mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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353 lines
11 KiB
Python
353 lines
11 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.vision.ops import DeformConv2D
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from paddle.regularizer import L2Decay
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from paddle.nn.initializer import Normal, Constant, XavierUniform
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__all__ = ["ResNet_vd", "ConvBNLayer", "DeformableConvV2"]
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class DeformableConvV2(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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weight_attr=None,
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bias_attr=None,
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lr_scale=1,
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regularizer=None,
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skip_quant=False,
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dcn_bias_regularizer=L2Decay(0.),
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dcn_bias_lr_scale=2.):
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super(DeformableConvV2, self).__init__()
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self.offset_channel = 2 * kernel_size**2 * groups
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self.mask_channel = kernel_size**2 * groups
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if bias_attr:
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# in FCOS-DCN head, specifically need learning_rate and regularizer
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dcn_bias_attr = ParamAttr(
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initializer=Constant(value=0),
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regularizer=dcn_bias_regularizer,
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learning_rate=dcn_bias_lr_scale)
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else:
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# in ResNet backbone, do not need bias
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dcn_bias_attr = False
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self.conv_dcn = DeformConv2D(
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in_channels,
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out_channels,
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kernel_size,
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stride=stride,
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padding=(kernel_size - 1) // 2 * dilation,
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dilation=dilation,
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deformable_groups=groups,
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weight_attr=weight_attr,
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bias_attr=dcn_bias_attr)
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if lr_scale == 1 and regularizer is None:
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offset_bias_attr = ParamAttr(initializer=Constant(0.))
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else:
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offset_bias_attr = ParamAttr(
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initializer=Constant(0.),
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learning_rate=lr_scale,
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regularizer=regularizer)
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self.conv_offset = nn.Conv2D(
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in_channels,
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groups * 3 * kernel_size**2,
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kernel_size,
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stride=stride,
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padding=(kernel_size - 1) // 2,
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weight_attr=ParamAttr(initializer=Constant(0.0)),
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bias_attr=offset_bias_attr)
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if skip_quant:
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self.conv_offset.skip_quant = True
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def forward(self, x):
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offset_mask = self.conv_offset(x)
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offset, mask = paddle.split(
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offset_mask,
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num_or_sections=[self.offset_channel, self.mask_channel],
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axis=1)
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mask = F.sigmoid(mask)
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y = self.conv_dcn(x, offset, mask=mask)
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return y
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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groups=1,
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dcn_groups=1,
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is_vd_mode=False,
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act=None,
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is_dcn=False):
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super(ConvBNLayer, self).__init__()
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self.is_vd_mode = is_vd_mode
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self._pool2d_avg = nn.AvgPool2D(
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kernel_size=2, stride=2, padding=0, ceil_mode=True)
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if not is_dcn:
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self._conv = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=(kernel_size - 1) // 2,
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groups=groups,
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bias_attr=False)
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else:
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self._conv = DeformableConvV2(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=(kernel_size - 1) // 2,
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groups=dcn_groups, #groups,
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bias_attr=False)
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self._batch_norm = nn.BatchNorm(out_channels, act=act)
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def forward(self, inputs):
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if self.is_vd_mode:
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inputs = self._pool2d_avg(inputs)
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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class BottleneckBlock(nn.Layer):
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def __init__(
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self,
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in_channels,
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out_channels,
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stride,
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shortcut=True,
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if_first=False,
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is_dcn=False, ):
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super(BottleneckBlock, self).__init__()
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self.conv0 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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act='relu')
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self.conv1 = ConvBNLayer(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=3,
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stride=stride,
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act='relu',
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is_dcn=is_dcn,
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dcn_groups=2)
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self.conv2 = ConvBNLayer(
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in_channels=out_channels,
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out_channels=out_channels * 4,
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kernel_size=1,
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act=None)
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if not shortcut:
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self.short = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels * 4,
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kernel_size=1,
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stride=1,
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is_vd_mode=False if if_first else True)
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self.shortcut = shortcut
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def forward(self, inputs):
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y = self.conv0(inputs)
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conv1 = self.conv1(y)
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conv2 = self.conv2(conv1)
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if self.shortcut:
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short = inputs
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else:
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short = self.short(inputs)
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y = paddle.add(x=short, y=conv2)
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y = F.relu(y)
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return y
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class BasicBlock(nn.Layer):
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def __init__(
