class Trainer(object):
def __init__(self, args, dataset):
self.args = args
self.cuda = args.cuda
self.dataset = dataset
if args.network_type in ['seq2seq'] and args.dataset in ['msrvtt']:
self.train_data = dataset['train']
self.valid_data = dataset['val']
self.test_data = dataset['test']
else:
raise Exception(f"Unknown network type: {args.network_type} and unknown dataset: {args.dataset} combination !!")
if args.use_tensorboard and args.mode == 'train':
self.tb = TensorBoard(args.model_dir)
else:
self.tb = None
self.build_model()
if self.args.load_path:
self.load_model()
if self.args.loss_function in ['rl','xe+rl'] and self.args.reward_type=='CIDEnt':
self.build_load_entailment_model()
def build_model(self):
self.start_epoch = self.epoch = 0
self.step = 0
if self.args.network_type == 'seq2seq':
self.model = Seq2seqAttention(self.args)
else:
raise NotImplemented(f"Network type `{self.args.network_type}` is not defined")
if self.args.num_gpu == 1:
self.model.cuda()
elif self.args.num_gpu > 1:
raise NotImplemented("`num_gpu > 1` is in progress")
self.ce = nn.CrossEntropyLoss()
logger.info(f"[*] # Parameters: {self.count_parameters}")
def build_load_entailment_model(self):
logger.info(f"Building Entailment model...")
vocab = data.common_loader.Vocab(self.args.snli_vocab_file, self.args.max_snli_vocab_size)
self.entailment_data = data.common_loader.SNLIBatcher(self.args.decoder_rnn_max_length, vocab)
self.entailment_model = CoattMaxPool(self.args)
if self.args.num_gpu == 1:
self.entailment_model.cuda()
self.entailment_model.load_state_dict(
t.load(self.args.load_entailment_path, map_location=None))
logger.info(f"[*] LOADED: {self.args.load_entailment_path}")
def train(self):
optimizer = get_optimizer(self.args.optim)
self.optim = optimizer(
self.model.parameters(),
lr=self.args.lr)
for self.epoch in range(self.start_epoch, self.args.max_epoch):
self.train_model()
if self.epoch % self.args.save_epoch == 0:
scores = self.test(mode='val')
self.save_model(save_criteria_score=scores)
def train_model(self):
total_loss = 0
model = self.model
model.train()
pbar = tqdm(total=self.train_data.num_steps, desc="train_model")
batcher = self.train_data.get_batcher()
for step in range(0,self.train_data.num_steps):
batch = next(batcher)
if self.args.network_type == 'seq2seq':
video_features = batch.get('video_batch')
flengths = batch.get('video_len_batch')
captions = batch.get('caption_batch')
clengths = batch.get('caption_len_batch')
video_features = to_var(self.args, video_features)
captions = to_var(self.args, captions)
if self.args.loss_function == 'xe':
outputs = self.model(video_features, flengths, captions, clengths)
targets = pack_padded_sequence(captions, clengths, batch_first=True)[0]
loss = self.ce(outputs, targets)
elif self.args.loss_function in ['rl', 'xe+rl']:
sampled_sequence, outputs = self.model.sample_rl(video_features, flengths, sampling='multinomial')
sampled_sequence_numpy = sampled_sequence.cpu().data.numpy()
argmax_sequence,_ = self.model.sample_rl(video_features, flengths, sampling='argmax')
argmax_sequence_numpy = argmax_sequence.cpu().data.numpy()
reward, seq_lengths = self.calculate_reward(sampled_sequence_numpy, batch.get('original_caption_dict'), batch.get('video_id'),
self.train_data.vocab)
base_reward, _ = self.calculate_reward(argmax_sequence_numpy, batch.get('original_caption_dict'), batch.get('video_id'),
self.train_data.vocab)
reward = reward - base_reward
reward = Variable(torch.FloatTensor(reward).cuda(), requires_grad=True).unsqueeze(2)
log_prob = F.log_softmax(outputs, 2)
target_one_hot = Variable(torch.FloatTensor(log_prob.size()).cuda().zero_().scatter_(2, sampled_sequence.unsqueeze(2).data, 1.0), requires_grad=True)
loss = -log_prob * target_one_hot * reward.expand_as(log_prob)
loss = loss.sum()/Variable(torch.FloatTensor(seq_lengths).cuda(), requires_grad=True).sum()
if self.args.loss_function == 'xe+rl':
outputs = pack_padded_sequence(outputs, clengths, batch_first=True)[0]
targets = pack_padded_sequence(captions, clengths, batch_first=True)[0]
ml_loss = self.ce(outputs,targets)
loss = self.args.gamma_ml_rl * loss + (1-self.args.gamma_ml_rl) * ml_loss
else:
raise Exception(f"Unknown network type: {self.args.network_type}")
# update
self.optim.zero_grad()
loss.backward()
t.nn.utils.clip_grad_norm(
model.parameters(), self.args.grad_clip)
self.optim.step()
total_loss += loss.data
pbar.set_description(f"train_model| loss: {loss.data[0]:5.3f}")
if step % self.args.log_step == 0 and step > 0:
cur_loss = total_loss[0] / self.args.log_step
ppl = math.exp(cur_loss)
logger.info(f'| epoch {self.epoch:3d} | lr {self.args.lr:8.6f} '
f'| loss {cur_loss:.2f} | ppl {ppl:8.2f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary("model/loss", cur_loss, self.step)
self.tb.scalar_summary("model/perplexity", ppl, self.step)
total_loss = 0
step += 1
self.step += 1
pbar.update(1)
def test(self, mode):
self.model.eval()
counter = 0
if mode == 'val':
batcher = self.valid_data.get_batcher()
num_steps = self.valid_data.num_steps
elif mode == 'test':
batcher = self.test_data.get_batcher()
num_steps = self.test_data.num_steps
else:
raise Exception("Unknow mode: {}".format(mode))
if self.args.network_type == 'seq2seq':
gts = {}
res = {}
for i in range(num_steps):
batch = next(batcher)
video_features = batch.get('video_batch')
flengths = batch.get('video_len_batch')
video_features = to_var(self.args, video_features)
if self.args.beam_size>1:
predicted_targets = self.model.beam_search(video_features, flengths, self.args.beam_size)
else:
predicted_targets = self.model.sample(video_features, flengths)
predicted_targets = predicted_targets.cpu().data.numpy()
for k,vid in enumerate(batch.get('video_id')):
caption = [self.valid_data.vocab.id2word(id_) for id_ in predicted_targets[k,:]]
punctuation = np.argmax(np.array(caption) == '[END]')
if punctuation == 0 and not caption:
caption = caption
else:
caption = caption[:punctuation]
caption = ' '.join(caption)
if not caption:
caption = '[UNK]'
print(caption)
res[counter] = [caption]
gts[counter] = batch.get('original_caption_dict')[vid]
counter += 1
scores = evaluate(gts, res, score_type='macro', tokenized=True)
scores_dict = {}
save_criteria_score = None
logger.info("Results:")
for method, score in scores:
if mode == 'val':
self.tb.scalar_summary(f"test/{mode}_{method}", score, self.epoch)
scores_dict[method] = score
logger.info("{}:{}".format(method,score))
if self.args.save_criteria == method:
save_criteria_score = score
if mode == 'test':
# save the result
if not self.args.load_path.endswith('.pth'):
if not os.path.exists(os.path.join(self.args.model_dir,'results')):
os.mkdir(os.path.join(self.args.model_dir,'results'))
result_save_path = self.result_path
final_dict = {}
final_dict['args'] = self.args.__dict__
final_dict['scores'] = scores_dict
with open(result_save_path, 'w') as fp:
json.dump(final_dict, fp, indent=4, sort_keys=True)
return save_criteria_score
def calculate_reward(self,sampled_sequence, gts, video_ids, vocab):
"""
:param sampled_sequence:
sampled sequence in the form of token_ids of size : batch_size x max_steps
:param ref_sequence:
dictionary of reference captions for the given videos
:param video_ids:
list of the video_ids
:param vocab:
vocab class object used to convert token ids to words
:param reward_type:
specify the reward
:return rewards:
rewards obtained from the sampled seq w.r.t. ref_seq (metric scores)
:return seq_lens
sampled sequence lengths array of size batch_size
"""
res = {}
gts_tmp = {}
seq_lens = []
batch_size, step_size = sampled_sequence.shape
counter = 0
for k in range(batch_size):
caption = [vocab.id2word(id_) for id_ in sampled_sequence[k,:]]
# print caption
punctuation = np.argmax(np.array(caption) == STOP_DECODING)
if punctuation == 0 and not caption:
caption = caption
else:
caption = caption[:punctuation]
caption = ' '.join(caption)
if not caption:
caption = UNKNOWN_TOKEN
res[counter] = [caption]
gts_tmp[counter] = gts[video_ids[k]]
counter +=1
seq_lens.append(len(caption.split())+1)
_,reward = evaluate(gts_tmp,res,metric='CIDEr' if self.args.reward_type=='CIDEnt' else self.args.reward_type ,score_type='micro',tokenized=True)[0]
if self.args.reward_type == 'CIDEnt':
entailment_scores = self.compute_entailment_scores(gts_tmp, res)
reward = [x-self.args.lambda_threshold if y<self.args.beta_threshold else x for x,y in zip(reward, entailment_scores)]
reward = np.array(reward)
reward = np.reshape(reward,[batch_size,1])
return reward, np.array(seq_lens)
def compute_entailment_scores(self,gts,res,length_norm=False):
scores = []
for key, value in res.items():
tmp_prem = gts[key]
tmp_hypo = [value[0] for _ in range(len(tmp_prem))]
batch = self.entailment_data.process_external_data(tmp_prem,tmp_hypo)
premise = batch.get('premise_batch')
premise_len = batch.get('premise_length')
premise = to_var(self.args, premise)
hypothesis = batch.get('hypothesis_batch')
hypothesis_len = batch.get('hypothesis_length')
hypothesis = to_var(self.args, hypothesis)
self.entailment_model.eval()
logits, batch_prob, preds = self.entailment_model(premise, premise_len, hypothesis, hypothesis_len)
batch_prob = batch_prob.cpu().data.numpy()
scores.append(batch_prob.max())
return scores
def save_model(self, save_criteria_score=None):
t.save(self.model.state_dict(), self.path)
logger.info(f"[*] SAVED: {self.path}")
epochs, steps = self.get_saved_models_info()
if save_criteria_score is not None:
if os.path.exists(os.path.join(self.args.model_dir,'checkpoint_tracker.dat')):
checkpoint_tracker = t.load(os.path.join(self.args.model_dir,'checkpoint_tracker.dat'))
else:
checkpoint_tracker = {}
key = f"{self.epoch}_{self.step}"
value = save_criteria_score
checkpoint_tracker[key] = value
if len(epochs)>=self.args.max_save_num:
low_value = 100000.0
remove_key = None
for key,value in checkpoint_tracker.items():
if low_value > value:
remove_key = key
low_value = value
del checkpoint_tracker[remove_key]
remove_epoch = remove_key.split("_")[0]
paths = glob(os.path.join(self.args.model_dir,f'*_epoch{remove_epoch}_*.pth'))
for path in paths:
remove_file(path)
# save back the checkpointer tracker
t.save(checkpoint_tracker, os.path.join(self.args.model_dir,'checkpoint_tracker.dat'))
else:
for epoch in epochs[:-self.args.max_save_num]:
paths = glob(os.path.join(self.args.model_dir, f'*_epoch{epoch}_*.pth'))
for path in paths:
remove_file(path)
def get_saved_models_info(self):
paths = glob(os.path.join(self.args.model_dir, '*.pth'))
paths.sort()
def get_numbers(items, delimiter, idx, replace_word, must_contain=''):
return list(set([int(
name.split(delimiter)[idx].replace(replace_word, ''))
for name in basenames if must_contain in name]))
basenames = [os.path.basename(path.rsplit('.', 1)[0]) for path in paths]
epochs = get_numbers(basenames, '_', 1, 'epoch')
steps = get_numbers(basenames, '_', 2, 'step', 'model')
epochs.sort()
steps.sort()
return epochs, steps
def load_model(self):
if self.args.load_path.endswith('.pth'):
map_location=None
self.model.load_state_dict(
t.load(self.args.load_path, map_location=map_location))
logger.info(f"[*] LOADED: {self.args.load_path}")
else:
if os.path.exists(os.path.join(self.args.load_path,'checkpoint_tracker.dat')):
checkpoint_tracker = t.load(os.path.join(self.args.load_path,'checkpoint_tracker.dat'))
best_key = None
best_score = -1.0
for key,value in checkpoint_tracker.items():
if value>best_score:
best_score = value
best_key = key
self.epoch = int(best_key.split("_")[0])
self.step = int(best_key.split("_")[1])
else:
epochs, steps = self.get_saved_models_info()
if len(epochs) == 0:
logger.info(f"[!] No checkpoint found in {self.args.model_dir}...")
return
self.epoch = self.start_epoch = max(epochs)
self.step = max(steps)
if self.args.num_gpu == 0:
map_location = lambda storage, loc: storage
else:
map_location = None
self.model.load_state_dict(
t.load(self.load_path, map_location=map_location))
logger.info(f"[*] LOADED: {self.load_path}")
def create_result_path(self, filename):
return f'{self.args.model_dir}/results/model_epoch{self.epoch}_step{self.step}_{filename}'
@property
def count_parameters(self):
return sum(p.numel() for p in self.model.parameters() if p.requires_grad)
@property
def path(self):
return f'{self.args.model_dir}/model_epoch{self.epoch}_step{self.step}.pth'
@property
def load_path(self):
return f'{self.args.load_path}/model_epoch{self.epoch}_step{self.step}.pth'
@property
def result_path(self):
return f'{self.args.model_dir}/results/model_epoch{self.epoch}_step{self.step}.json'
@property
def lr(self):
degree = max(self.epoch - self.args.decay_after + 1, 0)
return self.args.lr * (self.args.decay ** degree)
class Trainer(object):
"""A class to wrap training code."""
def __init__(self, args, dataset):
"""Constructor for training algorithm.
Args:
args: From command line, picked up by `argparse`.
dataset: Currently only `data.text.Corpus` is supported.
Initializes:
- Data: train, val and test.
- Model: shared and controller.
- Inference: optimizers for shared and controller parameters.
- Criticism: cross-entropy loss for training the shared model.
"""
self.args = args
if self.args.cuda:
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
self.controller_step = 0
self.cuda = args.cuda
self.dataset = dataset
self.epoch = 0
self.shared_step = 0
self.start_epoch = 0
# logger.info('regularizing:')
# for regularizer in [('activation regularization',
# self.args.activation_regularization),
# ('temporal activation regularization',
# self.args.temporal_activation_regularization),
# ('norm stabilizer regularization',
# self.args.norm_stabilizer_regularization)]:
# if regularizer[1]:
# logger.info(f'{regularizer[0]}')
self.train_data = utils.batchify(dataset.train, args.batch_size,
self.cuda)
# NOTE(brendan): The validation set data is batchified twice
# separately: once for computing rewards during the Train Controller
# phase (valid_data, batch size == 64), and once for evaluating ppl
# over the entire validation set (eval_data, batch size == 1)
self.valid_data = utils.batchify(dataset.valid, args.batch_size,
self.cuda)
self.eval_data = utils.batchify(dataset.valid, args.test_batch_size,
self.cuda)
self.test_data = utils.batchify(dataset.test, args.test_batch_size,
self.cuda)
self.max_length = self.args.shared_rnn_max_length
if args.use_tensorboard:
self.tb = TensorBoard(args.model_dir)
else:
self.tb = None
self.build_model()
if self.args.load_path:
self.load_model()
shared_optimizer = _get_optimizer(self.args.shared_optim)
controller_optimizer = _get_optimizer(self.args.controller_optim)
self.shared_optim = shared_optimizer(
self.shared.parameters(),
lr=self.shared_lr,
weight_decay=self.args.shared_l2_reg)
self.controller_optim = controller_optimizer(
self.controller.parameters(), lr=self.args.controller_lr)
self.ce = nn.CrossEntropyLoss()
def build_model(self):
"""Creates and initializes the shared and controller models."""
self.shared = models.RNN(self.args, self.dataset)
self.controller = models.Controller(self.args)
if self.args.num_gpu == 1:
self.shared.cuda()
self.controller.cuda()
elif self.args.num_gpu > 1:
raise NotImplementedError('`num_gpu > 1` is in progress')
def train(self, single=False):
"""Cycles through alternately training the shared parameters and the
controller, as described in Section 2.2, Training ENAS and Deriving
Architectures, of the paper.
From the paper (for Penn Treebank):
- In the first phase, shared parameters omega are trained for 400
steps, each on a minibatch of 64 examples.
- In the second phase, the controller's parameters are trained for 2000
steps.
Args:
single (bool): If True it won't train the controller and use the
same dag instead of derive().
"""
# dag = utils.load_dag(self.args) if single else None
dag = None
# if self.args.shared_initial_step > 0:
# self.train_shared(self.args.shared_initial_step)
# self.train_controller()
for self.epoch in range(self.start_epoch, self.args.max_epoch):
# 1. Training the shared parameters omega of the child models
self.train_shared(dag=dag)
# 2. Training the controller parameters theta
if not single:
self.train_controller()
if self.epoch % self.args.save_epoch == 0:
with _get_no_grad_ctx_mgr():
best_dag = dag if dag else self.derive()
self.evaluate(self.eval_data,
best_dag,
'val_best',
max_num=self.args.batch_size * 100)
self.save_model()
if self.epoch >= self.args.shared_decay_after:
utils.update_lr(self.shared_optim, self.shared_lr)
def get_loss(self, inputs, targets, hidden, dags, is_training=True):
"""Computes the loss for the same batch for M models.
