308 lines
10 KiB
Python
308 lines
10 KiB
Python
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#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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from paddle import nn, ParamAttr
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from paddle.nn import functional as F
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import paddle.fluid as fluid
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import paddle
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import numpy as np
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__all__ = ["ResNetFPN"]
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class ResNetFPN(nn.Layer):
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def __init__(self, in_channels=1, layers=50, **kwargs):
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super(ResNetFPN, self).__init__()
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supported_layers = {
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18: {
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'depth': [2, 2, 2, 2],
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'block_class': BasicBlock
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},
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34: {
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'depth': [3, 4, 6, 3],
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'block_class': BasicBlock
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},
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50: {
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'depth': [3, 4, 6, 3],
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'block_class': BottleneckBlock
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},
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101: {
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'depth': [3, 4, 23, 3],
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'block_class': BottleneckBlock
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},
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152: {
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'depth': [3, 8, 36, 3],
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'block_class': BottleneckBlock
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}
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}
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stride_list = [(2, 2), (2, 2), (1, 1), (1, 1)]
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num_filters = [64, 128, 256, 512]
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self.depth = supported_layers[layers]['depth']
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self.F = []
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self.conv = ConvBNLayer(
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in_channels=in_channels,
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out_channels=64,
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kernel_size=7,
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stride=2,
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act="relu",
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name="conv1")
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self.block_list = []
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in_ch = 64
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if layers >= 50:
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for block in range(len(self.depth)):
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for i in range(self.depth[block]):
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if layers in [101, 152] and block == 2:
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if i == 0:
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conv_name = "res" + str(block + 2) + "a"
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else:
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conv_name = "res" + str(block + 2) + "b" + str(i)
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else:
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conv_name = "res" + str(block + 2) + chr(97 + i)
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block_list = self.add_sublayer(
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"bottleneckBlock_{}_{}".format(block, i),
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BottleneckBlock(
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in_channels=in_ch,
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out_channels=num_filters[block],
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stride=stride_list[block] if i == 0 else 1,
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name=conv_name))
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in_ch = num_filters[block] * 4
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self.block_list.append(block_list)
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self.F.append(block_list)
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else:
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for block in range(len(self.depth)):
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for i in range(self.depth[block]):
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conv_name = "res" + str(block + 2) + chr(97 + i)
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if i == 0 and block != 0:
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stride = (2, 1)
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else:
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stride = (1, 1)
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basic_block = self.add_sublayer(
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conv_name,
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BasicBlock(
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in_channels=in_ch,
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out_channels=num_filters[block],
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stride=stride_list[block] if i == 0 else 1,
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is_first=block == i == 0,
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name=conv_name))
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in_ch = basic_block.out_channels
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self.block_list.append(basic_block)
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out_ch_list = [in_ch // 4, in_ch // 2, in_ch]
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self.base_block = []
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self.conv_trans = []
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self.bn_block = []
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for i in [-2, -3]:
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in_channels = out_ch_list[i + 1] + out_ch_list[i]
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self.base_block.append(
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self.add_sublayer(
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"F_{}_base_block_0".format(i),
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nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_ch_list[i],
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kernel_size=1,
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weight_attr=ParamAttr(trainable=True),
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bias_attr=ParamAttr(trainable=True))))
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self.base_block.append(
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self.add_sublayer(
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"F_{}_base_block_1".format(i),
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nn.Conv2D(
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in_channels=out_ch_list[i],
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out_channels=out_ch_list[i],
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kernel_size=3,
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padding=1,
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weight_attr=ParamAttr(trainable=True),
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bias_attr=ParamAttr(trainable=True))))
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self.base_block.append(
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self.add_sublayer(
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"F_{}_base_block_2".format(i),
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nn.BatchNorm(
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num_channels=out_ch_list[i],
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act="relu",
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param_attr=ParamAttr(trainable=True),
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bias_attr=ParamAttr(trainable=True))))
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self.base_block.append(
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self.add_sublayer(
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"F_{}_base_block_3".format(i),
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nn.Conv2D(
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in_channels=out_ch_list[i],
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out_channels=512,
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kernel_size=1,
