mirror of
https://github.com/deepinsight/insightface.git
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338 lines
11 KiB
Python
338 lines
11 KiB
Python
# 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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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, PReLU
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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from paddle.nn.initializer import XavierNormal, Constant
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import math
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__all__ = ["FresResNet50", "FresResNet100"]
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class ConvBNLayer(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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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None,
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data_format="NCHW"):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False,
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data_format=data_format)
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if name == "conv1":
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bn_name = "bn_" + name
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else:
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bn_name = "bn" + name[3:]
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self._batch_norm = BatchNorm(
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num_filters,
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act=act,
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epsilon=1e-05,
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param_attr=ParamAttr(name=bn_name + "_scale"),
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bias_attr=ParamAttr(bn_name + "_offset"),
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=bn_name + "_variance",
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data_layout=data_format)
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(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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data_format="NCHW"):
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super(BasicBlock, self).__init__()
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self.stride = stride
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bn_name = "bn_" + name[3:] + "_before"
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self._batch_norm = BatchNorm(
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num_channels,
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act=None,
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epsilon=1e-05,
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param_attr=ParamAttr(name=bn_name + "_scale"),
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bias_attr=ParamAttr(bn_name + "_offset"),
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=bn_name + "_variance",
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data_layout=data_format)
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=3,
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stride=1,
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act=None,
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name=name + "_branch2a",
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data_format=data_format)
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self.prelu = PReLU(num_parameters=1, name=name + "_branch2a_prelu")
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self.conv1 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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act=None,
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name=name + "_branch2b",
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data_format=data_format)
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if shortcut:
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self.short = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=1,
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stride=stride,
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act=None,
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name=name + "_branch1",
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data_format=data_format)
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self.shortcut = shortcut
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def forward(self, inputs):
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y = self._batch_norm(inputs)
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y = self.conv0(y)
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y = self.prelu(y)
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conv1 = self.conv1(y)
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if self.shortcut:
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short = self.short(inputs)
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else:
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short = inputs
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y = paddle.add(x=short, y=conv1)
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return y
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class FC(nn.Layer):
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def __init__(self,
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bn_channels,
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num_channels,
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num_classes,
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fc_type,
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dropout=0.4,
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name=None,
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data_format="NCHW"):
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super(FC, self).__init__()
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self.p = dropout
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self.fc_type = fc_type
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self.num_channels = num_channels
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bn_name = "bn_" + name
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if fc_type == "Z":
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self._batch_norm_1 = BatchNorm(
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bn_channels,
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act=None,
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epsilon=1e-05,
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param_attr=ParamAttr(name=bn_name + "_1_scale"),
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bias_attr=ParamAttr(bn_name + "_1_offset"),
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moving_mean_name=bn_name + "_1_mean",
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moving_variance_name=bn_name + "_1_variance",
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data_layout=data_format)
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if self.p > 0:
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self.dropout = Dropout(p=self.p, name=name + '_dropout')
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elif fc_type == "E":
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self._batch_norm_1 = BatchNorm(
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bn_channels,
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act=None,
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epsilon=1e-05,
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param_attr=ParamAttr(name=bn_name + "_1_scale"),
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bias_attr=ParamAttr(bn_name + "_1_offset"),
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moving_mean_name=bn_name + "_1_mean",
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moving_variance_name=bn_name + "_1_variance",
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data_layout=data_format)
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if self.p > 0:
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self.dropout = Dropout(p=self.p, name=name + '_dropout')
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self.fc = Linear(
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num_channels,
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num_classes,
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weight_attr=ParamAttr(
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initializer=XavierNormal(fan_in=0.0), name=name + ".w_0"),
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bias_attr=ParamAttr(
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initializer=Constant(), name=name + ".b_0"))
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self._batch_norm_2 = BatchNorm(
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num_classes,
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act=None,
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epsilon=1e-05,
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param_attr=ParamAttr(name=bn_name + "_2_scale"),
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bias_attr=ParamAttr(bn_name + "_2_offset"),
