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All Rights Reserved +""" +Transforms and data augmentation for both image + bbox. +""" +import os +import random + +import PIL +import torch +import torchvision.transforms as T +import torchvision.transforms.functional as F + +from groundingdino.util.box_ops import box_xyxy_to_cxcywh +from groundingdino.util.misc import interpolate + + +def crop(image, target, region): + cropped_image = F.crop(image, *region) + + target = target.copy() + i, j, h, w = region + + # should we do something wrt the original size? + target["size"] = torch.tensor([h, w]) + + fields = ["labels", "area", "iscrowd", "positive_map"] + + if "boxes" in target: + boxes = target["boxes"] + max_size = torch.as_tensor([w, h], dtype=torch.float32) + cropped_boxes = boxes - torch.as_tensor([j, i, j, i]) + cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size) + cropped_boxes = cropped_boxes.clamp(min=0) + area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1) + target["boxes"] = cropped_boxes.reshape(-1, 4) + target["area"] = area + fields.append("boxes") + + if "masks" in target: + # FIXME should we update the area here if there are no boxes? + target["masks"] = target["masks"][:, i : i + h, j : j + w] + fields.append("masks") + + # remove elements for which the boxes or masks that have zero area + if "boxes" in target or "masks" in target: + # favor boxes selection when defining which elements to keep + # this is compatible with previous implementation + if "boxes" in target: + cropped_boxes = target["boxes"].reshape(-1, 2, 2) + keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1) + else: + keep = target["masks"].flatten(1).any(1) + + for field in fields: + if field in target: + target[field] = target[field][keep] + + if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO": + # for debug and visualization only. + if "strings_positive" in target: + target["strings_positive"] = [ + _i for _i, _j in zip(target["strings_positive"], keep) if _j + ] + + return cropped_image, target + + +def hflip(image, target): + flipped_image = F.hflip(image) + + w, h = image.size + + target = target.copy() + if "boxes" in target: + boxes = target["boxes"] + boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor( + [w, 0, w, 0] + ) + target["boxes"] = boxes + + if "masks" in target: + target["masks"] = target["masks"].flip(-1) + + return flipped_image, target + + +def resize(image, target, size, max_size=None): + # size can be min_size (scalar) or (w, h) tuple + + def get_size_with_aspect_ratio(image_size, size, max_size=None): + w, h = image_size + if max_size is not None: + min_original_size = float(min((w, h))) + max_original_size = float(max((w, h))) + if max_original_size / min_original_size * size > max_size: + size = int(round(max_size * min_original_size / max_original_size)) + + if (w <= h and w == size) or (h <= w and h == size): + return (h, w) + + if w < h: + ow = size + oh = int(size * h / w) + else: + oh = size + ow = int(size * w / h) + + return (oh, ow) + + def get_size(image_size, size, max_size=None): + if isinstance(size, (list, tuple)): + return size[::-1] + else: + return get_size_with_aspect_ratio(image_size, size, max_size) + + size = get_size(image.size, size, max_size) + rescaled_image = F.resize(image, size) + + if target is None: + return rescaled_image, None + + ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size)) + ratio_width, ratio_height = ratios + + target = target.copy() + if "boxes" in target: + boxes = target["boxes"] + scaled_boxes = boxes * torch.as_tensor( + [ratio_width, ratio_height, ratio_width, ratio_height] + ) + target["boxes"] = scaled_boxes + + if "area" in target: + area = target["area"] + scaled_area = area * (ratio_width * ratio_height) + target["area"] = scaled_area + + h, w = size + target["size"] = torch.tensor([h, w]) + + if "masks" in target: + target["masks"] = ( + interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5 + ) + + return rescaled_image, target + + +def pad(image, target, padding): + # assumes that we only pad on the bottom right corners + padded_image = F.pad(image, (0, 0, padding[0], padding[1])) + if target is None: + return padded_image, None + target = target.copy() + # should we do something wrt the original size? + target["size"] = torch.tensor(padded_image.size[::-1]) + if "masks" in target: + target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1])) + return padded_image, target + + +class ResizeDebug(object): + def __init__(self, size): + self.size = size + + def __call__(self, img, target): + return resize(img, target, self.size) + + +class RandomCrop(object): + def __init__(self, size): + self.size = size + + def __call__(self, img, target): + region = T.RandomCrop.get_params(img, self.size) + return crop(img, target, region) + + +class RandomSizeCrop(object): + def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False): + # respect_boxes: True to keep all boxes + # False to tolerence box filter + self.min_size = min_size + self.max_size = max_size + self.respect_boxes = respect_boxes + + def __call__(self, img: PIL.Image.Image, target: dict): + init_boxes = len(target["boxes"]) + max_patience = 10 + for i in range(max_patience): + w = random.randint(self.min_size, min(img.width, self.max_size)) + h = random.randint(self.min_size, min(img.height, self.max_size)) + region = T.RandomCrop.get_params(img, [h, w]) + result_img, result_target = crop(img, target, region) + if ( + not self.respect_boxes + or len(result_target["boxes"]) == init_boxes + or i == max_patience - 1 + ): + return result_img, result_target + return result_img, result_target + + +class CenterCrop(object): + def __init__(self, size): + self.size = size + + def __call__(self, img, target): + image_width, image_height = img.size + crop_height, crop_width = self.size + crop_top = int(round((image_height - crop_height) / 2.0)) + crop_left = int(round((image_width - crop_width) / 2.0)) + return crop(img, target, (crop_top, crop_left, crop_height, crop_width)) + + +class RandomHorizontalFlip(object): + def __init__(self, p=0.5): + self.p = p + + def __call__(self, img, target): + if random.random() < self.p: + return hflip(img, target) + return img, target + + +class RandomResize(object): + def __init__(self, sizes, max_size=None): + assert isinstance(sizes, (list, tuple)) + self.sizes = sizes + self.max_size = max_size + + def __call__(self, img, target=None): + size = random.choice(self.sizes) + return resize(img, target, size, self.max_size) + + +class RandomPad(object): + def __init__(self, max_pad): + self.max_pad = max_pad + + def __call__(self, img, target): + pad_x = random.randint(0, self.max_pad) + pad_y = random.randint(0, self.max_pad) + return pad(img, target, (pad_x, pad_y)) + + +class RandomSelect(object): + """ + Randomly selects between transforms1 and transforms2, + with probability p for transforms1 and (1 - p) for transforms2 + """ + + def __init__(self, transforms1, transforms2, p=0.5): + self.transforms1 = transforms1 + self.transforms2 = transforms2 + self.p = p + + def __call__(self, img, target): + if random.random() < self.p: + return self.transforms1(img, target) + return self.transforms2(img, target) + + +class ToTensor(object): + def __call__(self, img, target): + return F.to_tensor(img), target + + +class RandomErasing(object): + def __init__(self, *args, **kwargs): + self.eraser = T.RandomErasing(*args, **kwargs) + + def __call__(self, img, target): + return self.eraser(img), target + + +class Normalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, image, target=None): + image = F.normalize(image, mean=self.mean, std=self.std) + if target is None: + return image, None + target = target.copy() + h, w = image.shape[-2:] + if "boxes" in target: + boxes = target["boxes"] + boxes = box_xyxy_to_cxcywh(boxes) + boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32) + target["boxes"] = boxes + return image, target + + +class Compose(object): + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, image, target): + for t in self.transforms: + image, target = t(image, target) + return image, target + + def __repr__(self): + format_string = self.__class__.__name__ + "(" + for t in self.transforms: + format_string += "\n" + format_string += " {0}".format(t) + format_string += "\n)" + return format_string diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/__init__.py b/GroundingDINO/groundingdino/models/GroundingDINO/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2af819d61d589cfec2e0ca46612a7456f42b831a --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/__init__.py @@ -0,0 +1,15 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Conditional DETR +# Copyright (c) 2021 Microsoft. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copied from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# ------------------------------------------------------------------------ + +from .groundingdino import build_groundingdino diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/backbone/__init__.py b/GroundingDINO/groundingdino/models/GroundingDINO/backbone/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..76e4b272b479a26c63d120c818c140870cd8c287 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/backbone/__init__.py @@ -0,0 +1 @@ +from .backbone import build_backbone diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py b/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..c8340c723fad8e07e2fc62daaa3912487498814b --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py @@ -0,0 +1,221 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Conditional DETR +# Copyright (c) 2021 Microsoft. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copied from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# ------------------------------------------------------------------------ + +""" +Backbone modules. +""" + +from typing import Dict, List + +import torch +import torch.nn.functional as F +import torchvision +from torch import nn +from torchvision.models._utils import IntermediateLayerGetter + +from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process + +from .position_encoding import build_position_encoding +from .swin_transformer import build_swin_transformer + + +class FrozenBatchNorm2d(torch.nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, + without which any other models than torchvision.models.resnet[18,34,50,101] + produce nans. + """ + + def __init__(self, n): + super(FrozenBatchNorm2d, self).__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super(FrozenBatchNorm2d, self)._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it fuser-friendly + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + rv = self.running_var.reshape(1, -1, 1, 1) + rm = self.running_mean.reshape(1, -1, 1, 1) + eps = 1e-5 + scale = w * (rv + eps).rsqrt() + bias = b - rm * scale + return x * scale + bias + + +class BackboneBase(nn.Module): + def __init__( + self, + backbone: nn.Module, + train_backbone: bool, + num_channels: int, + return_interm_indices: list, + ): + super().__init__() + for name, parameter in backbone.named_parameters(): + if ( + not train_backbone + or "layer2" not in name + and "layer3" not in name + and "layer4" not in name + ): + parameter.requires_grad_(False) + + return_layers = {} + for idx, layer_index in enumerate(return_interm_indices): + return_layers.update( + {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)} + ) + + # if len: + # if use_stage1_feature: + # return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} + # else: + # return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"} + # else: + # return_layers = {'layer4': "0"} + self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) + self.num_channels = num_channels + + def forward(self, tensor_list: NestedTensor): + xs = self.body(tensor_list.tensors) + out: Dict[str, NestedTensor] = {} + for name, x in xs.items(): + m = tensor_list.mask + assert m is not None + mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] + out[name] = NestedTensor(x, mask) + # import ipdb; ipdb.set_trace() + return out + + +class Backbone(BackboneBase): + """ResNet backbone with frozen BatchNorm.""" + + def __init__( + self, + name: str, + train_backbone: bool, + dilation: bool, + return_interm_indices: list, + batch_norm=FrozenBatchNorm2d, + ): + if name in ["resnet18", "resnet34", "resnet50", "resnet101"]: + backbone = getattr(torchvision.models, name)( + replace_stride_with_dilation=[False, False, dilation], + pretrained=is_main_process(), + norm_layer=batch_norm, + ) + else: + raise NotImplementedError("Why you can get here with name {}".format(name)) + # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 + assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available." + assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] + num_channels_all = [256, 512, 1024, 2048] + num_channels = num_channels_all[4 - len(return_interm_indices) :] + super().__init__(backbone, train_backbone, num_channels, return_interm_indices) + + +class Joiner(nn.Sequential): + def __init__(self, backbone, position_embedding): + super().__init__(backbone, position_embedding) + + def forward(self, tensor_list: NestedTensor): + xs = self[0](tensor_list) + out: List[NestedTensor] = [] + pos = [] + for name, x in xs.items(): + out.append(x) + # position encoding + pos.append(self[1](x).to(x.tensors.dtype)) + + return out, pos + + +def build_backbone(args): + """ + Useful args: + - backbone: backbone name + - lr_backbone: + - dilation + - return_interm_indices: available: [0,1,2,3], [1,2,3], [3] + - backbone_freeze_keywords: + - use_checkpoint: for swin only for now + + """ + position_embedding = build_position_encoding(args) + train_backbone = True + if not train_backbone: + raise ValueError("Please set lr_backbone > 0") + return_interm_indices = args.return_interm_indices + assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] + args.backbone_freeze_keywords + use_checkpoint = getattr(args, "use_checkpoint", False) + + if args.backbone in ["resnet50", "resnet101"]: + backbone = Backbone( + args.backbone, + train_backbone, + args.dilation, + return_interm_indices, + batch_norm=FrozenBatchNorm2d, + ) + bb_num_channels = backbone.num_channels + elif args.backbone in [ + "swin_T_224_1k", + "swin_B_224_22k", + "swin_B_384_22k", + "swin_L_224_22k", + "swin_L_384_22k", + ]: + pretrain_img_size = int(args.backbone.split("_")[-2]) + backbone = build_swin_transformer( + args.backbone, + pretrain_img_size=pretrain_img_size, + out_indices=tuple(return_interm_indices), + dilation=False, + use_checkpoint=use_checkpoint, + ) + + bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :] + else: + raise NotImplementedError("Unknown backbone {}".format(args.backbone)) + + assert len(bb_num_channels) == len( + return_interm_indices + ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}" + + model = Joiner(backbone, position_embedding) + model.num_channels = bb_num_channels + assert isinstance( + bb_num_channels, List + ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels)) + # import ipdb; ipdb.set_trace() + return model diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py b/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py new file mode 100644 index 0000000000000000000000000000000000000000..eac7e896bbe85a670824bfe8ef487d0535d5bd99 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py @@ -0,0 +1,186 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# DINO +# Copyright (c) 2022 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Conditional DETR +# Copyright (c) 2021 Microsoft. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copied from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# ------------------------------------------------------------------------ + +""" +Various positional encodings for the transformer. +""" +import math + +import torch +from torch import nn + +from groundingdino.util.misc import NestedTensor + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + mask = tensor_list.mask + assert mask is not None + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + # if os.environ.get("SHILONG_AMP", None) == '1': + # eps = 1e-4 + # else: + # eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +class PositionEmbeddingSineHW(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None + ): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperatureH = temperatureH + self.temperatureW = temperatureW + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + mask = tensor_list.mask + assert mask is not None + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + + # import ipdb; ipdb.set_trace() + + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats) + pos_x = x_embed[:, :, :, None] / dim_tx + + dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats) + pos_y = y_embed[:, :, :, None] / dim_ty + + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + + # import ipdb; ipdb.set_trace() + + return pos + + +class PositionEmbeddingLearned(nn.Module): + """ + Absolute pos embedding, learned. + """ + + def __init__(self, num_pos_feats=256): + super().__init__() + self.row_embed = nn.Embedding(50, num_pos_feats) + self.col_embed = nn.Embedding(50, num_pos_feats) + self.reset_parameters() + + def reset_parameters(self): + nn.init.uniform_(self.row_embed.weight) + nn.init.uniform_(self.col_embed.weight) + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + h, w = x.shape[-2:] + i = torch.arange(w, device=x.device) + j = torch.arange(h, device=x.device) + x_emb = self.col_embed(i) + y_emb = self.row_embed(j) + pos = ( + torch.cat( + [ + x_emb.unsqueeze(0).repeat(h, 1, 1), + y_emb.unsqueeze(1).repeat(1, w, 1), + ], + dim=-1, + ) + .permute(2, 0, 1) + .unsqueeze(0) + .repeat(x.shape[0], 1, 1, 1) + ) + return pos + + +def build_position_encoding(args): + N_steps = args.hidden_dim // 2 + if args.position_embedding in ("v2", "sine"): + # TODO find a better way of exposing other arguments + position_embedding = PositionEmbeddingSineHW( + N_steps, + temperatureH=args.pe_temperatureH, + temperatureW=args.pe_temperatureW, + normalize=True, + ) + elif args.position_embedding in ("v3", "learned"): + position_embedding = PositionEmbeddingLearned(N_steps) + else: + raise ValueError(f"not supported {args.position_embedding}") + + return position_embedding diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py b/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..1c66194deb5dd370e797e57e2712f44303e568cc --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py @@ -0,0 +1,802 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# DINO +# Copyright (c) 2022 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# -------------------------------------------------------- +# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py +# -------------------------------------------------------- + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + +from groundingdino.util.misc import NestedTensor + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__( + self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0 + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B_, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop + ) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask) + else: + x = blk(x, attn_mask) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + dilation (bool): if True, the output size if 16x downsample, ow 32x downsample. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + dilation=False, + use_checkpoint=False, + ): + super().__init__() + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.out_indices = out_indices + self.frozen_stages = frozen_stages + self.dilation = dilation + + # if use_checkpoint: + # print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!") + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + pretrain_img_size[0] // patch_size[0], + pretrain_img_size[1] // patch_size[1], + ] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + # prepare downsample list + downsamplelist = [PatchMerging for i in range(self.num_layers)] + downsamplelist[-1] = None + num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] + if self.dilation: + downsamplelist[-2] = None + num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2 + for i_layer in range(self.num_layers): + layer = BasicLayer( + # dim=int(embed_dim * 2 ** i_layer), + dim=num_features[i_layer], + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + downsample=downsamplelist[i_layer], + use_checkpoint=use_checkpoint, + ) + self.layers.append(layer) + + # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + for i_layer in out_indices: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + # def init_weights(self, pretrained=None): + # """Initialize the weights in backbone. + # Args: + # pretrained (str, optional): Path to pre-trained weights. + # Defaults to None. + # """ + + # def _init_weights(m): + # if isinstance(m, nn.Linear): + # trunc_normal_(m.weight, std=.02) + # if isinstance(m, nn.Linear) and m.bias is not None: + # nn.init.constant_(m.bias, 0) + # elif isinstance(m, nn.LayerNorm): + # nn.init.constant_(m.bias, 0) + # nn.init.constant_(m.weight, 1.0) + + # if isinstance(pretrained, str): + # self.apply(_init_weights) + # logger = get_root_logger() + # load_checkpoint(self, pretrained, strict=False, logger=logger) + # elif pretrained is None: + # self.apply(_init_weights) + # else: + # raise TypeError('pretrained must be a str or None') + + def forward_raw(self, x): + """Forward function.""" + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate( + self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" + ) + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + outs = [] + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + # import ipdb; ipdb.set_trace() + + if i in self.out_indices: + norm_layer = getattr(self, f"norm{i}") + x_out = norm_layer(x_out) + + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + # in: + # torch.Size([2, 3, 1024, 1024]) + # outs: + # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ + # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] + return tuple(outs) + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + + """Forward function.""" + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate( + self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" + ) + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + outs = [] + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + + if i in self.out_indices: + norm_layer = getattr(self, f"norm{i}") + x_out = norm_layer(x_out) + + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + # in: + # torch.Size([2, 3, 1024, 1024]) + # out: + # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \ + # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])] + + # collect for nesttensors + outs_dict = {} + for idx, out_i in enumerate(outs): + m = tensor_list.mask + assert m is not None + mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0] + outs_dict[idx] = NestedTensor(out_i, mask) + + return outs_dict + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +def build_swin_transformer(modelname, pretrain_img_size, **kw): + assert modelname in [ + "swin_T_224_1k", + "swin_B_224_22k", + "swin_B_384_22k", + "swin_L_224_22k", + "swin_L_384_22k", + ] + + model_para_dict = { + "swin_T_224_1k": dict( + embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7 + ), + "swin_B_224_22k": dict( + embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7 + ), + "swin_B_384_22k": dict( + embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12 + ), + "swin_L_224_22k": dict( + embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7 + ), + "swin_L_384_22k": dict( + embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12 + ), + } + kw_cgf = model_para_dict[modelname] + kw_cgf.update(kw) + model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf) + return model + + +if __name__ == "__main__": + model = build_swin_transformer("swin_L_384_22k", 384, dilation=True) + x = torch.rand(2, 3, 1024, 1024) + y = model.forward_raw(x) + import ipdb + + ipdb.set_trace() + x = torch.rand(2, 3, 384, 384) + y = model.forward_raw(x) diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/bertwarper.py b/GroundingDINO/groundingdino/models/GroundingDINO/bertwarper.py new file mode 100644 index 0000000000000000000000000000000000000000..f0cf9779b270e1aead32845006f8b881fcba37ad --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/bertwarper.py @@ -0,0 +1,273 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from torch import Tensor, nn +from torchvision.ops.boxes import nms +from transformers import BertConfig, BertModel, BertPreTrainedModel +from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions + + +class BertModelWarper(nn.Module): + def __init__(self, bert_model): + super().__init__() + # self.bert = bert_modelc + + self.config = bert_model.config + self.embeddings = bert_model.embeddings + self.encoder = bert_model.encoder + self.pooler = bert_model.pooler + + self.get_extended_attention_mask = bert_model.get_extended_attention_mask + self.invert_attention_mask = bert_model.invert_attention_mask + self.get_head_mask = bert_model.get_head_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = ( + output_attentions if output_attentions is not None else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': + # import ipdb; ipdb.set_trace() + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class TextEncoderShell(nn.Module): + def __init__(self, text_encoder): + super().__init__() + self.text_encoder = text_encoder + self.config = self.text_encoder.config + + def forward(self, **kw): + # feed into text encoder + return self.text_encoder(**kw) + + +def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer): + """Generate attention mask between each pair of special tokens + Args: + input_ids (torch.Tensor): input ids. Shape: [bs, num_token] + special_tokens_mask (list): special tokens mask. + Returns: + torch.Tensor: attention mask between each special tokens. + """ + input_ids = tokenized["input_ids"] + bs, num_token = input_ids.shape + # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens + special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool() + for special_token in special_tokens_list: + special_tokens_mask |= input_ids == special_token + + # idxs: each row is a list of indices of special tokens + idxs = torch.nonzero(special_tokens_mask) + + # generate attention mask and positional ids + attention_mask = ( + torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1) + ) + position_ids = torch.zeros((bs, num_token), device=input_ids.device) + previous_col = 0 + for i in range(idxs.shape[0]): + row, col = idxs[i] + if (col == 0) or (col == num_token - 1): + attention_mask[row, col, col] = True + position_ids[row, col] = 0 + else: + attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True + position_ids[row, previous_col + 1 : col + 1] = torch.arange( + 0, col - previous_col, device=input_ids.device + ) + + previous_col = col + + # # padding mask + # padding_mask = tokenized['attention_mask'] + # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool() + + return attention_mask, position_ids.to(torch.long) + + +def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer): + """Generate attention mask between each pair of special tokens + Args: + input_ids (torch.Tensor): input ids. Shape: [bs, num_token] + special_tokens_mask (list): special tokens mask. + Returns: + torch.Tensor: attention mask between each special tokens. + """ + input_ids = tokenized["input_ids"] + bs, num_token = input_ids.shape + # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens + special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool() + for special_token in special_tokens_list: + special_tokens_mask |= input_ids == special_token + + # idxs: each row is a list of indices of special tokens + idxs = torch.nonzero(special_tokens_mask) + + # generate attention mask and positional ids + attention_mask = ( + torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1) + ) + position_ids = torch.zeros((bs, num_token), device=input_ids.device) + cate_to_token_mask_list = [[] for _ in range(bs)] + previous_col = 0 + for i in range(idxs.shape[0]): + row, col = idxs[i] + if (col == 0) or (col == num_token - 1): + attention_mask[row, col, col] = True + position_ids[row, col] = 0 + else: + attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True + position_ids[row, previous_col + 1 : col + 1] = torch.arange( + 0, col - previous_col, device=input_ids.device + ) + c2t_maski = torch.zeros((num_token), device=input_ids.device).bool() + c2t_maski[previous_col + 1 : col] = True + cate_to_token_mask_list[row].append(c2t_maski) + previous_col = col + + cate_to_token_mask_list = [ + torch.stack(cate_to_token_mask_listi, dim=0) + for cate_to_token_mask_listi in cate_to_token_mask_list + ] + + # # padding mask + # padding_mask = tokenized['attention_mask'] + # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool() + + return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h new file mode 100644 index 0000000000000000000000000000000000000000..c7408eba007b424194618baa63726657e36875e3 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h @@ -0,0 +1,64 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#pragma once + +#include "ms_deform_attn_cpu.h" + +#ifdef WITH_CUDA +#include "ms_deform_attn_cuda.h" +#endif + +namespace groundingdino { + +at::Tensor +ms_deform_attn_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_forward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +std::vector +ms_deform_attn_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_backward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +} // namespace groundingdino \ No newline at end of file diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..551243fdadfd1682b5dc6628623b67a79b3f6c74 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp @@ -0,0 +1,43 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#include + +#include +#include + +namespace groundingdino { + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + +} // namespace groundingdino diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h new file mode 100644 index 0000000000000000000000000000000000000000..b2b88e8c46f19b6db0933163e57ccdb51180f517 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h @@ -0,0 +1,35 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#pragma once +#include + +namespace groundingdino { + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + +} // namespace groundingdino diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..d04fae8a9a45c11e4e74f3035e94762796da4096 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu @@ -0,0 +1,156 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#include +#include "ms_deform_im2col_cuda.cuh" + +#include +#include +#include +#include + +namespace groundingdino { + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); + + const int batch_n = im2col_step_; + auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto columns = output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] { + ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + columns.data()); + + })); + } + + output = output.view({batch, num_query, num_heads*channels}); + + return output; +} + + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto grad_value = at::zeros_like(value); + auto grad_sampling_loc = at::zeros_like(sampling_loc); + auto grad_attn_weight = at::zeros_like(attn_weight); + + const int batch_n = im2col_step_; + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto grad_output_g = grad_output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] { + ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), + grad_output_g.data(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + grad_value.data() + n * im2col_step_ * per_value_size, + grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size); + + })); + } + + return { + grad_value, grad_sampling_loc, grad_attn_weight + }; +} + +} // namespace groundingdino \ No newline at end of file diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h new file mode 100644 index 0000000000000000000000000000000000000000..ad1311a78f61303616504eb991aaa9c4a93d9948 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h @@ -0,0 +1,33 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +#pragma once +#include + +namespace groundingdino { + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + +} // namespace groundingdino \ No newline at end of file diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh new file mode 100644 index 0000000000000000000000000000000000000000..6bc2acb7aea0eab2e9e91e769a16861e1652c284 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh @@ -0,0 +1,1327 @@ +/*! +************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************** +* Modified from DCN (https://github.com/msracver/Deformable-ConvNets) +* Copyright (c) 2018 Microsoft +************************************************************************** +*/ + +#include +#include +#include + +#include +#include + +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N, const int num_threads) +{ + return (N + num_threads - 1) / num_threads; +} + + +template +__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + } + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + *grad_attn_weight = top_grad * val; + *grad_sampling_loc = width * grad_w_weight * top_grad_value; + *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + atomicAdd(grad_attn_weight, top_grad * val); + atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value); + atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value); +} + + +template +__global__ void ms_deformable_im2col_gpu_kernel(const int n, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + scalar_t *data_col_ptr = data_col + index; + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + scalar_t col = 0; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride); + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight; + } + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + } + } + *data_col_ptr = col; + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockSize; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockSize/2; s>0; s>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockDim.x; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]); + atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]); + atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]); + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear_gm( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + grad_sampling_loc, grad_attn_weight); + } + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +void ms_deformable_im2col_cuda(cudaStream_t stream, + const scalar_t* data_value, + const int64_t* data_spatial_shapes, + const int64_t* data_level_start_index, + const scalar_t* data_sampling_loc, + const scalar_t* data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* data_col) +{ + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + const int num_threads = CUDA_NUM_THREADS; + ms_deformable_im2col_gpu_kernel + <<>>( + num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, + batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +template +void ms_deformable_col2im_cuda(cudaStream_t stream, + const scalar_t* grad_col, + const scalar_t* data_value, + const int64_t * data_spatial_shapes, + const int64_t * data_level_start_index, + const scalar_t * data_sampling_loc, + const scalar_t * data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels; + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + if (channels > 1024) + { + if ((channels & 1023) == 0) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_gm + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + else{ + switch(channels) + { + case 1: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 2: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 4: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 8: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 16: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 32: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 64: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 128: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 256: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 512: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 1024: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + default: + if (channels < 64) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + } + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } + +} \ No newline at end of file diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/csrc/cuda_version.cu b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/cuda_version.cu new file mode 100644 index 0000000000000000000000000000000000000000..64569e34ffb250964de27e33e7a53f3822270b9e --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/cuda_version.cu @@ -0,0 +1,7 @@ +#include + +namespace groundingdino { +int get_cudart_version() { + return CUDART_VERSION; +} +} // namespace groundingdino diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c1f2c50c82909bbd5492c163d634af77a3ba1781 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp @@ -0,0 +1,58 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +#include "MsDeformAttn/ms_deform_attn.h" + +namespace groundingdino { + +#ifdef WITH_CUDA +extern int get_cudart_version(); +#endif + +std::string get_cuda_version() { +#ifdef WITH_CUDA + std::ostringstream oss; + + // copied from + // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231 + auto printCudaStyleVersion = [&](int v) { + oss << (v / 1000) << "." << (v / 10 % 100); + if (v % 10 != 0) { + oss << "." << (v % 10); + } + }; + printCudaStyleVersion(get_cudart_version()); + return oss.str(); +#else + return std::string("not available"); +#endif +} + +// similar to +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp +std::string get_compiler_version() { + std::ostringstream ss; +#if defined(__GNUC__) +#ifndef __clang__ + { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; } +#endif +#endif + +#if defined(__clang_major__) + { + ss << "clang " << __clang_major__ << "." << __clang_minor__ << "." + << __clang_patchlevel__; + } +#endif + +#if defined(_MSC_VER) + { ss << "MSVC " << _MSC_FULL_VER; } +#endif + return ss.str(); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward"); + m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward"); +} + +} // namespace groundingdino \ No newline at end of file diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py b/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..2753b3ddee43c7a9fe28d1824db5d786e7e1ad59 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py @@ -0,0 +1,297 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +import torch +import torch.nn as nn +import torch.nn.functional as F +from timm.models.layers import DropPath + + +class FeatureResizer(nn.Module): + """ + This class takes as input a set of embeddings of dimension C1 and outputs a set of + embedding of dimension C2, after a linear transformation, dropout and normalization (LN). + """ + + def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): + super().