batch_size = 3
num_layers = 3
cell_conf_l0 = pack(
[h_c, active_func, in_c, in_h, in_w, kernel_size, DEBUG],
ConvLSTMCell.get_init_keys())
cell_conf_l1 = pack(
[h_c, active_func, h_c, in_h, in_w, kernel_size, DEBUG],
ConvLSTMCell.get_init_keys())
cell_conf_l2 = pack(
[h_c, active_func, h_c, in_h, in_w, kernel_size, DEBUG],
ConvLSTMCell.get_init_keys())
cell_configs = [cell_conf_l0, cell_conf_l1, cell_conf_l2]
encoder_configs = pack([num_layers, cell_configs],
ConvLSTM.get_init_keys())
reconstruct_configs = pack([num_layers, cell_configs],
ConvLSTM.get_init_keys())
predict_configs = pack([num_layers, cell_configs],
ConvLSTM.get_init_keys())
model_configs = pack(
[encoder_configs, reconstruct_configs, predict_configs], _KEYS)
model = MMNIST_ConvLSTM(model_configs)
x_train = to_var(torch.randn(batch_size, time_steps, in_c, in_h, in_w))
x_predict = to_var(torch.randn(batch_size, time_steps, in_c, in_h, in_w))
data = pack([x_train, x_predict, None], ['x_train', 'x_predict', 'states'])
configs = pack([True, 6], ['use_gt', 'max_steps'])
def MMNIST_CONV_LSTM(extra_info):
_KEYS = ['encoder_configs', 'reconstruct_configs', 'predict_configs']
h_c = 16
active_func = nn.Tanh()
in_c = 1
in_h = 64
in_w = 64
kernel_size = 5
DEBUG = True
num_layers = 3
cell_conf_l0 = pack(
[h_c, active_func, in_c, in_h, in_w, kernel_size, DEBUG],
ConvLSTMCell.get_init_keys())
cell_conf_l1 = pack([8, active_func, h_c, in_h, in_w, kernel_size, DEBUG],
ConvLSTMCell.get_init_keys())
cell_conf_l2 = pack([8, active_func, 8, in_h, in_w, kernel_size, DEBUG],
ConvLSTMCell.get_init_keys())
cell_configs = [cell_conf_l0, cell_conf_l1, cell_conf_l2]
encoder_configs = pack([num_layers, cell_configs],
ConvLSTM.get_init_keys())
reconstruct_configs = pack([num_layers, cell_configs],
ConvLSTM.get_init_keys())
predict_configs = pack([num_layers, cell_configs],
ConvLSTM.get_init_keys())
model_info = pack([encoder_configs, reconstruct_configs, predict_configs],
_KEYS)
model_info['name'] = 'MMNIST_CONV_LSTM'
trainloader_info = {
'file_addr': './data/mmnist_train.npy',
'batch_size': 32,
'shuffle': True,
'num_workers': 2
}
valloader_info = {
'file_addr': './data/mmnist_val.npy',
'batch_size': 16,
'shuffle': False,
'num_workers': 2
}
testloader_info = {
'file_addr': './data/mmnist_test.npy',
'batch_size': 16,
'shuffle': False,
'num_workers': 2
}
seed = 666
folder_name = 'mmnist_convLSTM'
main_info = {
'clip': 0.25,
'num_epochs': 60,
'halve_every': 10,
'log_dir': './logs/%s' % folder_name,
'save_dir': './checkpoints/%s' % folder_name
}
optimizer_info = {
'lr': 1e-4,
'optim_alg': 'RMSprop',
'weight_decay': 0.9,
'momentum': 0
}
hparams = HParams(trainloader_info=trainloader_info,
valloader_info=valloader_info,
testloader_info=testloader_info,
model_info=model_info,
optimizer_info=optimizer_info,
main_info=main_info,
seed=seed)
return hparams