更换文档检测模型
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321
paddle_detection/ppdet/modeling/backbones/cspresnet.py
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321
paddle_detection/ppdet/modeling/backbones/cspresnet.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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from paddle.regularizer import L2Decay
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from paddle.nn.initializer import Constant
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from ppdet.modeling.ops import get_act_fn
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from ppdet.core.workspace import register, serializable
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from ..shape_spec import ShapeSpec
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__all__ = ['CSPResNet', 'BasicBlock', 'EffectiveSELayer', 'ConvBNLayer']
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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ch_in,
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ch_out,
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filter_size=3,
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stride=1,
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groups=1,
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padding=0,
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act=None):
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super(ConvBNLayer, self).__init__()
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self.conv = nn.Conv2D(
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in_channels=ch_in,
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out_channels=ch_out,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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groups=groups,
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bias_attr=False)
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self.bn = nn.BatchNorm2D(
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ch_out,
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weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
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bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
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self.act = get_act_fn(act) if act is None or isinstance(act, (
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str, dict)) else act
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.act(x)
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return x
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class RepVggBlock(nn.Layer):
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def __init__(self, ch_in, ch_out, act='relu', alpha=False):
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super(RepVggBlock, self).__init__()
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self.ch_in = ch_in
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self.ch_out = ch_out
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self.conv1 = ConvBNLayer(
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ch_in, ch_out, 3, stride=1, padding=1, act=None)
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self.conv2 = ConvBNLayer(
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ch_in, ch_out, 1, stride=1, padding=0, act=None)
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self.act = get_act_fn(act) if act is None or isinstance(act, (
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str, dict)) else act
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if alpha:
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self.alpha = self.create_parameter(
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shape=[1],
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attr=ParamAttr(initializer=Constant(value=1.)),
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dtype="float32")
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else:
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self.alpha = None
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def forward(self, x):
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if hasattr(self, 'conv'):
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y = self.conv(x)
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else:
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if self.alpha:
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y = self.conv1(x) + self.alpha * self.conv2(x)
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else:
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y = self.conv1(x) + self.conv2(x)
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y = self.act(y)
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return y
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def convert_to_deploy(self):
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if not hasattr(self, 'conv'):
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self.conv = nn.Conv2D(
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in_channels=self.ch_in,
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out_channels=self.ch_out,
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kernel_size=3,
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stride=1,
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padding=1,
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groups=1)
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kernel, bias = self.get_equivalent_kernel_bias()
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self.conv.weight.set_value(kernel)
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self.conv.bias.set_value(bias)
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self.__delattr__('conv1')
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self.__delattr__('conv2')
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def get_equivalent_kernel_bias(self):
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kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1)
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kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2)
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if self.alpha:
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return kernel3x3 + self.alpha * self._pad_1x1_to_3x3_tensor(
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kernel1x1), bias3x3 + self.alpha * bias1x1
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else:
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return kernel3x3 + self._pad_1x1_to_3x3_tensor(
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kernel1x1), bias3x3 + bias1x1
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def _pad_1x1_to_3x3_tensor(self, kernel1x1):
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if kernel1x1 is None:
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return 0
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else:
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return nn.functional.pad(kernel1x1, [1, 1, 1, 1])
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def _fuse_bn_tensor(self, branch):
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if branch is None:
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return 0, 0
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kernel = branch.conv.weight
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running_mean = branch.bn._mean
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running_var = branch.bn._variance
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gamma = branch.bn.weight
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beta = branch.bn.bias
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eps = branch.bn._epsilon
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std = (running_var + eps).sqrt()
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t = (gamma / std).reshape((-1, 1, 1, 1))
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return kernel * t, beta - running_mean * gamma / std
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class BasicBlock(nn.Layer):
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def __init__(self,
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ch_in,
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ch_out,
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act='relu',
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shortcut=True,
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use_alpha=False):
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super(BasicBlock, self).__init__()
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assert ch_in == ch_out
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self.conv1 = ConvBNLayer(ch_in, ch_out, 3, stride=1, padding=1, act=act)
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self.conv2 = RepVggBlock(ch_out, ch_out, act=act, alpha=use_alpha)
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self.shortcut = shortcut
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def forward(self, x):
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y = self.conv1(x)
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y = self.conv2(y)
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if self.shortcut:
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return paddle.add(x, y)
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else:
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return y
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class EffectiveSELayer(nn.Layer):
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""" Effective Squeeze-Excitation
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From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
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"""
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def __init__(self, channels, act='hardsigmoid'):
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super(EffectiveSELayer, self).__init__()
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self.fc = nn.Conv2D(channels, channels, kernel_size=1, padding=0)
