179 lines
4.9 KiB
Python
179 lines
4.9 KiB
Python
from collections.abc import Sequence
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import mmcv
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import numpy as np
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import torch
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from mmcv.parallel import DataContainer as DC
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from PIL import Image
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from ..builder import PIPELINES
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def to_tensor(data):
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"""Convert objects of various python types to :obj:`torch.Tensor`.
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Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
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:class:`Sequence`, :class:`int` and :class:`float`.
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"""
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if isinstance(data, torch.Tensor):
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return data
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elif isinstance(data, np.ndarray):
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return torch.from_numpy(data)
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elif isinstance(data, Sequence) and not mmcv.is_str(data):
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return torch.tensor(data)
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elif isinstance(data, int):
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return torch.LongTensor([data])
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elif isinstance(data, float):
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return torch.FloatTensor([data])
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else:
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raise TypeError(
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f'Type {type(data)} cannot be converted to tensor.'
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'Supported types are: `numpy.ndarray`, `torch.Tensor`, '
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'`Sequence`, `int` and `float`')
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@PIPELINES.register_module()
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class ToTensor(object):
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def __init__(self, keys):
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self.keys = keys
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def __call__(self, results):
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for key in self.keys:
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results[key] = to_tensor(results[key])
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return results
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def __repr__(self):
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return self.__class__.__name__ + f'(keys={self.keys})'
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@PIPELINES.register_module()
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class ImageToTensor(object):
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def __init__(self, keys):
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self.keys = keys
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def __call__(self, results):
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for key in self.keys:
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img = results[key]
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if len(img.shape) < 3:
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img = np.expand_dims(img, -1)
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results[key] = to_tensor(img.transpose(2, 0, 1))
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return results
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def __repr__(self):
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return self.__class__.__name__ + f'(keys={self.keys})'
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@PIPELINES.register_module()
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class Transpose(object):
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def __init__(self, keys, order):
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self.keys = keys
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self.order = order
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def __call__(self, results):
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for key in self.keys:
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results[key] = results[key].transpose(self.order)
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return results
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def __repr__(self):
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return self.__class__.__name__ + \
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f'(keys={self.keys}, order={self.order})'
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@PIPELINES.register_module()
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class ToPIL(object):
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def __init__(self):
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pass
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def __call__(self, results):
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results['img'] = Image.fromarray(results['img'])
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return results
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@PIPELINES.register_module()
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class ToNumpy(object):
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def __init__(self):
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pass
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def __call__(self, results):
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results['img'] = np.array(results['img'], dtype=np.float32)
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return results
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@PIPELINES.register_module()
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class Collect(object):
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"""
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Collect data from the loader relevant to the specific task.
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This is usually the last stage of the data loader pipeline. Typically keys
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is set to some subset of "img" and "gt_label".
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Args:
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keys (Sequence[str]): Keys of results to be collected in ``data``.
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meta_keys (Sequence[str], optional): Meta keys to be converted to
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``mmcv.DataContainer`` and collected in ``data[img_metas]``.
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Default: ``('filename', 'ori_shape', 'img_shape', 'flip',
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'flip_direction', 'img_norm_cfg')``
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Returns:
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dict: The result dict contains the following keys
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- keys in``self.keys``
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- ``img_metas`` if avaliable
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"""
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def __init__(self,
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keys,
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meta_keys=('filename', 'ori_shape', 'img_shape', 'flip',
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'flip_direction', 'img_norm_cfg')):
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self.keys = keys
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self.meta_keys = meta_keys
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def __call__(self, results):
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data = {}
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img_meta = {}
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for key in self.meta_keys:
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if key in results:
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img_meta[key] = results[key]
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data['img_metas'] = DC(img_meta, cpu_only=True)
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for key in self.keys:
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data[key] = results[key]
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return data
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def __repr__(self):
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return self.__class__.__name__ + \
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f'(keys={self.keys}, meta_keys={self.meta_keys})'
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@PIPELINES.register_module()
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class WrapFieldsToLists(object):
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"""Wrap fields of the data dictionary into lists for evaluation.
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This class can be used as a last step of a test or validation
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pipeline for single image evaluation or inference.
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Example:
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>>> test_pipeline = [
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>>> dict(type='LoadImageFromFile'),
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>>> dict(type='Normalize',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True),
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>>> dict(type='ImageToTensor', keys=['img']),
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>>> dict(type='Collect', keys=['img']),
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>>> dict(type='WrapIntoLists')
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>>> ]
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"""
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def __call__(self, results):
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# Wrap dict fields into lists
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for key, val in results.items():
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results[key] = [val]
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return results
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def __repr__(self):
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return f'{self.__class__.__name__}()'
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