class VisDialDataset(Dataset):
def __init__(self,
visdial_jsonpath: str,
config: Dict[str, Union[int, str]],
overfit: bool = False,
in_memory: bool = False):
super().__init__()
self.config = config
self.json_reader = VisDialJsonReader(visdial_jsonpath)
self.vocabulary = Vocabulary(
config["word_counts_json"], min_count=config["vocab_min_count"]
)
# initialize image features reader according to split
image_features_hdfpath = config["image_features_train_h5"]
if "val" in self.json_reader.split:
image_features_hdfpath = config["image_features_val_h5"]
elif "test" in self.json_reader.split:
image_features_hdfpath = config["image_features_test_h5"]
self.hdf_reader = ImageFeaturesHdfReader(image_features_hdfpath, in_memory)
# keep a list of image_ids as primary keys to access data
self.image_ids = list(self.json_reader.dialogs.keys())
if overfit:
self.image_ids = self.image_ids[:5]
@property
def split(self):
return self.json_reader.split
def __len__(self):
return len(self.image_ids)
def __getitem__(self, index):
# get image_id, which serves as a primary key for current instance
image_id = self.image_ids[index]
# get image features for this image_id using hdf reader
image_features = self.hdf_reader[image_id]
image_features = torch.tensor(image_features)
# normalize image features at zero-th dimension (since there's no batch dimension)
if self.config["img_norm"]:
image_features = normalize(image_features, dim=0, p=2)
# retrieve instance for this image_id using json reader
visdial_instance = self.json_reader[image_id]
caption = visdial_instance["caption"]
dialog = visdial_instance["dialog"]
# convert word tokens of caption, question, answer and answer options to integers
caption = self.vocabulary.to_indices(caption)
for i in range(len(dialog)):
dialog[i]["question"] = self.vocabulary.to_indices(dialog[i]["question"])
dialog[i]["answer"] = self.vocabulary.to_indices(dialog[i]["answer"])
for j in range(len(dialog[i]["answer_options"])):
dialog[i]["answer_options"][j] = self.vocabulary.to_indices(
dialog[i]["answer_options"][j]
)
questions, question_lengths = self._pad_sequences(
[dialog_round["question"] for dialog_round in dialog]
)
history, history_lengths = self._get_history(
caption,
[dialog_round["question"] for dialog_round in dialog],
[dialog_round["answer"] for dialog_round in dialog]
)
answer_options = []
answer_option_lengths = []
for dialog_round in dialog:
options, option_lengths = self._pad_sequences(dialog_round["answer_options"])
answer_options.append(options)
answer_option_lengths.append(option_lengths)
answer_options = torch.stack(answer_options, 0)
if "test" not in self.split:
answer_indices = [dialog_round["gt_index"] for dialog_round in dialog]
# collect everything as tensors for ``collate_fn`` of dataloader to work seemlessly
# questions, history, etc. are converted to LongTensors, for nn.Embedding input
item = {}
item["img_ids"] = torch.tensor(image_id).long()
item["img_feat"] = image_features
item["ques"] = questions.long()
item["hist"] = history.long()
item["opt"] = answer_options.long()
item["ques_len"] = torch.tensor(question_lengths).long()
item["hist_len"] = torch.tensor(history_lengths).long()
item["opt_len"] = torch.tensor(answer_option_lengths).long()
item["num_rounds"] = torch.tensor(visdial_instance["num_rounds"]).long()
if "test" not in self.split:
item["ans_ind"] = torch.tensor(answer_indices).long()
return item
def _pad_sequences(self, sequences: List[List[int]]):
"""Given tokenized sequences (either questions, answers or answer options, tokenized
in ``__getitem__``), padding them to maximum specified sequence length. Return as a
tensor of size ``(*, max_sequence_length)``.
This method is only called in ``__getitem__``, chunked out separately for readability.
Parameters
----------
sequences : List[List[int]]
List of tokenized sequences, each sequence is typically a List[int].
Returns
-------
torch.Tensor, torch.Tensor
Tensor of sequences padded to max length, and length of sequences before padding.
"""
for i in range(len(sequences)):
sequences[i] = sequences[i][: self.config["max_sequence_length"] - 1]
sequence_lengths = [len(sequence) for sequence in sequences]
# pad all sequences to max_sequence_length
maxpadded_sequences = torch.full(
(len(sequences), self.config["max_sequence_length"]),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_sequences = pad_sequence(
[torch.tensor(sequence) for sequence in sequences],
batch_first=True, padding_value=self.vocabulary.PAD_INDEX
)
maxpadded_sequences[:, :padded_sequences.size(1)] = padded_sequences
return maxpadded_sequences, sequence_lengths
def _get_history(self,
caption: List[int],
questions: List[List[int]],
answers: List[List[int]]):
# allow double length of caption, equivalent to a concatenated QA pair
caption = caption[: self.config["max_sequence_length"] * 2 - 1]
for i in range(len(questions)):
questions[i] = questions[i][: self.config["max_sequence_length"] - 1]
for i in range(len(answers)):
answers[i] = answers[i][: self.config["max_sequence_length"] - 1]
# history for first round is caption, else concatenated QA pair of previous round
history = []
history.append(caption)
for question, answer in zip(questions, answers):
history.append(question + answer + [self.vocabulary.EOS_INDEX])
# drop last entry from history (there's no eleventh question)
history = history[:-1]
max_history_length = self.config["max_sequence_length"] * 2
if self.config["concat_history"]:
# concatenated_history has similar structure as history, except it contains
# concatenated QA pairs from previous rounds
concatenated_history = []
concatenated_history.append(caption)
for i in range(1, len(history)):
concatenated_history.append([])
for j in range(i + 1):
concatenated_history[i].extend(history[j])
max_history_length = self.config["max_sequence_length"] * 2 * len(history)
history = concatenated_history
history_lengths = [len(round_history) for round_history in history]
maxpadded_history = torch.full(
(len(history), max_history_length),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_history = pad_sequence(
[torch.tensor(round_history) for round_history in history],
batch_first=True, padding_value=self.vocabulary.PAD_INDEX
)
maxpadded_history[:, :padded_history.size(1)] = padded_history
return maxpadded_history, history_lengths
class VisDialDataset(Dataset):
"""
A full representation of VisDial v1.0 (train/val/test) dataset. According
to the appropriate split, it returns dictionary of question, image,
history, ground truth answer, answer options, dense annotations etc.
