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529 lines
17 KiB
Python
529 lines
17 KiB
Python
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# copyright (c) 2021 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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"""
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This code is refer from:
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https://github.com/liyunsheng13/micronet/blob/main/backbone/micronet.py
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https://github.com/liyunsheng13/micronet/blob/main/backbone/activation.py
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"""
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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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from ppocr.modeling.backbones.det_mobilenet_v3 import make_divisible
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M0_cfgs = [
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# s, n, c, ks, c1, c2, g1, g2, c3, g3, g4, y1, y2, y3, r
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[2, 1, 8, 3, 2, 2, 0, 4, 8, 2, 2, 2, 0, 1, 1],
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[2, 1, 12, 3, 2, 2, 0, 8, 12, 4, 4, 2, 2, 1, 1],
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[2, 1, 16, 5, 2, 2, 0, 12, 16, 4, 4, 2, 2, 1, 1],
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[1, 1, 32, 5, 1, 4, 4, 4, 32, 4, 4, 2, 2, 1, 1],
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[2, 1, 64, 5, 1, 4, 8, 8, 64, 8, 8, 2, 2, 1, 1],
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[1, 1, 96, 3, 1, 4, 8, 8, 96, 8, 8, 2, 2, 1, 2],
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[1, 1, 384, 3, 1, 4, 12, 12, 0, 0, 0, 2, 2, 1, 2],
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]
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M1_cfgs = [
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# s, n, c, ks, c1, c2, g1, g2, c3, g3, g4
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[2, 1, 8, 3, 2, 2, 0, 6, 8, 2, 2, 2, 0, 1, 1],
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[2, 1, 16, 3, 2, 2, 0, 8, 16, 4, 4, 2, 2, 1, 1],
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[2, 1, 16, 5, 2, 2, 0, 16, 16, 4, 4, 2, 2, 1, 1],
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[1, 1, 32, 5, 1, 6, 4, 4, 32, 4, 4, 2, 2, 1, 1],
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[2, 1, 64, 5, 1, 6, 8, 8, 64, 8, 8, 2, 2, 1, 1],
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[1, 1, 96, 3, 1, 6, 8, 8, 96, 8, 8, 2, 2, 1, 2],
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[1, 1, 576, 3, 1, 6, 12, 12, 0, 0, 0, 2, 2, 1, 2],
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]
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M2_cfgs = [
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# s, n, c, ks, c1, c2, g1, g2, c3, g3, g4
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[2, 1, 12, 3, 2, 2, 0, 8, 12, 4, 4, 2, 0, 1, 1],
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[2, 1, 16, 3, 2, 2, 0, 12, 16, 4, 4, 2, 2, 1, 1],
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[1, 1, 24, 3, 2, 2, 0, 16, 24, 4, 4, 2, 2, 1, 1],
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[2, 1, 32, 5, 1, 6, 6, 6, 32, 4, 4, 2, 2, 1, 1],
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[1, 1, 32, 5, 1, 6, 8, 8, 32, 4, 4, 2, 2, 1, 2],
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[1, 1, 64, 5, 1, 6, 8, 8, 64, 8, 8, 2, 2, 1, 2],
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[2, 1, 96, 5, 1, 6, 8, 8, 96, 8, 8, 2, 2, 1, 2],
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[1, 1, 128, 3, 1, 6, 12, 12, 128, 8, 8, 2, 2, 1, 2],
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[1, 1, 768, 3, 1, 6, 16, 16, 0, 0, 0, 2, 2, 1, 2],
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]
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M3_cfgs = [
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# s, n, c, ks, c1, c2, g1, g2, c3, g3, g4
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[2, 1, 16, 3, 2, 2, 0, 12, 16, 4, 4, 0, 2, 0, 1],
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[2, 1, 24, 3, 2, 2, 0, 16, 24, 4, 4, 0, 2, 0, 1],
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[1, 1, 24, 3, 2, 2, 0, 24, 24, 4, 4, 0, 2, 0, 1],
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[2, 1, 32, 5, 1, 6, 6, 6, 32, 4, 4, 0, 2, 0, 1],
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[1, 1, 32, 5, 1, 6, 8, 8, 32, 4, 4, 0, 2, 0, 2],
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[1, 1, 64, 5, 1, 6, 8, 8, 48, 8, 8, 0, 2, 0, 2],
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[1, 1, 80, 5, 1, 6, 8, 8, 80, 8, 8, 0, 2, 0, 2],
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[1, 1, 80, 5, 1, 6, 10, 10, 80, 8, 8, 0, 2, 0, 2],
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[1, 1, 120, 5, 1, 6, 10, 10, 120, 10, 10, 0, 2, 0, 2],
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[1, 1, 120, 5, 1, 6, 12, 12, 120, 10, 10, 0, 2, 0, 2],
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[1, 1, 144, 3, 1, 6, 12, 12, 144, 12, 12, 0, 2, 0, 2],
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[1, 1, 432, 3, 1, 3, 12, 12, 0, 0, 0, 0, 2, 0, 2],
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]
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def get_micronet_config(mode):
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return eval(mode + '_cfgs')
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class MaxGroupPooling(nn.Layer):
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def __init__(self, channel_per_group=2):
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super(MaxGroupPooling, self).__init__()
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self.channel_per_group = channel_per_group
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def forward(self, x):
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if self.channel_per_group == 1:
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return x
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# max op