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bias_attr=ParamAttr(trainable=True),
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weight_attr=ParamAttr(trainable=True))))
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self.out_channels = 512
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def __call__(self, x):
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x = self.conv(x)
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fpn_list = []
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F = []
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for i in range(len(self.depth)):
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fpn_list.append(np.sum(self.depth[:i + 1]))
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for i, block in enumerate(self.block_list):
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x = block(x)
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for number in fpn_list:
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if i + 1 == number:
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F.append(x)
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base = F[-1]
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j = 0
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for i, block in enumerate(self.base_block):
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if i % 3 == 0 and i < 6:
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j = j + 1
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b, c, w, h = F[-j - 1].shape
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if [w, h] == list(base.shape[2:]):
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base = base
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else:
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base = self.conv_trans[j - 1](base)
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base = self.bn_block[j - 1](base)
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base = paddle.concat([base, F[-j - 1]], axis=1)
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base = block(base)
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return base
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class ConvBNLayer(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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kernel_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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super(ConvBNLayer, self).__init__()
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self.conv = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=2 if stride == (1, 1) else kernel_size,
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dilation=2 if stride == (1, 1) else 1,
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stride=stride,
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padding=(kernel_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(name=name + '.conv2d.output.1.w_0'),
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bias_attr=False, )
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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.bn = nn.BatchNorm(
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num_channels=out_channels,
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act=act,
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param_attr=ParamAttr(name=name + '.output.1.w_0'),
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bias_attr=ParamAttr(name=name + '.output.1.b_0'),
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=bn_name + "_variance")
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def __call__(self, x):
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x = self.conv(x)
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x = self.bn(x)
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return x
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class ShortCut(nn.Layer):
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def __init__(self, in_channels, out_channels, stride, name, is_first=False):
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super(ShortCut, self).__init__()
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self.use_conv = True
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if in_channels != out_channels or stride != 1 or is_first == True:
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if stride == (1, 1):
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self.conv = ConvBNLayer(
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in_channels, out_channels, 1, 1, name=name)
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else: # stride==(2,2)
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self.conv = ConvBNLayer(
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in_channels, out_channels, 1, stride, name=name)
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else:
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self.use_conv = False
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def forward(self, x):
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if self.use_conv:
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x = self.conv(x)
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return x
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class BottleneckBlock(nn.Layer):
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def __init__(self, in_channels, out_channels, stride, name):
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super(BottleneckBlock, self).__init__()
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self.conv0 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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act='relu',
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name=name + "_branch2a")
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self.conv1 = ConvBNLayer(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=3,
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stride=stride,
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act='relu',
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name=name + "_branch2b")
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self.conv2 = ConvBNLayer(
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in_channels=out_channels,
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out_channels=out_channels * 4,
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kernel_size=1,
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act=None,
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name=name + "_branch2c")
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self.short = ShortCut(
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in_channels=in_channels,
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out_channels=out_channels * 4,
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stride=stride,
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is_first=False,
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name=name + "_branch1")
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self.out_channels = out_channels * 4
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def forward(self, x):
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y = self.conv0(x)
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y = self.conv1(y)
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y = self.conv2(y)
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y = y + self.short(x)
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y = F.relu(y)
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return y
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class BasicBlock(nn.Layer):
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def __init__(self, in_channels, out_channels, stride, name, is_first):
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super(BasicBlock, self).__init__()
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self.conv0 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=3,
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act='relu',
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stride=stride,
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name=name + "_branch2a")
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self.conv1 = ConvBNLayer(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=3,
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act=None,
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name=name + "_branch2b")
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self.short = ShortCut(
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in_channels=in_channels,
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out_channels=out_channels,
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stride=stride,
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is_first=is_first,
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name=name + "_branch1")
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self.out_channels = out_channels
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def forward(self, x):
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y = self.conv0(x)
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y = self.conv1(y)
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y = y + self.short(x)
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return F.relu(y)
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