class AutoEncoder:
BATCH_SIZE = 32
def __init__(self, model):
# if torch.cuda.device_count() > 1:
# self.model.bak.bak.bak.bak = nn.DataParallel(model.bak.bak.bak.bak)
# else:
self.model = model
# Default is the same model.bak.bak.bak.bak
self.optimizer = None
self.scheduler = None
self.criterion = nn.MSELoss()
self.losses = []
def post_setup(self):
self.optimizer = Adagrad(self.model.parameters(),
lr=0.001,
weight_decay=0.0005)
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(self.optimizer,
factor=0.5,
patience=20,
threshold=0.0001)
def train_batches(self, episodes_count, files_list):
episode_counter = 0
prev_loss = 0.0
if LOAD_ALL_MEMORY:
dataset_loader = TrainingDataSampler(EPISODES_COUNT)
dataset_loader.load_all_training_data(files_list)
else:
dataset_loader = TrainingFileManager(PARENT_DIR_LIST,
EPISODES_COUNT)
dataset_loader.start()
while episode_counter < episodes_count:
cuboids = dataset_loader.get_training_data()
print("{} : Running episode {} and prev loss {}".format(
datetime.datetime.now(), str(episode_counter), prev_loss))
cuboids = cuboids.to(DEVICE)
output = self.model(cuboids)
output = output.to(DEVICE)
self.optimizer.zero_grad() # zero the gradient buffers
loss = self.criterion(output, cuboids)
loss.backward()
self.optimizer.step() # Does the update
self.losses.append(loss.item())
if episode_counter > 0 and episode_counter % SNAPSHOT_DURATION == 0:
np.save("{}-{}".format(LOSSES_FILE_PATH, str(episode_counter)),
np.array(self.losses))
torch.save(
auto_encoder, "{}-{}".format(MODEL_FILE_PATH,
str(episode_counter)))
cv2.imwrite("test1.png",
cuboids.cpu().detach().numpy()[0][0] * 255)
cv2.imwrite("test2_1.png",
output.cpu().detach().numpy()[0][0] * 255)
print("Loss for episode {} is {}".format(episode_counter, loss))
prev_loss = loss.item()
episode_counter += 1
np.save("{}-{}".format(LOSSES_FILE_PATH, str(episode_counter)),
np.array(self.losses))
torch.save(auto_encoder, "{}-{}".format(MODEL_FILE_PATH,
str(episode_counter)))
class Train(object):
def __init__(self):
self.vocab = Vocab(config.vocab_path, config.vocab_size)
self.batcher = Batcher(config.train_data_path,
self.vocab,
mode='train',
batch_size=config.batch_size,
single_pass=False)
time.sleep(15)
train_dir = os.path.join(config.log_root,
'train_{}'.format(int(time.time())))
if not os.path.exists(train_dir):
os.mkdir(train_dir)
self.model_dir = os.path.join(train_dir, 'model')
if not os.path.exists(self.model_dir):
os.mkdir(self.model_dir)
self.summary_writer = tf.summary.FileWriter(train_dir)
def save_model(self, running_avg_loss, iters):
state = {
'iter': iters,
'encoder_state_dict': self.model.encoder.state_dict(),
'decoder_state_dict': self.model.decoder.state_dict(),
'reduce_state_dict': self.model.reduce_state.state_dict(),
'optimizer': self.optimizer.state_dict(),
'current_loss': running_avg_loss
}
model_save_path = os.path.join(
self.model_dir, 'model_{}_{}'.format(iters, int(time.time())))
torch.save(state, model_save_path)
def setup_train(self, model_file_path=None):
self.model = Model(model_file_path)
params = list(self.model.encoder.parameters()) + list(self.model.decoder.parameters()) + \
list(self.model.reduce_state.parameters())
initial_lr = config.lr_coverage if config.is_coverage else config.lr
self.optimizer = Adagrad(
params,
lr=initial_lr,
initial_accumulator_value=config.adagrad_init_acc)
start_iter, start_loss = 0, 0
if model_file_path is not None:
state = torch.load(model_file_path,
map_location=lambda storage, location: storage)
start_iter = state['iter']
start_loss = state['current_loss']
if not config.is_coverage:
self.optimizer.load_state_dict(state['optimizer'])
if use_cuda:
for state in self.optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
return start_iter, start_loss
def train_one_batch(self, batch):
enc_batch, enc_padding_mask, enc_lens, enc_batch_extend_vocab, extra_zeros, c_t_1, coverage = \
get_input_from_batch(batch)
dec_batch, dec_padding_mask, max_dec_len, dec_lens_var, target_batch = \
get_output_from_batch(batch)
self.optimizer.zero_grad()
encoder_outputs, encoder_feature, encoder_hidden = self.model.encoder(
enc_batch, enc_lens)
s_t_1 = self.model.reduce_state(encoder_hidden)
step_losses = []
for di in range(min(max_dec_len, config.max_dec_steps)):
y_t_1 = dec_batch[:, di] # Teacher forcing
final_dist, s_t_1, c_t_1, attn_dist, p_gen, next_coverage = self.model.decoder(
