Note
You are reading the documentation for MMOCR 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMOCR 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the maintenance plan, changelog, code and documentation of MMOCR 1.0 for more details.
Source code for mmocr.models.textdet.dense_heads.textsnake_head
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.runner import BaseModule
from mmocr.models.builder import HEADS
from .head_mixin import HeadMixin
[docs]@HEADS.register_module()
class TextSnakeHead(HeadMixin, BaseModule):
"""The class for TextSnake head: TextSnake: A Flexible Representation for
Detecting Text of Arbitrary Shapes.
TextSnake: `A Flexible Representation for Detecting Text of Arbitrary
Shapes <https://arxiv.org/abs/1807.01544>`_.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
downsample_ratio (float): Downsample ratio.
loss (dict): Configuration dictionary for loss type.
postprocessor (dict): Config of postprocessor for TextSnake.
train_cfg, test_cfg: Depreciated.
init_cfg (dict or list[dict], optional): Initialization configs.
"""
def __init__(self,
in_channels,
out_channels=5,
downsample_ratio=1.0,
loss=dict(type='TextSnakeLoss'),
postprocessor=dict(
type='TextSnakePostprocessor', text_repr_type='poly'),
train_cfg=None,
test_cfg=None,
init_cfg=dict(
type='Normal',
override=dict(name='out_conv'),
mean=0,
std=0.01),
**kwargs):
old_keys = ['text_repr_type', 'decoding_type']
for key in old_keys:
if kwargs.get(key, None):
postprocessor[key] = kwargs.get(key)
warnings.warn(
f'{key} is deprecated, please specify '
'it in postprocessor config dict. See '
'https://github.com/open-mmlab/mmocr/pull/640 '
'for details.', UserWarning)
BaseModule.__init__(self, init_cfg=init_cfg)
HeadMixin.__init__(self, loss, postprocessor)
assert isinstance(in_channels, int)
self.in_channels = in_channels
self.out_channels = out_channels
self.downsample_ratio = downsample_ratio
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.out_conv = nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.out_channels,
kernel_size=1,
stride=1,
padding=0)
[docs] def forward(self, inputs):
"""
Args:
inputs (Tensor): Shape :math:`(N, C_{in}, H, W)`, where
:math:`C_{in}` is ``in_channels``. :math:`H` and :math:`W`
should be the same as the input of backbone.
Returns:
Tensor: A tensor of shape :math:`(N, 5, H, W)`.
"""
outputs = self.out_conv(inputs)
return outputs