def predict_and_save(dataset: GroundedScanDataset,
model: nn.Module,
output_file_path: str,
max_decoding_steps: int,
max_testing_examples=None,
**kwargs):
"""
Predict all data in dataset with a model and write the predictions to output_file_path.
:param dataset: a dataset with test examples
:param model: a trained model from model.py
:param output_file_path: a path where a .json file with predictions will be saved.
:param max_decoding_steps: after how many steps to force quit decoding
:param max_testing_examples: after how many examples to stop predicting, if None all examples will be evaluated
"""
cfg = locals().copy()
with open(output_file_path, mode='w') as outfile:
output = []
with torch.no_grad():
i = 0
for (input_sequence, derivation_spec, situation_spec,
output_sequence, target_sequence, attention_weights_commands,
attention_weights_situations, position_accuracy) in predict(
dataset.get_data_iterator(batch_size=1),
model=model,
max_decoding_steps=max_decoding_steps,
pad_idx=dataset.target_vocabulary.pad_idx,
sos_idx=dataset.target_vocabulary.sos_idx,
eos_idx=dataset.target_vocabulary.eos_idx):
i += 1
accuracy = sequence_accuracy(output_sequence,
target_sequence[0].tolist()[1:-1])
input_str_sequence = dataset.array_to_sentence(
input_sequence[0].tolist(), vocabulary="input")
input_str_sequence = input_str_sequence[
1:-1] # Get rid of <SOS> and <EOS>
target_str_sequence = dataset.array_to_sentence(
target_sequence[0].tolist(), vocabulary="target")
target_str_sequence = target_str_sequence[
1:-1] # Get rid of <SOS> and <EOS>
output_str_sequence = dataset.array_to_sentence(
output_sequence, vocabulary="target")
output.append({
"input": input_str_sequence,
"prediction": output_str_sequence,
"derivation": derivation_spec,
"target": target_str_sequence,
"situation": situation_spec,
"attention_weights_input": attention_weights_commands,
"attention_weights_situation":
attention_weights_situations,
"accuracy": accuracy,
"exact_match": True if accuracy == 100 else False,
"position_accuracy": position_accuracy
})
logger.info("Wrote predictions for {} examples.".format(i))
json.dump(output, outfile, indent=4)
return output_file_path
def predict_and_save(dataset: GroundedScanDataset, model: nn.Module, output_file_path: str, max_decoding_steps: int,
max_testing_examples=None, **kwargs):
"""
:param dataset:
:param model:
:param output_file_path:
:param max_decoding_steps:
:param max_testing_examples:
:param kwargs:
:return:
"""
cfg = locals().copy()
with open(output_file_path, mode='w') as outfile:
output = []
with torch.no_grad():
i = 0
for (input_sequence, derivation_spec, situation_spec, output_sequence, target_sequence,
attention_weights_commands, attention_weights_situations, _) in predict(
dataset.get_data_iterator(batch_size=1), model=model, max_decoding_steps=max_decoding_steps,
pad_idx=dataset.target_vocabulary.pad_idx, sos_idx=dataset.target_vocabulary.sos_idx,
eos_idx=dataset.target_vocabulary.eos_idx):
i += 1
accuracy = sequence_accuracy(output_sequence, target_sequence[0].tolist()[1:-1])
input_str_sequence = dataset.array_to_sentence(input_sequence[0].tolist(), vocabulary="input")
input_str_sequence = input_str_sequence[1:-1] # Get rid of <SOS> and <EOS>
target_str_sequence = dataset.array_to_sentence(target_sequence[0].tolist(), vocabulary="target")
target_str_sequence = target_str_sequence[1:-1] # Get rid of <SOS> and <EOS>
output_str_sequence = dataset.array_to_sentence(output_sequence, vocabulary="target")
output.append({"input": input_str_sequence, "prediction": output_str_sequence,
"derivation": derivation_spec,
"target": target_str_sequence, "situation": situation_spec,
"attention_weights_input": attention_weights_commands,
"attention_weights_situation": attention_weights_situations,
"accuracy": accuracy,
"exact_match": True if accuracy == 100 else False})
logger.info("Wrote predictions for {} examples.".format(i))
json.dump(output, outfile, indent=4)
return output_file_path
def train(
data_path: str,
data_directory: str,
generate_vocabularies: bool,