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self.act = get_act_fn(act) if act is None or isinstance(act, (
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str, dict)) else act
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def forward(self, x):
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x_se = x.mean((2, 3), keepdim=True)
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x_se = self.fc(x_se)
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return x * self.act(x_se)
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class CSPResStage(nn.Layer):
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def __init__(self,
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block_fn,
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ch_in,
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ch_out,
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n,
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stride,
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act='relu',
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attn='eca',
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use_alpha=False):
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super(CSPResStage, self).__init__()
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ch_mid = (ch_in + ch_out) // 2
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if stride == 2:
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self.conv_down = ConvBNLayer(
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ch_in, ch_mid, 3, stride=2, padding=1, act=act)
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else:
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self.conv_down = None
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self.conv1 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act)
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self.conv2 = ConvBNLayer(ch_mid, ch_mid // 2, 1, act=act)
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self.blocks = nn.Sequential(*[
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block_fn(
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ch_mid // 2,
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ch_mid // 2,
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act=act,
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shortcut=True,
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use_alpha=use_alpha) for i in range(n)
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])
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if attn:
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self.attn = EffectiveSELayer(ch_mid, act='hardsigmoid')
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else:
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self.attn = None
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self.conv3 = ConvBNLayer(ch_mid, ch_out, 1, act=act)
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def forward(self, x):
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if self.conv_down is not None:
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x = self.conv_down(x)
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y1 = self.conv1(x)
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y2 = self.blocks(self.conv2(x))
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y = paddle.concat([y1, y2], axis=1)
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if self.attn is not None:
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y = self.attn(y)
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y = self.conv3(y)
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return y
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@register
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@serializable
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class CSPResNet(nn.Layer):
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__shared__ = ['width_mult', 'depth_mult', 'trt']
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def __init__(self,
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layers=[3, 6, 6, 3],
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channels=[64, 128, 256, 512, 1024],
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act='swish',
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return_idx=[1, 2, 3],
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depth_wise=False,
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use_large_stem=False,
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width_mult=1.0,
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depth_mult=1.0,
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trt=False,
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use_checkpoint=False,
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use_alpha=False,
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**args):
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super(CSPResNet, self).__init__()
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self.use_checkpoint = use_checkpoint
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channels = [max(round(c * width_mult), 1) for c in channels]
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layers = [max(round(l * depth_mult), 1) for l in layers]
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act = get_act_fn(
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act, trt=trt) if act is None or isinstance(act,
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(str, dict)) else act
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if use_large_stem:
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self.stem = nn.Sequential(
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('conv1', ConvBNLayer(
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3, channels[0] // 2, 3, stride=2, padding=1, act=act)),
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('conv2', ConvBNLayer(
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channels[0] // 2,
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channels[0] // 2,
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3,
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stride=1,
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padding=1,
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act=act)), ('conv3', ConvBNLayer(
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channels[0] // 2,
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channels[0],
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3,
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stride=1,
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padding=1,
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act=act)))
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else:
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self.stem = nn.Sequential(
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('conv1', ConvBNLayer(
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3, channels[0] // 2, 3, stride=2, padding=1, act=act)),
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('conv2', ConvBNLayer(
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channels[0] // 2,
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channels[0],
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3,
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stride=1,
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padding=1,
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act=act)))
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n = len(channels) - 1
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self.stages = nn.Sequential(*[(str(i), CSPResStage(
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BasicBlock,
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channels[i],
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channels[i + 1],
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layers[i],
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2,
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act=act,
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use_alpha=use_alpha)) for i in range(n)])
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self._out_channels = channels[1:]
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self._out_strides = [4 * 2**i for i in range(n)]
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self.return_idx = return_idx
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if use_checkpoint:
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paddle.seed(0)
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def forward(self, inputs):
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x = inputs['image']
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x = self.stem(x)
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outs = []
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for idx, stage in enumerate(self.stages):
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if self.use_checkpoint and self.training:
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x = paddle.distributed.fleet.utils.recompute(
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stage, x, **{"preserve_rng_state": True})
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else:
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x = stage(x)
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if idx in self.return_idx:
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outs.append(x)
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return outs
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@property
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def out_shape(self):
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return [
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ShapeSpec(
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channels=self._out_channels[i], stride=self._out_strides[i])
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for i in self.return_idx
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]
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