"""
def __init__(
self,
config: Dict[str, Any],
dialogs_jsonpath: str,
dense_annotations_jsonpath: Optional[str] = None,
overfit: bool = False,
in_memory: bool = False,
return_options: bool = True,
add_boundary_toks: bool = False,
sample_flag: bool = False,
):
super().__init__()
self.config = config
self.return_options = return_options
self.add_boundary_toks = add_boundary_toks
self.dialogs_reader = DialogsReader(dialogs_jsonpath)
if "val" in self.split and dense_annotations_jsonpath is not None:
self.annotations_reader = DenseAnnotationsReader(
dense_annotations_jsonpath)
else:
self.annotations_reader = None
self.vocabulary = Vocabulary(config["word_counts_json"],
min_count=config["vocab_min_count"])
# Initialize image features reader according to split.
image_features_hdfpath = config["image_features_train_h5"]
if "val" in self.dialogs_reader.split:
image_features_hdfpath = config["image_features_val_h5"]
elif "test" in self.dialogs_reader.split:
image_features_hdfpath = config["image_features_test_h5"]
self.hdf_reader = ImageFeaturesHdfReader(image_features_hdfpath,
in_memory)
if sample_flag == False:
self.image_ids = list(self.dialogs_reader.dialogs.keys())
file = open('data/qt_count.json', 'r') ##load qt information
self.qt_file = json.loads(file.read())
self.qt_list = list(self.qt_file.keys())
file.close()
for i in range(len(self.qt_list)):
self.qt_list[i] = word_tokenize(self.qt_list[i])
# Keep a list of image_ids as primary keys to access data.
if sample_flag == True:
samplefile = open('data/visdial_1.0_train_dense_sample.json', 'r')
sample = json.loads(samplefile.read())
samplefile.close()
ndcg_id_list = []
for idx in range(len(sample)):
ndcg_id_list.append(sample[idx]['image_id'])
self.image_ids = ndcg_id_list
if overfit:
self.image_ids = self.image_ids[:5]
@property
def split(self):
return self.dialogs_reader.split
def __len__(self):
return len(self.image_ids)
def __getitem__(self, index):
# Get image_id, which serves as a primary key for current instance.
image_id = self.image_ids[index]
# Get image features for this image_id using hdf reader.
image_features = self.hdf_reader[image_id]
image_features = torch.tensor(image_features)
# Normalize image features at zero-th dimension (since there's no batch
# dimension).
if self.config["img_norm"]:
image_features = normalize(image_features, dim=0, p=2)
# Retrieve instance for this image_id using json reader.
# print(image_id)
visdial_instance = self.dialogs_reader[image_id]
caption = visdial_instance["caption"]
dialog = visdial_instance["dialog"]
opt_idx = visdial_instance['opt_list']
# Convert word tokens of caption, question, answer and answer options
# to integers.
qt = torch.full([10, 1], 54) #totally 55 question types
qt_len = torch.full([10, 1], 2) #maxmize about 6
caption = self.vocabulary.to_indices(caption)
for i in range(len(dialog)):
question_tmp = dialog[i]["question"]
for k in range(len(self.qt_list)):
if self.qt_list[k] == question_tmp[0:len(self.qt_list[k])]:
qt[i] = k
qt_len[i] = len(self.qt_list[k])
dialog[i]["question"] = self.vocabulary.to_indices(
dialog[i]["question"])
if self.add_boundary_toks:
dialog[i]["answer"] = self.vocabulary.to_indices(
[self.vocabulary.SOS_TOKEN] + dialog[i]["answer"] +
[self.vocabulary.EOS_TOKEN])
else:
dialog[i]["answer"] = self.vocabulary.to_indices(
dialog[i]["answer"])
if self.return_options:
for j in range(len(dialog[i]["answer_options"])):
if self.add_boundary_toks:
dialog[i]["answer_options"][
j] = self.vocabulary.to_indices(
[self.vocabulary.SOS_TOKEN] +
dialog[i]["answer_options"][j] +
[self.vocabulary.EOS_TOKEN])
else:
dialog[i]["answer_options"][
j] = self.vocabulary.to_indices(
dialog[i]["answer_options"][j])
questions, question_lengths = self._pad_sequences(
[dialog_round["question"] for dialog_round in dialog])
history, history_lengths = self._get_history(
caption,
[dialog_round["question"] for dialog_round in dialog],
[dialog_round["answer"] for dialog_round in dialog],
)
answers_in, answer_lengths = self._pad_sequences(
[dialog_round["answer"][:-1] for dialog_round in dialog]
# [dialog_round["answer"][:] for dialog_round in dialog]
)
answers_out, _ = self._pad_sequences(
# [dialog_round["answer"][:] for dialog_round in dialog]
[dialog_round["answer"][1:] for dialog_round in dialog])
answers, _ = self._pad_sequences(
# [dialog_round["answer"][:] for dialog_round in dialog]
[dialog_round["answer"] for dialog_round in dialog])
# Collect everything as tensors for ``collate_fn`` of dataloader to
# work seamlessly questions, history, etc. are converted to
# LongTensors, for nn.Embedding input.
item = {}
item['opt_idx'] = torch.tensor(opt_idx).long()
item["img_ids"] = torch.tensor(image_id).long()
item["img_feat"] = image_features
item["ques"] = questions.long()
item["qt"] = qt.long()
item["qt_len"] = qt_len.long()
item["ans"] = answers.long()
item["hist"] = history.long()
item["ans_in"] = answers_in.long()
item["ans_out"] = answers_out.long()
item["ques_len"] = torch.tensor(question_lengths).long()
item["hist_len"] = torch.tensor(history_lengths).long()
item["ans_len"] = torch.tensor(answer_lengths).long()
item["num_rounds"] = torch.tensor(
visdial_instance["num_rounds"]).long()
if self.return_options:
if self.add_boundary_toks:
answer_options_in, answer_options_out, answer_options = [], [], []
answer_option_lengths = []
for dialog_round in dialog:
options, option_lengths = self._pad_sequences([
option[:-1]
for option in dialog_round["answer_options"]
])
answer_options_in.append(options)
options, _ = self._pad_sequences([
option[1:] for option in dialog_round["answer_options"]
])
answer_options_out.append(options)
options, _ = self._pad_sequences([
option[:] for option in dialog_round["answer_options"]
])
answer_options.append(options)
answer_option_lengths.append(option_lengths)
answer_options_in = torch.stack(answer_options_in, 0)
answer_options_out = torch.stack(answer_options_out, 0)
answer_options = torch.stack(answer_options, 0)
item["opt"] = answer_options.long()
item["opt_in"] = answer_options_in.long()
item["opt_out"] = answer_options_out.long()
item["opt_len"] = torch.tensor(answer_option_lengths).long()
else:
answer_options = []
answer_option_lengths = []
for dialog_round in dialog:
options, option_lengths = self._pad_sequences(
dialog_round["answer_options"])
answer_options.append(options)
answer_option_lengths.append(option_lengths)
answer_options = torch.stack(answer_options, 0)
item["opt"] = answer_options.long()
item["opt_len"] = torch.tensor(answer_option_lengths).long()
if "test" not in self.split:
answer_indices = [
dialog_round["gt_index"] for dialog_round in dialog
]
item["ans_ind"] = torch.tensor(answer_indices).long()
