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# ------------------------------------------------------------------------
# DINO
# Copyright (c) 2022 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Mask2Former https://github.com/facebookresearch/Mask2Former by Feng Li and Hao Zhang.
import logging
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d
from detectron2.utils.registry import Registry
from detectron2.structures import BitMasks
from timm.models.layers import trunc_normal_
from .dino_decoder import TransformerDecoder, DeformableTransformerDecoderLayer
from ...utils.utils import MLP, gen_encoder_output_proposals, inverse_sigmoid
from ...utils import box_ops
TRANSFORMER_DECODER_REGISTRY = Registry("TRANSFORMER_MODULE")
TRANSFORMER_DECODER_REGISTRY.__doc__ = """
Registry for transformer module in MaskDINO.
"""
def build_transformer_decoder(cfg, in_channels, lang_encoder, mask_classification=True):
"""
Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.
"""
name = cfg.MODEL.MaskDINO.TRANSFORMER_DECODER_NAME
return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, lang_encoder, mask_classification)
@TRANSFORMER_DECODER_REGISTRY.register()
class MaskDINODecoder(nn.Module):
@configurable
def __init__(
self,
in_channels,
lang_encoder,
mask_classification=True,
*,
num_classes: int,
hidden_dim: int,
num_queries: int,
nheads: int,
dim_feedforward: int,
dec_layers: int,
mask_dim: int,
dim_projection: int,
enforce_input_project: bool,
two_stage: bool,
dn: str,
noise_scale:float,
dn_num:int,
initialize_box_type:bool,
initial_pred:bool,
learn_tgt: bool,
total_num_feature_levels: int = 4,
dropout: float = 0.0,
activation: str = 'relu',
nhead: int = 8,
dec_n_points: int = 4,
return_intermediate_dec: bool = True,
query_dim: int = 4,
dec_layer_share: bool = False,
semantic_ce_loss: bool = False,
cross_track_layer: bool = False,
):
"""
NOTE: this interface is experimental.
Args:
in_channels: channels of the input features
mask_classification: whether to add mask classifier or not
num_classes: number of classes
hidden_dim: Transformer feature dimension
num_queries: number of queries
nheads: number of heads
dim_feedforward: feature dimension in feedforward network
enc_layers: number of Transformer encoder layers
dec_layers: number of Transformer decoder layers
pre_norm: whether to use pre-LayerNorm or not
mask_dim: mask feature dimension
enforce_input_project: add input project 1x1 conv even if input
channels and hidden dim is identical
d_model: transformer dimension
dropout: dropout rate
activation: activation function
nhead: num heads in multi-head attention
dec_n_points: number of sampling points in decoder
return_intermediate_dec: return the intermediate results of decoder
query_dim: 4 -> (x, y, w, h)
dec_layer_share: whether to share each decoder layer
semantic_ce_loss: use ce loss for semantic segmentation
"""
super().__init__()
assert mask_classification, "Only support mask classification model"
self.mask_classification = mask_classification
self.num_feature_levels = total_num_feature_levels
self.initial_pred = initial_pred
self.lang_encoder = lang_encoder
# define Transformer decoder here
self.dn=dn
self.learn_tgt = learn_tgt
self.noise_scale=noise_scale
self.dn_num=dn_num
self.num_heads = nheads
self.num_layers = dec_layers
self.two_stage=two_stage
self.initialize_box_type = initialize_box_type
self.total_num_feature_levels = total_num_feature_levels
self.num_queries = num_queries
self.semantic_ce_loss = semantic_ce_loss
# learnable query features
if not two_stage or self.learn_tgt:
self.query_feat = nn.Embedding(num_queries, hidden_dim)
if not two_stage and initialize_box_type == 'no':
self.query_embed = nn.Embedding(num_queries, 4)
if two_stage:
self.enc_output = nn.Linear(hidden_dim, hidden_dim)
self.enc_output_norm = nn.LayerNorm(hidden_dim)
self.input_proj = nn.ModuleList()
for _ in range(self.num_feature_levels):
if in_channels != hidden_dim or enforce_input_project:
self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1))
weight_init.c2_xavier_fill(self.input_proj[-1])
else:
self.input_proj.append(nn.Sequential())
self.num_classes = {
'obj365':100,
'obj365_clip':100,
'lvis':100,
'openimage':100,
'lvis_clip':100,
'openimage_clip':100,
'grit':100,
'vg':200,
'coco':80,
'coco_clip':80,
'grounding':1,
'rvos':1,
'sa1b':1,
'sa1b_clip':1,
'bdd_det':10,
'bdd_inst':8,
'ytvis19':40,
'image_yt19':40,
'image_yt21':40,
'bdd_track_seg':8,
'bdd_track_box':8,
'ovis':25,
'image_o':25,
'ytvis21':40,
'uvo_video': 81,
'ytbvos':1,
}
# output FFNs
assert self.mask_classification, "why not class embedding?"
