251 lines
8.2 KiB
Python
251 lines
8.2 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.nn as nn
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from paddle import ParamAttr
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import paddle.nn.functional as F
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from paddle.nn import Conv2D, MaxPool2D, AdaptiveAvgPool2D, BatchNorm2D
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from paddle.nn.initializer import KaimingNormal
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from paddle.regularizer import L2Decay
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from 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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from ppdet.modeling.ops import channel_shuffle
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__all__ = ['ShuffleNetV2']
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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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groups=1,
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act=None):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2D(
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in_channels=in_channels,
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out_channels=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=groups,
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weight_attr=ParamAttr(initializer=KaimingNormal()),
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bias_attr=False)
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self._batch_norm = BatchNorm2D(
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out_channels,
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weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
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bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
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if act == "hard_swish":
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act = 'hardswish'
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self.act = act
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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if self.act:
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y = getattr(F, self.act)(y)
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return y
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class InvertedResidual(nn.Layer):
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def __init__(self, in_channels, out_channels, stride, act="relu"):
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super(InvertedResidual, self).__init__()
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self._conv_pw = ConvBNLayer(
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in_channels=in_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act)
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self._conv_dw = ConvBNLayer(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=out_channels // 2,
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act=None)
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self._conv_linear = ConvBNLayer(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act)
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def forward(self, inputs):
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x1, x2 = paddle.split(
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inputs,
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num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2],
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axis=1)
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x2 = self._conv_pw(x2)
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x2 = self._conv_dw(x2)
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x2 = self._conv_linear(x2)
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out = paddle.concat([x1, x2], axis=1)
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return channel_shuffle(out, 2)
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class InvertedResidualDS(nn.Layer):
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def __init__(self, in_channels, out_channels, stride, act="relu"):
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super(InvertedResidualDS, self).__init__()
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# branch1
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self._conv_dw_1 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=in_channels,
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act=None)
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self._conv_linear_1 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act)
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# branch2
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self._conv_pw_2 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act)
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self._conv_dw_2 = ConvBNLayer(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=out_channels // 2,
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act=None)
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self._conv_linear_2 = ConvBNLayer(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act)
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def forward(self, inputs):
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x1 = self._conv_dw_1(inputs)
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x1 = self._conv_linear_1(x1)
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x2 = self._conv_pw_2(inputs)
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x2 = self._conv_dw_2(x2)
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x2 = self._conv_linear_2(x2)
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out = paddle.concat([x1, x2], axis=1)
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return channel_shuffle(out, 2)
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@register
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@serializable
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class ShuffleNetV2(nn.Layer):
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def __init__(self, scale=1.0, act="relu", feature_maps=[5, 13, 17]):
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super(ShuffleNetV2, self).__init__()
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self.scale = scale
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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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stage_repeats = [4, 8, 4]
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if scale == 0.25:
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stage_out_channels = [-1, 24, 24, 48, 96, 512]
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elif scale == 0.33:
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stage_out_channels = [-1, 24, 32, 64, 128, 512]
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elif scale == 0.5:
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stage_out_channels = [-1, 24, 48, 96, 192, 1024]
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elif scale == 1.0:
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stage_out_channels = [-1, 24, 116, 232, 464, 1024]
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elif scale == 1.5:
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stage_out_channels = [-1, 24, 176, 352, 704, 1024]
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elif scale == 2.0:
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stage_out_channels = [-1, 24, 244, 488, 976, 2048]
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else:
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raise NotImplementedError("This scale size:[" + str(scale) +
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"] is not implemented!")
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self._out_channels = []
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self._feature_idx = 0
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# 1. conv1
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self._conv1 = ConvBNLayer(
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in_channels=3,
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out_channels=stage_out_channels[1],
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kernel_size=3,
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stride=2,
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padding=1,
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act=act)
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self._max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
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self._feature_idx += 1
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# 2. bottleneck sequences
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self._block_list = []
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for stage_id, num_repeat in enumerate(stage_repeats):
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for i in range(num_repeat):
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if i == 0:
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block = self.add_sublayer(
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name=str(stage_id + 2) + '_' + str(i + 1),
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sublayer=InvertedResidualDS(
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in_channels=stage_out_channels[stage_id + 1],
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out_channels=stage_out_channels[stage_id + 2],
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stride=2,
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act=act))
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else:
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block = self.add_sublayer(
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name=str(stage_id + 2) + '_' + str(i + 1),
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sublayer=InvertedResidual(
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in_channels=stage_out_channels[stage_id + 2],
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out_channels=stage_out_channels[stage_id + 2],
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stride=1,
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act=act))
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self._block_list.append(block)
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self._feature_idx += 1
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self._update_out_channels(stage_out_channels[stage_id + 2],
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self._feature_idx, self.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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y = self._conv1(inputs['image'])
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y = self._max_pool(y)
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outs = []
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for i, inv in enumerate(self._block_list):
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y = inv(y)
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if i + 2 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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