# Gather dense annotations.
if "val" in self.split:
dense_annotations = self.annotations_reader[image_id]
item["relevance"] = torch.tensor(
dense_annotations["gt_relevance"]).float()
item["round_id"] = torch.tensor(
dense_annotations["round_id"]).long()
return item
def _pad_sequences(self, sequences: List[List[int]]):
"""Given tokenized sequences (either questions, answers or answer
options, tokenized in ``__getitem__``), padding them to maximum
specified sequence length. Return as a tensor of size
``(*, max_sequence_length)``.
This method is only called in ``__getitem__``, chunked out separately
for readability.
Parameters
----------
sequences : List[List[int]]
List of tokenized sequences, each sequence is typically a
List[int].
Returns
-------
torch.Tensor, torch.Tensor
Tensor of sequences padded to max length, and length of sequences
before padding.
"""
for i in range(len(sequences)):
sequences[i] = sequences[i][:self.config["max_sequence_length"] -
1]
sequence_lengths = [len(sequence) for sequence in sequences]
# Pad all sequences to max_sequence_length.
maxpadded_sequences = torch.full(
(len(sequences), self.config["max_sequence_length"]),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_sequences = pad_sequence(
[torch.tensor(sequence) for sequence in sequences],
batch_first=True,
padding_value=self.vocabulary.PAD_INDEX,
)
maxpadded_sequences[:, :padded_sequences.size(1)] = padded_sequences
return maxpadded_sequences, sequence_lengths
def _get_history(
self,
caption: List[int],
questions: List[List[int]],
answers: List[List[int]],
):
# Allow double length of caption, equivalent to a concatenated QA pair.
caption = caption[:self.config["max_sequence_length"] * 2 - 1]
for i in range(len(questions)):
questions[i] = questions[i][:self.config["max_sequence_length"] -
1]
for i in range(len(answers)):
answers[i] = answers[i][:self.config["max_sequence_length"] - 1]
# History for first round is caption, else concatenated QA pair of
# previous round.
history = []
history.append(caption)
for question, answer in zip(questions, answers):
history.append(question + answer + [self.vocabulary.EOS_INDEX])
# Drop last entry from history (there's no eleventh question).
history = history[:-1] #切掉最后一个
max_history_length = self.config["max_sequence_length"] * 2
if self.config.get("concat_history", False):
# Concatenated_history has similar structure as history, except it
# contains concatenated QA pairs from previous rounds.
concatenated_history = []
concatenated_history.append(caption)
for i in range(1, len(history)):
concatenated_history.append([])
for j in range(i + 1):
concatenated_history[i].extend(history[j])
max_history_length = (self.config["max_sequence_length"] * 2 *
len(history))
history = concatenated_history
history_lengths = [len(round_history) for round_history in history]
maxpadded_history = torch.full(
(len(history), max_history_length),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_history = pad_sequence(
[torch.tensor(round_history) for round_history in history],
batch_first=True,
padding_value=self.vocabulary.PAD_INDEX,
)
maxpadded_history[:, :padded_history.size(1)] = padded_history
return maxpadded_history, history_lengths
class VisDialDataset(Dataset):
"""
A full representation of VisDial v1.0 (train/val/test) dataset. According
to the appropriate split, it returns dictionary of question, image,
history, ground truth answer, answer options, dense annotations etc.
"""
def __init__(
self,
config: Dict[str, Any],
dialogs_jsonpath: str,
dense_annotations_jsonpath: Optional[str] = None,
augment_dense_annotations_jsonpath: Optional[str] = None,
use_pretrained_emb: bool = False,
qa_emb_file_path: Optional[str] = None, # SA: todo remove this
hist_emb_file_path: Optional[str] = None, # SA: todo remove this
use_caption: bool = True,
num_hist_turns: int = 10,
finetune: bool = False,
overfit: bool = False,
in_memory: bool = False,
num_workers: int = 1,
return_options: bool = True,
add_boundary_toks: bool = False):
super().__init__()
self.config = config
# SA: embedding reader
self.use_pretrained_emb = use_pretrained_emb
self.return_options = return_options
self.add_boundary_toks = add_boundary_toks
self.dialogs_reader = DialogsReader(
dialogs_jsonpath,
num_examples=(5 if overfit else None),
num_workers=num_workers,
use_pretrained_emb=self.use_pretrained_emb)
self.finetune = finetune
self.use_caption = use_caption
# SA: embedding reader
if self.use_pretrained_emb:
assert qa_emb_file_path, "Did you forget to set emb file path?"
# @todo: for now coming through argparse
self.qa_emb_file_path = qa_emb_file_path
self.hist_emb_file_path = hist_emb_file_path
# hist_emb_file_path = config["hist_emb_file_path"]
# TransformerEmbeddingsHdfReader(embedding_path, in_memory)
# self.embedding_reader = TransformerEmbeddingsHdfReader(hist_emb_file_path,
# in_memory)
self.question_reader = QuesEmbeddingsHdfReader(
qa_emb_file_path, in_memory)
self.ans_reader = AnswerEmbeddingsHdfReader(
qa_emb_file_path, in_memory)
self.caption_reader = CaptionEmbeddingsHdfReader(
qa_emb_file_path, in_memory)
# SA: we dont pass in_memory here because history is too big
# SA: todo this key would change
self.hist_reader = HistEmbeddingsHdfReader(hist_emb_file_path,
hdfs_key="hist")
# SA: if finetuning for train/val otherwise just validation set
if self.finetune or ("val" in self.split
and dense_annotations_jsonpath is not None):
self.annotations_reader = DenseAnnotationsReader(
dense_annotations_jsonpath)
else:
self.annotations_reader = None
if augment_dense_annotations_jsonpath is not None:
self.augmented_annotations_reader = AugmentedDenseAnnotationsReader(
augment_dense_annotations_jsonpath)
self.use_augment_dense = True
else:
self.use_augment_dense = False
self.vocabulary = Vocabulary(config["word_counts_json"],
min_count=config["vocab_min_count"])
# Initialize image features reader according to split.
image_features_hdfpath = config["image_features_train_h5"]
if "val" in self.dialogs_reader.split:
image_features_hdfpath = config["image_features_val_h5"]
elif "test" in self.dialogs_reader.split:
image_features_hdfpath = config["image_features_test_h5"]
self.hdf_reader = ImageFeaturesHdfReader(image_features_hdfpath,
in_memory)