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b, c, h, w = x.shape
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# reshape
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y = paddle.reshape(x, [b, c // self.channel_per_group, -1, h, w])
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out = paddle.max(y, axis=2)
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return out
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class SpatialSepConvSF(nn.Layer):
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def __init__(self, inp, oups, kernel_size, stride):
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super(SpatialSepConvSF, self).__init__()
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oup1, oup2 = oups
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self.conv = nn.Sequential(
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nn.Conv2D(
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inp,
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oup1, (kernel_size, 1), (stride, 1), (kernel_size // 2, 0),
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bias_attr=False,
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groups=1),
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nn.BatchNorm2D(oup1),
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nn.Conv2D(
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oup1,
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oup1 * oup2, (1, kernel_size), (1, stride),
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(0, kernel_size // 2),
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bias_attr=False,
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groups=oup1),
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nn.BatchNorm2D(oup1 * oup2),
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ChannelShuffle(oup1), )
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def forward(self, x):
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out = self.conv(x)
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return out
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class ChannelShuffle(nn.Layer):
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def __init__(self, groups):
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super(ChannelShuffle, self).__init__()
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self.groups = groups
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def forward(self, x):
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b, c, h, w = x.shape
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channels_per_group = c // self.groups
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# reshape
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x = paddle.reshape(x, [b, self.groups, channels_per_group, h, w])
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x = paddle.transpose(x, (0, 2, 1, 3, 4))
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out = paddle.reshape(x, [b, -1, h, w])
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return out
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class StemLayer(nn.Layer):
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def __init__(self, inp, oup, stride, groups=(4, 4)):
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super(StemLayer, self).__init__()
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g1, g2 = groups
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self.stem = nn.Sequential(
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SpatialSepConvSF(inp, groups, 3, stride),
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MaxGroupPooling(2) if g1 * g2 == 2 * oup else nn.ReLU6())
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def forward(self, x):
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out = self.stem(x)
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return out
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class DepthSpatialSepConv(nn.Layer):
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def __init__(self, inp, expand, kernel_size, stride):
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super(DepthSpatialSepConv, self).__init__()
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exp1, exp2 = expand
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hidden_dim = inp * exp1
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oup = inp * exp1 * exp2
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self.conv = nn.Sequential(
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nn.Conv2D(
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inp,
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inp * exp1, (kernel_size, 1), (stride, 1),
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(kernel_size // 2, 0),
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bias_attr=False,
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groups=inp),
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nn.BatchNorm2D(inp * exp1),
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nn.Conv2D(
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hidden_dim,
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oup, (1, kernel_size),
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1, (0, kernel_size // 2),
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bias_attr=False,
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groups=hidden_dim),
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nn.BatchNorm2D(oup))
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def forward(self, x):
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x = self.conv(x)
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return x
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class GroupConv(nn.Layer):
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def __init__(self, inp, oup, groups=2):
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super(GroupConv, self).__init__()
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self.inp = inp
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self.oup = oup
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self.groups = groups
