def main(args):
# reconstruction loss
# (1 / L) * torch.sum(torch.log())
# mse?
batch_size = args.batch_size
dataset_size = args.total_data_size
# cfg_file = os.path.join(args.example_config_path, args.primitive) + ".yaml"
cfg = get_vae_defaults()
# cfg.merge_from_file(cfg_file)
cfg.freeze()
vae = VAE(args.input_dimension,
args.output_dimension,
args.latent_dimension,
hidden_layers=cfg.ENCODER_HIDDEN_LAYERS_MLP)
if torch.cuda.is_available():
vae.encoder.cuda()
vae.decoder.cuda()
data_dir = args.data_dir
data_loader = DataLoader(data_dir=data_dir)
data_loader.create_random_ordering(size=dataset_size)
total_loss = 0
for epoch in range(args.num_epochs):
print('Epoch: ' + str(epoch))
running_total_loss = 0
for i in range(0, dataset_size, batch_size):
vae.optimizer.zero_grad()
input_batch, target_batch = data_loader.load_batch(i, batch_size)
input_batch = to_var(torch.from_numpy(input_batch))
target_batch = to_var(torch.from_numpy(target_batch))
z, recon_mu, z_mu, z_logvar = vae.forward(input_batch)
# target = torch.normal(z_mu)
output = recon_mu
kl_loss = vae.kl_loss(z_mu, z_logvar)
recon_loss = vae.recon_loss(output, target_batch)
# loss = vae.total_loss(output, target_batch, z_mu, z_logvar)
loss = kl_loss + recon_loss
loss.backward()
vae.optimizer.step()
running_total_loss = running_total_loss + loss.data
print(" -- running loss: ")
print(running_total_loss)
print(' --avgerage loss: ')
print(running_total_loss / (dataset_size / batch_size))
total_loss = total_loss + running_total_loss
def main(args):
# cfg_file = os.path.join(args.example_config_path, args.primitive) + ".yaml"
cfg = get_vae_defaults()
# cfg.merge_from_file(cfg_file)
cfg.freeze()
batch_size = args.batch_size
dataset_size = args.total_data_size
if args.experiment_name is None:
experiment_name = args.model_name
else:
experiment_name = args.experiment_name
if not os.path.exists(os.path.join(args.log_dir, experiment_name)):
os.makedirs(os.path.join(args.log_dir, experiment_name))
description_txt = raw_input('Please enter experiment notes: \n')
if isinstance(description_txt, str):
with open(
os.path.join(args.log_dir, experiment_name,
experiment_name + '_description.txt'), 'wb') as f:
f.write(description_txt)
writer = SummaryWriter(os.path.join(args.log_dir, experiment_name))
# torch_seed = np.random.randint(low=0, high=1000)
# np_seed = np.random.randint(low=0, high=1000)
torch_seed = 0
np_seed = 0
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
trained_model_path = os.path.join(args.model_path, args.model_name)
if not os.path.exists(trained_model_path):
os.makedirs(trained_model_path)
if args.task == 'contact':
if args.start_rep == 'keypoints':
start_dim = 24
elif args.start_rep == 'pose':
start_dim = 7
if args.goal_rep == 'keypoints':
goal_dim = 24
elif args.goal_rep == 'pose':
goal_dim = 7
if args.skill_type == 'pull':
# + 7 because single arm palm pose
input_dim = start_dim + goal_dim + 7
else:
# + 14 because both arms palm pose
input_dim = start_dim + goal_dim + 14
output_dim = 7
decoder_input_dim = start_dim + goal_dim
vae = VAE(input_dim,
output_dim,
args.latent_dimension,
decoder_input_dim,