# Keep a list of image_ids as primary keys to access data.
# For finetune we use only those image id where we have dense annotations
if self.finetune:
self.image_ids = list(self.annotations_reader.keys)
else:
self.image_ids = list(self.dialogs_reader.dialogs.keys())
if overfit:
self.image_ids = self.image_ids[:5]
@property
def split(self):
return self.dialogs_reader.split
def __len__(self):
return len(self.image_ids)
def __getitem__(self, index):
# start = time.time()
# Get image_id, which serves as a primary key for current instance.
image_id = self.image_ids[index]
# Get image features for this image_id using hdf reader.
image_features = self.hdf_reader[image_id]
image_features = torch.tensor(image_features)
# Normalize image features at zero-th dimension (since there's no batch
# dimension).
if self.config["img_norm"]:
image_features = normalize(image_features, dim=0, p=2)
# Retrieve instance for this image_id using json reader.
visdial_instance = self.dialogs_reader[image_id]
caption = visdial_instance["caption"]
dialog = visdial_instance["dialog"]
# SA: reading embeddings here
if self.use_pretrained_emb:
# We need indexes to actually call the readers here now.
dialog_with_index = visdial_instance["dialog_with_index"]
original_index = visdial_instance["original_index"]
assert len(dialog) == len(
dialog_with_index
), "These should be equal => just saving the index instead of string"
# ideally should be in if-else clause
ques_embeddings = []
ans_embeddings = []
opts_embeddings = []
# Convert word tokens of caption, question, answer and answer options
# to integers.
caption = self.vocabulary.to_indices(caption)
for i in range(len(dialog)):
# SA: using embeddings here in the same loop
if self.use_pretrained_emb:
# SA: todo We dont need caption embeddings when we already have history???
# caption_embedding = self.caption_reader[original_index]
ques_embeddings.append(
self.question_reader[dialog_with_index[i]["question"]])
ans_embeddings.append(
self.ans_reader[dialog_with_index[i]["answer"]])
# SA: original code
dialog[i]["question"] = self.vocabulary.to_indices(
dialog[i]["question"])
if self.add_boundary_toks:
dialog[i]["answer"] = self.vocabulary.to_indices(
[self.vocabulary.SOS_TOKEN] + dialog[i]["answer"] +
[self.vocabulary.EOS_TOKEN])
else:
dialog[i]["answer"] = self.vocabulary.to_indices(
dialog[i]["answer"])
# for disc decoder
if self.return_options:
# Ideally should be in if-else clause
opts_round_embeddings = []
for j in range(len(dialog[i]["answer_options"])):
# SA: trying option encodings here now
if self.use_pretrained_emb:
opts_round_embeddings.append(self.ans_reader[
dialog_with_index[i]["answer_options"][j]])
if self.add_boundary_toks:
dialog[i]["answer_options"][
j] = self.vocabulary.to_indices(
[self.vocabulary.SOS_TOKEN] +
dialog[i]["answer_options"][j] +
[self.vocabulary.EOS_TOKEN])
else:
dialog[i]["answer_options"][
j] = self.vocabulary.to_indices(
dialog[i]["answer_options"][j])
# Ideally should be in if-else clause
opts_embeddings.append(opts_round_embeddings)
questions, question_lengths = self._pad_sequences(
[dialog_round["question"] for dialog_round in dialog])
history, history_lengths = self._get_history(
caption,
[dialog_round["question"] for dialog_round in dialog],
[dialog_round["answer"] for dialog_round in dialog],
)
answers_in, answer_lengths = self._pad_sequences(
[dialog_round["answer"][:-1] for dialog_round in dialog])
answers_out, _ = self._pad_sequences(
[dialog_round["answer"][1:] for dialog_round in dialog])
# Collect everything as tensors for ``collate_fn`` of dataloader to
# work seamlessly questions, history, etc. are converted to
# LongTensors, for nn.Embedding input.
item = {}
item["img_ids"] = torch.tensor(image_id).long()
item["img_feat"] = image_features
item["ques"] = questions.long()
item["hist"] = history.long()
item["ans_in"] = answers_in.long(
) # SA: probably useful for training gen
item["ans_out"] = answers_out.long(
) # SA: probably useful for training gen
item["ques_len"] = torch.tensor(question_lengths).long()
item["hist_len"] = torch.tensor(history_lengths).long()
item["ans_len"] = torch.tensor(answer_lengths).long()
item["num_rounds"] = torch.tensor(
visdial_instance["num_rounds"]).long()
## SA: pretrained embedding here
if self.use_pretrained_emb:
# See https://github.com/pytorch/pytorch/issues/13918
item["ques_embeddings"] = torch.tensor(
np.array(ques_embeddings)).float()
# now (10, 20, 768) ==> will be (bs, 10, 20, 768) (bert embeddings)
item["opts_embeddings"] = torch.tensor(
np.array(opts_embeddings)).float()
# ans_embeddings = torch.tensor(np.array(ans_embeddings)).float()
# caption_embedding = torch.tensor(np.array(caption_embedding)).float()
# SA: todo proxy hist embeddings
# hist_embeddings = self._get_history_embedding(caption_embedding, item["ques_embeddings"],
# ans_embeddings)
item["hist_embeddings"] = self.hist_reader[image_id]
# (10, 100, 20, 768) ==> will be (bs, 10, 100, 20, 768) (bert embeddings)
if self.return_options:
if self.add_boundary_toks:
answer_options_in, answer_options_out = [], []
answer_option_lengths = []
for dialog_round in dialog:
options, option_lengths = self._pad_sequences([
option[:-1]
for option in dialog_round["answer_options"]
])
answer_options_in.append(options)
options, _ = self._pad_sequences([
option[1:] for option in dialog_round["answer_options"]
])
answer_options_out.append(options)
answer_option_lengths.append(option_lengths)
answer_options_in = torch.stack(answer_options_in, 0)
answer_options_out = torch.stack(answer_options_out, 0)
item["opt_in"] = answer_options_in.long()
item["opt_out"] = answer_options_out.long()
item["opt_len"] = torch.tensor(answer_option_lengths).long()
else:
answer_options = []
answer_option_lengths = []
for dialog_round in dialog:
options, option_lengths = self._pad_sequences(
dialog_round["answer_options"])
answer_options.append(options)
answer_option_lengths.append(option_lengths)
answer_options = torch.stack(answer_options, 0)
# used by disc model
## options_length SA: used by model to select non-zero options
item["opt"] = answer_options.long()
item["opt_len"] = torch.tensor(answer_option_lengths).long()
if "test" not in self.split:
answer_indices = [
dialog_round["gt_index"] for dialog_round in dialog
]
