426 lines
12 KiB
Python
426 lines
12 KiB
Python
# copyright (c) 2020 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 sys
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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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from paddle.nn import Conv2d, Pool2D, BatchNorm, Linear
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from paddle.nn.initializer import Uniform
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import math
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__all__ = [
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"DPN",
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"DPN68",
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"DPN92",
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"DPN98",
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"DPN107",
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"DPN131",
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]
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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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pad=0,
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groups=1,
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act="relu",
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name=None):
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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=pad,
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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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self._batch_norm = BatchNorm(
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num_filters,
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act=act,
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param_attr=ParamAttr(name=name + '_bn_scale'),
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bias_attr=ParamAttr(name + '_bn_offset'),
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moving_mean_name=name + '_bn_mean',
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moving_variance_name=name + '_bn_variance')
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def forward(self, input):
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y = self._conv(input)
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y = self._batch_norm(y)
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return y
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class BNACConvLayer(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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pad=0,
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groups=1,
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act="relu",
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name=None):
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super(BNACConvLayer, self).__init__()
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self.num_channels = num_channels
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self._batch_norm = BatchNorm(
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num_channels,
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act=act,
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param_attr=ParamAttr(name=name + '_bn_scale'),
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bias_attr=ParamAttr(name + '_bn_offset'),
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moving_mean_name=name + '_bn_mean',
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moving_variance_name=name + '_bn_variance')
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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=pad,
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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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def forward(self, input):
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y = self._batch_norm(input)
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y = self._conv(y)
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return y
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class DualPathFactory(nn.Layer):
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def __init__(self,
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num_channels,
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num_1x1_a,
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num_3x3_b,
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num_1x1_c,
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inc,
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G,
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_type='normal',
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name=None):
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super(DualPathFactory, self).__init__()
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self.num_1x1_c = num_1x1_c
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self.inc = inc
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self.name = name
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kw = 3
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kh = 3
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pw = (kw - 1) // 2
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ph = (kh - 1) // 2
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# type
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if _type == 'proj':
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key_stride = 1
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self.has_proj = True
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elif _type == 'down':
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key_stride = 2
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self.has_proj = True
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elif _type == 'normal':
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key_stride = 1
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self.has_proj = False
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else:
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print("not implemented now!!!")
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sys.exit(1)
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data_in_ch = sum(num_channels) if isinstance(num_channels,
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list) else num_channels
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if self.has_proj:
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self.c1x1_w_func = BNACConvLayer(
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num_channels=data_in_ch,
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num_filters=num_1x1_c + 2 * inc,
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filter_size=(1, 1),
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pad=(0, 0),
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stride=(key_stride, key_stride),
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name=name + "_match")
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self.c1x1_a_func = BNACConvLayer(
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num_channels=data_in_ch,
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num_filters=num_1x1_a,
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filter_size=(1, 1),
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pad=(0, 0),
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name=name + "_conv1")
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self.c3x3_b_func = BNACConvLayer(
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num_channels=num_1x1_a,
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num_filters=num_3x3_b,
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filter_size=(kw, kh),
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pad=(pw, ph),
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stride=(key_stride, key_stride),
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groups=G,
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name=name + "_conv2")
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self.c1x1_c_func = BNACConvLayer(
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num_channels=num_3x3_b,
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num_filters=num_1x1_c + inc,
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filter_size=(1, 1),
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pad=(0, 0),
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name=name + "_conv3")
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def forward(self, input):
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# PROJ
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if isinstance(input, list):
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data_in = paddle.concat([input[0], input[1]], axis=1)
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else:
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data_in = input
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if self.has_proj:
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c1x1_w = self.c1x1_w_func(data_in)
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data_o1, data_o2 = paddle.split(
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c1x1_w, num_or_sections=[self.num_1x1_c, 2 * self.inc], axis=1)
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else:
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data_o1 = input[0]
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data_o2 = input[1]
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c1x1_a = self.c1x1_a_func(data_in)
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c3x3_b = self.c3x3_b_func(c1x1_a)
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c1x1_c = self.c1x1_c_func(c3x3_b)
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c1x1_c1, c1x1_c2 = paddle.split(
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c1x1_c, num_or_sections=[self.num_1x1_c, self.inc], axis=1)
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# OUTPUTS
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summ = paddle.elementwise_add(x=data_o1, y=c1x1_c1)
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dense = paddle.concat([data_o2, c1x1_c2], axis=1)
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# tensor, channels
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return [summ, dense]
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class DPN(nn.Layer):
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def __init__(self, layers=60, class_dim=1000):
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super(DPN, self).__init__()
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self._class_dim = class_dim
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args = self.get_net_args(layers)
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bws = args['bw']
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inc_sec = args['inc_sec']
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rs = args['r']
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k_r = args['k_r']
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k_sec = args['k_sec']
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G = args['G']
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init_num_filter = args['init_num_filter']
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init_filter_size = args['init_filter_size']
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init_padding = args['init_padding']
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self.k_sec = k_sec
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self.conv1_x_1_func = ConvBNLayer(
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num_channels=3,
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num_filters=init_num_filter,
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filter_size=3,
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stride=2,
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pad=1,
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act='relu',
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name="conv1")