This amounts to an estimate of the loss, which is turned into an
estimate for the gradients of the shared model.
"""
if not isinstance(dags, list):
dags = [dags]
loss = 0
for dag in dags:
output, hidden = self.shared(inputs,
dag,
prev_s=hidden,
is_training=is_training)
output_flat = output.view(-1, self.dataset.num_tokens)
sample_loss = (self.ce(output_flat, targets) /
self.args.shared_num_sample)
loss += sample_loss
assert len(dags) == 1, 'there are multiple `hidden` for multple `dags`'
return loss, hidden
def train_shared(self, max_step=None, dag=None):
"""Train the language model for 400 steps of minibatches of 64
examples.
Args:
max_step: Used to run extra training steps as a warm-up.
dag: If not None, is used instead of calling sample().
BPTT is truncated at 35 timesteps.
For each weight update, gradients are estimated by sampling M models
from the fixed controller policy, and averaging their gradients
computed on a batch of training data.
"""
model = self.shared
model.train()
self.controller.eval()
hidden = self.shared.init_hidden(self.args.batch_size)
if max_step is None:
max_step = self.args.shared_max_step
else:
max_step = min(self.args.shared_max_step, max_step)
abs_max_grad = 0
abs_max_hidden_norm = 0
step = 0
raw_total_loss = 0
total_loss = 0
train_idx = 0
# TODO(brendan): Why - 1 - 1?
while train_idx < self.train_data.size(0) - 1 - 1:
if step > max_step:
break
if dag:
dags = dag
else:
dags, sample_log_probs, sample_entropy = self.controller.sample(
self.args.shared_num_sample)
inputs, targets = self.get_batch(self.train_data, train_idx,
self.max_length)
loss, hidden = self.get_loss(inputs, targets, hidden, dags)
hidden = hidden.detach_()
raw_total_loss += loss.data
# loss += _apply_penalties(extra_out, self.args)
# update
self.shared_optim.zero_grad()
loss.backward()
# h1tohT = extra_out['hiddens']
# new_abs_max_hidden_norm = utils.to_item(
# h1tohT.norm(dim=-1).data.max())
# if new_abs_max_hidden_norm > abs_max_hidden_norm:
# abs_max_hidden_norm = new_abs_max_hidden_norm
# logger.info(f'max hidden {abs_max_hidden_norm}')
# abs_max_grad = _check_abs_max_grad(abs_max_grad, model)
torch.nn.utils.clip_grad_norm(model.parameters(),
self.args.shared_grad_clip)
self.shared_optim.step()
total_loss += loss.data
if ((step % self.args.log_step) == 0) and (step > 0):
self._summarize_shared_train(total_loss, raw_total_loss)
raw_total_loss = 0
total_loss = 0
step += 1
self.shared_step += 1
train_idx += self.max_length
def get_reward(self, dag, entropies, hidden, valid_idx=0):
"""Computes the perplexity of a single sampled model on a minibatch of
validation data.
"""
if not isinstance(entropies, np.ndarray):
entropies = entropies.data.cpu().numpy()
inputs, targets = self.get_batch(self.valid_data,
valid_idx,
self.max_length,
volatile=True)
valid_loss, hidden = self.get_loss(inputs,
targets,
hidden,
dag,
is_training=False)
valid_loss = utils.to_item(valid_loss.data)
valid_ppl = math.exp(valid_loss)
# TODO: we don't know reward_c
if self.args.ppl_square:
# TODO: but we do know reward_c=80 in the previous paper
R = self.args.reward_c / valid_ppl**2
else:
R = self.args.reward_c / valid_ppl
if self.args.entropy_mode == 'reward':
rewards = R + self.args.controller_entropy_weight * entropies
elif self.args.entropy_mode == 'regularizer':
rewards = R * np.ones_like(entropies)
else:
raise NotImplementedError(
f'Unkown entropy mode: {self.args.entropy_mode}')
return rewards, hidden
def train_controller(self):
"""Fixes the shared parameters and updates the controller parameters.
The controller is updated with a score function gradient estimator
(i.e., REINFORCE), with the reward being c/valid_ppl, where valid_ppl
is computed on a minibatch of validation data.
A moving average baseline is used.
The controller is trained for 2000 steps per epoch (i.e.,
first (Train Shared) phase -> second (Train Controller) phase).
"""
model = self.controller
model.train()
# TODO(brendan): Why can't we call shared.eval() here? Leads to loss
# being uniformly zero for the controller.
# self.shared.eval()
avg_reward_base = None
baseline = None
adv_history = []
entropy_history = []
reward_history = []
hidden = self.shared.init_hidden(self.args.batch_size)
total_loss = 0
valid_idx = 0
for step in range(self.args.controller_max_step):
# sample models, need M=10?
loss_avg = []
for m in range(1):
dags, log_probs, entropies = self.controller.sample(
with_details=True)
# calculate reward
np_entropies = entropies.data.cpu().numpy()
# NOTE(brendan): No gradients should be backpropagated to the
# shared model during controller training, obviously.
with _get_no_grad_ctx_mgr():
rewards, hidden = self.get_reward(dags, np_entropies,
hidden, valid_idx)
#hidden = hidden[-1].detach_() # should we reset immediately? like below
hidden = self.shared.init_hidden(self.args.batch_size)
# discount
# if 1 > self.args.discount > 0:
# rewards = discount(rewards, self.args.discount)
reward_history.extend(rewards)
entropy_history.extend(np_entropies)
# moving average baseline
if baseline is None:
baseline = rewards
else:
decay = self.args.ema_baseline_decay
baseline = decay * baseline + (1 - decay) * rewards
adv = rewards - baseline
adv_history.extend(adv)
# policy loss
loss = -log_probs * utils.get_variable(
adv, self.cuda, requires_grad=False)
loss_avg.append(loss)
# if self.args.entropy_mode == 'regularizer':
# loss -= self.args.entropy_coeff * entropies
loss = torch.stack(loss_avg)
loss = loss.sum()
#loss = loss.sum() # or loss.mean()
# update
self.controller_optim.zero_grad()
loss.backward()
if self.args.controller_grad_clip > 0:
torch.nn.utils.clip_grad_norm(model.parameters(),
self.args.controller_grad_clip)
self.controller_optim.step()
total_loss += utils.to_item(loss.data)
if ((step % self.args.log_step) == 0) and (step > 0):
self._summarize_controller_train(total_loss, adv_history,
entropy_history,
reward_history,
avg_reward_base, dags)
reward_history, adv_history, entropy_history = [], [], []
total_loss = 0
self.controller_step += 1
prev_valid_idx = valid_idx
valid_idx = ((valid_idx + self.max_length) %
(self.valid_data.size(0) - 1))
# NOTE(brendan): Whenever we wrap around to the beginning of the
# validation data, we reset the hidden states.
if prev_valid_idx > valid_idx:
hidden = self.shared.init_hidden(self.args.batch_size)
def evaluate(self, source, dag, name, batch_size=1, max_num=None):
"""Evaluate on the validation set.
NOTE(brendan): We should not be using the test set to develop the
algorithm (basic machine learning good practices).
"""
self.shared.eval()
self.controller.eval()
data = source[:max_num * self.max_length]
total_loss = 0
hidden = self.shared.init_hidden(batch_size)
pbar = range(0, data.size(0) - 1, self.max_length)
for count, idx in enumerate(pbar):
inputs, targets = self.get_batch(data, idx, volatile=True)
output, hidden = self.shared(inputs,
dag,
prev_s=hidden,
is_training=False)
output_flat = output.view(-1, self.dataset.num_tokens)
total_loss += len(inputs) * self.ce(output_flat, targets).data
hidden = hidden.detach_()
ppl = math.exp(
utils.to_item(total_loss) / (count + 1) / self.max_length)
val_loss = utils.to_item(total_loss) / len(data)
ppl = math.exp(val_loss)
self.tb.scalar_summary(f'eval/{name}_loss', val_loss, self.epoch)
self.tb.scalar_summary(f'eval/{name}_ppl', ppl, self.epoch)
logger.info(f'eval | loss: {val_loss:8.2f} | ppl: {ppl:8.2f}')
def derive(self, sample_num=None, valid_idx=0):
"""TODO(brendan): We are always deriving based on the very first batch
of validation data? This seems wrong...
"""
hidden = self.shared.init_hidden(self.args.batch_size)
if sample_num is None:
sample_num = self.args.derive_num_sample
dags, _, entropies = self.controller.sample(sample_num,
with_details=True)
dags = [dags] # only one sample for now
max_R = 0
best_dag = None
for dag in dags:
R, _ = self.get_reward(dag, entropies, hidden, valid_idx)
if R.max() > max_R:
max_R = R.max()
best_dag = dag
logger.info(f'derive | max_R: {max_R:8.6f}')
fname = (f'{self.epoch:03d}-{self.controller_step:06d}-'
f'{max_R:6.4f}-best.png')
path = os.path.join(self.args.model_dir, 'networks', fname)
#utils.draw_network(best_dag, path)
#self.tb.image_summary('derive/best', [path], self.epoch)
return best_dag
@property
def shared_lr(self):
degree = max(self.epoch - self.args.shared_decay_after + 1, 0)
return self.args.shared_lr * (self.args.shared_decay**degree)
@property
def controller_lr(self):
return self.args.controller_lr
def get_batch(self, source, idx, length=None, volatile=False):
# code from
# https://github.com/pytorch/examples/blob/master/word_language_model/main.py
length = min(length if length else self.max_length,
len(source) - 1 - idx)
data = source[idx:idx + length].clone().detach()
target = source[idx + 1:idx + 1 + length].view(-1).clone().detach()
return data, target
@property
def shared_path(self):
return f'{self.args.model_dir}/shared_epoch{self.epoch}_step{self.shared_step}.pth'
@property
def controller_path(self):
return f'{self.args.model_dir}/controller_epoch{self.epoch}_step{self.controller_step}.pth'
def get_saved_models_info(self):
paths = glob.glob(os.path.join(self.args.model_dir, '*.pth'))
paths.sort()
def get_numbers(items, delimiter, idx, replace_word, must_contain=''):
return list(
set([
int(name.split(delimiter)[idx].replace(replace_word, ''))
for name in basenames if must_contain in name
]))
basenames = [
os.path.basename(path.rsplit('.', 1)[0]) for path in paths
]
epochs = get_numbers(basenames, '_', 1, 'epoch')
shared_steps = get_numbers(basenames, '_', 2, 'step', 'shared')
controller_steps = get_numbers(basenames, '_', 2, 'step', 'controller')
epochs.sort()
shared_steps.sort()
controller_steps.sort()
return epochs, shared_steps, controller_steps
def save_model(self):
torch.save(self.shared.state_dict(), self.shared_path)
logger.info(f'[*] SAVED: {self.shared_path}')
torch.save(self.controller.state_dict(), self.controller_path)
logger.info(f'[*] SAVED: {self.controller_path}')
epochs, shared_steps, controller_steps = self.get_saved_models_info()
for epoch in epochs[:-self.args.max_save_num]:
paths = glob.glob(
os.path.join(self.args.model_dir, f'*_epoch{epoch}_*.pth'))
for path in paths:
utils.remove_file(path)
def load_model(self):
epochs, shared_steps, controller_steps = self.get_saved_models_info()
if len(epochs) == 0:
logger.info(f'[!] No checkpoint found in {self.args.model_dir}...')
return
self.epoch = self.start_epoch = max(epochs)
self.shared_step = max(shared_steps)
self.controller_step = max(controller_steps)
if self.args.num_gpu == 0:
map_location = lambda storage, loc: storage
else:
map_location = None
self.shared.load_state_dict(
torch.load(self.shared_path, map_location=map_location))
logger.info(f'[*] LOADED: {self.shared_path}')
self.controller.load_state_dict(
torch.load(self.controller_path, map_location=map_location))
logger.info(f'[*] LOADED: {self.controller_path}')
def _summarize_controller_train(self, total_loss, adv_history,
entropy_history, reward_history,
avg_reward_base, dags):
"""Logs the controller's progress for this training epoch."""
cur_loss = total_loss / self.args.log_step
avg_adv = np.mean(adv_history)
avg_entropy = np.mean(entropy_history)
avg_reward = np.mean(reward_history)
if avg_reward_base is None:
avg_reward_base = avg_reward
logger.info(f'| epoch {self.epoch:3d} | lr {self.controller_lr:.5f} '
f'| R {avg_reward:.5f} | entropy {avg_entropy:.4f} '
f'| loss {cur_loss:.5f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary('controller/loss', cur_loss,
self.controller_step)
self.tb.scalar_summary('controller/reward', avg_reward,
self.controller_step)
self.tb.scalar_summary('controller/reward-B_per_epoch',
avg_reward - avg_reward_base,
self.controller_step)
self.tb.scalar_summary('controller/entropy', avg_entropy,
self.controller_step)
self.tb.scalar_summary('controller/adv', avg_adv,
self.controller_step)
paths = []
for dag in dags:
fname = (f'{self.epoch:03d}-{self.controller_step:06d}-'
f'{avg_reward:6.4f}.png')
path = os.path.join(self.args.model_dir, 'networks', fname)
# utils.draw_network(dag, path)
paths.append(path)
# self.tb.image_summary('controller/sample',
# paths,
# self.controller_step)
def _summarize_shared_train(self, total_loss, raw_total_loss):
"""Logs a set of training steps."""
