301 lines
11 KiB
Python
301 lines
11 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 paddle import ParamAttr
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from paddle.nn.initializer import Constant
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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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__all__ = ['BiFPN']
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class SeparableConvLayer(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels=None,
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kernel_size=3,
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norm_type='bn',
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norm_groups=32,
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act='swish'):
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super(SeparableConvLayer, self).__init__()
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assert norm_type in ['bn', 'sync_bn', 'gn', None]
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assert act in ['swish', 'relu', None]
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self.in_channels = in_channels
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if out_channels is None:
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self.out_channels = self.in_channels
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self.norm_type = norm_type
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self.norm_groups = norm_groups
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self.depthwise_conv = nn.Conv2D(
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in_channels,
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in_channels,
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kernel_size,
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padding=kernel_size // 2,
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groups=in_channels,
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bias_attr=False)
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self.pointwise_conv = nn.Conv2D(in_channels, self.out_channels, 1)
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# norm type
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if self.norm_type in ['bn', 'sync_bn']:
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self.norm = nn.BatchNorm2D(self.out_channels)
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elif self.norm_type == 'gn':
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self.norm = nn.GroupNorm(
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num_groups=self.norm_groups, num_channels=self.out_channels)
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# activation
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if act == 'swish':
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self.act = nn.Swish()
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elif act == 'relu':
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self.act = nn.ReLU()
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def forward(self, x):
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if self.act is not None:
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x = self.act(x)
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out = self.depthwise_conv(x)
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out = self.pointwise_conv(out)
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if self.norm_type is not None:
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out = self.norm(out)
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return out
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class BiFPNCell(nn.Layer):
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def __init__(self,
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channels=256,
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num_levels=5,
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eps=1e-5,
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use_weighted_fusion=True,
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kernel_size=3,
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norm_type='bn',
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norm_groups=32,
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act='swish'):
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super(BiFPNCell, self).__init__()
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self.channels = channels
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self.num_levels = num_levels
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self.eps = eps
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self.use_weighted_fusion = use_weighted_fusion
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# up
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self.conv_up = nn.LayerList([
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SeparableConvLayer(
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self.channels,
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kernel_size=kernel_size,
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norm_type=norm_type,
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norm_groups=norm_groups,
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act=act) for _ in range(self.num_levels - 1)
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])
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# down
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self.conv_down = nn.LayerList([
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SeparableConvLayer(
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self.channels,
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kernel_size=kernel_size,
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norm_type=norm_type,
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norm_groups=norm_groups,
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act=act) for _ in range(self.num_levels - 1)
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])
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if self.use_weighted_fusion:
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self.up_weights = self.create_parameter(
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shape=[self.num_levels - 1, 2],
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attr=ParamAttr(initializer=Constant(1.)))
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self.down_weights = self.create_parameter(
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shape=[self.num_levels - 1, 3],
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attr=ParamAttr(initializer=Constant(1.)))
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def _feature_fusion_cell(self,
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conv_layer,
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lateral_feat,
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sampling_feat,
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route_feat=None,
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weights=None):
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if self.use_weighted_fusion:
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weights = F.relu(weights)
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weights = weights / (weights.sum() + self.eps)
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if route_feat is not None:
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out_feat = weights[0] * lateral_feat + \
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weights[1] * sampling_feat + \
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weights[2] * route_feat
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else:
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out_feat = weights[0] * lateral_feat + \
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weights[1] * sampling_feat
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else:
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if route_feat is not None:
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out_feat = lateral_feat + sampling_feat + route_feat
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else:
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out_feat = lateral_feat + sampling_feat
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out_feat = conv_layer(out_feat)
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return out_feat
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def forward(self, feats):
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# feats: [P3 - P7]
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lateral_feats = []
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# up
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up_feature = feats[-1]
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for i, feature in enumerate(feats[::-1]):
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if i == 0:
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lateral_feats.append(feature)
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else:
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shape = paddle.shape(feature)
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up_feature = F.interpolate(
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up_feature, size=[shape[2], shape[3]])
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lateral_feature = self._feature_fusion_cell(
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self.conv_up[i - 1],
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feature,
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up_feature,
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weights=self.up_weights[i - 1]
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if self.use_weighted_fusion else None)
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lateral_feats.append(lateral_feature)
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up_feature = lateral_feature
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out_feats = []
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# down
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down_feature = lateral_feats[-1]
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for i, (lateral_feature,
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route_feature) in enumerate(zip(lateral_feats[::-1], feats)):
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if i == 0:
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out_feats.append(lateral_feature)
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else:
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down_feature = F.max_pool2d(down_feature, 3, 2, 1)
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if i == len(feats) - 1:
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route_feature = None
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weights = self.down_weights[
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i - 1][:2] if self.use_weighted_fusion else None
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else:
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weights = self.down_weights[
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i - 1] if self.use_weighted_fusion else None
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out_feature = self._feature_fusion_cell(
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self.conv_down[i - 1],
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lateral_feature,
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down_feature,
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route_feature,
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weights=weights)
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out_feats.append(out_feature)
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down_feature = out_feature
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return out_feats
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@register
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@serializable
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class BiFPN(nn.Layer):
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"""
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Bidirectional Feature Pyramid Network, see https://arxiv.org/abs/1911.09070
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Args:
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in_channels (list[int]): input channels of each level which can be
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derived from the output shape of backbone by from_config.
