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from transformers import CLIPModel, CLIPTokenizer
import os
import json
import argparse
from random import shuffle, seed
import string
# non-standard dependencies:
import h5py
from six.moves import cPickle
import numpy as np
import torch
import torchvision.models as models
import skimage.io
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from PIL import Image
from torch import nn
class CLIPScore(nn.Module):
def __init__(self, clipscore_w=2.5, image_size=224, mode='clip_s', use_grammar=False, joint_out=False):
super(CLIPScore, self).__init__()
# from transformers import CLIPModel, CLIPTokenizer
self.clip_model = CLIPModel.from_pretrained(
'openai/clip-vit-base-patch32')
self.tokenizer = CLIPTokenizer.from_pretrained(
'openai/clip-vit-base-patch32')
self.clip_model.eval()
self.clipscore_w = clipscore_w
self.image_transform = self._transform(image_size)
self.mode = mode
assert mode in ['clip_s', 'refclip_s']
self.use_grammar = use_grammar
self.joint_out = joint_out
if self.use_grammar and self.joint_out is False:
self.grammar_score_head = nn.Sequential(
nn.Linear(self.clip_model.text_embed_dim, self.clip_model.projection_dim, bias=False),
nn.ReLU(),
nn.Linear(self.clip_model.projection_dim, 2, bias=False)
)
def _transform(self, n_px):
return Compose([
Resize(n_px, interpolation=Image.BICUBIC),
CenterCrop(n_px),
lambda image: image.convert("RGB"),
ToTensor(),
Normalize((0.48145466, 0.4578275, 0.40821073),
(0.26862954, 0.26130258, 0.27577711)),
])
def load_image(self, image_path):
image = Image.open(image_path)
return image
# @torch.no_grad()
def image_extract(self, image):
if isinstance(image, str):
image = self.load_image(image)
if not isinstance(image, torch.Tensor):
image = self.image_transform(image)
img_tensor = image.view(-1, 3, 224, 224)
device = next(self.clip_model.parameters()).device
img_tensor = img_tensor.to(device)
clip_model = self.clip_model
img_feat = clip_model.vision_model(img_tensor).pooler_output
img_feat = clip_model.visual_projection(img_feat)
img_feat = img_feat / img_feat.norm(dim=-1, keepdim=True)
return img_feat
# @torch.no_grad()
def text_extract(self, text, prompt="A photo depicts", proj_norm=True):
if isinstance(text, str):
text_batch = [" ".join([prompt, text])]
elif isinstance(text, list):
text_batch = [" ".join([prompt, txt]) for txt in text]
if isinstance(text, tuple) and isinstance(text[0], torch.Tensor):
input_ids, attention_mask = text
else:
input_text = text_batch
tokenized = self.tokenizer(
input_text, return_tensors='pt', padding=True)
input_ids = tokenized.input_ids
attention_mask = tokenized.attention_mask
clip_model = self.clip_model
device = next(self.clip_model.parameters()).device
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
text_feat = clip_model.text_model(input_ids, attention_mask).pooler_output
if proj_norm:
text_feat = clip_model.text_projection(text_feat)
text_feat = text_feat / text_feat.norm(dim=-1, keepdim=True)
return text_feat
# @torch.no_grad()
def calc_clip_s(self, img_feat, text_feat):
return self.clipscore_w * torch.relu((img_feat * text_feat).sum(dim=-1))
# @torch.no_grad()
def calc_refclip_s(self, img_feat=None, text_feat=None, ref_text_feat=None, ref_text_mask=None, clip_s=None):
if clip_s is None:
clip_s = self.calc_clip_s(img_feat, text_feat)
