def emmental_collate_fn(batch):
X_batch = defaultdict(list)
Y_batch = defaultdict(list)
for x_dict, y_dict in batch:
for field_name, value in x_dict.items():
X_batch[field_name].append(value)
for label_name, value in y_dict.items():
Y_batch[label_name].append(value)
for field_name, values in X_batch.items():
# Only merge list of tensors
if isinstance(values[0], torch.Tensor):
X_batch[field_name] = list_to_tensor(
values,
min_len=Meta.config["data_config"]["min_data_len"],
max_len=Meta.config["data_config"]["max_data_len"],
)
for label_name, values in Y_batch.items():
Y_batch[label_name] = list_to_tensor(
values,
min_len=Meta.config["data_config"]["min_data_len"],
max_len=Meta.config["data_config"]["max_data_len"],
)
return dict(X_batch), dict(Y_batch)
def test_list_to_tensor(caplog):
"""Unit test of list to tensor"""
caplog.set_level(logging.INFO)
# list of 1-D tensor with the different length
batch = [torch.Tensor([1, 2]), torch.Tensor([3]), torch.Tensor([4, 5, 6])]
padded_batch, mask_batch = list_to_tensor(batch)
assert torch.equal(padded_batch,
torch.Tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]))
assert torch.equal(
mask_batch, mask_batch.new_tensor([[0, 0, 1], [0, 1, 1], [0, 0, 0]]))
# list of 1-D tensor with the same length
batch = [
torch.Tensor([1, 2, 3]),
torch.Tensor([4, 5, 6]),
torch.Tensor([7, 8, 9])
]
padded_batch, mask_batch = list_to_tensor(batch)
assert torch.equal(padded_batch,
torch.Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
assert torch.equal(
mask_batch, mask_batch.new_tensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]]))
# list of 2-D tensor with the same size
batch = [
torch.Tensor([[1, 2, 3], [1, 2, 3]]),
torch.Tensor([[4, 5, 6], [4, 5, 6]]),
torch.Tensor([[7, 8, 9], [7, 8, 9]]),
]
padded_batch, mask_batch = list_to_tensor(batch)
assert torch.equal(
padded_batch,
torch.Tensor([[[1, 2, 3], [1, 2, 3]], [[4, 5, 6], [4, 5, 6]],
[[7, 8, 9], [7, 8, 9]]]),
)
assert mask_batch is None
# list of tensor with the different size
batch = [
torch.Tensor([[1, 2], [2, 3]]),
torch.Tensor([4, 5, 6]),
torch.Tensor([7, 8, 9, 0]),
]
padded_batch, mask_batch = list_to_tensor(batch)
assert torch.equal(
padded_batch, torch.Tensor([[1, 2, 2, 3], [4, 5, 6, 0], [7, 8, 9, 0]]))
assert torch.equal(
mask_batch,
mask_batch.new_tensor([[0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1]]))
def emmental_collate_fn(
batch: List[Tuple[Dict[str, Any], Dict[str, Tensor]]]
) -> Tuple[Dict[str, Any], Dict[str, Tensor]]:
r"""Collate function.
Args:
batch(Tuple[Dict[str, Any], Dict[str, Tensor]]): The batch to collate.
Returns:
Tuple[Dict[str, Any], Dict[str, Tensor]]: The collated batch.
"""
X_batch: defaultdict = defaultdict(list)
Y_batch: defaultdict = defaultdict(list)
for x_dict, y_dict in batch:
for field_name, value in x_dict.items():
if isinstance(value, list):
X_batch[field_name] += value
else:
X_batch[field_name].append(value)
for label_name, value in y_dict.items():
if isinstance(value, list):
Y_batch[label_name] += value
else:
Y_batch[label_name].append(value)
field_names = copy.deepcopy(list(X_batch.keys()))
for field_name in field_names:
values = X_batch[field_name]
# Only merge list of tensors
if isinstance(values[0], Tensor):
item_tensor, item_mask_tensor = list_to_tensor(
values,
min_len=Meta.config["data_config"]["min_data_len"],
max_len=Meta.config["data_config"]["max_data_len"],
)
X_batch[field_name] = item_tensor
if item_mask_tensor is not None:
X_batch[f"{field_name}_mask"] = item_mask_tensor
for label_name, values in Y_batch.items():
Y_batch[label_name] = list_to_tensor(
values,
min_len=Meta.config["data_config"]["min_data_len"],
max_len=Meta.config["data_config"]["max_data_len"],
)[0]
return dict(X_batch), dict(Y_batch)
def wrapped_f(dataset):
X_dict = defaultdict(list)
Y_dict = defaultdict(list)
examples = []
for x_dict, y_dict in dataset:
