I try to develop Convolution network deep learning for face recognition and right now when i try to run it said 'list' object has no attribute 'cuda' im not sure what went wrong can anyone check. the code below is for train the whole module and below that is for load the data
if name == 'main':
#set_trace()
args = edict({
'operation' : 'train',
'feature_file' : None,
'result_sample_path' : None,
'gpu' : 'GPU',
'path_image_query' : None,
'query_label' : 'Query label',
'dataset' : None,
'specific_dataset_folder_name' : 'lfw',
'img_extension' : 'jpg',
'preprocessing_method' : 'sphereface',
'model_name' : 'mobiface',
'batch_size' : 3,
'image_query':'/content/drive/My Drive/recfaces13/recfaces/datasets/LFW',
'train':True})
# selecting the size of the crop based on the network
if args.model_name == 'mobilefacenet' or args.model_name == 'sphereface':
crop_size = (96, 112)
elif args.model_name == 'mobiface' or args.model_name == 'shufflefacenet':
crop_size = (112, 112)
elif args.model_name == 'openface':
crop_size = (96, 96)
elif args.model_name == 'facenet':
crop_size = (160, 160)
else:
raise NotImplementedError("Model " + args.model_name + " not implemented")
if args.dataset is not None:
# process whole dataset
assert args.specific_dataset_folder_name is not None, 'To process a dataset, ' \
'the flag --specific_dataset_folder_name is required.'
process_dataset(args.operation, args.model_name, args.batch_size,
args.dataset, args.specific_dataset_folder_name,
args.img_extension, args.preprocessing_method, crop_size,
args.result_sample_path, args.feature_file)
#elif args.image_query is not None:
# process unique image
# dataset = ImageDataLoader(args.image_query, args.preprocessing_method,
# crop_size, args.operation == 'extract_features')
# dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=2, drop_last=False)
# features = None
elif args.operation == 'train':
##########set_trace()
net = load_net('mobilefacenet', 'gpu')
net = net.cuda()
model_name=args.model_name
dataset = LFW(args.image_query,args.specific_dataset_folder_name, args.img_extension, args.preprocessing_method, crop_size, args.train)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=2, drop_last=False)
# data_counter_per_class = np.zeros((len(dataloader)))
# for i in range(len(dataloader)):
# path = os.path.join('image_query', dataloader[i])
# files = get_files_from_folder(path)
# data_counter_per_class[i] = len(files)
# test_counter = np.round(data_counter_per_class * (1 - train_ratio))
#dataloader1=dataloader.split(',')
#train,test=train_test_split(dataloader,test_size=0.2)
#trainloader = torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=True, num_workers=2, drop_last=False)
# testloader = torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=False, num_workers=2, drop_last=False) //create path//
#create array of data path split that data path and
features = None
if args.feature_file is not None and os.path.isfile(args.feature_file):
features = scipy.io.loadmat(args.feature_file)
epoch = 2
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
train_loss = list()
#set_trace()
for i, data in enumerate(dataloader):
inps, labs = data
inps, labs = inps.cuda(args['device']), labs.cuda(args['device'])
inps.squeeze_(0)
labs.squeeze_(0)
inps = Variable(inps).cuda(args['device'])
labs = Variable(labs).cuda(args['device'])
optimizer.zero_grad()
outs = net(inps)
soft_outs = F.softmax(outs, dim=1)
prds = soft_outs.data.max(1)[1]
loss = criterion(outs, labs)
loss.backward()
optimizer.step()
prds = prds.squeeze_(1).squeeze_(0).cpu().numpy()
inps_np = inps.detach().squeeze(0).cpu().numpy()
labs_np = labs.detach().squeeze(0).cpu().numpy()
train_loss.append(loss.data.item())
print('[epoch %d], [iter %d / %d], [train loss %.5f]' % (epoch, i + 1, len(train_loader), np.asarray(train_loss).mean()))
Dataloader
class LFW(object):
def __init__(self, root, specific_folder, img_extension, preprocessing_method=None, crop_size=(96, 112)):
"""
Dataloader of the LFW dataset.
root: path to the dataset to be used.
specific_folder: specific folder inside the same dataset.
img_extension: extension of the dataset images.
preprocessing_method: string with the name of the preprocessing method.
crop_size: retrieval network specific crop size.
"""
self.preprocessing_method = preprocessing_method
self.crop_size = crop_size
self.imgl_list = []
self.classes = []
self.people = []
self.model_align = None
# read the file with the names and the number of images of each people in the dataset
with open(os.path.join(root, 'people.txt')) as f:
people = f.read().splitlines()[1:]
# get only the people that have more than 20 images
for p in people:
p = p.split('\t')
if len(p) > 1:
if int(p[1]) >= 20:
for num_img in range(1, int(p[1]) + 1):
self.imgl_list.append(os.path.join(root, specific_folder, p[0], p[0] + '_' +
'{:04}'.format(num_img) + '.' + img_extension))
self.classes.append(p[0])
self.people.append(p[0])
le = preprocessing.LabelEncoder()
self.classes = le.fit_transform(self.classes)
print(len(self.imgl_list), len(self.classes), len(self.people))
def __getitem__(self, index):
imgl = imageio.imread(self.imgl_list[index])
cl = self.classes[index]
# if image is grayscale, transform into rgb by repeating the image 3 times
if len(imgl.shape) == 2:
imgl = np.stack([imgl] * 3, 2)
imgl, bb = preprocess(imgl, self.preprocessing_method, crop_size=self.crop_size,
is_processing_dataset=True, return_only_largest_bb=True, execute_default=True)
# append image with its reverse
imglist = [imgl, imgl[:, ::-1, :]]
# normalization
for i in range(len(imglist)):
imglist[i] = (imglist[i] - 127.5) / 128.0
imglist[i] = imglist[i].transpose(2, 0, 1)
imgs = [torch.from_numpy(i).float() for i in imglist]
return imgs, cl, imgl, bb, self.imgl_list[index], self.people[index]
def __len__(self):
return len(self.imgl_list)
