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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 | import torch
import torchvision.transforms as transforms
import numpy as np
import cv2
import logging
from .model import Net
class Extractor(object):
def __init__(self, model_path, use_cuda=True):
self.net = Net(reid=True)
self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
state_dict = torch.load(model_path, map_location=torch.device(self.device))[
'net_dict']
self.net.load_state_dict(state_dict)
logger = logging.getLogger("root.tracker")
logger.info("Loading weights from {}... Done!".format(model_path))
self.net.to(self.device)
self.size = (64, 128)
self.norm = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
def _preprocess(self, im_crops):
"""
TODO:
1. to float with scale from 0 to 1
2. resize to (64, 128) as Market1501 dataset did
3. concatenate to a numpy array
3. to torch Tensor
4. normalize
"""
def _resize(im, size):
return cv2.resize(im.astype(np.float32)/255., size)
im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(
0) for im in im_crops], dim=0).float()
return im_batch
def __call__(self, im_crops):
im_batch = self._preprocess(im_crops)
with torch.no_grad():
im_batch = im_batch.to(self.device)
features = self.net(im_batch)
return features.cpu().numpy()
if __name__ == '__main__':
img = cv2.imread("demo.jpg")[:, :, (2, 1, 0)]
extr = Extractor("checkpoint/ckpt.t7")
feature = extr(img)
print(feature.shape)
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