input_vocab_path: str,
target_vocab_path: str,
embedding_dimension: int,
num_encoder_layers: int,
encoder_dropout_p: float,
encoder_bidirectional: bool,
training_batch_size: int,
test_batch_size: int,
max_decoding_steps: int,
num_decoder_layers: int,
decoder_dropout_p: float,
cnn_kernel_size: int,
cnn_dropout_p: float,
cnn_hidden_num_channels: int,
simple_situation_representation: bool,
decoder_hidden_size: int,
encoder_hidden_size: int,
learning_rate: float,
adam_beta_1: float,
adam_beta_2: float,
lr_decay: float,
lr_decay_steps: int,
resume_from_file: str,
max_training_iterations: int,
output_directory: str,
print_every: int,
evaluate_every: int,
conditional_attention: bool,
auxiliary_task: bool,
weight_target_loss: float,
attention_type: str,
k: int,
max_training_examples,
max_testing_examples,
# SeqGAN params begin
pretrain_gen_path,
pretrain_gen_epochs,
pretrain_disc_path,
pretrain_disc_epochs,
rollout_trails,
rollout_update_rate,
disc_emb_dim,
disc_hid_dim,
load_tensors_from_path,
# SeqGAN params end
seed=42,
**kwargs):
device = torch.device("cpu")
cfg = locals().copy()
torch.manual_seed(seed)
logger.info("Loading Training set...")
training_set = GroundedScanDataset(
data_path,
data_directory,
split="train",
input_vocabulary_file=input_vocab_path,
target_vocabulary_file=target_vocab_path,
generate_vocabulary=generate_vocabularies,
k=k)
training_set.read_dataset(
max_examples=max_training_examples,
simple_situation_representation=simple_situation_representation,
load_tensors_from_path=load_tensors_from_path
) # set this to False if no pickle file available
logger.info("Done Loading Training set.")
logger.info(" Loaded {} training examples.".format(
training_set.num_examples))
logger.info(" Input vocabulary size training set: {}".format(
training_set.input_vocabulary_size))
logger.info(" Most common input words: {}".format(
training_set.input_vocabulary.most_common(5)))
logger.info(" Output vocabulary size training set: {}".format(
training_set.target_vocabulary_size))
logger.info(" Most common target words: {}".format(
training_set.target_vocabulary.most_common(5)))
if generate_vocabularies:
training_set.save_vocabularies(input_vocab_path, target_vocab_path)
logger.info(
"Saved vocabularies to {} for input and {} for target.".format(
input_vocab_path, target_vocab_path))
# logger.info("Loading Dev. set...")
# test_set = GroundedScanDataset(data_path, data_directory, split="dev",
# input_vocabulary_file=input_vocab_path,
# target_vocabulary_file=target_vocab_path, generate_vocabulary=False, k=0)
# test_set.read_dataset(max_examples=max_testing_examples,
# simple_situation_representation=simple_situation_representation)
#
# # Shuffle the test set to make sure that if we only evaluate max_testing_examples we get a random part of the set.
# test_set.shuffle_data()
# logger.info("Done Loading Dev. set.")
generator = Model(
input_vocabulary_size=training_set.input_vocabulary_size,
target_vocabulary_size=training_set.target_vocabulary_size,
num_cnn_channels=training_set.image_channels,
input_padding_idx=training_set.input_vocabulary.pad_idx,
target_pad_idx=training_set.target_vocabulary.pad_idx,
target_eos_idx=training_set.target_vocabulary.eos_idx,
**cfg)
total_vocabulary = set(
list(training_set.input_vocabulary._word_to_idx.keys()) +
list(training_set.target_vocabulary._word_to_idx.keys()))
total_vocabulary_size = len(total_vocabulary)
discriminator = Discriminator(embedding_dim=disc_emb_dim,
hidden_dim=disc_hid_dim,
vocab_size=total_vocabulary_size,
max_seq_len=max_decoding_steps)
generator = generator.cuda() if use_cuda else generator
discriminator = discriminator.cuda() if use_cuda else discriminator
rollout = Rollout(generator, rollout_update_rate)
log_parameters(generator)
trainable_parameters = [
parameter for parameter in generator.parameters()
if parameter.requires_grad
]
optimizer = torch.optim.Adam(trainable_parameters,
lr=learning_rate,
betas=(adam_beta_1, adam_beta_2))
scheduler = LambdaLR(optimizer,
lr_lambda=lambda t: lr_decay**(t / lr_decay_steps))
