mmdet/datasets/pipelines/formatting.py

# Copyright (c) OpenMMLab. All rights reserved.
from collections.abc import Sequence

import mmcv
import numpy as np
import torch
from mmcv.parallel import DataContainer as DC

from ..builder import PIPELINES


def to_tensor(data):
    """Convert objects of various python types to :obj:`torch.Tensor`.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.

    Args:
        data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
            be converted.
    """

    if isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, Sequence) and not mmcv.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError(f'type {type(data)} cannot be converted to tensor.')


@PIPELINES.register_module()
class ToTensor:
    """Convert some results to :obj:`torch.Tensor` by given keys.

    Args:
        keys (Sequence[str]): Keys that need to be converted to Tensor.
    """

    def __init__(self, keys):
        self.keys = keys

    def __call__(self, results):
        """Call function to convert data in results to :obj:`torch.Tensor`.

        Args:
            results (dict): Result dict contains the data to convert.

        Returns:
            dict: The result dict contains the data converted
                to :obj:`torch.Tensor`.
        """
        for key in self.keys:
            results[key] = to_tensor(results[key])
        return results

    def __repr__(self):
        return self.__class__.__name__ + f'(keys={self.keys})'


@PIPELINES.register_module()
class ImageToTensor:
    """Convert image to :obj:`torch.Tensor` by given keys.

    The dimension order of input image is (H, W, C). The pipeline will convert
    it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
    (1, H, W).

    Args:
        keys (Sequence[str]): Key of images to be converted to Tensor.
    """

    def __init__(self, keys):
        self.keys = keys

    def __call__(self, results):
        """Call function to convert image in results to :obj:`torch.Tensor` and
        permute the channel order.

        Args:
            results (dict): Result dict contains the image data to convert.

        Returns:
            dict: The result dict contains the image converted
                to :obj:`torch.Tensor` and permuted to (C, H, W) order.
        """
        for key in self.keys:
            img = results[key]
            if len(img.shape) < 3:
                img = np.expand_dims(img, -1)
            results[key] = to_tensor(img).permute(2, 0, 1).contiguous()
        return results

    def __repr__(self):
        return self.__class__.__name__ + f'(keys={self.keys})'


@PIPELINES.register_module()
class Transpose:
    """Transpose some results by given keys.

    Args:
        keys (Sequence[str]): Keys of results to be transposed.
        order (Sequence[int]): Order of transpose.
    """

    def __init__(self, keys, order):
        self.keys = keys
        self.order = order

    def __call__(self, results):
        """Call function to transpose the channel order of data in results.

        Args:
            results (dict): Result dict contains the data to transpose.

        Returns:
            dict: The result dict contains the data transposed to \
                ``self.order``.
        """
        for key in self.keys:
            results[key] = results[key].transpose(self.order)
        return results

    def __repr__(self):
        return self.__class__.__name__ + \
               f'(keys={self.keys}, order={self.order})'


@PIPELINES.register_module()
class ToDataContainer:
    """Convert results to :obj:`mmcv.DataContainer` by given fields.

    Args:
        fields (Sequence[dict]): Each field is a dict like
            ``dict(key='xxx', **kwargs)``. The ``key`` in result will
            be converted to :obj:`mmcv.DataContainer` with ``**kwargs``.
            Default: ``(dict(key='img', stack=True), dict(key='gt_bboxes'),
            dict(key='gt_labels'))``.
    """

    def __init__(self,
                 fields=(dict(key='img', stack=True), dict(key='gt_bboxes'),
                         dict(key='gt_labels'))):
        self.fields = fields

    def __call__(self, results):
        """Call function to convert data in results to
        :obj:`mmcv.DataContainer`.

        Args:
            results (dict): Result dict contains the data to convert.

        Returns:
            dict: The result dict contains the data converted to \
                :obj:`mmcv.DataContainer`.
        """

        for field in self.fields:
            field = field.copy()
            key = field.pop('key')
            results[key] = DC(results[key], **field)
        return results

    def __repr__(self):
        return self.__class__.__name__ + f'(fields={self.fields})'


@PIPELINES.register_module()
class DefaultFormatBundle:
    """Default formatting bundle.

    It simplifies the pipeline of formatting common fields, including "img",
    "proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg".
    These fields are formatted as follows.

    - img: (1)transpose & to tensor, (2)to DataContainer (stack=True)
    - proposals: (1)to tensor, (2)to DataContainer
    - gt_bboxes: (1)to tensor, (2)to DataContainer
    - gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
    - gt_labels: (1)to tensor, (2)to DataContainer
    - gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
    - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \
                       (3)to DataContainer (stack=True)

    Args:
        img_to_float (bool): Whether to force the image to be converted to
            float type. Default: True.
        pad_val (dict): A dict for padding value in batch collating,
            the default value is `dict(img=0, masks=0, seg=255)`.
            Without this argument, the padding value of "gt_semantic_seg"
            will be set to 0 by default, which should be 255.
    """

    def __init__(self,
                 img_to_float=True,
                 pad_val=dict(img=0, masks=0, seg=255)):
        self.img_to_float = img_to_float
        self.pad_val = pad_val

    def __call__(self, results):
        """Call function to transform and format common fields in results.

        Args:
            results (dict): Result dict contains the data to convert.