cur_loss = utils.to_item(total_loss) / self.args.log_step
# NOTE(brendan): The raw loss, without adding in the activation
# regularization terms, should be used to compute ppl.
cur_raw_loss = utils.to_item(raw_total_loss) / self.args.log_step
ppl = math.exp(cur_raw_loss)
logger.info(f'| epoch {self.epoch:3d} '
f'| lr {self.shared_lr:4.2f} '
f'| raw loss {cur_raw_loss:.2f} '
f'| loss {cur_loss:.2f} '
f'| ppl {ppl:8.2f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary('shared/loss', cur_loss, self.shared_step)
self.tb.scalar_summary('shared/perplexity', ppl, self.shared_step)
def main(args):
# Step 1: init data folders
'''if os.path.exists('save_state/'+args.regime+'/normalization_stats.pkl'):
print('Loading normalization stats')
x_mean, x_sd = misc.load_file('save_state/'+args.regime+'/normalization_stats.pkl')
else:
x_mean, x_sd = preprocess.save_normalization_stats(args.regime)
print('x_mean: %.3f, x_sd: %.3f' % (x_mean, x_sd))'''
val_loader=load_data(args, "val")
tb=TensorBoard(args.model_dir)
# Step 2: init neural networks
print("network is:",args.net)
if args.net == 'Reab3p16':
model = Reab3p16(args)
elif args.net=='RN_mlp':
model =WildRelationNet()
if args.gpunum > 1:
model = nn.DataParallel(model, device_ids=range(args.gpunum))
weights_path = args.path_weight+"/"+args.load_weight
if os.path.exists(weights_path) and args.restore:
pretrained_dict = torch.load(weights_path)
model_dict = model.state_dict()
pretrained_dict1 = {}
for k, v in pretrained_dict.items():
if k in model_dict:
pretrained_dict1[k] = v
#print(k)
model_dict.update(pretrained_dict1)
model.load_state_dict(model_dict)
print('load weight')
style_raven={65:0, 129:1, 257:2, 66:3, 132:4, 36:5, 258:6, 136:7, 264:8, 72:9, 130:10
, 260:11, 40:12, 34:13, 49:14, 18:15, 20:16, 24:17}
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr,momentum=args.mo, weight_decay=5e-4)
if args.gpunum>1:
optimizer = nn.DataParallel(optimizer, device_ids=range(args.gpunum))
iter_count = 1
epoch_count = 1
#iter_epoch=int(len(train_files) / args.batch_size)
print(time.strftime('%H:%M:%S', time.localtime(time.time())), 'training')
style_raven_len = len(style_raven)
if args.rl_style=="dqn":
dqn = DQN()
elif args.rl_style=="ddpg":
ram = MemoryBuffer(1000)
ddpg = Trainer(style_raven_len*4+2, style_raven_len, 1, ram)
alpha_1=0.1
if args.rl_style=="dqn":
a = dqn.choose_action([0.5] * 3) # TODO
elif args.rl_style=="ddpg":
action_ = ddpg.get_exploration_action(np.zeros([style_raven_len*4+2]).astype(np.float32),alpha_1)
if args.type_loss:loss_fn=nn.BCELoss()
best_acc=0.0
while True:
since=time.time()
print(action_)
for i in range(style_raven_len):
tb.scalar_summary("action/a"+str(i), action_[i], epoch_count)
data_files = preprocess.provide_data(args.regime, style_raven_len, action_,style_raven)
train_files = [data_file for data_file in data_files if 'train' in data_file]
print("train_num:", len(train_files))
train_loader = torch.utils.data.DataLoader(Dataset(args,train_files), batch_size=args.batch_size, shuffle=True,
num_workers=args.numwork)
model.train()
iter_epoch = int(len(train_files) / args.batch_size)
acc_part_train=np.zeros([style_raven_len,2]).astype(np.float32)
mean_loss_train= np.zeros([style_raven_len, 2]).astype(np.float32)
loss_train=0
for x, y,style,me in train_loader:
if x.shape[0]<10:
print(x.shape[0])
break
x, y ,meta = Variable(x).cuda(), Variable(y).cuda(), Variable(me).cuda()
if args.gpunum > 1:
optimizer.module.zero_grad()
else:
optimizer.zero_grad()
if args.type_loss:
pred_train, pred_meta= model(x)
else:
pred_train = model(x)
loss_ = F.nll_loss(pred_train, y,reduce=False)
loss=loss_.mean() if not args.type_loss else loss_.mean()+10*loss_fn(pred_meta,meta)
loss.backward()
if args.gpunum > 1:
optimizer.module.step()
else:
optimizer.step()
iter_count += 1
pred = pred_train.data.max(1)[1]
correct = pred.eq(y.data).cpu()
loss_train+=loss.item()
for num, style_pers in enumerate(style):
style_pers = style_pers[:-4].split("/")[-1].split("_")[3:]
for style_per in style_pers:
style_per=int(style_per)
if correct[num] == 1:
acc_part_train[style_per, 0] += 1
acc_part_train[style_per, 1] += 1
#mean_pred_train[style_per,0] += pred_train[num,y[num].item()].data.cpu()
#mean_pred_train[style_per, 1] += 1
mean_loss_train[style_per,0] += loss_[num].item()
mean_loss_train[style_per, 1] += 1
accuracy_total = correct.sum() * 100.0 / len(y)
if iter_count %10 == 0:
iter_c = iter_count % iter_epoch
print(time.strftime('%H:%M:%S', time.localtime(time.time())),
('train_epoch:%d,iter_count:%d/%d, loss:%.3f, acc:%.1f') % (
epoch_count, iter_c, iter_epoch, loss, accuracy_total))
tb.scalar_summary("train_loss",loss,iter_count)
loss_train=loss_train/len(train_files)
#mean_pred_train=[x[0]/ x[1] for x in mean_pred_train]
mean_loss_train=[x[0]/ x[1] for x in mean_loss_train]
acc_part_train = [x[0] / x[1] if x[1]!=0 else 0 for x in acc_part_train]
print(acc_part_train)
if epoch_count %args.lr_step ==0:
print("change lr")
adjust_learning_rate(optimizer, epoch_count, args.lr_step,args.gpunum)
time_elapsed = time.time() - since
print('train epoch in {:.0f}h {:.0f}m {:.0f}s'.format(
time_elapsed // 3600, time_elapsed // 60 % 60, time_elapsed % 60))
#acc_p=np.array([x[0]/x[1] for x in acc_part])
#print(acc_p)
with torch.no_grad():
model.eval()
accuracy_all = []
iter_test=0
acc_part_val = np.zeros([style_raven_len, 2]).astype(np.float32)
for x, y, style,me in val_loader:
iter_test+=1
x, y = Variable(x).cuda(), Variable(y).cuda()
pred,_ = model(x)
pred = pred.data.max(1)[1]
correct = pred.eq(y.data).cpu().numpy()
accuracy = correct.sum() * 100.0 / len(y)
for num, style_pers in enumerate(style):
style_pers = style_pers[:-4].split("/")[-1].split("_")[3:]
for style_per in style_pers:
style_per = int(style_per)
if correct[num] == 1:
acc_part_val[style_per, 0] += 1
acc_part_val[style_per, 1] += 1
accuracy_all.append(accuracy)
# if iter_test % 10 == 0:
#
# print(time.strftime('%H:%M:%S', time.localtime(time.time())),
# ('test_iter:%d, acc:%.1f') % (
# iter_test, accuracy))
accuracy_all = sum(accuracy_all) / len(accuracy_all)
acc_part_val = [x[0] / x[1] if x[1]!=0 else 0 for x in acc_part_val ]
baseline_rl=70
reward=np.mean(acc_part_val)*100-baseline_rl
tb.scalar_summary("valreward", reward,epoch_count)
action_list=[x for x in a]
cur_state=np.array(acc_part_val+acc_part_train+action_list+mean_loss_train
+[loss_train]+[epoch_count]).astype(np.float32)
#np.expand_dims(, axis=0)
if args.rl_style == "dqn":
a = dqn.choose_action(cur_state) # TODO
elif args.rl_style == "ddpg":
a = ddpg.get_exploration_action(cur_state,alpha_1)
if alpha_1<1:
alpha_1+=0.005#0.1
if epoch_count > 1:
if args.rl_style == "dqn":dqn.store_transition(last_state, a, reward , cur_state)
elif args.rl_style == "ddpg":ram.add(last_state, a, reward, cur_state)
if epoch_count > 1:
if args.rl_style == "dqn":dqn.learn()
elif args.rl_style == "ddpg":loss_actor, loss_critic=ddpg.optimize()
print('------------------------------------')
print('learn q learning')
print('------------------------------------')
tb.scalar_summary("loss_actor", loss_actor, epoch_count)
tb.scalar_summary("loss_critic", loss_critic, epoch_count)
last_state=cur_state
time_elapsed = time.time() - since
print('test epoch in {:.0f}h {:.0f}m {:.0f}s'.format(
time_elapsed // 3600, time_elapsed // 60 % 60, time_elapsed % 60))
print('------------------------------------')
print(('epoch:%d, acc:%.1f') % (epoch_count, accuracy_all))
print('------------------------------------')
if accuracy_all>best_acc:
best_acc=max(best_acc,accuracy_all)
#ddpg.save_models(args.model_dir + '/', epoch_count)
save_state(model.state_dict(), args.model_dir + "/epochbest")
epoch_count += 1
if epoch_count%20==0:
print("save weights")
ddpg.save_models(args.model_dir+'/',epoch_count )
save_state(model.state_dict(), args.model_dir+"/epoch"+str(epoch_count))
class Trainer(object):
def __init__(self, args, dataset):
self.args = args
self.cuda = args.cuda
self.dataset = dataset
self.train_data = batchify(dataset.train, args.batch_size, self.cuda)
self.valid_data = batchify(dataset.valid, args.batch_size, self.cuda)
self.test_data = batchify(dataset.test, args.test_batch_size,
self.cuda)
self.max_length = self.args.shared_rnn_max_length
if args.use_tensorboard:
self.tb = TensorBoard(args.model_dir)
else:
self.tb = None
self.build_model()
if self.args.load_path:
self.load_model()
def build_model(self):
self.start_epoch = self.epoch = 0
self.shared_step, self.controller_step = 0, 0
if self.args.network_type == 'rnn':
self.shared = RNN(self.args, self.dataset)
elif self.args.network_type == 'cnn':
self.shared = CNN(self.args, self.dataset)
else:
raise NotImplemented(
f"Network type `{self.args.network_type}` is not defined")
self.controller = Controller(self.args)
if self.args.num_gpu == 1:
self.shared.cuda()
self.controller.cuda()
elif self.args.num_gpu > 1:
raise NotImplemented("`num_gpu > 1` is in progress")
self.ce = nn.CrossEntropyLoss()
def train(self):
shared_optimizer = get_optimizer(self.args.shared_optim)
controller_optimizer = get_optimizer(self.args.controller_optim)
self.shared_optim = shared_optimizer(
self.shared.parameters(),
lr=self.shared_lr,
weight_decay=self.args.shared_l2_reg)
self.controller_optim = controller_optimizer(
self.controller.parameters(), lr=self.args.controller_lr)
hidden = self.shared.init_hidden(self.args.batch_size)
for self.epoch in range(self.start_epoch, self.args.max_epoch):
# 1. Training the shared parameters ω of the child models
hidden = self.train_shared(hidden)
# 2. Training the controller parameters θ
self.train_controller()
if self.epoch % self.args.save_epoch == 0:
if self.epoch > 0:
best_dag = self.derive()
loss, ppl = self.test(self.test_data, best_dag,
"test_best")
self.save_model()
if self.epoch >= self.args.shared_decay_after:
update_lr(self.shared_optim, self.shared_lr)
def get_loss(self, inputs, targets, hidden, dags, with_hidden=False):
if type(dags) != list:
dags = [dags]
loss = 0
for dag in dags:
# previous hidden is useless
output, hidden = self.shared(inputs, hidden, dag)
output_flat = output.view(-1, self.dataset.num_tokens)
sample_loss = self.ce(output_flat,
targets) / self.args.shared_num_sample
loss += sample_loss
if with_hidden:
assert len(
dags) == 1, "there are multiple `hidden` for multple `dags`"
return loss, hidden
else:
return loss
def train_shared(self, hidden):
total_loss = 0
model = self.shared
model.train()
step, train_idx = 0, 0
pbar = tqdm(total=self.train_data.size(0), desc="train_shared")
while train_idx < self.train_data.size(0) - 1 - 1:
if step > self.args.shared_max_step:
break
dags = self.controller.sample(self.args.shared_num_sample)
inputs, targets = self.get_batch(self.train_data, train_idx,
self.max_length)
loss = self.get_loss(inputs, targets, hidden, dags)
# update
self.shared_optim.zero_grad()
loss.backward()
t.nn.utils.clip_grad_norm(model.parameters(),
self.args.shared_grad_clip)
self.shared_optim.step()
total_loss += loss.data
pbar.set_description(f"train_shared| loss: {loss.data[0]:5.3f}")
if step % self.args.log_step == 0 and step > 0:
cur_loss = total_loss[0] / self.args.log_step
ppl = math.exp(cur_loss)
logger.info(
f'| epoch {self.epoch:3d} | lr {self.shared_lr:4.2f} '
f'| loss {cur_loss:.2f} | ppl {ppl:8.2f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary("shared/loss", cur_loss,
self.shared_step)
self.tb.scalar_summary("shared/perplexity", ppl,
self.shared_step)
total_loss = 0
step += 1
self.shared_step += 1
train_idx += self.max_length
pbar.update(self.max_length)
def get_reward(self, dag, valid_idx=None):
if valid_idx:
valid_idx = 0
inputs, targets = self.get_batch(self.valid_data, valid_idx,
self.max_length)
valid_loss = self.get_loss(inputs, targets, None, dag)
valid_ppl = math.exp(valid_loss.data[0])
R = self.args.reward_c / valid_ppl
return R
def train_controller(self):
total_loss = 0
model = self.controller
model.train()
pbar = trange(self.args.controller_max_step, desc="train_controller")
baseline = None
reward_history, adv_history, entropy_history = [], [], []
valid_idx = 0
for step in pbar:
# sample models
dags, log_probs, entropies = self.controller.sample(
with_details=True)
# calculate reward
R = self.get_reward(dags, valid_idx)
reward_history.append(R)
entropy_history.extend(entropies)
# moving average baseline
if baseline is None:
baseline = R
else:
decay = self.args.ema_baseline_decay
baseline = decay * baseline + (1 - decay) * R
adv = R - baseline
adv_history.append(adv)
pbar.set_description(
f"train_controller| R: {R:8.6f} | R-b: {adv:8.6f}")
rewards = [0] * (2 * (self.args.num_blocks - 1)) + [adv]
# discount
if self.args.discount == 1:
rewards = [adv] * len(log_probs)
elif self.args.discount > 0:
rewards = discount(rewards, self.args.discount)
#rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps)
# policy loss
loss = 0
for log_prob, reward, entropy in zip(log_probs, rewards,
entropies):
loss = loss - log_prob * reward - self.args.entropy_coeff * entropy
# update
self.controller_optim.zero_grad()
loss.backward()
self.controller_optim.step()
total_loss += loss.data
if step % self.args.log_step == 0 and step > 0:
cur_loss = total_loss[0][0] / self.args.log_step
avg_reward = np.mean(reward_history)
avg_entropy = np.mean(entropy_history)
avg_adv = np.mean(adv_history)
logger.info(
f'| epoch {self.epoch:3d} | lr {self.controller_lr:.5f} '
f'| R {avg_reward:.5f} | entropy {avg_entropy:.4f} '
f'| loss {cur_loss:.5f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary("controller/loss", cur_loss,
self.controller_step)
self.tb.scalar_summary("controller/reward", avg_reward,
self.controller_step)
self.tb.scalar_summary("controller/entropy", avg_entropy,
self.controller_step)
self.tb.scalar_summary("controller/adv", avg_adv,
self.controller_step)
paths = []
for dag in dags:
fname = f"{self.epoch:03d}-{self.controller_step:06d}-{avg_reward:6.4f}.png"
path = os.path.join(self.args.model_dir, "networks",
fname)
draw_network(dag, path)
paths.append(path)
self.tb.image_summary("controller/sample", paths,
self.controller_step)
reward_history, adv_history, entropy_history = [], [], []
self.controller_step += 1
valid_idx = (valid_idx +
self.max_length) % (self.valid_data.size(0) - 1)
def test(self, source, dag, name, batch_size=1):
self.shared.eval()
self.controller.eval()
total_loss = 0
hidden = self.shared.init_hidden(batch_size)
pbar = trange(0, source.size(0) - 1, self.max_length, desc="test")
for count, idx in enumerate(pbar):
data, targets = self.get_batch(source, idx, evaluation=True)
output, hidden = self.shared(data, hidden, dag)
output_flat = output.view(-1, self.dataset.num_tokens)
total_loss += len(data) * self.ce(output_flat, targets).data
hidden = detach(hidden)
ppl = math.exp(total_loss[0] / (count + 1) / self.max_length)
pbar.set_description(f"test| ppl: {ppl:8.2f}")
test_loss = total_loss[0] / len(source)
ppl = math.exp(test_loss)
self.tb.scalar_summary(f"test/{name}_loss", test_loss, self.epoch)
self.tb.scalar_summary(f"test/{name}_ppl", ppl, self.epoch)
return test_loss, ppl
def derive(self, valid_idx=0, sample_num=None):
if sample_num is None:
sample_num = self.args.derive_num_sample
dags = self.controller.sample(sample_num)
max_R, best_dag = 0, None
pbar = tqdm(dags, desc="derive")
for dag in pbar:
R = self.get_reward(dag, valid_idx)
if R > max_R:
max_R = R
best_dag = dag
pbar.set_description(f"derive| max_R: {max_R:8.6f}")
fname = f"{self.epoch:03d}-{self.controller_step:06d}-{max_R:6.4f}-best.png"
path = os.path.join(self.args.model_dir, "networks", fname)
draw_network(best_dag, path)
self.tb.image_summary("derive/best", [path], self.epoch)
return best_dag
@property
def shared_lr(self):
degree = max(self.epoch - self.args.shared_decay_after + 1, 0)
return self.args.shared_lr * (self.args.shared_decay**degree)
@property
def controller_lr(self):
return self.args.controller_lr
def get_batch(self, source, idx, length=None, evaluation=False):
# code from https://github.com/pytorch/examples/blob/master/word_language_model/main.py
length = min(length if length else self.max_length,
len(source) - 1 - idx)
data = Variable(source[idx:idx + length], volatile=evaluation)
target = Variable(source[idx + 1:idx + 1 + length].view(-1))
return data, target
@property
def shared_path(self):
return f'{self.args.model_dir}/shared_epoch{self.epoch}_step{self.shared_step}.pth'
@property
def controller_path(self):
return f'{self.args.model_dir}/controller_epoch{self.epoch}_step{self.controller_step}.pth'
def get_saved_models_info(self):
paths = glob(os.path.join(self.args.model_dir, '*.pth'))
paths.sort()
def get_numbers(items, delimiter, idx, replace_word, must_contain=''):
return list(
set([
int(name.split(delimiter)[idx].replace(replace_word, ''))
for name in basenames if must_contain in name
]))
basenames = [
os.path.basename(path.rsplit('.', 1)[0]) for path in paths
]
epochs = get_numbers(basenames, '_', 1, 'epoch')
shared_steps = get_numbers(basenames, '_', 2, 'step', 'shared')
controller_steps = get_numbers(basenames, '_', 2, 'step', 'controller')
epochs.sort()
shared_steps.sort()
controller_steps.sort()
return epochs, shared_steps, controller_steps
def save_model(self):
t.save(self.shared.state_dict(), self.shared_path)
logger.info(f"[*] SAVED: {self.shared_path}")
t.save(self.controller.state_dict(), self.controller_path)
logger.info(f"[*] SAVED: {self.controller_path}")
epochs, shared_steps, controller_steps = self.get_saved_models_info()
for epoch in epochs[:-self.args.max_save_num]:
paths = glob(
os.path.join(self.args.model_dir, f'*_epoch{epoch}_*.pth'))
for path in paths:
remove_file(path)
def load_model(self):
epochs, shared_steps, controller_steps = self.get_saved_models_info()
if len(epochs) == 0:
logger.info(f"[!] No checkpoint found in {self.args.model_dir}...")
return
self.start_epoch = max(epochs)
self.shared_step = max(shared_steps)
self.controller_step = max(controller_steps)
if self.args.num_gpu == 0:
map_location = lambda storage, loc: storage
else:
map_location = None
self.shared.load_state_dict(
t.load(self.shared_path, map_location=map_location))
logger.info(f"[*] LOADED: {self.shared_path}")
self.controller.load_state_dict(
t.load(self.controller_path, map_location=map_location))
logger.info(f"[*] LOADED: {self.controller_path}")
def main(args):
# Step 1: init data folders
'''if os.path.exists('save_state/'+args.regime+'/normalization_stats.pkl'): ##to load raw data and preprocess it
print('Loading normalization stats')
x_mean, x_sd = misc.load_file('save_state/'+args.regime+'/normalization_stats.pkl')
else:
x_mean, x_sd = preprocess.save_normalization_stats(args.regime)
print('x_mean: %.3f, x_sd: %.3f' % (x_mean, x_sd))'''
val_loader=load_data(args, "val") ##loading already preprocessed validation/testing data
tb=TensorBoard(args.model_dir) ##The model_dir arguments represents the directory to save model parameters, graph and etc. This can also be used to
##load checkpoints from the directory into a estimator to continue training a previously saved model.
