def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(net=cfg.net, pretrained=False)
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
model = model.base
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
###################
# Extract Feature #
###################
im_dir = osp.expanduser(cfg.image_dir)
im_paths = get_im_names(im_dir,
pattern='*.jpg',
return_path=True,
return_np=False)
all_feat = []
import scipy.io as sci
for i, im_path in enumerate(im_paths):
im = np.asarray(Image.open(im_path).convert('RGB'))
im = pre_process_im(im, cfg)
im = Variable(TVT(torch.from_numpy(im).float()), volatile=True)
feat = model(im)
feat = feat.data.cpu().numpy()
all_feat.append(feat)
if (i + 1) % 100 == 0:
print('{}/{} images done'.format(i, len(im_paths)))
all_feat = np.concatenate(all_feat)
print('all_feat.shape:', all_feat.shape)
# all_feat = normalize(all_feat, axis=1)
# You can save your im_paths and features, or do anything else ...
sci.savemat(cfg.saved_feature_mat_file, {'features': all_feat})
def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(net=cfg.net, pretrained=False)
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
model = model.base
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
#######################
# Extract Image Crops #
#######################
all_feat = []
dataset = BoundingBoxCrops(cfg)
loader = DataLoader(dataset=dataset,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=cfg.num_workers)
print('Processing detections')
t = tqdm(iter(loader), leave=False, total=len(loader))
for i, data in enumerate(t):
imgs, bad_boxes = data
imgs = Variable(TVT(imgs))
with torch.no_grad():
feats = model(imgs)
feats = feats.data.cpu().numpy()
for j in range(len(bad_boxes)):
if bad_boxes[j] == True:
feats[j,:] = np.zeros(1280)
sci.savemat(cfg.saved_feature_mat_path+'/'+"{:05d}".format(i)+'.mat', {'features':feats})
def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(last_conv_stride=cfg.last_conv_stride)
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
im_dir = osp.expanduser('~/Dataset/market1501/Market-1501-v15.09.15/query')
im_paths = get_im_names(im_dir,
pattern='*.jpg',
return_path=True,
return_np=False)
all_feat = []
for i, im_path in enumerate(im_paths):
im = np.asarray(Image.open(im_path).convert('RGB'))
im = pre_process_im(im, cfg)
im = Variable(TVT(torch.from_numpy(im).float()), volatile=True)
feat = model(im)
feat = feat.data.cpu().numpy()
all_feat.append(feat)
if (i + 1) % 100 == 0:
print('{}/{} images done'.format(i, len(im_paths)))
all_feat = np.concatenate(all_feat)
print('all_feat.shape:', all_feat.shape)
all_feat = normalize(all_feat, axis=1)
def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(last_conv_stride=cfg.last_conv_stride)
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
im_dir = osp.expanduser('../../5Labeled Data/labeledNew')
im_paths = get_im_names(im_dir,
pattern='*.png',
return_path=True,
return_np=False)
currentData = {}
imagesKnown = set()
with open('../../7Data Features/dataFeatures.json') as json_file:
currentData = json.load(json_file)
for image in currentData["images"]:
imagesKnown.add(image["imageName"])
all_feat = []
for i, im_path in enumerate(im_paths):
im = np.asarray(Image.open(im_path).convert('RGB'))
im = pre_process_im(im, cfg)
im = Variable(TVT(torch.from_numpy(im).float()), volatile=True)
feat = model(im)
feat = feat.data.cpu().numpy()
all_feat.append(feat)
if (i + 1) % 100 == 0:
print('{}/{} images done'.format(i, len(im_paths)))
all_feat = np.concatenate(all_feat)
print('all_feat.shape:', all_feat.shape)
all_feat = normalize(all_feat, axis=1)
# You can save your im_paths and features, or do anything else ...
for i, im_path in enumerate(im_paths):
if (im_path in imagesKnown):
print("image", im_path, "already exists")
pass
else:
currentData['images'].append({
'imageName': im_path,
'features': all_feat[i].tolist()
})
if (i + 1) % 100 == 0:
print('{}/{} images saved'.format(i, len(im_paths)))
with open('../../7Data Features/dataFeatures.json', 'w') as outfile:
json.dump(currentData, outfile)