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self,
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in_channels,
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out_channels,
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stride,
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shortcut=True,
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if_first=False, ):
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super(BasicBlock, self).__init__()
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self.stride = stride
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self.conv0 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=3,
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stride=stride,
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act='relu')
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self.conv1 = ConvBNLayer(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=3,
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act=None)
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if not shortcut:
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self.short = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=1,
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is_vd_mode=False if if_first else True)
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self.shortcut = shortcut
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def forward(self, inputs):
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y = self.conv0(inputs)
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conv1 = self.conv1(y)
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if self.shortcut:
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short = inputs
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else:
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short = self.short(inputs)
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y = paddle.add(x=short, y=conv1)
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y = F.relu(y)
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return y
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class ResNet_vd(nn.Layer):
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def __init__(self,
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in_channels=3,
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layers=50,
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dcn_stage=None,
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out_indices=None,
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**kwargs):
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super(ResNet_vd, self).__init__()
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self.layers = layers
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supported_layers = [18, 34, 50, 101, 152, 200]
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assert layers in supported_layers, \
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"supported layers are {} but input layer is {}".format(
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supported_layers, layers)
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if layers == 18:
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depth = [2, 2, 2, 2]
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elif layers == 34 or layers == 50:
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depth = [3, 4, 6, 3]
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elif layers == 101:
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depth = [3, 4, 23, 3]
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elif layers == 152:
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depth = [3, 8, 36, 3]
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elif layers == 200:
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depth = [3, 12, 48, 3]
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num_channels = [64, 256, 512,
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1024] if layers >= 50 else [64, 64, 128, 256]
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num_filters = [64, 128, 256, 512]
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self.dcn_stage = dcn_stage if dcn_stage is not None else [
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False, False, False, False
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]
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self.out_indices = out_indices if out_indices is not None else [
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0, 1, 2, 3
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]
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self.conv1_1 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=32,
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kernel_size=3,
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stride=2,
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act='relu')
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self.conv1_2 = ConvBNLayer(
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in_channels=32,
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out_channels=32,
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kernel_size=3,
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stride=1,
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act='relu')
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self.conv1_3 = ConvBNLayer(
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in_channels=32,
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out_channels=64,
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kernel_size=3,
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stride=1,
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act='relu')
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self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
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self.stages = []
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self.out_channels = []
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if layers >= 50:
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for block in range(len(depth)):
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block_list = []
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shortcut = False
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is_dcn = self.dcn_stage[block]
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for i in range(depth[block]):
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bottleneck_block = self.add_sublayer(
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'bb_%d_%d' % (block, i),
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BottleneckBlock(
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in_channels=num_channels[block]
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if i == 0 else num_filters[block] * 4,
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out_channels=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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shortcut=shortcut,
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if_first=block == i == 0,
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is_dcn=is_dcn))
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shortcut = True
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block_list.append(bottleneck_block)
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if block in self.out_indices:
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self.out_channels.append(num_filters[block] * 4)
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self.stages.append(nn.Sequential(*block_list))
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else:
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for block in range(len(depth)):
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block_list = []
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shortcut = False
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for i in range(depth[block]):
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basic_block = self.add_sublayer(
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'bb_%d_%d' % (block, i),
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BasicBlock(
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in_channels=num_channels[block]
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if i == 0 else num_filters[block],
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out_channels=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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shortcut=shortcut,
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if_first=block == i == 0))
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shortcut = True
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block_list.append(basic_block)
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if block in self.out_indices:
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self.out_channels.append(num_filters[block])
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self.stages.append(nn.Sequential(*block_list))
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def forward(self, inputs):
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y = self.conv1_1(inputs)
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y = self.conv1_2(y)
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y = self.conv1_3(y)
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y = self.pool2d_max(y)
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out = []
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for i, block in enumerate(self.stages):
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y = block(y)
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if i in self.out_indices:
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out.append(y)
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return out
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