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405 lines
14 KiB
405 lines
14 KiB
2 years ago
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# Copyright (c) 2022 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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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 paddlers.models.ppdet.core.workspace import register, serializable
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from paddlers.models.ppdet.modeling.initializer import conv_init_
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from ..shape_spec import ShapeSpec
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__all__ = [
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'CSPDarkNet', 'BaseConv', 'DWConv', 'BottleNeck', 'SPPLayer', 'SPPFLayer'
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]
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class BaseConv(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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ksize,
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stride,
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groups=1,
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bias=False,
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act="silu"):
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super(BaseConv, self).__init__()
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self.conv = nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size=ksize,
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stride=stride,
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padding=(ksize - 1) // 2,
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groups=groups,
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bias_attr=bias)
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self.bn = nn.BatchNorm2D(
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out_channels,
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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._init_weights()
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def _init_weights(self):
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conv_init_(self.conv)
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def forward(self, x):
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# use 'x * F.sigmoid(x)' replace 'silu'
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x = self.bn(self.conv(x))
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y = x * F.sigmoid(x)
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return y
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class DWConv(nn.Layer):
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"""Depthwise Conv"""
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def __init__(self,
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in_channels,
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out_channels,
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ksize,
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stride=1,
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bias=False,
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act="silu"):
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super(DWConv, self).__init__()
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self.dw_conv = BaseConv(
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in_channels,
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in_channels,
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ksize=ksize,
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stride=stride,
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groups=in_channels,
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bias=bias,
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act=act)
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self.pw_conv = BaseConv(
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in_channels,
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out_channels,
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ksize=1,
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stride=1,
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groups=1,
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bias=bias,
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act=act)
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def forward(self, x):
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return self.pw_conv(self.dw_conv(x))
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class Focus(nn.Layer):
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"""Focus width and height information into channel space, used in YOLOX."""
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def __init__(self,
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in_channels,
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out_channels,
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ksize=3,
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stride=1,
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bias=False,
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act="silu"):
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super(Focus, self).__init__()
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self.conv = BaseConv(
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in_channels * 4,
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out_channels,
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ksize=ksize,
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stride=stride,
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bias=bias,
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act=act)
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def forward(self, inputs):
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# inputs [bs, C, H, W] -> outputs [bs, 4C, W/2, H/2]
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top_left = inputs[:, :, 0::2, 0::2]
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top_right = inputs[:, :, 0::2, 1::2]
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bottom_left = inputs[:, :, 1::2, 0::2]
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bottom_right = inputs[:, :, 1::2, 1::2]
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outputs = paddle.concat(
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[top_left, bottom_left, top_right, bottom_right], 1)
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return self.conv(outputs)
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class BottleNeck(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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shortcut=True,
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expansion=0.5,
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depthwise=False,
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bias=False,
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act="silu"):
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super(BottleNeck, self).__init__()
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hidden_channels = int(out_channels * expansion)
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Conv = DWConv if depthwise else BaseConv
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self.conv1 = BaseConv(
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in_channels, hidden_channels, ksize=1, stride=1, bias=bias, act=act)
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self.conv2 = Conv(
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hidden_channels,
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out_channels,
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ksize=3,
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stride=1,
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bias=bias,
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act=act)
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self.add_shortcut = shortcut and in_channels == out_channels
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def forward(self, x):
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y = self.conv2(self.conv1(x))
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if self.add_shortcut:
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y = y + x
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return y
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class SPPLayer(nn.Layer):
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"""Spatial Pyramid Pooling (SPP) layer used in YOLOv3-SPP and YOLOX"""
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def __init__(self,
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in_channels,
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out_channels,
