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89 lines
3.3 KiB
Python
89 lines
3.3 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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import numpy as np
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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 ..registry import BACKBONES
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from .deeplab import DeepLab
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class FPN(nn.Layer):
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"""FPN Layer"""
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def __init__(self, in_dim_4x, in_dim_8x, in_dim_16x, out_dim):
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super(FPN, self).__init__()
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self.toplayer = self._make_layer(in_dim_16x, out_dim)
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self.latlayer1 = self._make_layer(in_dim_8x, out_dim)
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self.latlayer2 = self._make_layer(in_dim_4x, out_dim)
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self.smooth1 = self._make_layer(out_dim,
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out_dim,
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kernel_size=3,
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padding=1)
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self.smooth2 = self._make_layer(out_dim,
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out_dim,
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kernel_size=3,
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padding=1)
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def _make_layer(self, in_dim, out_dim, kernel_size=1, padding=0):
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return nn.Sequential(
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nn.Conv2D(in_dim,
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out_dim,
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kernel_size=kernel_size,
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stride=1,
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padding=padding,
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bias_attr=False),
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nn.GroupNorm(num_groups=32, num_channels=out_dim))
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def forward(self, x_4x, x_8x, x_16x):
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""" forward function"""
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x_16x = self.toplayer(x_16x)
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x_8x = self.latlayer1(x_8x)
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x_4x = self.latlayer2(x_4x)
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x_8x = x_8x + F.interpolate(
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x_16x, size=x_8x.shape[-2:], mode='bilinear', align_corners=True)
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x_4x = x_4x + F.interpolate(
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x_8x, size=x_4x.shape[-2:], mode='bilinear', align_corners=True)
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x_8x = self.smooth1(x_8x)
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x_4x = self.smooth2(x_4x)
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return F.relu(x_4x), F.relu(x_8x), F.relu(x_16x)
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@BACKBONES.register()
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class CFBI(nn.Layer):
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"""CFBI plus backbone"""
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def __init__(self,
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backbone='resnet',
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freeze_bn=True,
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model_aspp_outdim=256,
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in_dim_8x=512,
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model_semantic_embedding_dim=256): #,epsilon=1e-05):
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super(CFBI, self).__init__()
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#self.epsilon = epsilon
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self.feature_extracter = DeepLab(backbone=backbone, freeze_bn=freeze_bn)
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self.fpn = FPN(in_dim_4x=model_aspp_outdim,
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in_dim_8x=in_dim_8x,
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in_dim_16x=model_aspp_outdim,
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out_dim=model_semantic_embedding_dim)
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def forward(self, x):
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"""forward function"""
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x, aspp_x, low_level, mid_level = self.feature_extracter(x, True)
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x_4x, x_8x, x_16x = self.fpn(x, mid_level, aspp_x)
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return x_4x, x_8x, x_16x, low_level
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