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import warnings |
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import torch.nn as nn |
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from mmcv.runner import BaseModule |
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from mmocr.models.builder import HEADS |
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from .head_mixin import HeadMixin |
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@HEADS.register_module() |
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class TextSnakeHead(HeadMixin, BaseModule): |
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"""The class for TextSnake head: TextSnake: A Flexible Representation for |
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Detecting Text of Arbitrary Shapes. |
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TextSnake: `A Flexible Representation for Detecting Text of Arbitrary |
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Shapes <https://arxiv.org/abs/1807.01544>`_. |
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Args: |
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in_channels (int): Number of input channels. |
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out_channels (int): Number of output channels. |
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downsample_ratio (float): Downsample ratio. |
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loss (dict): Configuration dictionary for loss type. |
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postprocessor (dict): Config of postprocessor for TextSnake. |
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train_cfg, test_cfg: Depreciated. |
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init_cfg (dict or list[dict], optional): Initialization configs. |
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""" |
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def __init__(self, |
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in_channels, |
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out_channels=5, |
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downsample_ratio=1.0, |
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loss=dict(type='TextSnakeLoss'), |
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postprocessor=dict( |
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type='TextSnakePostprocessor', text_repr_type='poly'), |
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train_cfg=None, |
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test_cfg=None, |
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init_cfg=dict( |
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type='Normal', |
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override=dict(name='out_conv'), |
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mean=0, |
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std=0.01), |
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**kwargs): |
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old_keys = ['text_repr_type', 'decoding_type'] |
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for key in old_keys: |
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if kwargs.get(key, None): |
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postprocessor[key] = kwargs.get(key) |
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warnings.warn( |
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f'{key} is deprecated, please specify ' |
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'it in postprocessor config dict. See ' |
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'https://github.com/open-mmlab/mmocr/pull/640 ' |
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'for details.', UserWarning) |
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BaseModule.__init__(self, init_cfg=init_cfg) |
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HeadMixin.__init__(self, loss, postprocessor) |
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assert isinstance(in_channels, int) |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.downsample_ratio = downsample_ratio |
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self.train_cfg = train_cfg |
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self.test_cfg = test_cfg |
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self.out_conv = nn.Conv2d( |
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in_channels=self.in_channels, |
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out_channels=self.out_channels, |
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kernel_size=1, |
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stride=1, |
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padding=0) |
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def forward(self, inputs): |
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""" |
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Args: |
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inputs (Tensor): Shape :math:`(N, C_{in}, H, W)`, where |
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:math:`C_{in}` is ``in_channels``. :math:`H` and :math:`W` |
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should be the same as the input of backbone. |
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Returns: |
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Tensor: A tensor of shape :math:`(N, 5, H, W)`. |
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""" |
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outputs = self.out_conv(inputs) |
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return outputs |
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