mmclassification/mmcls/datasets/pipelines/formating.py

from collections.abc import Sequence

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

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`.
    """
    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.'
            'Supported types are: `numpy.ndarray`, `torch.Tensor`, '
            '`Sequence`, `int` and `float`')


@PIPELINES.register_module()
class ToTensor(object):

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

    def __call__(self, results):
        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(object):

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

    def __call__(self, results):
        for key in self.keys:
            img = results[key]
            if len(img.shape) < 3:
                img = np.expand_dims(img, -1)
            results[key] = to_tensor(img.transpose(2, 0, 1))
        return results

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


@PIPELINES.register_module()
class Transpose(object):

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

    def __call__(self, results):
        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(object):

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

    def __call__(self, results):
        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 ToPIL(object):

    def __init__(self):
        pass

    def __call__(self, results):
        results['img'] = Image.fromarray(results['img'])
        return results


@PIPELINES.register_module()
class ToNumpy(object):

    def __init__(self):
        pass

    def __call__(self, results):
        results['img'] = np.array(results['img'], dtype=np.float32)
        # results['img'] = np.array(results['img'])
        return results


@PIPELINES.register_module()
class Collect(object):
    """
    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" and "gt_labels".

    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.

        - "filename": path to the image file

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

        - "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
    """

    def __init__(self, keys, meta_keys=('filename', 'ori_shape', 'img_shape')):
        self.keys = keys
        self.meta_keys = meta_keys

    def __call__(self, results):
        data = {}
        img_meta = {}
        for key in self.meta_keys:
            img_meta[key] = results[key]
        # data['img_metas'] = DC(img_meta, cpu_only=True)
        # data['img_metas'] = img_meta
        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(object):
    """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='ImageToTensor', keys=['img']),
        >>>    dict(type='Collect', keys=['img']),
        >>>    dict(type='WrapIntoLists')
        >>> ]
    """

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

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