# Step 2: init neural networks
print("network is:",args.net)
if args.net == 'Reab3p16': ##if want to use model Reab3p16
model = Reab3p16(args)
elif args.net=='RN_mlp': ##if want to use model WildRelationNet
model =WildRelationNet()
if args.gpunum > 1:
model = nn.DataParallel(model, device_ids=range(args.gpunum)) ##The nn package defines a set of Modules, which you can think of as a neural network layer that has produces output from
##input and may have some trainable weights.
##when more than one gpu, want to save model weights using DataParrallel module prefix
weights_path = args.path_weight+"/"+args.load_weight ##saved weigths of model
if os.path.exists(weights_path) and args.restore: ##pretrained weights
pretrained_dict = torch.load(weights_path) ##pretrained_dict is the state dictionary of the pre-trained model available
model_dict = model.state_dict() ## https://pytorch.org/tutorials/recipes/recipes/what_is_state_dict.htmlA state_dict is an integral entity
pretrained_dict1 = {} ##..if you are interested in saving or loading models from PyTorch
for k, v in pretrained_dict.items(): ##filter out unnecessary keys k
if k in model_dict: ##only when keys match(like conv2D..and so forth)
pretrained_dict1[k] = v
#print(k)
model_dict.update(pretrained_dict1) ##overwrite entries in the existing state dict
model.load_state_dict(model_dict) ##load the new state dict, new weights
print('load weight')
style_raven={65:0, 129:1, 257:2, 66:3, 132:4, 36:5, 258:6, 136:7, 264:8, 72:9, 130:10 ##dictionary(key:value pair of
, 260:11, 40:12, 34:13, 49:14, 18:15, 20:16, 24:17}
##After setting weights using optimizer for training.
##The standard way in PyTorch to train a model in multiple GPUs is to use nn.DataParallel which copies the model to the GPUs
##and during training splits the batch among them and combines the individual outputs.
##model.cuda() by default will send your model to the "current device"
#If you need to move a model to GPU via .cuda(), please do so before constructing optimizers for it. Parameters of a model
#after .cuda() will be different objects with those before the call.
##A very popular technique that is used along with SGD is called Momentum. Instead of using only the gradient of the current
##step to guide the search, momentum also accumulates the gradient of the past steps to determine the direction to go
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr,momentum=args.mo, weight_decay=5e-4) ##Adam has convergence problems that often SGD + momentum can converge better
##with longer training time. We often see a lot of papers in 2018 and 2019 were still using SGD
if args.gpunum>1:
optimizer = nn.DataParallel(optimizer, device_ids=range(args.gpunum))
##setting iter-count and epoch to 1 before starting training
iter_count = 1 ## number of batches of data the algorithm has seen (or simply the number of passes the algorithm has done on the dataset)
epoch_count = 1 ##number of times a learning algorithm sees the complete dataset
#iter_epoch=int(len(train_files) / args.batch_size)
print(time.strftime('%H:%M:%S', time.localtime(time.time())), 'training')
style_raven_len = len(style_raven) ##length of style raven dict
if args.rl_style=="dqn": ##calling reinforcemt model for training
dqn = DQN() ##if want to use dqn model
elif args.rl_style=="ddpg": ##if want to use ddpg model (aiming to use this)
ram = MemoryBuffer(1000)
ddpg = Trainer(style_raven_len*4+2, style_raven_len, 1, ram) ##creating an instance of Trainer class defined in rl folder (ddpg.py) why style_raven_len*4+2?
alpha_1=0.1
if args.rl_style=="dqn":
a = dqn.choose_action([0.5] * 3) # TODO
elif args.rl_style=="ddpg":
action_ = ddpg.get_exploration_action(np.zeros([style_raven_len*4+2]).astype(np.float32),alpha_1) ##calling exploration which returns action?
if args.type_loss:loss_fn=nn.BCELoss() ##Creates a criterion that measures the Binary Cross Entropy between the target and the output.
best_acc=0.0 ##setting accuracy to 0.0
while True: ##loop(train) until
since=time.time()
print(action_)
for i in range(style_raven_len):
tb.scalar_summary("action/a"+str(i), action_[i], epoch_count) ##saving summary such as poch counts and actions
data_files = preprocess.provide_data(args.regime, style_raven_len, action_,style_raven)
train_files = [data_file for data_file in data_files if 'train' in data_file] #creating a list of training files
print("train_num:", len(train_files))
##torch.utils.data.DataLoader` supports both map-style and iterable-style datasets with single- or multi-process loading,
##customizing loading order and optional automatic batching (collation) and memory pinning
##shuffle true because we want independent B training batches from Dataset
train_loader = torch.utils.data.DataLoader(Dataset(args,train_files), batch_size=args.batch_size, shuffle=True,
num_workers=args.numwork)
model.train() ##start training model
iter_epoch = int(len(train_files) / args.batch_size) ##setting iteration count for total dataset
acc_part_train=np.zeros([style_raven_len,2]).astype(np.float32) ##defining variable for saving part accuracy while training
mean_loss_train= np.zeros([style_raven_len, 2]).astype(np.float32) ##defining variable for saving mean loss while training
loss_train=0
for x, y,style,me in train_loader:
if x.shape[0]<10: ##x.shape[0] will give the number of rows in an array (10 by 1024 2D array)
print(x.shape[0])
break
x, y ,meta = Variable(x).cuda(), Variable(y).cuda(), Variable(me).cuda() ##Components are accessible as variable.x, variable.y, variable.z
if args.gpunum > 1:
optimizer.module.zero_grad() ##to set the gradient of the parameters in the model to 0, module beacause DataParallel
else:
optimizer.zero_grad() ## same as above set the gradient of the parameters to zero
if args.type_loss:
pred_train, pred_meta= model(x) ##applying model to x where x is from training data
else:
pred_train = model(x) ##x is images y is actual label/category
loss_ = F.nll_loss(pred_train, y,reduce=False) ##calculating loss occurred while training
loss=loss_.mean() if not args.type_loss else loss_.mean()+10*loss_fn(pred_meta,meta)##If your loss is not a scalar value, then you should certainly use either
loss.backward() ##loss.mean() or loss.sum() to convert it to a scalar before calling the backward. Otherwise, it will cause an error
#When you call loss.backward(), all it does is compute gradient of loss w.r.t all the parameters in loss that have
##requires_grad = True and store them in parameter.grad attribute for every parameter.
##optimizer.step() updates all the parameters based on parameter.grad
if args.gpunum > 1:
optimizer.module.step() ##module for DataParallel
else:
optimizer.step()
iter_count += 1 ##update iter-count by 1 evrytime
pred = pred_train.data.max(1)[1]
correct = pred.eq(y.data).cpu() ##compare actual and predicted category
loss_train+=loss.item() ##The average of the batch losses will give you an estimate of the “epoch loss” during training.
for num, style_pers in enumerate(style):
style_pers = style_pers[:-4].split("/")[-1].split("_")[3:]
for style_per in style_pers:
style_per=int(style_per)
if correct[num] == 1:
acc_part_train[style_per, 0] += 1
acc_part_train[style_per, 1] += 1
#mean_pred_train[style_per,0] += pred_train[num,y[num].item()].data.cpu()
#mean_pred_train[style_per, 1] += 1
mean_loss_train[style_per,0] += loss_[num].item()
mean_loss_train[style_per, 1] += 1
accuracy_total = correct.sum() * 100.0 / len(y) ####calc accuracy
if iter_count %10 == 0: ##do this for 10 iterations
iter_c = iter_count % iter_epoch
print(time.strftime('%H:%M:%S', time.localtime(time.time())),
('train_epoch:%d,iter_count:%d/%d, loss:%.3f, acc:%.1f') % (
epoch_count, iter_c, iter_epoch, loss, accuracy_total))
tb.scalar_summary("train_loss",loss,iter_count) ##saving train loss to summary
loss_train=loss_train/len(train_files) ##The average of the batch losses will give you an estimate of the “epoch loss” during training.
#mean_pred_train=[x[0]/ x[1] for x in mean_pred_train]
mean_loss_train=[x[0]/ x[1] for x in mean_loss_train]
acc_part_train = [x[0] / x[1] if x[1]!=0 else 0 for x in acc_part_train]
print(acc_part_train)
if epoch_count %args.lr_step ==0: ##adjusting learning rate after 30 epochs
print("change lr")
adjust_learning_rate(optimizer, epoch_count, args.lr_step,args.gpunum)
time_elapsed = time.time() - since
print('train epoch in {:.0f}h {:.0f}m {:.0f}s'.format(
time_elapsed // 3600, time_elapsed // 60 % 60, time_elapsed % 60))
#acc_p=np.array([x[0]/x[1] for x in acc_part])
#print(acc_p)
with torch.no_grad():
model.eval() ##evaluating model
accuracy_all = []
iter_test=0
acc_part_val = np.zeros([style_raven_len, 2]).astype(np.float32)
for x, y, style,me in val_loader: ##using validation data
iter_test+=1
x, y = Variable(x).cuda(), Variable(y).cuda()
pred,_ = model(x)
pred = pred.data.max(1)[1]
correct = pred.eq(y.data).cpu().numpy()
accuracy = correct.sum() * 100.0 / len(y) ##accuracy is calc basd on how many labels match
for num, style_pers in enumerate(style):
style_pers = style_pers[:-4].split("/")[-1].split("_")[3:]
for style_per in style_pers:
style_per = int(style_per)
if correct[num] == 1:
acc_part_val[style_per, 0] += 1
acc_part_val[style_per, 1] += 1
accuracy_all.append(accuracy) ##append to accuracy list
# if iter_test % 10 == 0:
#
# print(time.strftime('%H:%M:%S', time.localtime(time.time())),
# ('test_iter:%d, acc:%.1f') % (
# iter_test, accuracy))
accuracy_all = sum(accuracy_all) / len(accuracy_all) ##total accuracy is calculated
acc_part_val = [x[0] / x[1] if x[1]!=0 else 0 for x in acc_part_val ]
baseline_rl=70 ##baseline for accuracy
reward=np.mean(acc_part_val)*100-baseline_rl ##calculating reward using val accuracy
tb.scalar_summary("valreward", reward,epoch_count) ##saving summary
action_list=[x for x in a]
cur_state=np.array(acc_part_val+acc_part_train+action_list+mean_loss_train ##saving all calc in currnt state
+[loss_train]+[epoch_count]).astype(np.float32)
#np.expand_dims(, axis=0)
if args.rl_style == "dqn":
a = dqn.choose_action(cur_state) # TODO
elif args.rl_style == "ddpg": ##passing current state to rl model's get_exploration_action
a = ddpg.get_exploration_action(cur_state,alpha_1)
if alpha_1<1:
alpha_1+=0.005#0.1
if epoch_count > 1: ##saving last state and current state ,reward in memory for epoch >1
if args.rl_style == "dqn":dqn.store_transition(last_state, a, reward , cur_state)
elif args.rl_style == "ddpg":ram.add(last_state, a, reward, cur_state)
if epoch_count > 1:
if args.rl_style == "dqn":dqn.learn()
elif args.rl_style == "ddpg":loss_actor, loss_critic=ddpg.optimize() ##using rl ddpg model's optimize function to for teaching
print('------------------------------------')
print('learn q learning')
print('------------------------------------')
tb.scalar_summary("loss_actor", loss_actor, epoch_count)
tb.scalar_summary("loss_critic", loss_critic, epoch_count)
last_state=cur_state
time_elapsed = time.time() - since
print('test epoch in {:.0f}h {:.0f}m {:.0f}s'.format(
time_elapsed // 3600, time_elapsed // 60 % 60, time_elapsed % 60))
print('------------------------------------')
print(('epoch:%d, acc:%.1f') % (epoch_count, accuracy_all))
print('------------------------------------')
if accuracy_all>best_acc: ##save the best accuracy obtained from val data as best accuracy for next epoch
best_acc=max(best_acc,accuracy_all)
#ddpg.save_models(args.model_dir + '/', epoch_count)
save_state(model.state_dict(), args.model_dir + "/epochbest") ##saving the current state
epoch_count += 1 ##increasing epoch count by 1
if epoch_count%20==0: ##Do this for 20 epochs for complete dataset
print("save weights")
ddpg.save_models(args.model_dir+'/',epoch_count ) ##saving the model
save_state(model.state_dict(), args.model_dir+"/epoch"+str(epoch_count))
class Trainer(object):
"""A class to wrap training code."""
def __init__(self, args, dataset):
"""Constructor for training algorithm.
Args:
args: From command line, picked up by `argparse`.
dataset: Currently only `data.text.Corpus` is supported.
Initializes:
- Data: train, val and test.
- Model: shared and controller.
- Inference: optimizers for shared and controller parameters.
- Criticism: cross-entropy loss for training the shared model.
"""
self.args = args
self.controller_step = 0
self.cuda = args.cuda
self.device = gpu = torch.device("cuda:0")
self.dataset = dataset
self.epoch = 0
self.shared_step = 0
self.start_epoch = 0
self.compute_fisher = False
logger.info('regularizing:')
for regularizer in [('activation regularization',
self.args.activation_regularization),
('temporal activation regularization',
self.args.temporal_activation_regularization),
('norm stabilizer regularization',
self.args.norm_stabilizer_regularization)]:
if regularizer[1]:
logger.info(f'{regularizer[0]}')
self.image_dataset = isinstance(dataset, Image)
if self.image_dataset:
self._train_data = dataset.train
self._valid_data = dataset.valid
self._test_data = dataset.test
self._eval_data = dataset.valid
self.train_data = wrap_iterator_with_name(self._train_data,
'train')
self.valid_data = wrap_iterator_with_name(self._valid_data,
'valid')
self.test_data = wrap_iterator_with_name(self._test_data, 'test')
self.eval_data = wrap_iterator_with_name(self._eval_data, 'eval')
self.max_length = 0
else:
self.train_data = utils.batchify(dataset.train, args.batch_size,
self.cuda)
self.valid_data = utils.batchify(dataset.valid, args.batch_size,
self.cuda)
self.eval_data = utils.batchify(dataset.valid,
args.test_batch_size, self.cuda)
self.test_data = utils.batchify(dataset.test, args.test_batch_size,
self.cuda)
self.max_length = self.args.shared_rnn_max_length
self.train_data_size = self.train_data.size(
0) if not self.image_dataset else len(self.train_data)
self.valid_data_size = self.valid_data.size(
0) if not self.image_dataset else len(self.valid_data)
self.test_data_size = self.test_data.size(
0) if not self.image_dataset else len(self.test_data)
# Visualization
if args.use_tensorboard:
self.tb = TensorBoard(args.model_dir)
else:
self.tb = None
self.draw_network = utils.draw_network
self.build_model()
if self.args.load_path:
self.load_model()
shared_optimizer = _get_optimizer(self.args.shared_optim)
controller_optimizer = _get_optimizer(self.args.controller_optim)
# As fisher information, and it should be seen by this model, to get the loss.
self.shared_optim = shared_optimizer(
self.shared.parameters(),
lr=self.shared_lr,
weight_decay=self.args.shared_l2_reg)
self.controller_optim = controller_optimizer(
self.controller.parameters(), lr=self.args.controller_lr)
self.ce = nn.CrossEntropyLoss()
self.top_k_acc = top_k_accuracy
def build_model(self):
"""Creates and initializes the shared and controller models."""
if self.args.network_type == 'rnn':
self.shared = models.RNN(self.args, self.dataset)
self.controller = models.Controller(self.args)
elif self.args.network_type == 'micro_cnn':
self.shared = models.CNN(self.args, self.dataset)
self.controller = models.CNNMicroController(self.args)
else:
raise NotImplementedError(f'Network type '
f'`{self.args.network_type}` is not '
f'defined')
if self.args.num_gpu == 1:
if torch.__version__ == '0.3.1':
self.shared.cuda()
self.controller.cuda()
else:
self.shared.to(self.device)
self.controller.to(self.device)
elif self.args.num_gpu > 1:
raise NotImplementedError('`num_gpu > 1` is in progress')
def train(self):
"""Cycles through alternately training the shared parameters and the
controller, as described in Section 2.2, Training ENAS and Deriving
Architectures, of the paper.
From the paper (for Penn Treebank):
- In the first phase, shared parameters omega are trained for 400
steps, each on a minibatch of 64 examples.
- In the second phase, the controller's parameters are trained for 2000
steps.
