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# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
# Modified by Feng Liang from
# https://github.com/MendelXu/zsseg.baseline/blob/master/mask_former/modeling/clip_adapter/adapter.py
from typing import List
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.structures import BitMasks
from .utils import build_clip_model, crop_with_mask
from .text_template import PromptExtractor
PIXEL_MEAN = (0.48145466, 0.4578275, 0.40821073)
PIXEL_STD = (0.26862954, 0.26130258, 0.27577711)
class ClipAdapter(nn.Module):
def __init__(self, clip_model_name: str, mask_prompt_depth: int, text_templates: PromptExtractor):
super().__init__()
self.clip_model = build_clip_model(clip_model_name, mask_prompt_depth)
self.text_templates = text_templates
self.text_templates.init_buffer(self.clip_model)
self.text_feature_buffer = {}
def forward(self, image: torch.Tensor, text: List[str], **kwargs):
image = self._preprocess_image(image, **kwargs)
text_feature = self.get_text_features(text) # k,feat_dim
image_features = self.get_image_features(image)
return self.get_sim_logits(text_feature, image_features)
def _preprocess_image(self, image: torch.Tensor):
return image
def _get_text_features(self, noun_list: List[str]):
left_noun_list = [
noun for noun in noun_list if noun not in self.text_feature_buffer
]
if len(left_noun_list) > 0:
left_text_features = self.text_templates(
left_noun_list, self.clip_model
)
self.text_feature_buffer.update(
{
noun: text_feature
for noun, text_feature in zip(
left_noun_list, left_text_features
)
}
)
return torch.stack([self.text_feature_buffer[noun] for noun in noun_list])
def get_text_features(self, noun_list: List[str]):
return self._get_text_features(noun_list)
def get_image_features(self, image: torch.Tensor):
image_features = self.clip_model.visual(image)
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
return image_features
def get_sim_logits(
self,
text_features: torch.Tensor,
image_features: torch.Tensor,
temperature: float = 100,
):
return temperature * image_features @ text_features.T
def normalize_feature(self, feat: torch.Tensor):
return feat / feat.norm(dim=-1, keepdim=True)
class MaskFormerClipAdapter(ClipAdapter):
def __init__(
self,
clip_model_name: str,
text_templates: PromptExtractor,
mask_fill: str = "mean",
mask_expand_ratio: float = 1.0,
mask_thr: float = 0.5,
mask_matting: bool = False,
region_resized: bool = True,
mask_prompt_depth: int = 0,
mask_prompt_fwd: bool = False,
):
super().__init__(clip_model_name, mask_prompt_depth, text_templates)
self.non_object_embedding = nn.Parameter(
torch.empty(1, self.clip_model.text_projection.shape[-1])
)
nn.init.normal_(
self.non_object_embedding.data,
std=self.clip_model.transformer.width ** -0.5,
)
# for test
self.mask_fill = mask_fill
if self.mask_fill == "zero":
self.mask_fill = (0.0, 0.0, 0.0)
elif self.mask_fill == "mean":
self.mask_fill = [255.0 * c for c in PIXEL_MEAN]
else:
raise NotImplementedError(
"Unknown mask_fill method: {}".format(self.mask_fill)
)
self.mask_expand_ratio = mask_expand_ratio
self.mask_thr = mask_thr
self.mask_matting = mask_matting
self.region_resized = region_resized
self.mask_prompt_fwd = mask_prompt_fwd
self.register_buffer(
"pixel_mean", torch.Tensor(PIXEL_MEAN).reshape(1, 3, 1, 1) * 255.0
)
self.register_buffer(
"pixel_std", torch.Tensor(PIXEL_STD).reshape(1, 3, 1, 1) * 255.0
)
def forward(
self,
image: torch.Tensor,
text: List[str],
mask: torch.Tensor,
normalize: bool = True,
fwd_w_region_mask: bool = False,
):
(regions, unnorm_regions), region_masks, valid_flag = self._preprocess_image(image, mask, normalize=normalize)
if regions is None:
return None, valid_flag
if isinstance(regions, list):
assert NotImplementedError
image_features = torch.cat(
[self.get_image_features(image_i) for image_i in regions], dim=0
)
else:
if self.mask_prompt_fwd:
image_features = self.get_image_features(regions, region_masks)
else:
image_features = self.get_image_features(regions)
text_feature = self.get_text_features(text) # k,feat_dim
return self.get_sim_logits(text_feature, image_features), unnorm_regions, valid_flag
def get_image_features(self, image, region_masks=None):
image_features = self.clip_model.visual(image, region_masks)
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
return image_features
def _preprocess_image(
self, image: torch.Tensor, mask: torch.Tensor, normalize: bool = True
):
"""crop, mask and normalize the image
Args:
image ([type]): [C,H,W]
mask ([type]): [K,H,W
normalize (bool, optional): [description]. Defaults to True.
"""
dtype = mask.dtype
bin_mask = mask > self.mask_thr
valid = bin_mask.sum(dim=(-1, -2)) > 0
bin_mask = bin_mask[valid]
mask = mask[valid]
if not self.mask_matting:
mask = bin_mask
bin_mask = BitMasks(bin_mask)
bboxes = bin_mask.get_bounding_boxes()
# crop,mask
regions = []
region_masks = []
for bbox, single_mask in zip(bboxes, mask):
region, region_mask = crop_with_mask(
image.type(dtype),
single_mask.type(dtype),
bbox,
fill=self.mask_fill,
expand_ratio=self.mask_expand_ratio,
)
regions.append(region.unsqueeze(0))
region_masks.append(region_mask.unsqueeze(0))
if len(regions) == 0:
return None, valid
unnorm_regions = regions
if normalize:
regions = [(r - self.pixel_mean) / self.pixel_std for r in regions]
# resize
if self.region_resized:
regions = [
F.interpolate(r, size=(224, 224), mode="bicubic") for r in regions
]
regions = torch.cat(regions)
region_masks = [
F.interpolate(r, size=(224, 224), mode="nearest") for r in region_masks
]
region_masks = torch.cat(region_masks)
unnorm_regions = [
F.interpolate(r, size=(224, 224), mode="bicubic") for r in unnorm_regions
]
unnorm_regions = torch.cat(unnorm_regions)
return (regions, unnorm_regions), region_masks, valid
def get_text_features(self, noun_list: List[str]):
object_text_features = self._get_text_features(noun_list)
non_object_text_features = (
self.non_object_embedding
/ self.non_object_embedding.norm(dim=-1, keepdim=True)
)
return torch.cat([object_text_features, non_object_text_features], dim=0)
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