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| import argparse | |
| import pickle | |
| import os | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from torch.utils.data import DataLoader | |
| from torchvision import transforms | |
| from torchvision.models import inception_v3, Inception3 | |
| import numpy as np | |
| from tqdm import tqdm | |
| from inception import InceptionV3 | |
| from dataset import MultiResolutionDataset | |
| class Inception3Feature(Inception3): | |
| def forward(self, x): | |
| if x.shape[2] != 299 or x.shape[3] != 299: | |
| x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=True) | |
| x = self.Conv2d_1a_3x3(x) # 299 x 299 x 3 | |
| x = self.Conv2d_2a_3x3(x) # 149 x 149 x 32 | |
| x = self.Conv2d_2b_3x3(x) # 147 x 147 x 32 | |
| x = F.max_pool2d(x, kernel_size=3, stride=2) # 147 x 147 x 64 | |
| x = self.Conv2d_3b_1x1(x) # 73 x 73 x 64 | |
| x = self.Conv2d_4a_3x3(x) # 73 x 73 x 80 | |
| x = F.max_pool2d(x, kernel_size=3, stride=2) # 71 x 71 x 192 | |
| x = self.Mixed_5b(x) # 35 x 35 x 192 | |
| x = self.Mixed_5c(x) # 35 x 35 x 256 | |
| x = self.Mixed_5d(x) # 35 x 35 x 288 | |
| x = self.Mixed_6a(x) # 35 x 35 x 288 | |
| x = self.Mixed_6b(x) # 17 x 17 x 768 | |
| x = self.Mixed_6c(x) # 17 x 17 x 768 | |
| x = self.Mixed_6d(x) # 17 x 17 x 768 | |
| x = self.Mixed_6e(x) # 17 x 17 x 768 | |
| x = self.Mixed_7a(x) # 17 x 17 x 768 | |
| x = self.Mixed_7b(x) # 8 x 8 x 1280 | |
| x = self.Mixed_7c(x) # 8 x 8 x 2048 | |
| x = F.avg_pool2d(x, kernel_size=8) # 8 x 8 x 2048 | |
| return x.view(x.shape[0], x.shape[1]) # 1 x 1 x 2048 | |
| def load_patched_inception_v3(): | |
| # inception = inception_v3(pretrained=True) | |
| # inception_feat = Inception3Feature() | |
| # inception_feat.load_state_dict(inception.state_dict()) | |
| inception_feat = InceptionV3([3], normalize_input=False) | |
| return inception_feat | |
| def extract_features(loader, inception, device): | |
| pbar = tqdm(loader) | |
| feature_list = [] | |
| for img in pbar: | |
| img = img.to(device) | |
| feature = inception(img)[0].view(img.shape[0], -1) | |
| feature_list.append(feature.to('cpu')) | |
| features = torch.cat(feature_list, 0) | |
| return features | |
| if __name__ == '__main__': | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| parser = argparse.ArgumentParser( | |
| description='Calculate Inception v3 features for datasets' | |
| ) | |
| parser.add_argument('--size', type=int, default=256) | |
| parser.add_argument('--batch', default=64, type=int, help='batch size') | |
| parser.add_argument('--n_sample', type=int, default=50000) | |
| parser.add_argument('--flip', action='store_true') | |
| parser.add_argument('path', metavar='PATH', help='path to datset lmdb file') | |
| args = parser.parse_args() | |
| inception = load_patched_inception_v3() | |
| inception = nn.DataParallel(inception).eval().to(device) | |
| transform = transforms.Compose( | |
| [ | |
| transforms.RandomHorizontalFlip(p=0.5 if args.flip else 0), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), | |
| ] | |
| ) | |
| dset = MultiResolutionDataset(args.path, transform=transform, resolution=args.size) | |
| loader = DataLoader(dset, batch_size=args.batch, num_workers=4) | |
| features = extract_features(loader, inception, device).numpy() | |
| features = features[: args.n_sample] | |
| print(f'extracted {features.shape[0]} features') | |
| mean = np.mean(features, 0) | |
| cov = np.cov(features, rowvar=False) | |
| name = os.path.splitext(os.path.basename(args.path))[0] | |
| with open(f'inception_{name}.pkl', 'wb') as f: | |
| pickle.dump({'mean': mean, 'cov': cov, 'size': args.size, 'path': args.path}, f) | |