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#!/usr/bin/env python
# coding: utf-8
# In[37]:
import torch
import torch.nn as nn
from functools import partial
import clip
from einops import rearrange, repeat
from glob import glob
from PIL import Image
from torchvision import transforms as T
from tqdm import tqdm
import pickle
import os
import numpy as np
# In[17]:
device = 'cuda:0'
clip_norm = T.Normalize(mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711))
clip_transform = T.Compose([T.ToTensor(),
clip_norm])
# In[1]:
class ClipImageEncoder(nn.Module):
"""
Uses the CLIP image encoder.
"""
def __init__(
self,
model='ViT-L/14',
context_dim=[],
jit=False,
device='cuda',
):
super().__init__()
self.context_dim = context_dim
self.model, _ = clip.load(name=model, device=device, jit=jit)
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
@torch.no_grad()
def forward(self, x):
b, n, c, h, w = x.shape
batch = rearrange(x, 'b n c h w -> (b n) c h w ')
ret = self.model.encode_image(batch)
return rearrange(ret, '(b n) w -> b n w ', b=b, n=n)
def preprocess(self, style_file):
if os.path.exists(style_file):
style_image = Image.open(style_file)
else:
style_image = Image.fromarray(np.zeros((224,224,3), dtype=np.uint8))
x = clip_transform(style_image).unsqueeze(0).unsqueeze(0)
return x
def postprocess(self, x):
return x.squeeze(0).detach().cpu().numpy()
# In[23]:
encoder = ClipImageEncoder()
encoder = encoder.to(device)
# In[6]:
# style_files = glob("/home/soon/datasets/deepfashion_inshop/styles/**/*.jpg", recursive=True)
# # In[39]:
# for style_file in tqdm(style_files[:]):
# style_image = Image.open(style_file)
# x = clip_transform(style_image).unsqueeze(0).unsqueeze(0).to(device)
# emb = encoder(x).detach().cpu().squeeze(0).numpy()
# emb_file = style_file.replace('.jpg','.p')
# with open(emb_file, 'wb') as file:
# pickle.dump(emb, file)
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