hidden_layers=cfg.ENCODER_HIDDEN_LAYERS_MLP,
lr=args.learning_rate)
elif args.task == 'goal':
if args.start_rep == 'keypoints':
start_dim = 24
elif args.start_rep == 'pose':
start_dim = 7
if args.goal_rep == 'keypoints':
goal_dim = 24
elif args.goal_rep == 'pose':
goal_dim = 7
input_dim = start_dim + goal_dim
output_dim = goal_dim
decoder_input_dim = start_dim
vae = GoalVAE(input_dim,
output_dim,
args.latent_dimension,
decoder_input_dim,
hidden_layers=cfg.ENCODER_HIDDEN_LAYERS_MLP,
lr=args.learning_rate)
elif args.task == 'transformation':
input_dim = args.input_dimension
output_dim = args.output_dimension
decoder_input_dim = args.input_dimension - args.output_dimension
vae = GoalVAE(input_dim,
output_dim,
args.latent_dimension,
decoder_input_dim,
hidden_layers=cfg.ENCODER_HIDDEN_LAYERS_MLP,
lr=args.learning_rate)
else:
raise ValueError('training task not recognized')
if torch.cuda.is_available():
vae.encoder.cuda()
vae.decoder.cuda()
if args.start_epoch > 0:
start_epoch = args.start_epoch
num_epochs = args.num_epochs
fname = os.path.join(
trained_model_path,
args.model_name + '_epoch_%d.pt' % args.start_epoch)
torch_seed, np_seed = load_seed(fname)
load_net_state(vae, fname)
load_opt_state(vae, fname)
args = load_args(fname)
args.start_epoch = start_epoch
args.num_epochs = num_epochs
torch.manual_seed(torch_seed)
np.random.seed(np_seed)
data_dir = args.data_dir
data_loader = DataLoader(data_dir=data_dir)
data_loader.create_random_ordering(size=dataset_size)
dataset = data_loader.load_dataset(start_rep=args.start_rep,
goal_rep=args.goal_rep,
task=args.task)
total_loss = []
start_time = time.time()
print('Saving models to: ' + trained_model_path)
kl_weight = 1.0
print('Starting on epoch: ' + str(args.start_epoch))
for epoch in range(args.start_epoch, args.start_epoch + args.num_epochs):
print('Epoch: ' + str(epoch))
epoch_total_loss = 0
epoch_kl_loss = 0
epoch_pos_loss = 0
epoch_ori_loss = 0
epoch_recon_loss = 0
kl_coeff = 1 - kl_weight
kl_weight = args.kl_anneal_rate * kl_weight
print('KL coeff: ' + str(kl_coeff))
for i in range(0, dataset_size, batch_size):
vae.optimizer.zero_grad()
input_batch, decoder_input_batch, target_batch = \
data_loader.sample_batch(dataset, i, batch_size)
input_batch = to_var(torch.from_numpy(input_batch))
decoder_input_batch = to_var(torch.from_numpy(decoder_input_batch))
z, recon_mu, z_mu, z_logvar = vae.forward(input_batch,
decoder_input_batch)
kl_loss = vae.kl_loss(z_mu, z_logvar)
if args.task == 'contact':
output_r, output_l = recon_mu
if args.skill_type == 'grasp':
target_batch_right = to_var(
torch.from_numpy(target_batch[:, 0]))
target_batch_left = to_var(
torch.from_numpy(target_batch[:, 1]))
pos_loss_right = vae.mse(output_r[:, :3],
target_batch_right[:, :3])
ori_loss_right = vae.rotation_loss(
output_r[:, 3:], target_batch_right[:, 3:])
pos_loss_left = vae.mse(output_l[:, :3],
target_batch_left[:, :3])
ori_loss_left = vae.rotation_loss(output_l[:, 3:],
target_batch_left[:, 3:])
pos_loss = pos_loss_left + pos_loss_right
ori_loss = ori_loss_left + ori_loss_right
elif args.skill_type == 'pull':
target_batch = to_var(
torch.from_numpy(target_batch.squeeze()))