self.confidence_score = MLP(hidden_dim, hidden_dim, 1, 2)
self.category_embed = nn.Parameter(torch.rand(hidden_dim, dim_projection))
# trunc_normal_(self.category_embed, std=.02)
# self.track_embed = MLP(hidden_dim, hidden_dim, hidden_dim, 3)
self.coco_label_enc = nn.Embedding(80,hidden_dim)
self.obj365_label_enc = nn.Embedding(100, hidden_dim)
self.vg_label_enc = nn.Embedding(200, hidden_dim)
self.grounding_label_enc = nn.Embedding(1,hidden_dim)
self.ytvis19_label_enc = nn.Embedding(40,hidden_dim)
self.ytvis21_label_enc = nn.Embedding(40,hidden_dim)
self.ovis_label_enc = nn.Embedding(25,hidden_dim)
self.uvo_label_enc = nn.Embedding(81,hidden_dim)
self.bdd_det = nn.Embedding(10,hidden_dim)
self.bdd_inst = nn.Embedding(8,hidden_dim)
self.label_enc = {
'coco': self.coco_label_enc,
'coco_clip': self.coco_label_enc,
'coconomask': self.coco_label_enc,
'obj365': self.obj365_label_enc,
'lvis': self.obj365_label_enc,
'openimage': self.obj365_label_enc,
'grit': self.obj365_label_enc,
'vg': self.vg_label_enc,
'obj365_clip': self.obj365_label_enc,
'lvis_clip': self.obj365_label_enc,
'openimage_clip': self.obj365_label_enc,
'bdd_det':self.bdd_det,
'bdd_inst':self.bdd_inst,
'bdd_track_seg':self.bdd_inst,
'bdd_track_box':self.bdd_inst,
'sa1b': self.grounding_label_enc,
'sa1b_clip': self.grounding_label_enc,
'grounding': self.grounding_label_enc,
'rvos': self.grounding_label_enc,
'uvo_video':self.uvo_label_enc,
'ytvis19':self.ytvis19_label_enc,
'image_yt19': self.ytvis19_label_enc,
'ytvis21':self.ytvis21_label_enc,
'image_yt21':self.ytvis21_label_enc,
'ovis':self.ovis_label_enc,
'image_o': self.ovis_label_enc,
'burst':self.grounding_label_enc,
'ytbvos':self.grounding_label_enc,
}
self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
# init decoder
self.decoder_norm = decoder_norm = nn.LayerNorm(hidden_dim)
decoder_layer = DeformableTransformerDecoderLayer(hidden_dim, dim_feedforward,
dropout, activation,
self.num_feature_levels, nhead, dec_n_points)
self.decoder = TransformerDecoder(decoder_layer, self.num_layers, decoder_norm,
return_intermediate=return_intermediate_dec,
d_model=hidden_dim, query_dim=query_dim,
num_feature_levels=self.num_feature_levels,
dec_layer_share=dec_layer_share,
cross_track_layer = cross_track_layer,
n_levels=self.num_feature_levels, n_heads=nhead, n_points=dec_n_points
)
self.cross_track_layer = cross_track_layer
self.hidden_dim = hidden_dim
self._bbox_embed = _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)
box_embed_layerlist = [_bbox_embed for i in range(self.num_layers)] # share box prediction each layer
self.bbox_embed = nn.ModuleList(box_embed_layerlist)
self.decoder.bbox_embed = self.bbox_embed
@classmethod
def from_config(cls, cfg, in_channels, lang_encoder, mask_classification):
ret = {}
ret["in_channels"] = in_channels
ret["lang_encoder"] = lang_encoder
ret["mask_classification"] = mask_classification
ret["dim_projection"] = cfg.MODEL.DIM_PROJ
ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
ret["hidden_dim"] = cfg.MODEL.MaskDINO.HIDDEN_DIM
ret["num_queries"] = cfg.MODEL.MaskDINO.NUM_OBJECT_QUERIES
# Transformer parameters:
ret["nheads"] = cfg.MODEL.MaskDINO.NHEADS
ret["dim_feedforward"] = cfg.MODEL.MaskDINO.DIM_FEEDFORWARD
ret["dec_layers"] = cfg.MODEL.MaskDINO.DEC_LAYERS
ret["enforce_input_project"] = cfg.MODEL.MaskDINO.ENFORCE_INPUT_PROJ
ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
ret["two_stage"] =cfg.MODEL.MaskDINO.TWO_STAGE
ret["initialize_box_type"] = cfg.MODEL.MaskDINO.INITIALIZE_BOX_TYPE # ['no', 'bitmask', 'mask2box']
ret["dn"]=cfg.MODEL.MaskDINO.DN
ret["noise_scale"] =cfg.MODEL.MaskDINO.DN_NOISE_SCALE
