def concat_examples(batch, device=None):
if len(batch) == 0:
raise ValueError('batch is empty')
if device is None:
def to_device(x):
return x
elif device < 0:
to_device = cuda.to_cpu
else:
def to_device(x):
return cuda.to_gpu(x, device, cuda.Stream.null)
result = [
to_device(_concat_arrays([s[0] for s in batch], -1)), # ws
to_device(_concat_arrays([s[1] for s in batch], -1)), # ps
to_device(_concat_arrays([s[2] for s in batch], -1)), # ss
[s[3] for s in batch]
] # ls
if len(batch[0]) == 7:
result.append([to_device(s[4]) for s in batch]) # cat_ts
result.append([to_device(s[5]) for s in batch]) # dep_ts
result.append(to_device(_concat_arrays([s[6] for s in batch],
None))) # weights
return tuple(result)
def concat_examples(batch,
device=None,
padding=None,
indices_concat=None,
indices_to_device=None):
if len(batch) == 0:
raise ValueError('batch is empty')
first_elem = batch[0]
elem_size = len(first_elem)
if indices_concat is None:
indices_concat = range(elem_size)
if indices_to_device is None:
indices_to_device = range(elem_size)
result = []
if not isinstance(padding, tuple):
padding = [padding] * elem_size
for i in six.moves.range(elem_size):
res = [example[i] for example in batch]
if i in indices_concat:
res = _concat_arrays(res, padding[i])
if i in indices_to_device:
if i in indices_concat:
res = to_device(device, res)
else:
res = [to_device(device, r) for r in res]
result.append(res)
return tuple(result)
def list_examples(batch, device=None, padding=None):
if len(batch) == 0:
raise ValueError('batch is empty')
if padding is not None:
raise NotImplementedError
first_elem = batch[0]
if isinstance(first_elem, tuple):
result = []
if not isinstance(padding, tuple):
padding = [padding] * len(first_elem)
for i in six.moves.range(len(first_elem)):
result.append([to_device(device, example[i]) for example in batch])
return tuple(result)
elif isinstance(first_elem, dict):
result = {}
if not isinstance(padding, dict):
padding = {key: padding for key in first_elem}
for key in first_elem:
result[key] = [to_device(device, example[key])
for example in batch]
return result
else:
raise NotImplementedError
return to_device(device, _concat_arrays(batch, padding))
def convert_sg(batch, device):
if len(batch) == 0: raise ValueError('batch is empty')
result = [
to_device(device,
_concat_arrays([example[0] for example in batch], None)),
[example[1] for example in batch]
]
return tuple(result)
def batch_without_padding(name, batch, device=None):
"""Batch without padding (increase ndim by 1)
Args:
name (str): propaty name of graph data
batch (list[BaseGraphData]): list of base graph data
device (chainer.backend.Device, optional): device. Defaults to None.
Returns:
BaseGraphDataset: graph dataset sent to `device`
"""
feat = _concat_arrays([getattr(example, name) for example in batch], None)
return device.send(feat)
def concat_examples(batch, device=None):
# batch: img, mask, label, scale
if len(batch) == 0:
raise ValueError('batch is empty')
first_elem = batch[0]
result = []
for i in six.moves.range(len(first_elem)):
array = _concat_arrays([example[i] for example in batch], None)
if i == 0: # img
result.append(to_device(device, array))
else:
result.append(array)
return tuple(result)
def concat_examples(batch, device=None, padding=None):
if len(batch) == 0:
raise ValueError('batch is empty')
first_elem = batch[0]
result = []
if not isinstance(padding, tuple):
padding = [padding] * len(first_elem)
for i in six.moves.range(len(first_elem)):
res = _concat_arrays([example[i] for example in batch], padding[i])
if i in [0, 1]: # img, bbox
res = to_device(device, res)
result.append(res)
return tuple(result)