#TODO add flags for when we're training both arms
# output = recon_mu[0] # right arm is index [0]
# output = recon_mu[1] # left arm is index [1]
pos_loss_right = vae.mse(output_r[:, :3],
target_batch[:, :3])
ori_loss_right = vae.rotation_loss(output_r[:, 3:],
target_batch[:, 3:])
pos_loss = pos_loss_right
ori_loss = ori_loss_right
elif args.task == 'goal':
target_batch = to_var(torch.from_numpy(target_batch.squeeze()))
output = recon_mu
if args.goal_rep == 'pose':
pos_loss = vae.mse(output[:, :3], target_batch[:, :3])
ori_loss = vae.rotation_loss(output[:, 3:],
target_batch[:, 3:])
elif args.goal_rep == 'keypoints':
pos_loss = vae.mse(output, target_batch)
ori_loss = torch.zeros(pos_loss.shape)
elif args.task == 'transformation':
target_batch = to_var(torch.from_numpy(target_batch.squeeze()))
output = recon_mu
pos_loss = vae.mse(output[:, :3], target_batch[:, :3])
ori_loss = vae.rotation_loss(output[:, 3:], target_batch[:,
3:])
recon_loss = pos_loss + ori_loss
loss = kl_coeff * kl_loss + recon_loss
loss.backward()
vae.optimizer.step()
epoch_total_loss = epoch_total_loss + loss.data
epoch_kl_loss = epoch_kl_loss + kl_loss.data
epoch_pos_loss = epoch_pos_loss + pos_loss.data
epoch_ori_loss = epoch_ori_loss + ori_loss.data
epoch_recon_loss = epoch_recon_loss + recon_loss.data
writer.add_scalar('loss/train/ori_loss', ori_loss.data, i)
writer.add_scalar('loss/train/pos_loss', pos_loss.data, i)
writer.add_scalar('loss/train/kl_loss', kl_loss.data, i)
if (i / batch_size) % args.batch_freq == 0:
if args.skill_type == 'pull' or args.task == 'goal' or args.task == 'transformation':
print(
'Train Epoch: %d [%d/%d (%f)]\tLoss: %f\tKL: %f\tPos: %f\t Ori: %f'
% (epoch, i, dataset_size,
100.0 * i / dataset_size / batch_size, loss.item(),
kl_loss.item(), pos_loss.item(), ori_loss.item()))
elif args.skill_type == 'grasp' and args.task == 'contact':
print(
'Train Epoch: %d [%d/%d (%f)]\tLoss: %f\tKL: %f\tR Pos: %f\t R Ori: %f\tL Pos: %f\tL Ori: %f'
% (epoch, i, dataset_size, 100.0 * i / dataset_size /
batch_size, loss.item(), kl_loss.item(),
pos_loss_right.item(), ori_loss_right.item(),
pos_loss_left.item(), ori_loss_left.item()))
print(' --avgerage loss: ')
print(epoch_total_loss / (dataset_size / batch_size))
loss_dict = {
'epoch_total': epoch_total_loss / (dataset_size / batch_size),
'epoch_kl': epoch_kl_loss / (dataset_size / batch_size),
'epoch_pos': epoch_pos_loss / (dataset_size / batch_size),
'epoch_ori': epoch_ori_loss / (dataset_size / batch_size),
'epoch_recon': epoch_recon_loss / (dataset_size / batch_size)
}
total_loss.append(loss_dict)
if epoch % args.save_freq == 0:
print('\n--Saving model\n')
print('time: ' + str(time.time() - start_time))
save_state(net=vae,
torch_seed=torch_seed,
np_seed=np_seed,
args=args,
fname=os.path.join(
trained_model_path,
args.model_name + '_epoch_' + str(epoch) + '.pt'))
np.savez(os.path.join(
trained_model_path,
args.model_name + '_epoch_' + str(epoch) + '_loss.npz'),
loss=np.asarray(total_loss))
print('Done!')
save_state(net=vae,
torch_seed=torch_seed,
np_seed=np_seed,
args=args,
fname=os.path.join(
trained_model_path,
args.model_name + '_epoch_' + str(epoch) + '.pt'))