__init__() + self.do_ln = do_ln + # Object feature encoding + self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) + self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) + self.dropout = nn.Dropout(dropout) + + def forward(self, encoder_features): + x = self.fc(encoder_features) + if self.do_ln: + x = self.layer_norm(x) + output = self.dropout(x) + return output + + +def l1norm(X, dim, eps=1e-8): + """L1-normalize columns of X""" + norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps + X = torch.div(X, norm) + return X + + +def l2norm(X, dim, eps=1e-8): + """L2-normalize columns of X""" + norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps + X = torch.div(X, norm) + return X + + +def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8): + """ + query: (n_context, queryL, d) + context: (n_context, sourceL, d) + """ + batch_size_q, queryL = query.size(0), query.size(1) + batch_size, sourceL = context.size(0), context.size(1) + + # Get attention + # --> (batch, d, queryL) + queryT = torch.transpose(query, 1, 2) + + # (batch, sourceL, d)(batch, d, queryL) + # --> (batch, sourceL, queryL) + attn = torch.bmm(context, queryT) + if raw_feature_norm == "softmax": + # --> (batch*sourceL, queryL) + attn = attn.view(batch_size * sourceL, queryL) + attn = nn.Softmax()(attn) + # --> (batch, sourceL, queryL) + attn = attn.view(batch_size, sourceL, queryL) + elif raw_feature_norm == "l2norm": + attn = l2norm(attn, 2) + elif raw_feature_norm == "clipped_l2norm": + attn = nn.LeakyReLU(0.1)(attn) + attn = l2norm(attn, 2) + else: + raise ValueError("unknown first norm type:", raw_feature_norm) + # --> (batch, queryL, sourceL) + attn = torch.transpose(attn, 1, 2).contiguous() + # --> (batch*queryL, sourceL) + attn = attn.view(batch_size * queryL, sourceL) + attn = nn.Softmax()(attn * smooth) + # --> (batch, queryL, sourceL) + attn = attn.view(batch_size, queryL, sourceL) + # --> (batch, sourceL, queryL) + attnT = torch.transpose(attn, 1, 2).contiguous() + + # --> (batch, d, sourceL) + contextT = torch.transpose(context, 1, 2) + # (batch x d x sourceL)(batch x sourceL x queryL) + # --> (batch, d, queryL) + weightedContext = torch.bmm(contextT, attnT) + # --> (batch, queryL, d) + weightedContext = torch.transpose(weightedContext, 1, 2) + + return weightedContext, attnT + + +class BiMultiHeadAttention(nn.Module): + def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None): + super(BiMultiHeadAttention, self).__init__() + + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + self.v_dim = v_dim + self.l_dim = l_dim + + assert ( + self.head_dim * self.num_heads == self.embed_dim + ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." + self.scale = self.head_dim ** (-0.5) + self.dropout = dropout + + self.v_proj = nn.Linear(self.v_dim, self.embed_dim) + self.l_proj = nn.Linear(self.l_dim, self.embed_dim) + self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim) + self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim) + + self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim) + self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim) + + self.stable_softmax_2d = True + self.clamp_min_for_underflow = True + self.clamp_max_for_overflow = True + + self._reset_parameters() + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def _reset_parameters(self): + nn.init.xavier_uniform_(self.v_proj.weight) + self.v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.l_proj.weight) + self.l_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.values_v_proj.weight) + self.values_v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.values_l_proj.weight) + self.values_l_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_v_proj.weight) + self.out_v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_l_proj.weight) + self.out_l_proj.bias.data.fill_(0) + + def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): + """_summary_ + + Args: + v (_type_): bs, n_img, dim + l (_type_): bs, n_text, dim + attention_mask_v (_type_, optional): _description_. bs, n_img + attention_mask_l (_type_, optional): _description_. bs, n_text + + Returns: + _type_: _description_ + """ + # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': + # import ipdb; ipdb.set_trace() + bsz, tgt_len, _ = v.size() + + query_states = self.v_proj(v) * self.scale + key_states = self._shape(self.l_proj(l), -1, bsz) + value_v_states = self._shape(self.values_v_proj(v), -1, bsz) + value_l_states = self._shape(self.values_l_proj(l), -1, bsz) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_v_states = value_v_states.view(*proj_shape) + value_l_states = value_l_states.view(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" + ) + + if self.stable_softmax_2d: + attn_weights = attn_weights - attn_weights.max() + + if self.clamp_min_for_underflow: + attn_weights = torch.clamp( + attn_weights, min=-50000 + ) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attn_weights = torch.clamp( + attn_weights, max=50000 + ) # Do not increase 50000, data type half has quite limited range + + attn_weights_T = attn_weights.transpose(1, 2) + attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0] + if self.clamp_min_for_underflow: + attn_weights_l = torch.clamp( + attn_weights_l, min=-50000 + ) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attn_weights_l = torch.clamp( + attn_weights_l, max=50000 + ) # Do not increase 50000, data type half has quite limited range + + # mask vison for language + if attention_mask_v is not None: + attention_mask_v = ( + attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) + ) + attn_weights_l.masked_fill_(attention_mask_v, float("-inf")) + + attn_weights_l = attn_weights_l.softmax(dim=-1) + + # mask language for vision + if attention_mask_l is not None: + attention_mask_l = ( + attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1) + ) + attn_weights.masked_fill_(attention_mask_l, float("-inf")) + attn_weights_v = attn_weights.softmax(dim=-1) + + attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training) + attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training) + + attn_output_v = torch.bmm(attn_probs_v, value_l_states) + attn_output_l = torch.bmm(attn_probs_l, value_v_states) + + if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}" + ) + + if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim): + raise ValueError( + f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}" + ) + + attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output_v = attn_output_v.transpose(1, 2) + attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim) + + attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim) + attn_output_l = attn_output_l.transpose(1, 2) + attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim) + + attn_output_v = self.out_v_proj(attn_output_v) + attn_output_l = self.out_l_proj(attn_output_l) + + return attn_output_v, attn_output_l + + +# Bi-Direction MHA (text->image, image->text) +class BiAttentionBlock(nn.Module): + def __init__( + self, + v_dim, + l_dim, + embed_dim, + num_heads, + dropout=0.1, + drop_path=0.0, + init_values=1e-4, + cfg=None, + ): + """ + Inputs: + embed_dim - Dimensionality of input and attention feature vectors + hidden_dim - Dimensionality of hidden layer in feed-forward network + (usually 2-4x larger than embed_dim) + num_heads - Number of heads to use in the Multi-Head Attention block + dropout - Amount of dropout to apply in the feed-forward network + """ + super(BiAttentionBlock, self).__init__() + + # pre layer norm + self.layer_norm_v = nn.LayerNorm(v_dim) + self.layer_norm_l = nn.LayerNorm(l_dim) + self.attn = BiMultiHeadAttention( + v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout + ) + + # add layer scale for training stability + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True) + self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True) + + def forward(self, v, l, attention_mask_v=None, attention_mask_l=None): + v = self.layer_norm_v(v) + l = self.layer_norm_l(l) + delta_v, delta_l = self.attn( + v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l + ) + # v, l = v + delta_v, l + delta_l + v = v + self.drop_path(self.gamma_v * delta_v) + l = l + self.drop_path(self.gamma_l * delta_l) + return v, l + + # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None) diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py b/GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py new file mode 100644 index 0000000000000000000000000000000000000000..052df6220595a1b39b7e2aea37ca4872d113dfd2 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py @@ -0,0 +1,395 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Conditional DETR model and criterion classes. +# Copyright (c) 2021 Microsoft. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# ------------------------------------------------------------------------ +# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR) +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# ------------------------------------------------------------------------ +import copy +from typing import List + +import torch +import torch.nn.functional as F +from torch import nn +from torchvision.ops.boxes import nms +from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast + +from groundingdino.util import box_ops, get_tokenlizer +from groundingdino.util.misc import ( + NestedTensor, + accuracy, + get_world_size, + interpolate, + inverse_sigmoid, + is_dist_avail_and_initialized, + nested_tensor_from_tensor_list, +) +from groundingdino.util.utils import get_phrases_from_posmap +from groundingdino.util.visualizer import COCOVisualizer +from groundingdino.util.vl_utils import create_positive_map_from_span + +from ..registry import MODULE_BUILD_FUNCS +from .backbone import build_backbone +from .bertwarper import ( + BertModelWarper, + generate_masks_with_special_tokens, + generate_masks_with_special_tokens_and_transfer_map, +) +from .transformer import build_transformer +from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss + + +class GroundingDINO(nn.Module): + """This is the Cross-Attention Detector module that performs object detection""" + + def __init__( + self, + backbone, + transformer, + num_queries, + aux_loss=False, + iter_update=False, + query_dim=2, + num_feature_levels=1, + nheads=8, + # two stage + two_stage_type="no", # ['no', 'standard'] + dec_pred_bbox_embed_share=True, + two_stage_class_embed_share=True, + two_stage_bbox_embed_share=True, + num_patterns=0, + dn_number=100, + dn_box_noise_scale=0.4, + dn_label_noise_ratio=0.5, + dn_labelbook_size=100, + text_encoder_type="bert-base-uncased", + sub_sentence_present=True, + max_text_len=256, + ): + """Initializes the model. + Parameters: + backbone: torch module of the backbone to be used. See backbone.py + transformer: torch module of the transformer architecture. See transformer.py + num_queries: number of object queries, ie detection slot. This is the maximal number of objects + Conditional DETR can detect in a single image. For COCO, we recommend 100 queries. + aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. + """ + super().__init__() + self.num_queries = num_queries + self.transformer = transformer + self.hidden_dim = hidden_dim = transformer.d_model + self.num_feature_levels = num_feature_levels + self.nheads = nheads + self.max_text_len = 256 + self.sub_sentence_present = sub_sentence_present + + # setting query dim + self.query_dim = query_dim + assert query_dim == 4 + + # for dn training + self.num_patterns = num_patterns + self.dn_number = dn_number + self.dn_box_noise_scale = dn_box_noise_scale + self.dn_label_noise_ratio = dn_label_noise_ratio + self.dn_labelbook_size = dn_labelbook_size + + # bert + self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type) + self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type) + self.bert.pooler.dense.weight.requires_grad_(False) + self.bert.pooler.dense.bias.requires_grad_(False) + self.bert = BertModelWarper(bert_model=self.bert) + + self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True) + nn.init.constant_(self.feat_map.bias.data, 0) + nn.init.xavier_uniform_(self.feat_map.weight.data) + # freeze + + # special tokens + self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"]) + + # prepare input projection layers + if num_feature_levels > 1: + num_backbone_outs = len(backbone.num_channels) + input_proj_list = [] + for _ in range(num_backbone_outs): + in_channels = backbone.num_channels[_] + input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, hidden_dim, kernel_size=1), + nn.GroupNorm(32, hidden_dim), + ) + ) + for _ in range(num_feature_levels - num_backbone_outs): + input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1), + nn.GroupNorm(32, hidden_dim), + ) + ) + in_channels = hidden_dim + self.input_proj = nn.ModuleList(input_proj_list) + else: + assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!" + self.input_proj = nn.ModuleList( + [ + nn.Sequential( + nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1), + nn.GroupNorm(32, hidden_dim), + ) + ] + ) + + self.backbone = backbone + self.aux_loss = aux_loss + self.box_pred_damping = box_pred_damping = None + + self.iter_update = iter_update + assert iter_update, "Why not iter_update?" + + # prepare pred layers + self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share + # prepare class & box embed + _class_embed = ContrastiveEmbed() + + _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3) + nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0) + nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0) + + if dec_pred_bbox_embed_share: + box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)] + else: + box_embed_layerlist = [ + copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers) + ] + class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)] + self.bbox_embed = nn.ModuleList(box_embed_layerlist) + self.class_embed = nn.ModuleList(class_embed_layerlist) + self.transformer.decoder.bbox_embed = self.bbox_embed + self.transformer.decoder.class_embed = self.class_embed + + # two stage + self.two_stage_type = two_stage_type + assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format( + two_stage_type + ) + if two_stage_type != "no": + if two_stage_bbox_embed_share: + assert dec_pred_bbox_embed_share + self.transformer.enc_out_bbox_embed = _bbox_embed + else: + self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed) + + if two_stage_class_embed_share: + assert dec_pred_bbox_embed_share + self.transformer.enc_out_class_embed = _class_embed + else: + self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed) + + self.refpoint_embed = None + + self._reset_parameters() + + def _reset_parameters(self): + # init input_proj + for proj in self.input_proj: + nn.init.xavier_uniform_(proj[0].weight, gain=1) + nn.init.constant_(proj[0].bias, 0) + + def init_ref_points(self, use_num_queries): + self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim) + + def forward(self, samples: NestedTensor, targets: List = None, **kw): + """The forward expects a NestedTensor, which consists of: + - samples.tensor: batched images, of shape [batch_size x 3 x H x W] + - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels + + It returns a dict with the following elements: + - "pred_logits": the classification logits (including no-object) for all queries. + Shape= [batch_size x num_queries x num_classes] + - "pred_boxes": The normalized boxes coordinates for all queries, represented as + (center_x, center_y, width, height). These values are normalized in [0, 1], + relative to the size of each individual image (disregarding possible padding). + See PostProcess for information on how to retrieve the unnormalized bounding box. + - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of + dictionnaries containing the two above keys for each decoder layer. + """ + if targets is None: + captions = kw["captions"] + else: + captions = [t["caption"] for t in targets] + len(captions) + + # encoder texts + tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to( + samples.device + ) + ( + text_self_attention_masks, + position_ids, + cate_to_token_mask_list, + ) = generate_masks_with_special_tokens_and_transfer_map( + tokenized, self.specical_tokens, self.tokenizer + ) + + if text_self_attention_masks.shape[1] > self.max_text_len: + text_self_attention_masks = text_self_attention_masks[ + :, : self.max_text_len, : self.max_text_len + ] + position_ids = position_ids[:, : self.max_text_len] + tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len] + tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len] + tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len] + + # extract text embeddings + if self.sub_sentence_present: + tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"} + tokenized_for_encoder["attention_mask"] = text_self_attention_masks + tokenized_for_encoder["position_ids"] = position_ids + else: + # import ipdb; ipdb.set_trace() + tokenized_for_encoder = tokenized + + bert_output = self.bert(**tokenized_for_encoder) # bs, 195, 768 + + encoded_text = self.feat_map(bert_output["last_hidden_state"]) # bs, 195, d_model + text_token_mask = tokenized.attention_mask.bool() # bs, 195 + # text_token_mask: True for nomask, False for mask + # text_self_attention_masks: True for nomask, False for mask + + if encoded_text.shape[1] > self.max_text_len: + encoded_text = encoded_text[:, : self.max_text_len, :] + text_token_mask = text_token_mask[:, : self.max_text_len] + position_ids = position_ids[:, : self.max_text_len] + text_self_attention_masks = text_self_attention_masks[ + :, : self.max_text_len, : self.max_text_len + ] + + text_dict = { + "encoded_text": encoded_text, # bs, 195, d_model + "text_token_mask": text_token_mask, # bs, 195 + "position_ids": position_ids, # bs, 195 + "text_self_attention_masks": text_self_attention_masks, # bs, 195,195 + } + + # import ipdb; ipdb.set_trace() + + if isinstance(samples, (list, torch.Tensor)): + samples = nested_tensor_from_tensor_list(samples) + features, poss = self.backbone(samples) + + srcs = [] + masks = [] + for l, feat in enumerate(features): + src, mask = feat.decompose() + srcs.append(self.input_proj[l](src)) + masks.append(mask) + assert mask is not None + if self.num_feature_levels > len(srcs): + _len_srcs = len(srcs) + for l in range(_len_srcs, self.num_feature_levels): + if l == _len_srcs: + src = self.input_proj[l](features[-1].tensors) + else: + src = self.input_proj[l](srcs[-1]) + m = samples.mask + mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0] + pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype) + srcs.append(src) + masks.append(mask) + poss.append(pos_l) + + input_query_bbox = input_query_label = attn_mask = dn_meta = None + hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer( + srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict + ) + + # deformable-detr-like anchor update + outputs_coord_list = [] + for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate( + zip(reference[:-1], self.bbox_embed, hs) + ): + layer_delta_unsig = layer_bbox_embed(layer_hs) + layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig) + layer_outputs_unsig = layer_outputs_unsig.sigmoid() + outputs_coord_list.append(layer_outputs_unsig) + outputs_coord_list = torch.stack(outputs_coord_list) + + # output + outputs_class = torch.stack( + [ + layer_cls_embed(layer_hs, text_dict) + for layer_cls_embed, layer_hs in zip(self.class_embed, hs) + ] + ) + out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]} + + # # for intermediate outputs + # if self.aux_loss: + # out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list) + + # # for encoder output + # if hs_enc is not None: + # # prepare intermediate outputs + # interm_coord = ref_enc[-1] + # interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict) + # out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord} + # out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal} + + return out + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_coord): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + return [ + {"pred_logits": a, "pred_boxes": b} + for a, b in zip(outputs_class[:-1], outputs_coord[:-1]) + ] + + +@MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino") +def build_groundingdino(args): + + backbone = build_backbone(args) + transformer = build_transformer(args) + + dn_labelbook_size = args.dn_labelbook_size + dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share + sub_sentence_present = args.sub_sentence_present + + model = GroundingDINO( + backbone, + transformer, + num_queries=args.num_queries, + aux_loss=True, + iter_update=True, + query_dim=4, + num_feature_levels=args.num_feature_levels, + nheads=args.nheads, + dec_pred_bbox_embed_share=dec_pred_bbox_embed_share, + two_stage_type=args.two_stage_type, + two_stage_bbox_embed_share=args.two_stage_bbox_embed_share, + two_stage_class_embed_share=args.two_stage_class_embed_share, + num_patterns=args.num_patterns, + dn_number=0, + dn_box_noise_scale=args.dn_box_noise_scale, + dn_label_noise_ratio=args.dn_label_noise_ratio, + dn_labelbook_size=dn_labelbook_size, + text_encoder_type=args.text_encoder_type, + sub_sentence_present=sub_sentence_present, + max_text_len=args.max_text_len, + ) + + return model diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py b/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py new file mode 100644 index 0000000000000000000000000000000000000000..489d501bef364020212306d81e9b85c8daa27491 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py @@ -0,0 +1,413 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from: +# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py +# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py +# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py +# ------------------------------------------------------------------------------------------------ + +import math +import warnings +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.init import constant_, xavier_uniform_ + +try: + from groundingdino import _C +except: + warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!") + + +# helpers +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n - 1) == 0) and n != 0 + + +class MultiScaleDeformableAttnFunction(Function): + @staticmethod + def forward( + ctx, + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + im2col_step, + ): + ctx.im2col_step = im2col_step + output = _C.ms_deform_attn_forward( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + ctx.im2col_step, + ) + ctx.save_for_backward( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + ( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + ) = ctx.saved_tensors + grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward( + value, + value_spatial_shapes, + value_level_start_index, + sampling_locations, + attention_weights, + grad_output, + ctx.im2col_step, + ) + + return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None + + +def multi_scale_deformable_attn_pytorch( + value: torch.Tensor, + value_spatial_shapes: torch.Tensor, + sampling_locations: torch.Tensor, + attention_weights: torch.Tensor, +) -> torch.Tensor: + + bs, _, num_heads, embed_dims = value.shape + _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape + value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for level, (H_, W_) in enumerate(value_spatial_shapes): + # bs, H_*W_, num_heads, embed_dims -> + # bs, H_*W_, num_heads*embed_dims -> + # bs, num_heads*embed_dims, H_*W_ -> + # bs*num_heads, embed_dims, H_, W_ + value_l_ = ( + value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_) + ) + # bs, num_queries, num_heads, num_points, 2 -> + # bs, num_heads, num_queries, num_points, 2 -> + # bs*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1) + # bs*num_heads, embed_dims, num_queries, num_points + sampling_value_l_ = F.grid_sample( + value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False + ) + sampling_value_list.append(sampling_value_l_) + # (bs, num_queries, num_heads, num_levels, num_points) -> + # (bs, num_heads, num_queries, num_levels, num_points) -> + # (bs, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.transpose(1, 2).reshape( + bs * num_heads, 1, num_queries, num_levels * num_points + ) + output = ( + (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) + .sum(-1) + .view(bs, num_heads * embed_dims, num_queries) + ) + return output.transpose(1, 2).contiguous() + + +class MultiScaleDeformableAttention(nn.Module): + """Multi-Scale Deformable Attention Module used in Deformable-DETR + + `Deformable DETR: Deformable Transformers for End-to-End Object Detection. + `_. + + Args: + embed_dim (int): The embedding dimension of Attention. Default: 256. + num_heads (int): The number of attention heads. Default: 8. + num_levels (int): The number of feature map used in Attention. Default: 4. + num_points (int): The number of sampling points for each query + in each head. Default: 4. + img2col_steps (int): The step used in image_to_column. Defualt: 64. + dropout (float): Dropout layer used in output. Default: 0.1. + batch_first (bool): if ``True``, then the input and output tensor will be + provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)` + """ + + def __init__( + self, + embed_dim: int = 256, + num_heads: int = 8, + num_levels: int = 4, + num_points: int = 4, + img2col_step: int = 64, + batch_first: bool = False, + ): + super().__init__() + if embed_dim % num_heads != 0: + raise ValueError( + "embed_dim must be divisible by num_heads, but got {} and {}".format( + embed_dim, num_heads + ) + ) + head_dim = embed_dim // num_heads + + self.batch_first = batch_first + + if not _is_power_of_2(head_dim): + warnings.warn( + """ + You'd better set d_model in MSDeformAttn to make sure that + each dim of the attention head a power of 2, which is more efficient. + """ + ) + + self.im2col_step = img2col_step + self.embed_dim = embed_dim + self.num_heads = num_heads + self.num_levels = num_levels + self.num_points = num_points + self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2) + self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points) + self.value_proj = nn.Linear(embed_dim, embed_dim) + self.output_proj = nn.Linear(embed_dim, embed_dim) + + self.init_weights() + + def _reset_parameters(self): + return self.init_weights() + + def init_weights(self): + """ + Default initialization for Parameters of Module. + """ + constant_(self.sampling_offsets.weight.data, 0.0) + thetas = torch.arange(self.num_heads, dtype=torch.float32) * ( + 2.0 * math.pi / self.num_heads + ) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(self.num_heads, 1, 1, 2) + .repeat(1, self.num_levels, self.num_points, 1) + ) + for i in range(self.num_points): + grid_init[:, :, i, :] *= i + 1 + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.0) + constant_(self.attention_weights.bias.data, 0.0) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.0) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.0) + + def freeze_sampling_offsets(self): + print("Freeze sampling offsets") + self.sampling_offsets.weight.requires_grad = False + self.sampling_offsets.bias.requires_grad = False + + def freeze_attention_weights(self): + print("Freeze attention weights") + self.attention_weights.weight.requires_grad = False + self.attention_weights.bias.requires_grad = False + + def forward( + self, + query: torch.Tensor, + key: Optional[torch.Tensor] = None, + value: Optional[torch.Tensor] = None, + query_pos: Optional[torch.Tensor] = None, + key_padding_mask: Optional[torch.Tensor] = None, + reference_points: Optional[torch.Tensor] = None, + spatial_shapes: Optional[torch.Tensor] = None, + level_start_index: Optional[torch.Tensor] = None, + **kwargs + ) -> torch.Tensor: + + """Forward Function of MultiScaleDeformableAttention + + Args: + query (torch.Tensor): Query embeddings with shape + `(num_query, bs, embed_dim)` + key (torch.Tensor): Key embeddings with shape + `(num_key, bs, embed_dim)` + value (torch.Tensor): Value embeddings with shape + `(num_key, bs, embed_dim)` + query_pos (torch.Tensor): The position embedding for `query`. Default: None. + key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`, + indicating which elements within `key` to be ignored in attention. + reference_points (torch.Tensor): The normalized reference points + with shape `(bs, num_query, num_levels, 2)`, + all elements is range in [0, 1], top-left (0, 0), + bottom-right (1, 1), including padding are. + or `(N, Length_{query}, num_levels, 4)`, add additional + two dimensions `(h, w)` to form reference boxes. + spatial_shapes (torch.Tensor): Spatial shape of features in different levels. + With shape `(num_levels, 2)`, last dimension represents `(h, w)`. + level_start_index (torch.Tensor): The start index of each level. A tensor with + shape `(num_levels, )` which can be represented as + `[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`. + + Returns: + torch.Tensor: forward results with shape `(num_query, bs, embed_dim)` + """ + + if value is None: + value = query + + if query_pos is not None: + query = query + query_pos + + if not self.batch_first: + # change to (bs, num_query ,embed_dims) + query = query.permute(1, 0, 2) + value = value.permute(1, 0, 2) + + bs, num_query, _ = query.shape + bs, num_value, _ = value.shape + + assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value + + value = self.value_proj(value) + if key_padding_mask is not None: + value = value.masked_fill(key_padding_mask[..., None], float(0)) + value = value.view(bs, num_value, self.num_heads, -1) + sampling_offsets = self.sampling_offsets(query).view( + bs, num_query, self.num_heads, self.num_levels, self.num_points, 2 + ) + attention_weights = self.attention_weights(query).view( + bs, num_query, self.num_heads, self.num_levels * self.num_points + ) + attention_weights = attention_weights.softmax(-1) + attention_weights = attention_weights.view( + bs, + num_query, + self.num_heads, + self.num_levels, + self.num_points, + ) + + # bs, num_query, num_heads, num_levels, num_points, 2 + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif reference_points.shape[-1] == 4: + sampling_locations = ( + reference_points[:, :, None, :, None, :2] + + sampling_offsets + / self.num_points + * reference_points[:, :, None, :, None, 2:] + * 0.5 + ) + else: + raise ValueError( + "Last dim of reference_points must be 2 or 4, but get {} instead.".format( + reference_points.shape[-1] + ) + ) + + if torch.cuda.is_available() and value.is_cuda: + halffloat = False + if value.dtype == torch.float16: + halffloat = True + value = value.float() + sampling_locations = sampling_locations.float() + attention_weights = attention_weights.float() + + output = MultiScaleDeformableAttnFunction.apply( + value, + spatial_shapes, + level_start_index, + sampling_locations, + attention_weights, + self.im2col_step, + ) + + if halffloat: + output = output.half() + else: + output = multi_scale_deformable_attn_pytorch( + value, spatial_shapes, sampling_locations, attention_weights + ) + + output = self.output_proj(output) + + if not self.batch_first: + output = output.permute(1, 0, 2) + + return output + + +def create_dummy_class(klass, dependency, message=""): + """ + When a dependency of a class is not available, create a dummy class which throws ImportError + when used. + + Args: + klass (str): name of the class. + dependency (str): name of the dependency. + message: extra message to print + Returns: + class: a class object + """ + err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass) + if message: + err = err + " " + message + + class _DummyMetaClass(type): + # throw error on class attribute access + def __getattr__(_, __): # noqa: B902 + raise ImportError(err) + + class _Dummy(object, metaclass=_DummyMetaClass): + # throw error on constructor + def __init__(self, *args, **kwargs): + raise ImportError(err) + + return _Dummy + + +def create_dummy_func(func, dependency, message=""): + """ + When a dependency of a function is not available, create a dummy function which throws + ImportError when used. + + Args: + func (str): name of the function. + dependency (str or list[str]): name(s) of the dependency. + message: extra message to print + Returns: + function: a function object + """ + err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func) + if message: + err = err + " " + message + + if isinstance(dependency, (list, tuple)): + dependency = ",".join(dependency) + + def _dummy(*args, **kwargs): + raise ImportError(err) + + return _dummy diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py b/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..fcb8742dbdde6e80fd38b11d064211f6935aae76 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py @@ -0,0 +1,959 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# DINO +# Copyright (c) 2022 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Conditional DETR Transformer class. +# Copyright (c) 2021 Microsoft. