def knowledge_celebrity_test(context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder,
to_hidden: ToHidden, celebrity_memory: Memory,
text_decoder: TextDecoder, test_dataset: Dataset,
celebrity_scores, text_length: int,
vocab: Dict[str, int]):
"""Knowledge celebrity test.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
to_hidden (ToHidden): Context to hidden.
celebrity_memory (Memory): Celebrity Memory.
text_decoder (TextDecoder): Text decoder.
test_dataset (Dataset): Valid dataset.
celebrity_scores: Celebrity scores.
text_length (int): Text length.
vocab (Dict[str, int]): Vocabulary.
"""
id2word: List[str] = [None] * len(vocab)
for word, wid in vocab.items():
id2word[wid] = word
# Test dataset loader.
test_data_loader = DataLoader(
test_dataset,
batch_size=KnowledgeCelebrityTestConfig.batch_size,
num_workers=KnowledgeCelebrityTestConfig.num_data_loader_workers)
sum_loss = 0
# Switch to eval mode.
context_text_encoder.eval()
context_image_encoder.eval()
context_encoder.eval()
to_hidden.eval()
celebrity_memory.eval()
text_decoder.eval()
output_file = open('knowledge_celebrity.out', 'w')
with torch.no_grad():
for batch_id, test_data in enumerate(test_data_loader):
texts, text_lengths, images, utter_types = test_data
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, hiddens = encode_context(context_text_encoder,
context_image_encoder,
context_encoder, texts,
text_lengths, images)
# (batch_size, context_vector_size)
knowledge_entry = celebrity_scores
encode_knowledge_func = partial(celebrity_memory, knowledge_entry)
text_eval(to_hidden,
text_decoder,
text_length,
id2word,
context,
texts[-1],
hiddens,
encode_knowledge_func,
output_file=output_file)
output_file.close()
def intention_test(context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder, intention: Intention,
test_dataset: Dataset):
"""Intention test.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
intention (Intention): Intention.
test_dataset (Dataset): Test dataset.
"""
# Test dataset loader.
test_data_loader = DataLoader(
test_dataset,
batch_size=IntentionTestConfig.batch_size,
shuffle=False,
num_workers=IntentionTestConfig.num_data_loader_workers)
sum_accuracy = 0
# Switch to eval mode.
context_text_encoder.eval()
context_image_encoder.eval()
context_encoder.eval()
intention.eval()
with torch.no_grad():
for batch_id, valid_data in enumerate(test_data_loader):
texts, text_lengths, images, utter_types = valid_data
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, _ = encode_context(context_text_encoder,
context_image_encoder, context_encoder,
texts, text_lengths, images)
# (batch_size, context_vector_size)
intent_prob = intention(context)
# (batch_size, utterance_type_size)
intentions = torch.argmax(intent_prob, dim=1)
eqs = torch.eq(intentions, utter_types)
num_correct = torch.sum(eqs).item()
accuracy = num_correct * 1.0 / eqs.size(0)
sum_accuracy += accuracy
# Print.
print('pred:', intentions)
print('true:', utter_types)
print('# correct:', num_correct)
print('accuracy:', accuracy)
print('total accuracy:', sum_accuracy / (batch_id + 1))
def intention_valid(context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder, intention: Intention,
valid_dataset: Dataset):
"""Intention valid.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
intention (Intention): Intention.
valid_dataset (Dataset): Valid dataset.
"""
# Valid dataset loader.
valid_data_loader = DataLoader(
valid_dataset,
batch_size=IntentionValidConfig.batch_size,
shuffle=True,
num_workers=IntentionValidConfig.num_data_loader_workers)
sum_loss = 0
sum_accuracy = 0
num_batches = 0
# Switch to eval mode.
context_text_encoder.eval()
context_image_encoder.eval()
context_encoder.eval()
intention.eval()
with torch.no_grad():
for batch_id, valid_data in enumerate(valid_data_loader):