# Load model and vocabularies if resuming.
start_iteration = 1
best_iteration = 1
best_accuracy = 0
best_exact_match = 0
best_loss = float('inf')
if resume_from_file:
assert os.path.isfile(
resume_from_file), "No checkpoint found at {}".format(
resume_from_file)
logger.info(
"Loading checkpoint from file at '{}'".format(resume_from_file))
optimizer_state_dict = generator.load_model(resume_from_file)
optimizer.load_state_dict(optimizer_state_dict)
start_iteration = generator.trained_iterations
logger.info("Loaded checkpoint '{}' (iter {})".format(
resume_from_file, start_iteration))
if pretrain_gen_path is None:
print('Pretraining generator with MLE...')
pre_train_generator(training_set,
training_batch_size,
generator,
seed,
pretrain_gen_epochs,
name='pretrained_generator')
else:
print('Load pretrained generator weights')
generator_weights = torch.load(pretrain_gen_path)
generator.load_state_dict(generator_weights)
if pretrain_disc_path is None:
print('Pretraining Discriminator....')
train_discriminator(training_set,
discriminator,
training_batch_size,
generator,
seed,
pretrain_disc_epochs,
name="pretrained_discriminator")
else:
print('Loading Discriminator....')
discriminator_weights = torch.load(pretrain_disc_path)
discriminator.load_state_dict(discriminator_weights)
logger.info("Training starts..")
training_iteration = start_iteration
torch.autograd.set_detect_anomaly(True)
while training_iteration < max_training_iterations:
# Shuffle the dataset and loop over it.
training_set.shuffle_data()
for (input_batch, input_lengths, _, situation_batch, _, target_batch,
target_lengths, agent_positions, target_positions) in \
training_set.get_data_iterator(batch_size=training_batch_size):
is_best = False
generator.train()
# Forward pass.
samples = generator.sample(
batch_size=training_batch_size,
max_seq_len=max(target_lengths).astype(int),
commands_input=input_batch,
commands_lengths=input_lengths,
situations_input=situation_batch,
target_batch=target_batch,
sos_idx=training_set.input_vocabulary.sos_idx,
eos_idx=training_set.input_vocabulary.eos_idx)
rewards = rollout.get_reward(samples, rollout_trails, input_batch,
input_lengths, situation_batch,
target_batch,
training_set.input_vocabulary.sos_idx,
training_set.input_vocabulary.eos_idx,
discriminator)
assert samples.shape == rewards.shape
# calculate rewards
rewards = torch.exp(rewards).contiguous().view((-1, ))
if use_cuda:
rewards = rewards.cuda()
# get generator scores for sequence
target_scores = generator.get_normalized_logits(
commands_input=input_batch,
commands_lengths=input_lengths,
situations_input=situation_batch,
samples=samples,
sample_lengths=target_lengths,
sos_idx=training_set.input_vocabulary.sos_idx)
del samples
# calculate loss on the generated sequence given the rewards
loss = generator.get_gan_loss(target_scores, target_batch, rewards)
del rewards
# Backward pass and update model parameters.
loss.backward()
optimizer.step()
scheduler.step(training_iteration)
optimizer.zero_grad()
generator.update_state(is_best=is_best)
# Print current metrics.
if training_iteration % print_every == 0:
# accuracy, exact_match = generator.get_metrics(target_scores, target_batch)
learning_rate = scheduler.get_lr()[0]
logger.info("Iteration %08d, loss %8.4f, learning_rate %.5f," %
(training_iteration, loss, learning_rate))