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Modified from DETR (https://github.com/facebookresearch/detr) +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# ------------------------------------------------------------------------ + +from typing import Optional + +import torch +import torch.utils.checkpoint as checkpoint +from torch import Tensor, nn + +from groundingdino.util.misc import inverse_sigmoid + +from .fuse_modules import BiAttentionBlock +from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn +from .transformer_vanilla import TransformerEncoderLayer +from .utils import ( + MLP, + _get_activation_fn, + _get_clones, + gen_encoder_output_proposals, + gen_sineembed_for_position, + get_sine_pos_embed, +) + + +class Transformer(nn.Module): + def __init__( + self, + d_model=256, + nhead=8, + num_queries=300, + num_encoder_layers=6, + num_unicoder_layers=0, + num_decoder_layers=6, + dim_feedforward=2048, + dropout=0.0, + activation="relu", + normalize_before=False, + return_intermediate_dec=False, + query_dim=4, + num_patterns=0, + # for deformable encoder + num_feature_levels=1, + enc_n_points=4, + dec_n_points=4, + # init query + learnable_tgt_init=False, + # two stage + two_stage_type="no", # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1'] + embed_init_tgt=False, + # for text + use_text_enhancer=False, + use_fusion_layer=False, + use_checkpoint=False, + use_transformer_ckpt=False, + use_text_cross_attention=False, + text_dropout=0.1, + fusion_dropout=0.1, + fusion_droppath=0.0, + ): + super().__init__() + self.num_feature_levels = num_feature_levels + self.num_encoder_layers = num_encoder_layers + self.num_unicoder_layers = num_unicoder_layers + self.num_decoder_layers = num_decoder_layers + self.num_queries = num_queries + assert query_dim == 4 + + # choose encoder layer type + encoder_layer = DeformableTransformerEncoderLayer( + d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points + ) + + if use_text_enhancer: + text_enhance_layer = TransformerEncoderLayer( + d_model=d_model, + nhead=nhead // 2, + dim_feedforward=dim_feedforward // 2, + dropout=text_dropout, + ) + else: + text_enhance_layer = None + + if use_fusion_layer: + feature_fusion_layer = BiAttentionBlock( + v_dim=d_model, + l_dim=d_model, + embed_dim=dim_feedforward // 2, + num_heads=nhead // 2, + dropout=fusion_dropout, + drop_path=fusion_droppath, + ) + else: + feature_fusion_layer = None + + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + assert encoder_norm is None + self.encoder = TransformerEncoder( + encoder_layer, + num_encoder_layers, + d_model=d_model, + num_queries=num_queries, + text_enhance_layer=text_enhance_layer, + feature_fusion_layer=feature_fusion_layer, + use_checkpoint=use_checkpoint, + use_transformer_ckpt=use_transformer_ckpt, + ) + + # choose decoder layer type + decoder_layer = DeformableTransformerDecoderLayer( + d_model, + dim_feedforward, + dropout, + activation, + num_feature_levels, + nhead, + dec_n_points, + use_text_cross_attention=use_text_cross_attention, + ) + + decoder_norm = nn.LayerNorm(d_model) + self.decoder = TransformerDecoder( + decoder_layer, + num_decoder_layers, + decoder_norm, + return_intermediate=return_intermediate_dec, + d_model=d_model, + query_dim=query_dim, + num_feature_levels=num_feature_levels, + ) + + self.d_model = d_model + self.nhead = nhead + self.dec_layers = num_decoder_layers + self.num_queries = num_queries # useful for single stage model only + self.num_patterns = num_patterns + if not isinstance(num_patterns, int): + Warning("num_patterns should be int but {}".format(type(num_patterns))) + self.num_patterns = 0 + + if num_feature_levels > 1: + if self.num_encoder_layers > 0: + self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) + else: + self.level_embed = None + + self.learnable_tgt_init = learnable_tgt_init + assert learnable_tgt_init, "why not learnable_tgt_init" + self.embed_init_tgt = embed_init_tgt + if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"): + self.tgt_embed = nn.Embedding(self.num_queries, d_model) + nn.init.normal_(self.tgt_embed.weight.data) + else: + self.tgt_embed = None + + # for two stage + self.two_stage_type = two_stage_type + assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format( + two_stage_type + ) + if two_stage_type == "standard": + # anchor selection at the output of encoder + self.enc_output = nn.Linear(d_model, d_model) + self.enc_output_norm = nn.LayerNorm(d_model) + self.two_stage_wh_embedding = None + + if two_stage_type == "no": + self.init_ref_points(num_queries) # init self.refpoint_embed + + self.enc_out_class_embed = None + self.enc_out_bbox_embed = None + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MSDeformAttn): + m._reset_parameters() + if self.num_feature_levels > 1 and self.level_embed is not None: + nn.init.normal_(self.level_embed) + + def get_valid_ratio(self, mask): + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def init_ref_points(self, use_num_queries): + self.refpoint_embed = nn.Embedding(use_num_queries, 4) + + def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None): + """ + Input: + - srcs: List of multi features [bs, ci, hi, wi] + - masks: List of multi masks [bs, hi, wi] + - refpoint_embed: [bs, num_dn, 4]. None in infer + - pos_embeds: List of multi pos embeds [bs, ci, hi, wi] + - tgt: [bs, num_dn, d_model]. None in infer + + """ + # prepare input for encoder + src_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): + bs, c, h, w = src.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + + src = src.flatten(2).transpose(1, 2) # bs, hw, c + mask = mask.flatten(1) # bs, hw + pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c + if self.num_feature_levels > 1 and self.level_embed is not None: + lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) + else: + lvl_pos_embed = pos_embed + lvl_pos_embed_flatten.append(lvl_pos_embed) + src_flatten.append(src) + mask_flatten.append(mask) + src_flatten = torch.cat(src_flatten, 1) # bs, \sum{hxw}, c + mask_flatten = torch.cat(mask_flatten, 1) # bs, \sum{hxw} + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) # bs, \sum{hxw}, c + spatial_shapes = torch.as_tensor( + spatial_shapes, dtype=torch.long, device=src_flatten.device + ) + level_start_index = torch.cat( + (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]) + ) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) + + # two stage + enc_topk_proposals = enc_refpoint_embed = None + + ######################################################### + # Begin Encoder + ######################################################### + memory, memory_text = self.encoder( + src_flatten, + pos=lvl_pos_embed_flatten, + level_start_index=level_start_index, + spatial_shapes=spatial_shapes, + valid_ratios=valid_ratios, + key_padding_mask=mask_flatten, + memory_text=text_dict["encoded_text"], + text_attention_mask=~text_dict["text_token_mask"], + # we ~ the mask . False means use the token; True means pad the token + position_ids=text_dict["position_ids"], + text_self_attention_masks=text_dict["text_self_attention_masks"], + ) + ######################################################### + # End Encoder + # - memory: bs, \sum{hw}, c + # - mask_flatten: bs, \sum{hw} + # - lvl_pos_embed_flatten: bs, \sum{hw}, c + # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c) + # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c) + ######################################################### + text_dict["encoded_text"] = memory_text + # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + # if memory.isnan().any() | memory.isinf().any(): + # import ipdb; ipdb.set_trace() + + if self.two_stage_type == "standard": + output_memory, output_proposals = gen_encoder_output_proposals( + memory, mask_flatten, spatial_shapes + ) + output_memory = self.enc_output_norm(self.enc_output(output_memory)) + + if text_dict is not None: + enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict) + else: + enc_outputs_class_unselected = self.enc_out_class_embed(output_memory) + + topk_logits = enc_outputs_class_unselected.max(-1)[0] + enc_outputs_coord_unselected = ( + self.enc_out_bbox_embed(output_memory) + output_proposals + ) # (bs, \sum{hw}, 4) unsigmoid + topk = self.num_queries + + topk_proposals = torch.topk(topk_logits, topk, dim=1)[1] # bs, nq + + # gather boxes + refpoint_embed_undetach = torch.gather( + enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4) + ) # unsigmoid + refpoint_embed_ = refpoint_embed_undetach.detach() + init_box_proposal = torch.gather( + output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4) + ).sigmoid() # sigmoid + + # gather tgt + tgt_undetach = torch.gather( + output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model) + ) + if self.embed_init_tgt: + tgt_ = ( + self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1) + ) # nq, bs, d_model + else: + tgt_ = tgt_undetach.detach() + + if refpoint_embed is not None: + refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1) + tgt = torch.cat([tgt, tgt_], dim=1) + else: + refpoint_embed, tgt = refpoint_embed_, tgt_ + + elif self.two_stage_type == "no": + tgt_ = ( + self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1) + ) # nq, bs, d_model + refpoint_embed_ = ( + self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1) + ) # nq, bs, 4 + + if refpoint_embed is not None: + refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1) + tgt = torch.cat([tgt, tgt_], dim=1) + else: + refpoint_embed, tgt = refpoint_embed_, tgt_ + + if self.num_patterns > 0: + tgt_embed = tgt.repeat(1, self.num_patterns, 1) + refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1) + tgt_pat = self.patterns.weight[None, :, :].repeat_interleave( + self.num_queries, 1 + ) # 1, n_q*n_pat, d_model + tgt = tgt_embed + tgt_pat + + init_box_proposal = refpoint_embed_.sigmoid() + + else: + raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type)) + ######################################################### + # End preparing tgt + # - tgt: bs, NQ, d_model + # - refpoint_embed(unsigmoid): bs, NQ, d_model + ######################################################### + + ######################################################### + # Begin Decoder + ######################################################### + hs, references = self.decoder( + tgt=tgt.transpose(0, 1), + memory=memory.transpose(0, 1), + memory_key_padding_mask=mask_flatten, + pos=lvl_pos_embed_flatten.transpose(0, 1), + refpoints_unsigmoid=refpoint_embed.transpose(0, 1), + level_start_index=level_start_index, + spatial_shapes=spatial_shapes, + valid_ratios=valid_ratios, + tgt_mask=attn_mask, + memory_text=text_dict["encoded_text"], + text_attention_mask=~text_dict["text_token_mask"], + # we ~ the mask . False means use the token; True means pad the token + ) + ######################################################### + # End Decoder + # hs: n_dec, bs, nq, d_model + # references: n_dec+1, bs, nq, query_dim + ######################################################### + + ######################################################### + # Begin postprocess + ######################################################### + if self.two_stage_type == "standard": + hs_enc = tgt_undetach.unsqueeze(0) + ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0) + else: + hs_enc = ref_enc = None + ######################################################### + # End postprocess + # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None + # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None + ######################################################### + + return hs, references, hs_enc, ref_enc, init_box_proposal + # hs: (n_dec, bs, nq, d_model) + # references: sigmoid coordinates. (n_dec+1, bs, bq, 4) + # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None + # ref_enc: sigmoid coordinates. \ + # (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None + + +class TransformerEncoder(nn.Module): + def __init__( + self, + encoder_layer, + num_layers, + d_model=256, + num_queries=300, + enc_layer_share=False, + text_enhance_layer=None, + feature_fusion_layer=None, + use_checkpoint=False, + use_transformer_ckpt=False, + ): + """_summary_ + + Args: + encoder_layer (_type_): _description_ + num_layers (_type_): _description_ + norm (_type_, optional): _description_. Defaults to None. + d_model (int, optional): _description_. Defaults to 256. + num_queries (int, optional): _description_. Defaults to 300. + enc_layer_share (bool, optional): _description_. Defaults to False. + + """ + super().__init__() + # prepare layers + self.layers = [] + self.text_layers = [] + self.fusion_layers = [] + if num_layers > 0: + self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share) + + if text_enhance_layer is not None: + self.text_layers = _get_clones( + text_enhance_layer, num_layers, layer_share=enc_layer_share + ) + if feature_fusion_layer is not None: + self.fusion_layers = _get_clones( + feature_fusion_layer, num_layers, layer_share=enc_layer_share + ) + else: + self.layers = [] + del encoder_layer + + if text_enhance_layer is not None: + self.text_layers = [] + del text_enhance_layer + if feature_fusion_layer is not None: + self.fusion_layers = [] + del feature_fusion_layer + + self.query_scale = None + self.num_queries = num_queries + self.num_layers = num_layers + self.d_model = d_model + + self.use_checkpoint = use_checkpoint + self.use_transformer_ckpt = use_transformer_ckpt + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + reference_points_list = [] + for lvl, (H_, W_) in enumerate(spatial_shapes): + + ref_y, ref_x = torch.meshgrid( + torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), + torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device), + ) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def forward( + self, + # for images + src: Tensor, + pos: Tensor, + spatial_shapes: Tensor, + level_start_index: Tensor, + valid_ratios: Tensor, + key_padding_mask: Tensor, + # for texts + memory_text: Tensor = None, + text_attention_mask: Tensor = None, + pos_text: Tensor = None, + text_self_attention_masks: Tensor = None, + position_ids: Tensor = None, + ): + """ + Input: + - src: [bs, sum(hi*wi), 256] + - pos: pos embed for src. [bs, sum(hi*wi), 256] + - spatial_shapes: h,w of each level [num_level, 2] + - level_start_index: [num_level] start point of level in sum(hi*wi). + - valid_ratios: [bs, num_level, 2] + - key_padding_mask: [bs, sum(hi*wi)] + + - memory_text: bs, n_text, 256 + - text_attention_mask: bs, n_text + False for no padding; True for padding + - pos_text: bs, n_text, 256 + + - position_ids: bs, n_text + Intermedia: + - reference_points: [bs, sum(hi*wi), num_level, 2] + Outpus: + - output: [bs, sum(hi*wi), 256] + """ + + output = src + + # preparation and reshape + if self.num_layers > 0: + reference_points = self.get_reference_points( + spatial_shapes, valid_ratios, device=src.device + ) + + if self.text_layers: + # generate pos_text + bs, n_text, text_dim = memory_text.shape + if pos_text is None and position_ids is None: + pos_text = ( + torch.arange(n_text, device=memory_text.device) + .float() + .unsqueeze(0) + .unsqueeze(-1) + .repeat(bs, 1, 1) + ) + pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False) + if position_ids is not None: + pos_text = get_sine_pos_embed( + position_ids[..., None], num_pos_feats=256, exchange_xy=False + ) + + # main process + for layer_id, layer in enumerate(self.layers): + # if output.isnan().any() or memory_text.isnan().any(): + # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': + # import ipdb; ipdb.set_trace() + if self.fusion_layers: + if self.use_checkpoint: + output, memory_text = checkpoint.checkpoint( + self.fusion_layers[layer_id], + output, + memory_text, + key_padding_mask, + text_attention_mask, + ) + else: + output, memory_text = self.fusion_layers[layer_id]( + v=output, + l=memory_text, + attention_mask_v=key_padding_mask, + attention_mask_l=text_attention_mask, + ) + + if self.text_layers: + memory_text = self.text_layers[layer_id]( + src=memory_text.transpose(0, 1), + src_mask=~text_self_attention_masks, # note we use ~ for mask here + src_key_padding_mask=text_attention_mask, + pos=(pos_text.transpose(0, 1) if pos_text is not None else None), + ).transpose(0, 1) + + # main process + if self.use_transformer_ckpt: + output = checkpoint.checkpoint( + layer, + output, + pos, + reference_points, + spatial_shapes, + level_start_index, + key_padding_mask, + ) + else: + output = layer( + src=output, + pos=pos, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + level_start_index=level_start_index, + key_padding_mask=key_padding_mask, + ) + + return output, memory_text + + +class TransformerDecoder(nn.Module): + def __init__( + self, + decoder_layer, + num_layers, + norm=None, + return_intermediate=False, + d_model=256, + query_dim=4, + num_feature_levels=1, + ): + super().__init__() + if num_layers > 0: + self.layers = _get_clones(decoder_layer, num_layers) + else: + self.layers = [] + self.num_layers = num_layers + self.norm = norm + self.return_intermediate = return_intermediate + assert return_intermediate, "support return_intermediate only" + self.query_dim = query_dim + assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim) + self.num_feature_levels = num_feature_levels + + self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2) + self.query_pos_sine_scale = None + + self.query_scale = None + self.bbox_embed = None + self.class_embed = None + + self.d_model = d_model + + self.ref_anchor_head = None + + def forward( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + refpoints_unsigmoid: Optional[Tensor] = None, # num_queries, bs, 2 + # for memory + level_start_index: Optional[Tensor] = None, # num_levels + spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2 + valid_ratios: Optional[Tensor] = None, + # for text + memory_text: Optional[Tensor] = None, + text_attention_mask: Optional[Tensor] = None, + ): + """ + Input: + - tgt: nq, bs, d_model + - memory: hw, bs, d_model + - pos: hw, bs, d_model + - refpoints_unsigmoid: nq, bs, 2/4 + - valid_ratios/spatial_shapes: bs, nlevel, 2 + """ + output = tgt + + intermediate = [] + reference_points = refpoints_unsigmoid.sigmoid() + ref_points = [reference_points] + + for layer_id, layer in enumerate(self.layers): + + if reference_points.shape[-1] == 4: + reference_points_input = ( + reference_points[:, :, None] + * torch.cat([valid_ratios, valid_ratios], -1)[None, :] + ) # nq, bs, nlevel, 4 + else: + assert reference_points.shape[-1] == 2 + reference_points_input = reference_points[:, :, None] * valid_ratios[None, :] + query_sine_embed = gen_sineembed_for_position( + reference_points_input[:, :, 0, :] + ) # nq, bs, 256*2 + + # conditional query + raw_query_pos = self.ref_point_head(query_sine_embed) # nq, bs, 256 + pos_scale = self.query_scale(output) if self.query_scale is not None else 1 + query_pos = pos_scale * raw_query_pos + # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + # if query_pos.isnan().any() | query_pos.isinf().any(): + # import ipdb; ipdb.set_trace() + + # main process + output = layer( + tgt=output, + tgt_query_pos=query_pos, + tgt_query_sine_embed=query_sine_embed, + tgt_key_padding_mask=tgt_key_padding_mask, + tgt_reference_points=reference_points_input, + memory_text=memory_text, + text_attention_mask=text_attention_mask, + memory=memory, + memory_key_padding_mask=memory_key_padding_mask, + memory_level_start_index=level_start_index, + memory_spatial_shapes=spatial_shapes, + memory_pos=pos, + self_attn_mask=tgt_mask, + cross_attn_mask=memory_mask, + ) + if output.isnan().any() | output.isinf().any(): + print(f"output layer_id {layer_id} is nan") + try: + num_nan = output.isnan().sum().item() + num_inf = output.isinf().sum().item() + print(f"num_nan {num_nan}, num_inf {num_inf}") + except Exception as e: + print(e) + # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1': + # import ipdb; ipdb.set_trace() + + # iter update + if self.bbox_embed is not None: + # box_holder = self.bbox_embed(output) + # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points) + # new_reference_points = box_holder[..., :self.query_dim].sigmoid() + + reference_before_sigmoid = inverse_sigmoid(reference_points) + delta_unsig = self.bbox_embed[layer_id](output) + outputs_unsig = delta_unsig + reference_before_sigmoid + new_reference_points = outputs_unsig.sigmoid() + + reference_points = new_reference_points.detach() + # if layer_id != self.num_layers - 1: + ref_points.append(new_reference_points) + + intermediate.append(self.norm(output)) + + return [ + [itm_out.transpose(0, 1) for itm_out in intermediate], + [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points], + ] + + +class DeformableTransformerEncoderLayer(nn.Module): + def __init__( + self, + d_model=256, + d_ffn=1024, + dropout=0.1, + activation="relu", + n_levels=4, + n_heads=8, + n_points=4, + ): + super().__init__() + + # self attention + self.self_attn = MSDeformAttn( + embed_dim=d_model, + num_levels=n_levels, + num_heads=n_heads, + num_points=n_points, + batch_first=True, + ) + self.dropout1 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, d_ffn) + self.activation = _get_activation_fn(activation, d_model=d_ffn) + self.dropout2 = nn.Dropout(dropout) + self.linear2 = nn.Linear(d_ffn, d_model) + self.dropout3 = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, src): + src2 = self.linear2(self.dropout2(self.activation(self.linear1(src)))) + src = src + self.dropout3(src2) + src = self.norm2(src) + return src + + def forward( + self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None + ): + # self attention + # import ipdb; ipdb.set_trace() + src2 = self.self_attn( + query=self.with_pos_embed(src, pos), + reference_points=reference_points, + value=src, + spatial_shapes=spatial_shapes, + level_start_index=level_start_index, + key_padding_mask=key_padding_mask, + ) + src = src + self.dropout1(src2) + src = self.norm1(src) + + # ffn + src = self.forward_ffn(src) + + return src + + +class DeformableTransformerDecoderLayer(nn.Module): + def __init__( + self, + d_model=256, + d_ffn=1024, + dropout=0.1, + activation="relu", + n_levels=4, + n_heads=8, + n_points=4, + use_text_feat_guide=False, + use_text_cross_attention=False, + ): + super().__init__() + + # cross attention + self.cross_attn = MSDeformAttn( + embed_dim=d_model, + num_levels=n_levels, + num_heads=n_heads, + num_points=n_points, + batch_first=True, + ) + self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.norm1 = nn.LayerNorm(d_model) + + # cross attention text + if use_text_cross_attention: + self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) + self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.catext_norm = nn.LayerNorm(d_model) + + # self attention + self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout) + self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.norm2 = nn.LayerNorm(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, d_ffn) + self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1) + self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.linear2 = nn.Linear(d_ffn, d_model) + self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity() + self.norm3 = nn.LayerNorm(d_model) + + self.key_aware_proj = None + self.use_text_feat_guide = use_text_feat_guide + assert not use_text_feat_guide + self.use_text_cross_attention = use_text_cross_attention + + def rm_self_attn_modules(self): + self.self_attn = None + self.dropout2 = None + self.norm2 = None + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, tgt): + with torch.cuda.amp.autocast(enabled=False): + tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout4(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward( + self, + # for tgt + tgt: Optional[Tensor], # nq, bs, d_model + tgt_query_pos: Optional[Tensor] = None, # pos for query. MLP(Sine(pos)) + tgt_query_sine_embed: Optional[Tensor] = None, # pos for query. Sine(pos) + tgt_key_padding_mask: Optional[Tensor] = None, + tgt_reference_points: Optional[Tensor] = None, # nq, bs, 4 + memory_text: Optional[Tensor] = None, # bs, num_token, d_model + text_attention_mask: Optional[Tensor] = None, # bs, num_token + # for memory + memory: Optional[Tensor] = None, # hw, bs, d_model + memory_key_padding_mask: Optional[Tensor] = None, + memory_level_start_index: Optional[Tensor] = None, # num_levels + memory_spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2 + memory_pos: Optional[Tensor] = None, # pos for memory + # sa + self_attn_mask: Optional[Tensor] = None, # mask used for self-attention + cross_attn_mask: Optional[Tensor] = None, # mask used for cross-attention + ): + """ + Input: + - tgt/tgt_query_pos: nq, bs, d_model + - + """ + assert cross_attn_mask is None + + # self attention + if self.self_attn is not None: + # import ipdb; ipdb.set_trace() + q = k = self.with_pos_embed(tgt, tgt_query_pos) + tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + + if self.use_text_cross_attention: + tgt2 = self.ca_text( + self.with_pos_embed(tgt, tgt_query_pos), + memory_text.transpose(0, 1), + memory_text.transpose(0, 1), + key_padding_mask=text_attention_mask, + )[0] + tgt = tgt + self.catext_dropout(tgt2) + tgt = self.catext_norm(tgt) + + tgt2 = self.cross_attn( + query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1), + reference_points=tgt_reference_points.transpose(0, 1).contiguous(), + value=memory.transpose(0, 1), + spatial_shapes=memory_spatial_shapes, + level_start_index=memory_level_start_index, + key_padding_mask=memory_key_padding_mask, + ).transpose(0, 1) + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + + # ffn + tgt = self.forward_ffn(tgt) + + return tgt + + +def build_transformer(args): + return Transformer( + d_model=args.hidden_dim, + dropout=args.dropout, + nhead=args.nheads, + num_queries=args.num_queries, + dim_feedforward=args.dim_feedforward, + num_encoder_layers=args.enc_layers, + num_decoder_layers=args.dec_layers, + normalize_before=args.pre_norm, + return_intermediate_dec=True, + query_dim=args.query_dim, + activation=args.transformer_activation, + num_patterns=args.num_patterns, + num_feature_levels=args.num_feature_levels, + enc_n_points=args.enc_n_points, + dec_n_points=args.dec_n_points, + learnable_tgt_init=True, + # two stage + two_stage_type=args.two_stage_type, # ['no', 'standard', 'early'] + embed_init_tgt=args.embed_init_tgt, + use_text_enhancer=args.use_text_enhancer, + use_fusion_layer=args.use_fusion_layer, + use_checkpoint=args.use_checkpoint, + use_transformer_ckpt=args.use_transformer_ckpt, + use_text_cross_attention=args.use_text_cross_attention, + text_dropout=args.text_dropout, + fusion_dropout=args.fusion_dropout, + fusion_droppath=args.fusion_droppath, + ) diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py b/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py new file mode 100644 index 0000000000000000000000000000000000000000..10c0920c1a217af5bb3e1b13077568035ab3b7b5 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py @@ -0,0 +1,123 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +DETR Transformer class. + +Copy-paste from torch.nn.Transformer with modifications: + * positional encodings are passed in MHattention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers +""" +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from .utils import ( + MLP, + _get_activation_fn, + _get_clones, + gen_encoder_output_proposals, + gen_sineembed_for_position, + sigmoid_focal_loss, +) + + +class TextTransformer(nn.Module): + def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1): + super().__init__() + self.num_layers = num_layers + self.d_model = d_model + self.nheads = nheads + self.dim_feedforward = dim_feedforward + self.norm = None + + single_encoder_layer = TransformerEncoderLayer( + d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout + ) + self.layers = _get_clones(single_encoder_layer, num_layers) + + def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor): + """ + + Args: + text_attention_mask: bs, num_token + memory_text: bs, num_token, d_model + + Raises: + RuntimeError: _description_ + + Returns: + output: bs, num_token, d_model + """ + + output = memory_text.transpose(0, 1) + + for layer in self.layers: + output = layer(output, src_key_padding_mask=text_attention_mask) + + if self.norm is not None: + output = self.norm(output) + + return output.transpose(0, 1) + + +class TransformerEncoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + self.nhead = nhead + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + # repeat attn mask + if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]: + # bs, num_q, num_k + src_mask = src_mask.repeat(self.nhead, 1, 1) + + q = k = self.with_pos_embed(src, pos) + + src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0] + + # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src diff --git a/GroundingDINO/groundingdino/models/GroundingDINO/utils.py b/GroundingDINO/groundingdino/models/GroundingDINO/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5bd18f70225e12b2e27fdb4eabcde91d959f8e31 --- /dev/null +++ b/GroundingDINO/groundingdino/models/GroundingDINO/utils.py @@ -0,0 +1,268 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +import copy +import math + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + + +def _get_clones(module, N, layer_share=False): + # import ipdb; ipdb.set_trace() + if layer_share: + return nn.ModuleList([module for i in range(N)]) + else: + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def get_sine_pos_embed( + pos_tensor: torch.Tensor, + num_pos_feats: int = 128, + temperature: int = 10000, + exchange_xy: bool = True, +): + """generate sine position embedding from a position tensor + Args: + pos_tensor (torch.Tensor): shape: [..., n]. + num_pos_feats (int): projected shape for each float in the tensor. + temperature (int): temperature in the sine/cosine function. + exchange_xy (bool, optional): exchange pos x and pos y. \ + For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True. + Returns: + pos_embed (torch.Tensor): shape: [..., n*num_pos_feats]. + """ + scale = 2 * math.pi + dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device) + dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats) + + def sine_func(x: torch.Tensor): + sin_x = x * scale / dim_t + sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2) + return sin_x + + pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)] + if exchange_xy: + pos_res[0], pos_res[1] = pos_res[1], pos_res[0] + pos_res = torch.cat(pos_res, dim=-1) + return pos_res + + +def gen_encoder_output_proposals( + memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None +): + """ + Input: + - memory: bs, \sum{hw}, d_model + - memory_padding_mask: bs, \sum{hw} + - spatial_shapes: nlevel, 2 + - learnedwh: 2 + Output: + - output_memory: bs, \sum{hw}, d_model + - output_proposals: bs, \sum{hw}, 4 + """ + N_, S_, C_ = memory.shape + proposals = [] + _cur = 0 + for lvl, (H_, W_) in enumerate(spatial_shapes): + mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1) + valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) + valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) + + # import ipdb; ipdb.set_trace() + + grid_y, grid_x = torch.meshgrid( + torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device), + torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device), + ) + grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) # H_, W_, 2 + + scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2) + grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale + + if learnedwh is not None: + # import ipdb; ipdb.set_trace() + wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl) + else: + wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) + + # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1) + # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale + # wh = torch.ones_like(grid) / scale + proposal = torch.cat((grid, wh), -1).view(N_, -1, 4) + proposals.append(proposal) + _cur += H_ * W_ + # import ipdb; ipdb.set_trace() + output_proposals = torch.cat(proposals, 1) + output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all( + -1, keepdim=True + ) + output_proposals = torch.log(output_proposals / (1 - output_proposals)) # unsigmoid + output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf")) + output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) + + output_memory = memory + output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) + output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) + + # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf')) + # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf')) + + return output_memory, output_proposals + + +class RandomBoxPerturber: + def __init__( + self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2 + ) -> None: + self.noise_scale = torch.Tensor( + [x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale] + ) + + def __call__(self, refanchors: Tensor) -> Tensor: + nq, bs, query_dim = refanchors.shape + device = refanchors.device + + noise_raw = torch.rand_like(refanchors) + noise_scale = self.noise_scale.to(device)[:query_dim] + + new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale) + return new_refanchors.clamp_(0, 1) + + +def sigmoid_focal_loss( + inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False +): + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + alpha: (optional) Weighting factor in range (0,1) to balance + positive vs negative examples. Default = -1 (no weighting). + gamma: Exponent of the modulating factor (1 - p_t) to + balance easy vs hard examples. + Returns: + Loss tensor + """ + prob = inputs.sigmoid() + ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + p_t = prob * targets + (1 - prob) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + if no_reduction: + return loss + + return loss.mean(1).sum() / num_boxes + + +class MLP(nn.Module): + """Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList( + nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) + ) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +def _get_activation_fn(activation, d_model=256, batch_dim=0): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + if activation == "prelu": + return nn.PReLU() + if activation == "selu": + return F.selu + + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") + + +def gen_sineembed_for_position(pos_tensor): + # n_query, bs, _ = pos_tensor.size() + # sineembed_tensor = torch.zeros(n_query, bs, 256) + scale = 2 * math.pi + dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device) + dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128) + x_embed = pos_tensor[:, :, 0] * scale + y_embed = pos_tensor[:, :, 1] * scale + pos_x = x_embed[:, :, None] / dim_t + pos_y = y_embed[:, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) + pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2) + if pos_tensor.size(-1) == 2: + pos = torch.cat((pos_y, pos_x), dim=2) + elif pos_tensor.size(-1) == 4: + w_embed = pos_tensor[:, :, 2] * scale + pos_w = w_embed[:, :, None] / dim_t + pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2) + + h_embed = pos_tensor[:, :, 3] * scale + pos_h = h_embed[:, :, None] / dim_t + pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2) + + pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2) + else: + raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1))) + return pos + + +class ContrastiveEmbed(nn.Module): + def __init__(self, max_text_len=256): + """ + Args: + max_text_len: max length of text. + """ + super().