item["ans_ind"] = torch.tensor(
answer_indices).long() # Used by evaluate for ndcg
# Gather dense annotations.
if self.finetune or ("val" in self.split):
dense_annotations = self.annotations_reader[image_id]
# SA: have to do this because of changed dic key in train
if "val" in self.split:
item["gt_relevance"] = torch.tensor(
dense_annotations["gt_relevance"]).float()
elif "train" in self.split:
item["gt_relevance"] = torch.tensor(
dense_annotations["relevance"]).float()
item["round_id"] = torch.tensor(
dense_annotations["round_id"]).long()
# end = time.time()
# time_taken = end - start
# print('Time for loading item: ',time_taken)
if self.use_augment_dense:
augmented_dense_annotations = self.augmented_annotations_reader[
image_id]
item["augmented_gt_relevance"] = torch.tensor(
augmented_dense_annotations["augmented_gt_relevance"]).float()
return item
def _pad_sequences(self, sequences: List[List[int]]):
"""Given tokenized sequences (either questions, answers or answer
options, tokenized in ``__getitem__``), padding them to maximum
specified sequence length. Return as a tensor of size
``(*, max_sequence_length)``.
This method is only called in ``__getitem__``, chunked out separately
for readability.
Parameters
----------
sequences : List[List[int]]
List of tokenized sequences, each sequence is typically a
List[int].
Returns
-------
torch.Tensor, torch.Tensor
Tensor of sequences padded to max length, and length of sequences
before padding.
"""
for i in range(len(sequences)):
sequences[i] = sequences[i][:self.config["max_sequence_length"] -
1]
sequence_lengths = [len(sequence) for sequence in sequences]
# Pad all sequences to max_sequence_length.
maxpadded_sequences = torch.full(
(len(sequences), self.config["max_sequence_length"]),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_sequences = pad_sequence(
[torch.tensor(sequence) for sequence in sequences],
batch_first=True,
padding_value=self.vocabulary.PAD_INDEX,
)
maxpadded_sequences[:, :padded_sequences.size(1)] = padded_sequences
return maxpadded_sequences, sequence_lengths
def _get_history(
self,
caption: List[int],
questions: List[List[int]],
answers: List[List[int]],
):
# Allow double length of caption, equivalent to a concatenated QA pair.
caption = caption[:self.config["max_sequence_length"] * 2 - 1]
for i in range(len(questions)):
questions[i] = questions[i][:self.config["max_sequence_length"] -
1]
for i in range(len(answers)):
answers[i] = answers[i][:self.config["max_sequence_length"] - 1]
# History for first round is caption, else concatenated QA pair of
# previous round.
history = []
## SA: appending EOS after caption
caption = caption + [self.vocabulary.EOS_INDEX]
if self.use_caption:
history.append(caption)
else:
history.append([self.vocabulary.EOS_INDEX])
# print("Not using caption in history.")
for question, answer in zip(questions, answers):
history.append(question + answer + [self.vocabulary.EOS_INDEX])
# Drop last entry from history (there's no eleventh question).
history = history[:-1]
max_history_length = self.config["max_sequence_length"] * 2
if self.config.get("concat_history", False):
# Concatenated_history has similar structure as history, except it
# contains concatenated QA pairs from previous rounds.
concatenated_history = []
concatenated_history.append(caption)
for i in range(1, len(history)):
concatenated_history.append([])
for j in range(i + 1):
concatenated_history[i].extend(history[j])
max_history_length = (self.config["max_sequence_length"] * 2 *
len(history))
history = concatenated_history
history_lengths = [len(round_history) for round_history in history]
maxpadded_history = torch.full(
(len(history), max_history_length),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_history = pad_sequence(
[torch.tensor(round_history) for round_history in history],
batch_first=True,
padding_value=self.vocabulary.PAD_INDEX,
)
maxpadded_history[:, :padded_history.size(1)] = padded_history
return maxpadded_history, history_lengths
def _get_history_embedding(self, caption, questions, answers):
"""
only for one dialogue here
num_rounds = 10
:param caption: (40, 768) ==> cross check
:param questions: (10, 20, 768)
:param answers: (10, 20, 768)
:return:
"""
concatenated_qa_history = torch.cat([questions, answers], 1)
# print(concatenated_qa_history.size())
# Drop last
concatenated_qa_history = concatenated_qa_history[:-1]
caption = caption.unsqueeze(0)
# Concatenate along batch now
concatenated_qa_history = torch.cat([caption, concatenated_qa_history],
0) # shape (10, 40, 768)
if self.config.get("concat_history", False):
max_history_length = (self.config["max_sequence_length"] * 2 *
len(concatenated_qa_history)) # 400
history_list = []
num_rounds, _, rep_size = concatenated_qa_history.size(
) # (10, 40, 768)
# hist_tensor = concatenated_qa_history.view(-1, rep_size) # (10*40, 768)
# hist_tensor = hist_tensor.unsqueeze(0).repeat(num_rounds,1,1) # (10, 400, 768)
# zero_array =
for i in range(1, num_rounds + 1):
pad_array = torch.zeros(
max_history_length -
self.config["max_sequence_length"] * 2 * (i), rep_size)
hist_array = concatenated_qa_history[:i].view(-1, rep_size)
hist_round = torch.cat([hist_array, pad_array], 0)
history_list.append(hist_round)
history = torch.stack(history_list, 0)
else:
history = concatenated_qa_history
return history
def _get_combined_ques_caption_or_hist(self, caption: List[int],
questions: List[List[int]],
answers: List[List[int]]):
# Allow double length of caption, equivalent to a concatenated QA pair.
caption = caption[:self.config["max_sequence_length"] * 2 - 1]
for i in range(len(questions)):
questions[i] = questions[i][:self.config["max_sequence_length"] -
1]
for i in range(len(answers)):
answers[i] = answers[i][:self.config["max_sequence_length"] - 1]
args.test_json,
caption_jsonpath=args.captions_test_json,
overfit=args.overfit,
in_memory=args.in_memory)
val_dataloader = DataLoader(val_dataset,
batch_size=config["solver"]["batch_size"],
num_workers=args.cpu_workers)
with open(config["dataset"]["glovepath"], "r") as glove_file:
glove = json.load(glove_file)
glovevocabulary = Vocabulary(config["dataset"]["word_counts_json"],
min_count=config["dataset"]["vocab_min_count"])
KAT = []
for key in glove.keys():
keylist = [key]
token = glovevocabulary.to_indices(keylist)
key_and_token = keylist + token
KAT.append(key_and_token)
glove_token = {}
for item in KAT:
glove_token[item[1]] = glove[item[0]]
glove_list = []
for i in range(len(glovevocabulary)):
if i in glove_token.keys():
glove_list.append(glove_token[i])
else:
randArray = random.random(size=(1, 300)).tolist()
glove_list.append(randArray[0])
glove_token = torch.Tensor(glove_list).view(len(glovevocabulary), -1)
class VisDialDataset(Dataset):
"""
A full representation of VisDial v1.0 (train/val/test) dataset. According to the appropriate
split, it returns dictionary of question, image, history, ground truth answer, answer options,
dense annotations etc.