"""
if self.args.shared_initial_step > 0:
self.train_shared(self.args.shared_initial_step)
self.train_controller()
for self.epoch in range(self.start_epoch, self.args.max_epoch):
if self.epoch >= self.args.start_using_fisher:
self.compute_fisher = True
if self.args.set_fisher_zero_per_iter > 0 \
and self.epoch % self.args.set_fisher_zero_per_iter == 0:
self.shared.set_fisher_zero()
# 1. Training the shared parameters omega of the child models
self.train_shared()
# 2. Training the controller parameters theta
if self.args.train_controller:
if self.epoch < self.args.stop_training_controller:
self.train_controller()
if self.epoch % self.args.save_epoch == 0:
with _get_no_grad_ctx_mgr():
best_dag = self.derive()
self.evaluate(self.eval_data,
best_dag,
'val_best',
max_num=self.args.batch_size * 100)
self.save_model()
if self.epoch >= self.args.shared_decay_after:
utils.update_lr(self.shared_optim, self.shared_lr)
def get_loss(self, inputs, targets, dags, **kwargs):
"""Computes the loss for the same batch for M models.
This amounts to an estimate of the loss, which is turned into an
estimate for the gradients of the shared model.
We store, compute the new WPL.
:param **kwargs: passed into self.shared(, such as hidden)
"""
if not isinstance(dags, list):
dags = [dags]
loss = 0
for dag in dags:
output, hidden, extra_out = self.shared(inputs, dag, **kwargs)
output_flat = output.view(-1, self.dataset.num_classes)
sample_loss = (self.ce(output_flat, targets) /
self.args.shared_num_sample)
# Get WPL part
if self.compute_fisher:
wpl = self.shared.compute_weight_plastic_loss_with_update_fisher(
dag)
wpl = 0.5 * wpl
loss += sample_loss + wpl
rest_loss = wpl
else:
loss += sample_loss
rest_loss = Variable(torch.zeros(1))
# logger.info(f'Loss {loss.data[0]} = '
# f'sample_loss {sample_loss.data[0]}')
#assert len(dags) == 1, 'there are multiple `hidden` for multple `dags`'
return loss, sample_loss, rest_loss, hidden, extra_out
def train_shared(self, max_step=None):
"""Train the language model for 400 steps of minibatches of 64
examples.
Args:
max_step: Used to run extra training steps as a warm-up.
BPTT is truncated at 35 timesteps.
For each weight update, gradients are estimated by sampling M models
from the fixed controller policy, and averaging their gradients
computed on a batch of training data.
"""
valid_ppls = []
valid_ppls_after = []
model = self.shared
model.train()
self.controller.eval()
hidden = self.shared.init_training(self.args.batch_size)
v_hidden = self.shared.init_training(self.args.batch_size)
if max_step is None:
max_step = self.args.shared_max_step
else:
max_step = min(self.args.shared_max_step, max_step)
abs_max_grad = 0
abs_max_hidden_norm = 0
step = 0
raw_total_loss = 0
total_loss = 0
total_sample_loss = 0
total_rest_loss = 0
train_idx = 0
valid_idx = 0
def _run_shared_one_batch(inputs, targets, hidden, dags,
raw_total_loss):
# global abs_max_grad
# global abs_max_hidden_norm
# global raw_total_loss
loss, sample_loss, rest_loss, hidden, extra_out = self.get_loss(
inputs, targets, dags, hidden=hidden)
# Detach the hidden
# Because they are input from previous state.
hidden = utils.detach(hidden)
raw_total_loss += sample_loss.data / self.args.num_batch_per_iter
penalty_loss = _apply_penalties(extra_out, self.args)
loss += penalty_loss
rest_loss += penalty_loss
return loss, sample_loss, rest_loss, hidden, extra_out, raw_total_loss
def _clip_gradient(abs_max_grad, abs_max_hidden_norm):
h1tohT = extra_out['hiddens']
new_abs_max_hidden_norm = utils.to_item(
h1tohT.norm(dim=-1).data.max())
if new_abs_max_hidden_norm > abs_max_hidden_norm:
abs_max_hidden_norm = new_abs_max_hidden_norm
logger.info(f'max hidden {abs_max_hidden_norm}')
abs_max_grad = _check_abs_max_grad(abs_max_grad, model)
torch.nn.utils.clip_grad_norm(model.parameters(),
self.args.shared_grad_clip)
return abs_max_grad, abs_max_hidden_norm
def _evaluate_valid(dag):
hidden_eval = self.shared.init_training(self.args.batch_size)
inputs_eval, targets_eval = self.get_batch(self.valid_data,
0,
self.max_length,
volatile=True)
_, valid_loss_eval, _, _, _ = self.get_loss(inputs_eval,
targets_eval,
dag,
hidden=hidden_eval)
valid_loss_eval = utils.to_item(valid_loss_eval.data)
valid_ppl_eval = math.exp(valid_loss_eval)
# return valid_ppl_eval
dags_eval = []
while train_idx < self.train_data_size - 1 - 1:
if step > max_step:
break
dags = self.controller.sample(self.args.shared_num_sample)
dags_eval.append(dags[0])
for b in range(0, self.args.num_batch_per_iter):
# For each model, do the update for 30 batches.
inputs, targets = self.get_batch(self.train_data, train_idx,
self.max_length)
loss, sample_loss, rest_loss, hidden, extra_out, raw_total_loss = \
_run_shared_one_batch(
inputs, targets, hidden, dags, raw_total_loss)
# update with complete logic
# First, normally we compute one loss and do update accordingly.
# if in the last batch, we compute the fisher information
# based on two kinds of loss, complete or ce-loss only.
self.shared_optim.zero_grad()
# If it is the last training batch. Update the Fisher information
if self.compute_fisher and (not self.args.shared_valid_fisher):
if b == self.args.num_batch_per_iter - 1:
sample_loss.backward()
if self.args.shared_ce_fisher:
self.shared.update_fisher(dags[0])
rest_loss.backward()
else:
rest_loss.backward()
self.shared.update_fisher(dags[0])
else:
loss.backward()
else:
loss.backward()
abs_max_grad, abs_max_hidden_norm = _clip_gradient(
abs_max_grad, abs_max_hidden_norm)
self.shared_optim.step()
total_loss += loss.data / self.args.num_batch_per_iter
total_sample_loss += sample_loss.data / self.args.num_batch_per_iter
total_rest_loss += rest_loss.data / self.args.num_batch_per_iter
train_idx = ((train_idx + self.max_length) %
(self.train_data_size - 1))
if self.epoch > self.args.start_evaluate_diff:
valid_ppl_eval = _evaluate_valid(dags[0])
valid_ppls.append(valid_ppl_eval)
logger.info(
f'Step {step}'
f'Loss {utils.to_item(total_loss) / (step + 1):.5f} = '
f'sample_loss {utils.to_item(total_sample_loss) / (step + 1):.5f} + '
f'wpl {utils.to_item(total_rest_loss) / (step + 1):.5f}')
if ((step % self.args.log_step) == 0) and (step > 0):
self._summarize_shared_train(total_loss, raw_total_loss)
raw_total_loss = 0
total_loss = 0
total_sample_loss = 0
total_rest_loss = 0
if self.compute_fisher:
# Update with the validation dataset for fisher information after each step,
# with update the optimal weights.
v_inputs, v_targets = self.get_batch(self.valid_data,
valid_idx,
self.max_length)
v_loss, v_sample_loss, _, v_hidden, v_extra_out, _ = _run_shared_one_batch(
v_inputs, v_targets, v_hidden, dags, 0)
self.shared_optim.zero_grad()
if self.args.shared_ce_fisher:
v_sample_loss.backward()
else:
v_loss.backward()
self.shared.update_fisher(dags[0], self.epoch)
self.shared.update_optimal_weights()
valid_idx = ((valid_idx + self.max_length) %
(self.valid_data_size - 1))
step += 1
self.shared_step += 1
if self.epoch > self.args.start_evaluate_diff:
for arch in dags_eval:
valid_ppl_eval = _evaluate_valid(arch)
valid_ppls_after.append(valid_ppl_eval)
logger.info(f'valid_ppl {valid_ppl_eval}')
diff = np.array(valid_ppls_after) - np.array(valid_ppls)
logger.info(f'Mean_diff {np.mean(diff)}')
logger.info(f'Max_diff {np.amax(diff)}')
self.tb.scalar_summary(f'Mean difference', np.mean(diff),
self.epoch)
self.tb.scalar_summary(f'Max difference', np.amax(diff),
self.epoch)
self.tb.scalar_summary(f'Mean valid_ppl after training',
np.mean(np.array(valid_ppls_after)),
self.epoch)
self.tb.scalar_summary(f'Mean valid_ppl before training',
np.mean(np.array(valid_ppls)), self.epoch)
self.tb.scalar_summary(f'std_diff', np.std(np.array(diff)),
self.epoch)
def get_reward(self, dags, entropies, hidden, valid_idx=None):
"""
Computes the reward of a single sampled model or multiple on a minibatch of
validation data.
"""
if not isinstance(entropies, np.ndarray):
entropies = entropies.data.cpu().numpy()
if valid_idx is None:
valid_idx = 0
inputs, targets = self.get_batch(self.valid_data,
valid_idx,
self.max_length,
volatile=True)
_, valid_loss, _, hidden, _ = self.get_loss(inputs,
targets,
dags,
hidden=hidden)
valid_loss = utils.to_item(valid_loss.data)
valid_ppl = math.exp(valid_loss)
if self.args.ppl_square:
R = self.args.reward_c / valid_ppl**2
else:
R = self.args.reward_c / valid_ppl
if self.args.entropy_mode == 'reward':
rewards = R + self.args.entropy_coeff * entropies
elif self.args.entropy_mode == 'regularizer':
rewards = R * np.ones_like(entropies)
else:
raise NotImplementedError(
f'Unkown entropy mode: {self.args.entropy_mode}')
return rewards, hidden
def train_controller(self):
"""Fixes the shared parameters and updates the controller parameters.
The controller is updated with a score function gradient estimator
(i.e., REINFORCE), with the reward being c/valid_ppl, where valid_ppl
is computed on a minibatch of validation data.
A moving average baseline is used.
The controller is trained for 2000 steps per epoch (i.e.,
first (Train Shared) phase -> second (Train Controller) phase).
"""
model = self.controller
model.train()
avg_reward_base = None
baseline = None
adv_history = []
entropy_history = []
reward_history = []
hidden = self.shared.init_training(self.args.batch_size)
total_loss = 0
valid_idx = 0
for step in range(self.args.controller_max_step):
# print("************ train controller ****************")
# sample models
dags, log_probs, entropies = self.controller.sample(
batch_size=self.args.policy_batch_size, with_details=True)
# calculate reward
np_entropies = entropies.data.cpu().numpy()
with _get_no_grad_ctx_mgr():
rewards, hidden = self.get_reward(dags, np_entropies, hidden,
valid_idx)
# discount
if 1 > self.args.discount > 0:
rewards = discount(rewards, self.args.discount)
reward_history.extend(rewards)
entropy_history.extend(np_entropies)
# moving average baseline
if baseline is None:
baseline = rewards
else:
decay = self.args.ema_baseline_decay
baseline = decay * baseline + (1 - decay) * rewards
adv = rewards - baseline
adv_history.extend(adv)
# policy loss
loss = -log_probs * utils.get_variable(
adv, self.cuda, requires_grad=False)
if self.args.entropy_mode == 'regularizer':
loss -= self.args.entropy_coeff * entropies
loss = loss.sum() # or loss.mean()
# update
self.controller_optim.zero_grad()
loss.backward()
if self.args.controller_grad_clip > 0:
torch.nn.utils.clip_grad_norm(model.parameters(),
self.args.controller_grad_clip)
self.controller_optim.step()
total_loss += utils.to_item(loss.data)
if ((step % self.args.log_step) == 0) and (step > 0):
self._summarize_controller_train(total_loss, adv_history,
entropy_history,
reward_history,
avg_reward_base, dags)
reward_history, adv_history, entropy_history = [], [], []
total_loss = 0
self.controller_step += 1
prev_valid_idx = valid_idx
valid_idx = ((valid_idx + self.max_length) %
(self.valid_data_size - 1))
if prev_valid_idx > valid_idx:
hidden = self.shared.init_training(self.args.batch_size)
def evaluate(self, source, dag, name, batch_size=1, max_num=None):
"""Evaluate on the validation set.
"""
self.shared.eval()
self.controller.eval()
if self.image_dataset:
data = source
else:
data = source[:max_num * self.max_length]
total_loss = 0
hidden = self.shared.init_training(batch_size)
pbar = range(0, self.valid_data_size - 1, self.max_length)
for count, idx in enumerate(pbar):
inputs, targets = self.get_batch(data, idx, volatile=True)
output, hidden, _ = self.shared(inputs,
dag,
hidden=hidden,
is_train=False)
output_flat = output.view(-1, self.dataset.num_classes)
total_loss += len(inputs) * self.ce(output_flat, targets).data
hidden = utils.detach(hidden)
ppl = math.exp(
utils.to_item(total_loss) / (count + 1) / self.max_length)
val_loss = utils.to_item(total_loss) / len(data)
ppl = math.exp(val_loss)
self.tb.scalar_summary(f'eval/{name}_loss', val_loss, self.epoch)
self.tb.scalar_summary(f'eval/{name}_ppl', ppl, self.epoch)
logger.info(f'eval | loss: {val_loss:8.2f} | ppl: {ppl:8.2f}')
def derive(self, sample_num=None, valid_idx=0):
if sample_num is None:
sample_num = self.args.derive_num_sample
dags, _, entropies = self.controller.sample(sample_num,
with_details=True)
max_R = 0
best_dag = None
for dag in dags:
if self.image_dataset:
R, _ = self.get_reward([dag], entropies, valid_idx)
else:
hidden = self.shared.init_training(self.args.batch_size)
R, _ = self.get_reward(dag, entropies, hidden, valid_idx)
if R.max() > max_R:
max_R = R.max()
best_dag = dag
logger.info(f'derive | max_R: {max_R:8.6f}')
fname = (f'{self.epoch:03d}-{self.controller_step:06d}-'
f'{max_R:6.4f}-best.png')
path = os.path.join(self.args.model_dir, 'networks', fname)
success = self.draw_network(best_dag, path)
if success:
self.tb.image_summary('derive/best', [path], self.epoch)
return best_dag
def reset_dataloader_by_name(self, name):
""" Works for only reset _DataLoaderIter by DataLoader with name """
try:
new_iter = wrap_iterator_with_name(
iter(getattr(self, f'_{name}_data')), name)
setattr(self, f'{name}_data', new_iter)
except Exception as e:
print(e)
return new_iter
@property
def shared_lr(self):
degree = max(self.epoch - self.args.shared_decay_after + 1, 0)
return self.args.shared_lr * (self.args.shared_decay**degree)
@property
def controller_lr(self):
return self.args.controller_lr
def get_batch(self, source, idx, length=None, volatile=False):
# code from
# https://github.com/pytorch/examples/blob/master/word_language_model/main.py
if not self.image_dataset:
length = min(length if length else self.max_length,
len(source) - 1 - idx)
data = Variable(source[idx:idx + length], volatile=volatile)
target = Variable(source[idx + 1:idx + 1 + length].view(-1),
volatile=volatile)
else:
# Try the dataloader logic.
# type is _DataLoaderIter
try:
data, target = next(source)
except StopIteration as e:
print(f'{e}')
name = source.name
source = self.reset_dataloader_by_name(name)
data, target = next(source)
# data.to(self.device)
return data.to(self.device), target.to(self.device)
return data, target
@property
def shared_path(self):
return f'{self.args.model_dir}/shared_epoch{self.epoch}_step{self.shared_step}.pth'
@property
def controller_path(self):
return f'{self.args.model_dir}/controller_epoch{self.epoch}_step{self.controller_step}.pth'
def get_saved_models_info(self):
paths = glob.glob(os.path.join(self.args.model_dir, '*.pth'))
paths.sort()
def get_numbers(items, delimiter, idx, replace_word, must_contain=''):
return list(
set([
int(name.split(delimiter)[idx].replace(replace_word, ''))
for name in basenames if must_contain in name
]))
basenames = [
os.path.basename(path.rsplit('.', 1)[0]) for path in paths
]
epochs = get_numbers(basenames, '_', 1, 'epoch')
shared_steps = get_numbers(basenames, '_', 2, 'step', 'shared')
controller_steps = get_numbers(basenames, '_', 2, 'step', 'controller')
epochs.sort()
shared_steps.sort()
controller_steps.sort()
return epochs, shared_steps, controller_steps
def save_model(self):
torch.save(self.shared.state_dict(), self.shared_path)
logger.info(f'[*] SAVED: {self.shared_path}')
torch.save(self.controller.state_dict(), self.controller_path)
logger.info(f'[*] SAVED: {self.controller_path}')
epochs, shared_steps, controller_steps = self.get_saved_models_info()
for epoch in epochs[:-self.args.max_save_num]:
paths = glob.glob(
os.path.join(self.args.model_dir, f'*_epoch{epoch}_*.pth'))
for path in paths:
utils.remove_file(path)
def load_model(self):
epochs, shared_steps, controller_steps = self.get_saved_models_info()
if len(epochs) == 0:
logger.info(f'[!] No checkpoint found in {self.args.model_dir}...')
return
self.epoch = self.start_epoch = max(epochs)
self.shared_step = max(shared_steps)
self.controller_step = max(controller_steps)
if self.args.num_gpu == 0:
map_location = lambda storage, loc: storage
else:
map_location = None
self.shared.load_state_dict(
torch.load(self.shared_path, map_location=map_location))
logger.info(f'[*] LOADED: {self.shared_path}')
self.controller.load_state_dict(
torch.load(self.controller_path, map_location=map_location))
logger.info(f'[*] LOADED: {self.controller_path}')
def _summarize_controller_train(self, total_loss, adv_history,
entropy_history, reward_history,
avg_reward_base, dags):
"""Logs the controller's progress for this training epoch."""
cur_loss = total_loss / self.args.log_step
avg_adv = np.mean(adv_history)
avg_entropy = np.mean(entropy_history)
avg_reward = np.mean(reward_history)
if avg_reward_base is None:
avg_reward_base = avg_reward
logger.info(f'| epoch {self.epoch:3d} | lr {self.controller_lr:.5f} '
f'| R {avg_reward:.5f} | entropy {avg_entropy:.4f} '
f'| loss {cur_loss:.5f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary('controller/loss', cur_loss,
self.controller_step)
self.tb.scalar_summary('controller/reward', avg_reward,
self.controller_step)
self.tb.scalar_summary('controller/std/reward',
np.std(reward_history),
self.controller_step)
self.tb.scalar_summary('controller/reward-B_per_epoch',
avg_reward - avg_reward_base,
self.controller_step)
self.tb.scalar_summary('controller/entropy', avg_entropy,
self.controller_step)
self.tb.scalar_summary('controller/adv', avg_adv,
self.controller_step)
paths = []
for dag in dags:
fname = (f'{self.epoch:03d}-{self.controller_step:06d}-'
f'{avg_reward:6.4f}.png')
path = os.path.join(self.args.model_dir, 'networks', fname)
self.draw_network(dag, path)
paths.append(path)
self.tb.image_summary('controller/sample', paths,
self.controller_step)
def _summarize_shared_train(self, total_loss, raw_total_loss):
"""Logs a set of training steps."""
cur_loss = utils.to_item(total_loss) / self.args.log_step
cur_raw_loss = utils.to_item(raw_total_loss) / self.args.log_step
try:
ppl = math.exp(cur_raw_loss)
except RuntimeError as e:
print(f"Got error {e}")
logger.info(f'| epoch {self.epoch:3d} '
f'| lr {self.shared_lr:4.2f} '
f'| raw loss {cur_raw_loss:.2f} '
f'| loss {cur_loss:.2f} '
f'| ppl {ppl:8.2f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary('shared/loss', cur_loss, self.shared_step)
self.tb.scalar_summary('shared/perplexity', ppl, self.shared_step)
class Trainer(object):
"""A class to wrap training code."""
def __init__(self, args, dataset):
"""Constructor for training algorithm.