__init__() + self.max_text_len = max_text_len + + def forward(self, x, text_dict): + """_summary_ + + Args: + x (_type_): _description_ + text_dict (_type_): _description_ + { + 'encoded_text': encoded_text, # bs, 195, d_model + 'text_token_mask': text_token_mask, # bs, 195 + # True for used tokens. False for padding tokens + } + Returns: + _type_: _description_ + """ + assert isinstance(text_dict, dict) + + y = text_dict["encoded_text"] + text_token_mask = text_dict["text_token_mask"] + + res = x @ y.transpose(-1, -2) + res.masked_fill_(~text_token_mask[:, None, :], float("-inf")) + + # padding to max_text_len + new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device) + new_res[..., : res.shape[-1]] = res + + return new_res diff --git a/GroundingDINO/groundingdino/models/__init__.py b/GroundingDINO/groundingdino/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e3413961d1d184b99835eb1e919b052d70298bc6 --- /dev/null +++ b/GroundingDINO/groundingdino/models/__init__.py @@ -0,0 +1,18 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .GroundingDINO import build_groundingdino + + +def build_model(args): + # we use register to maintain models from catdet6 on. + from .registry import MODULE_BUILD_FUNCS + + assert args.modelname in MODULE_BUILD_FUNCS._module_dict + build_func = MODULE_BUILD_FUNCS.get(args.modelname) + model = build_func(args) + return model diff --git a/GroundingDINO/groundingdino/models/registry.py b/GroundingDINO/groundingdino/models/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..2d22a59eec79a2a19b83fa1779f2adaf5753aec6 --- /dev/null +++ b/GroundingDINO/groundingdino/models/registry.py @@ -0,0 +1,66 @@ +# ------------------------------------------------------------------------ +# Grounding DINO +# url: https://github.com/IDEA-Research/GroundingDINO +# Copyright (c) 2023 IDEA. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ +# -*- coding: utf-8 -*- +# @Author: Yihao Chen +# @Date: 2021-08-16 16:03:17 +# @Last Modified by: Shilong Liu +# @Last Modified time: 2022-01-23 15:26 +# modified from mmcv + +import inspect +from functools import partial + + +class Registry(object): + def __init__(self, name): + self._name = name + self._module_dict = dict() + + def __repr__(self): + format_str = self.__class__.__name__ + "(name={}, items={})".format( + self._name, list(self._module_dict.keys()) + ) + return format_str + + def __len__(self): + return len(self._module_dict) + + @property + def name(self): + return self._name + + @property + def module_dict(self): + return self._module_dict + + def get(self, key): + return self._module_dict.get(key, None) + + def registe_with_name(self, module_name=None, force=False): + return partial(self.register, module_name=module_name, force=force) + + def register(self, module_build_function, module_name=None, force=False): + """Register a module build function. + Args: + module (:obj:`nn.Module`): Module to be registered. + """ + if not inspect.isfunction(module_build_function): + raise TypeError( + "module_build_function must be a function, but got {}".format( + type(module_build_function) + ) + ) + if module_name is None: + module_name = module_build_function.__name__ + if not force and module_name in self._module_dict: + raise KeyError("{} is already registered in {}".format(module_name, self.name)) + self._module_dict[module_name] = module_build_function + + return module_build_function + + +MODULE_BUILD_FUNCS = Registry("model build functions") diff --git a/GroundingDINO/groundingdino/util/__init__.py b/GroundingDINO/groundingdino/util/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..168f9979a4623806934b0ff1102ac166704e7dec --- /dev/null +++ b/GroundingDINO/groundingdino/util/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved diff --git a/GroundingDINO/groundingdino/util/box_ops.py b/GroundingDINO/groundingdino/util/box_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..781068d294e576954edb4bd07b6e0f30e4e1bcd9 --- /dev/null +++ b/GroundingDINO/groundingdino/util/box_ops.py @@ -0,0 +1,140 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Utilities for bounding box manipulation and GIoU. +""" +import torch +from torchvision.ops.boxes import box_area + + +def box_cxcywh_to_xyxy(x): + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] + return torch.stack(b, dim=-1) + + +def box_xyxy_to_cxcywh(x): + x0, y0, x1, y1 = x.unbind(-1) + b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)] + return torch.stack(b, dim=-1) + + +# modified from torchvision to also return the union +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + # import ipdb; ipdb.set_trace() + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / (union + 1e-6) + return iou, union + + +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + The boxes should be in [x0, y0, x1, y1] format + + Returns a [N, M] pairwise matrix, where N = len(boxes1) + and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + # except: + # import ipdb; ipdb.set_trace() + iou, union = box_iou(boxes1, boxes2) + + lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,M,2] + area = wh[:, :, 0] * wh[:, :, 1] + + return iou - (area - union) / (area + 1e-6) + + +# modified from torchvision to also return the union +def box_iou_pairwise(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2] + rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2] + + wh = (rb - lt).clamp(min=0) # [N,2] + inter = wh[:, 0] * wh[:, 1] # [N] + + union = area1 + area2 - inter + + iou = inter / union + return iou, union + + +def generalized_box_iou_pairwise(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + Input: + - boxes1, boxes2: N,4 + Output: + - giou: N, 4 + """ + # degenerate boxes gives inf / nan results + # so do an early check + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + assert boxes1.shape == boxes2.shape + iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4 + + lt = torch.min(boxes1[:, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,2] + area = wh[:, 0] * wh[:, 1] + + return iou - (area - union) / area + + +def masks_to_boxes(masks): + """Compute the bounding boxes around the provided masks + + The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. + + Returns a [N, 4] tensors, with the boxes in xyxy format + """ + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device) + + h, w = masks.shape[-2:] + + y = torch.arange(0, h, dtype=torch.float) + x = torch.arange(0, w, dtype=torch.float) + y, x = torch.meshgrid(y, x) + + x_mask = masks * x.unsqueeze(0) + x_max = x_mask.flatten(1).max(-1)[0] + x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + y_mask = masks * y.unsqueeze(0) + y_max = y_mask.flatten(1).max(-1)[0] + y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + return torch.stack([x_min, y_min, x_max, y_max], 1) + + +if __name__ == "__main__": + x = torch.rand(5, 4) + y = torch.rand(3, 4) + iou, union = box_iou(x, y) + import ipdb + + ipdb.set_trace() diff --git a/GroundingDINO/groundingdino/util/get_tokenlizer.py b/GroundingDINO/groundingdino/util/get_tokenlizer.py new file mode 100644 index 0000000000000000000000000000000000000000..f7dcf7e95f03f95b20546b26442a94225924618b --- /dev/null +++ b/GroundingDINO/groundingdino/util/get_tokenlizer.py @@ -0,0 +1,26 @@ +from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast + + +def get_tokenlizer(text_encoder_type): + if not isinstance(text_encoder_type, str): + # print("text_encoder_type is not a str") + if hasattr(text_encoder_type, "text_encoder_type"): + text_encoder_type = text_encoder_type.text_encoder_type + elif text_encoder_type.get("text_encoder_type", False): + text_encoder_type = text_encoder_type.get("text_encoder_type") + else: + raise ValueError( + "Unknown type of text_encoder_type: {}".format(type(text_encoder_type)) + ) + print("final text_encoder_type: {}".format(text_encoder_type)) + + tokenizer = AutoTokenizer.from_pretrained(text_encoder_type) + return tokenizer + + +def get_pretrained_language_model(text_encoder_type): + if text_encoder_type == "bert-base-uncased": + return BertModel.from_pretrained(text_encoder_type) + if text_encoder_type == "roberta-base": + return RobertaModel.from_pretrained(text_encoder_type) + raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type)) diff --git a/GroundingDINO/groundingdino/util/inference.py b/GroundingDINO/groundingdino/util/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..7c9b8a0b382f615bcda0ef8220f79afc0892e641 --- /dev/null +++ b/GroundingDINO/groundingdino/util/inference.py @@ -0,0 +1,257 @@ +from typing import Tuple, List + +import re +import cv2 +import numpy as np +import supervision as sv +import torch +from PIL import Image +from torchvision.ops import box_convert + +import groundingdino.datasets.transforms as T +from groundingdino.models import build_model +from groundingdino.util.misc import clean_state_dict +from groundingdino.util.slconfig import SLConfig +from groundingdino.util.utils import get_phrases_from_posmap + +# ---------------------------------------------------------------------------------------------------------------------- +# OLD API +# ---------------------------------------------------------------------------------------------------------------------- + + +def preprocess_caption(caption: str) -> str: + result = caption.lower().strip() + if result.endswith("."): + return result + return result + "." + + +def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"): + args = SLConfig.fromfile(model_config_path) + args.device = device + model = build_model(args) + checkpoint = torch.load(model_checkpoint_path, map_location="cpu") + model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) + model.eval() + return model + + +def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]: + transform = T.Compose( + [ + T.RandomResize([800], max_size=1333), + T.ToTensor(), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ] + ) + image_source = Image.open(image_path).convert("RGB") + image = np.asarray(image_source) + image_transformed, _ = transform(image_source, None) + return image, image_transformed + + +def predict( + model, + image: torch.Tensor, + caption: str, + box_threshold: float, + text_threshold: float, + device: str = "cuda" +) -> Tuple[torch.Tensor, torch.Tensor, List[str]]: + caption = preprocess_caption(caption=caption) + + model = model.to(device) + image = image.to(device) + + with torch.no_grad(): + outputs = model(image[None], captions=[caption]) + + prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0] # prediction_logits.shape = (nq, 256) + prediction_boxes = outputs["pred_boxes"].cpu()[0] # prediction_boxes.shape = (nq, 4) + + mask = prediction_logits.max(dim=1)[0] > box_threshold + logits = prediction_logits[mask] # logits.shape = (n, 256) + boxes = prediction_boxes[mask] # boxes.shape = (n, 4) + + tokenizer = model.tokenizer + tokenized = tokenizer(caption) + + phrases = [ + get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '') + for logit + in logits + ] + + return boxes, logits.max(dim=1)[0], phrases + + +def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray: + h, w, _ = image_source.shape + boxes = boxes * torch.Tensor([w, h, w, h]) + xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() + detections = sv.Detections(xyxy=xyxy) + + labels = [ + f"{phrase} {logit:.2f}" + for phrase, logit + in zip(phrases, logits) + ] + + box_annotator = sv.BoxAnnotator() + annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR) + annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels) + return annotated_frame + + +# ---------------------------------------------------------------------------------------------------------------------- +# NEW API +# ---------------------------------------------------------------------------------------------------------------------- + + +class Model: + + def __init__( + self, + model_config_path: str, + model_checkpoint_path: str, + device: str = "cuda" + ): + self.model = load_model( + model_config_path=model_config_path, + model_checkpoint_path=model_checkpoint_path, + device=device + ).to(device) + self.device = device + + def predict_with_caption( + self, + image: np.ndarray, + caption: str, + box_threshold: float = 0.35, + text_threshold: float = 0.25 + ) -> Tuple[sv.Detections, List[str]]: + """ + import cv2 + + image = cv2.imread(IMAGE_PATH) + + model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH) + detections, labels = model.predict_with_caption( + image=image, + caption=caption, + box_threshold=BOX_THRESHOLD, + text_threshold=TEXT_THRESHOLD + ) + + import supervision as sv + + box_annotator = sv.BoxAnnotator() + annotated_image = box_annotator.annotate(scene=image, detections=detections, labels=labels) + """ + processed_image = Model.preprocess_image(image_bgr=image).to(self.device) + boxes, logits, phrases = predict( + model=self.model, + image=processed_image, + caption=caption, + box_threshold=box_threshold, + text_threshold=text_threshold, + device=self.device) + source_h, source_w, _ = image.shape + detections = Model.post_process_result( + source_h=source_h, + source_w=source_w, + boxes=boxes, + logits=logits) + return detections, phrases + + def predict_with_classes( + self, + image: np.ndarray, + classes: List[str], + box_threshold: float, + text_threshold: float + ) -> sv.Detections: + """ + import cv2 + + image = cv2.imread(IMAGE_PATH) + + model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH) + detections = model.predict_with_classes( + image=image, + classes=CLASSES, + box_threshold=BOX_THRESHOLD, + text_threshold=TEXT_THRESHOLD + ) + + + import supervision as sv + + box_annotator = sv.BoxAnnotator() + annotated_image = box_annotator.annotate(scene=image, detections=detections) + """ + caption = ". ".join(classes) + processed_image = Model.preprocess_image(image_bgr=image).to(self.device) + boxes, logits, phrases = predict( + model=self.model, + image=processed_image, + caption=caption, + box_threshold=box_threshold, + text_threshold=text_threshold, + device=self.device) + source_h, source_w, _ = image.shape + detections = Model.post_process_result( + source_h=source_h, + source_w=source_w, + boxes=boxes, + logits=logits) + class_id = Model.phrases2classes(phrases=phrases, classes=classes) + detections.class_id = class_id + return detections + + @staticmethod + def preprocess_image(image_bgr: np.ndarray) -> torch.Tensor: + transform = T.Compose( + [ + T.RandomResize([800], max_size=1333), + T.ToTensor(), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ] + ) + image_pillow = Image.fromarray(cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)) + image_transformed, _ = transform(image_pillow, None) + return image_transformed + + @staticmethod + def post_process_result( + source_h: int, + source_w: int, + boxes: torch.Tensor, + logits: torch.Tensor + ) -> sv.Detections: + boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h]) + xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() + confidence = logits.numpy() + return sv.Detections(xyxy=xyxy, confidence=confidence) + + @staticmethod + def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndarray: + class_ids = [] + for phrase in phrases: + try: + # class_ids.append(classes.index(phrase)) + class_ids.append(Model.find_index(phrase, classes)) + except ValueError: + class_ids.append(None) + return np.array(class_ids) + + @staticmethod + def find_index(string, lst): + # if meet string like "lake river" will only keep "lake" + # this is an hack implementation for visualization which will be updated in the future + string = string.lower().split()[0] + for i, s in enumerate(lst): + if string in s.lower(): + return i + print("There's a wrong phrase happen, this is because of our post-process merged wrong tokens, which will be modified in the future. We will assign it with a random label at this time.") + return 0 \ No newline at end of file diff --git a/GroundingDINO/groundingdino/util/logger.py b/GroundingDINO/groundingdino/util/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..18145f54c927abd59b95f3fa6e6da8002bc2ce97 --- /dev/null +++ b/GroundingDINO/groundingdino/util/logger.py @@ -0,0 +1,93 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import functools +import logging +import os +import sys + +from termcolor import colored + + +class _ColorfulFormatter(logging.Formatter): + def __init__(self, *args, **kwargs): + self._root_name = kwargs.pop("root_name") + "." + self._abbrev_name = kwargs.pop("abbrev_name", "") + if len(self._abbrev_name): + self._abbrev_name = self._abbrev_name + "." + super(_ColorfulFormatter, self).__init__(*args, **kwargs) + + def formatMessage(self, record): + record.name = record.name.replace(self._root_name, self._abbrev_name) + log = super(_ColorfulFormatter, self).formatMessage(record) + if record.levelno == logging.WARNING: + prefix = colored("WARNING", "red", attrs=["blink"]) + elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL: + prefix = colored("ERROR", "red", attrs=["blink", "underline"]) + else: + return log + return prefix + " " + log + + +# so that calling setup_logger multiple times won't add many handlers +@functools.lru_cache() +def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None): + """ + Initialize the detectron2 logger and set its verbosity level to "INFO". + + Args: + output (str): a file name or a directory to save log. If None, will not save log file. + If ends with ".txt" or ".log", assumed to be a file name. + Otherwise, logs will be saved to `output/log.txt`. + name (str): the root module name of this logger + + Returns: + logging.Logger: a logger + """ + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + logger.propagate = False + + if abbrev_name is None: + abbrev_name = name + + plain_formatter = logging.Formatter( + "[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S" + ) + # stdout logging: master only + if distributed_rank == 0: + ch = logging.StreamHandler(stream=sys.stdout) + ch.setLevel(logging.DEBUG) + if color: + formatter = _ColorfulFormatter( + colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s", + datefmt="%m/%d %H:%M:%S", + root_name=name, + abbrev_name=str(abbrev_name), + ) + else: + formatter = plain_formatter + ch.setFormatter(formatter) + logger.addHandler(ch) + + # file logging: all workers + if output is not None: + if output.endswith(".txt") or output.endswith(".log"): + filename = output + else: + filename = os.path.join(output, "log.txt") + if distributed_rank > 0: + filename = filename + f".rank{distributed_rank}" + os.makedirs(os.path.dirname(filename), exist_ok=True) + + fh = logging.StreamHandler(_cached_log_stream(filename)) + fh.setLevel(logging.DEBUG) + fh.setFormatter(plain_formatter) + logger.addHandler(fh) + + return logger + + +# cache the opened file object, so that different calls to `setup_logger` +# with the same file name can safely write to the same file. +@functools.lru_cache(maxsize=None) +def _cached_log_stream(filename): + return open(filename, "a") diff --git a/GroundingDINO/groundingdino/util/misc.py b/GroundingDINO/groundingdino/util/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..d64b84ef24bea0c98e76824feb1903f6bfebe7a5 --- /dev/null +++ b/GroundingDINO/groundingdino/util/misc.py @@ -0,0 +1,717 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +import colorsys +import datetime +import functools +import io +import json +import os +import pickle +import subprocess +import time +from collections import OrderedDict, defaultdict, deque +from typing import List, Optional + +import numpy as np +import torch +import torch.distributed as dist + +# needed due to empty tensor bug in pytorch and torchvision 0.5 +import torchvision +from torch import Tensor + +__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7 +if __torchvision_need_compat_flag: + from torchvision.ops import _new_empty_tensor + from torchvision.ops.misc import _output_size + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + if d.shape[0] == 0: + return 0 + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + if os.environ.get("SHILONG_AMP", None) == "1": + eps = 1e-4 + else: + eps = 1e-6 + return self.total / (self.count + eps) + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value, + ) + + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + + return dist.group.WORLD + + +def all_gather_cpu(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + + world_size = get_world_size() + if world_size == 1: + return [data] + + cpu_group = _get_global_gloo_group() + + buffer = io.BytesIO() + torch.save(data, buffer) + data_view = buffer.getbuffer() + device = "cuda" if cpu_group is None else "cpu" + tensor = torch.ByteTensor(data_view).to(device) + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long) + size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)] + if cpu_group is None: + dist.all_gather(size_list, local_size) + else: + print("gathering on cpu") + dist.all_gather(size_list, local_size, group=cpu_group) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + assert isinstance(local_size.item(), int) + local_size = int(local_size.item()) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device)) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device) + tensor = torch.cat((tensor, padding), dim=0) + if cpu_group is None: + dist.all_gather(tensor_list, tensor) + else: + dist.all_gather(tensor_list, tensor, group=cpu_group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + tensor = torch.split(tensor, [size, max_size - size], dim=0)[0] + buffer = io.BytesIO(tensor.cpu().numpy()) + obj = torch.load(buffer) + data_list.append(obj) + + return data_list + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + + if os.getenv("CPU_REDUCE") == "1": + return all_gather_cpu(data) + + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device="cuda") + size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + # print(name, str(meter)) + # import ipdb;ipdb.set_trace() + if meter.count > 0: + loss_str.append("{}: {}".format(name, str(meter))) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None, logger=None): + if logger is None: + print_func = print + else: + print_func = logger.info + + i = 0 + if not header: + header = "" + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt="{avg:.4f}") + data_time = SmoothedValue(fmt="{avg:.4f}") + space_fmt = ":" + str(len(str(len(iterable)))) + "d" + if torch.cuda.is_available(): + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + "max mem: {memory:.0f}", + ] + ) + else: + log_msg = self.delimiter.join( + [ + header, + "[{0" + space_fmt + "}/{1}]", + "eta: {eta}", + "{meters}", + "time: {time}", + "data: {data}", + ] + ) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + # import ipdb; ipdb.set_trace() + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print_func( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB, + ) + ) + else: + print_func( + log_msg.format( + i, + len(iterable), + eta=eta_string, + meters=str(self), + time=str(iter_time), + data=str(data_time), + ) + ) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print_func( + "{} Total time: {} ({:.4f} s / it)".format( + header, total_time_str, total_time / len(iterable) + ) + ) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode("ascii").strip() + + sha = "N/A" + diff = "clean" + branch = "N/A" + try: + sha = _run(["git", "rev-parse", "HEAD"]) + subprocess.check_output(["git", "diff"], cwd=cwd) + diff = _run(["git", "diff-index", "HEAD"]) + diff = "has uncommited changes" if diff else "clean" + branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"]) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def collate_fn(batch): + # import ipdb; ipdb.set_trace() + batch = list(zip(*batch)) + batch[0] = nested_tensor_from_tensor_list(batch[0]) + return tuple(batch) + + +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + if mask == "auto": + self.mask = torch.zeros_like(tensors).to(tensors.device) + if self.mask.dim() == 3: + self.mask = self.mask.sum(0).to(bool) + elif self.mask.dim() == 4: + self.mask = self.mask.sum(1).to(bool) + else: + raise ValueError( + "tensors dim must be 3 or 4 but {}({})".format( + self.tensors.dim(), self.tensors.shape + ) + ) + + def imgsize(self): + res = [] + for i in range(self.tensors.shape[0]): + mask = self.mask[i] + maxH = (~mask).sum(0).max() + maxW = (~mask).sum(1).max() + res.append(torch.Tensor([maxH, maxW])) + return res + + def to(self, device): + # type: (Device) -> NestedTensor # noqa + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def to_img_list_single(self, tensor, mask): + assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim()) + maxH = (~mask).sum(0).max() + maxW = (~mask).sum(1).max() + img = tensor[:, :maxH, :maxW] + return img + + def to_img_list(self): + """remove the padding and convert to img list + + Returns: + [type]: [description] + """ + if self.tensors.dim() == 3: + return self.to_img_list_single(self.tensors, self.mask) + else: + res = [] + for i in range(self.tensors.shape[0]): + tensor_i = self.tensors[i] + mask_i = self.mask[i] + res.append(self.to_img_list_single(tensor_i, mask_i)) + return res + + @property + def device(self): + return self.tensors.device + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + @property + def shape(self): + return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape} + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + # TODO make this more general + if tensor_list[0].ndim == 3: + if torchvision._is_tracing(): + # nested_tensor_from_tensor_list() does not export well to ONNX + # call _onnx_nested_tensor_from_tensor_list() instead + return _onnx_nested_tensor_from_tensor_list(tensor_list) + + # TODO make it support different-sized images + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], : img.shape[2]] = False + else: + raise ValueError("not supported") + return NestedTensor(tensor, mask) + + +# _onnx_nested_tensor_from_tensor_list() is an implementation of +# nested_tensor_from_tensor_list() that is supported by ONNX tracing. +@torch.jit.unused +def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: + max_size = [] + for i in range(tensor_list[0].dim()): + max_size_i = torch.max( + torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32) + ).to(torch.int64) + max_size.append(max_size_i) + max_size = tuple(max_size) + + # work around for + # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + # m[: img.shape[1], :img.shape[2]] = False + # which is not yet supported in onnx + padded_imgs = [] + padded_masks = [] + for img in tensor_list: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) + padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) + padded_masks.append(padded_mask.to(torch.bool)) + + tensor = torch.stack(padded_imgs) + mask = torch.stack(padded_masks) + + return NestedTensor(tensor, mask=mask) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "": # 'RANK' in os.environ and + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"]) + + # launch by torch.distributed.launch + # Single node + # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ... + # Multi nodes + # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ... + # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ... + # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK')) + # local_world_size = int(os.environ['GPU_PER_NODE_COUNT']) + # args.world_size = args.world_size * local_world_size + # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK']) + # args.rank = args.rank * local_world_size + args.local_rank + print( + "world size: {}, rank: {}, local rank: {}".format( + args.world_size, args.rank, args.local_rank + ) + ) + print(json.dumps(dict(os.environ), indent=2)) + elif "SLURM_PROCID" in os.environ: + args.rank = int(os.environ["SLURM_PROCID"]) + args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"]) + args.world_size = int(os.environ["SLURM_NPROCS"]) + + print( + "world size: {}, world rank: {}, local rank: {}, device_count: {}".format( + args.world_size, args.rank, args.local_rank, torch.cuda.device_count() + ) + ) + else: + print("Not using distributed mode") + args.distributed = False + args.world_size = 1 + args.rank = 0 + args.local_rank = 0 + return + + print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank)) + args.distributed = True + torch.cuda.set_device(args.local_rank) + args.dist_backend = "nccl" + print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) + + torch.distributed.init_process_group( + backend=args.dist_backend, + world_size=args.world_size, + rank=args.rank, + init_method=args.dist_url, + ) + + print("Before torch.distributed.barrier()") + torch.distributed.barrier() + print("End torch.distributed.barrier()") + setup_for_distributed(args.rank == 0) + + +@torch.no_grad() +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + if target.numel() == 0: + return [torch.zeros([], device=output.device)] + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +@torch.no_grad() +def accuracy_onehot(pred, gt): + """_summary_ + + Args: + pred (_type_): n, c + gt (_type_): n, c + """ + tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum() + acc = tp / gt.shape[0] * 100 + return acc + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor + """ + Equivalent to nn.functional.interpolate, but with support for empty batch sizes. + This will eventually be supported natively by PyTorch, and this + class can go away. + """ + if __torchvision_need_compat_flag < 0.7: + if input.numel() > 0: + return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners) + + output_shape = _output_size(2, input, size, scale_factor) + output_shape = list(input.shape[:-2]) + list(output_shape) + return _new_empty_tensor(input, output_shape) + else: + return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) + + +class color_sys: + def __init__(self, num_colors) -> None: + self.num_colors = num_colors + colors = [] + for i in np.arange(0.0, 360.0, 360.0 / num_colors): + hue = i / 360.0 + lightness = (50 + np.random.rand() * 10) / 100.0 + saturation = (90 + np.random.rand() * 10) / 100.0 + colors.append( + tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)]) + ) + self.colors = colors + + def __call__(self, idx): + return self.colors[idx] + + +def inverse_sigmoid(x, eps=1e-3): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +def clean_state_dict(state_dict): + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + if k[:7] == "module.": + k = k[7:] # remove `module.