# Only valid `ValidConfig.num_batches` batches.
if batch_id >= IntentionValidConfig.num_batches:
break
num_batches += 1
texts, text_lengths, images, utter_types = valid_data
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, _ = encode_context(context_text_encoder,
context_image_encoder, context_encoder,
texts, text_lengths, images)
# (batch_size, context_vector_size)
intent_prob = intention(context)
# (batch_size, utterance_type_size)
loss = nll_loss(intent_prob, utter_types)
sum_loss += loss
eqs = torch.eq(torch.argmax(intent_prob, dim=1), utter_types)
accuracy = torch.sum(eqs).item() * 1.0 / eqs.size(0)
sum_accuracy += accuracy
# Switch to train mode.
context_text_encoder.train()
context_image_encoder.train()
context_encoder.train()
intention.train()
return sum_loss / num_batches, sum_accuracy / num_batches
def knowledge_attribute_train(context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder,
train_dataset: Dataset,
valid_dataset: Dataset,
test_dataset: Dataset,
model_file: str,
attribute_data: AttributeData,
vocab: Dict[str, int],
embed_init=None):
"""Knowledge styletip train.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
train_dataset (Dataset): Train dataset.
valid_dataset (Dataset): Valid dataset.
test_dataset (Dataset): Test dataset.
model_file (str): Saved model file.
attribute_data (AttributeData): Attribute data.
vocab (Dict[str, int]): Vocabulary.
embed_init: Initial embedding (vocab_size, embed_size).
"""
# Data loader.
train_data_loader = DataLoader(
dataset=train_dataset,
batch_size=KnowledgeAttributeTrainConfig.batch_size,
shuffle=True,
num_workers=KnowledgeAttributeTrainConfig.num_data_loader_workers)
# Model.
vocab_size = len(vocab)
attribute_kv_memory_config = AttributeKVMemoryConfig(
len(attribute_data.key_vocab), len(attribute_data.value_vocab))
text_decoder_config = KnowledgeTextDecoderConfig(vocab_size,
MemoryConfig.memory_size,
MemoryConfig.output_size,
embed_init)
to_hidden = ToHidden(text_decoder_config)
to_hidden = to_hidden.to(GlobalConfig.device)
attribute_kv_memory = KVMemory(attribute_kv_memory_config)
attribute_kv_memory = attribute_kv_memory.to(GlobalConfig.device)
text_decoder = TextDecoder(text_decoder_config)
text_decoder = text_decoder.to(GlobalConfig.device)
# Model parameters.
params = list(
chain.from_iterable([
list(model.parameters()) for model in [
context_text_encoder, context_image_encoder, context_encoder,
to_hidden, attribute_kv_memory, text_decoder
]
]))
optimizer = Adam(params, lr=KnowledgeAttributeTrainConfig.learning_rate)
epoch_id = 0
min_valid_loss = None
# Load saved state.
if isfile(model_file):
state = torch.load(model_file)
to_hidden.load_state_dict(state['to_hidden'])
attribute_kv_memory.load_state_dict(state['attribute_kv_memory'])
text_decoder.load_state_dict(state['text_decoder'])
optimizer.load_state_dict(state['optimizer'])
epoch_id = state['epoch_id']
min_valid_loss = state['min_valid_loss']
# Loss.
sum_loss = 0
bad_loss_cnt = 0
# Switch to train mode.
context_text_encoder.train()
context_image_encoder.train()
context_encoder.train()
to_hidden.train()
attribute_kv_memory.train()
text_decoder.train()
finished = False
for epoch_id in range(epoch_id,
KnowledgeAttributeTrainConfig.num_iterations):
for batch_id, train_data in enumerate(train_data_loader):
