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af8092c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | import torch
import torch.backends.cudnn as cudnn
import torchvision
import argparse
import os
from model import Net
parser = argparse.ArgumentParser(description="Train on market1501")
parser.add_argument("--data-dir", default='data', type=str)
parser.add_argument("--no-cuda", action="store_true")
parser.add_argument("--gpu-id", default=0, type=int)
args = parser.parse_args()
# device
device = "cuda:{}".format(
args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu"
if torch.cuda.is_available() and not args.no_cuda:
cudnn.benchmark = True
# data loader
root = args.data_dir
query_dir = os.path.join(root, "query")
gallery_dir = os.path.join(root, "gallery")
transform = torchvision.transforms.Compose([
torchvision.transforms.Resize((128, 64)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
[0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
queryloader = torch.utils.data.DataLoader(
torchvision.datasets.ImageFolder(query_dir, transform=transform),
batch_size=64, shuffle=False
)
galleryloader = torch.utils.data.DataLoader(
torchvision.datasets.ImageFolder(gallery_dir, transform=transform),
batch_size=64, shuffle=False
)
# net definition
net = Net(reid=True)
assert os.path.isfile(
"./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
print('Loading from checkpoint/ckpt.t7')
checkpoint = torch.load("./checkpoint/ckpt.t7")
net_dict = checkpoint['net_dict']
net.load_state_dict(net_dict, strict=False)
net.eval()
net.to(device)
# compute features
query_features = torch.tensor([]).float()
query_labels = torch.tensor([]).long()
gallery_features = torch.tensor([]).float()
gallery_labels = torch.tensor([]).long()
with torch.no_grad():
for idx, (inputs, labels) in enumerate(queryloader):
inputs = inputs.to(device)
features = net(inputs).cpu()
query_features = torch.cat((query_features, features), dim=0)
query_labels = torch.cat((query_labels, labels))
for idx, (inputs, labels) in enumerate(galleryloader):
inputs = inputs.to(device)
features = net(inputs).cpu()
gallery_features = torch.cat((gallery_features, features), dim=0)
gallery_labels = torch.cat((gallery_labels, labels))
gallery_labels -= 2
# save features
features = {
"qf": query_features,
"ql": query_labels,
"gf": gallery_features,
"gl": gallery_labels
}
torch.save(features, "features.pth")
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