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You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Source code for mmcls.datasets.pipelines.formatting

# 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 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`')


[docs]@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})'
[docs]@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})'
[docs]@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})'
[docs]@PIPELINES.register_module() class ToPIL(object): def __init__(self): pass def __call__(self, results): results['img'] = Image.fromarray(results['img']) return results
[docs]@PIPELINES.register_module() class ToNumpy(object): def __init__(self): pass def __call__(self, results): results['img'] = np.array(results['img'], dtype=np.float32) return results
[docs]@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_label". 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_shape', 'img_shape', 'flip', 'flip_direction', 'img_norm_cfg') Returns: dict: The result dict contains the following keys - keys in ``self.keys`` - ``img_metas`` if available """ def __init__(self, keys, meta_keys=('filename', 'ori_filename', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'img_norm_cfg')): self.keys = keys self.meta_keys = meta_keys def __call__(self, results): data = {} img_meta = {} for key in self.meta_keys: if key in results: 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(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__}()' @PIPELINES.register_module() class ToHalf(object): def __init__(self, keys): self.keys = keys def __call__(self, results): for k in self.keys: if isinstance(results[k], torch.Tensor): results[k] = results[k].to(torch.half) else: results[k] = results[k].astype(np.float16) return results
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