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from typing import Union, List, Tuple
from collections import OrderedDict
from functools import partial
import numpy as np
import torch
import torch.nn as nn
import torchvision.transforms as transforms
class CLIPScoreWrapper(nn.Module):
def __init__(self,
clip_model_name: str,
download_root: str = None,
device: torch.device = "cuda" if torch.cuda.is_available() else "cpu",
jit: bool = False,
# additional params
visual_score: bool = False,
feats_loss_type: str = None,
feats_loss_weights: List[float] = None,
fc_loss_weight: float = None,
context_length: int = 77):
super().__init__()
import clip # local import
# check model info
self.clip_model_name = clip_model_name
self.device = device
self.available_models = clip.available_models()
assert clip_model_name in self.available_models, f"A model backbone: {clip_model_name} that does not exist"
# load CLIP
self.model, self.preprocess = clip.load(clip_model_name, device, jit=jit, download_root=download_root)
self.model.eval()
# load tokenize
self.tokenize_fn = partial(clip.tokenize, context_length=context_length)
# load CLIP visual
self.visual_encoder = VisualEncoderWrapper(self.model, clip_model_name).to(device)
self.visual_encoder.eval()
# check loss weights
self.visual_score = visual_score
if visual_score:
assert feats_loss_type in ["l1", "l2", "cosine"], f"{feats_loss_type} is not exist."
if clip_model_name.startswith("ViT"): assert len(feats_loss_weights) == 12
if clip_model_name.startswith("RN"): assert len(feats_loss_weights) == 5
# load visual loss wrapper
self.visual_loss_fn = CLIPVisualLossWrapper(self.visual_encoder, feats_loss_type,
feats_loss_weights,
fc_loss_weight)
@property
def input_resolution(self):
return self.model.visual.input_resolution # default: 224
@property
def resize(self): # Resize only
return transforms.Compose([self.preprocess.transforms[0]])
@property
def normalize(self):
return transforms.Compose([
self.preprocess.transforms[0], # Resize
self.preprocess.transforms[1], # CenterCrop
self.preprocess.transforms[-1], # Normalize
])
@property
def norm_(self): # Normalize only
return transforms.Compose([self.preprocess.transforms[-1]])
def encode_image_layer_wise(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
semantic_vec, feature_maps = self.visual_encoder(x)
return semantic_vec, feature_maps
def encode_text(self, text: Union[str, List[str]], norm: bool = True) -> torch.Tensor:
tokens = self.tokenize_fn(text).to(self.device)
text_features = self.model.encode_text(tokens)
if norm:
text_features = text_features.mean(axis=0, keepdim=True)
text_features_norm = text_features / text_features.norm(dim=-1, keepdim=True)
return text_features_norm
return text_features
def encode_image(self, image: torch.Tensor, norm: bool = True) -> torch.Tensor:
image_features = self.model.encode_image(image)
if norm:
image_features_norm = image_features / image_features.norm(dim=-1, keepdim=True)
return image_features_norm
return image_features
@torch.no_grad()
def predict(self,
image: torch.Tensor,
text: Union[str, List[str]]) -> Tuple[torch.Tensor, torch.Tensor, np.ndarray]:
image_features = self.model.encode_image(image)
text_tokenize = self.tokenize_fn(text).to(self.device)
text_features = self.model.encode_text(text_tokenize)
logits_per_image, logits_per_text = self.model(image, text)
probs = logits_per_image.softmax(dim=-1).cpu().numpy()
return image_features, text_features, probs
def compute_text_visual_distance(
self, image: torch.Tensor, text: Union[str, List[str]]
) -> torch.Tensor:
image_features = self.model.encode_image(image)
text_tokenize = self.tokenize_fn(text).to(self.device)
with torch.no_grad():
text_features = self.model.encode_text(text_tokenize)
image_features_norm = image_features / image_features.norm(dim=-1, keepdim=True)
text_features_norm = text_features / text_features.norm(dim=-1, keepdim=True)
# loss = - (image_features_norm @ text_features_norm.T)
loss = 1 - torch.cosine_similarity(image_features_norm, text_features_norm, dim=1)
return loss.mean()
def directional_text_visual_distance(self, src_text, src_img, tar_text, tar_img):
src_image_features = self.model.encode_image(src_img).detach()
tar_image_features = self.model.encode_image(tar_img)
src_text_tokenize = self.tokenize_fn(src_text).to(self.device)
tar_text_tokenize = self.tokenize_fn(tar_text).to(self.device)
with torch.no_grad():
src_text_features = self.model.encode_text(src_text_tokenize)
tar_text_features = self.model.encode_text(tar_text_tokenize)
delta_image_features = tar_image_features - src_image_features
delta_text_features = tar_text_features - src_text_features
# # avold zero divisor
# delta_image_features_norm = delta_image_features / delta_image_features.norm(dim=-1, keepdim=True)
# delta_text_features_norm = delta_text_features / delta_text_features.norm(dim=-1, keepdim=True)
loss = 1 - torch.cosine_similarity(delta_image_features, delta_text_features, dim=1, eps=1e-3)
return loss.mean()
def compute_visual_distance(
self, x: torch.Tensor, y: torch.Tensor, clip_norm: bool = True,
) -> Tuple[torch.Tensor, List]:
# return a fc loss and the list of feat loss
assert self.visual_score is True
assert x.shape == y.shape
assert x.shape[-1] == self.input_resolution and x.shape[-2] == self.input_resolution
assert y.shape[-1] == self.input_resolution and y.shape[-2] == self.input_resolution
if clip_norm:
return self.visual_loss_fn(self.normalize(x), self.normalize(y))
else:
return self.visual_loss_fn(x, y)
class VisualEncoderWrapper(nn.Module):
"""
semantic features and layer by layer feature maps are obtained from CLIP visual encoder.