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self.conv = nn.Sequential(
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nn.Conv2D(
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inp, oup, 1, 1, 0, bias_attr=False, groups=self.groups[0]),
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nn.BatchNorm2D(oup))
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def forward(self, x):
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x = self.conv(x)
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return x
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class DepthConv(nn.Layer):
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def __init__(self, inp, oup, kernel_size, stride):
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super(DepthConv, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2D(
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inp,
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oup,
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kernel_size,
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stride,
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kernel_size // 2,
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bias_attr=False,
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groups=inp),
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nn.BatchNorm2D(oup))
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def forward(self, x):
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out = self.conv(x)
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return out
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class DYShiftMax(nn.Layer):
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def __init__(self,
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inp,
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oup,
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reduction=4,
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act_max=1.0,
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act_relu=True,
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init_a=[0.0, 0.0],
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init_b=[0.0, 0.0],
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relu_before_pool=False,
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g=None,
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expansion=False):
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super(DYShiftMax, self).__init__()
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self.oup = oup
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self.act_max = act_max * 2
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self.act_relu = act_relu
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self.avg_pool = nn.Sequential(nn.ReLU() if relu_before_pool == True else
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nn.Sequential(), nn.AdaptiveAvgPool2D(1))
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self.exp = 4 if act_relu else 2
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self.init_a = init_a
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self.init_b = init_b
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# determine squeeze
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squeeze = make_divisible(inp // reduction, 4)
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if squeeze < 4:
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squeeze = 4
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self.fc = nn.Sequential(
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nn.Linear(inp, squeeze),
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nn.ReLU(), nn.Linear(squeeze, oup * self.exp), nn.Hardsigmoid())
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if g is None:
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g = 1
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self.g = g[1]
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if self.g != 1 and expansion:
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self.g = inp // self.g
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self.gc = inp // self.g
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index = paddle.to_tensor([range(inp)])
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index = paddle.reshape(index, [1, inp, 1, 1])
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index = paddle.reshape(index, [1, self.g, self.gc, 1, 1])
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indexgs = paddle.split(index, [1, self.g - 1], axis=1)
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indexgs = paddle.concat((indexgs[1], indexgs[0]), axis=1)
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indexs = paddle.split(indexgs, [1, self.gc - 1], axis=2)
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indexs = paddle.concat((indexs[1], indexs[0]), axis=2)
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self.index = paddle.reshape(indexs, [inp])
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self.expansion = expansion
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def forward(self, x):
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x_in = x
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x_out = x
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b, c, _, _ = x_in.shape
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y = self.avg_pool(x_in)
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y = paddle.reshape(y, [b, c])
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y = self.fc(y)
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y = paddle.reshape(y, [b, self.oup * self.exp, 1, 1])
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y = (y - 0.5) * self.act_max
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n2, c2, h2, w2 = x_out.shape
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x2 = paddle.to_tensor(x_out.numpy()[:, self.index.numpy(), :, :])
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if self.exp == 4:
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temp = y.shape
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a1, b1, a2, b2 = paddle.split(y, temp[1] // self.oup, axis=1)
