更换文档检测模型
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226
paddle_detection/ppdet/modeling/backbones/hardnet.py
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226
paddle_detection/ppdet/modeling/backbones/hardnet.py
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# 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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import paddle
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import paddle.nn as nn
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from ppdet.core.workspace import register
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from ..shape_spec import ShapeSpec
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__all__ = ['HarDNet']
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def ConvLayer(in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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bias_attr=False):
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layer = nn.Sequential(
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('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=kernel_size // 2,
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groups=1,
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bias_attr=bias_attr)), ('norm', nn.BatchNorm2D(out_channels)),
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('relu', nn.ReLU6()))
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return layer
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def DWConvLayer(in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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bias_attr=False):
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layer = nn.Sequential(
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('dwconv', 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=1,
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groups=out_channels,
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bias_attr=bias_attr)), ('norm', nn.BatchNorm2D(out_channels)))
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return layer
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def CombConvLayer(in_channels, out_channels, kernel_size=1, stride=1):
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layer = nn.Sequential(
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('layer1', ConvLayer(
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in_channels, out_channels, kernel_size=kernel_size)),
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('layer2', DWConvLayer(
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out_channels, out_channels, stride=stride)))
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return layer
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class HarDBlock(nn.Layer):
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def __init__(self,
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in_channels,
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growth_rate,
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grmul,
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n_layers,
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keepBase=False,
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residual_out=False,
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dwconv=False):
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super().__init__()
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self.keepBase = keepBase
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self.links = []
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layers_ = []
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self.out_channels = 0
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for i in range(n_layers):
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outch, inch, link = self.get_link(i + 1, in_channels, growth_rate,
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grmul)
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self.links.append(link)
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if dwconv:
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layers_.append(CombConvLayer(inch, outch))
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else:
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layers_.append(ConvLayer(inch, outch))
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if (i % 2 == 0) or (i == n_layers - 1):
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self.out_channels += outch
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self.layers = nn.LayerList(layers_)
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def get_out_ch(self):
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return self.out_channels
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def get_link(self, layer, base_ch, growth_rate, grmul):
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if layer == 0:
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return base_ch, 0, []
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out_channels = growth_rate
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link = []
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for i in range(10):
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dv = 2**i
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if layer % dv == 0:
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k = layer - dv
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link.append(k)
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if i > 0:
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out_channels *= grmul
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out_channels = int(int(out_channels + 1) / 2) * 2
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in_channels = 0
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for i in link:
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ch, _, _ = self.get_link(i, base_ch, growth_rate, grmul)
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in_channels += ch
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return out_channels, in_channels, link
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def forward(self, x):
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layers_ = [x]
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for layer in range(len(self.layers)):
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link = self.links[layer]
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tin = []
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for i in link:
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tin.append(layers_[i])
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if len(tin) > 1:
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x = paddle.concat(tin, 1)
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else:
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x = tin[0]
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out = self.layers[layer](x)
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layers_.append(out)
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t = len(layers_)
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out_ = []
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for i in range(t):
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if (i == 0 and self.keepBase) or (i == t - 1) or (i % 2 == 1):
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out_.append(layers_[i])
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out = paddle.concat(out_, 1)
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return out
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@register
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class HarDNet(nn.Layer):
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def __init__(self, depth_wise=False, return_idx=[1, 3, 8, 13], arch=85):
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super(HarDNet, self).__init__()
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assert arch in [68, 85], "HarDNet-{} is not supported.".format(arch)
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if arch == 85:
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first_ch = [48, 96]
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second_kernel = 3
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ch_list = [192, 256, 320, 480, 720]
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grmul = 1.7
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gr = [24, 24, 28, 36, 48]
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n_layers = [8, 16, 16, 16, 16]
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elif arch == 68:
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first_ch = [32, 64]
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second_kernel = 3
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ch_list = [128, 256, 320, 640]
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grmul = 1.7
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gr = [14, 16, 20, 40]
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n_layers = [8, 16, 16, 16]
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else:
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raise ValueError("HarDNet-{} is not supported.".format(arch))
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self.return_idx = return_idx
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self._out_channels = [96, 214, 458, 784]
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avg_pool = True
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if depth_wise:
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second_kernel = 1
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avg_pool = False
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blks = len(n_layers)
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self.base = nn.LayerList([])
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# First Layer: Standard Conv3x3, Stride=2
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self.base.append(
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ConvLayer(
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in_channels=3,
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out_channels=first_ch[0],
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kernel_size=3,
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stride=2,
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bias_attr=False))
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# Second Layer
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self.base.append(
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ConvLayer(
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first_ch[0], first_ch[1], kernel_size=second_kernel))
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# Avgpooling or DWConv3x3 downsampling
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if avg_pool:
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self.base.append(nn.AvgPool2D(kernel_size=3, stride=2, padding=1))
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else:
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self.base.append(DWConvLayer(first_ch[1], first_ch[1], stride=2))
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# Build all HarDNet blocks
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ch = first_ch[1]
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for i in range(blks):
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blk = HarDBlock(ch, gr[i], grmul, n_layers[i], dwconv=depth_wise)
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ch = blk.out_channels
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self.base.append(blk)
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if i != blks - 1:
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self.base.append(ConvLayer(ch, ch_list[i], kernel_size=1))
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ch = ch_list[i]
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if i == 0:
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self.base.append(
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nn.AvgPool2D(
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kernel_size=2, stride=2, ceil_mode=True))
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elif i != blks - 1 and i != 1 and i != 3:
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self.base.append(nn.AvgPool2D(kernel_size=2, stride=2))
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def forward(self, inputs):
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x = inputs['image']
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outs = []
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for i, layer in enumerate(self.base):
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x = layer(x)
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if i in self.return_idx:
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outs.append(x)
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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=self._out_channels[i]) for i in range(4)]
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