def CreateFeature(model, phase, outputPath='.'):
"""
Create h5py dataset for feature extraction.
ARGS:
outputPath : h5py output path
model : used model
labelList : list of corresponding groundtruth texts
"""
featurenet = feature_net(model)
if use_gpu:
featurenet.cuda()
feature_map = torch.FloatTensor()
label_map = torch.LongTensor()
for data in tqdm(dataloader[phase]):
img, label = data
if use_gpu:
img = Variable(img, volatile=True).cuda()
else:
img = Variable(img, volatile=True)
out = featurenet(img)
feature_map = torch.cat((feature_map, out.cpu().data), 0)
label_map = torch.cat((label_map, label), 0)
feature_map = feature_map.numpy()
label_map = label_map.numpy()
file_name = '_feature_{}.hd5f'.format(model)
h5_path = os.path.join(outputPath, phase) + file_name
with h5py.File(h5_path, 'w') as h:
h.create_dataset('data', data=feature_map)
h.create_dataset('label', data=label_map)
def CreateFeature(model, phase, outputPath='.'):
"""
Create h5py dataset for feature extraction.
ARGS:
outputPath : h5py output path
model : used model
labelList : list of corresponding groundtruth texts
"""
featurenet = feature_net(model)
if use_gpu:
featurenet.cuda()
feature_map = torch.FloatTensor()
label_map = torch.LongTensor()
for data in tqdm(dataloader[phase]):
img, label = data
if use_gpu:
img = Variable(img, volatile=True).cuda()
else:
img = Variable(img, volatile=True)
out = featurenet(img)
feature_map = torch.cat((feature_map, out.cpu().data), 0)
label_map = torch.cat((label_map, label), 0)
feature_map = feature_map.numpy()
label_map = label_map.numpy()
file_name = '_feature_{}.hd5f'.format(model)
h5_path = os.path.join(outputPath, phase) + file_name
with h5py.File(h5_path, 'w') as h:
h.create_dataset('data', data=feature_map)
h.create_dataset('label', data=label_map)