Args:
args: From command line, picked up by `argparse`.
dataset: Currently only `data.text.Corpus` is supported.
Initializes:
- Data: train, val and test.
- Model: shared and controller.
- Inference: optimizers for shared and controller parameters.
- Criticism: cross-entropy loss for training the shared model.
"""
self.args = args
self.controller_step = 0
self.cuda = args.cuda
self.dataset = dataset
self.epoch = 0
self.shared_step = 0
self.start_epoch = 0
logger.info('regularizing:')
for regularizer in [('activation regularization',
self.args.activation_regularization),
('temporal activation regularization',
self.args.temporal_activation_regularization),
('norm stabilizer regularization',
self.args.norm_stabilizer_regularization)]:
if regularizer[1]:
logger.info('{0}'.format(regularizer[0]))
self.train_data = utils.batchify(dataset.train, args.batch_size,
self.cuda)
# NOTE(brendan): The validation set data is batchified twice
# separately: once for computing rewards during the Train Controller
# phase (valid_data, batch size == 64), and once for evaluating ppl
# over the entire validation set (eval_data, batch size == 1)
self.valid_data = utils.batchify(dataset.valid, args.batch_size,
self.cuda)
self.eval_data = utils.batchify(dataset.valid, args.test_batch_size,
self.cuda)
self.test_data = utils.batchify(dataset.test, args.test_batch_size,
self.cuda)
self.max_length = self.args.shared_rnn_max_length # default=35
if args.use_tensorboard:
self.tb = TensorBoard(args.model_dir)
else:
self.tb = None
self.build_model() # 创建一个模型存入self.shared中，这里可以是RNN或CNN，再创建一个Controler
if self.args.load_path:
self.load_model()
shared_optimizer = _get_optimizer(self.args.shared_optim)
controller_optimizer = _get_optimizer(self.args.controller_optim)
self.shared_optim = shared_optimizer(
self.shared.parameters(),
lr=self.shared_lr,
weight_decay=self.args.shared_l2_reg)
self.controller_optim = controller_optimizer(
self.controller.parameters(), lr=self.args.controller_lr)
self.ce = nn.CrossEntropyLoss()
def build_model(self):
"""Creates and initializes the shared and controller models."""
if self.args.network_type == 'rnn':
self.shared = models.RNN(self.args, self.dataset)
elif self.args.network_type == 'cnn':
self.shared = models.CNN(self.args, self.dataset)
else:
raise NotImplementedError(
'Network type `{0}` is not defined'.format(
self.args.network_type))
self.controller = models.Controller(
self.args
) # 构建了一个orward：Embedding（130,100）->lstm（100,100）->decoder的列表，对应25个decoder
if self.args.num_gpu == 1:
self.shared.cuda()
self.controller.cuda()
elif self.args.num_gpu > 1:
raise NotImplementedError('`num_gpu > 1` is in progress')
def train(self, single=False):
"""Cycles through alternately training the shared parameters and the
controller, as described in Section 2.2, Training ENAS and Deriving
Architectures, of the paper.
From the paper (for Penn Treebank):
- In the first phase, shared parameters omega are trained for 400
steps, each on a minibatch of 64 examples.
- In the second phase, the controller's parameters are trained for 2000
steps.
Args:
single (bool): If True it won't train the controller and use the
same dag instead of derive().
"""
dag = utils.load_dag(self.args) if single else None # 初始训练dag=None
if self.args.shared_initial_step > 0: # self.args.shared_initial_step default=0
self.train_shared(self.args.shared_initial_step)
self.train_controller()
for self.epoch in range(
self.start_epoch,
self.args.max_epoch): # start_epoch=0,max_epoch=150
# 1. Training the shared parameters omega of the child models
# 训练RNN，先用Controller随机生成一个dag，然后用这个dag构建一个RNNcell，然后用这个RNNcell去做下一个词预测，得到loss
self.train_shared(dag=dag)
# 2. Training the controller parameters theta
if not single:
self.train_controller()
if self.epoch % self.args.save_epoch == 0:
with _get_no_grad_ctx_mgr():
best_dag = dag if dag else self.derive()
self.evaluate(self.eval_data,
best_dag,
'val_best',
max_num=self.args.batch_size * 100)
self.save_model()
#应该是逐渐降低学习率
if self.epoch >= self.args.shared_decay_after:
utils.update_lr(self.shared_optim, self.shared_lr)
def get_loss(self, inputs, targets, hidden, dags):
"""
:param inputs:输入数据，[35,64]
:param targets: 目标数据（相当于标签）[35,64] 输入的词后移一个词
:param hidden: 隐藏层参数
:param dags: RNN 的cell结构
:return: decoded(35,64,10000),hidden(64,1000),extra_out{dropped_output(35,64,1000),h1tohT(35,64,1000),raw_output(35,64,1000)
"""
"""Computes the loss for the same batch for M models.
This amounts to an estimate of the loss, which is turned into an
estimate for the gradients of the shared model.
"""
if not isinstance(dags, list):
dags = [dags]
loss = 0
for dag in dags:
# decoded(35,64,10000),hidden(64,1000),extra_out{dropped_output(35,64,1000),h1tohT(35,64,1000),raw_output(35,64,1000)
output, hidden, extra_out = self.shared(
inputs, dag, hidden=hidden) # RNN.forward
output_flat = output.view(-1,
self.dataset.num_tokens) # (2240,10000)
# self.ce=nn.CrossEntropyLoss() target(2240) shared_num_sample=1
sample_loss = (self.ce(output_flat, targets) /
self.args.shared_num_sample)
loss += sample_loss
assert len(dags) == 1, 'there are multiple `hidden` for multple `dags`'
return loss, hidden, extra_out
def train_shared(self, max_step=None, dag=None):
"""Train the language model for 400 steps of minibatches of 64
examples.
Args:
max_step: Used to run extra training steps as a warm-up.
dag: If not None, is used instead of calling sample().
BPTT is truncated at 35 timesteps. #基于时间的反向传播算法BPTT(Back Propagation Trough Time)
For each weight update, gradients are estimated by sampling M models
from the fixed controller policy, and averaging their gradients
computed on a batch of training data.
"""
model = self.shared # model.RNN
model.train(
) # set RNN.training属性为true 即当前训练的是RNN而不训练Controller https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module.train
self.controller.eval(
) # Sets the module in evaluation mode. This is equivalent with self.train(False).
# 功能：初始化variable，即全零的Tensor
hidden = self.shared.init_hidden(self.args.batch_size)
if max_step is None:
max_step = self.args.shared_max_step # shared_max_step=150
else:
max_step = min(self.args.shared_max_step, max_step)
abs_max_grad = 0
abs_max_hidden_norm = 0
step = 0
raw_total_loss = 0 # 用于统计结果的，和计算过程无关
total_loss = 0
train_idx = 0
# TODO(brendan): Why - 1 - 1?为什么-1-1？
# TODO(为什么-1-1)这里的train_idx是批次的编号，一共14524个batch（每个batch有64个词）为了训练输入数据不可能取最后一个batch
# TODO(为什么-1-1)因为如果是最后一个batch就没有target了，因此最后一个batch是倒数第二个，而倒数第二个的下标是 size-2
# self.train_data.size(0) 14524
while train_idx < self.train_data.size(0) - 1 - 1:
if step > max_step:
break
# Controller负责sample一个dag出来，是一个list，里面有一个defaultdict，存储了dag的连接信息
# 这一步只是提取Controller的值，并没有训练，初始的时候也是随机得出来的一个dag
dags = dag if dag else self.controller.sample(
batch_size=self.args.shared_num_sample
) # shared_num_sample:default=1
# 提取一个max_length长度的数据集（35,64），35个批次，每个批次64个词，组成一个训练批次
# input是训练数据，target是每个输入的词后面的词，用于训练RNN的
inputs, targets = self.get_batch(self.train_data, train_idx,
self.max_length) # max_length=35
# get_loss完成了由dag生成的RNNcell的前向计算
loss, hidden, extra_out = self.get_loss(inputs, targets, hidden,
dags)
# Detaches the Tensor from the graph that created it, making it a leaf. Views cannot be detached in-place.
hidden.detach_()
raw_total_loss += loss.data
# 根据命令行参数加一下正则惩罚项
loss += _apply_penalties(extra_out, self.args)
# update
self.shared_optim.zero_grad()
loss.backward() # 反向更新
h1tohT = extra_out['hiddens']
# 和日志有关，和计算无关
new_abs_max_hidden_norm = utils.to_item(
h1tohT.norm(dim=-1).data.max())
if new_abs_max_hidden_norm > abs_max_hidden_norm:
abs_max_hidden_norm = new_abs_max_hidden_norm
logger.info('max hidden {0}'.format(abs_max_hidden_norm))
# 函数的功能是获取Tensor图中的最大梯度，来检测是否出现梯度爆炸，但好像后面没有使用
abs_max_grad = _check_abs_max_grad(abs_max_grad, model)
# Clips gradient norm of an iterable of parameters.
# The norm is computed over all gradients together, as if they were concatenated into a single vector.
# Gradients are modified in-place.
torch.nn.utils.clip_grad_norm(
model.parameters(),
self.args.shared_grad_clip) # shared_grad_clip=0.25
self.shared_optim.step() # Performs a single optimization step.
total_loss += loss.data
# 和log有关
if ((step % self.args.log_step) == 0) and (step > 0):
self._summarize_shared_train(total_loss, raw_total_loss)
raw_total_loss = 0
total_loss = 0
step += 1
self.shared_step += 1
train_idx += self.max_length # max_length:35，下一个batch
def get_reward(self, dag, entropies, hidden, valid_idx=0):
"""Computes the perplexity of a single sampled model on a minibatch of
validation data.
计算模型的PPL：每个词的条件预测概率（即已知前n个词预测第n+1个词的概率）的累积的倒数开N（全体词的数量）次方
"""
if not isinstance(entropies, np.ndarray):
entropies = entropies.data.cpu().numpy()
inputs, targets = self.get_batch(self.valid_data,
valid_idx,
self.max_length,
volatile=True)
valid_loss, hidden, _ = self.get_loss(inputs, targets, hidden,
dag) #RNN.forward
valid_loss = utils.to_item(valid_loss.data)
valid_ppl = math.exp(valid_loss) #计算PPL
# TODO: we don't know reward_c
if self.args.ppl_square: #default:false
# TODO: but we do know reward_c=80 in the previous paper
R = self.args.reward_c / valid_ppl**2
else:
R = self.args.reward_c / valid_ppl #这个值的作用在NAS(Zoph and Le, 2017) page 8 states that c is a constant
if self.args.entropy_mode == 'reward': #entroy_mode：default：reward
rewards = R + self.args.entropy_coeff * entropies # entropy_coeff：default=1e-4
elif self.args.entropy_mode == 'regularizer':
rewards = R * np.ones_like(entropies)
else:
raise NotImplementedError('Unkown entropy mode: {0}'.format(
self.args.entropy_mode))
return rewards, hidden
def train_controller(self):
"""Fixes the shared parameters and updates the controller parameters.
The controller is updated with a score function gradient estimator
(i.e., REINFORCE), with the reward being c/valid_ppl, where valid_ppl
is computed on a minibatch of validation data.
A moving average baseline is used.
The controller is trained for 2000 steps per epoch (i.e.,
first (Train Shared) phase -> second (Train Controller) phase).
"""
model = self.controller
model.train() # 设置Controller的train属性为true，当前训练Controller
# 这里为什么不调用hared.eval()? 这是因为会导致Controller的loss一直为零。
# self.shared.eval()，上面的解释应该是Brendon这个人测试之后的结论
avg_reward_base = None
baseline = None
# 这几个是用于统计信息的
adv_history = []
entropy_history = []
reward_history = []
hidden = self.shared.init_hidden(self.args.batch_size)
total_loss = 0
valid_idx = 0
for step in range(self.args.controller_max_step): #controller_max_step
# sample models
#dags:list([1])(defaultdict([25])),log_probs:Tensor.size([23]),entropies:Tensor.size([23])交叉熵：-ylogy
dags, log_probs, entropies = self.controller.sample(
with_details=True)
# calculate reward
np_entropies = entropies.data.cpu().numpy()
# NOTE(brendan): No gradients should be backpropagated to the
# shared model during controller training, obviously.
"""
with 语句实质是上下文管理。
1、上下文管理协议。包含方法__enter__() 和 __exit__()，支持该协议对象要实现这两个方法。
2、上下文管理器，定义执行with语句时要建立的运行时上下文，负责执行with语句块上下文中的进入与退出操作。
3、进入上下文的时候执行__enter__方法，如果设置as var语句，var变量接受__enter__()方法返回值。
4、如果运行时发生了异常，就退出上下文管理器。调用管理器__exit__方法。
"""
# 创建了一个torch.no_grad()的上下文，执行get_reward的时候是不需要计算梯度的，执行完get_reward在恢复计算梯度模式
with _get_no_grad_ctx_mgr():
rewards, hidden = self.get_reward(dags, np_entropies, hidden,
valid_idx)
# discount 默认未启用
if 1 > self.args.discount > 0: #discout:default=1
rewards = discount(rewards, self.args.discount)
reward_history.extend(rewards)
entropy_history.extend(np_entropies)
# moving average baseline
if baseline is None:
baseline = rewards
else:
decay = self.args.ema_baseline_decay #****ema_baseline_decay:default=0.95 very important
baseline = decay * baseline + (1 - decay) * rewards
adv = rewards - baseline
adv_history.extend(adv)
# policy loss
loss = -log_probs * utils.get_variable(
adv, self.cuda, requires_grad=False)
if self.args.entropy_mode == 'regularizer': #entropy_mode:default='reward'
loss -= self.args.entropy_coeff * entropies
loss = loss.sum() # or loss.mean()
# update
self.controller_optim.zero_grad()
loss.backward()
if self.args.controller_grad_clip > 0:
torch.nn.utils.clip_grad_norm(model.parameters(),
self.args.controller_grad_clip)
self.controller_optim.step()
total_loss += utils.to_item(loss.data)
if ((step % self.args.log_step) == 0) and (step > 0):
self._summarize_controller_train(total_loss, adv_history,
entropy_history,
reward_history,
avg_reward_base, dags)
reward_history, adv_history, entropy_history = [], [], []
total_loss = 0
self.controller_step += 1
prev_valid_idx = valid_idx
valid_idx = ((valid_idx + self.max_length) %
(self.valid_data.size(0) - 1))
# NOTE(brendan): Whenever we wrap around to the beginning of the
# validation data, we reset the hidden states.
if prev_valid_idx > valid_idx:
hidden = self.shared.init_hidden(self.args.batch_size)
def evaluate(self, source, dag, name, batch_size=1, max_num=None):
"""Evaluate on the validation set.
NOTE(brendan): We should not be using the test set to develop the
algorithm (basic machine learning good practices).