# TODO: Consider making sure aug_x_dict is not x_dict!
aug_x_dict, aug_y_dict = f(x_dict, y_dict)
if aug_x_dict is not None and aug_y_dict is not None:
examples.append((aug_x_dict, aug_y_dict))
for x_dict, y_dict in examples:
for k, v in x_dict.items():
X_dict[k].append(v)
for k, v in y_dict.items():
Y_dict[k].append(v)
for k, v in Y_dict.items():
Y_dict[k] = list_to_tensor(v)
# X_dict, Y_dict = emmental_collate_fn(examples)
aug_dataset = EmmentalDataset(name=f.__name__,
X_dict=X_dict,
Y_dict=Y_dict)
logger.info(
f"Total {len(aug_dataset)} augmented examples were created "
f"from AF {f.__name__}")
return aug_dataset
def emmental_collate_fn(
batch: Union[List[Tuple[Dict[str, Any], Dict[str, Tensor]]],
List[Dict[str, Any]]],
min_data_len: int = 0,
max_data_len: int = 0,
) -> Union[Tuple[Dict[str, Any], Dict[str, Tensor]], Dict[str, Any]]:
"""Collate function.
Args:
batch: The batch to collate.
min_data_len: The minimal data sequence length, defaults to 0.
max_data_len: The maximal data sequence length (0 means no limit), defaults to 0.
Returns:
The collated batch.
"""
X_batch: defaultdict = defaultdict(list)
Y_batch: defaultdict = defaultdict(list)
for item in batch:
# Check if batch is (x_dict, y_dict) pair
if isinstance(item, dict):
x_dict = item
y_dict: Dict[str, Any] = dict()
else:
x_dict, y_dict = item
for field_name, value in x_dict.items():
if isinstance(value, list):
X_batch[field_name] += value
else:
X_batch[field_name].append(value)
for label_name, value in y_dict.items():
if isinstance(value, list):
Y_batch[label_name] += value
else:
Y_batch[label_name].append(value)
field_names = copy.deepcopy(list(X_batch.keys()))
for field_name in field_names:
values = X_batch[field_name]
# Only merge list of tensors
if isinstance(values[0], Tensor):
item_tensor, item_mask_tensor = list_to_tensor(
values,
min_len=min_data_len,
max_len=max_data_len,
)
X_batch[field_name] = item_tensor
if item_mask_tensor is not None:
X_batch[f"{field_name}_mask"] = item_mask_tensor
for label_name, values in Y_batch.items():
Y_batch[label_name] = list_to_tensor(
values,
min_len=min_data_len,
max_len=max_data_len,
)[0]
if len(Y_batch) != 0:
return dict(X_batch), dict(Y_batch)
else:
return dict(X_batch)
def emmental_collate_fn(
batch: Union[List[Tuple[Dict[str, Any], Dict[str, Tensor]]],
List[Dict[str, Any]]]
) -> Union[Tuple[Dict[str, Any], Dict[str, Tensor]], Dict[str, Any]]:
"""Collate function.
Args:
batch: The batch to collate.
Returns:
The collated batch.
"""
X_batch: defaultdict = defaultdict(list)
Y_batch: defaultdict = defaultdict(list)
# Learnable batch should be a pair of dict, while non learnable batch is a dict
is_learnable = True if not isinstance(batch[0], dict) else False
if is_learnable:
for x_dict, y_dict in batch:
if isinstance(x_dict, dict) and isinstance(y_dict, dict):
for field_name, value in x_dict.items():
if isinstance(value, list):
X_batch[field_name] += value
else:
X_batch[field_name].append(value)
for label_name, value in y_dict.items():
if isinstance(value, list):
Y_batch[label_name] += value
else:
Y_batch[label_name].append(value)
else:
for x_dict in batch: # type: ignore
for field_name, value in x_dict.items(): # type: ignore
if isinstance(value, list):
X_batch[field_name] += value
else:
X_batch[field_name].append(value)
field_names = copy.deepcopy(list(X_batch.keys()))
for field_name in field_names:
values = X_batch[field_name]
# Only merge list of tensors
if isinstance(values[0], Tensor):
item_tensor, item_mask_tensor = list_to_tensor(
values,
min_len=Meta.config["data_config"]["min_data_len"],
max_len=Meta.config["data_config"]["max_data_len"],
)
X_batch[field_name] = item_tensor
if item_mask_tensor is not None:
X_batch[f"{field_name}_mask"] = item_mask_tensor
if is_learnable:
for label_name, values in Y_batch.items():
Y_batch[label_name] = list_to_tensor(
values,
min_len=Meta.config["data_config"]["min_data_len"],
max_len=Meta.config["data_config"]["max_data_len"],
)[0]
if is_learnable:
return dict(X_batch), dict(Y_batch)
else:
return dict(X_batch)
def bootleg_collate_fn(
batch: Union[List[Tuple[Dict[str, Any], Dict[str, Tensor]]],
List[Dict[str, Any]]]
) -> Union[Tuple[Dict[str, Any], Dict[str, Tensor]], Dict[str, Any]]:
"""Collate function (modified from emmental collate fn). The main
difference is our collate function handles the kg_adj dictionary items from
the dataset. We collate each value of each dict key.