ret["dn_num"] =cfg.MODEL.MaskDINO.DN_NUM
ret["initial_pred"] =cfg.MODEL.MaskDINO.INITIAL_PRED
ret["learn_tgt"] = cfg.MODEL.MaskDINO.LEARN_TGT
ret["total_num_feature_levels"] = cfg.MODEL.SEM_SEG_HEAD.TOTAL_NUM_FEATURE_LEVELS
ret["semantic_ce_loss"] = cfg.MODEL.MaskDINO.TEST.SEMANTIC_ON and cfg.MODEL.MaskDINO.SEMANTIC_CE_LOSS and ~cfg.MODEL.MaskDINO.TEST.PANOPTIC_ON
ret["cross_track_layer"] = cfg.MODEL.CROSS_TRACK
return ret
def prepare_for_dn(self, targets, tgt, refpoint_emb, batch_size,task):
"""
modified from dn-detr. You can refer to dn-detr
https://github.com/IDEA-Research/DN-DETR/blob/main/models/dn_dab_deformable_detr/dn_components.py
for more details
:param dn_args: scalar, noise_scale
:param tgt: original tgt (content) in the matching part
:param refpoint_emb: positional anchor queries in the matching part
:param batch_size: bs
"""
if self.training:
scalar, noise_scale = self.dn_num,self.noise_scale
known = [(torch.ones_like(t['labels'])).cuda() for t in targets]
know_idx = [torch.nonzero(t) for t in known]
known_num = [sum(k) for k in known]
# use fix number of dn queries
if max(known_num)>0:
scalar = scalar//(int(max(known_num)))
else:
scalar = 0
if scalar == 0:
input_query_label = None
input_query_bbox = None
attn_mask = None
mask_dict = None
return input_query_label, input_query_bbox, attn_mask, mask_dict
# can be modified to selectively denosie some label or boxes; also known label prediction
unmask_bbox = unmask_label = torch.cat(known)
labels = torch.cat([t['labels'] for t in targets])
boxes = torch.cat([t['boxes'] for t in targets])
batch_idx = torch.cat([torch.full_like(t['labels'].long(), i) for i, t in enumerate(targets)])
# known
known_indice = torch.nonzero(unmask_label + unmask_bbox)
known_indice = known_indice.view(-1)
# noise
known_indice = known_indice.repeat(scalar, 1).view(-1)
known_labels = labels.repeat(scalar, 1).view(-1)
known_bid = batch_idx.repeat(scalar, 1).view(-1)
known_bboxs = boxes.repeat(scalar, 1)
known_labels_expaned = known_labels.clone()
known_bbox_expand = known_bboxs.clone()
# noise on the label
if noise_scale > 0:
p = torch.rand_like(known_labels_expaned.float())
chosen_indice = torch.nonzero(p < (noise_scale * 0.5)).view(-1) # half of bbox prob
new_label = torch.randint_like(chosen_indice, 0, self.num_classes[task]) # randomly put a new one here
known_labels_expaned.scatter_(0, chosen_indice, new_label)
if noise_scale > 0:
diff = torch.zeros_like(known_bbox_expand)
diff[:, :2] = known_bbox_expand[:, 2:] / 2
diff[:, 2:] = known_bbox_expand[:, 2:]
known_bbox_expand += torch.mul((torch.rand_like(known_bbox_expand) * 2 - 1.0),
diff).cuda() * noise_scale
known_bbox_expand = known_bbox_expand.clamp(min=0.0, max=1.0)
m = known_labels_expaned.long().to('cuda')
input_label_embed = self.label_enc[task](m)
input_bbox_embed = inverse_sigmoid(known_bbox_expand)
single_pad = int(max(known_num))
pad_size = int(single_pad * scalar)
padding_label = torch.zeros(pad_size, self.hidden_dim).cuda()
padding_bbox = torch.zeros(pad_size, 4).cuda()
if not refpoint_emb is None:
input_query_label = torch.cat([padding_label, tgt], dim=0).repeat(batch_size, 1, 1)
input_query_bbox = torch.cat([padding_bbox, refpoint_emb], dim=0).repeat(batch_size, 1, 1)
else:
input_query_label=padding_label.repeat(batch_size, 1, 1)
input_query_bbox = padding_bbox.repeat(batch_size, 1, 1)
# map
map_known_indice = torch.tensor([]).to('cuda')
if len(known_num):
map_known_indice = torch.cat([torch.tensor(range(num)) for num in known_num]) # [1,2, 1,2,3]
map_known_indice = torch.cat([map_known_indice + single_pad * i for i in range(scalar)]).long()