"""
def __init__(self, clip_model: nn.Module, clip_model_name: str):
super().__init__()
self.clip_model = clip_model
self.clip_model_name = clip_model_name
if clip_model_name.startswith("ViT"):
self.feature_maps = OrderedDict()
for i in range(12): # 12 ResBlocks in ViT visual transformer
self.clip_model.visual.transformer.resblocks[i].register_forward_hook(
self.make_hook(i)
)
if clip_model_name.startswith("RN"):
layers = list(self.clip_model.visual.children())
init_layers = torch.nn.Sequential(*layers)[:8]
self.layer1 = layers[8]
self.layer2 = layers[9]
self.layer3 = layers[10]
self.layer4 = layers[11]
self.att_pool2d = layers[12]
def make_hook(self, name):
def hook(module, input, output):
if len(output.shape) == 3:
# LND -> NLD (B, 77, 768)
self.feature_maps[name] = output.permute(1, 0, 2)
else:
self.feature_maps[name] = output
return hook
def _forward_vit(self, x: torch.Tensor) -> Tuple[torch.Tensor, List]:
fc_feature = self.clip_model.encode_image(x).float()
feature_maps = [self.feature_maps[k] for k in range(12)]
# fc_feature len: 1 ,feature_maps len: 12
return fc_feature, feature_maps
def _forward_resnet(self, x: torch.Tensor) -> Tuple[torch.Tensor, List]:
def stem(m, x):
for conv, bn, relu in [(m.conv1, m.bn1, m.relu1), (m.conv2, m.bn2, m.relu2), (m.conv3, m.bn3, m.relu3)]:
x = torch.relu(bn(conv(x)))
x = m.avgpool(x)
return x
x = x.type(self.clip_model.visual.conv1.weight.dtype)
x = stem(self.clip_model.visual, x)
x1 = self.layer1(x)
x2 = self.layer2(x1)
x3 = self.layer3(x2)
x4 = self.layer4(x3)
y = self.att_pool2d(x4)
# fc_features len: 1 ,feature_maps len: 5
return y, [x, x1, x2, x3, x4]
def forward(self, x) -> Tuple[torch.Tensor, List[torch.Tensor]]:
if self.clip_model_name.startswith("ViT"):
fc_feat, visual_feat_maps = self._forward_vit(x)
if self.clip_model_name.startswith("RN"):
fc_feat, visual_feat_maps = self._forward_resnet(x)
return fc_feat, visual_feat_maps
class CLIPVisualLossWrapper(nn.Module):
"""
Visual Feature Loss + FC loss
"""
def __init__(
self,
visual_encoder: nn.Module,
feats_loss_type: str = None,
feats_loss_weights: List[float] = None,
fc_loss_weight: float = None,
):
super().__init__()
self.visual_encoder = visual_encoder
self.feats_loss_weights = feats_loss_weights
self.fc_loss_weight = fc_loss_weight
self.layer_criterion = layer_wise_distance(feats_loss_type)
def forward(self, x: torch.Tensor, y: torch.Tensor):
x_fc_feature, x_feat_maps = self.visual_encoder(x)
y_fc_feature, y_feat_maps = self.visual_encoder(y)
# visual feature loss
if sum(self.feats_loss_weights) == 0:
feats_loss_list = [torch.tensor(0, device=x.device)]
else:
feats_loss = self.layer_criterion(x_feat_maps, y_feat_maps, self.visual_encoder.clip_model_name)
feats_loss_list = []
for layer, w in enumerate(self.feats_loss_weights):
if w:
feats_loss_list.append(feats_loss[layer] * w)
# visual fc loss, default: cosine similarity
if self.fc_loss_weight == 0:
fc_loss = torch.tensor(0, device=x.device)
else:
fc_loss = (1 - torch.cosine_similarity(x_fc_feature, y_fc_feature, dim=1)).mean()
fc_loss = fc_loss * self.fc_loss_weight
return fc_loss, feats_loss_list
#################################################################################
# layer wise metric #
#################################################################################
def layer_wise_distance(metric_name: str):
return {
"l1": l1_layer_wise,
"l2": l2_layer_wise,
"cosine": cosine_layer_wise
}.get(metric_name.lower())
def l2_layer_wise(x_features, y_features, clip_model_name):
return [
torch.square(x_conv - y_conv).mean()
for x_conv, y_conv in zip(x_features, y_features)
]
def l1_layer_wise(x_features, y_features, clip_model_name):
return [
torch.abs(x_conv - y_conv).mean()
for x_conv, y_conv in zip(x_features, y_features)
]
def cosine_layer_wise(x_features, y_features, clip_model_name):
if clip_model_name.startswith("RN"):
return [
(1 - torch.cosine_similarity(x_conv, y_conv, dim=1)).mean()
for x_conv, y_conv in zip(x_features, y_features)
]
return [
(1 - torch.cosine_similarity(x_conv, y_conv, dim=1)).mean()
for x_conv, y_conv in zip(x_features, y_features)
]
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