generate_dataset.save_images()
generate_dataset.save_images('finaltest')
generate_dataset.save_images('val')
if args.hyperparams:
epochs = args.epochs
lr = args.learning_rate
batchsize = args.batch_size
else:
epochs = 100
lr = 0.005
batchsize = 128
if args.train:
net = Deep_Emotion()
net.to(device)
print("Model archticture: ", net)
traincsv_file = args.data + '/' + 'train.csv'
validationcsv_file = args.data + '/' + 'val.csv'
train_img_dir = args.data + '/' + 'train/'
validation_img_dir = args.data + '/' + 'val/'
transformation = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))])
train_dataset = Plain_Dataset(csv_file=traincsv_file,
img_dir=train_img_dir,
datatype='train',
transform=transformation)
validation_dataset = Plain_Dataset(csv_file=validationcsv_file,
img_dir=validation_img_dir,
# batchsize = 128
# if args.train:
# net = Deep_Emotion()
# net.to(device)
# print("Model archticture: ", net)
# traincsv_file = args.data+'/'+'train.csv'
# validationcsv_file = args.data+'/'+'val.csv'
# train_img_dir = args.data+'/'+'train/'
# validation_img_dir = args.data+'/'+'val/'
epochs = 100
lr = 0.005 # Learning rate
batchsize = 128
net = Deep_Emotion() ## CREATING THE MODEL BY CALLING DEEPMOTION.PY
net.to(device) ## MOVING IT TO GPU / CPU
print("Model archticture: ", net)
traincsv_file = 'data' + '/' + 'train.csv'
validationcsv_file = 'data' + '/' + 'val.csv'
train_img_dir = 'data' + '/' + 'train/'
validation_img_dir = 'data' + '/' + 'val/'
transformation = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))])
train_dataset = Plain_Dataset(csv_file=traincsv_file,
img_dir=train_img_dir,
datatype='train',
transform=transformation)
validation_dataset = Plain_Dataset(csv_file=validationcsv_file,
img_dir=validation_img_dir,