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_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 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()
""" 7) Create Model """
VOCAB_SIZE = len(vocab.word2index)
IMAGE_EMB_DIM = 256
WORD_EMB_DIM = 256
HIDDEN_DIM = 512
word_embedding = torch.nn.Embedding(
num_embeddings=VOCAB_SIZE,
embedding_dim=WORD_EMB_DIM,
)
image_encoder = ImageEncoder(out_dim=IMAGE_EMB_DIM)
image_decoder = CaptionRNN(num_classes=VOCAB_SIZE,
word_emb_dim=WORD_EMB_DIM,
img_emb_dim=IMAGE_EMB_DIM,
hidden_dim=HIDDEN_DIM)
word_embedding.eval()
image_encoder.eval()
image_decoder.eval()
""" 9) Load Weights """
LOAD_WEIGHTS = True
EMBEDDING_WEIGHT_FILE = 'checkpoints/BIGDATASET-weights-embedding-epoch-3.pt'
ENCODER_WEIGHT_FILE = 'checkpoints/BIGDATASET-weights-encoder-epoch-3.pt'
DECODER_WEIGHT_FILE = 'checkpoints/BIGDATASET-weights-decoder-epoch-3.pt'
if LOAD_WEIGHTS:
print("Loading pretrained weights...")
word_embedding.load_state_dict(torch.load(EMBEDDING_WEIGHT_FILE))
image_encoder.load_state_dict(torch.load(ENCODER_WEIGHT_FILE))
image_decoder.load_state_dict(torch.load(DECODER_WEIGHT_FILE))
""" 10) Device Setup"""
device = 'cuda:1'
device = torch.device(device)
def valid(epoch=1,
checkpoints_dir='./checkpoints',
use_bert=False,
data_path=None,
out_path='../prediction_result/valid_pred.json',
output_ndcg=True):
print("valid epoch{}".format(epoch))
if data_path is not None:
kdd_dataset = ValidDataset(data_path, use_bert=use_bert)
else:
kdd_dataset = ValidDataset(data_path, use_bert=use_bert)
loader = DataLoader(kdd_dataset,
collate_fn=collate_fn_valid,
batch_size=128,
shuffle=False,
num_workers=8)
tbar = tqdm(loader)
text_encoder = TextEncoder(kdd_dataset.unknown_token + 1,
1024,
256,
use_bert=use_bert).cuda()
image_encoder = ImageEncoder(input_dim=2048, output_dim=1024,
nhead=4).cuda()
score_model = ScoreModel(1024, 256).cuda()
# category_embedding = model.CategoryEmbedding(768).cuda()
checkpoints = torch.load(
os.path.join(checkpoints_dir, 'model-epoch{}.pth'.format(epoch)))
text_encoder.load_state_dict(checkpoints['query'])
image_encoder.load_state_dict(checkpoints['item'])
score_model.load_state_dict(checkpoints['score'])
# score_model.load_state_dict(checkpoints['score'])
outputs = {}
image_encoder.eval()
text_encoder.eval()
score_model.eval()
for query_id, product_id, query, query_len, features, boxes, category, obj_len in tbar:
query, query_len = query.cuda(), query_len.cuda()
query, hidden = text_encoder(query, query_len)
features, boxes, obj_len = features.cuda(), boxes.cuda(), obj_len.cuda(
)
features = image_encoder(features, boxes, obj_len)
score = score_model(query, hidden, query_len, features)
score = score.data.cpu().numpy()
# print(score2)
for q_id, p_id, s in zip(query_id.data.numpy(),
product_id.data.numpy(), score):
outputs.setdefault(str(q_id), [])
outputs[str(q_id)].append((p_id, s))
for k, v in outputs.items():
v = sorted(v, key=lambda x: x[1], reverse=True)
v = [(str(x[0]), float(x[1])) for x in v]
outputs[k] = v
with open(out_path, 'w') as f:
json.dump(outputs, f)
if output_ndcg:
pred = read_json(out_path)
gt = read_json('../data/valid/valid_answer.json')
score = 0
k = 5
for key, val in gt.items():
ground_truth_ids = [str(x) for x in val]
predictions = [x[0] for x in pred[key][:k]]
ref_vec = [1.0] * len(ground_truth_ids)
pred_vec = [
1.0 if pid in ground_truth_ids else 0.0 for pid in predictions
]
score += get_ndcg(pred_vec, ref_vec, k)
# print(key)
# print([pid for pid in predictions if pid not in ground_truth_ids])
# print('========')
# score += len(set(predictions).intersection(ground_truth_ids)) / len(ground_truth_ids)
score = score / len(gt)
print('ndcg@%d: %.4f' % (k, score))
return score
else:
return None