mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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237 lines
7.7 KiB
Python
237 lines
7.7 KiB
Python
# copyright (c) 2022 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 numpy as np
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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.nn import Conv2D, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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from paddle.nn.initializer import Uniform
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import math
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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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from .det_resnet_vd import DeformableConvV2, ConvBNLayer
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class BottleneckBlock(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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stride,
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shortcut=True,
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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=num_channels,
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out_channels=num_filters,
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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=num_filters,
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out_channels=num_filters,
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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=1, )
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self.conv2 = ConvBNLayer(
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in_channels=num_filters,
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out_channels=num_filters * 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=num_channels,
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out_channels=num_filters * 4,
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kernel_size=1,
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stride=stride, )
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self.shortcut = shortcut
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self._num_channels_out = num_filters * 4
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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__(self,
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num_channels,
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num_filters,
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stride,
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shortcut=True,
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name=None):
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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=num_channels,
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out_channels=num_filters,
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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=num_filters,
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out_channels=num_filters,
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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=num_channels,
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out_channels=num_filters,
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kernel_size=1,
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stride=stride)
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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(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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out_indices=None,
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dcn_stage=None):
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super(ResNet, self).__init__()
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self.layers = layers
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self.input_image_channel = in_channels
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supported_layers = [18, 34, 50, 101, 152]
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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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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.conv = ConvBNLayer(
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in_channels=self.input_image_channel,
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out_channels=64,
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kernel_size=7,
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stride=2,
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act="relu", )
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self.pool2d_max = MaxPool2D(
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kernel_size=3,
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stride=2,
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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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shortcut = False
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block_list = []
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is_dcn = self.dcn_stage[block]
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for i in range(depth[block]):
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if layers in [101, 152] and block == 2:
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if i == 0:
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conv_name = "res" + str(block + 2) + "a"
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else:
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conv_name = "res" + str(block + 2) + "b" + str(i)
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else:
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conv_name = "res" + str(block + 2) + chr(97 + i)
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bottleneck_block = self.add_sublayer(
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conv_name,
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BottleneckBlock(
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num_channels=num_channels[block]
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if i == 0 else num_filters[block] * 4,
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num_filters=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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is_dcn=is_dcn))
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block_list.append(bottleneck_block)
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shortcut = True
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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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shortcut = False
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block_list = []
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for i in range(depth[block]):
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conv_name = "res" + str(block + 2) + chr(97 + i)
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basic_block = self.add_sublayer(
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conv_name,
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BasicBlock(
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num_channels=num_channels[block]
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if i == 0 else num_filters[block],
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num_filters=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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block_list.append(basic_block)
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shortcut = True
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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.conv(inputs)
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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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