"""
self.shared.eval()
self.controller.eval()
data = source[:max_num * self.max_length]
total_loss = 0
hidden = self.shared.init_hidden(batch_size)
pbar = range(0, data.size(0) - 1, self.max_length)
for count, idx in enumerate(pbar):
inputs, targets = self.get_batch(data, idx, volatile=True)
output, hidden, _ = self.shared(inputs,
dag,
hidden=hidden,
is_train=False)
output_flat = output.view(-1, self.dataset.num_tokens)
total_loss += len(inputs) * self.ce(output_flat, targets).data
hidden.detach_()
ppl = math.exp(
utils.to_item(total_loss) / (count + 1) / self.max_length)
val_loss = utils.to_item(total_loss) / len(data)
ppl = math.exp(val_loss)
self.tb.scalar_summary('eval/{0}_loss'.format(name), val_loss,
self.epoch)
self.tb.scalar_summary('eval/{0}_ppl'.format(name), ppl, self.epoch)
logger.info('eval | loss: {0:8.2f} | ppl: {1:8.2f}'.format(
val_loss, ppl))
def derive(self, sample_num=None, valid_idx=0):
"""TODO(brendan): We are always deriving based on the very first batch
of validation data? This seems wrong...
"""
hidden = self.shared.init_hidden(self.args.batch_size)
if sample_num is None:
sample_num = self.args.derive_num_sample
dags, _, entropies = self.controller.sample(sample_num,
with_details=True)
max_R = 0
best_dag = None
for dag in dags:
R, _ = self.get_reward(dag, entropies, hidden, valid_idx)
if R.max() > max_R:
max_R = R.max()
best_dag = dag
logger.info('derive | max_R: {0:8.6f}'.format(max_R))
fname = ('{0:03d}-{1:06d}-{2:6.4f}-best.png'.format(
self.epoch, self.controller_step, max_R))
path = os.path.join(self.args.model_dir, 'networks', fname)
#utils.draw_network(best_dag, path)
#self.tb.image_summary('derive/best', [path], self.epoch)
return best_dag
@property
def shared_lr(self):
degree = max(self.epoch - self.args.shared_decay_after + 1, 0)
return self.args.shared_lr * (self.args.shared_decay**degree)
@property #将类方法转换为类属性，可以用 . 直接获取属性值或者对属性进行赋值
def controller_lr(self):
return self.args.controller_lr
def get_batch(self, source, idx, length=None, volatile=False):
"""
这个函数的作用是从数据集中取得length长度的数据组成一个Variable（这个操作在pytorch中已经过时了，可以直接使用Tensor来生成计算，而不用
再使用Variable来封装Tensor来计算
这里的batch指的是取词窗口组成的batch，length是最多取多少个batch_size的词
:param source:数据集train_data
:param idx: 当前数据样本索引值
:param length:max_length=35？
:param volatile（易变的）:Volatile is recommended for purely inference mode, when you’re sure you won’t be even calling .backward()
设定volatie选项为true的话则只是取值模式，而不会进行反向计算
:return:
"""
# code from
# https://github.com/pytorch/examples/blob/master/word_language_model/main.py
length = min(length if length else self.max_length,
len(source) - 1 - idx)
#UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
data = Variable(source[idx:idx + length],
volatile=volatile) # shape(35,64) 取35个批次，每个批次64个词
target = Variable(source[idx + 1:idx + 1 + length].view(-1),
volatile=volatile) # view（35,64）->(2240)
# 这里target=data+1的意思是从data中推断下一个词
return data, target
@property
def shared_path(self):
return '{0}/shared_epoch{1:d}_step{2:d}.pth'.format(
self.args.model_dir, self.epoch, self.shared_step)
@property
def controller_path(self):
return '{}/controller_epoch{}_step{}.pth'.format(
self.args.model_dir, self.epoch, self.controller_step)
def get_saved_models_info(self):
paths = glob.glob(os.path.join(self.args.model_dir, '*.pth'))
paths.sort()
def get_numbers(items, delimiter, idx, replace_word, must_contain=''):
return list(
set([
int(name.split(delimiter)[idx].replace(replace_word, ''))
for name in basenames if must_contain in name
]))
basenames = [
os.path.basename(path.rsplit('.', 1)[0]) for path in paths
]
epochs = get_numbers(basenames, '_', 1, 'epoch')
shared_steps = get_numbers(basenames, '_', 2, 'step', 'shared')
controller_steps = get_numbers(basenames, '_', 2, 'step', 'controller')
epochs.sort()
shared_steps.sort()
controller_steps.sort()
return epochs, shared_steps, controller_steps
def save_model(self):
torch.save(self.shared.state_dict(), self.shared_path)
logger.info('[*] SAVED: {0}'.format(self.shared_path))
torch.save(self.controller.state_dict(), self.controller_path)
logger.info('[*] SAVED: {0}'.format(self.controller_path))
epochs, shared_steps, controller_steps = self.get_saved_models_info()
for epoch in epochs[:-self.args.max_save_num]:
paths = glob.glob(
os.path.join(self.args.model_dir,
'*_epoch{0}_*.pth'.format(epoch)))
for path in paths:
utils.remove_file(path)
def load_model(self):
epochs, shared_steps, controller_steps = self.get_saved_models_info()
if len(epochs) == 0:
logger.info('[!] No checkpoint found in {0}...'.format(
self.args.model_dir))
return
self.epoch = self.start_epoch = max(epochs)
self.shared_step = max(shared_steps)
self.controller_step = max(controller_steps)
if self.args.num_gpu == 0:
map_location = lambda storage, loc: storage
else:
map_location = None
self.shared.load_state_dict(
torch.load(self.shared_path, map_location=map_location))
logger.info('[*] LOADED: {0}'.format(self.shared_path))
self.controller.load_state_dict(
torch.load(self.controller_path, map_location=map_location))
logger.info('[*] LOADED: {0}'.format(self.controller_path))
def _summarize_controller_train(self, total_loss, adv_history,
entropy_history, reward_history,
avg_reward_base, dags):
"""Logs the controller's progress for this training epoch."""
cur_loss = total_loss / self.args.log_step
avg_adv = np.mean(adv_history)
avg_entropy = np.mean(entropy_history)
avg_reward = np.mean(reward_history)
if avg_reward_base is None:
avg_reward_base = avg_reward
logger.info(
'| epoch {0:3d} | lr {1:.5f} | R {2:.5f} | entropy {3:.4f} | loss {:.5f}'
.format(self.epoch, self.controller_lr, avg_reward, avg_entropy,
cur_loss))
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary('controller/loss', cur_loss,
self.controller_step)
self.tb.scalar_summary('controller/reward', avg_reward,
self.controller_step)
self.tb.scalar_summary('controller/reward-B_per_epoch',
avg_reward - avg_reward_base,
self.controller_step)
self.tb.scalar_summary('controller/entropy', avg_entropy,
self.controller_step)
self.tb.scalar_summary('controller/adv', avg_adv,
self.controller_step)
paths = []
for dag in dags:
fname = ('{0:03d}-{1:06d}-{2:6.4f}.png'.format(
self.epoch, self.controller_step, avg_reward))
path = os.path.join(self.args.model_dir, 'networks', fname)
utils.draw_network(dag, path)
paths.append(path)
self.tb.image_summary('controller/sample', paths,
self.controller_step)
def _summarize_shared_train(self, total_loss, raw_total_loss):
"""Logs a set of training steps."""
cur_loss = utils.to_item(total_loss) / self.args.log_step
# NOTE(brendan): The raw loss, without adding in the activation
# regularization terms, should be used to compute ppl.
cur_raw_loss = utils.to_item(raw_total_loss) / self.args.log_step
ppl = math.exp(cur_raw_loss)
logger.info(
'| epoch {0:3d} | lr {1:4.2f} | raw loss {2:.2f} | loss {3:.2f} | ppl {4:8.2f}'
.format(self.epoch, self.shared_lr, cur_raw_loss, cur_loss, ppl))
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary('shared/loss', cur_loss, self.shared_step)
self.tb.scalar_summary('shared/perplexity', ppl, self.shared_step)
def main(args):
# Step 1: init data folders
'''if os.path.exists('save_state/'+args.regime+'/normalization_stats.pkl'):
print('Loading normalization stats')
x_mean, x_sd = misc.load_file('save_state/'+args.regime+'/normalization_stats.pkl')
else:
x_mean, x_sd = preprocess.save_normalization_stats(args.regime)
print('x_mean: %.3f, x_sd: %.3f' % (x_mean, x_sd))'''
data_dir = args.datapath
data_files = []
for x in os.listdir(data_dir):
for y in os.listdir(data_dir + x):
data_files.append(data_dir + x + "/" + y)
test_files = [
data_file for data_file in data_files
if 'val' in data_file and 'npz' in data_file
]
train_files = [
data_file for data_file in data_files
if 'train' in data_file and 'npz' in data_file
]
print("train_num:", len(train_files), "test_num:", len(test_files))
train_loader = torch.utils.data.DataLoader(Dataset(args, train_files),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.numwork) #
test_loader = torch.utils.data.DataLoader(Dataset(args, test_files),
batch_size=args.batch_size,
num_workers=args.numwork)
tb = TensorBoard(args.model_dir)
# Step 2: init neural networks
print("network is:", args.net)
if args.net == 'Reab3p16':
model = Reab3p16(args)
if args.gpunum > 1:
model = nn.DataParallel(model, device_ids=range(args.gpunum))
weights_path = args.path_weight
if os.path.exists(weights_path):
pretrained_dict = torch.load(weights_path)
model_dict = model.state_dict()
pretrained_dict1 = {}
for k, v in pretrained_dict.items():
if k in model_dict:
pretrained_dict1[k] = v
#print(k)
model_dict.update(pretrained_dict1)
model.load_state_dict(model_dict)
print('load weight: ' + weights_path)
model.cuda()
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.mo,
weight_decay=5e-4)
#optimizer = optim.Adam(model.parameters(), lr=args.lr)
if args.gpunum > 1:
optimizer = nn.DataParallel(optimizer, device_ids=range(args.gpunum))
iter_count = 1
epoch_count = 1
#iter_epoch=int(len(train_files) / args.batch_size)
print(time.strftime('%H:%M:%S', time.localtime(time.time())), 'training')
while True:
since = time.time()
with torch.no_grad():
model.eval()
accuracy_all = []
for x, y, style, me in test_loader:
x, y = Variable(x).cuda(), Variable(y).cuda()
pred = model(x)
pred = pred.data.max(1)[1]
correct = pred.eq(y.data).cpu().numpy()
accuracy = correct.sum() * 100.0 / len(y)
accuracy_all.append(accuracy)
accuracy_all = sum(accuracy_all) / len(accuracy_all)
reward = accuracy_all * 100
tb.scalar_summary("test_acc", reward, epoch_count)
# np.expand_dims(, axis=0)
time_elapsed = time.time() - since
print('test epoch in {:.0f}h {:.0f}m {:.0f}s'.format(
time_elapsed // 3600, time_elapsed // 60 % 60, time_elapsed % 60))
print('------------------------------------')
print(('epoch:%d, acc:%.1f') % (epoch_count, accuracy_all))
print('------------------------------------')
model.train()
iter_epoch = int(len(train_files) / args.batch_size)
for x, y, style, me in train_loader:
if x.shape[0] < 10:
print(x.shape[0])
break
x, y = Variable(x).cuda(), Variable(y).cuda()
if args.gpunum > 1:
optimizer.module.zero_grad()
else:
optimizer.zero_grad()
pred = model(x)
loss = F.nll_loss(pred, y, reduce=False)
#train_loss=loss
loss = loss.mean()
loss.backward()
if args.gpunum > 1:
optimizer.module.step()
else:
optimizer.step()
iter_count += 1
pred = pred.data.max(1)[1]
correct = pred.eq(y.data).cpu()
accuracy_total = correct.sum() * 100.0 / len(y)
if iter_count % 100 == 0:
iter_c = iter_count % iter_epoch
print(
time.strftime('%H:%M:%S', time.localtime(time.time())),
('train_epoch:%d,iter_count:%d/%d, loss:%.3f, acc:%.1f') %
(epoch_count, iter_c, iter_epoch, loss, accuracy_total))
tb.scalar_summary("train_loss", loss, iter_count)
#print(acc_part_train)
if epoch_count % args.lr_step == 0:
print("change lr")
adjust_learning_rate(optimizer, epoch_count, args.lr_step,
args.gpunum)
time_elapsed = time.time() - since
print('train epoch in {:.0f}h {:.0f}m {:.0f}s'.format(
time_elapsed // 3600, time_elapsed // 60 % 60, time_elapsed % 60))
#acc_p=np.array([x[0]/x[1] for x in acc_part])
#print(acc_p)
epoch_count += 1
if epoch_count % 1 == 0:
print("save!!!!!!!!!!!!!!!!")
save_state(model.state_dict(),
args.model_dir + "/epoch" + str(epoch_count))
class Trainer(object):
"""A class to wrap training code."""
def __init__(self,
dataset,
n_tranformers,
n_scalers,
n_constructers,
n_selecters,
n_models,
lstm_size,
temperature,
tanh_constant,
save_dir,
func_names,
controller_max_step=100,
controller_grad_clip=0,
optimizer='sgd',
controller_lr=0.001,
entropy_weight=0.001,
ema_baseline_decay=0.95,
use_tensorboard=True,
model_dir=None,
log_step=10):
self.dataset = dataset
self.controller_max_step = controller_max_step
self.controller_grad_clip = controller_grad_clip
self.n_tranformers = n_tranformers
self.n_scalers = n_scalers
self.n_constructers = n_constructers
self.n_selecters = n_selecters
self.n_models = n_models
self.lstm_size = lstm_size
self.temperature = temperature
self.tanh_constant = tanh_constant
self.save_dir = save_dir
self.optimizer = optimizer
self.controller_lr = controller_lr
self.entropy_weight = entropy_weight
self.ema_baseline_decay = ema_baseline_decay
self.func_names = func_names
self.use_tensorboard = use_tensorboard
self.log_step = log_step
self.model_dir = model_dir
if self.use_tensorboard:
self.tb = TensorBoard(self.model_dir)
else:
self.tb = None
self.controller_step = 0
def get_reward(self, actions):
reward = models.fit(actions, self.dataset)
return reward
def random_actions(self):
num_tokens = [
self.n_tranformers, self.n_scalers, self.n_constructers,
self.n_selecters, self.n_models
]
skip_index = [np.random.randint(i, size=1) for i in range(1, 5)]
func_index = [np.random.randint(i, size=1) for i in num_tokens]
actions = []
for x in range(4):
actions.append(skip_index[x][0])
actions.append(func_index[x][0])
actions.append(func_index[-1][0])
return actions
def train_controller(self):
avg_reward_base = None
baseline = None
adv_history = []
entropy_history = []
reward_history = []
controller = models.Controller(self.n_tranformers, self.n_scalers,
self.n_constructers, self.n_selecters,
self.n_models, self.func_names,
self.lstm_size, self.temperature,
self.tanh_constant, self.save_dir)
controller_optimizer = _get_optimizer(self.optimizer)
controller_optim = controller_optimizer(controller.parameters(),
lr=self.controller_lr)
controller.train()
total_loss = 0
results_dag = []
results_acc = []
random_history = []
acc_history = []
for step in range(self.controller_max_step):
# sample models
dags, actions, sample_entropy, sample_log_probs = controller()
sample_entropy = torch.sum(sample_entropy)
sample_log_probs = torch.sum(sample_log_probs)
# print(sample_log_probs)
print(actions)
random_actions = self.random_actions()
with torch.no_grad():
acc = self.get_reward(actions)
random_acc = self.get_reward(torch.LongTensor(random_actions))
random_history.append(random_acc)
results_acc.append(acc)
results_dag.append(dags)
acc_history.append(acc)
rewards = torch.tensor(acc)
if self.entropy_weight is not None:
rewards += self.entropy_weight * sample_entropy
reward_history.append(rewards)
entropy_history.append(sample_entropy)
# moving average baseline
if baseline is None:
baseline = rewards
else:
decay = self.ema_baseline_decay
baseline = decay * baseline + (1 - decay) * rewards
adv = rewards - baseline
adv_history.append(adv)
# policy loss
loss = sample_log_probs * adv
# update
controller_optim.zero_grad()
loss.backward()
if self.controller_grad_clip > 0:
torch.nn.utils.clip_grad_norm(controller.parameters(),
self.controller_grad_clip)
controller_optim.step()
total_loss += loss.item()
if ((step % self.log_step) == 0) and (step > 0):
self._summarize_controller_train(total_loss, adv_history,
entropy_history,
reward_history, acc_history,
random_history,
avg_reward_base, dags)
reward_history, adv_history, entropy_history,acc_history,random_history = [], [], [],[],[]
total_loss = 0
self.controller_step += 1
max_acc = np.max(results_acc)
max_dag = results_dag[np.argmax(results_acc)]
path = os.path.join(self.model_dir, 'networks', 'best.png')
utils.draw_network(max_dag[0], path)
# np.sort(results_acc)[-10:]
return np.sort(list(set(results_acc)))[-10:]
def _summarize_controller_train(self, total_loss, adv_history,
entropy_history, reward_history,
acc_history, random_history,
avg_reward_base, dags):
"""Logs the controller's progress for this training epoch."""
cur_loss = total_loss / self.log_step
avg_adv = np.mean(adv_history)
avg_entropy = np.mean(entropy_history)
avg_reward = np.mean(reward_history)
avg_acc = np.mean(acc_history)
avg_random = np.mean(random_history)
if avg_reward_base is None:
avg_reward_base = avg_reward
logger.info(f'| lr {self.controller_lr:.5f} '
f'| R {avg_reward:.5f} | entropy {avg_entropy:.4f} '
f'| loss {cur_loss:.5f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary('controller/loss', cur_loss,
self.controller_step)
self.tb.scalar_summary('controller/reward', avg_reward,
self.controller_step)
self.tb.scalar_summary('controller/reward-B_per_epoch',
avg_reward - avg_reward_base,
self.controller_step)
self.tb.scalar_summary('controller/entropy', avg_entropy,
self.controller_step)
self.tb.scalar_summary('controller/adv', avg_adv,
self.controller_step)
self.tb.scalar_summary('controller/acc', avg_acc,
self.controller_step)
self.tb.scalar_summary('controller/random', avg_random,
self.controller_step)
paths = []
class Trainer(object):
"""A class to wrap training code."""
def __init__(self, args, dataset):
"""Constructor for training algorithm.
