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