def train(args):
base_dir = args.save_dir
if not os.path.exists(base_dir):
os.mkdir(base_dir)
# logging_config(folder=base_dir, name="training")
save_movingmnist_cfg(base_dir)
batch_size = cfg.MODEL.TRAIN.BATCH_SIZE
max_iterations = cfg.MODEL.TRAIN.MAX_ITER
test_iteration_interval = cfg.MODEL.VALID_ITER
test_and_save_checkpoint_iterations = cfg.MODEL.SAVE_ITER
LR_step_size = cfg.MODEL.TRAIN.LR_STEP
gamma = cfg.MODEL.TRAIN.GAMMA1
LR = cfg.MODEL.TRAIN.LR
criterion = Weighted_mse_mae().to(cfg.GLOBAL.DEVICE)
if cfg.MODEL.TYPE == 'TrajGRU':
encoder_subnet, encoder_net = encoder_params_mnist()
forecaster_subnet, forecaster_net = forecaster_params_mnist()
elif cfg.MODEL.TYPE == 'ConvLSTM':
encoder_subnet, encoder_net = convlstm_encoder_params_mnist()
forecaster_subnet, forecaster_net = convlstm_forecaster_params_mnist()
else:
raise NotImplementedError('Model is not found.')
encoder = Encoder(encoder_subnet, encoder_net).to(cfg.GLOBAL.DEVICE)
forecaster = Forecaster(forecaster_subnet,
forecaster_net).to(cfg.GLOBAL.DEVICE)
encoder_forecaster = EF(encoder, forecaster).to(cfg.GLOBAL.DEVICE)
optimizer = torch.optim.Adam(encoder_forecaster.parameters(), lr=LR)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer,
step_size=LR_step_size,
gamma=gamma)
folder_name = os.path.split(os.path.dirname(os.path.abspath(__file__)))[-1]
train_mnist(encoder_forecaster, optimizer, criterion, exp_lr_scheduler,
batch_size, max_iterations, test_iteration_interval,
test_and_save_checkpoint_iterations, folder_name, base_dir)
import os from experiments.net_params import convlstm_encoder_params, convlstm_forecaster_params ### Config batch_size = cfg.GLOBAL.BATCH_SZIE max_iterations = 100000 test_iteration_interval = 1000 test_and_save_checkpoint_iterations = 1000 LR_step_size = 20000 gamma = 0.7 LR = 1e-4 criterion = Weighted_mse_mae().to(cfg.GLOBAL.DEVICE) encoder = Encoder(convlstm_encoder_params[0], convlstm_encoder_params[1]).to(cfg.GLOBAL.DEVICE) forecaster = Forecaster(convlstm_forecaster_params[0], convlstm_forecaster_params[1]).to(cfg.GLOBAL.DEVICE) encoder_forecaster = EF(encoder, forecaster).to(cfg.GLOBAL.DEVICE) optimizer = torch.optim.Adam(encoder_forecaster.parameters(), lr=LR) exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=LR_step_size, gamma=gamma) folder_name = os.path.split(os.path.dirname(os.path.abspath(__file__)))[-1] train_and_test_milan(encoder_forecaster, optimizer, criterion, exp_lr_scheduler, batch_size, max_iterations, test_iteration_interval, test_and_save_checkpoint_iterations, folder_name)