if len(known_bid):
input_query_label[(known_bid.long(), map_known_indice)] = input_label_embed
input_query_bbox[(known_bid.long(), map_known_indice)] = input_bbox_embed
tgt_size = pad_size + self.num_queries
attn_mask = torch.ones(tgt_size, tgt_size).to('cuda') < 0
# match query cannot see the reconstruct
attn_mask[pad_size:, :pad_size] = True
# reconstruct cannot see each other
for i in range(scalar):
if i == 0:
attn_mask[single_pad * i:single_pad * (i + 1), single_pad * (i + 1):pad_size] = True
if i == scalar - 1:
attn_mask[single_pad * i:single_pad * (i + 1), :single_pad * i] = True
else:
attn_mask[single_pad * i:single_pad * (i + 1), single_pad * (i + 1):pad_size] = True
attn_mask[single_pad * i:single_pad * (i + 1), :single_pad * i] = True
mask_dict = {
'known_indice': torch.as_tensor(known_indice).long(),
'batch_idx': torch.as_tensor(batch_idx).long(),
'map_known_indice': torch.as_tensor(map_known_indice).long(),
'known_lbs_bboxes': (known_labels, known_bboxs),
'know_idx': know_idx,
'pad_size': pad_size,
'scalar': scalar,
}
else:
if not refpoint_emb is None:
input_query_label = tgt.repeat(batch_size, 1, 1)
input_query_bbox = refpoint_emb.repeat(batch_size, 1, 1)
else:
input_query_label=None
input_query_bbox=None
attn_mask = None
mask_dict=None
# 100*batch*256
if not input_query_bbox is None:
input_query_label = input_query_label
input_query_bbox = input_query_bbox
return input_query_label,input_query_bbox,attn_mask,mask_dict
def dn_post_process(self,outputs_class,outputs_score,outputs_coord,mask_dict,outputs_mask):
"""
post process of dn after output from the transformer
put the dn part in the mask_dict
"""
assert mask_dict['pad_size'] > 0
output_known_class = outputs_class[:, :, :mask_dict['pad_size'], :]
outputs_class = outputs_class[:, :, mask_dict['pad_size']:, :]
output_known_score = outputs_score[:, :, :mask_dict['pad_size'], :]
outputs_score = outputs_score[:, :, mask_dict['pad_size']:, :]
output_known_coord = outputs_coord[:, :, :mask_dict['pad_size'], :]
outputs_coord = outputs_coord[:, :, mask_dict['pad_size']:, :]
if outputs_mask is not None:
output_known_mask = outputs_mask[:, :, :mask_dict['pad_size'], :]
outputs_mask = outputs_mask[:, :, mask_dict['pad_size']:, :]
out = {'pred_logits': output_known_class[-1], 'pred_scores':output_known_score[-1],'pred_boxes': output_known_coord[-1],'pred_masks': output_known_mask[-1]}
out['aux_outputs'] = self._set_aux_loss(output_known_class, output_known_score, output_known_mask, output_known_coord)
mask_dict['output_known_lbs_bboxes']=out
return outputs_class, outputs_score, outputs_coord, outputs_mask
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 pred_box(self, reference, hs, ref0=None):
"""
:param reference: reference box coordinates from each decoder layer
:param hs: content
:param ref0: whether there are prediction from the first layer
"""
device = reference[0].device
if ref0 is None:
outputs_coord_list = []
else:
outputs_coord_list = [ref0.to(device)]
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).to(device)
layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig).to(device)
layer_outputs_unsig = layer_outputs_unsig.sigmoid()
outputs_coord_list.append(layer_outputs_unsig)
outputs_coord_list = torch.stack(outputs_coord_list)
return outputs_coord_list
def forward(self, x, mask_features, extra, task, masks, targets=None):
"""
:param x: input, a list of multi-scale feature
:param mask_features: is the per-pixel embeddings with resolution 1/4 of the original image,
obtained by fusing backbone encoder encoded features. This is used to produce binary masks.
