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[Docs] Add code-spell pre-commit hook and fix a large mount of typos. (…
…open-mmlab#470) * Add code spell check hook * Add codespell config * Fix a lot of typos. * Add formating.py to keep compatibility.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,180 +1,9 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| from collections.abc import Sequence | ||
| # flake8: noqa | ||
| import warnings | ||
|
|
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| import mmcv | ||
| import numpy as np | ||
| import torch | ||
| from mmcv.parallel import DataContainer as DC | ||
| from PIL import Image | ||
| from .formatting import * | ||
|
|
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| from ..builder import PIPELINES | ||
|
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| 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`') | ||
|
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| @PIPELINES.register_module() | ||
| class ToTensor(object): | ||
|
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| def __init__(self, keys): | ||
| self.keys = keys | ||
|
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| 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})' | ||
|
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|
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| @PIPELINES.register_module() | ||
| class ImageToTensor(object): | ||
|
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| 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})' | ||
|
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|
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| @PIPELINES.register_module() | ||
| class Transpose(object): | ||
|
|
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| 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})' | ||
|
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|
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| @PIPELINES.register_module() | ||
| class ToPIL(object): | ||
|
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| def __init__(self): | ||
| pass | ||
|
|
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| def __call__(self, results): | ||
| results['img'] = Image.fromarray(results['img']) | ||
| return results | ||
|
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|
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| @PIPELINES.register_module() | ||
| class ToNumpy(object): | ||
|
|
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| def __init__(self): | ||
| pass | ||
|
|
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| def __call__(self, results): | ||
| results['img'] = np.array(results['img'], dtype=np.float32) | ||
| return results | ||
|
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|
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| @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 avaliable | ||
| """ | ||
|
|
||
| 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 | ||
|
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||
| def __repr__(self): | ||
| return self.__class__.__name__ + \ | ||
| f'(keys={self.keys}, meta_keys={self.meta_keys})' | ||
|
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| @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__}()' | ||
| warnings.warn('DeprecationWarning: mmcls.datasets.pipelines.formating will be ' | ||
| 'deprecated in 2021, please replace it with ' | ||
| 'mmcls.datasets.pipelines.formatting.') |
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