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a1 = a1 + self.init_a[0]
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a2 = a2 + self.init_a[1]
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b1 = b1 + self.init_b[0]
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b2 = b2 + self.init_b[1]
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z1 = x_out * a1 + x2 * b1
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z2 = x_out * a2 + x2 * b2
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out = paddle.maximum(z1, z2)
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elif self.exp == 2:
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temp = y.shape
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a1, b1 = paddle.split(y, temp[1] // self.oup, axis=1)
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a1 = a1 + self.init_a[0]
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b1 = b1 + self.init_b[0]
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out = x_out * a1 + x2 * b1
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return out
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class DYMicroBlock(nn.Layer):
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def __init__(self,
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inp,
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oup,
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kernel_size=3,
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stride=1,
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ch_exp=(2, 2),
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ch_per_group=4,
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groups_1x1=(1, 1),
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depthsep=True,
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shuffle=False,
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activation_cfg=None):
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super(DYMicroBlock, self).__init__()
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self.identity = stride == 1 and inp == oup
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y1, y2, y3 = activation_cfg['dy']
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act_reduction = 8 * activation_cfg['ratio']
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init_a = activation_cfg['init_a']
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init_b = activation_cfg['init_b']
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t1 = ch_exp
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gs1 = ch_per_group
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hidden_fft, g1, g2 = groups_1x1
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hidden_dim2 = inp * t1[0] * t1[1]
|
||
|
|
|
||
|
|
if gs1[0] == 0:
|
||
|
|
self.layers = nn.Sequential(
|
||
|
|
DepthSpatialSepConv(inp, t1, kernel_size, stride),
|
||
|
|
DYShiftMax(
|
||
|
|
hidden_dim2,
|
||
|
|
hidden_dim2,
|
||
|
|
act_max=2.0,
|
||
|
|
act_relu=True if y2 == 2 else False,
|
||
|
|
init_a=init_a,
|
||
|
|
reduction=act_reduction,
|
||
|
|
init_b=init_b,
|
||
|
|
g=gs1,
|
||
|
|
expansion=False) if y2 > 0 else nn.ReLU6(),
|
||
|
|
ChannelShuffle(gs1[1]) if shuffle else nn.Sequential(),
|
||
|
|
ChannelShuffle(hidden_dim2 // 2)
|
||
|
|
if shuffle and y2 != 0 else nn.Sequential(),
|
||
|
|
GroupConv(hidden_dim2, oup, (g1, g2)),
|
||
|
|
DYShiftMax(
|
||
|
|
oup,
|
||
|
|
oup,
|
||
|
|
act_max=2.0,
|
||
|
|
act_relu=False,
|
||
|
|
init_a=[1.0, 0.0],
|
||
|
|
reduction=act_reduction // 2,
|
||
|
|
init_b=[0.0, 0.0],
|
||
|
|
g=(g1, g2),
|
||
|
|
expansion=False) if y3 > 0 else nn.Sequential(),
|
||
|
|
ChannelShuffle(g2) if shuffle else nn.Sequential(),
|
||
|
|
ChannelShuffle(oup // 2)
|
||
|
|
if shuffle and oup % 2 == 0 and y3 != 0 else nn.Sequential(), )
|
||
|
|
elif g2 == 0:
|
||
|
|
self.layers = nn.Sequential(
|
||
|
|
GroupConv(inp, hidden_dim2, gs1),
|
||
|
|
DYShiftMax(
|
||
|
|
hidden_dim2,
|
||
|
|
hidden_dim2,
|
||
|
|
act_max=2.0,
|
||
|
|
act_relu=False,
|
||
|
|
init_a=[1.0, 0.0],
|
||
|
|
reduction=act_reduction,
|
||
|
|
init_b=[0.0, 0.0],
|
||
|
|
g=gs1,
|
||
|
|
expansion=False) if y3 > 0 else nn.Sequential(), )
|
||
|
|
else:
|
||
|
|
self.layers = nn.Sequential(
|
||
|
|
GroupConv(inp, hidden_dim2, gs1),
|
||
|
|
DYShiftMax(
|
||
|
|
hidden_dim2,
|
||
|
|
hidden_dim2,
|
||
|
|
act_max=2.0,
|
||
|
|
act_relu=True if y1 == 2 else False,
|
||
|
|
init_a=init_a,
|
||
|
|
reduction=act_reduction,
|
||
|
|
init_b=init_b,
|
||
|
|
g=gs1,
|
||
|
|
expansion=False) if y1 > 0 else nn.ReLU6(),
|
||
|
|
ChannelShuffle(gs1[1]) if shuffle else nn.Sequential(),
|
||
|
|
DepthSpatialSepConv(hidden_dim2, (1, 1), kernel_size, stride)
|
||
|
|
if depthsep else
|
||
|
|
DepthConv(hidden_dim2, hidden_dim2, kernel_size, stride),
|
||
|
|
nn.Sequential(),
|
||
|
|
DYShiftMax(
|
||
|
|
hidden_dim2,
|
||
|
|
hidden_dim2,
|
||
|
|
act_max=2.0,
|
||
|
|
act_relu=True if y2 == 2 else False,
|
||
|
|
init_a=init_a,
|
||
|
|
reduction=act_reduction,
|
||
|
|
init_b=init_b,
|
||
|
|
g=gs1,
|
||
|
|
expansion=True) if y2 > 0 else nn.ReLU6(),
|
||
|
|
ChannelShuffle(hidden_dim2 // 4)
|
||
|
|
if shuffle and y1 != 0 and y2 != 0 else nn.Sequential()
|
||
|
|
if y1 == 0 and y2 == 0 else ChannelShuffle(hidden_dim2 // 2),
|
||
|
|
GroupConv(hidden_dim2, oup, (g1, g2)),
|
||
|
|
DYShiftMax(
|
||
|
|
oup,
|
||
|
|
oup,
|
||
|
|
act_max=2.0,
|
||
|
|
act_relu=False,
|
||
|
|
init_a=[1.0, 0.0],
|
||
|
|
reduction=act_reduction // 2
|
||
|
|
if oup < hidden_dim2 else act_reduction,
|
||
|
|
init_b=[0.0, 0.0],
|
||
|
|
g=(g1, g2),
|
||
|
|
expansion=False) if y3 > 0 else nn.Sequential(),
|
||
|
|
ChannelShuffle(g2) if shuffle else nn.Sequential(),
|
||
|
|
ChannelShuffle(oup // 2)
|
||
|
|
if shuffle and y3 != 0 else nn.Sequential(), )
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
identity = x
|
||
|
|
out = self.layers(x)
|
||
|
|
|
||
|
|
if self.identity:
|
||
|
|
out = out + identity
|
||
|
|
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
class MicroNet(nn.Layer):
|
||
|
|
"""
|
||
|
|
the MicroNet backbone network for recognition module.
