forked from PulseFocusPlatform/PulseFocusPlatform
244 lines
7.0 KiB
Python
244 lines
7.0 KiB
Python
# Copyright (c) 2021 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.layers import ConvNormLayer
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from ..shape_spec import ShapeSpec
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DLA_cfg = {34: ([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512])}
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class BasicBlock(nn.Layer):
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def __init__(self, ch_in, ch_out, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = ConvNormLayer(
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ch_in,
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ch_out,
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filter_size=3,
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stride=stride,
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bias_on=False,
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norm_decay=None)
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self.conv2 = ConvNormLayer(
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ch_out,
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ch_out,
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filter_size=3,
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stride=1,
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bias_on=False,
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norm_decay=None)
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def forward(self, inputs, residual=None):
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if residual is None:
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residual = inputs
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out = self.conv1(inputs)
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out = F.relu(out)
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out = self.conv2(out)
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out = paddle.add(x=out, y=residual)
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out = F.relu(out)
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return out
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class Root(nn.Layer):
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def __init__(self, ch_in, ch_out, kernel_size, residual):
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super(Root, self).__init__()
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self.conv = ConvNormLayer(
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ch_in,
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ch_out,
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filter_size=1,
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stride=1,
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bias_on=False,
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norm_decay=None)
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self.residual = residual
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def forward(self, inputs):
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children = inputs
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out = self.conv(paddle.concat(inputs, axis=1))
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if self.residual:
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out = paddle.add(x=out, y=children[0])
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out = F.relu(out)
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return out
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class Tree(nn.Layer):
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def __init__(self,
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level,
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block,
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ch_in,
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ch_out,
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stride=1,
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level_root=False,
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root_dim=0,
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root_kernel_size=1,
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root_residual=False):
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super(Tree, self).__init__()
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if root_dim == 0:
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root_dim = 2 * ch_out
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if level_root:
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root_dim += ch_in
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if level == 1:
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self.tree1 = block(ch_in, ch_out, stride)
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self.tree2 = block(ch_out, ch_out, 1)
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else:
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self.tree1 = Tree(
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level - 1,
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block,
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ch_in,
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ch_out,
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stride,
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root_dim=0,
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root_kernel_size=root_kernel_size,
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root_residual=root_residual)
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self.tree2 = Tree(
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level - 1,
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block,
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ch_out,
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ch_out,
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1,
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root_dim=root_dim + ch_out,
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root_kernel_size=root_kernel_size,
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root_residual=root_residual)
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if level == 1:
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self.root = Root(root_dim, ch_out, root_kernel_size, root_residual)
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self.level_root = level_root
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self.root_dim = root_dim
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self.downsample = None
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self.project = None
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self.level = level
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if stride > 1:
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self.downsample = nn.MaxPool2D(stride, stride=stride)
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if ch_in != ch_out:
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self.project = ConvNormLayer(
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ch_in,
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ch_out,
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filter_size=1,
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stride=1,
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bias_on=False,
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norm_decay=None)
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def forward(self, x, residual=None, children=None):
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children = [] if children is None else children
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bottom = self.downsample(x) if self.downsample else x
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residual = self.project(bottom) if self.project else bottom
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if self.level_root:
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children.append(bottom)
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x1 = self.tree1(x, residual)
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if self.level == 1:
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x2 = self.tree2(x1)
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x = self.root([x2, x1] + children)
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else:
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children.append(x1)
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x = self.tree2(x1, children=children)
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return x
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@register
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@serializable
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class DLA(nn.Layer):
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"""
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DLA, see https://arxiv.org/pdf/1707.06484.pdf
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Args:
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depth (int): DLA depth, should be 34.
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residual_root (bool): whether use a reidual layer in the root block
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"""
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def __init__(self, depth=34, residual_root=False):
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super(DLA, self).__init__()
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levels, channels = DLA_cfg[depth]
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if depth == 34:
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block = BasicBlock
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self.channels = channels
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self.base_layer = nn.Sequential(
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ConvNormLayer(
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3,
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channels[0],
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filter_size=7,
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stride=1,
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bias_on=False,
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norm_decay=None),
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nn.ReLU())
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self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
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self.level1 = self._make_conv_level(
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channels[0], channels[1], levels[1], stride=2)
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self.level2 = Tree(
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levels[2],
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block,
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channels[1],
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channels[2],
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2,
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level_root=False,
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root_residual=residual_root)
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self.level3 = Tree(
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levels[3],
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block,
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channels[2],
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channels[3],
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2,
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level_root=True,
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root_residual=residual_root)
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self.level4 = Tree(
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levels[4],
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block,
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channels[3],
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channels[4],
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2,
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level_root=True,
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root_residual=residual_root)
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self.level5 = Tree(
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levels[5],
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block,
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channels[4],
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channels[5],
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2,
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level_root=True,
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root_residual=residual_root)
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def _make_conv_level(self, ch_in, ch_out, conv_num, stride=1):
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modules = []
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for i in range(conv_num):
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modules.extend([
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ConvNormLayer(
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ch_in,
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ch_out,
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filter_size=3,
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stride=stride if i == 0 else 1,
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bias_on=False,
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norm_decay=None), nn.ReLU()
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])
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ch_in = ch_out
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return nn.Sequential(*modules)
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@property
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def out_shape(self):
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return [ShapeSpec(channels=self.channels[i]) for i in range(6)]
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def forward(self, inputs):
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outs = []
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im = inputs['image']
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feats = self.base_layer(im)
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for i in range(6):
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feats = getattr(self, 'level{}'.format(i))(feats)
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outs.append(feats)
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return outs
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