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kernel_sizes=(5, 9, 13),
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bias=False,
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act="silu"):
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super(SPPLayer, self).__init__()
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hidden_channels = in_channels // 2
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self.conv1 = BaseConv(
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in_channels, hidden_channels, ksize=1, stride=1, bias=bias, act=act)
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self.maxpoolings = nn.LayerList([
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nn.MaxPool2D(
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kernel_size=ks, stride=1, padding=ks // 2)
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for ks in kernel_sizes
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])
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conv2_channels = hidden_channels * (len(kernel_sizes) + 1)
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self.conv2 = BaseConv(
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conv2_channels, out_channels, ksize=1, stride=1, bias=bias, act=act)
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def forward(self, x):
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x = self.conv1(x)
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x = paddle.concat([x] + [mp(x) for mp in self.maxpoolings], axis=1)
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x = self.conv2(x)
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return x
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class SPPFLayer(nn.Layer):
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""" Spatial Pyramid Pooling - Fast (SPPF) layer used in YOLOv5 by Glenn Jocher,
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equivalent to SPP(k=(5, 9, 13))
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"""
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def __init__(self,
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in_channels,
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out_channels,
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ksize=5,
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bias=False,
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act='silu'):
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super(SPPFLayer, self).__init__()
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hidden_channels = in_channels // 2
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self.conv1 = BaseConv(
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in_channels, hidden_channels, ksize=1, stride=1, bias=bias, act=act)
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self.maxpooling = nn.MaxPool2D(
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kernel_size=ksize, stride=1, padding=ksize // 2)
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conv2_channels = hidden_channels * 4
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self.conv2 = BaseConv(
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conv2_channels, out_channels, ksize=1, stride=1, bias=bias, act=act)
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def forward(self, x):
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x = self.conv1(x)
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y1 = self.maxpooling(x)
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y2 = self.maxpooling(y1)
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y3 = self.maxpooling(y2)
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concats = paddle.concat([x, y1, y2, y3], axis=1)
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out = self.conv2(concats)
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return out
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class CSPLayer(nn.Layer):
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"""CSP (Cross Stage Partial) layer with 3 convs, named C3 in YOLOv5"""
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def __init__(self,
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in_channels,
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out_channels,
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num_blocks=1,
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shortcut=True,
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expansion=0.5,
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depthwise=False,
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bias=False,
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act="silu"):
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super(CSPLayer, self).__init__()
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hidden_channels = int(out_channels * expansion)
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self.conv1 = BaseConv(
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in_channels, hidden_channels, ksize=1, stride=1, bias=bias, act=act)
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self.conv2 = BaseConv(
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in_channels, hidden_channels, ksize=1, stride=1, bias=bias, act=act)
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self.bottlenecks = nn.Sequential(*[
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BottleNeck(
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hidden_channels,
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hidden_channels,
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shortcut=shortcut,
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expansion=1.0,
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depthwise=depthwise,
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bias=bias,
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act=act) for _ in range(num_blocks)
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])
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self.conv3 = BaseConv(
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hidden_channels * 2,
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out_channels,
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ksize=1,
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stride=1,
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bias=bias,
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act=act)
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def forward(self, x):
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x_1 = self.conv1(x)
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x_1 = self.bottlenecks(x_1)
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x_2 = self.conv2(x)
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x = paddle.concat([x_1, x_2], axis=1)
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x = self.conv3(x)
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return x
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@register
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@serializable
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class CSPDarkNet(nn.Layer):
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"""
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CSPDarkNet backbone.
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Args:
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arch (str): Architecture of CSPDarkNet, from {P5, P6, X}, default as X,
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and 'X' means used in YOLOX, 'P5/P6' means used in YOLOv5.
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depth_mult (float): Depth multiplier, multiply number of channels in
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each layer, default as 1.0.
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width_mult (float): Width multiplier, multiply number of blocks in
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CSPLayer, default as 1.0.
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depthwise (bool): Whether to use depth-wise conv layer.
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act (str): Activation function type, default as 'silu'.
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return_idx (list): Index of stages whose feature maps are returned.
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"""
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__shared__ = ['depth_mult', 'width_mult', 'act', 'trt']
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# in_channels, out_channels, num_blocks, add_shortcut, use_spp(use_sppf)
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# 'X' means setting used in YOLOX, 'P5/P6' means setting used in YOLOv5.