|
||
|
|
Args:
|
||
|
|
mode(str): {'M0', 'M1', 'M2', 'M3'}
|
||
|
|
Four models are proposed based on four different computational costs (4M, 6M, 12M, 21M MAdds)
|
||
|
|
Default: 'M3'.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, mode='M3', **kwargs):
|
||
|
|
super(MicroNet, self).__init__()
|
||
|
|
|
||
|
|
self.cfgs = get_micronet_config(mode)
|
||
|
|
|
||
|
|
activation_cfg = {}
|
||
|
|
if mode == 'M0':
|
||
|
|
input_channel = 4
|
||
|
|
stem_groups = 2, 2
|
||
|
|
out_ch = 384
|
||
|
|
activation_cfg['init_a'] = 1.0, 1.0
|
||
|
|
activation_cfg['init_b'] = 0.0, 0.0
|
||
|
|
elif mode == 'M1':
|
||
|
|
input_channel = 6
|
||
|
|
stem_groups = 3, 2
|
||
|
|
out_ch = 576
|
||
|
|
activation_cfg['init_a'] = 1.0, 1.0
|
||
|
|
activation_cfg['init_b'] = 0.0, 0.0
|
||
|
|
elif mode == 'M2':
|
||
|
|
input_channel = 8
|
||
|
|
stem_groups = 4, 2
|
||
|
|
out_ch = 768
|
||
|
|
activation_cfg['init_a'] = 1.0, 1.0
|
||
|
|
activation_cfg['init_b'] = 0.0, 0.0
|
||
|
|
elif mode == 'M3':
|
||
|
|
input_channel = 12
|
||
|
|
stem_groups = 4, 3
|
||
|
|
out_ch = 432
|
||
|
|
activation_cfg['init_a'] = 1.0, 0.5
|
||
|
|
activation_cfg['init_b'] = 0.0, 0.5
|
||
|
|
else:
|
||
|
|
raise NotImplementedError("mode[" + mode +
|
||
|
|
"_model] is not implemented!")
|
||
|
|
|
||
|
|
layers = [StemLayer(3, input_channel, stride=2, groups=stem_groups)]
|
||
|
|
|
||
|
|
for idx, val in enumerate(self.cfgs):
|
||
|
|
s, n, c, ks, c1, c2, g1, g2, c3, g3, g4, y1, y2, y3, r = val
|
||
|
|
|
||
|
|
t1 = (c1, c2)
|
||
|
|
gs1 = (g1, g2)
|
||
|
|
gs2 = (c3, g3, g4)
|
||
|
|
activation_cfg['dy'] = [y1, y2, y3]
|
||
|
|
activation_cfg['ratio'] = r
|
||
|
|
|
||
|
|
output_channel = c
|
||
|
|
layers.append(
|
||
|
|
DYMicroBlock(
|
||
|
|
input_channel,
|
||
|
|
output_channel,
|
||
|
|
kernel_size=ks,
|
||
|
|
stride=s,
|
||
|
|
ch_exp=t1,
|
||
|
|
ch_per_group=gs1,
|
||
|
|
groups_1x1=gs2,
|
||
|
|
depthsep=True,
|
||
|
|
shuffle=True,
|
||
|
|
activation_cfg=activation_cfg, ))
|
||
|
|
input_channel = output_channel
|
||
|
|
for i in range(1, n):
|
||
|
|
layers.append(
|
||
|
|
DYMicroBlock(
|
||
|
|
input_channel,
|
||
|
|
output_channel,
|
||
|
|
kernel_size=ks,
|
||
|
|
stride=1,
|
||
|
|
ch_exp=t1,
|
||
|
|
ch_per_group=gs1,
|
||
|
|
groups_1x1=gs2,
|
||
|
|
depthsep=True,
|
||
|
|
shuffle=True,
|
||
|
|
activation_cfg=activation_cfg, ))
|
||
|
|
input_channel = output_channel
|
||
|
|
self.features = nn.Sequential(*layers)
|
||
|
|
|
||
|
|
self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
|
||
|
|
|
||
|
|
self.out_channels = make_divisible(out_ch)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.features(x)
|
||
|
|
x = self.pool(x)
|
||
|
|
return x
|