` + new_state_dict[k] = v + return new_state_dict diff --git a/GroundingDINO/groundingdino/util/slconfig.py b/GroundingDINO/groundingdino/util/slconfig.py new file mode 100644 index 0000000000000000000000000000000000000000..3f293e3aff215a3c7c2f7d21d27853493b6ebfbc --- /dev/null +++ b/GroundingDINO/groundingdino/util/slconfig.py @@ -0,0 +1,427 @@ +# ========================================================== +# Modified from mmcv +# ========================================================== +import ast +import os.path as osp +import shutil +import sys +import tempfile +from argparse import Action +from importlib import import_module +import platform + +from addict import Dict +from yapf.yapflib.yapf_api import FormatCode + +BASE_KEY = "_base_" +DELETE_KEY = "_delete_" +RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"] + + +def check_file_exist(filename, msg_tmpl='file "{}" does not exist'): + if not osp.isfile(filename): + raise FileNotFoundError(msg_tmpl.format(filename)) + + +class ConfigDict(Dict): + def __missing__(self, name): + raise KeyError(name) + + def __getattr__(self, name): + try: + value = super(ConfigDict, self).__getattr__(name) + except KeyError: + ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'") + except Exception as e: + ex = e + else: + return value + raise ex + + +class SLConfig(object): + """ + config files. + only support .py file as config now. + + ref: mmcv.utils.config + + Example: + >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1]))) + >>> cfg.a + 1 + >>> cfg.b + {'b1': [0, 1]} + >>> cfg.b.b1 + [0, 1] + >>> cfg = Config.fromfile('tests/data/config/a.py') + >>> cfg.filename + "/home/kchen/projects/mmcv/tests/data/config/a.py" + >>> cfg.item4 + 'test' + >>> cfg + "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: " + "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}" + """ + + @staticmethod + def _validate_py_syntax(filename): + with open(filename) as f: + content = f.read() + try: + ast.parse(content) + except SyntaxError: + raise SyntaxError("There are syntax errors in config " f"file {filename}") + + @staticmethod + def _file2dict(filename): + filename = osp.abspath(osp.expanduser(filename)) + check_file_exist(filename) + if filename.lower().endswith(".py"): + with tempfile.TemporaryDirectory() as temp_config_dir: + temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py") + temp_config_name = osp.basename(temp_config_file.name) + if platform.system() == 'Windows': + temp_config_file.close() + shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name)) + temp_module_name = osp.splitext(temp_config_name)[0] + sys.path.insert(0, temp_config_dir) + SLConfig._validate_py_syntax(filename) + mod = import_module(temp_module_name) + sys.path.pop(0) + cfg_dict = { + name: value for name, value in mod.__dict__.items() if not name.startswith("__") + } + # delete imported module + del sys.modules[temp_module_name] + # close temp file + temp_config_file.close() + elif filename.lower().endswith((".yml", ".yaml", ".json")): + from .slio import slload + + cfg_dict = slload(filename) + else: + raise IOError("Only py/yml/yaml/json type are supported now!") + + cfg_text = filename + "\n" + with open(filename, "r") as f: + cfg_text += f.read() + + # parse the base file + if BASE_KEY in cfg_dict: + cfg_dir = osp.dirname(filename) + base_filename = cfg_dict.pop(BASE_KEY) + base_filename = base_filename if isinstance(base_filename, list) else [base_filename] + + cfg_dict_list = list() + cfg_text_list = list() + for f in base_filename: + _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f)) + cfg_dict_list.append(_cfg_dict) + cfg_text_list.append(_cfg_text) + + base_cfg_dict = dict() + for c in cfg_dict_list: + if len(base_cfg_dict.keys() & c.keys()) > 0: + raise KeyError("Duplicate key is not allowed among bases") + # TODO Allow the duplicate key while warnning user + base_cfg_dict.update(c) + + base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict) + cfg_dict = base_cfg_dict + + # merge cfg_text + cfg_text_list.append(cfg_text) + cfg_text = "\n".join(cfg_text_list) + + return cfg_dict, cfg_text + + @staticmethod + def _merge_a_into_b(a, b): + """merge dict `a` into dict `b` (non-inplace). + values in `a` will overwrite `b`. + copy first to avoid inplace modification + + Args: + a ([type]): [description] + b ([type]): [description] + + Returns: + [dict]: [description] + """ + # import ipdb; ipdb.set_trace() + if not isinstance(a, dict): + return a + + b = b.copy() + for k, v in a.items(): + if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False): + + if not isinstance(b[k], dict) and not isinstance(b[k], list): + # if : + # import ipdb; ipdb.set_trace() + raise TypeError( + f"{k}={v} in child config cannot inherit from base " + f"because {k} is a dict in the child config but is of " + f"type {type(b[k])} in base config. You may set " + f"`{DELETE_KEY}=True` to ignore the base config" + ) + b[k] = SLConfig._merge_a_into_b(v, b[k]) + elif isinstance(b, list): + try: + _ = int(k) + except: + raise TypeError( + f"b is a list, " f"index {k} should be an int when input but {type(k)}" + ) + b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)]) + else: + b[k] = v + + return b + + @staticmethod + def fromfile(filename): + cfg_dict, cfg_text = SLConfig._file2dict(filename) + return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename) + + def __init__(self, cfg_dict=None, cfg_text=None, filename=None): + if cfg_dict is None: + cfg_dict = dict() + elif not isinstance(cfg_dict, dict): + raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}") + for key in cfg_dict: + if key in RESERVED_KEYS: + raise KeyError(f"{key} is reserved for config file") + + super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict)) + super(SLConfig, self).__setattr__("_filename", filename) + if cfg_text: + text = cfg_text + elif filename: + with open(filename, "r") as f: + text = f.read() + else: + text = "" + super(SLConfig, self).__setattr__("_text", text) + + @property + def filename(self): + return self._filename + + @property + def text(self): + return self._text + + @property + def pretty_text(self): + + indent = 4 + + def _indent(s_, num_spaces): + s = s_.split("\n") + if len(s) == 1: + return s_ + first = s.pop(0) + s = [(num_spaces * " ") + line for line in s] + s = "\n".join(s) + s = first + "\n" + s + return s + + def _format_basic_types(k, v, use_mapping=False): + if isinstance(v, str): + v_str = f"'{v}'" + else: + v_str = str(v) + + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f"{k_str}: {v_str}" + else: + attr_str = f"{str(k)}={v_str}" + attr_str = _indent(attr_str, indent) + + return attr_str + + def _format_list(k, v, use_mapping=False): + # check if all items in the list are dict + if all(isinstance(_, dict) for _ in v): + v_str = "[\n" + v_str += "\n".join( + f"dict({_indent(_format_dict(v_), indent)})," for v_ in v + ).rstrip(",") + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f"{k_str}: {v_str}" + else: + attr_str = f"{str(k)}={v_str}" + attr_str = _indent(attr_str, indent) + "]" + else: + attr_str = _format_basic_types(k, v, use_mapping) + return attr_str + + def _contain_invalid_identifier(dict_str): + contain_invalid_identifier = False + for key_name in dict_str: + contain_invalid_identifier |= not str(key_name).isidentifier() + return contain_invalid_identifier + + def _format_dict(input_dict, outest_level=False): + r = "" + s = [] + + use_mapping = _contain_invalid_identifier(input_dict) + if use_mapping: + r += "{" + for idx, (k, v) in enumerate(input_dict.items()): + is_last = idx >= len(input_dict) - 1 + end = "" if outest_level or is_last else "," + if isinstance(v, dict): + v_str = "\n" + _format_dict(v) + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f"{k_str}: dict({v_str}" + else: + attr_str = f"{str(k)}=dict({v_str}" + attr_str = _indent(attr_str, indent) + ")" + end + elif isinstance(v, list): + attr_str = _format_list(k, v, use_mapping) + end + else: + attr_str = _format_basic_types(k, v, use_mapping) + end + + s.append(attr_str) + r += "\n".join(s) + if use_mapping: + r += "}" + return r + + cfg_dict = self._cfg_dict.to_dict() + text = _format_dict(cfg_dict, outest_level=True) + # copied from setup.cfg + yapf_style = dict( + based_on_style="pep8", + blank_line_before_nested_class_or_def=True, + split_before_expression_after_opening_paren=True, + ) + text, _ = FormatCode(text, style_config=yapf_style, verify=True) + + return text + + def __repr__(self): + return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}" + + def __len__(self): + return len(self._cfg_dict) + + def __getattr__(self, name): + # # debug + # print('+'*15) + # print('name=%s' % name) + # print("addr:", id(self)) + # # print('type(self):', type(self)) + # print(self.__dict__) + # print('+'*15) + # if self.__dict__ == {}: + # raise ValueError + + return getattr(self._cfg_dict, name) + + def __getitem__(self, name): + return self._cfg_dict.__getitem__(name) + + def __setattr__(self, name, value): + if isinstance(value, dict): + value = ConfigDict(value) + self._cfg_dict.__setattr__(name, value) + + def __setitem__(self, name, value): + if isinstance(value, dict): + value = ConfigDict(value) + self._cfg_dict.__setitem__(name, value) + + def __iter__(self): + return iter(self._cfg_dict) + + def dump(self, file=None): + # import ipdb; ipdb.set_trace() + if file is None: + return self.pretty_text + else: + with open(file, "w") as f: + f.write(self.pretty_text) + + def merge_from_dict(self, options): + """Merge list into cfg_dict + + Merge the dict parsed by MultipleKVAction into this cfg. + + Examples: + >>> options = {'model.backbone.depth': 50, + ... 'model.backbone.with_cp':True} + >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet')))) + >>> cfg.merge_from_dict(options) + >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict') + >>> assert cfg_dict == dict( + ... model=dict(backbone=dict(depth=50, with_cp=True))) + + Args: + options (dict): dict of configs to merge from. + """ + option_cfg_dict = {} + for full_key, v in options.items(): + d = option_cfg_dict + key_list = full_key.split(".") + for subkey in key_list[:-1]: + d.setdefault(subkey, ConfigDict()) + d = d[subkey] + subkey = key_list[-1] + d[subkey] = v + + cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict") + super(SLConfig, self).__setattr__( + "_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict) + ) + + # for multiprocess + def __setstate__(self, state): + self.__init__(state) + + def copy(self): + return SLConfig(self._cfg_dict.copy()) + + def deepcopy(self): + return SLConfig(self._cfg_dict.deepcopy()) + + +class DictAction(Action): + """ + argparse action to split an argument into KEY=VALUE form + on the first = and append to a dictionary. List options should + be passed as comma separated values, i.e KEY=V1,V2,V3 + """ + + @staticmethod + def _parse_int_float_bool(val): + try: + return int(val) + except ValueError: + pass + try: + return float(val) + except ValueError: + pass + if val.lower() in ["true", "false"]: + return True if val.lower() == "true" else False + if val.lower() in ["none", "null"]: + return None + return val + + def __call__(self, parser, namespace, values, option_string=None): + options = {} + for kv in values: + key, val = kv.split("=", maxsplit=1) + val = [self._parse_int_float_bool(v) for v in val.split(",")] + if len(val) == 1: + val = val[0] + options[key] = val + setattr(namespace, self.dest, options) diff --git a/GroundingDINO/groundingdino/util/slio.py b/GroundingDINO/groundingdino/util/slio.py new file mode 100644 index 0000000000000000000000000000000000000000..72c1f0f7b82cdc931d381feef64fe15815ba657e --- /dev/null +++ b/GroundingDINO/groundingdino/util/slio.py @@ -0,0 +1,177 @@ +# ========================================================== +# Modified from mmcv +# ========================================================== + +import json +import pickle +from abc import ABCMeta, abstractmethod +from pathlib import Path + +import yaml + +try: + from yaml import CLoader as Loader, CDumper as Dumper +except ImportError: + from yaml import Loader, Dumper + + +# =========================== +# Rigister handler +# =========================== + + +class BaseFileHandler(metaclass=ABCMeta): + @abstractmethod + def load_from_fileobj(self, file, **kwargs): + pass + + @abstractmethod + def dump_to_fileobj(self, obj, file, **kwargs): + pass + + @abstractmethod + def dump_to_str(self, obj, **kwargs): + pass + + def load_from_path(self, filepath, mode="r", **kwargs): + with open(filepath, mode) as f: + return self.load_from_fileobj(f, **kwargs) + + def dump_to_path(self, obj, filepath, mode="w", **kwargs): + with open(filepath, mode) as f: + self.dump_to_fileobj(obj, f, **kwargs) + + +class JsonHandler(BaseFileHandler): + def load_from_fileobj(self, file): + return json.load(file) + + def dump_to_fileobj(self, obj, file, **kwargs): + json.dump(obj, file, **kwargs) + + def dump_to_str(self, obj, **kwargs): + return json.dumps(obj, **kwargs) + + +class PickleHandler(BaseFileHandler): + def load_from_fileobj(self, file, **kwargs): + return pickle.load(file, **kwargs) + + def load_from_path(self, filepath, **kwargs): + return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs) + + def dump_to_str(self, obj, **kwargs): + kwargs.setdefault("protocol", 2) + return pickle.dumps(obj, **kwargs) + + def dump_to_fileobj(self, obj, file, **kwargs): + kwargs.setdefault("protocol", 2) + pickle.dump(obj, file, **kwargs) + + def dump_to_path(self, obj, filepath, **kwargs): + super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs) + + +class YamlHandler(BaseFileHandler): + def load_from_fileobj(self, file, **kwargs): + kwargs.setdefault("Loader", Loader) + return yaml.load(file, **kwargs) + + def dump_to_fileobj(self, obj, file, **kwargs): + kwargs.setdefault("Dumper", Dumper) + yaml.dump(obj, file, **kwargs) + + def dump_to_str(self, obj, **kwargs): + kwargs.setdefault("Dumper", Dumper) + return yaml.dump(obj, **kwargs) + + +file_handlers = { + "json": JsonHandler(), + "yaml": YamlHandler(), + "yml": YamlHandler(), + "pickle": PickleHandler(), + "pkl": PickleHandler(), +} + +# =========================== +# load and dump +# =========================== + + +def is_str(x): + """Whether the input is an string instance. + + Note: This method is deprecated since python 2 is no longer supported. + """ + return isinstance(x, str) + + +def slload(file, file_format=None, **kwargs): + """Load data from json/yaml/pickle files. + + This method provides a unified api for loading data from serialized files. + + Args: + file (str or :obj:`Path` or file-like object): Filename or a file-like + object. + file_format (str, optional): If not specified, the file format will be + inferred from the file extension, otherwise use the specified one. + Currently supported formats include "json", "yaml/yml" and + "pickle/pkl". + + Returns: + The content from the file. + """ + if isinstance(file, Path): + file = str(file) + if file_format is None and is_str(file): + file_format = file.split(".")[-1] + if file_format not in file_handlers: + raise TypeError(f"Unsupported format: {file_format}") + + handler = file_handlers[file_format] + if is_str(file): + obj = handler.load_from_path(file, **kwargs) + elif hasattr(file, "read"): + obj = handler.load_from_fileobj(file, **kwargs) + else: + raise TypeError('"file" must be a filepath str or a file-object') + return obj + + +def sldump(obj, file=None, file_format=None, **kwargs): + """Dump data to json/yaml/pickle strings or files. + + This method provides a unified api for dumping data as strings or to files, + and also supports custom arguments for each file format. + + Args: + obj (any): The python object to be dumped. + file (str or :obj:`Path` or file-like object, optional): If not + specified, then the object is dump to a str, otherwise to a file + specified by the filename or file-like object. + file_format (str, optional): Same as :func:`load`. + + Returns: + bool: True for success, False otherwise. + """ + if isinstance(file, Path): + file = str(file) + if file_format is None: + if is_str(file): + file_format = file.split(".")[-1] + elif file is None: + raise ValueError("file_format must be specified since file is None") + if file_format not in file_handlers: + raise TypeError(f"Unsupported format: {file_format}") + + handler = file_handlers[file_format] + if file is None: + return handler.dump_to_str(obj, **kwargs) + elif is_str(file): + handler.dump_to_path(obj, file, **kwargs) + elif hasattr(file, "write"): + handler.dump_to_fileobj(obj, file, **kwargs) + else: + raise TypeError('"file" must be a filename str or a file-object') diff --git a/GroundingDINO/groundingdino/util/time_counter.py b/GroundingDINO/groundingdino/util/time_counter.py new file mode 100644 index 0000000000000000000000000000000000000000..0aedb2e4d61bfbe7571dca9d50053f0fedaa1359 --- /dev/null +++ b/GroundingDINO/groundingdino/util/time_counter.py @@ -0,0 +1,62 @@ +import json +import time + + +class TimeCounter: + def __init__(self) -> None: + pass + + def clear(self): + self.timedict = {} + self.basetime = time.perf_counter() + + def timeit(self, name): + nowtime = time.perf_counter() - self.basetime + self.timedict[name] = nowtime + self.basetime = time.perf_counter() + + +class TimeHolder: + def __init__(self) -> None: + self.timedict = {} + + def update(self, _timedict: dict): + for k, v in _timedict.items(): + if k not in self.timedict: + self.timedict[k] = AverageMeter(name=k, val_only=True) + self.timedict[k].update(val=v) + + def final_res(self): + return {k: v.avg for k, v in self.timedict.items()} + + def __str__(self): + return json.dumps(self.final_res(), indent=2) + + +class AverageMeter(object): + """Computes and stores the average and current value""" + + def __init__(self, name, fmt=":f", val_only=False): + self.name = name + self.fmt = fmt + self.val_only = val_only + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def __str__(self): + if self.val_only: + fmtstr = "{name} {val" + self.fmt + "}" + else: + fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})" + return fmtstr.format(**self.__dict__) diff --git a/GroundingDINO/groundingdino/util/utils.py b/GroundingDINO/groundingdino/util/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e9f0318e306fa04bff0ada70486b41aaa69b07c8 --- /dev/null +++ b/GroundingDINO/groundingdino/util/utils.py @@ -0,0 +1,608 @@ +import argparse +import json +import warnings +from collections import OrderedDict +from copy import deepcopy +from typing import Any, Dict, List + +import numpy as np +import torch +from transformers import AutoTokenizer + +from groundingdino.util.slconfig import SLConfig + + +def slprint(x, name="x"): + if isinstance(x, (torch.Tensor, np.ndarray)): + print(f"{name}.shape:", x.shape) + elif isinstance(x, (tuple, list)): + print("type x:", type(x)) + for i in range(min(10, len(x))): + slprint(x[i], f"{name}[{i}]") + elif isinstance(x, dict): + for k, v in x.items(): + slprint(v, f"{name}[{k}]") + else: + print(f"{name}.type:", type(x)) + + +def clean_state_dict(state_dict): + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + if k[:7] == "module.": + k = k[7:] # remove `module.` + new_state_dict[k] = v + return new_state_dict + + +def renorm( + img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] +) -> torch.FloatTensor: + # img: tensor(3,H,W) or tensor(B,3,H,W) + # return: same as img + assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim() + if img.dim() == 3: + assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % ( + img.size(0), + str(img.size()), + ) + img_perm = img.permute(1, 2, 0) + mean = torch.Tensor(mean) + std = torch.Tensor(std) + img_res = img_perm * std + mean + return img_res.permute(2, 0, 1) + else: # img.dim() == 4 + assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % ( + img.size(1), + str(img.size()), + ) + img_perm = img.permute(0, 2, 3, 1) + mean = torch.Tensor(mean) + std = torch.Tensor(std) + img_res = img_perm * std + mean + return img_res.permute(0, 3, 1, 2) + + +class CocoClassMapper: + def __init__(self) -> None: + self.category_map_str = { + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6, + "7": 7, + "8": 8, + "9": 9, + "10": 10, + "11": 11, + "13": 12, + "14": 13, + "15": 14, + "16": 15, + "17": 16, + "18": 17, + "19": 18, + "20": 19, + "21": 20, + "22": 21, + "23": 22, + "24": 23, + "25": 24, + "27": 25, + "28": 26, + "31": 27, + "32": 28, + "33": 29, + "34": 30, + "35": 31, + "36": 32, + "37": 33, + "38": 34, + "39": 35, + "40": 36, + "41": 37, + "42": 38, + "43": 39, + "44": 40, + "46": 41, + "47": 42, + "48": 43, + "49": 44, + "50": 45, + "51": 46, + "52": 47, + "53": 48, + "54": 49, + "55": 50, + "56": 51, + "57": 52, + "58": 53, + "59": 54, + "60": 55, + "61": 56, + "62": 57, + "63": 58, + "64": 59, + "65": 60, + "67": 61, + "70": 62, + "72": 63, + "73": 64, + "74": 65, + "75": 66, + "76": 67, + "77": 68, + "78": 69, + "79": 70, + "80": 71, + "81": 72, + "82": 73, + "84": 74, + "85": 75, + "86": 76, + "87": 77, + "88": 78, + "89": 79, + "90": 80, + } + self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()} + self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()} + + def origin2compact(self, idx): + return self.origin2compact_mapper[int(idx)] + + def compact2origin(self, idx): + return self.compact2origin_mapper[int(idx)] + + +def to_device(item, device): + if isinstance(item, torch.Tensor): + return item.to(device) + elif isinstance(item, list): + return [to_device(i, device) for i in item] + elif isinstance(item, dict): + return {k: to_device(v, device) for k, v in item.items()} + else: + raise NotImplementedError( + "Call Shilong if you use other containers! type: {}".format(type(item)) + ) + + +# +def get_gaussian_mean(x, axis, other_axis, softmax=True): + """ + + Args: + x (float): Input images(BxCxHxW) + axis (int): The index for weighted mean + other_axis (int): The other index + + Returns: weighted index for axis, BxC + + """ + mat2line = torch.sum(x, axis=other_axis) + # mat2line = mat2line / mat2line.mean() * 10 + if softmax: + u = torch.softmax(mat2line, axis=2) + else: + u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6) + size = x.shape[axis] + ind = torch.linspace(0, 1, size).to(x.device) + batch = x.shape[0] + channel = x.shape[1] + index = ind.repeat([batch, channel, 1]) + mean_position = torch.sum(index * u, dim=2) + return mean_position + + +def get_expected_points_from_map(hm, softmax=True): + """get_gaussian_map_from_points + B,C,H,W -> B,N,2 float(0, 1) float(0, 1) + softargmax function + + Args: + hm (float): Input images(BxCxHxW) + + Returns: + weighted index for axis, BxCx2. float between 0 and 1. + + """ + # hm = 10*hm + B, C, H, W = hm.shape + y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax) # B,C + x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax) # B,C + # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2) + return torch.stack([x_mean, y_mean], dim=2) + + +# Positional encoding (section 5.1) +# borrow from nerf +class Embedder: + def __init__(self, **kwargs): + self.kwargs = kwargs + self.create_embedding_fn() + + def create_embedding_fn(self): + embed_fns = [] + d = self.kwargs["input_dims"] + out_dim = 0 + if self.kwargs["include_input"]: + embed_fns.append(lambda x: x) + out_dim += d + + max_freq = self.kwargs["max_freq_log2"] + N_freqs = self.kwargs["num_freqs"] + + if self.kwargs["log_sampling"]: + freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs) + else: + freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs) + + for freq in freq_bands: + for p_fn in self.kwargs["periodic_fns"]: + embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq)) + out_dim += d + + self.embed_fns = embed_fns + self.out_dim = out_dim + + def embed(self, inputs): + return torch.cat([fn(inputs) for fn in self.embed_fns], -1) + + +def get_embedder(multires, i=0): + import torch.nn as nn + + if i == -1: + return nn.Identity(), 3 + + embed_kwargs = { + "include_input": True, + "input_dims": 3, + "max_freq_log2": multires - 1, + "num_freqs": multires, + "log_sampling": True, + "periodic_fns": [torch.sin, torch.cos], + } + + embedder_obj = Embedder(**embed_kwargs) + embed = lambda x, eo=embedder_obj: eo.embed(x) + return embed, embedder_obj.out_dim + + +class APOPMeter: + def __init__(self) -> None: + self.tp = 0 + self.fp = 0 + self.tn = 0 + self.fn = 0 + + def update(self, pred, gt): + """ + Input: + pred, gt: Tensor() + """ + assert pred.shape == gt.shape + self.tp += torch.logical_and(pred == 1, gt == 1).sum().item() + self.fp += torch.logical_and(pred == 1, gt == 0).sum().item() + self.tn += torch.logical_and(pred == 0, gt == 0).sum().item() + self.tn += torch.logical_and(pred == 1, gt == 0).sum().item() + + def update_cm(self, tp, fp, tn, fn): + self.tp += tp + self.fp += fp + self.tn += tn + self.tn += fn + + +def inverse_sigmoid(x, eps=1e-5): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +def get_raw_dict(args): + """ + return the dicf contained in args. + + e.g: + >>> with open(path, 'w') as f: + json.dump(get_raw_dict(args), f, indent=2) + """ + if isinstance(args, argparse.Namespace): + return vars(args) + elif isinstance(args, dict): + return args + elif isinstance(args, SLConfig): + return args._cfg_dict + else: + raise NotImplementedError("Unknown type {}".format(type(args))) + + +def stat_tensors(tensor): + assert tensor.dim() == 1 + tensor_sm = tensor.softmax(0) + entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum() + + return { + "max": tensor.max(), + "min": tensor.min(), + "mean": tensor.mean(), + "var": tensor.var(), + "std": tensor.var() ** 0.5, + "entropy": entropy, + } + + +class NiceRepr: + """Inherit from this class and define ``__nice__`` to "nicely" print your + objects. + + Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function + Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``. + If the inheriting class has a ``__len__``, method then the default + ``__nice__`` method will return its length. + + Example: + >>> class Foo(NiceRepr): + ... def __nice__(self): + ... return 'info' + >>> foo = Foo() + >>> assert str(foo) == '' + >>> assert repr(foo).startswith('>> class Bar(NiceRepr): + ... pass + >>> bar = Bar() + >>> import pytest + >>> with pytest.warns(None) as record: + >>> assert 'object at' in str(bar) + >>> assert 'object at' in repr(bar) + + Example: + >>> class Baz(NiceRepr): + ... def __len__(self): + ... return 5 + >>> baz = Baz() + >>> assert str(baz) == '' + """ + + def __nice__(self): + """str: a "nice" summary string describing this module""" + if hasattr(self, "__len__"): + # It is a common pattern for objects to use __len__ in __nice__ + # As a convenience we define a default __nice__ for these objects + return str(len(self)) + else: + # In all other cases force the subclass to overload __nice__ + raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}") + + def __repr__(self): + """str: the string of the module""" + try: + nice = self.__nice__() + classname = self.__class__.__name__ + return f"<{classname}({nice}) at {hex(id(self))}>" + except NotImplementedError as ex: + warnings.warn(str(ex), category=RuntimeWarning) + return object.__repr__(self) + + def __str__(self): + """str: the string of the module""" + try: + classname = self.__class__.__name__ + nice = self.__nice__() + return f"<{classname}({nice})>" + except NotImplementedError as ex: + warnings.warn(str(ex), category=RuntimeWarning) + return object.__repr__(self) + + +def ensure_rng(rng=None): + """Coerces input into a random number generator. + + If the input is None, then a global random state is returned. + + If the input is a numeric value, then that is used as a seed to construct a + random state. Otherwise the input is returned as-is. + + Adapted from [1]_. + + Args: + rng (int | numpy.random.RandomState | None): + if None, then defaults to the global rng. Otherwise this can be an + integer or a RandomState class + Returns: + (numpy.random.RandomState) : rng - + a numpy random number generator + + References: + .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501 + """ + + if rng is None: + rng = np.random.mtrand._rand + elif isinstance(rng, int): + rng = np.random.RandomState(rng) + else: + rng = rng + return rng + + +def random_boxes(num=1, scale=1, rng=None): + """Simple version of ``kwimage.Boxes.random`` + + Returns: + Tensor: shape (n, 4) in x1, y1, x2, y2 format. + + References: + https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 + + Example: + >>> num = 3 + >>> scale = 512 + >>> rng = 0 + >>> boxes = random_boxes(num, scale, rng) + >>> print(boxes) + tensor([[280.9925, 278.9802, 308.6148, 366.1769], + [216.9113, 330.6978, 224.0446, 456.5878], + [405.3632, 196.3221, 493.3953, 270.7942]]) + """ + rng = ensure_rng(rng) + + tlbr = rng.rand(num, 4).astype(np.float32) + + tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2]) + tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3]) + br_x = np.maximum(tlbr[:, 0], tlbr[:, 2]) + br_y = np.maximum(tlbr[:, 1], tlbr[:, 3]) + + tlbr[:, 0] = tl_x * scale + tlbr[:, 1] = tl_y * scale + tlbr[:, 2] = br_x * scale + tlbr[:, 3] = br_y * scale + + boxes = torch.from_numpy(tlbr) + return boxes + + +class ModelEma(torch.nn.Module): + def __init__(self, model, decay=0.9997, device=None): + super(ModelEma, self).__init__() + # make a copy of the model for accumulating moving average of weights + self.module = deepcopy(model) + self.module.eval() + + # import ipdb; ipdb.set_trace() + + self.decay = decay + self.device = device # perform ema on different device from model if set + if self.device is not None: + self.module.to(device=device) + + def _update(self, model, update_fn): + with torch.no_grad(): + for ema_v, model_v in zip( + self.module.state_dict().values(), model.state_dict().values() + ): + if self.device is not None: + model_v = model_v.to(device=self.device) + ema_v.copy_(update_fn(ema_v, model_v)) + + def update(self, model): + self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m) + + def set(self, model): + self._update(model, update_fn=lambda e, m: m) + + +class BestMetricSingle: + def __init__(self, init_res=0.0, better="large") -> None: + self.init_res = init_res + self.best_res = init_res + self.best_ep = -1 + + self.better = better + assert better in ["large", "small"] + + def isbetter(self, new_res, old_res): + if self.better == "large": + return new_res > old_res + if self.better == "small": + return new_res < old_res + + def update(self, new_res, ep): + if self.isbetter(new_res, self.best_res): + self.best_res = new_res + self.best_ep = ep + return True + return False + + def __str__(self) -> str: + return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep) + + def __repr__(self) -> str: + return self.__str__() + + def summary(self) -> dict: + return { + "best_res": self.best_res, + "best_ep": self.best_ep, + } + + +class BestMetricHolder: + def __init__(self, init_res=0.0, better="large", use_ema=False) -> None: + self.best_all = BestMetricSingle(init_res, better) + self.use_ema = use_ema + if use_ema: + self.best_ema = BestMetricSingle(init_res, better) + self.best_regular = BestMetricSingle(init_res, better) + + def update(self, new_res, epoch, is_ema=False): + """ + return if the results is the best. + """ + if not self.use_ema: + return self.best_all.update(new_res, epoch) + else: + if is_ema: + self.best_ema.update(new_res, epoch) + return self.best_all.update(new_res, epoch) + else: + self.best_regular.update(new_res, epoch) + return self.best_all.update(new_res, epoch) + + def summary(self): + if not self.use_ema: + return self.best_all.summary() + + res = {} + res.update({f"all_{k}": v for k, v in self.best_all.summary().items()}) + res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()}) + res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()}) + return res + + def __repr__(self) -> str: + return json.dumps(self.summary(), indent=2) + + def __str__(self) -> str: + return self.