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out_channel (int): output channel of each level.
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num_extra_levels (int): the number of extra stages added to the last level.
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default: 2
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fpn_strides (List): The stride of each level.
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num_stacks (int): the number of stacks for BiFPN, default: 1.
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use_weighted_fusion (bool): use weighted feature fusion in BiFPN, default: True.
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norm_type (string|None): the normalization type in BiFPN module. If
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norm_type is None, norm will not be used after conv and if
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norm_type is string, bn, gn, sync_bn are available. default: bn.
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norm_groups (int): if you use gn, set this param.
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act (string|None): the activation function of BiFPN.
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"""
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def __init__(self,
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in_channels=(512, 1024, 2048),
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out_channel=256,
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num_extra_levels=2,
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fpn_strides=[8, 16, 32, 64, 128],
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num_stacks=1,
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use_weighted_fusion=True,
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norm_type='bn',
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norm_groups=32,
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act='swish'):
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super(BiFPN, self).__init__()
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assert num_stacks > 0, "The number of stacks of BiFPN is at least 1."
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assert norm_type in ['bn', 'sync_bn', 'gn', None]
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assert act in ['swish', 'relu', None]
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assert num_extra_levels >= 0, \
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"The `num_extra_levels` must be non negative(>=0)."
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self.in_channels = in_channels
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self.out_channel = out_channel
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self.num_extra_levels = num_extra_levels
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self.num_stacks = num_stacks
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self.use_weighted_fusion = use_weighted_fusion
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self.norm_type = norm_type
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self.norm_groups = norm_groups
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self.act = act
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self.num_levels = len(self.in_channels) + self.num_extra_levels
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if len(fpn_strides) != self.num_levels:
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for i in range(self.num_extra_levels):
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fpn_strides += [fpn_strides[-1] * 2]
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self.fpn_strides = fpn_strides
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self.lateral_convs = nn.LayerList()
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for in_c in in_channels:
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self.lateral_convs.append(
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ConvNormLayer(in_c, self.out_channel, 1, 1))
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if self.num_extra_levels > 0:
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self.extra_convs = nn.LayerList()
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for i in range(self.num_extra_levels):
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if i == 0:
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self.extra_convs.append(
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ConvNormLayer(self.in_channels[-1], self.out_channel, 3,
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2))
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else:
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self.extra_convs.append(nn.MaxPool2D(3, 2, 1))
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self.bifpn_cells = nn.LayerList()
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for i in range(self.num_stacks):
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self.bifpn_cells.append(
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BiFPNCell(
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self.out_channel,
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self.num_levels,
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use_weighted_fusion=self.use_weighted_fusion,
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norm_type=self.norm_type,
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norm_groups=self.norm_groups,
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act=self.act))
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@classmethod
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def from_config(cls, cfg, input_shape):
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return {
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'in_channels': [i.channels for i in input_shape],
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'fpn_strides': [i.stride for i in input_shape]
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}
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@property
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def out_shape(self):
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return [
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ShapeSpec(
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channels=self.out_channel, stride=s) for s in self.fpn_strides
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]
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def forward(self, feats):
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assert len(feats) == len(self.in_channels)
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fpn_feats = []
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for conv_layer, feature in zip(self.lateral_convs, feats):
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fpn_feats.append(conv_layer(feature))
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if self.num_extra_levels > 0:
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feat = feats[-1]
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for conv_layer in self.extra_convs:
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feat = conv_layer(feat)
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fpn_feats.append(feat)
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for bifpn_cell in self.bifpn_cells:
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fpn_feats = bifpn_cell(fpn_feats)
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return fpn_feats
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