# logger.info("Iteration %08d, loss %8.4f, accuracy %5.2f, exact match %5.2f, learning_rate %.5f,"
# % (training_iteration, loss, accuracy, exact_match, learning_rate))
del target_scores, target_batch
# # Evaluate on test set.
# if training_iteration % evaluate_every == 0:
# with torch.no_grad():
# generator.eval()
# logger.info("Evaluating..")
# accuracy, exact_match, target_accuracy = evaluate(
# test_set.get_data_iterator(batch_size=1), model=generator,
# max_decoding_steps=max_decoding_steps, pad_idx=test_set.target_vocabulary.pad_idx,
# sos_idx=test_set.target_vocabulary.sos_idx,
# eos_idx=test_set.target_vocabulary.eos_idx,
# max_examples_to_evaluate=kwargs["max_testing_examples"])
# logger.info(" Evaluation Accuracy: %5.2f Exact Match: %5.2f "
# " Target Accuracy: %5.2f" % (accuracy, exact_match, target_accuracy))
# if exact_match > best_exact_match:
# is_best = True
# best_accuracy = accuracy
# best_exact_match = exact_match
# generator.update_state(accuracy=accuracy, exact_match=exact_match, is_best=is_best)
# file_name = "checkpoint.pth.tar".format(str(training_iteration))
# if is_best:
# generator.save_checkpoint(file_name=file_name, is_best=is_best,
# optimizer_state_dict=optimizer.state_dict())
rollout.update_params()
train_discriminator(training_set,
discriminator,
training_batch_size,
generator,
seed,
epochs=1,
name="training_discriminator")
training_iteration += 1
if training_iteration > max_training_iterations:
break
del loss
torch.save(
generator.state_dict(),
'{}/{}'.format(output_directory,
'gen_{}_{}.ckpt'.format(training_iteration, seed)))
torch.save(
discriminator.state_dict(),
'{}/{}'.format(output_directory,
'dis_{}_{}.ckpt'.format(training_iteration, seed)))
logger.info("Finished training.")
def train(data_path: str,
data_directory: str,
generate_vocabularies: bool,
input_vocab_path: str,
target_vocab_path: str,
embedding_dimension: int,
num_encoder_layers: int,
encoder_dropout_p: float,
encoder_bidirectional: bool,
training_batch_size: int,
test_batch_size: int,
max_decoding_steps: int,
num_decoder_layers: int,
decoder_dropout_p: float,
cnn_kernel_size: int,
cnn_dropout_p: float,
cnn_hidden_num_channels: int,
simple_situation_representation: bool,
decoder_hidden_size: int,
encoder_hidden_size: int,
learning_rate: float,
adam_beta_1: float,
adam_beta_2: float,
lr_decay: float,
lr_decay_steps: int,
resume_from_file: str,
max_training_iterations: int,
output_directory: str,
print_every: int,
evaluate_every: int,
conditional_attention: bool,
auxiliary_task: bool,
weight_target_loss: float,
attention_type: str,
max_training_examples=None,
seed=42,
**kwargs):
device = torch.device(type='cuda') if use_cuda else torch.device(
type='cpu')
cfg = locals().copy()
torch.manual_seed(seed)
logger.info("Loading Training set...")
training_set = GroundedScanDataset(
data_path,
data_directory,
split="train",
input_vocabulary_file=input_vocab_path,
target_vocabulary_file=target_vocab_path,
generate_vocabulary=generate_vocabularies)
training_set.read_dataset(
max_examples=max_training_examples,
simple_situation_representation=simple_situation_representation)
logger.info("Done Loading Training set.")
logger.info(" Loaded {} training examples.".format(
training_set.num_examples))
logger.info(" Input vocabulary size training set: {}".format(
training_set.input_vocabulary_size))
logger.info(" Most common input words: {}".format(
training_set.input_vocabulary.most_common(5)))
logger.info(" Output vocabulary size training set: {}".format(
training_set.target_vocabulary_size))
logger.info(" Most common target words: {}".format(
training_set.target_vocabulary.most_common(5)))
if generate_vocabularies:
training_set.save_vocabularies(input_vocab_path, target_vocab_path)
logger.info(
"Saved vocabularies to {} for input and {} for target.".format(
input_vocab_path, target_vocab_path))
logger.info("Loading Test set...")
test_set = GroundedScanDataset(
data_path,
data_directory,
split="test", # TODO: use dev set here
input_vocabulary_file=input_vocab_path,
target_vocabulary_file=target_vocab_path,
generate_vocabulary=False)
test_set.read_dataset(
max_examples=None,
simple_situation_representation=simple_situation_representation)
# Shuffle the test set to make sure that if we only evaluate max_testing_examples we get a random part of the set.
test_set.shuffle_data()
logger.info("Done Loading Test set.")
