Hi i create the face reconition CNN and im not sure how would i able to save it so i can load can anyone suggest me a way? .
this is my code
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,
'device':'cuda'
})
print(args)
# 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.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)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=2, drop_last=False)
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 = Variable(inps).cuda(args['device'])
labs = Variable(labs).cuda(args['device'])
optimizer.zero_grad()
outs = net(inps.permute(0, 3, 1, 2).float())
soft_outs = F.softmax(outs, dim=1)
prds = soft_outs.data.max(1)[1]
loss = criterion(outs, labs)
loss.backward()
optimizer.step()
prds = prds.cpu().numpy()
inps_np = inps.detach().cpu().numpy()
labs_np = labs.detach().cpu().numpy()
train_loss.append(loss.data.item
())
print('[epoch %d], [iter %d / %d], [train loss %.5f]' % (epoch, i + 1, len(dataloader), np.asarray(train_loss).mean()))
```.
are there easy way to save that model? right now it run properly but i not sure how to recall this model so i can load it