"""
def __init__(self,
config: Dict[str, Any],
dialogs_jsonpath: str,
caption_jsonpath: str,
dense_annotations_jsonpath: Optional[str] = None,
overfit: bool = False,
in_memory: bool = False):
super().__init__()
self.config = config
self.dialogs_reader = DialogsReader(dialogs_jsonpath)
if "val" in self.split and dense_annotations_jsonpath is not None:
self.annotations_reader = DenseAnnotationsReader(
dense_annotations_jsonpath)
else:
self.annotations_reader = None
self.vocabulary = Vocabulary(config["word_counts_json"],
min_count=config["vocab_min_count"])
# Initialize image features reader according to split.
image_features_hdfpath = config["image_features_train_h5"]
if "val" in self.dialogs_reader.split:
image_features_hdfpath = config["image_features_val_h5"]
elif "test" in self.dialogs_reader.split:
image_features_hdfpath = config["image_features_test_h5"]
self.hdf_reader = ImageFeaturesHdfReader(image_features_hdfpath,
in_memory)
# Keep a list of image_ids as primary keys to access data.
self.image_ids = list(self.dialogs_reader.dialogs.keys())
if overfit:
self.image_ids = self.image_ids[:5]
self.captions_reader = CaptionReader(caption_jsonpath)
@property
def split(self):
return self.dialogs_reader.split
def __len__(self):
return len(self.image_ids)
def __getitem__(self, index):
# Get image_id, which serves as a primary key for current instance.
image_id = self.image_ids[index]
# Get image features for this image_id using hdf reader.
image_features, image_relation = self.hdf_reader[image_id]
image_features = torch.tensor(image_features)
image_relation = torch.tensor(image_relation) #relation
# Normalize image features at zero-th dimension (since there's no batch dimension).
if self.config["img_norm"]:
image_features = normalize(image_features, dim=0, p=2)
# Retrieve instance for this image_id using json reader.
visdial_instance = self.dialogs_reader[image_id]
caption = visdial_instance["caption"]
dialog = visdial_instance["dialog"]
# Convert word tokens of caption, question, answer and answer options to integers.
caption = self.vocabulary.to_indices(caption)
for i in range(len(dialog)):
dialog[i]["question"] = self.vocabulary.to_indices(
dialog[i]["question"])
dialog[i]["answer"] = self.vocabulary.to_indices(
dialog[i]["answer"])
for j in range(len(dialog[i]["answer_options"])):
dialog[i]["answer_options"][j] = self.vocabulary.to_indices(
dialog[i]["answer_options"][j])
questions, question_lengths = self._pad_sequences(
[dialog_round["question"] for dialog_round in dialog])
history, history_lengths = self._get_history(
caption, [dialog_round["question"] for dialog_round in dialog],
[dialog_round["answer"] for dialog_round in dialog])
answer_options = []
answer_option_lengths = []
for dialog_round in dialog:
options, option_lengths = self._pad_sequences(
dialog_round["answer_options"])
answer_options.append(options)
answer_option_lengths.append(option_lengths)
answer_options = torch.stack(answer_options, 0)
if "test" not in self.split:
answer_indices = [
dialog_round["gt_index"] for dialog_round in dialog
]
captions_dic = self.captions_reader[image_id]
captions_mul = captions_dic["captions"]
captions_new = []
for i in range(len(captions_mul)):
captions_each = self.vocabulary.to_indices(captions_mul[i])
captions_new.append(captions_each)
captions_new, captions_len = self._pad_captions(captions_new)
# Collect everything as tensors for ``collate_fn`` of dataloader to work seemlessly
# questions, history, etc. are converted to LongTensors, for nn.Embedding input.
item = {}
item["img_ids"] = torch.tensor(image_id).long()
item["img_feat"] = image_features
item["relations"] = image_relation
item["ques"] = questions.long()
item["hist"] = history.long()
item["opt"] = answer_options.long()
item["ques_len"] = torch.tensor(question_lengths).long()
item["hist_len"] = torch.tensor(history_lengths).long()
item["opt_len"] = torch.tensor(answer_option_lengths).long()
item["num_rounds"] = torch.tensor(
visdial_instance["num_rounds"]).long()
if "test" not in self.split:
item["ans_ind"] = torch.tensor(answer_indices).long()
# Gather dense annotations.
if "val" in self.split:
dense_annotations = self.annotations_reader[image_id]
item["gt_relevance"] = torch.tensor(
dense_annotations["gt_relevance"]).float()
item["round_id"] = torch.tensor(
dense_annotations["round_id"]).long()
#关于caption的相关参数
item["captions_len"] = torch.tensor(captions_len).long()
item["captions"] = captions_new.long()
return item
def _pad_sequences(self, sequences: List[List[int]]):
"""Given tokenized sequences (either questions, answers or answer options, tokenized
in ``__getitem__``), padding them to maximum specified sequence length. Return as a
tensor of size ``(*, max_sequence_length)``.
This method is only called in ``__getitem__``, chunked out separately for readability.
Parameters
----------
sequences : List[List[int]]
List of tokenized sequences, each sequence is typically a List[int].
Returns
-------
torch.Tensor, torch.Tensor
Tensor of sequences padded to max length, and length of sequences before padding.
