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213 lines
6.6 KiB
213 lines
6.6 KiB
# Copyright (c) 2022 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.functional as F |
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from paddle import ParamAttr |
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import paddle.nn as nn |
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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 ..shape_spec import ShapeSpec |
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__all__ = ['BlazeNeck'] |
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def hard_swish(x): |
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return x * F.relu6(x + 3) / 6. |
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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=0.1, |
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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, initializer=KaimingNormal()), |
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bias_attr=False) |
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if norm_type in ['sync_bn', 'bn']: |
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self._batch_norm = nn.BatchNorm2D(out_channels) |
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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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elif self.act == 'leaky': |
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x = F.leaky_relu(x) |
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elif self.act == 'hard_swish': |
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x = hard_swish(x) |
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return x |
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class FPN(nn.Layer): |
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def __init__(self, in_channels, out_channels, name=None): |
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super(FPN, self).__init__() |
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self.conv1_fpn = ConvBNLayer( |
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in_channels, |
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out_channels // 2, |
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kernel_size=1, |
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padding=0, |
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stride=1, |
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act='leaky', |
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name=name + '_output1') |
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self.conv2_fpn = ConvBNLayer( |
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in_channels, |
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out_channels // 2, |
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kernel_size=1, |
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padding=0, |
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stride=1, |
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act='leaky', |
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name=name + '_output2') |
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self.conv3_fpn = ConvBNLayer( |
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out_channels // 2, |
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out_channels // 2, |
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kernel_size=3, |
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padding=1, |
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stride=1, |
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act='leaky', |
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name=name + '_merge') |
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def forward(self, input): |
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output1 = self.conv1_fpn(input[0]) |
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output2 = self.conv2_fpn(input[1]) |
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up2 = F.upsample( |
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output2, size=paddle.shape(output1)[-2:], mode='nearest') |
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output1 = paddle.add(output1, up2) |
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output1 = self.conv3_fpn(output1) |
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return output1, output2 |
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class SSH(nn.Layer): |
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def __init__(self, in_channels, out_channels, name=None): |
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super(SSH, self).__init__() |
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assert out_channels % 4 == 0 |
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self.conv0_ssh = ConvBNLayer( |
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in_channels, |
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out_channels // 2, |
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kernel_size=3, |
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padding=1, |
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stride=1, |
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act=None, |
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name=name + 'ssh_conv3') |
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self.conv1_ssh = ConvBNLayer( |
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out_channels // 2, |
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out_channels // 4, |
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kernel_size=3, |
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padding=1, |
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stride=1, |
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act='leaky', |
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name=name + 'ssh_conv5_1') |
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self.conv2_ssh = ConvBNLayer( |
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out_channels // 4, |
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out_channels // 4, |
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kernel_size=3, |
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padding=1, |
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stride=1, |
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act=None, |
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name=name + 'ssh_conv5_2') |
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self.conv3_ssh = ConvBNLayer( |
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out_channels // 4, |
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out_channels // 4, |
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kernel_size=3, |
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padding=1, |
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stride=1, |
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act='leaky', |
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name=name + 'ssh_conv7_1') |
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self.conv4_ssh = ConvBNLayer( |
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out_channels // 4, |
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out_channels // 4, |
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kernel_size=3, |
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padding=1, |
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stride=1, |
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act=None, |
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name=name + 'ssh_conv7_2') |
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def forward(self, x): |
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conv0 = self.conv0_ssh(x) |
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conv1 = self.conv1_ssh(conv0) |
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conv2 = self.conv2_ssh(conv1) |
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conv3 = self.conv3_ssh(conv2) |
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conv4 = self.conv4_ssh(conv3) |
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concat = paddle.concat([conv0, conv2, conv4], axis=1) |
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return F.relu(concat) |
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@register |
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@serializable |
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class BlazeNeck(nn.Layer): |
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def __init__(self, in_channel, neck_type="None", data_format='NCHW'): |
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super(BlazeNeck, self).__init__() |
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self.neck_type = neck_type |
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self.reture_input = False |
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self._out_channels = in_channel |
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if self.neck_type == 'None': |
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self.reture_input = True |
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if "fpn" in self.neck_type: |
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self.fpn = FPN(self._out_channels[0], |
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self._out_channels[1], |
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name='fpn') |
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self._out_channels = [ |
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self._out_channels[0] // 2, self._out_channels[1] // 2 |
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] |
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if "ssh" in self.neck_type: |
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self.ssh1 = SSH(self._out_channels[0], |
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self._out_channels[0], |
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name='ssh1') |
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self.ssh2 = SSH(self._out_channels[1], |
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self._out_channels[1], |
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name='ssh2') |
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self._out_channels = [self._out_channels[0], self._out_channels[1]] |
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def forward(self, inputs): |
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if self.reture_input: |
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return inputs |
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output1, output2 = None, None |
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if "fpn" in self.neck_type: |
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backout_4, backout_1 = inputs |
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output1, output2 = self.fpn([backout_4, backout_1]) |
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if self.neck_type == "only_fpn": |
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return [output1, output2] |
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if self.neck_type == "only_ssh": |
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output1, output2 = inputs |
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feature1 = self.ssh1(output1) |
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feature2 = self.ssh2(output2) |
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return [feature1, feature2] |
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@property |
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def out_shape(self): |
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return [ |
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ShapeSpec(channels=c) |
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for c in [self._out_channels[0], self._out_channels[1]] |
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]
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