:param masks: mask in the original image
:param targets: used for denoising training
"""
if 'spatial_query_pos_mask' in extra:
visual_P = True
else:
visual_P = False
assert len(x) == self.num_feature_levels
device = x[0].device
size_list = []
# disable mask, it does not affect performance
enable_mask = 0
if masks is not None:
for src in x:
if src.size(2) % 32 or src.size(3) % 32:
enable_mask = 1
if enable_mask == 0:
masks = [torch.zeros((src.size(0), src.size(2), src.size(3)), device=src.device, dtype=torch.bool) for src in x]
src_flatten = []
mask_flatten = []
spatial_shapes = []
for i in range(self.num_feature_levels):
idx=self.num_feature_levels-1-i
bs, c , h, w=x[idx].shape
size_list.append(x[i].shape[-2:])
spatial_shapes.append(x[idx].shape[-2:])
src_flatten.append(self.input_proj[idx](x[idx]).flatten(2).transpose(1, 2))
mask_flatten.append(masks[i].flatten(1))
src_flatten = torch.cat(src_flatten, 1) # bs, \sum{hxw}, c
mask_flatten = torch.cat(mask_flatten, 1) # bs, \sum{hxw}
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)
predictions_federate = []
predictions_score = []
predictions_class = []
predictions_mask = []
if self.two_stage:
output_memory, output_proposals = gen_encoder_output_proposals(src_flatten, mask_flatten, spatial_shapes)
output_memory = self.enc_output_norm(self.enc_output(output_memory))
if task in ['grounding','rvos']:
class_embed = output_memory @ self.category_embed
enc_outputs_class_unselected = torch.einsum("bqc,bc->bq", class_embed, extra['grounding_class']).unsqueeze(-1) #[bz,numq,1]
elif visual_P:
enc_outputs_class_unselected = self.confidence_score(output_memory)
else:
class_embed = output_memory @ self.category_embed # [bz,num_q,projectdim]
enc_outputs_class_unselected = torch.einsum("bqc,nc->bqn", class_embed, extra['class_embeddings']) #[bz,n,80]
enc_outputs_coord_unselected = self._bbox_embed(
output_memory) + output_proposals # (bs, \sum{hw}, 4) unsigmoid
topk = self.num_queries
topk_proposals = torch.topk(enc_outputs_class_unselected.max(-1)[0], topk, dim=1)[1]
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() #[bz,num_q,4]
tgt_undetach = torch.gather(output_memory, 1,
topk_proposals.unsqueeze(-1).repeat(1, 1, self.hidden_dim)) # unsigmoid #[bz,num_q.256]
conf_score, outputs_class, outputs_mask,_ = self.forward_prediction_heads(tgt_undetach.transpose(0, 1), mask_features, task, extra, mask_dict = None)
tgt = tgt_undetach.detach()
if self.learn_tgt:
tgt = self.query_feat.weight[None].repeat(bs, 1, 1)
interm_outputs=dict()
interm_outputs['pred_logits'] = outputs_class
interm_outputs['pred_scores'] = conf_score
interm_outputs['pred_boxes'] = refpoint_embed_undetach.sigmoid()
interm_outputs['pred_masks'] = outputs_mask
elif not self.two_stage:
tgt = self.query_feat.weight[None].repeat(bs, 1, 1)
refpoint_embed = self.query_embed.weight[None].repeat(bs, 1, 1)
tgt_mask = None
mask_dict = None
if self.dn != "no" and self.training:
assert targets is not None
input_query_label, input_query_bbox, tgt_mask, mask_dict = \
self.prepare_for_dn(targets, None, None, x[0].shape[0],task)
if mask_dict is not None:
tgt=torch.cat([input_query_label, tgt],dim=1)
# direct prediction from the matching and denoising part in the begining
if self.initial_pred:
conf_score, outputs_class, outputs_mask, pred_federat = self.forward_prediction_heads(tgt.transpose(0, 1), mask_features, task, extra, mask_dict, self.training)
predictions_score.append(conf_score)
predictions_class.append(outputs_class)
predictions_mask.append(outputs_mask)
predictions_federate.append(pred_federat)
if self.dn != "no" and self.training and mask_dict is not None:
refpoint_embed=torch.cat([input_query_bbox,refpoint_embed],dim=1)
