forked from PulseFocusPlatform/PulseFocusPlatform
341 lines
11 KiB
Python
341 lines
11 KiB
Python
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# Copyright (c) 2020 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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import paddle.nn as nn
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import paddle.nn.functional as F
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from ppdet.core.workspace import register, serializable
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from ppdet.modeling.ops import batch_norm, mish
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from ..shape_spec import ShapeSpec
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__all__ = ['DarkNet', 'ConvBNLayer']
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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ch_in,
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ch_out,
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filter_size=3,
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stride=1,
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groups=1,
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padding=0,
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norm_type='bn',
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norm_decay=0.,
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act="leaky",
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freeze_norm=False,
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data_format='NCHW',
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name=''):
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"""
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conv + bn + activation layer
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Args:
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ch_in (int): input channel
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ch_out (int): output channel
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filter_size (int): filter size, default 3
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stride (int): stride, default 1
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groups (int): number of groups of conv layer, default 1
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padding (int): padding size, default 0
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norm_type (str): batch norm type, default bn
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norm_decay (str): decay for weight and bias of batch norm layer, default 0.
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act (str): activation function type, default 'leaky', which means leaky_relu
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freeze_norm (bool): whether to freeze norm, default False
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data_format (str): data format, NCHW or NHWC
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"""
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super(ConvBNLayer, self).__init__()
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self.conv = nn.Conv2D(
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in_channels=ch_in,
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out_channels=ch_out,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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groups=groups,
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data_format=data_format,
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bias_attr=False)
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self.batch_norm = batch_norm(
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ch_out,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format)
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self.act = act
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def forward(self, inputs):
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out = self.conv(inputs)
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out = self.batch_norm(out)
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if self.act == 'leaky':
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out = F.leaky_relu(out, 0.1)
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elif self.act == 'mish':
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out = mish(out)
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return out
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class DownSample(nn.Layer):
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def __init__(self,
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ch_in,
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ch_out,
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filter_size=3,
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stride=2,
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padding=1,
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norm_type='bn',
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norm_decay=0.,
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freeze_norm=False,
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data_format='NCHW'):
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"""
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downsample layer
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Args:
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ch_in (int): input channel
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ch_out (int): output channel
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filter_size (int): filter size, default 3
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stride (int): stride, default 2
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padding (int): padding size, default 1
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norm_type (str): batch norm type, default bn
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norm_decay (str): decay for weight and bias of batch norm layer, default 0.
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freeze_norm (bool): whether to freeze norm, default False
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data_format (str): data format, NCHW or NHWC
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"""
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super(DownSample, self).__init__()
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self.conv_bn_layer = ConvBNLayer(
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ch_in=ch_in,
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ch_out=ch_out,
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filter_size=filter_size,
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stride=stride,
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padding=padding,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format)
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self.ch_out = ch_out
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def forward(self, inputs):
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out = self.conv_bn_layer(inputs)
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return out
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class BasicBlock(nn.Layer):
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def __init__(self,
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ch_in,
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ch_out,
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norm_type='bn',
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norm_decay=0.,
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freeze_norm=False,
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data_format='NCHW'):
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"""
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BasicBlock layer of DarkNet
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Args:
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ch_in (int): input channel
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ch_out (int): output channel
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norm_type (str): batch norm type, default bn
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norm_decay (str): decay for weight and bias of batch norm layer, default 0.
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freeze_norm (bool): whether to freeze norm, default False
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data_format (str): data format, NCHW or NHWC
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"""
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super(BasicBlock, self).__init__()
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self.conv1 = ConvBNLayer(
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ch_in=ch_in,
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ch_out=ch_out,
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filter_size=1,
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stride=1,
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padding=0,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format)
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self.conv2 = ConvBNLayer(
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ch_in=ch_out,
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ch_out=ch_out * 2,
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filter_size=3,
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stride=1,
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padding=1,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format)
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def forward(self, inputs):
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conv1 = self.conv1(inputs)
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conv2 = self.conv2(conv1)
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out = paddle.add(x=inputs, y=conv2)
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return out
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class Blocks(nn.Layer):
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def __init__(self,
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ch_in,
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ch_out,
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count,
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norm_type='bn',
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norm_decay=0.,
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freeze_norm=False,
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name=None,
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data_format='NCHW'):
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"""
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Blocks layer, which consist of some BaickBlock layers
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Args:
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ch_in (int): input channel
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ch_out (int): output channel
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count (int): number of BasicBlock layer
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norm_type (str): batch norm type, default bn
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norm_decay (str): decay for weight and bias of batch norm layer, default 0.