__repr__() + + +def targets_to(targets: List[Dict[str, Any]], device): + """Moves the target dicts to the given device.""" + excluded_keys = [ + "questionId", + "tokens_positive", + "strings_positive", + "tokens", + "dataset_name", + "sentence_id", + "original_img_id", + "nb_eval", + "task_id", + "original_id", + "token_span", + "caption", + "dataset_type", + ] + return [ + {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets + ] + + +def get_phrases_from_posmap( + posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer +): + assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor" + if posmap.dim() == 1: + non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist() + token_ids = [tokenized["input_ids"][i] for i in non_zero_idx] + return tokenizer.decode(token_ids) + else: + raise NotImplementedError("posmap must be 1-dim") diff --git a/GroundingDINO/groundingdino/util/visualizer.py b/GroundingDINO/groundingdino/util/visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..7a1b7b101e9b73f75f9136bc67f2063c7c1cf1c1 --- /dev/null +++ b/GroundingDINO/groundingdino/util/visualizer.py @@ -0,0 +1,318 @@ +# -*- coding: utf-8 -*- +""" +@File : visualizer.py +@Time : 2022/04/05 11:39:33 +@Author : Shilong Liu +@Contact : slongliu86@gmail.com +""" + +import datetime +import os + +import cv2 +import matplotlib.pyplot as plt +import numpy as np +import torch +from matplotlib import transforms +from matplotlib.collections import PatchCollection +from matplotlib.patches import Polygon +from pycocotools import mask as maskUtils + + +def renorm( + img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] +) -> torch.FloatTensor: + # img: tensor(3,H,W) or tensor(B,3,H,W) + # return: same as img + assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim() + if img.dim() == 3: + assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % ( + img.size(0), + str(img.size()), + ) + img_perm = img.permute(1, 2, 0) + mean = torch.Tensor(mean) + std = torch.Tensor(std) + img_res = img_perm * std + mean + return img_res.permute(2, 0, 1) + else: # img.dim() == 4 + assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % ( + img.size(1), + str(img.size()), + ) + img_perm = img.permute(0, 2, 3, 1) + mean = torch.Tensor(mean) + std = torch.Tensor(std) + img_res = img_perm * std + mean + return img_res.permute(0, 3, 1, 2) + + +class ColorMap: + def __init__(self, basergb=[255, 255, 0]): + self.basergb = np.array(basergb) + + def __call__(self, attnmap): + # attnmap: h, w. np.uint8. + # return: h, w, 4. np.uint8. + assert attnmap.dtype == np.uint8 + h, w = attnmap.shape + res = self.basergb.copy() + res = res[None][None].repeat(h, 0).repeat(w, 1) # h, w, 3 + attn1 = attnmap.copy()[..., None] # h, w, 1 + res = np.concatenate((res, attn1), axis=-1).astype(np.uint8) + return res + + +def rainbow_text(x, y, ls, lc, **kw): + """ + Take a list of strings ``ls`` and colors ``lc`` and place them next to each + other, with text ls[i] being shown in color lc[i]. + + This example shows how to do both vertical and horizontal text, and will + pass all keyword arguments to plt.text, so you can set the font size, + family, etc. + """ + t = plt.gca().transData + fig = plt.gcf() + plt.show() + + # horizontal version + for s, c in zip(ls, lc): + text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw) + text.draw(fig.canvas.get_renderer()) + ex = text.get_window_extent() + t = transforms.offset_copy(text._transform, x=ex.width, units="dots") + + # #vertical version + # for s,c in zip(ls,lc): + # text = plt.text(x,y," "+s+" ",color=c, transform=t, + # rotation=90,va='bottom',ha='center',**kw) + # text.draw(fig.canvas.get_renderer()) + # ex = text.get_window_extent() + # t = transforms.offset_copy(text._transform, y=ex.height, units='dots') + + +class COCOVisualizer: + def __init__(self, coco=None, tokenlizer=None) -> None: + self.coco = coco + + def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"): + """ + img: tensor(3, H, W) + tgt: make sure they are all on cpu. + must have items: 'image_id', 'boxes', 'size' + """ + plt.figure(dpi=dpi) + plt.rcParams["font.size"] = "5" + ax = plt.gca() + img = renorm(img).permute(1, 2, 0) + # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': + # import ipdb; ipdb.set_trace() + ax.imshow(img) + + self.addtgt(tgt) + + if tgt is None: + image_id = 0 + elif "image_id" not in tgt: + image_id = 0 + else: + image_id = tgt["image_id"] + + if caption is None: + savename = "{}/{}-{}.png".format( + savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-") + ) + else: + savename = "{}/{}-{}-{}.png".format( + savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-") + ) + print("savename: {}".format(savename)) + os.makedirs(os.path.dirname(savename), exist_ok=True) + plt.savefig(savename) + plt.close() + + def addtgt(self, tgt): + """ """ + if tgt is None or not "boxes" in tgt: + ax = plt.gca() + + if "caption" in tgt: + ax.set_title(tgt["caption"], wrap=True) + + ax.set_axis_off() + return + + ax = plt.gca() + H, W = tgt["size"] + numbox = tgt["boxes"].shape[0] + + color = [] + polygons = [] + boxes = [] + for box in tgt["boxes"].cpu(): + unnormbbox = box * torch.Tensor([W, H, W, H]) + unnormbbox[:2] -= unnormbbox[2:] / 2 + [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist() + boxes.append([bbox_x, bbox_y, bbox_w, bbox_h]) + poly = [ + [bbox_x, bbox_y], + [bbox_x, bbox_y + bbox_h], + [bbox_x + bbox_w, bbox_y + bbox_h], + [bbox_x + bbox_w, bbox_y], + ] + np_poly = np.array(poly).reshape((4, 2)) + polygons.append(Polygon(np_poly)) + c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0] + color.append(c) + + p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1) + ax.add_collection(p) + p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2) + ax.add_collection(p) + + if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0: + assert ( + len(tgt["strings_positive"]) == numbox + ), f"{len(tgt['strings_positive'])} = {numbox}, " + for idx, strlist in enumerate(tgt["strings_positive"]): + cate_id = int(tgt["labels"][idx]) + _string = str(cate_id) + ":" + " ".join(strlist) + bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx] + # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1}) + ax.text( + bbox_x, + bbox_y, + _string, + color="black", + bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1}, + ) + + if "box_label" in tgt: + assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, " + for idx, bl in enumerate(tgt["box_label"]): + _string = str(bl) + bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx] + # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1}) + ax.text( + bbox_x, + bbox_y, + _string, + color="black", + bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1}, + ) + + if "caption" in tgt: + ax.set_title(tgt["caption"], wrap=True) + # plt.figure() + # rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(), + # ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black']) + + if "attn" in tgt: + # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': + # import ipdb; ipdb.set_trace() + if isinstance(tgt["attn"], tuple): + tgt["attn"] = [tgt["attn"]] + for item in tgt["attn"]: + attn_map, basergb = item + attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3) + attn_map = (attn_map * 255).astype(np.uint8) + cm = ColorMap(basergb) + heatmap = cm(attn_map) + ax.imshow(heatmap) + ax.set_axis_off() + + def showAnns(self, anns, draw_bbox=False): + """ + Display the specified annotations. + :param anns (array of object): annotations to display + :return: None + """ + if len(anns) == 0: + return 0 + if "segmentation" in anns[0] or "keypoints" in anns[0]: + datasetType = "instances" + elif "caption" in anns[0]: + datasetType = "captions" + else: + raise Exception("datasetType not supported") + if datasetType == "instances": + ax = plt.gca() + ax.set_autoscale_on(False) + polygons = [] + color = [] + for ann in anns: + c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0] + if "segmentation" in ann: + if type(ann["segmentation"]) == list: + # polygon + for seg in ann["segmentation"]: + poly = np.array(seg).reshape((int(len(seg) / 2), 2)) + polygons.append(Polygon(poly)) + color.append(c) + else: + # mask + t = self.imgs[ann["image_id"]] + if type(ann["segmentation"]["counts"]) == list: + rle = maskUtils.frPyObjects( + [ann["segmentation"]], t["height"], t["width"] + ) + else: + rle = [ann["segmentation"]] + m = maskUtils.decode(rle) + img = np.ones((m.shape[0], m.shape[1], 3)) + if ann["iscrowd"] == 1: + color_mask = np.array([2.0, 166.0, 101.0]) / 255 + if ann["iscrowd"] == 0: + color_mask = np.random.random((1, 3)).tolist()[0] + for i in range(3): + img[:, :, i] = color_mask[i] + ax.imshow(np.dstack((img, m * 0.5))) + if "keypoints" in ann and type(ann["keypoints"]) == list: + # turn skeleton into zero-based index + sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1 + kp = np.array(ann["keypoints"]) + x = kp[0::3] + y = kp[1::3] + v = kp[2::3] + for sk in sks: + if np.all(v[sk] > 0): + plt.plot(x[sk], y[sk], linewidth=3, color=c) + plt.plot( + x[v > 0], + y[v > 0], + "o", + markersize=8, + markerfacecolor=c, + markeredgecolor="k", + markeredgewidth=2, + ) + plt.plot( + x[v > 1], + y[v > 1], + "o", + markersize=8, + markerfacecolor=c, + markeredgecolor=c, + markeredgewidth=2, + ) + + if draw_bbox: + [bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"] + poly = [ + [bbox_x, bbox_y], + [bbox_x, bbox_y + bbox_h], + [bbox_x + bbox_w, bbox_y + bbox_h], + [bbox_x + bbox_w, bbox_y], + ] + np_poly = np.array(poly).reshape((4, 2)) + polygons.append(Polygon(np_poly)) + color.append(c) + + # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) + # ax.add_collection(p) + p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2) + ax.add_collection(p) + elif datasetType == "captions": + for ann in anns: + print(ann["caption"]) diff --git a/GroundingDINO/groundingdino/util/vl_utils.py b/GroundingDINO/groundingdino/util/vl_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c91bb02f584398f08a28e6b7719e2b99f6e28616 --- /dev/null +++ b/GroundingDINO/groundingdino/util/vl_utils.py @@ -0,0 +1,100 @@ +import os +import random +from typing import List + +import torch + + +def create_positive_map_from_span(tokenized, token_span, max_text_len=256): + """construct a map such that positive_map[i,j] = True iff box i is associated to token j + Input: + - tokenized: + - input_ids: Tensor[1, ntokens] + - attention_mask: Tensor[1, ntokens] + - token_span: list with length num_boxes. + - each item: [start_idx, end_idx] + """ + positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float) + for j, tok_list in enumerate(token_span): + for (beg, end) in tok_list: + beg_pos = tokenized.char_to_token(beg) + end_pos = tokenized.char_to_token(end - 1) + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + + assert beg_pos is not None and end_pos is not None + if os.environ.get("SHILONG_DEBUG_ONLY_ONE_POS", None) == "TRUE": + positive_map[j, beg_pos] = 1 + break + else: + positive_map[j, beg_pos : end_pos + 1].fill_(1) + + return positive_map / (positive_map.sum(-1)[:, None] + 1e-6) + + +def build_captions_and_token_span(cat_list, force_lowercase): + """ + Return: + captions: str + cat2tokenspan: dict + { + 'dog': [[0, 2]], + ... + } + """ + + cat2tokenspan = {} + captions = "" + for catname in cat_list: + class_name = catname + if force_lowercase: + class_name = class_name.lower() + if "/" in class_name: + class_name_list: List = class_name.strip().split("/") + class_name_list.append(class_name) + class_name: str = random.choice(class_name_list) + + tokens_positive_i = [] + subnamelist = [i.strip() for i in class_name.strip().split(" ")] + for subname in subnamelist: + if len(subname) == 0: + continue + if len(captions) > 0: + captions = captions + " " + strat_idx = len(captions) + end_idx = strat_idx + len(subname) + tokens_positive_i.append([strat_idx, end_idx]) + captions = captions + subname + + if len(tokens_positive_i) > 0: + captions = captions + " ." + cat2tokenspan[class_name] = tokens_positive_i + + return captions, cat2tokenspan + + +def build_id2posspan_and_caption(category_dict: dict): + """Build id2pos_span and caption from category_dict + + Args: + category_dict (dict): category_dict + """ + cat_list = [item["name"].lower() for item in category_dict] + id2catname = {item["id"]: item["name"].lower() for item in category_dict} + caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True) + id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()} + return id2posspan, caption diff --git a/GroundingDINO/groundingdino/version.py b/GroundingDINO/groundingdino/version.py new file mode 100644 index 0000000000000000000000000000000000000000..b794fd409a5e3b3b65ad76a43d6a01a318877640 --- /dev/null +++ b/GroundingDINO/groundingdino/version.py @@ -0,0 +1 @@ +__version__ = '0.1.0' diff --git a/GroundingDINO/setup.py b/GroundingDINO/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..a58340d44eca86b09cb69630465dfbdfe8acb742 --- /dev/null +++ b/GroundingDINO/setup.py @@ -0,0 +1,216 @@ +# coding=utf-8 +# Copyright 2022 The IDEA Authors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ------------------------------------------------------------------------------------------------ +# Modified from +# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/setup.py +# https://github.com/facebookresearch/detectron2/blob/main/setup.py +# https://github.com/open-mmlab/mmdetection/blob/master/setup.py +# https://github.com/Oneflow-Inc/libai/blob/main/setup.py +# ------------------------------------------------------------------------------------------------ + +import glob +import os +import subprocess + +import torch +from setuptools import find_packages, setup +from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension + +# groundingdino version info +version = "0.1.0" +package_name = "groundingdino" +cwd = os.path.dirname(os.path.abspath(__file__)) + + +sha = "Unknown" +try: + sha = subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=cwd).decode("ascii").strip() +except Exception: + pass + + +def write_version_file(): + version_path = os.path.join(cwd, "groundingdino", "version.py") + with open(version_path, "w") as f: + f.write(f"__version__ = '{version}'\n") + # f.write(f"git_version = {repr(sha)}\n") + + +requirements = ["torch", "torchvision"] + +torch_ver = [int(x) for x in torch.__version__.split(".")[:2]] + + +def get_extensions(): + this_dir = os.path.dirname(os.path.abspath(__file__)) + extensions_dir = os.path.join(this_dir, "groundingdino", "models", "GroundingDINO", "csrc") + + main_source = os.path.join(extensions_dir, "vision.cpp") + sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu")) + glob.glob( + os.path.join(extensions_dir, "*.cu") + ) + + sources = [main_source] + sources + + # We need these variables to build with CUDA when we create the Docker image + # It solves https://github.com/IDEA-Research/Grounded-Segment-Anything/issues/53 + # and https://github.com/IDEA-Research/Grounded-Segment-Anything/issues/84 when running + # inside a Docker container. + am_i_docker = os.environ.get('AM_I_DOCKER', '').casefold() in ['true', '1', 't'] + use_cuda = os.environ.get('BUILD_WITH_CUDA', '').casefold() in ['true', '1', 't'] + + extension = CppExtension + + extra_compile_args = {"cxx": []} + define_macros = [] + + if (torch.cuda.is_available() and CUDA_HOME is not None) or \ + (am_i_docker and use_cuda): + print("Compiling with CUDA") + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + else: + print("Compiling without CUDA") + define_macros += [("WITH_HIP", None)] + extra_compile_args["nvcc"] = [] + return None + + sources = [os.path.join(extensions_dir, s) for s in sources] + include_dirs = [extensions_dir] + + ext_modules = [ + extension( + "groundingdino._C", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + + return ext_modules + + +def parse_requirements(fname="requirements.txt", with_version=True): + """Parse the package dependencies listed in a requirements file but strips + specific versioning information. + + Args: + fname (str): path to requirements file + with_version (bool, default=False): if True include version specs + + Returns: + List[str]: list of requirements items + + CommandLine: + python -c "import setup; print(setup.parse_requirements())" + """ + import re + import sys + from os.path import exists + + require_fpath = fname + + def parse_line(line): + """Parse information from a line in a requirements text file.""" + if line.startswith("-r "): + # Allow specifying requirements in other files + target = line.split(" ")[1] + for info in parse_require_file(target): + yield info + else: + info = {"line": line} + if line.startswith("-e "): + info["package"] = line.split("#egg=")[1] + elif "@git+" in line: + info["package"] = line + else: + # Remove versioning from the package + pat = "(" + "|".join([">=", "==", ">"]) + ")" + parts = re.split(pat, line, maxsplit=1) + parts = [p.strip() for p in parts] + + info["package"] = parts[0] + if len(parts) > 1: + op, rest = parts[1:] + if ";" in rest: + # Handle platform specific dependencies + # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies + version, platform_deps = map(str.strip, rest.split(";")) + info["platform_deps"] = platform_deps + else: + version = rest # NOQA + info["version"] = (op, version) + yield info + + def parse_require_file(fpath): + with open(fpath, "r") as f: + for line in f.readlines(): + line = line.strip() + if line and not line.startswith("#"): + for info in parse_line(line): + yield info + + def gen_packages_items(): + if exists(require_fpath): + for info in parse_require_file(require_fpath): + parts = [info["package"]] + if with_version and "version" in info: + parts.extend(info["version"]) + if not sys.version.startswith("3.4"): + # apparently package_deps are broken in 3.4 + platform_deps = info.get("platform_deps") + if platform_deps is not None: + parts.append(";" + platform_deps) + item = "".join(parts) + yield item + + packages = list(gen_packages_items()) + return packages + + +if __name__ == "__main__": + print(f"Building wheel {package_name}-{version}") + + with open("LICENSE", "r", encoding="utf-8") as f: + license = f.read() + + write_version_file() + + setup( + name="groundingdino", + version="0.1.0", + author="International Digital Economy Academy, Shilong Liu", + url="https://github.com/IDEA-Research/GroundingDINO", + description="open-set object detector", + license=license, + install_requires=parse_requirements("requirements.txt"), + packages=find_packages( + exclude=( + "configs", + "tests", + ) + ), + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, + ) diff --git a/README.md b/README.md index ccf34fe62e5aa7672a9f50bcbc3949ba8849ed0d..3cf8aac67e2a129f5b306f6160f240bddea492c7 100644 --- a/README.md +++ b/README.md @@ -1,188 +1,21 @@ --- -license: mit -title: RAM_Tag2Text -sdk: gradio +title: Recognize Detect Segment Anything emoji: 🐠 -colorFrom: blue -colorTo: yellow +colorFrom: yellow +colorTo: gray +sdk: gradio +sdk_version: 3.35.2 app_file: app.py pinned: false +license: mit --- +# Recognize Anything Model + Grounded-SAM DEMO -# :label: Recognize Anything: A Strong Image Tagging Model & Tag2Text: Guiding Vision-Language Model via Image Tagging - -Official PyTorch Implementation of the Recognize Anything Model (RAM) and the Tag2Text Model. - -- RAM is a strong image tagging model, which can recognize any common category with high accuracy. -- Tag2Text is an efficient and controllable vision-language model with tagging guidance. - - -When combined with localization models ([Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything)), Tag2Text and RAM form a strong and general pipeline for visual semantic analysis. - -![](./images/ram_grounded_sam.jpg) - -## :sun_with_face: Helpful Tutorial - - -- :apple: [[Access RAM Homepage](https://recognize-anything.github.io/)] -- :grapes: [[Access Tag2Text Homepage](https://tag2text.github.io/)] -- :sunflower: [[Read RAM arXiv Paper](https://arxiv.org/abs/2306.03514)] -- :rose: [[Read Tag2Text arXiv Paper](https://arxiv.org/abs/2303.05657)] -- :mushroom: [[Try our Tag2Text web Demo! 🤗](https://huggingface.co./spaces/xinyu1205/Tag2Text)] - - - -## :bulb: Highlight -**Recognition and localization are two foundation computer vision tasks.** -- **The Segment Anything Model (SAM)** excels in **localization capabilities**, while it falls short when it comes to **recognition tasks**. -- **The Recognize Anything Model (RAM) and Tag2Text** exhibits **exceptional recognition abilities**, in terms of **both accuracy and scope**. - -

- - - - -
-

- - -
- -Tag2Text for Vision-Language Tasks. - - -- **Tagging.** Without manual annotations, Tag2Text achieves **superior** image tag recognition ability of [**3,429**](./data/tag_list.txt) commonly human-used categories. -- **Efficient.** Tagging guidance effectively enhances the performance of vision-language models on both **generation-based** and **alignment-based** tasks. -- **Controllable.** Tag2Text permits users to input **desired tags**, providing the flexibility in composing corresponding texts based on the input tags. - - -

- - - - -
-

-
- - -
- -Advancements of RAM on Tag2Text. - - -- **Accuracy.** RAM utilizes a data engine to generate additional annotations and clean incorrect ones, resulting higher accuracy compared to Tag2Text. -- **Scope.** Tag2Text recognizes 3,400+ fixed tags. RAM upgrades the number to 6,400+, covering more valuable categories. With open-set capability, RAM is feasible to recognize any common category. - - -
- - -## :sparkles: Highlight Projects with other Models -- [Tag2Text/RAM with Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) is trong and general pipeline for visual semantic analysis, which can automatically **recognize**, detect, and segment for an image! -- [Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) is a multifunctional video question answering tool. Tag2Text provides powerful tagging and captioning capabilities as a fundamental component. -- [Prompt-can-anything](https://github.com/positive666/Prompt-Can-Anything) is a gradio web library that integrates SOTA multimodal large models, including Tag2text as the core model for graphic understanding - - - - -## :fire: News - -- **`2023/06/07`**: We release the [Recognize Anything Model (RAM)](https://recognize-anything.github.io/), a strong image tagging model! -- **`2023/06/05`**: Tag2Text is combined with [Prompt-can-anything](https://github.com/OpenGVLab/Ask-Anything). -- **`2023/05/20`**: Tag2Text is combined with [VideoChat](https://github.com/OpenGVLab/Ask-Anything). -- **`2023/04/20`**: We marry Tag2Text with with [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything). -- **`2023/04/10`**: Code and checkpoint is available Now! -- **`2023/03/14`**: [Tag2Text web demo 🤗](https://huggingface.co./spaces/xinyu1205/Tag2Text) is available on Hugging Face Space! - - - - - - -## :writing_hand: TODO - -- [x] Release Tag2Text demo. -- [x] Release checkpoints. -- [x] Release inference code. -- [ ] Release RAM demo and checkpoints (until June 14th at the latest). -- [ ] Release training codes (until August 1st at the latest). -- [ ] Release training datasets (until August 1st at the latest). - - - -## :toolbox: Checkpoints - - - - - - - - - - - - - - - - - - - - - - - -
namebackboneDataIllustrationCheckpoint
1Tag2Text-SwinSwin-BaseCOCO, VG, SBU, CC-3M, CC-12MDemo version with comprehensive captions.Download link
- - -## :running: Tag2Text Inference - -1. Install the dependencies, run: - -
pip install -r requirements.txt
- -2. Download Tag2Text pretrained checkpoints. - -3. Get the tagging and captioning results: -
-python inference.py  --image images/1641173_2291260800.jpg \
---pretrained pretrained/tag2text_swin_14m.pth
-
-Or get the tagging and sepcifed captioning results (optional): -
python inference.py  --image images/1641173_2291260800.jpg \
---pretrained pretrained/tag2text_swin_14m.pth \
---specified-tags "cloud,sky"
- - -## :black_nib: Citation -If you find our work to be useful for your research, please consider citing. - -``` -@misc{zhang2023recognize, - title={Recognize Anything: A Strong Image Tagging Model}, - author={Youcai Zhang and Xinyu Huang and Jinyu Ma and Zhaoyang Li and Zhaochuan Luo and Yanchun Xie and Yuzhuo Qin and Tong Luo and Yaqian Li and Shilong Liu and Yandong Guo and Lei Zhang}, - year={2023}, - eprint={2306.03514}, - archivePrefix={arXiv}, - primaryClass={cs.CV} -} - -@article{huang2023tag2text, - title={Tag2Text: Guiding Vision-Language Model via Image Tagging}, - author={Huang, Xinyu and Zhang, Youcai and Ma, Jinyu and Tian, Weiwei and Feng, Rui and Zhang, Yuejie and Li, Yaqian and Guo, Yandong and Zhang, Lei}, - journal={arXiv preprint arXiv:2303.05657}, - year={2023} -} -``` - -## :hearts: Acknowledgements -This work is done with the help of the amazing code base of [BLIP](https://github.com/salesforce/BLIP), thanks very much! - -We also want to thank @Cheng Rui @Shilong Liu @Ren Tianhe for their help in [marrying Tag2Text with Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything). +Please refer to these homepages for details of these projects: +- Recognize Anthing Model: [https://recognize-anything.github.io/](https://recognize-anything.github.io/) +- Tag2Text Model: [https://https://tag2text.github.io/](https://https://tag2text.github.io/) +- Grounded-SAM: [https://github.com/IDEA-Research/Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) diff --git a/app.py b/app.py index 3383ebef0415a3efe7dc9c614c15610cc6d1c5cb..359848e0523e3520b5b9d7bc7d2beba99ea1f9a7 100644 --- a/app.py +++ b/app.py @@ -1,209 +1,387 @@ -import numpy as np -import random - -import torch -import torchvision.transforms as transforms - -from PIL import Image -from models.tag2text import tag2text_caption, ram - -import gradio as gr - -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - -image_size = 384 - -normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225 -]) -transform = transforms.Compose([transforms.Resize((image_size, image_size)),transforms.ToTensor(),normalize]) - -#######Tag2Text Model -pretrained = 'tag2text_swin_14m.pth' +import os +import sys +from pathlib import Path + +# setup Grouded-Segment-Anything +# building GroundingDINO requires torch but imports it before installing, +# so directly installing in requirements.txt causes dependency error. +# 1. build with "-e" option to keep the bin file in ./GroundingDINO/groundingdino/, rather than in site-package dir. +os.system("pip install -e ./GroundingDINO/") +# 2. for unknown reason, "import groundingdino" will fill due to unable to find the module, even after installing. +# add ./GroundingDINO/ to PATH, so package "groundingdino" can be imported. +sys.path.append(str(Path(__file__).parent / "GroundingDINO")) + +import random # noqa: E402 + +import cv2 # noqa: E402 +import groundingdino.datasets.transforms as T # noqa: E402 +import numpy as np # noqa: E402 +import torch # noqa: E402 +import torchvision # noqa: E402 +import torchvision.transforms as TS # noqa: E402 +from groundingdino.models import build_model # noqa: E402 +from groundingdino.util.slconfig import SLConfig # noqa: E402 +from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap # noqa: E402 +from PIL import Image, ImageDraw, ImageFont # noqa: E402 +from ram import inference_ram # noqa: E402 +from ram import inference_tag2text # noqa: E402 +from ram.models import ram # noqa: E402 +from ram.models import tag2text_caption # noqa: E402 +from segment_anything import SamPredictor, build_sam # noqa: E402 + + +# args +config_file = "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py" +ram_checkpoint = "./ram_swin_large_14m.pth" +tag2text_checkpoint = "./tag2text_swin_14m.pth" +grounded_checkpoint = "./groundingdino_swint_ogc.pth" +sam_checkpoint = "./sam_vit_h_4b8939.pth" +box_threshold = 0.25 +text_threshold = 0.2 +iou_threshold = 0.5 +device = "cpu" + + +def load_model(model_config_path, model_checkpoint_path, device): + args = SLConfig.fromfile(model_config_path) + args.device = device + model = build_model(args) + checkpoint = torch.load(model_checkpoint_path, map_location="cpu") + load_res = model.load_state_dict( + clean_state_dict(checkpoint["model"]), strict=False) + print(load_res) + _ = model.eval() + return model + + +def get_grounding_output(model, image, caption, box_threshold, text_threshold, device="cpu"): + caption = caption.lower() + caption = caption.strip() + if not caption.endswith("."): + caption = caption + "." + model = model.to(device) + image = image.to(device) + with torch.no_grad(): + outputs = model(image[None], captions=[caption]) + logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256) + boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4) + logits.shape[0] + + # filter output + logits_filt = logits.clone() + boxes_filt = boxes.clone() + filt_mask = logits_filt.max(dim=1)[0] > box_threshold + logits_filt = logits_filt[filt_mask] # num_filt, 256 + boxes_filt = boxes_filt[filt_mask] # num_filt, 4 + logits_filt.shape[0] + + # get phrase + tokenlizer = model.tokenizer + tokenized = tokenlizer(caption) + # build pred + pred_phrases = [] + scores = [] + for logit, box in zip(logits_filt, boxes_filt): + pred_phrase = get_phrases_from_posmap( + logit > text_threshold, tokenized, tokenlizer) + pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") + scores.append(logit.max().item()) + + return boxes_filt, torch.Tensor(scores), pred_phrases + + +def draw_mask(mask, draw, random_color=False): + if random_color: + color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 153) + else: + color = (30, 144, 255, 153) -model_tag2text = tag2text_caption(pretrained=pretrained, image_size=image_size, vit='swin_b' ) + nonzero_coords = np.transpose(np.nonzero(mask)) -model_tag2text.eval() -model_tag2text = model_tag2text.to(device) + for coord in nonzero_coords: + draw.point(coord[::-1], fill=color) -#######RAM Model -pretrained = 'ram_swin_large_14m.pth' +def draw_box(box, draw, label): + # random color + color = tuple(np.random.randint(0, 255, size=3).tolist()) + line_width = int(max(4, min(20, 0.006*max(draw.im.size)))) + draw.rectangle(((box[0], box[1]), (box[2], box[3])), outline=color, width=line_width) -model_ram = ram(pretrained=pretrained, image_size=image_size, vit='swin_l' ) + if label: + font_path = os.path.join( + cv2.__path__[0], 'qt', 'fonts', 'DejaVuSans.ttf') + font_size = int(max(12, min(60, 0.02*max(draw.im.size)))) + font = ImageFont.truetype(font_path, size=font_size) + if hasattr(font, "getbbox"): + bbox = draw.textbbox((box[0], box[1]), str(label), font) + else: + w, h = draw.textsize(str(label), font) + bbox = (box[0], box[1], w + box[0], box[1] + h) + draw.rectangle(bbox, fill=color) + draw.text((box[0], box[1]), str(label), fill="white", font=font) + + draw.text((box[0], box[1]), label, font=font) + + +@torch.no_grad() +def inference( + raw_image, specified_tags, do_det_seg, + tagging_model_type, tagging_model, grounding_dino_model, sam_model +): + print(f"Start processing, image size {raw_image.size}") + raw_image = raw_image.convert("RGB") + + # run tagging model + normalize = TS.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + transform = TS.Compose([ + TS.Resize((384, 384)), + TS.ToTensor(), + normalize + ]) + + image = raw_image.resize((384, 384)) + image = transform(image).unsqueeze(0).to(device) + + # Currently ", " is better for detecting single tags + # while ". " is a little worse in some case + if tagging_model_type == "RAM": + res = inference_ram(image, tagging_model) + tags = res[0].strip(' ').replace(' ', ' ').replace(' |', ',') + tags_chinese = res[1].strip(' ').replace(' ', ' ').replace(' |', ',') + print("Tags: ", tags) + print("图像标签: ", tags_chinese) + else: + res = inference_tag2text(image, tagging_model, specified_tags) + tags = res[0].strip(' ').replace(' ', ' ').replace(' |', ',') + caption = res[2] + print(f"Tags: {tags}") + print(f"Caption: {caption}") + + # return + if not do_det_seg: + if tagging_model_type == "RAM": + return tags.replace(", ", " | "), tags_chinese.replace(", ", " | "), None + else: + return tags.replace(", ", " | "), caption, None + + # run groundingDINO + transform = T.Compose([ + T.RandomResize([800], max_size=1333), + T.ToTensor(), + T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ]) + + image, _ = transform(raw_image, None) # 3, h, w + + boxes_filt, scores, pred_phrases = get_grounding_output( + grounding_dino_model, image, tags, box_threshold, text_threshold, device=device + ) + print("GroundingDINO finished") + + # run SAM + image = np.asarray(raw_image) + sam_model.set_image(image) + + size = raw_image.size + H, W = size[1], size[0] + for i in range(boxes_filt.size(0)): + boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H]) + boxes_filt[i][:2] -= boxes_filt[i][2:] / 2 + boxes_filt[i][2:] += boxes_filt[i][:2] + + boxes_filt = boxes_filt.cpu() + # use NMS to handle overlapped boxes + print(f"Before NMS: {boxes_filt.shape[0]} boxes") + nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist() + boxes_filt = boxes_filt[nms_idx] + pred_phrases = [pred_phrases[idx] for idx in nms_idx] + print(f"After NMS: {boxes_filt.shape[0]} boxes") + + transformed_boxes = sam_model.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device) + + masks, _, _ = sam_model.predict_torch( + point_coords=None, + point_labels=None, + boxes=transformed_boxes.to(device), + multimask_output=False, + ) + print("SAM finished") + + # draw output image + mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0)) + + mask_draw = ImageDraw.Draw(mask_image) + for mask in masks: + draw_mask(mask[0].cpu().numpy(), mask_draw, random_color=True) + + image_draw = ImageDraw.Draw(raw_image) + + for box, label in zip(boxes_filt, pred_phrases): + draw_box(box, image_draw, label) + + out_image = raw_image.convert('RGBA') + out_image.alpha_composite(mask_image) + + # return + if tagging_model_type == "RAM": + return tags.replace(", ", " | "), tags_chinese.replace(", ", " | "), out_image + else: + return tags.replace(", ", " | "), caption, out_image -model_ram.eval() -model_ram = model_ram.to(device) +if __name__ == "__main__": + import gradio as gr + + # load RAM + ram_model = ram(pretrained=ram_checkpoint, image_size=384, vit='swin_l') + ram_model.eval() + ram_model = ram_model.to(device) + + # load Tag2Text + delete_tag_index = [] # filter out attributes and action categories which are difficult to grounding + for i in range(3012, 3429): + delete_tag_index.append(i) + + tag2text_model = tag2text_caption(pretrained=tag2text_checkpoint, + image_size=384, + vit='swin_b', + delete_tag_index=delete_tag_index) + tag2text_model.threshold = 0.64 # we reduce the threshold to obtain more tags + tag2text_model.eval() + tag2text_model = tag2text_model.to(device) + + # load groundingDINO + grounding_dino_model = load_model(config_file, grounded_checkpoint, device=device) + + # load SAM + sam_model = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device)) + + # build GUI + def build_gui(): + + description = """ +
Recognize Anything Model + Grounded-SAM
+
+ Welcome to the RAM/Tag2Text + Grounded-SAM demo!

+
  • + Recognize Anything Model: Upload your image to get the English and Chinese tags! +
    • +
  • + Tag2Text Model: Upload your image to get the tags and caption! + (Optional: Specify tags to get the corresponding caption.) +
    • +
  • + Grounded-SAM: Tick the checkbox to get boxes and masks of tags! +
    • +
    + Great thanks to Ma Jinyu, the major contributor of this demo! + """ # noqa + + article = """ +

    + RAM and Tag2Text are trained on open-source datasets, and we are persisting in refining and iterating upon it.
    + Grounded-SAM is a combination of Grounding DINO and SAM aming to detect and segment anything with text inputs.
    + Recognize Anything: A Strong Image Tagging Model + | + Tag2Text: Guiding Language-Image Model via Image Tagging + | + Grounded-Segment-Anything +

    + """ # noqa + + def inference_with_ram(img, do_det_seg): + return inference( + img, None, do_det_seg, + "RAM", ram_model, grounding_dino_model, sam_model + ) -def inference(raw_image, model_n , input_tag): - raw_image = raw_image.resize((image_size, image_size)) + def inference_with_t2t(img, input_tags, do_det_seg): + return inference( + img, input_tags, do_det_seg, + "Tag2Text", tag2text_model, grounding_dino_model, sam_model + ) - image = transform(raw_image).unsqueeze(0).to(device) - if model_n == 'Recognize Anything Model': - model = model_ram - with torch.no_grad(): - tags, tags_chinese = model.generate_tag(image) - return tags[0],tags_chinese[0], 'none' - else: - model = model_tag2text - model.threshold = 0.68 - if input_tag == '' or input_tag == 'none' or input_tag == 'None': - input_tag_list = None - else: - input_tag_list = [] - input_tag_list.append(input_tag.replace(',',' | ')) - with torch.no_grad(): - - - caption, tag_predict = model.generate(image,tag_input = input_tag_list,max_length = 50, return_tag_predict = True) - if input_tag_list == None: - tag_1 = tag_predict - tag_2 = ['none'] - else: - _, tag_1 = model.generate(image,tag_input = None, max_length = 50, return_tag_predict = True) - tag_2 = tag_predict - - return tag_1[0],'none',caption[0] - - -def build_gui(): - - description = """ -
    Recognize Anything Model
    -
    - Welcome to the Recognize Anything Model (RAM) and Tag2Text Model demo!

    -
  • - Recognize Anything Model: Upload your image to get the English and Chinese outputs of the image tags! -
    • -
  • - Tag2Text Model: Upload your image to get the tags and caption of the image. - Optional: You can also input specified tags to get the corresponding caption. -
    • - """ # noqa - - article = """ -

    - RAM and Tag2Text is training on open-source datasets, and we are persisting in refining and iterating upon it.