        Returns:
            dict: The result dict contains the data that is formatted with \
                default bundle.
        """

        if 'img' in results:
            img = results['img']
            if self.img_to_float is True and img.dtype == np.uint8:
                # Normally, image is of uint8 type without normalization.
                # At this time, it needs to be forced to be converted to
                # flot32, otherwise the model training and inference
                # will be wrong. Only used for YOLOX currently .
                img = img.astype(np.float32)
            # add default meta keys
            results = self._add_default_meta_keys(results)
            if len(img.shape) < 3:
                img = np.expand_dims(img, -1)
            # To improve the computational speed by by 3-5 times, apply:
            # If image is not contiguous, use
            # `numpy.transpose()` followed by `numpy.ascontiguousarray()`
            # If image is already contiguous, use
            # `torch.permute()` followed by `torch.contiguous()`
            # Refer to https://github.com/open-mmlab/mmdetection/pull/9533
            # for more details
            if not img.flags.c_contiguous:
                img = np.ascontiguousarray(img.transpose(2, 0, 1))
                img = to_tensor(img)
            else:
                img = to_tensor(img).permute(2, 0, 1).contiguous()
            results['img'] = DC(
                img, padding_value=self.pad_val['img'], stack=True)
        for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']:
            if key not in results:
                continue
            results[key] = DC(to_tensor(results[key]))
        if 'gt_masks' in results:
            results['gt_masks'] = DC(
                results['gt_masks'],
                padding_value=self.pad_val['masks'],
                cpu_only=True)
        if 'gt_semantic_seg' in results:
            results['gt_semantic_seg'] = DC(
                to_tensor(results['gt_semantic_seg'][None, ...]),
                padding_value=self.pad_val['seg'],
                stack=True)
        return results

    def _add_default_meta_keys(self, results):
        """Add default meta keys.

        We set default meta keys including `pad_shape`, `scale_factor` and
        `img_norm_cfg` to avoid the case where no `Resize`, `Normalize` and
        `Pad` are implemented during the whole pipeline.

        Args:
            results (dict): Result dict contains the data to convert.

        Returns:
            results (dict): Updated result dict contains the data to convert.
        """
        img = results['img']
        results.setdefault('pad_shape', img.shape)
        results.setdefault('scale_factor', 1.0)
        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
        results.setdefault(
            'img_norm_cfg',
            dict(
                mean=np.zeros(num_channels, dtype=np.float32),
                std=np.ones(num_channels, dtype=np.float32),
                to_rgb=False))
        return results

    def __repr__(self):
        return self.__class__.__name__ + \
               f'(img_to_float={self.img_to_float})'


@PIPELINES.register_module()
class Collect:
    """Collect data from the loader relevant to the specific task.

    This is usually the last stage of the data loader pipeline. Typically keys
    is set to some subset of "img", "proposals", "gt_bboxes",
    "gt_bboxes_ignore", "gt_labels", and/or "gt_masks".

    The "img_meta" item is always populated.  The contents of the "img_meta"
    dictionary depends on "meta_keys". By default this includes:

        - "img_shape": shape of the image input to the network as a tuple \
            (h, w, c).  Note that images may be zero padded on the \
            bottom/right if the batch tensor is larger than this shape.

        - "scale_factor": a float indicating the preprocessing scale

        - "flip": a boolean indicating if image flip transform was used

        - "filename": path to the image file

        - "ori_shape": original shape of the image as a tuple (h, w, c)

        - "pad_shape": image shape after padding

        - "img_norm_cfg": a dict of normalization information:

            - mean - per channel mean subtraction
            - std - per channel std divisor
            - to_rgb - bool indicating if bgr was converted to rgb

    Args:
        keys (Sequence[str]): Keys of results to be collected in ``data``.
        meta_keys (Sequence[str], optional): Meta keys to be converted to
            ``mmcv.DataContainer`` and collected in ``data[img_metas]``.
            Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape',
            'pad_shape', 'scale_factor', 'flip', 'flip_direction',
            'img_norm_cfg')``
    """

    def __init__(self,
                 keys,
                 meta_keys=('filename', 'ori_filename', 'ori_shape',
                            'img_shape', 'pad_shape', 'scale_factor', 'flip',
                            'flip_direction', 'img_norm_cfg')):
        self.keys = keys
        self.meta_keys = meta_keys

    def __call__(self, results):
        """Call function to collect keys in results. The keys in ``meta_keys``
        will be converted to :obj:mmcv.DataContainer.

        Args:
            results (dict): Result dict contains the data to collect.

        Returns:
            dict: The result dict contains the following keys

                - keys in``self.keys``
                - ``img_metas``
        """

        data = {}
        img_meta = {}
        for key in self.meta_keys:
            img_meta[key] = results[key]
        data['img_metas'] = DC(img_meta, cpu_only=True)
        for key in self.keys:
            data[key] = results[key]
        return data

    def __repr__(self):
        return self.__class__.__name__ + \
               f'(keys={self.keys}, meta_keys={self.meta_keys})'


@PIPELINES.register_module()
class WrapFieldsToLists:
    """Wrap fields of the data dictionary into lists for evaluation.

    This class can be used as a last step of a test or validation
    pipeline for single image evaluation or inference.

    Example:
        >>> test_pipeline = [
        >>>    dict(type='LoadImageFromFile'),
        >>>    dict(type='Normalize',
                    mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True),
        >>>    dict(type='Pad', size_divisor=32),
        >>>    dict(type='ImageToTensor', keys=['img']),
        >>>    dict(type='Collect', keys=['img']),
        >>>    dict(type='WrapFieldsToLists')
        >>> ]
    """

    def __call__(self, results):
        """Call function to wrap fields into lists.

        Args:
            results (dict): Result dict contains the data to wrap.

        Returns:
            dict: The result dict where value of ``self.keys`` are wrapped \
                into list.
        """

        # Wrap dict fields into lists
        for key, val in results.items():
            results[key] = [val]
        return results

    def __repr__(self):
        return f'{self.__class__.__name__}()'