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# Copyright (c) OpenMMLab. All rights reserved.
from collections.abc import Mapping, Sequence
import torch
import torch.nn.functional as F
from torch.utils.data.dataloader import default_collate
from .data_container import DataContainer
def collate(batch, samples_per_gpu=1):
"""Puts each data field into a tensor/DataContainer with outer dimension
batch size.
Extend default_collate to add support for
:type:`~mmcv.parallel.DataContainer`. There are 3 cases.
1. cpu_only = True, e.g., meta data
2. cpu_only = False, stack = True, e.g., images tensors
3. cpu_only = False, stack = False, e.g., gt bboxes
"""
if not isinstance(batch, Sequence):
raise TypeError(f'{batch.dtype} is not supported.')
if isinstance(batch[0], DataContainer):
stacked = []
if batch[0].cpu_only:
for i in range(0, len(batch), samples_per_gpu):
stacked.append(
[sample.data for sample in batch[i:i + samples_per_gpu]])
return DataContainer(
stacked, batch[0].stack, batch[0].padding_value, cpu_only=True)
elif batch[0].stack:
for i in range(0, len(batch), samples_per_gpu):
assert isinstance(batch[i].data, torch.Tensor)
if batch[i].pad_dims is not None:
ndim = batch[i].dim()
assert ndim > batch[i].pad_dims
max_shape = [0 for _ in range(batch[i].pad_dims)]
for dim in range(1, batch[i].pad_dims + 1):
max_shape[dim - 1] = batch[i].size(-dim)
for sample in batch[i:i + samples_per_gpu]:
for dim in range(0, ndim - batch[i].pad_dims):
assert batch[i].size(dim) == sample.size(dim)
for dim in range(1, batch[i].pad_dims + 1):
max_shape[dim - 1] = max(max_shape[dim - 1],
sample.size(-dim))
padded_samples = []
for sample in batch[i:i + samples_per_gpu]:
pad = [0 for _ in range(batch[i].pad_dims * 2)]
for dim in range(1, batch[i].pad_dims + 1):
pad[2 * dim -
1] = max_shape[dim - 1] - sample.size(-dim)
padded_samples.append(
F.pad(
sample.data, pad, value=sample.padding_value))
stacked.append(default_collate(padded_samples))
elif batch[i].pad_dims is None:
stacked.append(
default_collate([
sample.data
for sample in batch[i:i + samples_per_gpu]
]))
else:
raise ValueError(
'pad_dims should be either None or integers (1-3)')
else:
for i in range(0, len(batch), samples_per_gpu):
stacked.append(
[sample.data for sample in batch[i:i + samples_per_gpu]])
return DataContainer(stacked, batch[0].stack, batch[0].padding_value)
elif isinstance(batch[0], Sequence):
transposed = zip(*batch)
return [collate(samples, samples_per_gpu) for samples in transposed]
elif isinstance(batch[0], Mapping):
return {
key: collate([d[key] for d in batch], samples_per_gpu)
for key in batch[0]
}
else:
return default_collate(batch)
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