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411 lines
13 KiB
411 lines
13 KiB
# Copyright (c) 2022 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 paddle.nn as nn |
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import paddle.nn.functional as F |
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from paddle import ParamAttr |
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from paddle.regularizer import L2Decay |
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from paddle.nn.initializer import KaimingNormal |
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from paddlers.models.ppdet.core.workspace import register, serializable |
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from numbers import Integral |
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from ..shape_spec import ShapeSpec |
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__all__ = ['MobileNet'] |
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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, |
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padding, |
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num_groups=1, |
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act='relu', |
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conv_lr=1., |
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conv_decay=0., |
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norm_decay=0., |
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norm_type='bn', |
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name=None): |
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super(ConvBNLayer, self).__init__() |
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self.act = act |
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self._conv = nn.Conv2D( |
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in_channels, |
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out_channels, |
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kernel_size=kernel_size, |
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stride=stride, |
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padding=padding, |
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groups=num_groups, |
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weight_attr=ParamAttr( |
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learning_rate=conv_lr, |
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initializer=KaimingNormal(), |
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regularizer=L2Decay(conv_decay)), |
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bias_attr=False) |
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param_attr = ParamAttr(regularizer=L2Decay(norm_decay)) |
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bias_attr = ParamAttr(regularizer=L2Decay(norm_decay)) |
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if norm_type in ['sync_bn', 'bn']: |
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self._batch_norm = nn.BatchNorm2D( |
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out_channels, weight_attr=param_attr, bias_attr=bias_attr) |
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def forward(self, x): |
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x = self._conv(x) |
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x = self._batch_norm(x) |
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if self.act == "relu": |
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x = F.relu(x) |
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elif self.act == "relu6": |
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x = F.relu6(x) |
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return x |
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class DepthwiseSeparable(nn.Layer): |
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def __init__(self, |
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in_channels, |
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out_channels1, |
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out_channels2, |
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num_groups, |
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stride, |
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scale, |
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conv_lr=1., |
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conv_decay=0., |
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norm_decay=0., |
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norm_type='bn', |
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name=None): |
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super(DepthwiseSeparable, self).__init__() |
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self._depthwise_conv = ConvBNLayer( |
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in_channels, |
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int(out_channels1 * scale), |
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kernel_size=3, |
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stride=stride, |
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padding=1, |
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num_groups=int(num_groups * scale), |
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conv_lr=conv_lr, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name=name + "_dw") |
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self._pointwise_conv = ConvBNLayer( |
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int(out_channels1 * scale), |
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int(out_channels2 * scale), |
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kernel_size=1, |
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stride=1, |
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padding=0, |
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conv_lr=conv_lr, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name=name + "_sep") |
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def forward(self, x): |
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x = self._depthwise_conv(x) |
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x = self._pointwise_conv(x) |
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return x |
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class ExtraBlock(nn.Layer): |
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def __init__(self, |
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in_channels, |
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out_channels1, |
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out_channels2, |
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num_groups=1, |
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stride=2, |
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conv_lr=1., |
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conv_decay=0., |
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norm_decay=0., |
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norm_type='bn', |
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name=None): |
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super(ExtraBlock, self).__init__() |
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self.pointwise_conv = ConvBNLayer( |
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in_channels, |
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int(out_channels1), |
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kernel_size=1, |
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stride=1, |
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padding=0, |
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num_groups=int(num_groups), |
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act='relu6', |
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conv_lr=conv_lr, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name=name + "_extra1") |
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self.normal_conv = ConvBNLayer( |
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int(out_channels1), |
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int(out_channels2), |
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kernel_size=3, |
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stride=stride, |
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padding=1, |
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num_groups=int(num_groups), |
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act='relu6', |
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conv_lr=conv_lr, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name=name + "_extra2") |
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def forward(self, x): |
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x = self.pointwise_conv(x) |
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x = self.normal_conv(x) |
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return x |
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@register |
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@serializable |
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class MobileNet(nn.Layer): |
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__shared__ = ['norm_type'] |
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def __init__(self, |
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norm_type='bn', |
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norm_decay=0., |
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conv_decay=0., |
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scale=1, |
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conv_learning_rate=1.0, |
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feature_maps=[4, 6, 13], |
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with_extra_blocks=False, |
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extra_block_filters=[[256, 512], [128, 256], [128, 256], |
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[64, 128]]): |
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super(MobileNet, self).__init__() |
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if isinstance(feature_maps, Integral): |
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feature_maps = [feature_maps] |
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self.feature_maps = feature_maps |
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self.with_extra_blocks = with_extra_blocks |
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self.extra_block_filters = extra_block_filters |
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self._out_channels = [] |
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self.conv1 = ConvBNLayer( |
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in_channels=3, |
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out_channels=int(32 * scale), |
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kernel_size=3, |
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stride=2, |
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padding=1, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv1") |
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self.dwsl = [] |
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dws21 = self.add_sublayer( |
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"conv2_1", |
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sublayer=DepthwiseSeparable( |
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in_channels=int(32 * scale), |
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out_channels1=32, |
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out_channels2=64, |
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num_groups=32, |
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stride=1, |