B, dim = img_feat.size()
ref_text_feat = ref_text_feat.view(B, -1, dim)
K = ref_text_feat.size(1)
text_feat = text_feat.view(B, 1, dim).expand(-1, K, -1)
assert ref_text_feat.size() == text_feat.size(
), (ref_text_feat.size(), text_feat.size())
ref_score = self.calc_clip_s(text_feat, ref_text_feat)
if ref_text_mask is not None:
if not isinstance(ref_text_mask, torch.Tensor):
ref_text_mask = torch.tensor(
ref_text_mask, dtype=ref_score.dtype, device=ref_score.device)
ref_score = ref_score.view(B, K) * ref_text_mask.view(B, K)
ref_score = ref_score.view(B, K).max(dim=1).values
assert clip_s.size() == (B,)
assert clip_s.size() == ref_score.size()
# harmonic mean
refclip_s = 2 / (1 / clip_s + 1 / ref_score)
return refclip_s
# # @torch.no_grad()
# def forward(self,
# images=None, text=None,
# img_feat=None, text_feat=None,
# ref_text=None, ref_text_feat=None, ref_text_mask=None,
# prompt="A photo depicts",
# mode=None):
# if img_feat is None:
# img_feat = self.image_extract(images)
# img_feat = img_feat.view(-1, 512)
# if text_feat is None:
# text_feat = self.text_extract(text, prompt=prompt)
# text_feat = text_feat.view(-1, 512)
# if mode is None:
# mode = self.mode
# assert mode in ['clip_s', 'refclip_s']
# if mode == 'clip_s':
# clip_s = self.calc_clip_s(img_feat, text_feat)
# return clip_s
# elif mode == 'refclip_s':
# if ref_text_feat is None:
# ref_text_feat = self.text_extract(ref_text, prompt=prompt)
# ref_text_feat = ref_text_feat.view(-1, 512)
# refclip_s = self.calc_refclip_s(
# img_feat, text_feat, ref_text_feat, ref_text_mask=ref_text_mask)
# return refclip_s
def train_step(self,
images=None, text=None,
img_feat=None, text_feat=None,
neg_text=None, neg_text_feat=None,
# ref_text=None, ref_text_feat=None, ref_text_mask=None,
prompt="A photo depicts",
# return_loss=True,
**kwargs):
if img_feat is None:
img_feat = self.image_extract(images)
img_feat = img_feat.view(-1, 512)
B = img_feat.size(0)
if self.joint_out:
pos_text_feat = self.text_extract(text, prompt=prompt, proj_norm=False).view(B, 512)
neg_text_feat = self.text_extract(neg_text, prompt=prompt, proj_norm=False).view(-1, 512)
neg_B = neg_text_feat.size(0)
# [B+neg_B, 512]
text_feat = torch.cat([pos_text_feat, neg_text_feat], dim=0)
text_cont_feat = self.clip_model.text_projection(text_feat)
text_cont_feat = text_cont_feat / text_cont_feat.norm(dim=-1, keepdim=True)
text_cont_feat = text_cont_feat.view(B+neg_B, 512)
logit_scale = self.clip_model.logit_scale.exp()
# [B+neg_B * B]
logits_per_text = torch.matmul(text_cont_feat, img_feat.t()) * logit_scale
# image-to-text label: positive text
caption_loss = -torch.diag(nn.functional.log_softmax(logits_per_text, dim=0)[:B]).mean()
# calculate text-to-image only on positive text
image_loss = -torch.diag(nn.functional.log_softmax(logits_per_text[:B], dim=1)).mean()
clip_loss = (caption_loss + image_loss) / 2.0
out = {
'clip_loss': clip_loss,
'img_feat': img_feat,
'text_feat': text_cont_feat[:B].detach(),
# 'neg_text_feat': neg_text_feat,
}
return out
else:
if text_feat is None:
text_feat = self.text_extract(text, prompt=prompt, proj_norm=False)
text_cont_feat = self.clip_model.text_projection(text_feat)
text_cont_feat = text_cont_feat / \
text_cont_feat.norm(dim=-1, keepdim=True)
text_cont_feat = text_cont_feat.view(B, 512)
# cosine similarity as logits
logit_scale = self.clip_model.logit_scale.exp()