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self.pool2d_max = Pool2D(
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pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
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num_channel_dpn = init_num_filter
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self.dpn_func_list = []
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#conv2 - conv5
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match_list, num = [], 0
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for gc in range(4):
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bw = bws[gc]
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inc = inc_sec[gc]
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R = (k_r * bw) // rs[gc]
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if gc == 0:
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_type1 = 'proj'
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_type2 = 'normal'
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match = 1
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else:
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_type1 = 'down'
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_type2 = 'normal'
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match = match + k_sec[gc - 1]
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match_list.append(match)
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self.dpn_func_list.append(
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self.add_sublayer(
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"dpn{}".format(match),
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DualPathFactory(
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num_channels=num_channel_dpn,
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num_1x1_a=R,
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num_3x3_b=R,
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num_1x1_c=bw,
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inc=inc,
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G=G,
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_type=_type1,
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name="dpn" + str(match))))
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num_channel_dpn = [bw, 3 * inc]
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for i_ly in range(2, k_sec[gc] + 1):
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num += 1
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if num in match_list:
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num += 1
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self.dpn_func_list.append(
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self.add_sublayer(
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"dpn{}".format(num),
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DualPathFactory(
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num_channels=num_channel_dpn,
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num_1x1_a=R,
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num_3x3_b=R,
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num_1x1_c=bw,
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inc=inc,
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G=G,
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_type=_type2,
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name="dpn" + str(num))))
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num_channel_dpn = [
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num_channel_dpn[0], num_channel_dpn[1] + inc
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]
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out_channel = sum(num_channel_dpn)
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self.conv5_x_x_bn = BatchNorm(
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num_channels=sum(num_channel_dpn),
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act="relu",
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param_attr=ParamAttr(name='final_concat_bn_scale'),
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bias_attr=ParamAttr('final_concat_bn_offset'),
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moving_mean_name='final_concat_bn_mean',
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moving_variance_name='final_concat_bn_variance')
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self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
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stdv = 0.01
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self.out = Linear(
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out_channel,
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class_dim,
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weight_attr=ParamAttr(
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initializer=Uniform(-stdv, stdv), name="fc_weights"),
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bias_attr=ParamAttr(name="fc_offset"))
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def forward(self, input):
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conv1_x_1 = self.conv1_x_1_func(input)
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convX_x_x = self.pool2d_max(conv1_x_1)
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dpn_idx = 0
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for gc in range(4):
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convX_x_x = self.dpn_func_list[dpn_idx](convX_x_x)
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dpn_idx += 1
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for i_ly in range(2, self.k_sec[gc] + 1):
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convX_x_x = self.dpn_func_list[dpn_idx](convX_x_x)
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dpn_idx += 1
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conv5_x_x = paddle.concat(convX_x_x, axis=1)
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conv5_x_x = self.conv5_x_x_bn(conv5_x_x)
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y = self.pool2d_avg(conv5_x_x)
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y = paddle.reshape(y, shape=[0, -1])
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y = self.out(y)
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return y
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def get_net_args(self, layers):
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if layers == 68:
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k_r = 128
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G = 32
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k_sec = [3, 4, 12, 3]
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inc_sec = [16, 32, 32, 64]
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bw = [64, 128, 256, 512]
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r = [64, 64, 64, 64]
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init_num_filter = 10
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init_filter_size = 3
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init_padding = 1
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elif layers == 92:
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k_r = 96
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G = 32
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k_sec = [3, 4, 20, 3]
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inc_sec = [16, 32, 24, 128]
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bw = [256, 512, 1024, 2048]
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r = [256, 256, 256, 256]
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init_num_filter = 64
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init_filter_size = 7
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init_padding = 3
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elif layers == 98:
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k_r = 160
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G = 40
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k_sec = [3, 6, 20, 3]
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inc_sec = [16, 32, 32, 128]
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bw = [256, 512, 1024, 2048]
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r = [256, 256, 256, 256]
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init_num_filter = 96
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init_filter_size = 7
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init_padding = 3
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elif layers == 107:
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k_r = 200
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G = 50
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k_sec = [4, 8, 20, 3]
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inc_sec = [20, 64, 64, 128]
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bw = [256, 512, 1024, 2048]
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r = [256, 256, 256, 256]
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init_num_filter = 128
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init_filter_size = 7
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init_padding = 3
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elif layers == 131:
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k_r = 160
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G = 40
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k_sec = [4, 8, 28, 3]
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inc_sec = [16, 32, 32, 128]
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bw = [256, 512, 1024, 2048]
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r = [256, 256, 256, 256]
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init_num_filter = 128
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init_filter_size = 7
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init_padding = 3
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else:
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raise NotImplementedError
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net_arg = {
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'k_r': k_r,
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'G': G,
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'k_sec': k_sec,
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'inc_sec': inc_sec,
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'bw': bw,
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'r': r
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}
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net_arg['init_num_filter'] = init_num_filter
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net_arg['init_filter_size'] = init_filter_size
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net_arg['init_padding'] = init_padding
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return net_arg
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def DPN68(**args):
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model = DPN(layers=68, **args)
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return model
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def DPN92(**args):
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model = DPN(layers=92, **args)
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return model
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def DPN98(**args):
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model = DPN(layers=98, **args)
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return model
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def DPN107(**args):
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model = DPN(layers=107, **args)
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return model
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def DPN131(**args):
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model = DPN(layers=131, **args)
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return model
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