y_t_1, s_t_1, encoder_outputs, encoder_feature,
enc_padding_mask, c_t_1, extra_zeros, enc_batch_extend_vocab,
coverage, di)
target = target_batch[:, di]
gold_probs = torch.gather(final_dist, 1,
target.unsqueeze(1)).squeeze()
step_loss = -torch.log(gold_probs + config.eps)
if config.is_coverage:
step_coverage_loss = torch.sum(torch.min(attn_dist, coverage),
1)
step_loss = step_loss + config.cov_loss_wt * step_coverage_loss
coverage = next_coverage
step_mask = dec_padding_mask[:, di]
step_loss = step_loss * step_mask
step_losses.append(step_loss)
sum_losses = torch.sum(torch.stack(step_losses, 1), 1)
batch_avg_loss = sum_losses / dec_lens_var
loss = torch.mean(batch_avg_loss)
loss.backward()
self.norm = clip_grad_norm_(self.model.encoder.parameters(),
config.max_grad_norm)
clip_grad_norm_(self.model.decoder.parameters(), config.max_grad_norm)
clip_grad_norm_(self.model.reduce_state.parameters(),
config.max_grad_norm)
self.optimizer.step()
return loss.item()
def trainIters(self, n_iters, model_file_path=None):
iter, running_avg_loss = self.setup_train(model_file_path)
start = time.time()
while iter < n_iters:
batch = self.batcher.next_batch()
loss = self.train_one_batch(batch)
running_avg_loss = calc_running_avg_loss(loss, running_avg_loss,
self.summary_writer, iter)
iter += 1
if iter % 100 == 0:
self.summary_writer.flush()
print_interval = 1000
if iter % print_interval == 0:
print('steps %d, seconds for %d batch: %.2f , loss: %f' %
(iter, print_interval, time.time() - start, loss))
start = time.time()
if iter % 5000 == 0:
self.save_model(running_avg_loss, iter)
class AutoEncoder:
def __init__(self, model_name, model, optimizer=None, device=None):
# if torch.cuda.device_count() > 1:
# self.model = nn.DataParallel(model)
# else:
self.model = model
self.model_name = model_name
self.optimizer = optimizer
self.scheduler = None
self.criterion = nn.MSELoss()
self.losses = []
fh = logging.FileHandler('{}/{}.log'.format(LOGS_PATH,
self.model_name))
fh.setLevel(logging.DEBUG)
self.logger = logging.getLogger(self.model_name)
self.logger.addHandler(fh)
self.device = DEVICE if not device else device
def post_setup(self, optimizer=None):
if optimizer is None:
print("None opt")
self.optimizer = Adagrad(self.model.parameters(),
lr=LEARNING_RATE,
weight_decay=0.0005)
else:
print("Opt filled")
self.optimizer = optimizer
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(self.optimizer,
factor=0.5,
patience=20,
threshold=0.0001)
def train_batches(self,
episodes_count,
training_data_sampler,
start_episodes_counter=0):
episode_counter = start_episodes_counter
prev_loss = 0.0
#self.logger.warning("Available keys - %s", training_data_sampler.available_keys)
#self.logger.warning("Probability map - %s", training_data_sampler.prob_map)
training_data_sampler.start()
while episode_counter < episodes_count:
cuboids = training_data_sampler.get_training_data()
self.logger.warning(
"{} : Running episode {} and prev loss {}".format(
datetime.datetime.now(), str(episode_counter), prev_loss))
cuboids = cuboids.to(self.device)
output = self.model(cuboids)
output = output.to(self.device)
self.optimizer.zero_grad() # zero the gradient buffers
loss = self.criterion(output, cuboids)
loss.backward()
self.optimizer.step() # Does the update
self.losses.append(loss.item())
if episode_counter > 0 and episode_counter % SNAPSHOT_DURATION == 0:
np.save(
"{}-{}-{}".format(LOSSES_FILE_PATH, self.model_name,
str(episode_counter)),
np.array(self.losses))
torch.save(
{
'optimizer': self.optimizer.state_dict(),
'model': self.model.state_dict()
},
"{}-{}-state-{}".format(MODEL_FILE_PATH, self.model_name,
episode_counter))
cv2.imwrite(
"{}/{}-{}-test1.png".format(LOGS_PATH, self.model_name,
episode_counter),
cuboids.cpu().detach().numpy()[0][0] * 255)
cv2.imwrite(
"{}/{}-{}-test2_1.png".format(LOGS_PATH, self.model_name,
episode_counter),
output.cpu().detach().numpy()[0][0] * 255)
self.logger.warning("Loss for episode {} is {}".format(
episode_counter, loss))
prev_loss = loss.item()
episode_counter += 1
np.save(
"{}-{}-{}".format(LOSSES_FILE_PATH, self.model_name,
str(episode_counter)), np.array(self.losses))
torch.save(
{
'optimizer': self.optimizer.state_dict(),
'model': self.model.state_dict()
}, "{}-{}-state-{}".format(MODEL_FILE_PATH, self.model_name,
episode_counter))