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freeze_norm (bool): whether to freeze norm, default False
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name (str): layer name
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data_format (str): data format, NCHW or NHWC
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"""
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super(Blocks, self).__init__()
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self.basicblock0 = BasicBlock(
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ch_in,
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ch_out,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format)
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self.res_out_list = []
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for i in range(1, count):
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block_name = '{}.{}'.format(name, i)
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res_out = self.add_sublayer(
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block_name,
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BasicBlock(
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ch_out * 2,
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ch_out,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format))
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self.res_out_list.append(res_out)
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self.ch_out = ch_out
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def forward(self, inputs):
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y = self.basicblock0(inputs)
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for basic_block_i in self.res_out_list:
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y = basic_block_i(y)
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return y
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DarkNet_cfg = {53: ([1, 2, 8, 8, 4])}
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@register
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@serializable
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class DarkNet(nn.Layer):
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__shared__ = ['norm_type', 'data_format']
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def __init__(self,
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depth=53,
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freeze_at=-1,
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return_idx=[2, 3, 4],
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num_stages=5,
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norm_type='bn',
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norm_decay=0.,
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freeze_norm=False,
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data_format='NCHW'):
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"""
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Darknet, see https://pjreddie.com/darknet/yolo/
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Args:
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depth (int): depth of network
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freeze_at (int): freeze the backbone at which stage
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filter_size (int): filter size, default 3
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return_idx (list): index of stages whose feature maps are returned
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norm_type (str): batch norm type, default bn
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norm_decay (str): decay for weight and bias of batch norm layer, default 0.
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data_format (str): data format, NCHW or NHWC
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"""
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super(DarkNet, self).__init__()
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self.depth = depth
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self.freeze_at = freeze_at
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self.return_idx = return_idx
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self.num_stages = num_stages
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self.stages = DarkNet_cfg[self.depth][0:num_stages]
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self.conv0 = ConvBNLayer(
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ch_in=3,
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ch_out=32,
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filter_size=3,
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stride=1,
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padding=1,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format)
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self.downsample0 = DownSample(
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ch_in=32,
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ch_out=32 * 2,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format)
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self._out_channels = []
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self.darknet_conv_block_list = []
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self.downsample_list = []
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ch_in = [64, 128, 256, 512, 1024]
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for i, stage in enumerate(self.stages):
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name = 'stage.{}'.format(i)
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conv_block = self.add_sublayer(
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name,
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Blocks(
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int(ch_in[i]),
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32 * (2**i),
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stage,
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format,
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name=name))
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self.darknet_conv_block_list.append(conv_block)
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if i in return_idx:
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self._out_channels.append(64 * (2**i))
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for i in range(num_stages - 1):
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down_name = 'stage.{}.downsample'.format(i)
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downsample = self.add_sublayer(
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down_name,
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DownSample(
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ch_in=32 * (2**(i + 1)),
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ch_out=32 * (2**(i + 2)),
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norm_type=norm_type,
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norm_decay=norm_decay,
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freeze_norm=freeze_norm,
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data_format=data_format))
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self.downsample_list.append(downsample)
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def forward(self, inputs):
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x = inputs['image']
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out = self.conv0(x)
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out = self.downsample0(out)
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blocks = []
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for i, conv_block_i in enumerate(self.darknet_conv_block_list):
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out = conv_block_i(out)
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if i == self.freeze_at:
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out.stop_gradient = True
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if i in self.return_idx:
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blocks.append(out)
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if i < self.num_stages - 1:
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out = self.downsample_list[i](out)
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return blocks
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@property
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def out_shape(self):
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return [ShapeSpec(channels=c) for c in self._out_channels]
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