"""
for i in range(len(sequences)):
sequences[i] = sequences[i][:self.config["max_sequence_length"] -
1]
sequence_lengths = [len(sequence) for sequence in sequences]
# Pad all sequences to max_sequence_length.
maxpadded_sequences = torch.full(
(len(sequences), self.config["max_sequence_length"]),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_sequences = pad_sequence(
[torch.tensor(sequence) for sequence in sequences],
batch_first=True,
padding_value=self.vocabulary.PAD_INDEX)
maxpadded_sequences[:, :padded_sequences.size(1)] = padded_sequences
return maxpadded_sequences, sequence_lengths
def _get_history(self, caption: List[int], questions: List[List[int]],
answers: List[List[int]]):
# Allow double length of caption, equivalent to a concatenated QA pair.
caption = caption[:self.config["max_sequence_length"] * 2 - 1]
for i in range(len(questions)):
questions[i] = questions[i][:self.config["max_sequence_length"] -
1]
for i in range(len(answers)):
answers[i] = answers[i][:self.config["max_sequence_length"] - 1]
# History for first round is caption, else concatenated QA pair of previous round.
history = []
history.append(caption)
for question, answer in zip(questions, answers):
history.append(question + answer + [self.vocabulary.EOS_INDEX])
# Drop last entry from history (there's no eleventh question).
history = history[:-1]
max_history_length = self.config["max_sequence_length"] * 2
if self.config.get("concat_history", False):
# Concatenated_history has similar structure as history, except it contains
# concatenated QA pairs from previous rounds.
concatenated_history = []
concatenated_history.append(caption)
for i in range(1, len(history)):
concatenated_history.append([])
for j in range(i + 1):
concatenated_history[i].extend(history[j])
max_history_length = self.config["max_sequence_length"] * 2 * len(
history)
history = concatenated_history
history_lengths = [len(round_history) for round_history in history]
maxpadded_history = torch.full(
(len(history), max_history_length),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_history = pad_sequence(
[torch.tensor(round_history) for round_history in history],
batch_first=True,
padding_value=self.vocabulary.PAD_INDEX)
maxpadded_history[:, :padded_history.size(1)] = padded_history
return maxpadded_history, history_lengths
def _pad_captions(self, sequences: List[List[int]]):
LEN_S = len(sequences)
if LEN_S > self.config["caption_round_num"]:
for i in range(LEN_S - self.config["caption_round_num"]):
sequences.pop(-1)
#caption_len.pop(-1)
caption_len = []
for i in range(len(sequences)):
LEN = len(sequences[i])
if LEN < self.config["caption_maxlen_each"]:
caption_len.append(len(sequences[i]))
for j in range(self.config["caption_maxlen_each"] - LEN):
sequences[i].append(0)
elif LEN > self.config["caption_maxlen_each"]:
for j in range(LEN - self.config["caption_maxlen_each"]):
sequences[i].pop(-1)
caption_len.append(len(sequences[i]))
else:
caption_len.append(len(sequences[i]))
LEN_S = len(sequences)
if LEN_S < self.config["caption_round_num"]:
j = 0
#LENS = len(sequences)
for i in range(self.config["caption_round_num"] - LEN_S):
if j >= LEN_S - 1:
j = 0
else:
j += 1
sequences.append(sequences[j])
length_new = caption_len[j]
caption_len.append(length_new)
sequences = torch.tensor(sequences).view(
self.config["caption_round_num"],
self.config["caption_maxlen_each"])
return sequences, caption_len
class KFoldVisDialDataset(Dataset):
"""
A full representation of VisDial v1.0 (train/val/test) dataset. According
to the appropriate split, it returns dictionary of question, image,
history, ground truth answer, answer options, dense annotations etc.
"""
def __init__(
self,
config: Dict[str, Any],
dialogs_jsonpath: str,
dense_annotations_jsonpath: str,
fold_split: Optional[str] = 'train',
fold: int = -1,
return_adjusted_gt_relevance: bool = False,
overfit: bool = False,
in_memory: bool = False,
return_options: bool = True,
add_boundary_toks: bool = False,
):
super().__init__()
self.config = config
self.return_options = return_options
self.return_adjusted_gt_relevance = return_adjusted_gt_relevance
self.add_boundary_toks = add_boundary_toks
self.dialogs_reader = DialogsReader(dialogs_jsonpath)
self.annotations_reader = DenseAnnotationsReader(
dense_annotations_jsonpath
)
self.fold_split = fold_split
self.fold = fold
self.n_folds = config['n_folds']
if config['word_embedding_type'] == 'glove':
self.vocabulary = GloveVocabulary(
word_counts_path = config['word_counts_json'],
min_count=config['vocab_min_count'],
glove_weight_path=config['glove_weight_txt'],
vec_size=config['glove_emb_dim'],
glove_vec_num=config['glove_vec_num']
)
else:
self.vocabulary = Vocabulary(
word_counts_path=config["word_counts_json"],
min_count=config["vocab_min_count"]
)
# Initialize image features reader according to split.
image_features_hdfpath = config["image_features_train_h5"]
if ("val" in self.dialogs_reader.split
and "fake" not in self.dialogs_reader.split):
image_features_hdfpath = config["image_features_val_h5"]
elif "test" in self.dialogs_reader.split:
image_features_hdfpath = config["image_features_test_h5"]
self.hdf_reader = ImageFeaturesHdfReader(
image_features_hdfpath, in_memory
)
# Keep a list of image_ids as primary keys to access data.
all_image_ids = np.array(list(self.dialogs_reader.dialogs.keys()))
if fold < 0 or fold_split == 'test':
self.image_ids = all_image_ids.tolist()
else:
kf = KFold(n_splits=self.n_folds, shuffle=False, random_state=606)
train_index, val_index = list(kf.split(all_image_ids))[fold]
if fold_split == 'train':
self.image_ids = all_image_ids[train_index].tolist()
elif fold_split == 'val':
self.image_ids = all_image_ids[val_index].tolist()
else:
raise NotImplementedError()
if overfit:
self.image_ids = self.image_ids[:5]
@property
def split(self):
return self.dialogs_reader.split
def __len__(self):
return len(self.image_ids)
def __getitem__(self, index):
# Get image_id, which serves as a primary key for current instance.
image_id = self.image_ids[index]
# Get image features for this image_id using hdf reader.
image_features = self.hdf_reader[image_id]
image_features = torch.tensor(image_features)
# Normalize image features at zero-th dimension (since there's no batch
# dimension).
if self.config["img_norm"]:
image_features = normalize(image_features, dim=0, p=2)
# Retrieve instance for this image_id using json reader.
visdial_instance = self.dialogs_reader[image_id]
caption = visdial_instance["caption"]
dialog = visdial_instance["dialog"]
