mirror of
https://github.com/deepinsight/insightface.git
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163 lines
5.0 KiB
Python
163 lines
5.0 KiB
Python
# 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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import paddle
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from paddle import nn
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import math
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__all__ = ['MobileFaceNet_128']
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MobileFaceNet_BottleNeck_Setting = [
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# t, c , n ,s
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[2, 64, 5, 2],
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[4, 128, 1, 2],
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[2, 128, 6, 1],
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[4, 128, 1, 2],
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[2, 128, 2, 1]
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]
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class BottleNeck(nn.Layer):
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def __init__(self, inp, oup, stride, expansion):
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super().__init__()
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self.connect = stride == 1 and inp == oup
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self.conv = nn.Sequential(
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# 1*1 conv
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nn.Conv2D(
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inp, inp * expansion, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(inp * expansion),
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nn.PReLU(inp * expansion),
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# 3*3 depth wise conv
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nn.Conv2D(
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inp * expansion,
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inp * expansion,
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3,
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stride,
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1,
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groups=inp * expansion,
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bias_attr=False),
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nn.BatchNorm2D(inp * expansion),
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nn.PReLU(inp * expansion),
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# 1*1 conv
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nn.Conv2D(
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inp * expansion, oup, 1, 1, 0, bias_attr=False),
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nn.BatchNorm2D(oup), )
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def forward(self, x):
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if self.connect:
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return x + self.conv(x)
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else:
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return self.conv(x)
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class ConvBlock(nn.Layer):
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def __init__(self, inp, oup, k, s, p, dw=False, linear=False):
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super().__init__()
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self.linear = linear
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if dw:
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self.conv = nn.Conv2D(
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inp, oup, k, s, p, groups=inp, bias_attr=False)
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else:
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self.conv = nn.Conv2D(inp, oup, k, s, p, bias_attr=False)
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self.bn = nn.BatchNorm2D(oup)
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if not linear:
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self.prelu = nn.PReLU(oup)
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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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if self.linear:
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return x
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else:
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return self.prelu(x)
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class MobileFaceNet(nn.Layer):
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def __init__(self,
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feature_dim=128,
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bottleneck_setting=MobileFaceNet_BottleNeck_Setting,
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**args):
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super().__init__()
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self.conv1 = ConvBlock(3, 64, 3, 2, 1)
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self.dw_conv1 = ConvBlock(64, 64, 3, 1, 1, dw=True)
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self.cur_channel = 64
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block = BottleNeck
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self.blocks = self._make_layer(block, bottleneck_setting)
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self.conv2 = ConvBlock(128, 512, 1, 1, 0)
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self.linear7 = ConvBlock(512, 512, 7, 1, 0, dw=True, linear=True)
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self.linear1 = ConvBlock(512, feature_dim, 1, 1, 0, linear=True)
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for m in self.sublayers():
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if isinstance(m, nn.Conv2D):
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# ks * ks * out_ch
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n = m.weight.shape[1] * m.weight.shape[2] * m.weight.shape[3]
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m.weight = paddle.create_parameter(
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shape=m.weight.shape,
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dtype=m.weight.dtype,
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default_initializer=nn.initializer.Normal(
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mean=0.0, std=math.sqrt(2.0 / n)))
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elif isinstance(m, (nn.BatchNorm, nn.BatchNorm2D, nn.GroupNorm)):
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m.weight = paddle.create_parameter(
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shape=m.weight.shape,
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dtype=m.weight.dtype,
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default_initializer=nn.initializer.Constant(value=1.0))
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m.bias = paddle.create_parameter(
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shape=m.bias.shape,
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dtype=m.bias.dtype,
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default_initializer=nn.initializer.Constant(value=0.0))
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def _make_layer(self, block, setting):
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layers = []
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for t, c, n, s in setting:
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for i in range(n):
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if i == 0:
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layers.append(block(self.cur_channel, c, s, t))
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else:
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layers.append(block(self.cur_channel, c, 1, t))
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self.cur_channel = c
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.dw_conv1(x)
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x = self.blocks(x)
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x = self.conv2(x)
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x = self.linear7(x)
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x = self.linear1(x)
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x = x.reshape([x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]])
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return x
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def MobileFaceNet_128(num_features=128, **args):
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model = MobileFaceNet(feature_dim=num_features, **args)
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return model
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# if __name__ == "__main__":
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# paddle.set_device("cpu")
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# x = paddle.rand([2, 3, 112, 112])
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# net = MobileFaceNet()
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# print(net)
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# x = net(x)
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# print(x.shape)
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