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arch_settings = {
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'X': [[64, 128, 3, True, False], [128, 256, 9, True, False],
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[256, 512, 9, True, False], [512, 1024, 3, False, True]],
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'P5': [[64, 128, 3, True, False], [128, 256, 6, True, False],
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[256, 512, 9, True, False], [512, 1024, 3, True, True]],
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'P6': [[64, 128, 3, True, False], [128, 256, 6, True, False],
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[256, 512, 9, True, False], [512, 768, 3, True, False],
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[768, 1024, 3, True, True]],
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}
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def __init__(self,
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arch='X',
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depth_mult=1.0,
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width_mult=1.0,
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depthwise=False,
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act='silu',
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trt=False,
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return_idx=[2, 3, 4]):
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super(CSPDarkNet, self).__init__()
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self.arch = arch
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self.return_idx = return_idx
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Conv = DWConv if depthwise else BaseConv
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arch_setting = self.arch_settings[arch]
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base_channels = int(arch_setting[0][0] * width_mult)
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# Note: differences between the latest YOLOv5 and the original YOLOX
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# 1. self.stem, use SPPF(in YOLOv5) or SPP(in YOLOX)
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# 2. use SPPF(in YOLOv5) or SPP(in YOLOX)
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# 3. put SPPF before(YOLOv5) or SPP after(YOLOX) the last cspdark block's CSPLayer
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# 4. whether SPPF(SPP)'CSPLayer add shortcut, True in YOLOv5, False in YOLOX
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if arch in ['P5', 'P6']:
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# in the latest YOLOv5, use Conv stem, and SPPF (fast, only single spp kernal size)
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self.stem = Conv(
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3, base_channels, ksize=6, stride=2, bias=False, act=act)
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spp_kernal_sizes = 5
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elif arch in ['X']:
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# in the original YOLOX, use Focus stem, and SPP (three spp kernal sizes)
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self.stem = Focus(
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3, base_channels, ksize=3, stride=1, bias=False, act=act)
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spp_kernal_sizes = (5, 9, 13)
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else:
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raise AttributeError("Unsupported arch type: {}".format(arch))
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_out_channels = [base_channels]
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layers_num = 1
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self.csp_dark_blocks = []
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for i, (in_channels, out_channels, num_blocks, shortcut,
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use_spp) in enumerate(arch_setting):
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in_channels = int(in_channels * width_mult)
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out_channels = int(out_channels * width_mult)
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_out_channels.append(out_channels)
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num_blocks = max(round(num_blocks * depth_mult), 1)
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stage = []
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conv_layer = self.add_sublayer(
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'layers{}.stage{}.conv_layer'.format(layers_num, i + 1),
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Conv(
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in_channels, out_channels, 3, 2, bias=False, act=act))
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stage.append(conv_layer)
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layers_num += 1
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if use_spp and arch in ['X']:
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# in YOLOX use SPPLayer
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spp_layer = self.add_sublayer(
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'layers{}.stage{}.spp_layer'.format(layers_num, i + 1),
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SPPLayer(
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out_channels,
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out_channels,
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kernel_sizes=spp_kernal_sizes,
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bias=False,
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act=act))
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stage.append(spp_layer)
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layers_num += 1
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csp_layer = self.add_sublayer(
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'layers{}.stage{}.csp_layer'.format(layers_num, i + 1),
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CSPLayer(
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out_channels,
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out_channels,
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num_blocks=num_blocks,
|
||
|
|
shortcut=shortcut,
|
||
|
|
depthwise=depthwise,
|
||
|
|
bias=False,
|
||
|
|
act=act))
|
||
|
|
stage.append(csp_layer)
|
||
|
|
layers_num += 1
|
||
|
|
|
||
|
|
if use_spp and arch in ['P5', 'P6']:
|
||
|
|
# in latest YOLOv5 use SPPFLayer instead of SPPLayer
|
||
|
|
sppf_layer = self.add_sublayer(
|
||
|
|
'layers{}.stage{}.sppf_layer'.format(layers_num, i + 1),
|
||
|
|
SPPFLayer(
|
||
|
|
out_channels,
|
||
|
|
out_channels,
|
||
|
|
ksize=5,
|
||
|
|
bias=False,
|
||
|
|
act=act))
|
||
|
|
stage.append(sppf_layer)
|
||
|
|
layers_num += 1
|
||
|
|
|
||
|
|
self.csp_dark_blocks.append(nn.Sequential(*stage))
|
||
|
|
|
||
|
|
self._out_channels = [_out_channels[i] for i in self.return_idx]
|
||
|
|
self.strides = [[2, 4, 8, 16, 32, 64][i] for i in self.return_idx]
|
||
|
|
|
||
|
|
def forward(self, inputs):
|
||
|
|
x = inputs['image']
|
||
|
|
outputs = []
|
||
|
|
x = self.stem(x)
|
||
|
|
for i, layer in enumerate(self.csp_dark_blocks):
|
||
|
|
x = layer(x)
|
||
|
|
if i + 1 in self.return_idx:
|
||
|
|
outputs.append(x)
|
||
|
|
return outputs
|
||
|
|
|
||
|
|
@property
|
||
|
|
def out_shape(self):
|
||
|
|
return [
|
||
|
|
ShapeSpec(
|
||
|
|
channels=c, stride=s)
|
||
|
|
for c, s in zip(self._out_channels, self.strides)
|
||
|
|
]
|