# Set gradients to 0.
optimizer.zero_grad()
train_data, products = train_data
keys, values, pair_length = products
keys = keys.to(GlobalConfig.device)
values = values.to(GlobalConfig.device)
pair_length = pair_length.to(GlobalConfig.device)
texts, text_lengths, images, utter_types = train_data
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, hiddens = encode_context(context_text_encoder,
context_image_encoder,
context_encoder, texts,
text_lengths, images)
# (batch_size, context_vector_size)
encode_knowledge_func = partial(attribute_kv_memory, keys, values,
pair_length)
loss, n_totals = text_loss(to_hidden, text_decoder,
text_decoder_config.text_length,
context, texts[-1], text_lengths[-1],
hiddens, encode_knowledge_func)
sum_loss += loss / text_decoder_config.text_length
loss.backward()
optimizer.step()
# Print loss every `TrainConfig.print_freq` batches.
if (batch_id + 1) % KnowledgeAttributeTrainConfig.print_freq == 0:
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
sum_loss /= KnowledgeAttributeTrainConfig.print_freq
print('epoch: {} \tbatch: {} \tloss: {} \ttime: {}'.format(
epoch_id + 1, batch_id + 1, sum_loss, cur_time))
sum_loss = 0
# Valid every `TrainConfig.valid_freq` batches.
if (batch_id + 1) % KnowledgeAttributeTrainConfig.valid_freq == 0:
valid_loss = knowledge_attribute_valid(
context_text_encoder, context_image_encoder,
context_encoder, to_hidden, attribute_kv_memory,
text_decoder, valid_dataset,
text_decoder_config.text_length)
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('valid_loss: {} \ttime: {}'.format(valid_loss, cur_time))
# Save current best model.
if min_valid_loss is None or valid_loss < min_valid_loss:
min_valid_loss = valid_loss
bad_loss_cnt = 0
save_dict = {
'task': KNOWLEDGE_ATTRIBUTE_SUBTASK,
'epoch_id': epoch_id,
'min_valid_loss': min_valid_loss,
'optimizer': optimizer.state_dict(),
'context_text_encoder':
context_text_encoder.state_dict(),
'context_image_encoder':
context_image_encoder.state_dict(),
'context_encoder': context_encoder.state_dict(),
'to_hidden': to_hidden.state_dict(),
'attribute_kv_memory':
attribute_kv_memory.state_dict(),
'text_decoder': text_decoder.state_dict()
}
torch.save(save_dict, model_file)
print('Best model saved.')
else:
bad_loss_cnt += 1
if bad_loss_cnt > KnowledgeAttributeTrainConfig.patience:
knowledge_attribute_test(
context_text_encoder, context_image_encoder,
context_encoder, to_hidden, attribute_kv_memory,
text_decoder, test_dataset,
text_decoder_config.text_length, vocab)
finished = True
break
if finished:
break
def knowledge_celebrity_valid(
context_text_encoder: TextEncoder, context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder, to_hidden: ToHidden,
celebrity_memory: Memory, text_decoder: TextDecoder,
valid_dataset: Dataset, celebrity_scores, text_length: int):
"""Knowledge celebrity valid.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
to_hidden (ToHidden): Context to hidden.
celebrity_memory (Memory): Celebrity Memory.
text_decoder (TextDecoder): Text decoder.
valid_dataset (Dataset): Valid dataset.
celebrity_scores: Celebrity scores.
text_length (int): Text length.
"""
# Valid dataset loader.
valid_data_loader = DataLoader(
valid_dataset,
batch_size=KnowledgeCelebrityValidConfig.batch_size,
shuffle=True,
num_workers=KnowledgeCelebrityValidConfig.num_data_loader_workers)
sum_loss = 0
num_batches = 0
# Switch to eval mode.
context_text_encoder.eval()
context_image_encoder.eval()
context_encoder.eval()
to_hidden.eval()
celebrity_memory.eval()
text_decoder.eval()
with torch.no_grad():
for batch_id, valid_data in enumerate(valid_data_loader):
# Only valid `ValidConfig.num_batches` batches.
if batch_id >= KnowledgeCelebrityValidConfig.num_batches:
break
num_batches += 1
texts, text_lengths, images, utter_types = valid_data
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
# utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, hiddens = encode_context(context_text_encoder,
context_image_encoder,
context_encoder, texts,
text_lengths, images)
# (batch_size, context_vector_size)
knowledge_entry = celebrity_scores
encode_knowledge_func = partial(celebrity_memory, knowledge_entry)
loss, n_totals = text_loss(to_hidden, text_decoder, text_length,
context, texts[-1], text_lengths[-1],
hiddens, encode_knowledge_func)