model = Model(input_vocabulary_size=training_set.input_vocabulary_size,
target_vocabulary_size=training_set.target_vocabulary_size,
num_cnn_channels=training_set.image_channels,
input_padding_idx=training_set.input_vocabulary.pad_idx,
target_pad_idx=training_set.target_vocabulary.pad_idx,
target_eos_idx=training_set.target_vocabulary.eos_idx,
**cfg)
model = model.cuda() if use_cuda else model
log_parameters(model)
trainable_parameters = [
parameter for parameter in model.parameters()
if parameter.requires_grad
]
optimizer = torch.optim.Adam(trainable_parameters,
lr=learning_rate,
betas=(adam_beta_1, adam_beta_2))
scheduler = LambdaLR(optimizer,
lr_lambda=lambda t: lr_decay**(t / lr_decay_steps))
# Load model and vocabularies if resuming.
start_iteration = 1
best_iteration = 1
best_accuracy = 0
best_exact_match = 0
best_loss = float('inf')
if resume_from_file:
assert os.path.isfile(
resume_from_file), "No checkpoint found at {}".format(
resume_from_file)
logger.info(
"Loading checkpoint from file at '{}'".format(resume_from_file))
optimizer_state_dict = model.load_model(resume_from_file)
optimizer.load_state_dict(optimizer_state_dict)
start_iteration = model.trained_iterations
logger.info("Loaded checkpoint '{}' (iter {})".format(
resume_from_file, start_iteration))
logger.info("Training starts..")
training_iteration = start_iteration
while training_iteration < max_training_iterations:
# Shuffle the dataset and loop over it.
training_set.shuffle_data()
for (input_batch, input_lengths, _, situation_batch, _, target_batch,
target_lengths, agent_positions,
target_positions) in training_set.get_data_iterator(
batch_size=training_batch_size):
is_best = False
model.train()
# Forward pass.
target_scores, target_position_scores = model(
commands_input=input_batch,
commands_lengths=input_lengths,
situations_input=situation_batch,
target_batch=target_batch,
target_lengths=target_lengths)
loss = model.get_loss(target_scores, target_batch)
if auxiliary_task:
target_loss = model.get_auxiliary_loss(target_position_scores,
target_positions)
else:
target_loss = 0
loss += weight_target_loss * target_loss
# Backward pass and update model parameters.
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
model.update_state(is_best=is_best)
# Print current metrics.
if training_iteration % print_every == 0:
accuracy, exact_match = model.get_metrics(
target_scores, target_batch)
if auxiliary_task:
auxiliary_accuracy_target = model.get_auxiliary_accuracy(
target_position_scores, target_positions)
else:
auxiliary_accuracy_target = 0.
learning_rate = scheduler.get_lr()[0]
logger.info(
"Iteration %08d, loss %8.4f, accuracy %5.2f, exact match %5.2f, learning_rate %.5f,"
" aux. accuracy target pos %5.2f" %
(training_iteration, loss, accuracy, exact_match,
learning_rate, auxiliary_accuracy_target))
# Evaluate on test set.
if training_iteration % evaluate_every == 0:
with torch.no_grad():
model.eval()
logger.info("Evaluating..")
accuracy, exact_match, target_accuracy = evaluate(
test_set.get_data_iterator(batch_size=1),
model=model,
max_decoding_steps=max_decoding_steps,
pad_idx=test_set.target_vocabulary.pad_idx,
sos_idx=test_set.target_vocabulary.sos_idx,
eos_idx=test_set.target_vocabulary.eos_idx,
max_examples_to_evaluate=kwargs["max_testing_examples"]
)
logger.info(
" Evaluation Accuracy: %5.2f Exact Match: %5.2f "
" Target Accuracy: %5.2f" %
(accuracy, exact_match, target_accuracy))
if exact_match > best_exact_match:
is_best = True
best_accuracy = accuracy
best_exact_match = exact_match
model.update_state(accuracy=accuracy,
exact_match=exact_match,
is_best=is_best)
file_name = "checkpoint.pth.tar".format(
str(training_iteration))
if is_best:
model.save_checkpoint(
file_name=file_name,
is_best=is_best,
optimizer_state_dict=optimizer.state_dict())
training_iteration += 1
if training_iteration > max_training_iterations:
break
logger.info("Finished training.")