    - Recognize Anything: A Strong Image Tagging Model - | - Tag2Text: Guiding Language-Image Model via Image Tagging - | - Github Repo -

    - """ # noqa - - def inference_with_ram(img): - res = inference(img, "Recognize Anything Model", None) - return res[0], res[1] - - def inference_with_t2t(img, input_tags): - res = inference(img, "Tag2Text Model", input_tags) - return res[0], res[2] - - with gr.Blocks(title="Recognize Anything Model") as demo: - ############### - # components - ############### - gr.HTML(description) - - with gr.Tab(label="Recognize Anything Model"): - with gr.Row(): - with gr.Column(): - ram_in_img = gr.Image(type="pil") - with gr.Row(): - ram_btn_run = gr.Button(value="Run") - ram_btn_clear = gr.Button(value="Clear") - with gr.Column(): - ram_out_tag = gr.Textbox(label="Tags") - ram_out_biaoqian = gr.Textbox(label="标签") - gr.Examples( - examples=[ - ["images/demo1.jpg"], - ["images/demo2.jpg"], - ["images/demo4.jpg"], - ], + with gr.Blocks(title="Recognize Anything Model") as demo: + ############### + # components + ############### + gr.HTML(description) + + with gr.Tab(label="Recognize Anything Model"): + with gr.Row(): + with gr.Column(): + ram_in_img = gr.Image(type="pil") + ram_opt_det_seg = gr.Checkbox(label="Get Boxes and Masks with Grounded-SAM", value=True) + with gr.Row(): + ram_btn_run = gr.Button(value="Run") + ram_btn_clear = gr.ClearButton() + with gr.Column(): + ram_out_img = gr.Image(type="pil") + ram_out_tag = gr.Textbox(label="Tags") + ram_out_biaoqian = gr.Textbox(label="标签") + gr.Examples( + examples=[ + ["images/demo1.jpg", True], + ["images/demo2.jpg", True], + ["images/demo4.jpg", True], + ], + fn=inference_with_ram, + inputs=[ram_in_img, ram_opt_det_seg], + outputs=[ram_out_tag, ram_out_biaoqian, ram_out_img], + cache_examples=True + ) + + with gr.Tab(label="Tag2Text Model"): + with gr.Row(): + with gr.Column(): + t2t_in_img = gr.Image(type="pil") + t2t_in_tag = gr.Textbox(label="User Specified Tags (Optional, separated by comma)") + t2t_opt_det_seg = gr.Checkbox(label="Get Boxes and Masks with Grounded-SAM", value=True) + with gr.Row(): + t2t_btn_run = gr.Button(value="Run") + t2t_btn_clear = gr.ClearButton() + with gr.Column(): + t2t_out_img = gr.Image(type="pil") + t2t_out_tag = gr.Textbox(label="Tags") + t2t_out_cap = gr.Textbox(label="Caption") + gr.Examples( + examples=[ + ["images/demo4.jpg", "", True], + ["images/demo4.jpg", "power line", False], + ["images/demo4.jpg", "track, train", False], + ], + fn=inference_with_t2t, + inputs=[t2t_in_img, t2t_in_tag, t2t_opt_det_seg], + outputs=[t2t_out_tag, t2t_out_cap, t2t_out_img], + cache_examples=True + ) + + gr.HTML(article) + + ############### + # events + ############### + # run inference + ram_btn_run.click( fn=inference_with_ram, - inputs=[ram_in_img], - outputs=[ram_out_tag, ram_out_biaoqian], - cache_examples=True + inputs=[ram_in_img, ram_opt_det_seg], + outputs=[ram_out_tag, ram_out_biaoqian, ram_out_img] ) - - with gr.Tab(label="Tag2Text Model"): - with gr.Row(): - with gr.Column(): - t2t_in_img = gr.Image(type="pil") - t2t_in_tag = gr.Textbox(label="User Specified Tags (Optional, separated by comma)") - with gr.Row(): - t2t_btn_run = gr.Button(value="Run") - t2t_btn_clear = gr.Button(value="Clear") - with gr.Column(): - t2t_out_tag = gr.Textbox(label="Tags") - t2t_out_cap = gr.Textbox(label="Caption") - gr.Examples( - examples=[ - ["images/demo4.jpg", ""], - ["images/demo4.jpg", "power line"], - ["images/demo4.jpg", "track, train"], - ], + t2t_btn_run.click( fn=inference_with_t2t, - inputs=[t2t_in_img, t2t_in_tag], - outputs=[t2t_out_tag, t2t_out_cap], - cache_examples=True + inputs=[t2t_in_img, t2t_in_tag, t2t_opt_det_seg], + outputs=[t2t_out_tag, t2t_out_cap, t2t_out_img] ) - gr.HTML(article) - - ############### - # events - ############### - # run inference - ram_btn_run.click( - fn=inference_with_ram, - inputs=[ram_in_img], - outputs=[ram_out_tag, ram_out_biaoqian] - ) - t2t_btn_run.click( - fn=inference_with_t2t, - inputs=[t2t_in_img, t2t_in_tag], - outputs=[t2t_out_tag, t2t_out_cap] - ) - - # # images of two image panels should keep the same - # # and clear old outputs when image changes - # # slow due to internet latency when deployed on huggingface, comment out - # def sync_img(v): - # return [gr.update(value=v)] + [gr.update(value="")] * 4 - - # ram_in_img.upload(fn=sync_img, inputs=[ram_in_img], outputs=[ - # t2t_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap - # ]) - # ram_in_img.clear(fn=sync_img, inputs=[ram_in_img], outputs=[ - # t2t_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap - # ]) - # t2t_in_img.clear(fn=sync_img, inputs=[t2t_in_img], outputs=[ - # ram_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap - # ]) - # t2t_in_img.upload(fn=sync_img, inputs=[t2t_in_img], outputs=[ - # ram_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap - # ]) - - # clear all - def clear_all(): - return [gr.update(value=None)] * 2 + [gr.update(value="")] * 5 - - ram_btn_clear.click(fn=clear_all, inputs=[], outputs=[ - ram_in_img, t2t_in_img, - ram_out_tag, ram_out_biaoqian, t2t_in_tag, t2t_out_tag, t2t_out_cap - ]) - t2t_btn_clear.click(fn=clear_all, inputs=[], outputs=[ - ram_in_img, t2t_in_img, - ram_out_tag, ram_out_biaoqian, t2t_in_tag, t2t_out_tag, t2t_out_cap - ]) - - return demo + # hide or show image output + ram_opt_det_seg.change(fn=lambda b: gr.update(visible=b), inputs=[ram_opt_det_seg], outputs=[ram_out_img]) + t2t_opt_det_seg.change(fn=lambda b: gr.update(visible=b), inputs=[t2t_opt_det_seg], outputs=[t2t_out_img]) + # clear + ram_btn_clear.add([ram_in_img, ram_out_img, ram_out_tag, ram_out_biaoqian]) + t2t_btn_clear.add([t2t_in_img, t2t_in_tag, t2t_out_img, t2t_out_tag, t2t_out_cap]) -if __name__ == "__main__": - demo = build_gui() - demo.launch(enable_queue=True) + return demo + + build_gui().launch(enable_queue=True) diff --git a/configs/med_config.json b/configs/med_config.json deleted file mode 100644 index 49d64f890cc38d558c4fd3bab048cc521a69a2be..0000000000000000000000000000000000000000 --- a/configs/med_config.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "architectures": [ - "BertModel" - ], - "attention_probs_dropout_prob": 0.1, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 768, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-12, - "max_position_embeddings": 512, - "model_type": "bert", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 0, - "type_vocab_size": 2, - "vocab_size": 30524, - "encoder_width": 768, - "add_cross_attention": true - } \ No newline at end of file diff --git a/configs/q2l_config.json b/configs/q2l_config.json deleted file mode 100644 index a8eba56c27769cadd1506c8e88fe33aced92668f..0000000000000000000000000000000000000000 --- a/configs/q2l_config.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "architectures": [ - "BertModel" - ], - "attention_probs_dropout_prob": 0.1, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 768, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-12, - "max_position_embeddings": 512, - "model_type": "bert", - "num_attention_heads": 4, - "num_hidden_layers": 2, - "pad_token_id": 0, - "type_vocab_size": 2, - "vocab_size": 30522, - "encoder_width": 768, - "add_cross_attention": true, - "add_tag_cross_attention": false - } \ No newline at end of file diff --git a/configs/swin/config_swinB_224.json b/configs/swin/config_swinB_224.json deleted file mode 100644 index 331dd101266f0ba246e539eb50948cfca78b8753..0000000000000000000000000000000000000000 --- a/configs/swin/config_swinB_224.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", - "vision_width": 1024, - "image_res": 224, - "window_size": 7, - "embed_dim": 128, - "depths": [ 2, 2, 18, 2 ], - "num_heads": [ 4, 8, 16, 32 ] - } - \ No newline at end of file diff --git a/configs/swin/config_swinB_384.json b/configs/swin/config_swinB_384.json deleted file mode 100644 index 7910c99721e75a51ddbcc0e5822f2e7a6920a5cd..0000000000000000000000000000000000000000 --- a/configs/swin/config_swinB_384.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", - "vision_width": 1024, - "image_res": 384, - "window_size": 12, - "embed_dim": 128, - "depths": [ 2, 2, 18, 2 ], - "num_heads": [ 4, 8, 16, 32 ] - } - \ No newline at end of file diff --git a/configs/swin/config_swinB_480.json b/configs/swin/config_swinB_480.json deleted file mode 100644 index ce0972f1ff6135b26a0a4ca0410f98f1fb80470e..0000000000000000000000000000000000000000 --- a/configs/swin/config_swinB_480.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", - "vision_width": 1024, - "image_res": 480, - "window_size": 15, - "embed_dim": 128, - "depths": [ 2, 2, 18, 2 ], - "num_heads": [ 4, 8, 16, 32 ] -} \ No newline at end of file diff --git a/configs/swin/config_swinB_576.json b/configs/swin/config_swinB_576.json deleted file mode 100644 index 8ba05f6ce690ae3ea59e76d2e7546162907a2c23..0000000000000000000000000000000000000000 --- a/configs/swin/config_swinB_576.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", - "vision_width": 1024, - "image_res": 576, - "window_size": 18, - "embed_dim": 128, - "depths": [ 2, 2, 18, 2 ], - "num_heads": [ 4, 8, 16, 32 ] -} \ No newline at end of file diff --git a/configs/swin/config_swinB_608.json b/configs/swin/config_swinB_608.json deleted file mode 100644 index 74409a72ff423560ab481513a6f8712d7eae36bc..0000000000000000000000000000000000000000 --- a/configs/swin/config_swinB_608.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "ckpt": "pretrain_model/swin_base_patch4_window7_224_22k.pth", - "vision_width": 1024, - "image_res": 608, - "window_size": 19, - "embed_dim": 128, - "depths": [ 2, 2, 18, 2 ], - "num_heads": [ 4, 8, 16, 32 ] -} \ No newline at end of file diff --git a/configs/swin/config_swinL_384.json b/configs/swin/config_swinL_384.json deleted file mode 100644 index e6443a2d209fef96f4a183b7499323976f3a88e5..0000000000000000000000000000000000000000 --- a/configs/swin/config_swinL_384.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "ckpt": "pretrain_model/swin_large_patch4_window12_384_22k.pth", - "vision_width": 1536, - "image_res": 384, - "window_size": 12, - "embed_dim": 192, - "depths": [ 2, 2, 18, 2 ], - "num_heads": [ 6, 12, 24, 48 ] - } \ No newline at end of file diff --git a/configs/tag2text_caption.yaml b/configs/tag2text_caption.yaml deleted file mode 100644 index eefbb7de93299b5ab86cada1081bfaf35cdb2680..0000000000000000000000000000000000000000 --- a/configs/tag2text_caption.yaml +++ /dev/null @@ -1,33 +0,0 @@ -image_root: '/home/notebook/data/group/projects/tagging/caption/datasets/public/coco/' - -ann_root: 'dataset/caption_dataset' -coco_gt_root: 'dataset/caption_dataset' - -pretrained: '/home/notebook/code/personal/S9049611/BLIP/output/pretrain_caption_tagtotext_v2_bert_asl' - -# size of vit model; base or large -vit: 'swin_b' -vit_grad_ckpt: False -vit_ckpt_layer: 0 - -batch_size: 35 -init_lr: 5e-6 - -image_size: 384 - -# generation configs -max_length: 20 -min_length: 5 -num_beams: 3 -prompt: 'a picture of ' - -# optimizer -weight_decay: 0.05 -min_lr: 0 -max_epoch: 10 - -text_pretrain: 'bert' - -class_num: 3429 -threshold: 0.7 - diff --git a/data/__pycache__/tag_class.cpython-37.pyc b/data/__pycache__/tag_class.cpython-37.pyc deleted file mode 100644 index 332387d0ca72801d69a22ac70bec291dbd20a146..0000000000000000000000000000000000000000 Binary files a/data/__pycache__/tag_class.cpython-37.pyc and /dev/null differ diff --git a/data/ram_tag_list.txt b/data/ram_tag_list.txt deleted file mode 100644 index 49c840b71915f639fb79cd83ac4a3e313cfbc2b1..0000000000000000000000000000000000000000 --- a/data/ram_tag_list.txt +++ /dev/null @@ -1,4585 +0,0 @@ -3D CG rendering -3D 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-winter sport -winter storm -wire -wisteria -witch -witch hat -wok -wolf -woman -wood -wood duck -wood floor -wood wall -wood-burning stove -wooden spoon -woodland -woodpecker -woodworking plane -wool -job -work card -workbench -worker -workplace -workshop -world -worm -worship -wound -wrap -wrap dress -wrapping paper -wrestle -wrestler -wrinkle -wristband -write -writer -writing -writing brush -writing desk -yacht -yak -yard -yellow -yoga -yoga mat -yoghurt -yoke -yolk -youth -youth hostel -yurt -zebra -zebra crossing -zen garden -zip -zipper -zombie -zongzi -zoo \ No newline at end of file diff --git a/data/ram_tag_list_chinese.txt b/data/ram_tag_list_chinese.txt deleted file mode 100644 index 83a736f000fa357231c64c4affc26977da2995ee..0000000000000000000000000000000000000000 --- a/data/ram_tag_list_chinese.txt +++ /dev/null @@ -1,4585 +0,0 @@ -三维CG渲染 -3d眼镜 -算盘 -鲍鱼 -修道院 -肚子 -学院 -附件 -事故 -手风琴 -橡子 -丙烯颜料 -表演 -行动 -动作电影 -活动 -演员 -改编本 -添加 -胶带 -调整 -成人 -冒险 -广告 -天线 -有氧运动 -喷雾罐 -爆炸头 -农业 -帮助 -空调 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a/models/__pycache__/vit.cpython-37.pyc and /dev/null differ diff --git a/models/bert.py b/models/bert.py deleted file mode 100644 index cb90b794284d2262d171aa0f93fdf20854a9059b..0000000000000000000000000000000000000000 --- a/models/bert.py +++ /dev/null @@ -1,1035 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li - * Based on huggingface code base - * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert -''' - -import math -import os -import warnings -from dataclasses import dataclass -from typing import Optional, Tuple - -import torch -from torch import Tensor, device, dtype, nn -import torch.utils.checkpoint -from torch import nn -from torch.nn import CrossEntropyLoss -import torch.nn.functional as F - -from transformers.activations import ACT2FN -from transformers.file_utils import ( - ModelOutput, -) -from transformers.modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - MaskedLMOutput, - MultipleChoiceModelOutput, - NextSentencePredictorOutput, - QuestionAnsweringModelOutput, - SequenceClassifierOutput, - TokenClassifierOutput, -) -from transformers.modeling_utils import ( - PreTrainedModel, - apply_chunking_to_forward, - find_pruneable_heads_and_indices, - prune_linear_layer, -) -from transformers.utils import logging -from transformers.models.bert.configuration_bert import BertConfig - - -logger = logging.get_logger(__name__) - - -class BertEmbeddings_nopos(nn.Module): - """Construct the embeddings from word and position embeddings.""" - - def __init__(self, config): - super().__init__() - self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) - # self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) - - # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load - # any TensorFlow checkpoint file - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - # position_ids (1, len position emb) is contiguous in memory and exported when serialized - # self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) - # self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - - self.config = config - - def forward( - self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 - ): - if input_ids is not None: - input_shape = input_ids.size() - else: - input_shape = inputs_embeds.size()[:-1] - - seq_length = input_shape[1] - - # if position_ids is None: - # position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] - - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - - embeddings = inputs_embeds - - # if self.position_embedding_type == "absolute": - # position_embeddings = self.position_embeddings(position_ids) - # # print('add position_embeddings!!!!') - # embeddings += position_embeddings - embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings - - - - -class BertEmbeddings(nn.Module): - """Construct the embeddings from word and position embeddings.""" - - def __init__(self, config): - super().__init__() - self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) - self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) - - # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load - # any TensorFlow checkpoint file - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - # position_ids (1, len position emb) is contiguous in memory and exported when serialized - self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - - self.config = config - - def forward( - self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 - ): - if input_ids is not None: - input_shape = input_ids.size() - else: - input_shape = inputs_embeds.size()[:-1] - - seq_length = input_shape[1] - - if position_ids is None: - position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] - - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - - embeddings = inputs_embeds - - if self.position_embedding_type == "absolute": - position_embeddings = self.position_embeddings(position_ids) - # print('add position_embeddings!!!!') - embeddings += position_embeddings - embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings - - -class BertSelfAttention(nn.Module): - def __init__(self, config, is_cross_attention): - super().__init__() - self.config = config - if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): - raise ValueError( - "The hidden size (%d) is not a multiple of the number of attention " - "heads (%d)" % (config.hidden_size, config.num_attention_heads) - ) - - self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - - self.query = nn.Linear(config.hidden_size, self.all_head_size) - if is_cross_attention: - self.key = nn.Linear(config.encoder_width, self.all_head_size) - self.value = nn.Linear(config.encoder_width, self.all_head_size) - else: - self.key = nn.Linear(config.hidden_size, self.all_head_size) - self.value = nn.Linear(config.hidden_size, self.all_head_size) - - self.dropout = nn.Dropout(config.attention_probs_dropout_prob) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - self.max_position_embeddings = config.max_position_embeddings - self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) - self.save_attention = False - - def save_attn_gradients(self, attn_gradients): - self.attn_gradients = attn_gradients - - def get_attn_gradients(self): - return self.attn_gradients - - def save_attention_map(self, attention_map): - self.attention_map = attention_map - - def get_attention_map(self): - return self.attention_map - - def transpose_for_scores(self, x): - new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) - x = x.view(*new_x_shape) - return x.permute(0, 2, 1, 3) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - mixed_query_layer = self.query(hidden_states) - - # If this is instantiated as a cross-attention module, the keys - # and values come from an encoder; the attention mask needs to be - # such that the encoder's padding tokens are not attended to. - is_cross_attention = encoder_hidden_states is not None - - if is_cross_attention: - # print(self.key.weight.shape) - key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) - value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) - attention_mask = encoder_attention_mask - elif past_key_value is not None: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - key_layer = torch.cat([past_key_value[0], key_layer], dim=2) - value_layer = torch.cat([past_key_value[1], value_layer], dim=2) - else: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - - query_layer = self.transpose_for_scores(mixed_query_layer) - - past_key_value = (key_layer, value_layer) - - # compatible with higher versions of transformers - if key_layer.shape[0] > query_layer.shape[0]: - key_layer = key_layer[:query_layer.shape[0], :, :, :] - attention_mask = attention_mask[:query_layer.shape[0], :, :] - value_layer = value_layer[:query_layer.shape[0], :, :, :] - - # Take the dot product between "query" and "key" to get the raw attention scores. - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - seq_length = hidden_states.size()[1] - position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) - position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) - distance = position_ids_l - position_ids_r - positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) - positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility - - if self.position_embedding_type == "relative_key": - relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores - elif self.position_embedding_type == "relative_key_query": - relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key - - attention_scores = attention_scores / math.sqrt(self.attention_head_size) - if attention_mask is not None: - # Apply the attention mask is (precomputed for all layers in BertModel forward() function) - attention_scores = attention_scores + attention_mask - - # Normalize the attention scores to probabilities. - attention_probs = nn.Softmax(dim=-1)(attention_scores) - - if is_cross_attention and self.save_attention: - self.save_attention_map(attention_probs) - attention_probs.register_hook(self.save_attn_gradients) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs_dropped = self.dropout(attention_probs) - - # Mask heads if we want to - if head_mask is not None: - attention_probs_dropped = attention_probs_dropped * head_mask - - context_layer = torch.matmul(attention_probs_dropped, value_layer) - - context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - context_layer = context_layer.view(*new_context_layer_shape) - - outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) - - outputs = outputs + (past_key_value,) - return outputs - - -class BertSelfOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertAttention(nn.Module): - def __init__(self, config, is_cross_attention=False): - super().__init__() - self.self = BertSelfAttention(config, is_cross_attention) - self.output = BertSelfOutput(config) - self.pruned_heads = set() - - def prune_heads(self, heads): - if len(heads) == 0: - return - heads, index = find_pruneable_heads_and_indices( - heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads - ) - - # Prune linear layers - self.self.query = prune_linear_layer(self.self.query, index) - self.self.key = prune_linear_layer(self.self.key, index) - self.self.value = prune_linear_layer(self.self.value, index) - self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) - - # Update hyper params and store pruned heads - self.self.num_attention_heads = self.self.num_attention_heads - len(heads) - self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads - self.pruned_heads = self.pruned_heads.union(heads) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - self_outputs = self.self( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - ) - attention_output = self.output(self_outputs[0], hidden_states) - outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them - return outputs - - -class BertIntermediate(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.intermediate_size) - if isinstance(config.hidden_act, str): - self.intermediate_act_fn = ACT2FN[config.hidden_act] - else: - self.intermediate_act_fn = config.hidden_act - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.intermediate_act_fn(hidden_states) - return hidden_states - - -class BertOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.intermediate_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertLayer(nn.Module): - def __init__(self, config, layer_num): - super().__init__() - self.config = config - self.chunk_size_feed_forward = config.chunk_size_feed_forward - self.seq_len_dim = 1 - self.attention = BertAttention(config) - self.layer_num = layer_num - if self.config.add_cross_attention: - self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) - self.intermediate = BertIntermediate(config) - self.output = BertOutput(config) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - mode=None, - ): - - if mode == 'tagging': - - assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" - - cross_attention_outputs = self.crossattention( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - output_attentions=output_attentions, - ) - attention_output = cross_attention_outputs[0] - outputs = cross_attention_outputs[1:-1] # add cross attentions if we output attention weights - - present_key_value = cross_attention_outputs[-1] - - else: - # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 - self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None - self_attention_outputs = self.attention( - hidden_states, - attention_mask, - head_mask, - output_attentions=output_attentions, - past_key_value=self_attn_past_key_value, - ) - attention_output = self_attention_outputs[0] - - outputs = self_attention_outputs[1:-1] - present_key_value = self_attention_outputs[-1] - - if mode=='multimodal': - assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" - - cross_attention_outputs = self.crossattention( - attention_output, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - output_attentions=output_attentions, - ) - attention_output = cross_attention_outputs[0] - outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights - layer_output = apply_chunking_to_forward( - self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output - ) - outputs = (layer_output,) + outputs - - outputs = outputs + (present_key_value,) - - return outputs - - def feed_forward_chunk(self, attention_output): - intermediate_output = self.intermediate(attention_output) - layer_output = self.output(intermediate_output, attention_output) - return layer_output - - -class BertEncoder(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) - self.gradient_checkpointing = False - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=False, - output_hidden_states=False, - return_dict=True, - mode='multimodal', - ): - all_hidden_states = () if output_hidden_states else None - all_self_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - - next_decoder_cache = () if use_cache else None - - for i in range(self.config.num_hidden_layers): - layer_module = self.layer[i] - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer_head_mask = head_mask[i] if head_mask is not None else None - past_key_value = past_key_values[i] if past_key_values is not None else None - - if self.gradient_checkpointing and self.training: - - if use_cache: - logger.warn( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs, past_key_value, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(layer_module), - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - mode=mode, - ) - else: - layer_outputs = layer_module( - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - mode=mode, - ) - - hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache += (layer_outputs[-1],) - if output_attentions: - all_self_attentions = all_self_attentions + (layer_outputs[1],) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple( - v - for v in [ - hidden_states, - next_decoder_cache, - all_hidden_states, - all_self_attentions, - all_cross_attentions, - ] - if v is not None - ) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) - - -class BertPooler(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.activation = nn.Tanh() - - def forward(self, hidden_states): - # We "pool" the model by simply taking the hidden state corresponding - # to the first token. - first_token_tensor = hidden_states[:, 0] - pooled_output = self.dense(first_token_tensor) - pooled_output = self.activation(pooled_output) - return pooled_output - - -class BertPredictionHeadTransform(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - if isinstance(config.hidden_act, str): - self.transform_act_fn = ACT2FN[config.hidden_act] - else: - self.transform_act_fn = config.hidden_act - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.transform_act_fn(hidden_states) - hidden_states = self.LayerNorm(hidden_states) - return hidden_states - - -class BertLMPredictionHead(nn.Module): - def __init__(self, config): - super().__init__() - self.transform = BertPredictionHeadTransform(config) - - # The output weights are the same as the input embeddings, but there is - # an output-only bias for each token. - self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - self.bias = nn.Parameter(torch.zeros(config.vocab_size)) - - # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` - self.decoder.bias = self.bias - - def forward(self, hidden_states): - hidden_states = self.transform(hidden_states) - hidden_states = self.decoder(hidden_states) - return hidden_states - - -class BertOnlyMLMHead(nn.Module): - def __init__(self, config): - super().__init__() - self.predictions = BertLMPredictionHead(config) - - def forward(self, sequence_output): - prediction_scores = self.predictions(sequence_output) - return prediction_scores - - -class BertPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = BertConfig - base_model_prefix = "bert" - _keys_to_ignore_on_load_missing = [r"position_ids"] - - def _init_weights(self, module): - """ Initialize the weights """ - if isinstance(module, (nn.Linear, nn.Embedding)): - # Slightly different from the TF version which uses truncated_normal for initialization - # cf https://github.com/pytorch/pytorch/pull/5617 - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - if isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - - -class BertModel(BertPreTrainedModel): - """ - The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of - cross-attention is added between the self-attention layers, following the architecture described in `Attention is - all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, - Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. - argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an - input to the forward pass. - """ - - def __init__(self, config, add_pooling_layer=True): - super().__init__(config) - self.config = config - - self.embeddings = BertEmbeddings(config) - - self.encoder = BertEncoder(config) - - self.pooler = BertPooler(config) if add_pooling_layer else None - - self.init_weights() - - - def get_input_embeddings(self): - return self.embeddings.word_embeddings - - def set_input_embeddings(self, value): - self.embeddings.word_embeddings = value - - def _prune_heads(self, heads_to_prune): - """ - Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base - class PreTrainedModel - """ - for layer, heads in heads_to_prune.items(): - self.encoder.layer[layer].attention.prune_heads(heads) - - - def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: - """ - Makes broadcastable attention and causal masks so that future and masked tokens are ignored. - - Arguments: - attention_mask (:obj:`torch.Tensor`): - Mask with ones indicating tokens to attend to, zeros for tokens to ignore. - input_shape (:obj:`Tuple[int]`): - The shape of the input to the model. - device: (:obj:`torch.device`): - The device of the input to the model. - - Returns: - :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. - """ - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - if attention_mask.dim() == 3: - extended_attention_mask = attention_mask[:, None, :, :] - elif attention_mask.dim() == 2: - # Provided a padding mask of dimensions [batch_size, seq_length] - # - if the model is a decoder, apply a causal mask in addition to the padding mask - # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] - if is_decoder: - batch_size, seq_length = input_shape - - seq_ids = torch.arange(seq_length, device=device) - causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] - # in case past_key_values are used we need to add a prefix ones mask to the causal mask - # causal and attention masks must have same type with pytorch version < 1.3 - causal_mask = causal_mask.to(attention_mask.dtype) - - if causal_mask.shape[1] < attention_mask.shape[1]: - prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] - causal_mask = torch.cat( - [ - torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), - causal_mask, - ], - axis=-1, - ) - - extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] - else: - extended_attention_mask = attention_mask[:, None, None, :] - else: - raise ValueError( - "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( - input_shape, attention_mask.shape - ) - ) - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and -10000.0 for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility - extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 - return extended_attention_mask - - def forward( - self, - input_ids=None, - attention_mask=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - is_decoder=False, - mode='multimodal', - ): - r""" - encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` - (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` - instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. - use_cache (:obj:`bool`, `optional`): - If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up - decoding (see :obj:`past_key_values`). - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if is_decoder: - use_cache = use_cache if use_cache is not None else self.config.use_cache - else: - use_cache = False - - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - batch_size, seq_length = input_shape - device = input_ids.device - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - batch_size, seq_length = input_shape - device = inputs_embeds.device - elif encoder_embeds is not None: - input_shape = encoder_embeds.size()[:-1] - batch_size, seq_length = input_shape - device = encoder_embeds.device - else: - raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") - - # past_key_values_length - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - - if attention_mask is None: - attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) - - # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] - # ourselves in which case we just need to make it broadcastable to all heads. - extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, - device, is_decoder) - - # If a 2D or 3D attention mask is provided for the cross-attention - # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] - if encoder_hidden_states is not None: - if type(encoder_hidden_states) == list: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() - else: - encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() - encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) - - if type(encoder_attention_mask) == list: - encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] - elif encoder_attention_mask is None: - encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) - else: - encoder_extended_attention_mask = None - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] - # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] - head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - - if encoder_embeds is None: - embedding_output = self.embeddings( - input_ids=input_ids, - position_ids=position_ids, - inputs_embeds=inputs_embeds, - past_key_values_length=past_key_values_length, - ) - else: - embedding_output = encoder_embeds - - encoder_outputs = self.encoder( - embedding_output, - attention_mask=extended_attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_extended_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - mode=mode, - ) - sequence_output = encoder_outputs[0] - pooled_output = self.pooler(sequence_output) if self.pooler is not None else None - - if not return_dict: - return (sequence_output, pooled_output) + encoder_outputs[1:] - - return BaseModelOutputWithPoolingAndCrossAttentions( - last_hidden_state=sequence_output, - pooler_output=pooled_output, - past_key_values=encoder_outputs.past_key_values, - hidden_states=encoder_outputs.hidden_states, - attentions=encoder_outputs.attentions, - cross_attentions=encoder_outputs.cross_attentions, - ) - - -class BertLMHeadModel(BertPreTrainedModel): - - _keys_to_ignore_on_load_unexpected = [r"pooler"] - _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] - - def __init__(self, config): - super().__init__(config) - - self.bert = BertModel(config, add_pooling_layer=False) - self.cls = BertOnlyMLMHead(config) - - self.init_weights() - - def get_output_embeddings(self): - return self.cls.predictions.decoder - - def set_output_embeddings(self, new_embeddings): - self.cls.predictions.decoder = new_embeddings - - def forward( - self, - input_ids=None, - attention_mask=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - labels=None, - past_key_values=None, - use_cache=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - return_logits=False, - is_decoder=True, - reduction='mean', - mode='multimodal', - ): - r""" - encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if - the model is configured as a decoder. - encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in - the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): - Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in - ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are - ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` - past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. - If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` - (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` - instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. - use_cache (:obj:`bool`, `optional`): - If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up - decoding (see :obj:`past_key_values`). - Returns: - Example:: - >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig - >>> import torch - >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') - >>> config = BertConfig.from_pretrained("bert-base-cased") - >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) - >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") - >>> outputs = model(**inputs) - >>> prediction_logits = outputs.logits - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - if labels is not None: - use_cache = False - - outputs = self.bert( - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - is_decoder=is_decoder, - mode=mode, - ) - - sequence_output = outputs[0] - prediction_scores = self.cls(sequence_output) - # sequence_output.shape torch.Size([85, 30, 768]) - # prediction_scores.shape torch.Size([85, 30, 30524]) - # labels.shape torch.Size([85, 30]) - - - if return_logits: - return prediction_scores[:, :-1, :].contiguous() - - lm_loss = None - if labels is not None: - # we are doing next-token prediction; shift prediction scores and input ids by one - shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() - labels = labels[:, 1:].contiguous() - loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) - lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) - if reduction=='none': - lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((lm_loss,) + output) if lm_loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=lm_loss, - logits=prediction_scores, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - - def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): - input_shape = input_ids.shape - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_shape) - - # cut decoder_input_ids if past is used - if past is not None: - input_ids = input_ids[:, -1:] - - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "past_key_values": past, - "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), - "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), - "is_decoder": True, - } - - def _reorder_cache(self, past, beam_idx): - reordered_past = () - for layer_past in past: - reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) - return reordered_past - - diff --git a/models/swin_transformer.py b/models/swin_transformer.py deleted file mode 100644 index c1affc9a8695474e831ad060343c1988d750dc5f..0000000000000000000000000000000000000000 --- a/models/swin_transformer.py +++ /dev/null @@ -1,654 +0,0 @@ -# -------------------------------------------------------- -# Swin Transformer -# Copyright (c) 2021 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ze Liu -# -------------------------------------------------------- - -import numpy as np -from scipy import interpolate - -import torch -import torch.nn as nn -import torch.utils.checkpoint as checkpoint -from timm.models.layers import DropPath, to_2tuple, trunc_normal_ - - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - r""" Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - def extra_repr(self) -> str: - return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' - - def flops(self, N): - # calculate flops for 1 window with token length of N - flops = 0 - # qkv = self.qkv(x) - flops += N * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * N * (self.dim // self.num_heads) * N - # x = (attn @ v) - flops += self.num_heads * N * N * (self.dim // self.num_heads) - # x = self.proj(x) - flops += N * self.dim * self.dim - return flops - - -class SwinTransformerBlock(nn.Module): - r""" Swin Transformer Block. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resulotion. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - if self.shift_size > 0: - # calculate attention mask for SW-MSA - H, W = self.input_resolution - img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - else: - attn_mask = None - - self.register_buffer("attn_mask", attn_mask) - - def forward(self, x): - H, W = self.input_resolution - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - else: - shifted_x = x - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ - f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" - - def flops(self): - flops = 0 - H, W = self.input_resolution - # norm1 - flops += self.dim * H * W - # W-MSA/SW-MSA - nW = H * W / self.window_size / self.window_size - flops += nW * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * H * W - return flops - - -class PatchMerging(nn.Module): - r""" Patch Merging Layer. - - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x): - """ - x: B, H*W, C - """ - H, W = self.input_resolution - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." - - x = x.view(B, H, W, C) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - def extra_repr(self) -> str: - return f"input_resolution={self.input_resolution}, dim={self.dim}" - - def flops(self): - H, W = self.input_resolution - flops = H * W * self.dim - flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim - return flops - - -class BasicLayer(nn.Module): - """ A basic Swin Transformer layer for one stage. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, dim, input_resolution, depth, num_heads, window_size, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): - - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList([ - SwinTransformerBlock(dim=dim, input_resolution=input_resolution, - num_heads=num_heads, window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop, attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) - for i in range(depth)]) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x): - for blk in self.blocks: - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x) - else: - x = blk(x) - if self.downsample is not None: - x = self.downsample(x) - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" - - def flops(self): - flops = 0 - for blk in self.blocks: - flops += blk.flops() - if self.downsample is not None: - flops += self.downsample.flops() - return flops - - -class PatchEmbed(nn.Module): - r""" Image to Patch Embedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - B, C, H, W = x.shape - # FIXME look at relaxing size constraints - assert H == self.img_size[0] and W == self.img_size[1], \ - f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." - x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C - if self.norm is not None: - x = self.norm(x) - return x - - def flops(self): - Ho, Wo = self.patches_resolution - flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) - if self.norm is not None: - flops += Ho * Wo * self.embed_dim - return flops - - -class SwinTransformer(nn.Module): - r""" Swin Transformer - A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - - https://arxiv.org/pdf/2103.14030 - - Args: - img_size (int | tuple(int)): Input image size. Default 224 - patch_size (int | tuple(int)): Patch size. Default: 4 - in_chans (int): Number of input image channels. Default: 3 - num_classes (int): Number of classes for classification head. Default: 1000 - embed_dim (int): Patch embedding dimension. Default: 96 - depths (tuple(int)): Depth of each Swin Transformer layer. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. Default: 7 - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False - patch_norm (bool): If True, add normalization after patch embedding. Default: True - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, - embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], - window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, - drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, - norm_layer=nn.LayerNorm, ape=False, patch_norm=True, - use_checkpoint=False, **kwargs): - super().__init__() - - self.num_classes = num_classes - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = int(embed_dim * 2 ** (self.num_layers - 1)) - self.mlp_ratio = mlp_ratio - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - # absolute position embedding - if self.ape: - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - - # build layers - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer), - input_resolution=(patches_resolution[0] // (2 ** i_layer), - patches_resolution[1] // (2 ** i_layer)), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], - norm_layer=norm_layer, - downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, - use_checkpoint=use_checkpoint) - self.layers.append(layer) - - self.norm = norm_layer(self.num_features) - self.avgpool = nn.AdaptiveAvgPool1d(1) - # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'absolute_pos_embed'} - - @torch.jit.ignore - def no_weight_decay_keywords(self): - return {'relative_position_bias_table'} - - def forward(self, x, idx_to_group_img=None, image_atts=None, **kwargs): - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x) - - x = self.norm(x) # B L C - - x_cls = self.avgpool(x.transpose(1, 2)) # B C 1 - - if idx_to_group_img is None: - return torch.cat([x_cls.transpose(1, 2), x], dim=1) - else: - x_bs = torch.gather(x, dim=0, index=idx_to_group_img.view(-1, 1, 1).expand(-1, x.shape[1], x.shape[2])) - weights = image_atts[:, 1:].unsqueeze(2) # B L 1 - x_bs_cls = torch.sum((weights * x_bs).transpose(1, 2), dim=-1, keepdim=True) # B C 1 - x_bs_cls = x_bs_cls / torch.sum(weights.transpose(1, 2), dim=-1, keepdim=True) # avgpool - - return torch.cat([x_bs_cls.transpose(1, 2), x_bs], dim=1), \ - torch.cat([x_cls.transpose(1, 2), x], dim=1) - - def flops(self): - flops = 0 - flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): - flops += layer.flops() - flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) - flops += self.num_features * self.num_classes - return flops - - -def interpolate_relative_pos_embed(rel_pos_bias, dst_num_pos, param_name=''): - # from: https://github.com/microsoft/unilm/blob/8a0a1c1f4e7326938ea7580a00d56d7f17d65612/beit/run_class_finetuning.py#L348 - - # rel_pos_bias: relative_position_bias_table - src_num_pos, num_attn_heads = rel_pos_bias.size() - - num_extra_tokens = 0 - src_size = int((src_num_pos - num_extra_tokens) ** 0.5) - dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) - if src_size != dst_size: - print("Position interpolate %s from %dx%d to %dx%d" % (param_name, src_size, src_size, dst_size, dst_size)) - - # extra_tokens = rel_pos_bias[-num_extra_tokens:, :] - # rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] - - def geometric_progression(a, r, n): - return a * (1.0 - r ** n) / (1.0 - r) - - left, right = 1.01, 1.5 - while right - left > 1e-6: - q = (left + right) / 2.0 - gp = geometric_progression(1, q, src_size // 2) - if gp > dst_size // 2: - right = q - else: - left = q - - # if q > 1.090307: - # q = 1.090307 - - dis = [] - cur = 1 - for i in range(src_size // 2): - dis.append(cur) - cur += q ** (i + 1) - - r_ids = [-_ for _ in reversed(dis)] - - x = r_ids + [0] + dis - y = r_ids + [0] + dis - - t = dst_size // 2.0 - dx = np.arange(-t, t + 0.1, 1.0) - dy = np.arange(-t, t + 0.1, 1.0) - - # print("Original positions = %s" % str(x)) - # print("Target positions = %s" % str(dx)) - - all_rel_pos_bias = [] - - for i in range(num_attn_heads): - z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() - f = interpolate.interp2d(x, y, z, kind='cubic') - all_rel_pos_bias.append( - torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) - - rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) - - return rel_pos_bias \ No newline at end of file diff --git a/models/tag2text.py b/models/tag2text.py deleted file mode 100644 index 05ca945b24e6b684a61d43f6d5378622fe247f89..0000000000000000000000000000000000000000 --- a/models/tag2text.py +++ /dev/null @@ -1,487 +0,0 @@ -''' - * The Recognize Anything Model (RAM) & Tag2Text Model - * Written by Xinyu Huang -''' -import numpy as np -import json -import torch -import warnings - -from torch import nn -from models.bert import BertConfig, BertModel, BertLMHeadModel -from models.vit import VisionTransformer -from models.swin_transformer import SwinTransformer -from data.ram_tag_list_threshold import ram_class_threshold - -from models.utils import * - -warnings.filterwarnings("ignore") - -class RAM(nn.Module): - def __init__(self, - med_config=f'{CONFIG_PATH}/configs/med_config.json', - image_size=384, - vit='base', - vit_grad_ckpt=False, - vit_ckpt_layer=0, - prompt='a picture of ', - threshold=0.68, - delete_tag_index=[], - tag_list=f'{CONFIG_PATH}/data/ram_tag_list.txt', - tag_list_chinese=f'{CONFIG_PATH}/data/ram_tag_list_chinese.txt'): - r""" The Recognize Anything Model (RAM) inference module. - RAM is a strong image tagging model, which can recognize any common category with high accuracy. - Described in the paper " Recognize Anything: A Strong Image Tagging Model" https://recognize-anything.github.io/ - - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - threshold (int): tagging threshold - delete_tag_index (list): delete some tags that may disturb captioning - """ - super().__init__() - - # create image encoder - if vit == 'swin_b': - if image_size == 224: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json' - elif image_size == 384: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json' - vision_config = read_json(vision_config_path) - assert image_size == vision_config['image_res'] - # assert config['patch_size'] == 32 - vision_width = vision_config['vision_width'] - - self.visual_encoder = SwinTransformer( - img_size=vision_config['image_res'], - patch_size=4, - in_chans=3, - embed_dim=vision_config['embed_dim'], - depths=vision_config['depths'], - num_heads=vision_config['num_heads'], - window_size=vision_config['window_size'], - mlp_ratio=4., - qkv_bias=True, - drop_rate=0.0, - drop_path_rate=0.1, - ape=False, - patch_norm=True, - use_checkpoint=False) - - elif vit == 'swin_l': - if image_size == 224: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_224.json' - elif image_size == 384: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_384.json' - vision_config = read_json(vision_config_path) - assert image_size == vision_config['image_res'] - # assert config['patch_size'] == 32 - vision_width = vision_config['vision_width'] - - self.visual_encoder = SwinTransformer( - img_size=vision_config['image_res'], - patch_size=4, - in_chans=3, - embed_dim=vision_config['embed_dim'], - depths=vision_config['depths'], - num_heads=vision_config['num_heads'], - window_size=vision_config['window_size'], - mlp_ratio=4., - qkv_bias=True, - drop_rate=0.0, - drop_path_rate=0.1, - ape=False, - patch_norm=True, - use_checkpoint=False) - - else: - self.visual_encoder, vision_width = create_vit( - vit, image_size, vit_grad_ckpt, vit_ckpt_layer) - - # create tokenzier - self.tokenizer = init_tokenizer() - - # Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder - # create image-tag interaction encoder - encoder_config = BertConfig.from_json_file(med_config) - encoder_config.encoder_width = 512 - self.tag_encoder = BertModel(config=encoder_config, - add_pooling_layer=False) - - # create image-tag-text decoder - decoder_config = BertConfig.from_json_file(med_config) - self.text_decoder = BertLMHeadModel(config=decoder_config) - - self.delete_tag_index = delete_tag_index - self.prompt = prompt - self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1 - - # load tag list - self.tag_list = self.load_tag_list(tag_list) - self.tag_list_chinese = self.load_tag_list(tag_list_chinese) - - # create image-tag recognition decoder - self.threshold = threshold - self.num_class = len(self.tag_list) - q2l_config = BertConfig.from_json_file(f'{CONFIG_PATH}/configs/q2l_config.json') - q2l_config.encoder_width = 512 - self.tagging_head = BertModel(config=q2l_config, - add_pooling_layer=False) - self.tagging_head.resize_token_embeddings(len(self.tokenizer)) - self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size) - - if q2l_config.hidden_size != 512: - self.wordvec_proj = nn.Linear(512, q2l_config.hidden_size) - else: - self.wordvec_proj = nn.Identity() - - self.fc = nn.Linear(q2l_config.hidden_size, 1) - - self.del_selfattention() - - # share weights of the lowest 2-layer of "image-tag interaction encoder" with the "image-tag recogntion decoder" - tie_encoder_decoder_weights(self.tag_encoder, self.tagging_head, '', - ' ') - self.image_proj = nn.Linear(vision_width, 512) - self.label_embed = nn.Parameter(torch.load('data/textual_label_embedding.pth',map_location='cpu').float()) - - # adjust thresholds for some tags - self.class_threshold = torch.ones(self.num_class) * self.threshold - for key,value in enumerate(ram_class_threshold): - self.class_threshold[key] = value - - def load_tag_list(self, tag_list_file): - with open(tag_list_file, 'r', encoding="utf8") as f: - tag_list = f.read().splitlines() - tag_list = np.array(tag_list) - return tag_list - - # delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label - def del_selfattention(self): - del self.tagging_head.embeddings - for layer in self.tagging_head.encoder.layer: - del layer.attention - - def generate_tag(self, - image, - threshold=0.68, - tag_input=None, - ): - - label_embed = torch.nn.functional.relu(self.wordvec_proj(self.label_embed)) - - image_embeds = self.image_proj(self.visual_encoder(image)) - image_atts = torch.ones(image_embeds.size()[:-1], - dtype=torch.long).to(image.device) - - # recognized image tags using image-tag recogntiion decoder - image_cls_embeds = image_embeds[:, 0, :] - image_spatial_embeds = image_embeds[:, 1:, :] - - bs = image_spatial_embeds.shape[0] - label_embed = label_embed.unsqueeze(0).repeat(bs, 1, 1) - tagging_embed = self.tagging_head( - encoder_embeds=label_embed, - encoder_hidden_states=image_embeds, - encoder_attention_mask=image_atts, - return_dict=False, - mode='tagging', - ) - - logits = self.fc(tagging_embed[0]).squeeze(-1) - - targets = torch.where( - torch.sigmoid(logits) > self.class_threshold.to(image.device), - torch.tensor(1.0).to(image.device), - torch.zeros(self.num_class).to(image.device)) - - tag = targets.cpu().numpy() - tag[:,self.delete_tag_index] = 0 - tag_output = [] - tag_output_chinese = [] - for b in range(bs): - index = np.argwhere(tag[b] == 1) - token = self.tag_list[index].squeeze(axis=1) - tag_output.append(' | '.join(token)) - token_chinese = self.tag_list_chinese[index].squeeze(axis=1) - tag_output_chinese.append(' | '.join(token_chinese)) - - - return tag_output, tag_output_chinese - - -class Tag2Text_Caption(nn.Module): - - def __init__(self, - med_config=f'{CONFIG_PATH}/configs/med_config.json', - image_size=384, - vit='base', - vit_grad_ckpt=False, - vit_ckpt_layer=0, - prompt='a picture of ', - threshold=0.68, - delete_tag_index=[127,2961, 3351, 3265, 3338, 3355, 3359], - tag_list=f'{CONFIG_PATH}/data/tag_list.txt'): - r""" Tag2Text inference module, both captioning and tagging are included. - Tag2Text is an efficient and controllable vision-language pre-training framework. - Described in the paper "Tag2Text: Guiding Vision-Language Model via Image Tagging" https://arxiv.org/abs/2303.05657 - - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - threshold (int): tagging threshold - delete_tag_index (list): delete some tags that may disturb captioning - """ - super().__init__() - - # create image encoder - if vit == 'swin_b': - if image_size == 224: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json' - elif image_size == 384: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json' - vision_config = read_json(vision_config_path) - assert image_size == vision_config['image_res'] - # assert config['patch_size'] == 32 - vision_width = vision_config['vision_width'] - - self.visual_encoder = SwinTransformer( - img_size=vision_config['image_res'], - patch_size=4, - in_chans=3, - embed_dim=vision_config['embed_dim'], - depths=vision_config['depths'], - num_heads=vision_config['num_heads'], - window_size=vision_config['window_size'], - mlp_ratio=4., - qkv_bias=True, - drop_rate=0.0, - drop_path_rate=0.1, - ape=False, - patch_norm=True, - use_checkpoint=False) - - else: - self.visual_encoder, vision_width = create_vit( - vit, image_size, vit_grad_ckpt, vit_ckpt_layer) - - # create tokenzier - self.tokenizer = init_tokenizer() - - # Tag2Text employ encoder-decoder architecture for image-tag-text generation: image-tag interaction encoder and image-tag-text decoder - # create image-tag interaction encoder - encoder_config = BertConfig.from_json_file(med_config) - encoder_config.encoder_width = vision_width - self.tag_encoder = BertModel(config=encoder_config, - add_pooling_layer=False) - - # create image-tag-text decoder - decoder_config = BertConfig.from_json_file(med_config) - self.text_decoder = BertLMHeadModel(config=decoder_config) - - # delete some tags that may disturb captioning - # 127: "quarter"; 2961: "back"; 3351: "two"; 3265: "three"; 3338: "four"; 3355: "five"; 3359: "one" - self.delete_tag_index = delete_tag_index - self.prompt = prompt - self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1 - - # load tag list - self.tag_list = self.load_tag_list(tag_list) - - # create image-tag recognition decoder - self.threshold = threshold - self.num_class = len(self.tag_list) - q2l_config = BertConfig.from_json_file(f'{CONFIG_PATH}/configs/q2l_config.json') - q2l_config.encoder_width = vision_width - self.tagging_head = BertModel(config=q2l_config, - add_pooling_layer=False) - self.tagging_head.resize_token_embeddings(len(self.tokenizer)) - self.label_embed = nn.Embedding(self.num_class, q2l_config.hidden_size) - self.fc = GroupWiseLinear(self.num_class, - q2l_config.hidden_size, - bias=True) - self.del_selfattention() - - # share weights of the lowest 2-layer of "image-tag interaction encoder" with the "image-tag recogntion decoder" - tie_encoder_decoder_weights(self.tag_encoder, self.tagging_head, '', - ' ') - - # adjust thresholds for some tags - # default threshold: 0.68 - # 2701: "person"; 2828: "man"; 1167: "woman"; - tag_thrshold = {2701:0.7, 2828: 0.7, 1167: 0.7} - self.class_threshold = torch.ones(self.num_class) * self.threshold - for key,value in tag_thrshold.items(): - self.class_threshold[key] = value - - def load_tag_list(self, tag_list_file): - with open(tag_list_file, 'r') as f: - tag_list = f.read().splitlines() - tag_list = np.array(tag_list) - return tag_list - - # delete self-attention layer of image-tag recognition decoder to reduce computation, follower Query2Label - def del_selfattention(self): - del self.tagging_head.embeddings - for layer in self.tagging_head.encoder.layer: - del layer.attention - - def generate(self, - image, - sample=False, - num_beams=3, - max_length=30, - min_length=10, - top_p=0.9, - repetition_penalty=1.0, - tag_input=None, - return_tag_predict=False): - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1], - dtype=torch.long).to(image.device) - - # if not user specified tags, recognized image tags using image-tag recogntiion decoder - if tag_input == None: - image_cls_embeds = image_embeds[:, 0, :] - image_spatial_embeds = image_embeds[:, 1:, :] - - bs = image_spatial_embeds.shape[0] - label_embed = self.label_embed.weight.unsqueeze(0).repeat(bs, 1, 1) - tagging_embed = self.tagging_head( - encoder_embeds=label_embed, - encoder_hidden_states=image_embeds, - encoder_attention_mask=image_atts, - return_dict=False, - mode='tagging', - ) - - logits = self.fc(tagging_embed[0]) - - targets = torch.where( - torch.sigmoid(logits) > self.class_threshold.to(image.device), - torch.tensor(1.0).to(image.device), - torch.zeros(self.num_class).to(image.device)) - - tag = targets.cpu().numpy() - - # delete some tags that may disturb captioning - tag[:, self.delete_tag_index] = 0 - - tag_input = [] - for b in range(bs): - index = np.argwhere(tag[b] == 1) - token = self.tag_list[index].squeeze(axis=1) - tag_input.append(' | '.join(token)) - - tag_output = tag_input - - # beam search for text generation(default) - if not sample: - image_embeds = image_embeds.repeat_interleave(num_beams, dim=0) - tag_input_temp = [] - for tag in tag_input: - for i in range(num_beams): - tag_input_temp.append(tag) - tag_input = tag_input_temp - - image_atts = torch.ones(image_embeds.size()[:-1], - dtype=torch.long).to(image.device) - - # tokenizer input tags - tag_input_tokenzier = self.tokenizer(tag_input, - padding='max_length', - truncation=True, - max_length=40, - return_tensors="pt").to( - image.device) - encoder_input_ids = tag_input_tokenzier.input_ids - encoder_input_ids[:, 0] = self.tokenizer.enc_token_id - - # put input tag into image-tag interaction encoder to interact with image embeddings - output_tagembedding = self.tag_encoder( - encoder_input_ids, - attention_mask=tag_input_tokenzier.attention_mask, - encoder_hidden_states=image_embeds, - encoder_attention_mask=image_atts, - return_dict=True, - ) - - # prompt trick for better captioning, followed BLIP - prompt = [self.prompt] * image.size(0) - input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to( - image.device) - input_ids[:, 0] = self.tokenizer.bos_token_id - input_ids = input_ids[:, :-1] - - if sample: - # nucleus sampling - model_kwargs = { - "encoder_hidden_states": output_tagembedding.last_hidden_state, - "encoder_attention_mask": None - } - outputs = self.text_decoder.generate( - input_ids=input_ids, - max_length=max_length, - min_length=min_length, - do_sample=True, - top_p=top_p, - num_return_sequences=1, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - repetition_penalty=1.1, - **model_kwargs) - else: - # beam search (default) - model_kwargs = { - "encoder_hidden_states": output_tagembedding.last_hidden_state, - "encoder_attention_mask": None - } - outputs = self.text_decoder.generate( - input_ids=input_ids, - max_length=max_length, - min_length=min_length, - num_beams=num_beams, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - repetition_penalty=repetition_penalty, - **model_kwargs) - - captions = [] - for output in outputs: - caption = self.tokenizer.decode(output, skip_special_tokens=True) - captions.append(caption[len(self.prompt):]) - if return_tag_predict == True: - return captions, tag_output - return captions - - -# load Tag2Text pretrained model parameters -def tag2text_caption(pretrained='', **kwargs): - model = Tag2Text_Caption(**kwargs) - if pretrained: - if kwargs['vit'] == 'swin_b': - model, msg = load_checkpoint_swinbase(model, pretrained, kwargs) - else: - model, msg = load_checkpoint(model, pretrained) - print('vit:', kwargs['vit']) - print('msg', msg) - return model - - -# load RAM pretrained model parameters -def ram(pretrained='', **kwargs): - model = RAM(**kwargs) - if pretrained: - if kwargs['vit'] == 'swin_b': - model, msg = load_checkpoint_swinbase(model, pretrained, kwargs) - elif kwargs['vit'] == 'swin_l': - model, msg = load_checkpoint_swinlarge(model, pretrained, kwargs) - else: - model, msg = load_checkpoint(model, pretrained) - print('vit:', kwargs['vit']) - print('msg', msg) - return model - diff --git a/models/utils.py b/models/utils.py deleted file mode 100644 index 9e1f12763aea4754c9892e6626e6d6b1ff673db3..0000000000000000000000000000000000000000 --- a/models/utils.py +++ /dev/null @@ -1,278 +0,0 @@ -import os -import json -import torch -import math - -from torch import nn -from typing import List -from transformers import BertTokenizer -from urllib.parse import urlparse -from timm.models.hub import download_cached_file -from models.vit import interpolate_pos_embed -from models.swin_transformer import interpolate_relative_pos_embed -from pathlib import Path -CONFIG_PATH=(Path(__file__).resolve().parents[1]) - -def read_json(rpath): - with open(rpath, 'r') as f: - return json.load(f) - - -def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, - base_model_prefix: str, skip_key: str): - uninitialized_encoder_weights: List[str] = [] - if decoder.__class__ != encoder.__class__: - logger.info( - f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized." - ) - - def tie_encoder_to_decoder_recursively( - decoder_pointer: nn.Module, - encoder_pointer: nn.Module, - module_name: str, - uninitialized_encoder_weights: List[str], - skip_key: str, - depth=0, - ): - assert isinstance(decoder_pointer, nn.Module) and isinstance( - encoder_pointer, nn.Module - ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module" - if hasattr(decoder_pointer, "weight") and skip_key not in module_name: - assert hasattr(encoder_pointer, "weight") - encoder_pointer.weight = decoder_pointer.weight - if hasattr(decoder_pointer, "bias"): - assert hasattr(encoder_pointer, "bias") - encoder_pointer.bias = decoder_pointer.bias - print(module_name + ' is tied') - return - - encoder_modules = encoder_pointer._modules - decoder_modules = decoder_pointer._modules - if len(decoder_modules) > 0: - assert ( - len(encoder_modules) > 0 - ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}" - - all_encoder_weights = set([ - module_name + "/" + sub_name - for sub_name in encoder_modules.keys() - ]) - encoder_layer_pos = 0 - for name, module in decoder_modules.items(): - if name.isdigit(): - encoder_name = str(int(name) + encoder_layer_pos) - decoder_name = name - if not isinstance( - decoder_modules[decoder_name], - type(encoder_modules[encoder_name])) and len( - encoder_modules) != len(decoder_modules): - # this can happen if the name corresponds to the position in a list module list of layers - # in this case the decoder has added a cross-attention that the encoder does not have - # thus skip this step and subtract one layer pos from encoder - encoder_layer_pos -= 1 - continue - elif name not in encoder_modules: - continue - elif depth > 500: - raise ValueError( - "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model." - ) - else: - decoder_name = encoder_name = name - tie_encoder_to_decoder_recursively( - decoder_modules[decoder_name], - encoder_modules[encoder_name], - module_name + "/" + name, - uninitialized_encoder_weights, - skip_key, - depth=depth + 1, - ) - all_encoder_weights.remove(module_name + "/" + encoder_name) - - uninitialized_encoder_weights += list(all_encoder_weights) - - # tie weights recursively - tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, - uninitialized_encoder_weights, skip_key) - - -class GroupWiseLinear(nn.Module): - # could be changed to: - # output = torch.einsum('ijk,zjk->ij', x, self.W) - # or output = torch.einsum('ijk,jk->ij', x, self.W[0]) - def __init__(self, num_class, hidden_dim, bias=True): - super().__init__() - self.num_class = num_class - self.hidden_dim = hidden_dim - self.bias = bias - - self.W = nn.Parameter(torch.Tensor(1, num_class, hidden_dim)) - if bias: - self.b = nn.Parameter(torch.Tensor(1, num_class)) - self.reset_parameters() - - def reset_parameters(self): - stdv = 1. / math.sqrt(self.W.size(2)) - for i in range(self.num_class): - self.W[0][i].data.uniform_(-stdv, stdv) - if self.bias: - for i in range(self.num_class): - self.b[0][i].data.uniform_(-stdv, stdv) - - def forward(self, x): - # x: B,K,d - x = (self.W * x).sum(-1) - if self.bias: - x = x + self.b - return x - - -def init_tokenizer(): - tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') - tokenizer.add_special_tokens({'bos_token': '[DEC]'}) - tokenizer.add_special_tokens({'additional_special_tokens': ['[ENC]']}) - tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] - return tokenizer - - -def create_vit(vit, - image_size, - use_grad_checkpointing=False, - ckpt_layer=0, - drop_path_rate=0): - - assert vit in ['base', 'large'], "vit parameter must be base or large" - if vit == 'base': - vision_width = 768 - visual_encoder = VisionTransformer( - img_size=image_size, - patch_size=16, - embed_dim=vision_width, - depth=12, - num_heads=12, - use_grad_checkpointing=use_grad_checkpointing, - ckpt_layer=ckpt_layer, - drop_path_rate=0 or drop_path_rate) - elif vit == 'large': - vision_width = 1024 - visual_encoder = VisionTransformer( - img_size=image_size, - patch_size=16, - embed_dim=vision_width, - depth=24, - num_heads=16, - use_grad_checkpointing=use_grad_checkpointing, - ckpt_layer=ckpt_layer, - drop_path_rate=0.1 or drop_path_rate) - return visual_encoder, vision_width - - -def is_url(url_or_filename): - parsed = urlparse(url_or_filename) - return parsed.scheme in ("http", "https") - - -def load_checkpoint(model, url_or_filename): - if is_url(url_or_filename): - cached_file = download_cached_file(url_or_filename, - check_hash=False, - progress=True) - checkpoint = torch.load(cached_file, map_location='cpu') - elif os.path.isfile(url_or_filename): - checkpoint = torch.load(url_or_filename, map_location='cpu') - else: - raise RuntimeError('checkpoint url or path is invalid') - - state_dict = checkpoint['model'] - - state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed( - state_dict['visual_encoder.pos_embed'], model.visual_encoder) - if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): - state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed( - state_dict['visual_encoder_m.pos_embed'], model.visual_encoder_m) - for key in model.state_dict().keys(): - if key in state_dict.keys(): - if state_dict[key].shape != model.state_dict()[key].shape: - del state_dict[key] - - msg = model.load_state_dict(state_dict, strict=False) - print('load checkpoint from %s' % url_or_filename) - return model, msg - - -def load_checkpoint_swinbase(model, url_or_filename, kwargs): - if kwargs['image_size'] == 224: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_224.json' - elif kwargs['image_size'] == 384: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinB_384.json' - window_size = read_json(vision_config_path)['window_size'] - print('--------------') - print(url_or_filename) - print('--------------') - if is_url(url_or_filename): - cached_file = download_cached_file(url_or_filename, - check_hash=False, - progress=True) - checkpoint = torch.load(cached_file, map_location='cpu') - elif os.path.isfile(url_or_filename): - checkpoint = torch.load(url_or_filename, map_location='cpu') - else: - raise RuntimeError('checkpoint url or path is invalid') - - state_dict = checkpoint['model'] - - for k in list(state_dict.keys()): - if 'relative_position_bias_table' in k: - dst_num_pos = (2 * window_size - 1)**2 - state_dict[k] = interpolate_relative_pos_embed(state_dict[k], - dst_num_pos, - param_name=k) - elif ('relative_position_index' in k) or ('attn_mask' in k): - del state_dict[k] - elif "vision_multi" in k: - state_dict[k.replace("vision_multi", - "tagging_head")] = state_dict.pop(k) - - msg = model.load_state_dict(state_dict, strict=False) - print('load checkpoint from %s' % url_or_filename) - return model, msg - - -def load_checkpoint_swinlarge(model, url_or_filename, kwargs): - if kwargs['image_size'] == 224: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_224.json' - elif kwargs['image_size'] == 384: - vision_config_path = f'{CONFIG_PATH}/configs/swin/config_swinL_384.json' - window_size = read_json(vision_config_path)['window_size'] - print('--------------') - print(url_or_filename) - print('--------------') - if is_url(url_or_filename): - cached_file = download_cached_file(url_or_filename, - check_hash=False, - progress=True) - checkpoint = torch.load(cached_file, map_location='cpu') - elif os.path.isfile(url_or_filename): - checkpoint = torch.load(url_or_filename, map_location='cpu') - else: - raise RuntimeError('checkpoint url or path is invalid') - - state_dict = checkpoint['model'] - - for k in list(state_dict.keys()): - if 'relative_position_bias_table' in k: - dst_num_pos = (2 * window_size - 1)**2 - state_dict[k] = interpolate_relative_pos_embed(state_dict[k], - dst_num_pos, - param_name=k) - elif ('relative_position_index' in k) or ('attn_mask' in k): - del state_dict[k] - elif "vision_multi" in k: - state_dict[k.replace("vision_multi", - "tagging_head")] = state_dict.pop(k) - - msg = model.load_state_dict(state_dict, strict=False) - print('load checkpoint from %s' % url_or_filename) - return model, msg - - diff --git a/models/vit.py b/models/vit.py deleted file mode 100644 index cec3d8e08ed4451d65392feb2e9f4848d1ef3899..0000000000000000000000000000000000000000 --- a/models/vit.py +++ /dev/null @@ -1,305 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li - * Based on timm code base - * https://github.com/rwightman/pytorch-image-models/tree/master/timm -''' - -import torch -import torch.nn as nn -import torch.nn.functional as F -from functools import partial - -from timm.models.vision_transformer import _cfg, PatchEmbed -from timm.models.registry import register_model -from timm.models.layers import trunc_normal_, DropPath -from timm.models.helpers import named_apply, adapt_input_conv - -from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper - -class Mlp(nn.Module): - """ MLP as used in Vision Transformer, MLP-Mixer and related networks - """ - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class Attention(nn.Module): - def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights - self.scale = qk_scale or head_dim ** -0.5 - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - self.attn_gradients = None - self.attention_map = None - - def save_attn_gradients(self, attn_gradients): - self.attn_gradients = attn_gradients - - def get_attn_gradients(self): - return self.attn_gradients - - def save_attention_map(self, attention_map): - self.attention_map = attention_map - - def get_attention_map(self): - return self.attention_map - - def forward(self, x, register_hook=False): - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - if register_hook: - self.save_attention_map(attn) - attn.register_hook(self.save_attn_gradients) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class Block(nn.Module): - - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): - super().__init__() - self.norm1 = norm_layer(dim) - self.attn = Attention( - dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - if use_grad_checkpointing: - self.attn = checkpoint_wrapper(self.attn) - self.mlp = checkpoint_wrapper(self.mlp) - - def forward(self, x, register_hook=False): - x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) - x = x + self.drop_path(self.mlp(self.norm2(x))) - return x - - -class VisionTransformer(nn.Module): - """ Vision Transformer - A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - - https://arxiv.org/abs/2010.11929 - """ - def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, - num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, - drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, - use_grad_checkpointing=False, ckpt_layer=0): - """ - Args: - img_size (int, tuple): input image size - patch_size (int, tuple): patch size - in_chans (int): number of input channels - num_classes (int): number of classes for classification head - embed_dim (int): embedding dimension - depth (int): depth of transformer - num_heads (int): number of attention heads - mlp_ratio (int): ratio of mlp hidden dim to embedding dim - qkv_bias (bool): enable bias for qkv if True - qk_scale (float): override default qk scale of head_dim ** -0.5 if set - representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set - drop_rate (float): dropout rate - attn_drop_rate (float): attention dropout rate - drop_path_rate (float): stochastic depth rate - norm_layer: (nn.Module): normalization layer - """ - super().__init__() - self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models - norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) - - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) - - num_patches = self.patch_embed.num_patches - - self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) - self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) - self.pos_drop = nn.Dropout(p=drop_rate) - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule - self.blocks = nn.ModuleList([ - Block( - dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, - use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) - ) - for i in range(depth)]) - self.norm = norm_layer(embed_dim) - - trunc_normal_(self.pos_embed, std=.02) - trunc_normal_(self.cls_token, std=.02) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'pos_embed', 'cls_token'} - - def forward(self, x, register_blk=-1): - B = x.shape[0] - x = self.patch_embed(x) - - cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks - x = torch.cat((cls_tokens, x), dim=1) - - x = x + self.pos_embed[:,:x.size(1),:] - x = self.pos_drop(x) - - for i,blk in enumerate(self.blocks): - x = blk(x, register_blk==i) - x = self.norm(x) - - return x - - @torch.jit.ignore() - def load_pretrained(self, checkpoint_path, prefix=''): - _load_weights(self, checkpoint_path, prefix) - - -@torch.no_grad() -def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): - """ Load weights from .npz checkpoints for official Google Brain Flax implementation - """ - import numpy as np - - def _n2p(w, t=True): - if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: - w = w.flatten() - if t: - if w.ndim == 4: - w = w.transpose([3, 2, 0, 1]) - elif w.ndim == 3: - w = w.transpose([2, 0, 1]) - elif w.ndim == 2: - w = w.transpose([1, 0]) - return torch.from_numpy(w) - - w = np.load(checkpoint_path) - if not prefix and 'opt/target/embedding/kernel' in w: - prefix = 'opt/target/' - - if hasattr(model.patch_embed, 'backbone'): - # hybrid - backbone = model.patch_embed.backbone - stem_only = not hasattr(backbone, 'stem') - stem = backbone if stem_only else backbone.stem - stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) - stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) - stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) - if not stem_only: - for i, stage in enumerate(backbone.stages): - for j, block in enumerate(stage.blocks): - bp = f'{prefix}block{i + 1}/unit{j + 1}/' - for r in range(3): - getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) - getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) - getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) - if block.downsample is not None: - block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) - block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) - block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) - embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) - else: - embed_conv_w = adapt_input_conv( - model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) - model.patch_embed.proj.weight.copy_(embed_conv_w) - model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) - model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) - pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) - if pos_embed_w.shape != model.pos_embed.shape: - pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights - pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) - model.pos_embed.copy_(pos_embed_w) - model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) - model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) -# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: -# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) -# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) -# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: -# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) -# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) - for i, block in enumerate(model.blocks.children()): - block_prefix = f'{prefix}Transformer/encoderblock_{i}/' - mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' - block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) - block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) - block.attn.qkv.weight.copy_(torch.cat([ - _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) - block.attn.qkv.bias.copy_(torch.cat([ - _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) - block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) - block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) - for r in range(2): - getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) - getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) - block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) - block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) - - -def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): - # interpolate position embedding - embedding_size = pos_embed_checkpoint.shape[-1] - num_patches = visual_encoder.patch_embed.num_patches - num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches - # height (== width) for the checkpoint position embedding - orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) - # height (== width) for the new position embedding - new_size = int(num_patches ** 0.5) - - if orig_size!=new_size: - # class_token and dist_token are kept unchanged - extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] - # only the position tokens are interpolated - pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] - pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) - pos_tokens = torch.nn.functional.interpolate( - pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) - pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) - new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) - print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) - - return new_pos_embed - else: - return pos_embed_checkpoint \ No newline at end of file diff --git a/ram_swin_large_14m.pth b/ram_swin_large_14m.pth deleted file mode 100644 index a477cb695d2f8da77df0af6a77b687d3cf78fd4a..0000000000000000000000000000000000000000 --- a/ram_swin_large_14m.pth +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:15c729c793af28b9d107c69f85836a1356d76ea830d4714699fb62e55fcc08ed -size 5625634877 diff --git a/requirements.txt b/requirements.txt index 17ec3b8f35232f8ec37d477922387d9740557195..6148b64add2070f90f7b932a265eedc5438be45a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,4 +5,23 @@ pycocoevalcap torch torchvision Pillow -scipy \ No newline at end of file +scipy +git+https://github.com/openai/CLIP.git +git+https://github.com/IDEA-Research/Grounded-Segment-Anything.git/#subdirectory=segment_anything +git+https://github.com/xinyu1205/recognize-anything.git +addict +diffusers[torch] +gradio +huggingface_hub +matplotlib +numpy +onnxruntime +opencv_python +pycocotools +PyYAML +requests +setuptools +supervision +termcolor +yapf +nltk diff --git a/sam_vit_h_4b8939.pth b/sam_vit_h_4b8939.pth deleted file mode 100644 index 8523acce9ddab1cf7e355628a08b1aab8ce08a72..0000000000000000000000000000000000000000 --- a/sam_vit_h_4b8939.pth +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a7bf3b02f3ebf1267aba913ff637d9a2d5c33d3173bb679e46d9f338c26f262e -size 2564550879 diff --git a/tag2text_swin_14m.pth b/tag2text_swin_14m.pth deleted file mode 100644 index c80eac01adf5565cdf80f728b6dc58fb8b8f1041..0000000000000000000000000000000000000000 --- a/tag2text_swin_14m.pth +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4ce96f0ce98f940a6680d567f66a38ccc9ca8c4e638e5f5c5c2e881a0e3502ac -size 4478705095