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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
@torch.no_grad()
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)
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