Args:
args: From command line, picked up by 'argparse'
dataset: Currently only `data.text.Corpus` is supported.
Initializes:
- Data: train, val and test.
- Model: shared and controller.
- Inference: optimizers for shared and controller parameters.
- Criticism: cross-entropy loss for training the shared model.
"""
#TODO 加个检查准确率的
self.args = args
self.controller_step = 0
self.cuda = args.cuda
self.dataset = dataset
self.epoch = 0
self.shared_step = 0
self.start_epoch = 0
print('regularizing:')
for regularizer in [('activation regularization',
self.args.activation_regularization),
('temporal activation regularization',
self.args.temporal_activation_regularization),
('norm stabilizer regularization',
self.args.norm_stabilizer_regularization)]:
if regularizer[1]:
print(f'{regularizer[0]}')
# self.train_data = utils.batchify(dataset.train,
# args.batch_size,
# self.cuda)
# NOTE(brendan): The validation set data is batchified twice
# separately: once for computing rewards during the Train Controller
# phase (valid_data, batch size == 64), and once for evaluating ppl
# over the entire validation set (eval_data, batch size == 1)
self.train_data = dataset.train
self.valid_data = dataset.valid
self.test_data = dataset.test
# self.max_length = self.args.shared_rnn_max_length
if args.use_tensorboard:
self.tb = TensorBoard(args.model_dir)
else:
self.tb = None
#TODO initialize controller and shared model
self.build_model()
# print("11111111")
if self.args.load_path:
print("=======load_path=======")
self.load_model()
shared_optimizer = _get_optimizer(self.args.shared_optim)
controller_optimizer = _get_optimizer(self.args.controller_optim)
print("=======make optimizer========")
self.shared_optim = shared_optimizer(
self.shared.parameters(),
lr=self.shared_lr,
weight_decay=self.args.shared_l2_reg)
print("=======make optimizer========")
self.controller_optim = controller_optimizer(
self.controller.parameters(), lr=self.args.controller_lr)
self.ce = nn.CrossEntropyLoss()
print("finish init")
def build_model(self):
"""Creates and initializes the shared and controller models."""
if self.args.network_type == 'rnn':
self.shared = models.RNN(self.args, self.dataset)
elif self.args.network_type == 'cnn':
print("----- begin to init cnn------")
self.shared = models.CNN(self.args, self.dataset)
# self.shared = self.shared.cuda()
else:
raise NotImplementedError(f'Network type '
f'`{self.args.network_type}` is not '
f'defined')
print("---- begin to init controller-----")
self.controller = models.Controller(self.args)
#self.controller = self.controller.cuda()
print("===begin to cuda")
if True:
print("cuda")
self.shared.cuda()
self.controller.cuda()
print("finish cuda")
elif self.args.num_gpu > 1:
raise NotImplementedError('`num_gpu > 1` is in process')
def train(self):
"""Cycles through alternately training the shared parameters and the
controller, as described in Section2.4 Training ENAS and deriving
Architectures, of the paraer.
"""
if self.args.shared_initial_step > 0:
self.train_shared(self.args.shared_initial_step)
self.train_controller()
for self.epoch in range(self.start_epoch, self.args.max_epoch):
# 1. Training the shared parameters omega of the child models
self.train_shared()
# 2. Training the controller parameters theta
#self.train_controller()
if self.epoch == 0:
with _get_no_grad_ctx_mgr():
best_dag = self.derive()
self.evaluate(iter(self.test_data),
best_dag,
'val_best',
max_num=self.args.batch_size * 100)
self.save_model()
if self.epoch % self.args.save_epoch == 0:
with _get_no_grad_ctx_mgr():
best_dag = self.derive()
self.evaluate(iter(self.test_data),
best_dag,
'val_best',
max_num=self.args.batch_size * 100)
self.save_model()
if self.epoch >= self.args.shared_decay_after:
utils.update_lr(self.shared_optim, self.shared_lr)
def get_loss(self, inputs, targets, dags):
"""Computes the loss for the same batch for M models.
This amounts to an estimate of the loss, which is turned into an
estimate for the gradients of the shared model.
"""
if not isinstance(dags, list):
dags = [dags]
loss = 0
for dag in dags:
inputs = Variable(inputs.cuda())
targets = Variable(targets.cuda())
# inputs = inputs.cuda()
#targets = targets.cuda()
#self.shared = self.shared.cuda()
output = self.shared(inputs, dag)
sample_loss = (self.ce(output, targets) /
self.args.shared_num_sample)
loss += sample_loss
assert len(
dags) == 1, 'there are multiple `hidden` for multiple `dags`'
return loss
def train_shared(self, max_step=None):
"""Train the image classification model for 310 steps
"""
#TODO check if it is right that create a new dag for every batch and may be
#one epoch one bathc will improve efficient
model = self.shared
model.train()
self.controller.eval()
if max_step is None:
max_step = self.args.shared_max_step
else:
max_step = min(self.args.shared_max_step, max_step)
step = 0
raw_total_loss = 0
total_loss = 0
# train_idx = 0
train_iter = iter(self.train_data)
#TODO understanding how it train
while True:
if step > max_step:
break
dags = self.controller.sample(self.args.shared_num_sample)
#print(dags)
#TODO use iterator to create batch but need to add StopIteration
#may be have some method to improve
try:
inputs, targets = train_iter.next()
except StopIteration:
print("====>train_shared<====== finish one epoch")
break
train_iter = iter(self.train_data)
#print(dags)
loss = self.get_loss(inputs, targets, dags)
raw_total_loss += loss.data
#TODO understand penality
# loss += _apply_penalties()
self.shared_optim.zero_grad()
loss.backward()
self.shared_optim.step()
total_loss += loss.data
#if step % 20 == 0:
# print("loss, ", total_loss, step, total_loss /(step+1))
if ((step % self.args.log_step) == 0) and (step > 0):
self._summarize_shared_train(total_loss, raw_total_loss)
raw_total_loss = 0
total_loss = 0
step += 1
self.shared_step += 1
# train_idx += self.max_length
def get_reward(self, dag, entropies, data_iter):
"""Computes the perplexity of a single sampled model on a minibatch of
validation data.
"""
if not isinstance(entropies, np.ndarray):
entropies = entropies.data.cpu().numpy()
try:
inputs, targets = data_iter.next()
except StopIteration:
data_iter = iter(self.valid_data)
inputs, targets = data_iter.next()
#TODO 怎么做volidate
valid_loss = self.get_loss(inputs, targets, dag)
# convert valid_loss to numpy ndarray
valid_loss = utils.to_item(valid_loss.data)
valid_ppl = math.exp(valid_loss)
# TODO we don't knoe reward_c
if self.args.ppl_square:
#TODO: but we do know reward_c =80 in the previous paper need to read previous paper
R = self.args.reward_c / valid_ppl**2
else:
R = self.args.reward_c / valid_ppl
if self.args.entropy_mode == 'reward':
rewards = R + self.args.entropy_coeff * entropies
elif self.args.entropy_mode == 'regularizer':
rewards = R * np.ones_like(entropies)
else:
raise NotImplementedError(
f'Unknown entropy mode: {self.args.entropy_mode}')
return rewards
def train_controller(self):
"""Fixes the shared parameters and updates the controller parameters.
The controller is updated with a score function gradient estimator
(i.e., REINFORCE), with the reward being c/valid_ppl. where valid_ppl
is computed on a minibatch of vlaidation data.
A moving average baseline is used.
The controller is trained for 2000 steps per epoch (i.e.,
first (Train Shared) phase -. Second (Train Controller) phase).
"""
model = self.controller
model.train()
avg_reward_base = None
baseline = None
adv_history = []
entropy_history = []
reward_history = []
valid_iter = iter(self.valid_data)
total_loss = 0
for step in range(self.args.controller_max_step):
dags, log_probs, entropies = self.controller.sample(
with_details=True)
#print(dags)
np_entropies = entropies.data.cpu().numpy()
with _get_no_grad_ctx_mgr():
rewards = self.get_reward(dags, np_entropies, valid_iter)
if 1 > self.args.discount > 0:
rewards = discount(rewards, self.args.discount)
reward_history.extend(rewards)
entropy_history.extend(np_entropies)
# moving average baseline
if baseline is None:
baseline = rewards
else:
decay = self.args.ema_baseline_decay
baseline = decay * baseline + (1 - decay) * rewards
adv = rewards - baseline
adv_history.extend(adv)
#policy loss
loss = -log_probs * utils.get_variable(
adv, self.cuda, requires_grad=False)
if self.args.entropy_mode == 'regularizer':
loss -= self.args.entropy_coeff * np_entropies
loss = loss.sum()
self.controller_optim.zero_grad()
loss.backward()
if self.args.controller_grad_clip > 0:
torch.nn.utils.clip_grad_norm(model.parameters(),
self.args.controller_grad_clip)
self.controller_optim.step()
total_loss += utils.to_item(loss.data)
#if step%20 ==0:
# print("total loss", total_loss, step, total_loss / (step+1))
if ((step % self.args.log_step) == 0) and (step > 0):
self._summarize_controller_train(total_loss, adv_history,
entropy_history,
reward_history,
avg_reward_base, dags)
reward_history, adv_history, entropy_history = [], [], []
total_loss = 0
self.controller_step += 1
# prev_valid_idx = valid_idx
# valid_idx = ((valid_idx + self.max_length) %
# (self.valid_data.size(0) - 1))
# NOTE(brendan): Whenever we wrap around to the beginning of the
# validation data, we reset the hidden states.
def evaluate(self, test_iter, dag, name, batch_size=1, max_num=None):
"""Evaluate on the validation set.
(lianqing)what is the data of source ?
NOTE: use validation to check reward but test set is the same as valid set
"""
self.shared.eval()
self.controller.eval()
acc = AverageMeter()
# data = source[:max_num*self.max_length]
total_loss = 0
# pbar = range(0, data.size(0) - 1, self.max_length)
count = 0
while True:
try:
count += 1
inputs, targets = next(test_iter)
except StopIteration:
print("========> finish evaluate on one epoch<======")
break
test_iter = iter(self.test_data)
inputs, targets = next(test_iter)
# inputs = Variable(inputs)
#check if is train the controller will have what difference
inputs = Variable(inputs.cuda())
targets = Variable(targets.cuda())
# inputs = inputs.cuda()
#targets = targets.cuda()
output = self.shared(inputs, dag, is_train=False)
# check is self.loss wil work ?:
total_loss += len(inputs) * self.ce(output, targets).data
ppl = math.exp(utils.to_item(total_loss) / (count + 1))
acc.update(utils.get_accuracy(targets, output))
val_loss = utils.to_item(total_loss) / count
ppl = math.exp(val_loss)
#TODO it's fix for rnn need to fix for cnn
#self.tb.scalar_summary(f'eval/{name}_loss', val_loss, self.epoch)
#self.tb.scalar_summary(f'eval/{name}_ppl', ppl, self.epoch)
print(
f'eval | loss: {val_loss:8.2f} | ppl: {ppl:8.2f} | accuracy: {acc.avg:8.2f}'
)
def derive(self, sample_num=None, valid_iter=None):
"""
pass sample_num is always to 1 test if batch_size > 1 will work ? for controller.sample
"""
if sample_num is None:
sample_num = self.args.derive_num_sample
if valid_iter == None:
valid_iter = iter(self.valid_data)
dags, _, entropies = self.controller.sample(sample_num,
with_details=True)
max_R = 0
best_dag = None
for dag in dags:
R = self.get_reward(dag, entropies, valid_iter)
if R.max() > max_R:
max_R = R.max()
best_dag = dag
print(f'derive | max_R: {max_R:8.6f}')
fname = (f'{self.epoch:03d}-{self.controller_step:06d}-'
f'{max_R:6.4}-best.png')
path = os.path.join(self.args.model_dir, 'networks', fname)
# utils.draw_network(best_dag, path)
# self.tb.image_summary('derive/best', [path], self.epoch)
return best_dag
@property
def shared_lr(self):
degree = max(self.epoch - self.args.shared_decay_after + 1, 0)
return self.args.shared_lr * (self.args.shared_decay**degree)
@property
def controller_lr(self):
return self.args.controller_lr
@property
def shared_path(self):
return f'{self.args.model_dir}/shared_epoch{self.epoch}_step{self.shared_step}.pth'
@property
def controller_path(self):
return f'{self.args.model_dir}/controller_epoch{self.epoch}_step{self.controller_step}.pth'
def get_saved_models_info(self):
paths = glob.glob(os.path.join(self.args.model_dir, '*.pth'))
paths.sort()
def get_numbers(items, delimiter, idx, replace_word, must_contain=''):
return list(
set([
int(name.split(delimiter)[idx].replace(replace_word, ''))
for name in basenames if must_contain in name
]))
basenames = [
os.path.basename(path.rsplit('.', 1)[0]) for path in paths
]
epochs = get_numbers(basenames, '_', 1, 'epoch')
shared_steps = get_numbers(basenames, '_', 2, 'step', 'shared')
controller_steps = get_numbers(basenames, '_', 2, 'step', 'controller')
epochs.sort()
shared_steps.sort()
controller_steps.sort()
return epochs, shared_steps, controller_steps
def save_model(self):
torch.save(self.shared.state_dict(), self.shared_path)
print(f'[*] SAVED: {self.shared_path}')
torch.save(self.controller.state_dict(), self.controller_path)
print(f'[*] SAVED: {self.controller_path}')
epochs, shared_steps, controller_steps = self.get_saved_models_info()
for epoch in epochs[:-self.args.max_save_num]:
paths = glob.glob(
os.path.join(self.args.model_dir, f'*_epoch{epoch}_*.pth'))
for path in paths:
utils.remove_file(path)
def load_model(self):
epochs, shared_steps, controller_steps = self.get_saved_models_info()
if len(epochs) == 0:
print(f'[!] No checkpoint found in {self.args.model_dir}...')
return
self.epoch = self.start_epoch = max(epochs)
self.shared_step = max(shared_steps)
self.controller_step = max(controller_steps)
if self.args.num_gpu == 0:
map_location = lambda storage, loc: storage
else:
map_location = None
self.shared.load_state_dict(
torch.load(self.shared_path, map_location=map_location))
print(f'[*] LOADED: {self.shared_path}')
self.controller.load_state_dict(
torch.load(self.controller_path, map_location=map_location))
print(f'[*] LOADED: {self.controller_path}')
def _summarize_controller_train(self, total_loss, adv_history,
entropy_history, reward_history,
avg_reward_base, dags):
"""Logs the controller's progress for this training epoch."""
cur_loss = total_loss / self.args.log_step
avg_adv = np.mean(adv_history)
avg_entropy = np.mean(entropy_history)
avg_reward = np.mean(reward_history)
if avg_reward_base is None:
avg_reward_base = avg_reward
print(f'| epoch {self.epoch:3d} | lr {self.controller_lr:.5f} '
f'| R {avg_reward:.5f} | entropy {avg_entropy:.4f} '
f'| loss {cur_loss:.5f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary('controller/loss', cur_loss,
self.controller_step)
self.tb.scalar_summary('controller/reward', avg_reward,
self.controller_step)
self.tb.scalar_summary('controller/reward-B_per_epoch',
avg_reward - avg_reward_base,
self.controller_step)
self.tb.scalar_summary('controller/entropy', avg_entropy,
self.controller_step)
self.tb.scalar_summary('controller/adv', avg_adv,
self.controller_step)
paths = []
for dag in dags:
fname = (f'{self.epoch:03d}-{self.controller_step:06d}-'
f'{avg_reward:6.4f}.png')
path = os.path.join(self.args.model_dir, 'networks', fname)
# utils.draw_network(dag, path)
paths.append(path)
self.tb.image_summary('controller/sample', paths,
self.controller_step)
def _summarize_shared_train(self, total_loss, raw_total_loss):
"""Logs a set of training steps."""
cur_loss = utils.to_item(total_loss) / self.args.log_step
# NOTE(brendan): The raw loss, without adding in the activation
# regularization terms, should be used to compute ppl.
cur_raw_loss = utils.to_item(raw_total_loss) / self.args.log_step
ppl = math.exp(cur_raw_loss)
print(f'| epoch {self.epoch:3d} '
f'| lr {self.shared_lr:4.2f} '
f'| raw loss {cur_raw_loss:.2f} '
f'| loss {cur_loss:.2f} '
f'| ppl {ppl:8.2f}')
# Tensorboard
if self.tb is not None:
self.tb.scalar_summary('shared/loss', cur_loss, self.shared_step)
self.tb.scalar_summary('shared/perplexity', ppl, self.shared_step)