Args:
batch: The batch to collate.
Returns:
The collated batch.
"""
X_batch: defaultdict = defaultdict(list)
# In Bootleg, we may have a nested dictionary in x_dict; we want to keep this structure but
# collate the subtensors
X_sub_batch: defaultdict = defaultdict(lambda: defaultdict(list))
Y_batch: defaultdict = defaultdict(list)
# Learnable batch should be a pair of dict, while non learnable batch is a dict
is_learnable = True if not isinstance(batch[0], dict) else False
if is_learnable:
for x_dict, y_dict in batch:
if isinstance(x_dict, dict) and isinstance(y_dict, dict):
for field_name, value in x_dict.items():
if isinstance(value, list):
X_batch[field_name] += value
elif isinstance(value, dict):
# We reinstantiate the field_name here in case there is not kg adj data
# This keeps the field_name key intact
if field_name not in X_sub_batch:
X_sub_batch[field_name] = defaultdict(list)
for sub_field_name, sub_value in value.items():
if isinstance(sub_value, list):
X_sub_batch[field_name][
sub_field_name] += sub_value
else:
X_sub_batch[field_name][sub_field_name].append(
sub_value)
else:
X_batch[field_name].append(value)
for label_name, value in y_dict.items():
if isinstance(value, list):
Y_batch[label_name] += value
else:
Y_batch[label_name].append(value)
else:
for x_dict in batch: # type: ignore
for field_name, value in x_dict.items(): # type: ignore
if isinstance(value, list):
X_batch[field_name] += value
elif isinstance(value, dict):
# We reinstantiate the field_name here in case there is not kg adj data
# This keeps the field_name key intact
if field_name not in X_sub_batch:
X_sub_batch[field_name] = defaultdict(list)
for sub_field_name, sub_value in value.items():
if isinstance(sub_value, list):
X_sub_batch[field_name][
sub_field_name] += sub_value
else:
X_sub_batch[field_name][sub_field_name].append(
sub_value)
else:
X_batch[field_name].append(value)
field_names = copy.deepcopy(list(X_batch.keys()))
for field_name in field_names:
values = X_batch[field_name]
# Only merge list of tensors
if isinstance(values[0], Tensor):
item_tensor, item_mask_tensor = list_to_tensor(
values,
min_len=Meta.config["data_config"]["min_data_len"],
max_len=Meta.config["data_config"]["max_data_len"],
)
X_batch[field_name] = item_tensor
if item_mask_tensor is not None:
X_batch[f"{field_name}_mask"] = item_mask_tensor
field_names = copy.deepcopy(list(X_sub_batch.keys()))
for field_name in field_names:
sub_field_names = copy.deepcopy(list(X_sub_batch[field_name].keys()))
for sub_field_name in sub_field_names:
values = X_sub_batch[field_name][sub_field_name]
# Only merge list of tensors
if isinstance(values[0], Tensor):
item_tensor, item_mask_tensor = list_to_tensor(
values,
min_len=Meta.config["data_config"]["min_data_len"],
max_len=Meta.config["data_config"]["max_data_len"],
)
X_sub_batch[field_name][sub_field_name] = item_tensor
if item_mask_tensor is not None:
X_sub_batch[field_name][
f"{sub_field_name}_mask"] = item_mask_tensor
# Add sub batch to batch
for field_name in field_names:
X_batch[field_name] = dict(X_sub_batch[field_name])
if is_learnable:
for label_name, values in Y_batch.items():
Y_batch[label_name] = list_to_tensor(
values,
min_len=Meta.config["data_config"]["min_data_len"],
max_len=Meta.config["data_config"]["max_data_len"],
)[0]
if is_learnable:
return dict(X_batch), dict(Y_batch)
else:
return dict(X_batch)
