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from PIL import Image
# import requests
from transformers import CLIPProcessor, CLIPModel
import torch
from torch.utils.data import DataLoader, Dataset
from pathlib import Path
from tqdm import tqdm
import json
import argparse
import numpy as np
class COCODataset(Dataset):
def __init__(self,
coco_root="/nas-ssd/jmincho/datasets/COCO/",
gen_caption_path=None,
is_gt=True):
super().__init__()
self.coco_root = Path(coco_root)
self.image_dir = self.coco_root.joinpath('images/val2014')
if is_gt:
print("Loading karpathy splits")
data_info_path = self.coco_root.joinpath('dataset_coco.json')
with open(data_info_path) as f:
karpathy_data = json.load(f)
data = []
for datum in karpathy_data['images']:
# karpathy test split
if datum['split'] == 'test':
img_id = datum['filename'].split('.')[0]
new_datum = {
'img_id': img_id,
'captions': [d['raw'].strip() for d in datum['sentences']],
}
data.append(new_datum)
else:
print("Loading generated captions")
gen_caption_path = Path(gen_caption_path)
with open(gen_caption_path) as f:
# karpathy_data = json.load(f)
imgTogen_results = json.load(f)['imgToEval']
data = []
for img_id, img_data in imgTogen_results.items():
new_datum = {
'img_id': img_id,
'captions': [img_data['caption']],
}
data.append(new_datum)
self.data = data
print('# images:', len(self.data))
self.img_transform = processor.feature_extractor
self.tokenizer = processor.tokenizer
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
datum = self.data[idx]
img_id = datum['img_id']
if 'COCO' not in img_id:
img_id = f'COCO_val2014_{str(img_id).zfill(12)}'
img_fname = f"{img_id}.jpg"
# COCO_val2014_000000522418.jpg
img_path = self.image_dir.joinpath(img_fname)
img = Image.open(img_path).convert("RGB")
# take first caption
caption = datum['captions'][0]
return {
"img": img,
"caption": caption,
}
def collate_fn(self, datum_list):
B = len(datum_list)
imgs = [datum['img'] for datum in datum_list]
images = self.img_transform(imgs, return_tensors="pt")
captions = [datum['caption'] for datum in datum_list]
text_tokens = self.tokenizer(captions, return_tensors="pt", padding=True)
batch = {
'images': images,
'captions': text_tokens,
}
return batch
def compute_similarity(image_features, text_features, bs = 1000):
# compute similarity
max_pairs = image_features.shape[0]
similarity_scores = torch.zeros(max_pairs, max_pairs)
for v in range(0, max_pairs, bs):
for t in range(0, max_pairs, bs):
# print('Processing Visual '+str(v)+' Text '+str(t), end='\r')
batch_visual_emb = image_features[v:v+bs]
batch_caption_emb = text_features[t:t+bs]
logits = batch_visual_emb @ batch_caption_emb.t()
similarity_scores[v:v+bs,t:t+bs] = logits
print('Done similarity')
return similarity_scores
def compute_retrieval(a2b_sims, return_ranks=True):
"""
Args:
a2b_sims: Result of computing similarity between two sets of embeddings (emb1 @ emb2.T)
with shape (num_datapoints, num_datapoints).
Returns:
Retrieval metrics for that similarity.
"""
npts = a2b_sims.shape[0]
ranks = np.zeros(npts)
top1 = np.zeros(npts)
# loop source embedding indices
for index in range(npts):
# get order of similarities to target embeddings
inds = np.argsort(a2b_sims[index])[::-1]
# find where the correct embedding is ranked
where = np.where(inds == index)
rank = where[0][0]
ranks[index] = rank
# save the top1 result as well
top1[index] = inds[0]
# Compute metrics
r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
r50 = 100.0 * len(np.where(ranks < 50)[0]) / len(ranks)
medr = np.floor(np.median(ranks)) + 1
meanr = ranks.mean() + 1
report_dict = {"r1": r1, "r5": r5, "r10": r10, "r50": r50, "medr": medr, "meanr": meanr, "sum": r1 + r5 + r10}
if return_ranks:
return report_dict, (ranks, top1)
else:
return report_dict
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--coco_root', type=str, default="/nas-ssd/jmincho/datasets/COCO/")
parser.add_argument('--gt', action='store_true')
parser.add_argument('--gen_caption_path', type=str, default="./eval_results/clipRN50_cider_test.json")
args = parser.parse_args()
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
device = "cuda"
model = model.to(device)
model.eval()
print(f"Loaded CLIP at {device}")
batch_size = 1000
dataset = COCODataset(
coco_root="/nas-ssd/jmincho/datasets/COCO/",
gen_caption_path=args.gen_caption_path,
is_gt=args.gt
)
data_loader = DataLoader(
dataset,
batch_size=batch_size,
collate_fn=dataset.collate_fn,
shuffle=False,
num_workers=8)
# fwd all samples
image_features = []
text_features = []
for batch_idx, batch in enumerate(tqdm(data_loader)):
# print('Evaluating batch {}/{}'.format(batch_idx, len(data_loader)), end="\r")
# images, texts = batch
with torch.no_grad():
images = batch["images"].to(device)
texts = batch["captions"].to(device)
vision_outputs = model.vision_model(**batch['images'])
text_outputs = model.text_model(**batch['captions'])
image_embeds = vision_outputs[1]
image_embeds = model.visual_projection(image_embeds)
text_embeds = text_outputs[1]
text_embeds = model.text_projection(text_embeds)
# normalized features
image_embeds = image_embeds / image_embeds.norm(dim=-1, keepdim=True)
text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)
text_features.append(text_embeds.detach().cpu())
image_features.append(image_embeds.detach().cpu())
image_features = torch.cat(image_features, 0)
text_features = torch.cat(text_features, 0)
print('Done forward')
# normalized features
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
# if not single_caption:
# for cap_idx in range(text_features.shape[1]):
# similarity_scores = compute_similarity(image_features, text_features[:,cap_idx,:])
# i2t_dict = compute_retrieval(similarity_scores.numpy())
# t2i_dict = compute_retrieval(similarity_scores.t().numpy())
# print(cap_idx, 'i2t', i2t_dict)
# print(cap_idx, 't2i', t2i_dict)
# else:
similarity_scores = compute_similarity(image_features, text_features)
i2t_dict = compute_retrieval(similarity_scores.numpy())
t2i_dict = compute_retrieval(similarity_scores.t().numpy())
print('i2t', i2t_dict)
print('t2i', t2i_dict)
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