# Convert word tokens of caption, question, answer and answer options
# to integers.
caption = self.vocabulary.to_indices(caption)
for i in range(len(dialog)):
dialog[i]["question"] = self.vocabulary.to_indices(
dialog[i]["question"]
)
if self.add_boundary_toks:
dialog[i]["answer"] = self.vocabulary.to_indices(
[self.vocabulary.SOS_TOKEN]
+ dialog[i]["answer"]
+ [self.vocabulary.EOS_TOKEN]
)
else:
dialog[i]["answer"] = self.vocabulary.to_indices(
dialog[i]["answer"]
)
if self.return_options:
for j in range(len(dialog[i]["answer_options"])):
if self.add_boundary_toks:
dialog[i]["answer_options"][
j
] = self.vocabulary.to_indices(
[self.vocabulary.SOS_TOKEN]
+ dialog[i]["answer_options"][j]
+ [self.vocabulary.EOS_TOKEN]
)
else:
dialog[i]["answer_options"][
j
] = self.vocabulary.to_indices(
dialog[i]["answer_options"][j]
)
questions, question_lengths = self._pad_sequences(
[dialog_round["question"] for dialog_round in dialog]
)
history, history_lengths = self._get_history(
caption,
[dialog_round["question"] for dialog_round in dialog],
[dialog_round["answer"] for dialog_round in dialog],
)
answers_in, answer_lengths = self._pad_sequences(
[dialog_round["answer"][:-1] for dialog_round in dialog]
)
answers_out, _ = self._pad_sequences(
[dialog_round["answer"][1:] for dialog_round in dialog]
)
# Collect everything as tensors for ``collate_fn`` of dataloader to
# work seamlessly questions, history, etc. are converted to
# LongTensors, for nn.Embedding input.
item = {}
item["img_ids"] = torch.tensor(image_id).long()
item["img_feat"] = image_features
item["ques"] = questions.long()
item["hist"] = history.long()
item["ans_in"] = answers_in.long()
item["ans_out"] = answers_out.long()
item["ques_len"] = torch.tensor(question_lengths).long()
item["hist_len"] = torch.tensor(history_lengths).long()
item["ans_len"] = torch.tensor(answer_lengths).long()
item["num_rounds"] = torch.tensor(
visdial_instance["num_rounds"]
).long()
if self.return_options:
if self.add_boundary_toks:
answer_options_in, answer_options_out = [], []
answer_option_lengths = []
for dialog_round in dialog:
options, option_lengths = self._pad_sequences(
[
option[:-1]
for option in dialog_round["answer_options"]
]
)
answer_options_in.append(options)
options, _ = self._pad_sequences(
[
option[1:]
for option in dialog_round["answer_options"]
]
)
answer_options_out.append(options)
answer_option_lengths.append(option_lengths)
answer_options_in = torch.stack(answer_options_in, 0)
answer_options_out = torch.stack(answer_options_out, 0)
item["opt_in"] = answer_options_in.long()
item["opt_out"] = answer_options_out.long()
item["opt_len"] = torch.tensor(answer_option_lengths).long()
else:
answer_options = []
answer_option_lengths = []
for dialog_round in dialog:
options, option_lengths = self._pad_sequences(
dialog_round["answer_options"]
)
answer_options.append(options)
answer_option_lengths.append(option_lengths)
answer_options = torch.stack(answer_options, 0)
item["opt"] = answer_options.long()
item["opt_len"] = torch.tensor(answer_option_lengths).long()
if "test" not in self.split:
answer_indices = [
dialog_round["gt_index"] for dialog_round in dialog
]
item["ans_ind"] = torch.tensor(answer_indices).long()
# Gather dense annotations.
if "val" in self.split or "dense" in self.split:
dense_annotations = self.annotations_reader[image_id]
item["gt_relevance"] = torch.tensor(
dense_annotations["gt_relevance"]
).float()
item["round_id"] = torch.tensor(
dense_annotations["round_id"]
).long()
if self.return_adjusted_gt_relevance:
item['adjusted_gt_relevance'] = torch.tensor(
dense_annotations['adjusted_gt_relevance']
).float()
return item
def _pad_sequences(self, sequences: List[List[int]]):
"""Given tokenized sequences (either questions, answers or answer
options, tokenized in ``__getitem__``), padding them to maximum
specified sequence length. Return as a tensor of size
``(*, max_sequence_length)``.
This method is only called in ``__getitem__``, chunked out separately
for readability.
Parameters
----------
sequences : List[List[int]]
List of tokenized sequences, each sequence is typically a
List[int].
Returns
-------
torch.Tensor, torch.Tensor
Tensor of sequences padded to max length, and length of sequences
before padding.
"""
for i in range(len(sequences)):
sequences[i] = sequences[i][
: self.config["max_sequence_length"] - 1
]
sequence_lengths = [len(sequence) for sequence in sequences]
# Pad all sequences to max_sequence_length.
maxpadded_sequences = torch.full(
(len(sequences), self.config["max_sequence_length"]),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_sequences = pad_sequence(
[torch.tensor(sequence) for sequence in sequences],
batch_first=True,
padding_value=self.vocabulary.PAD_INDEX,
)
maxpadded_sequences[:, : padded_sequences.size(1)] = padded_sequences
return maxpadded_sequences, sequence_lengths
def _get_history(
self,
caption: List[int],
questions: List[List[int]],
answers: List[List[int]],
):
# Allow double length of caption, equivalent to a concatenated QA pair.
caption = caption[: self.config["max_sequence_length"] * 2 - 1]
for i in range(len(questions)):
questions[i] = questions[i][
: self.config["max_sequence_length"] - 1
]
for i in range(len(answers)):
answers[i] = answers[i][: self.config["max_sequence_length"] - 1]
# History for first round is caption, else concatenated QA pair of
# previous round.
history = []
history.append(caption)
for question, answer in zip(questions, answers):
history.append(question + answer + [self.vocabulary.EOS_INDEX])
# Drop last entry from history (there's no eleventh question).
history = history[:-1]
max_history_length = self.config["max_sequence_length"] * 2
if self.config.get("concat_history", False):
# Concatenated_history has similar structure as history, except it
# contains concatenated QA pairs from previous rounds.
concatenated_history = []
concatenated_history.append(caption)
for i in range(1, len(history)):
concatenated_history.append([])
for j in range(i + 1):
concatenated_history[i].extend(history[j])
max_history_length = (
self.config["max_sequence_length"] * 2 * len(history)
)
history = concatenated_history
history_lengths = [len(round_history) for round_history in history]
maxpadded_history = torch.full(
(len(history), max_history_length),
fill_value=self.vocabulary.PAD_INDEX,
)
padded_history = pad_sequence(
[torch.tensor(round_history) for round_history in history],
batch_first=True,
padding_value=self.vocabulary.PAD_INDEX,
)
maxpadded_history[:, : padded_history.size(1)] = padded_history
return maxpadded_history, history_lengths