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scale=scale, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv2_1")) |
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self.dwsl.append(dws21) |
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self._update_out_channels( |
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int(64 * scale), len(self.dwsl), feature_maps) |
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dws22 = self.add_sublayer( |
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"conv2_2", |
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sublayer=DepthwiseSeparable( |
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in_channels=int(64 * scale), |
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out_channels1=64, |
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out_channels2=128, |
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num_groups=64, |
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stride=2, |
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scale=scale, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv2_2")) |
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self.dwsl.append(dws22) |
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self._update_out_channels( |
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int(128 * scale), len(self.dwsl), feature_maps) |
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# 1/4 |
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dws31 = self.add_sublayer( |
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"conv3_1", |
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sublayer=DepthwiseSeparable( |
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in_channels=int(128 * scale), |
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out_channels1=128, |
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out_channels2=128, |
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num_groups=128, |
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stride=1, |
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scale=scale, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv3_1")) |
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self.dwsl.append(dws31) |
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self._update_out_channels( |
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int(128 * scale), len(self.dwsl), feature_maps) |
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dws32 = self.add_sublayer( |
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"conv3_2", |
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sublayer=DepthwiseSeparable( |
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in_channels=int(128 * scale), |
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out_channels1=128, |
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out_channels2=256, |
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num_groups=128, |
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stride=2, |
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scale=scale, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv3_2")) |
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self.dwsl.append(dws32) |
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self._update_out_channels( |
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int(256 * scale), len(self.dwsl), feature_maps) |
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# 1/8 |
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dws41 = self.add_sublayer( |
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"conv4_1", |
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sublayer=DepthwiseSeparable( |
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in_channels=int(256 * scale), |
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out_channels1=256, |
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out_channels2=256, |
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num_groups=256, |
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stride=1, |
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scale=scale, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv4_1")) |
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self.dwsl.append(dws41) |
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self._update_out_channels( |
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int(256 * scale), len(self.dwsl), feature_maps) |
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dws42 = self.add_sublayer( |
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"conv4_2", |
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sublayer=DepthwiseSeparable( |
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in_channels=int(256 * scale), |
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out_channels1=256, |
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out_channels2=512, |
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num_groups=256, |
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stride=2, |
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scale=scale, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv4_2")) |
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self.dwsl.append(dws42) |
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self._update_out_channels( |
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int(512 * scale), len(self.dwsl), feature_maps) |
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# 1/16 |
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for i in range(5): |
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tmp = self.add_sublayer( |
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"conv5_" + str(i + 1), |
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sublayer=DepthwiseSeparable( |
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in_channels=int(512 * scale), |
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out_channels1=512, |
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out_channels2=512, |
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num_groups=512, |
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stride=1, |
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scale=scale, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv5_" + str(i + 1))) |
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self.dwsl.append(tmp) |
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self._update_out_channels( |
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int(512 * scale), len(self.dwsl), feature_maps) |
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dws56 = self.add_sublayer( |
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"conv5_6", |
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sublayer=DepthwiseSeparable( |
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in_channels=int(512 * scale), |
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out_channels1=512, |
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out_channels2=1024, |
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num_groups=512, |
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stride=2, |
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scale=scale, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv5_6")) |
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self.dwsl.append(dws56) |
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self._update_out_channels( |
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int(1024 * scale), len(self.dwsl), feature_maps) |
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# 1/32 |
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dws6 = self.add_sublayer( |
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"conv6", |
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sublayer=DepthwiseSeparable( |
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in_channels=int(1024 * scale), |
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out_channels1=1024, |
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out_channels2=1024, |
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num_groups=1024, |
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stride=1, |
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scale=scale, |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv6")) |
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self.dwsl.append(dws6) |
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self._update_out_channels( |
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int(1024 * scale), len(self.dwsl), feature_maps) |
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if self.with_extra_blocks: |
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self.extra_blocks = [] |
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for i, block_filter in enumerate(self.extra_block_filters): |
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in_c = 1024 if i == 0 else self.extra_block_filters[i - 1][1] |
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conv_extra = self.add_sublayer( |
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"conv7_" + str(i + 1), |
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sublayer=ExtraBlock( |
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in_c, |
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block_filter[0], |
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block_filter[1], |
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conv_lr=conv_learning_rate, |
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conv_decay=conv_decay, |
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norm_decay=norm_decay, |
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norm_type=norm_type, |
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name="conv7_" + str(i + 1))) |
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self.extra_blocks.append(conv_extra) |
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self._update_out_channels( |
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block_filter[1], |
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len(self.dwsl) + len(self.extra_blocks), feature_maps) |
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def _update_out_channels(self, channel, feature_idx, feature_maps): |
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if feature_idx in feature_maps: |
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self._out_channels.append(channel) |
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def forward(self, inputs): |
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outs = [] |
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y = self.conv1(inputs['image']) |
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for i, block in enumerate(self.dwsl): |
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y = block(y) |
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if i + 1 in self.feature_maps: |
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outs.append(y) |
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if not self.with_extra_blocks: |
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return outs |
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y = outs[-1] |
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for i, block in enumerate(self.extra_blocks): |
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idx = i + len(self.dwsl) |
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y = block(y) |
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if idx + 1 in self.feature_maps: |
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outs.append(y) |
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return outs |
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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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