hs, references, cross_track_embed = self.decoder(
tgt=tgt.transpose(0, 1),
memory=src_flatten.transpose(0, 1),
memory_key_padding_mask=mask_flatten,
pos=None,
refpoints_unsigmoid=refpoint_embed.transpose(0, 1),
level_start_index=level_start_index,
spatial_shapes=spatial_shapes,
valid_ratios=valid_ratios,
tgt_mask=tgt_mask,
task=task,
extra=extra,
)
for i, output in enumerate(hs):
conf_score, outputs_class, outputs_mask,pred_federat = self.forward_prediction_heads(output.transpose(0, 1), mask_features, task, extra, mask_dict, self.training or (i == len(hs)-1))
predictions_score.append(conf_score)
predictions_class.append(outputs_class)
predictions_mask.append(outputs_mask)
predictions_federate.append(pred_federat)
# iteratively box prediction
if self.initial_pred:
out_boxes = self.pred_box(references, hs, refpoint_embed.sigmoid())
assert len(predictions_class) == self.num_layers + 1
else:
out_boxes = self.pred_box(references, hs)
if mask_dict is not None:
predictions_mask=torch.stack(predictions_mask)
predictions_class=torch.stack(predictions_class)
predictions_score = torch.stack(predictions_score)
predictions_class, predictions_score, out_boxes, predictions_mask=\
self.dn_post_process(predictions_class, predictions_score, out_boxes,mask_dict,predictions_mask)
predictions_class, predictions_score, predictions_mask=list(predictions_class), list(predictions_score), list(predictions_mask)
elif self.training: # this is to insure self.label_enc participate in the model
predictions_class[-1] += 0.0*self.label_enc[task].weight.sum()
if mask_dict is not None:
track_embed = hs[-1][:, mask_dict['pad_size']:, :]
else:
track_embed = hs[-1]
out = {
'pred_federat':predictions_federate[-1],
'pred_logits': predictions_class[-1],
'pred_scores': predictions_score[-1],
'pred_masks': predictions_mask[-1],
'pred_boxes':out_boxes[-1],
'pred_track_embed': track_embed,
'visual_P': visual_P,
'aux_outputs': self._set_aux_loss(
predictions_class if self.mask_classification else None, predictions_score, predictions_mask, out_boxes, predictions_federate, visual_P
)
}
if self.two_stage:
out['interm_outputs'] = interm_outputs
return out, mask_dict
def forward_prediction_heads(self, output, mask_features, task, extra,mask_dict, pred_mask=True, visual_P=False):
decoder_output = self.decoder_norm(output)
decoder_output = decoder_output.transpose(0, 1)
# outputs_class = self.class_embed(decoder_output)
conf_score = self.confidence_score(decoder_output) # if visual_P else None
class_embed = decoder_output @ self.category_embed # [bz,num_q,projectdim]
if task in ['grounding', 'rvos']:
outputs_class = torch.einsum("bqc,bc->bq", class_embed, extra['grounding_class']).unsqueeze(-1) #[bz,numq,1]
else:
outputs_class = torch.einsum("bqc,nc->bqn", class_embed, extra['class_embeddings']) #[bz,n,80]
outputs_mask = None
if pred_mask:
mask_embed = self.mask_embed(decoder_output)
outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features)
return conf_score, outputs_class, outputs_mask, None
@torch.jit.unused
def _set_aux_loss(self, outputs_class, outputs_score, outputs_seg_masks, out_boxes, predictions_federate=None, visual_P=False):
# 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.
# if self.mask_classification:
if predictions_federate is None:
return [
{"pred_logits": a, "pred_scores": b, "pred_masks": c, "pred_boxes":d, 'visual_P': visual_P}
for a, b, c, d in zip(outputs_class[:-1], outputs_score[:-1], outputs_seg_masks[:-1], out_boxes[:-1])
]
else:
return [
{"pred_logits": a, "pred_scores": b, "pred_masks": c, "pred_boxes":d, 'pred_federat':e,'visual_P': visual_P}
for a, b, c, d, e in zip(outputs_class[:-1], outputs_score[:-1], outputs_seg_masks[:-1], out_boxes[:-1], predictions_federate[:-1])
] |