sum_loss += loss / text_length
# Switch to train mode.
context_text_encoder.train()
context_image_encoder.train()
context_encoder.train()
to_hidden.train()
celebrity_memory.train()
text_decoder.train()
return sum_loss / num_batches
def recommend_train(context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder,
train_dataset: Dataset,
valid_dataset: Dataset,
test_dataset: Dataset,
model_file: str,
vocab_size: int,
embed_init=None):
"""Recommend train.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
train_dataset (Dataset): Train dataset.
valid_dataset (Dataset): Valid dataset.
test_dataset (Dataset): Test dataset.
model_file (str): Saved model file.
vocab_size (int): Vocabulary size.
embed_init: Initial embedding (vocab_size, embed_size).
"""
# Data loader.
train_data_loader = DataLoader(
dataset=train_dataset,
batch_size=RecommendTrainConfig.batch_size,
shuffle=True,
num_workers=RecommendTrainConfig.num_data_loader_workers)
# Model.
similarity_config = SimilarityConfig(vocab_size, embed_init)
similarity = Similarity(similarity_config).to(GlobalConfig.device)
# Model parameters.
params = list(
chain.from_iterable([
list(model.parameters()) for model in [
context_text_encoder, context_image_encoder, context_encoder,
similarity
]
]))
optimizer = Adam(params, lr=RecommendTrainConfig.learning_rate)
epoch_id = 0
min_valid_loss = None
# Load saved state.
if isfile(model_file):
state = torch.load(model_file)
similarity.load_state_dict(state['similarity'])
optimizer.load_state_dict(state['optimizer'])
epoch_id = state['epoch_id']
min_valid_loss = state['min_valid_loss']
# Loss.
sum_loss = 0
bad_loss_cnt = 0
# Switch to train mode.
context_text_encoder.train()
context_image_encoder.train()
context_encoder.train()
similarity.train()
finished = False
for epoch_id in range(epoch_id, RecommendTrainConfig.num_iterations):
for batch_id, train_data in enumerate(train_data_loader):
# Sets gradients to 0.
optimizer.zero_grad()
context_dialog, pos_products, neg_products = train_data
texts, text_lengths, images, utter_types = context_dialog
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
batch_size = texts.size(0)
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
# utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, _ = encode_context(context_text_encoder,
context_image_encoder, context_encoder,
texts, text_lengths, images)
# (batch_size, context_vector_size)
loss = recommend_loss(similarity, batch_size, context,
pos_products, neg_products)
sum_loss += loss
loss.backward()
optimizer.step()
# Print loss every `TrainConfig.print_freq` batches.
if (batch_id + 1) % RecommendTrainConfig.print_freq == 0:
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
sum_loss /= RecommendTrainConfig.print_freq
print('epoch: {} \tbatch: {} \tloss: {} \ttime: {}'.format(
epoch_id + 1, batch_id + 1, sum_loss, cur_time))
sum_loss = 0
# Valid every `TrainConfig.valid_freq` batches.
if (batch_id + 1) % RecommendTrainConfig.valid_freq == 0:
valid_loss = recommend_valid(context_text_encoder,
context_image_encoder,
context_encoder, similarity,
valid_dataset)
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('valid_loss: {} \ttime: {}'.format(valid_loss, cur_time))
# Save current best model.
if min_valid_loss is None or valid_loss < min_valid_loss:
min_valid_loss = valid_loss
bad_loss_cnt = 0
save_dict = {
'task': RECOMMEND_TASK,
'epoch_id': epoch_id,
'min_valid_loss': min_valid_loss,
'optimizer': optimizer.state_dict(),
'context_text_encoder':
context_text_encoder.state_dict(),
'context_image_encoder':
context_image_encoder.state_dict(),
'context_encoder': context_encoder.state_dict(),
'similarity': similarity.state_dict()
}
torch.save(save_dict, model_file)
print('Best model saved.')
else:
bad_loss_cnt += 1
if bad_loss_cnt > RecommendTrainConfig.patience:
recommend_test(context_text_encoder, context_image_encoder,
context_encoder, similarity, test_dataset)
finished = True
break
if finished:
break
def recommend_valid(
context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder,
similarity: Similarity,
valid_dataset: Dataset):
"""Recommend valid.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
similarity (Similarity): Intention.
valid_dataset (Dataset): Valid dataset.