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moving_mean_name=bn_name + "_2_mean",
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moving_variance_name=bn_name + "_2_variance",
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data_layout=data_format)
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elif fc_type == "FC":
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self._batch_norm_1 = BatchNorm(
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bn_channels,
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act=None,
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epsilon=1e-05,
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param_attr=ParamAttr(name=bn_name + "_1_scale"),
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bias_attr=ParamAttr(bn_name + "_1_offset"),
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moving_mean_name=bn_name + "_1_mean",
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moving_variance_name=bn_name + "_1_variance",
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data_layout=data_format)
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self.fc = Linear(
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num_channels,
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num_classes,
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weight_attr=ParamAttr(
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initializer=XavierNormal(fan_in=0.0), name=name + ".w_0"),
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bias_attr=ParamAttr(
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initializer=Constant(), name=name + ".b_0"))
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self._batch_norm_2 = BatchNorm(
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num_classes,
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act=None,
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epsilon=1e-05,
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param_attr=ParamAttr(name=bn_name + "_2_scale"),
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bias_attr=ParamAttr(bn_name + "_2_offset"),
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moving_mean_name=bn_name + "_2_mean",
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moving_variance_name=bn_name + "_2_variance",
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data_layout=data_format)
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def forward(self, inputs):
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if self.fc_type == "Z":
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y = self._batch_norm_1(inputs)
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y = paddle.reshape(y, shape=[-1, self.num_channels])
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if self.p > 0:
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y = self.dropout(y)
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elif self.fc_type == "E":
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y = self._batch_norm_1(inputs)
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y = paddle.reshape(y, shape=[-1, self.num_channels])
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if self.p > 0:
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y = self.dropout(y)
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y = self.fc(y)
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y = self._batch_norm_2(y)
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elif self.fc_type == "FC":
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y = self._batch_norm_1(inputs)
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y = paddle.reshape(y, shape=[-1, self.num_channels])
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y = self.fc(y)
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y = self._batch_norm_2(y)
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return y
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class FresResNet(nn.Layer):
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def __init__(self,
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layers=50,
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num_features=512,
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fc_type='E',
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dropout=0.4,
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input_image_channel=3,
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input_image_width=112,
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input_image_height=112,
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data_format="NCHW"):
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super(FresResNet, self).__init__()
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self.layers = layers
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self.data_format = data_format
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self.input_image_channel = input_image_channel
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supported_layers = [50, 100]
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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 == 50:
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units = [3, 4, 14, 3]
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elif layers == 100:
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units = [3, 13, 30, 3]
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num_channels = [64, 64, 128, 256]
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num_filters = [64, 128, 256, 512]
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self.conv = ConvBNLayer(
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num_channels=self.input_image_channel,
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num_filters=64,
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filter_size=3,
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stride=1,
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act=None,
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name="conv1",
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data_format=self.data_format)
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self.prelu = PReLU(num_parameters=1, name="prelu1")
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self.block_list = paddle.nn.LayerList()
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for block in range(len(units)):
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shortcut = True
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for i in range(units[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 shortcut else 1,
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shortcut=shortcut,
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name=conv_name,
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data_format=self.data_format))
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self.block_list.append(basic_block)
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shortcut = False
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assert input_image_width % 16 == 0
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assert input_image_height % 16 == 0
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feat_w = input_image_width // 16
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feat_h = input_image_height // 16
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self.fc_channels = num_filters[-1] * feat_w * feat_h
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self.fc = FC(num_filters[-1],
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self.fc_channels,
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num_features,
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fc_type,
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dropout,
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name='fc')
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def forward(self, inputs):
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if self.data_format == "NHWC":
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inputs = paddle.tensor.transpose(inputs, [0, 2, 3, 1])
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inputs.stop_gradient = True
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y = self.conv(inputs)
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y = self.prelu(y)
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for block in self.block_list:
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y = block(y)
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y = self.fc(y)
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return y
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def FresResNet50(**args):
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model = FresResNet(layers=50, **args)
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return model
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def FresResNet100(**args):
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model = FresResNet(layers=100, **args)
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return model
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