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