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 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_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_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_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()