logits_per_text = torch.matmul(text_cont_feat, img_feat.t()) * logit_scale
# logits_per_image = logits_per_text.T
clip_loss = clip_loss_fn(logits_per_text)
# negative sampling
pos_text_feat = text_feat.view(B, 512)
neg_text_feat = self.text_extract(neg_text, prompt=prompt, proj_norm=False).view(B, 512)
grammar_text_feat = torch.cat([pos_text_feat, neg_text_feat], dim=0)
# 2B, 1
grammar_text_logit = self.grammar_score_head(grammar_text_feat)
grammar_labels = torch.LongTensor([1] * B + [0] * B).to(grammar_text_logit.device).view(2 * B)
grammar_loss = torch.nn.functional.cross_entropy(grammar_text_logit, grammar_labels)
grammar_pred = grammar_text_logit.argmax(dim=1, keepdim=False)
grammar_pos_pred = grammar_pred[:B]
grammar_neg_pred = grammar_pred[B:]
# grammar_acc = (grammar_pred == grammar_labels).float().mean()
out = {
'clip_loss': clip_loss,
'grammar_loss': grammar_loss,
'img_feat': img_feat,
'text_feat': text_cont_feat,
'neg_text_feat': neg_text_feat,
'grammar_pos_pred': grammar_pos_pred,
'grammar_neg_pred': grammar_neg_pred,
}
return out
def train_step_old(self,
images=None, text=None,
img_feat=None, text_feat=None,
neg_text=None, neg_text_feat=None,
# ref_text=None, ref_text_feat=None, ref_text_mask=None,
prompt="A photo depicts",
# return_loss=True,
**kwargs):
if img_feat is None:
img_feat = self.image_extract(images)
img_feat = img_feat.view(-1, 512)
B = img_feat.size(0)
if text_feat is None:
text_feat = self.text_extract(text, prompt=prompt, proj_norm=False)
text_cont_feat = self.clip_model.text_projection(text_feat)
text_cont_feat = text_cont_feat / text_cont_feat.norm(dim=-1, keepdim=True)
text_cont_feat = text_cont_feat.view(B, 512)
# cosine similarity as logits
logit_scale = self.clip_model.logit_scale.exp()
logits_per_text = torch.matmul(text_cont_feat, img_feat.t()) * logit_scale
# logits_per_image = logits_per_text.T
clip_loss = clip_loss_fn(logits_per_text)
# negative sampling
pos_text_feat = text_feat.view(B, 512)
neg_text_feat = self.text_extract(neg_text, prompt=prompt, proj_norm=False).view(B, 512)
grammar_text_feat = torch.cat([pos_text_feat, neg_text_feat], dim=0)
# 2B, 1
grammar_text_logit = self.grammar_score_head(grammar_text_feat)
grammar_labels = torch.LongTensor([1] * B + [0] * B).to(grammar_text_logit.device).view(2 * B)
grammar_loss = torch.nn.functional.cross_entropy(grammar_text_logit, grammar_labels)
grammar_pred = grammar_text_logit.argmax(dim=1, keepdim=False)
grammar_pos_pred = grammar_pred[:B]
grammar_neg_pred = grammar_pred[B:]
# grammar_acc = (grammar_pred == grammar_labels).float().mean()
out = {
'clip_loss': clip_loss,
'grammar_loss': grammar_loss,
'img_feat': img_feat,
'text_feat': text_cont_feat,
'neg_text_feat': neg_text_feat,
'grammar_pos_pred': grammar_pos_pred,
'grammar_neg_pred': grammar_neg_pred,
}
return out
# contrastive loss function, adapted from
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
def contrastive_loss(logits: torch.Tensor, dim: int) -> torch.Tensor:
neg_ce = torch.diag(nn.functional.log_softmax(logits, dim=dim))
return -neg_ce.mean()
def clip_loss_fn(similarity: torch.Tensor) -> torch.Tensor:
caption_loss = contrastive_loss(similarity, dim=0)
image_loss = contrastive_loss(similarity, dim=1)
return (caption_loss + image_loss) / 2.0
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