"""
# Valid dataset loader.
valid_data_loader = DataLoader(
valid_dataset,
batch_size=RecommendValidConfig.batch_size,
shuffle=True,
num_workers=RecommendValidConfig.num_data_loader_workers
)
sum_loss = 0
num_batches = 0
# Switch to eval mode.
context_text_encoder.eval()
context_image_encoder.eval()
context_encoder.eval()
# similarity.eval()
# There might be a bug in the implement of resnet.
num_ranks = torch.zeros(DatasetConfig.neg_images_max_num + 1,
dtype=torch.long)
num_ranks = num_ranks.to(GlobalConfig.device)
total_samples = 0
with torch.no_grad():
for batch_id, valid_data in enumerate(valid_data_loader):
# Only valid `ValidConfig.num_batches` batches.
if batch_id >= RecommendValidConfig.num_batches:
break
num_batches += 1
context_dialog, pos_products, neg_products = valid_data
texts, text_lengths, images, utter_types = context_dialog
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
batch_size = texts.size(0)
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
# utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, _ = encode_context(
context_text_encoder,
context_image_encoder,
context_encoder,
texts,
text_lengths,
images
)
# (batch_size, context_vector_size)
loss = recommend_loss(similarity, batch_size, context,
pos_products, neg_products)
sum_loss += loss
num_rank = recommend_eval(
similarity,
batch_size,
context,
pos_products,
neg_products
)
total_samples += batch_size
num_ranks += num_rank
for i in range(DatasetConfig.neg_images_max_num):
print('total recall@{} = {}'.format(
i + 1,
torch.sum(num_ranks[:i + 1]).item() / total_samples))
# Switch to train mode.
context_text_encoder.train()
context_image_encoder.train()
context_encoder.train()
similarity.train()
return sum_loss / num_batches
def knowledge_test(
context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder,
to_hidden: ToHidden,
attribute_kv_memory: KVMemory,
text_decoder: TextDecoder,
test_dataset: Dataset,
text_length: int,
vocab: Dict[str, int]):
"""Knowledge attribute test.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
to_hidden (ToHidden): Context to hidden.
attribute_kv_memory (KVMemory): Attribute Key-Value Memory.
text_decoder (TextDecoder): Text decoder.
test_dataset (Dataset): Valid dataset.
text_length (int): Text length.
vocab (Dict[str, int]): Vocabulary.
"""
id2word: List[str] = [None] * len(vocab)
for word, wid in vocab.items():
id2word[wid] = word
# Test dataset loader.
test_data_loader = DataLoader(
test_dataset,
batch_size=KnowledgeAttributeTestConfig.batch_size,
num_workers=KnowledgeAttributeTestConfig.num_data_loader_workers
)
sum_loss = 0
num_batches = 0
# Switch to eval mode.
context_text_encoder.eval()
context_image_encoder.eval()
context_encoder.eval()
to_hidden.eval()
attribute_kv_memory.eval()
text_decoder.eval()
output_file = open('knowledge_attribute.out', 'w')
with torch.no_grad():
for batch_id, test_data in enumerate(test_data_loader):
num_batches += 1
test_data, products = test_data
keys, values, pair_length = products
keys = keys.to(GlobalConfig.device)
values = values.to(GlobalConfig.device)
pair_length = pair_length.to(GlobalConfig.device)
texts, text_lengths, images, utter_types = test_data
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, hiddens = encode_context(
context_text_encoder,
context_image_encoder,
context_encoder,
texts,
text_lengths,
images
)
# (batch_size, context_vector_size)
encode_knowledge_func = partial(attribute_kv_memory,
keys, values, pair_length)
text_eval(to_hidden, text_decoder, text_length, id2word,
context, texts[-1], hiddens,
encode_knowledge_func, output_file=output_file)
output_file.close()
def intention_train(context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder, train_dataset: Dataset,
valid_dataset: Dataset, test_dataset: Dataset,
model_file: str):
"""Intention train.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
train_dataset (Dataset): Train dataset.
