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# ------------------------------------------------------------------------------------
# Minimal DALL-E
# Copyright (c) 2021 KakaoBrain. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------
import os
import random
import urllib
import hashlib
import tarfile
import torch
import clip
import numpy as np
from PIL import Image
from torch.nn import functional as F
from tqdm import tqdm
def set_seed(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
@torch.no_grad()
def clip_score(prompt: str,
images: np.ndarray,
model_clip: torch.nn.Module,
preprocess_clip,
device: str) -> np.ndarray:
images = [preprocess_clip(Image.fromarray((image*255).astype(np.uint8))) for image in images]
images = torch.stack(images, dim=0).to(device=device)
texts = clip.tokenize(prompt).to(device=device)
texts = torch.repeat_interleave(texts, images.shape[0], dim=0)
image_features = model_clip.encode_image(images)
text_features = model_clip.encode_text(texts)
scores = F.cosine_similarity(image_features, text_features).squeeze()
rank = torch.argsort(scores, descending=True).cpu().numpy()
return rank
def download(url: str, root: str) -> str:
os.makedirs(root, exist_ok=True)
filename = os.path.basename(url)
pathname = filename[:-len('.tar.gz')]
expected_md5 = url.split("/")[-2]
download_target = os.path.join(root, filename)
result_path = os.path.join(root, pathname)
if os.path.isfile(download_target) and (os.path.exists(result_path) and not os.path.isfile(result_path)):
return result_path
with urllib.request.urlopen(url) as source, open(download_target, 'wb') as output:
with tqdm(total=int(source.info().get('Content-Length')), ncols=80, unit='iB', unit_scale=True,
unit_divisor=1024) as loop:
while True:
buffer = source.read(8192)
if not buffer:
break
output.write(buffer)
loop.update(len(buffer))
if hashlib.md5(open(download_target, 'rb').read()).hexdigest() != expected_md5:
raise RuntimeError(f'Model has been downloaded but the md5 checksum does not not match')
with tarfile.open(download_target, 'r:gz') as f:
pbar = tqdm(f.getmembers(), total=len(f.getmembers()))
for member in pbar:
pbar.set_description(f'extracting: {member.name} (size:{member.size // (1024 * 1024)}MB)')
f.extract(member=member, path=root)
return result_path
def realpath_url_or_path(url_or_path: str, root: str = None) -> str:
if urllib.parse.urlparse(url_or_path).scheme in ('http', 'https'):
return download(url_or_path, root)
return url_or_path
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