valid_dataset (Dataset): Valid dataset.
test_dataset (Dataset): Test dataset.
model_file (str): Saved model file.
"""
# Data loader.
train_data_loader = DataLoader(
dataset=train_dataset,
batch_size=IntentionTrainConfig.batch_size,
shuffle=True,
num_workers=IntentionTrainConfig.num_data_loader_workers)
# Model.
intention_config = IntentionConfig()
intention = Intention(intention_config).to(GlobalConfig.device)
# Model parameters.
params = list(
chain.from_iterable([
list(model.parameters()) for model in [
context_text_encoder, context_image_encoder, context_encoder,
intention
]
]))
optimizer = Adam(params, lr=IntentionTrainConfig.learning_rate)
epoch_id = 0
min_valid_loss = None
# Load saved state.
if isfile(model_file):
state = torch.load(model_file)
intention.load_state_dict(state['intention'])
optimizer.load_state_dict(state['optimizer'])
epoch_id = state['epoch_id']
min_valid_loss = state['min_valid_loss']
# Loss.
sum_loss = 0
bad_loss_cnt = 0
# Switch to train mode.
context_text_encoder.train()
context_image_encoder.train()
context_encoder.train()
intention.train()
finished = False
for epoch_id in range(epoch_id, IntentionTrainConfig.num_iterations):
for batch_id, train_data in enumerate(train_data_loader):
# Sets gradients to 0.
optimizer.zero_grad()
texts, text_lengths, images, utter_types = train_data
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, _ = encode_context(context_text_encoder,
context_image_encoder, context_encoder,
texts, text_lengths, images)
# (batch_size, context_vector_size)
intent_prob = intention(context)
# (batch_size, utterance_type_size)
loss = nll_loss(intent_prob, utter_types)
sum_loss += loss
loss.backward()
optimizer.step()
# Print loss every `TrainConfig.print_freq` batches.
if (batch_id + 1) % IntentionTrainConfig.print_freq == 0:
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
sum_loss /= IntentionTrainConfig.print_freq
print('epoch: {} \tbatch: {} \tloss: {} \ttime: {}'.format(
epoch_id + 1, batch_id + 1, sum_loss, cur_time))
sum_loss = 0
# Valid every `TrainConfig.valid_freq` batches.
if (batch_id + 1) % IntentionTrainConfig.valid_freq == 0:
valid_loss, accuracy = intention_valid(context_text_encoder,
context_image_encoder,
context_encoder,
intention,
valid_dataset)
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('valid_loss: {} \taccuracy: {} \ttime: {}'.format(
valid_loss, accuracy, cur_time))
# Save current best model.
if min_valid_loss is None or valid_loss < min_valid_loss:
min_valid_loss = valid_loss
bad_loss_cnt = 0
save_dict = {
'task': INTENTION_TASK,
'epoch_id': epoch_id,
'min_valid_loss': min_valid_loss,
'optimizer': optimizer.state_dict(),
'context_text_encoder':
context_text_encoder.state_dict(),
'context_image_encoder':
context_image_encoder.state_dict(),
'context_encoder': context_encoder.state_dict(),
'intention': intention.state_dict()
}
torch.save(save_dict, model_file)
print('Best model saved.')
else:
bad_loss_cnt += 1
if bad_loss_cnt > IntentionTrainConfig.patience:
intention_test(context_text_encoder,
context_image_encoder, context_encoder,
intention, test_dataset)
finished = True
break
if finished:
break
