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| # copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| This code is refer from: | |
| https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textdet/necks/fpn_unet.py | |
| """ | |
| import paddle | |
| import paddle.nn as nn | |
| import paddle.nn.functional as F | |
| class UpBlock(nn.Layer): | |
| def __init__(self, in_channels, out_channels): | |
| super().__init__() | |
| assert isinstance(in_channels, int) | |
| assert isinstance(out_channels, int) | |
| self.conv1x1 = nn.Conv2D( | |
| in_channels, in_channels, kernel_size=1, stride=1, padding=0) | |
| self.conv3x3 = nn.Conv2D( | |
| in_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| self.deconv = nn.Conv2DTranspose( | |
| out_channels, out_channels, kernel_size=4, stride=2, padding=1) | |
| def forward(self, x): | |
| x = F.relu(self.conv1x1(x)) | |
| x = F.relu(self.conv3x3(x)) | |
| x = self.deconv(x) | |
| return x | |
| class FPN_UNet(nn.Layer): | |
| def __init__(self, in_channels, out_channels): | |
| super().__init__() | |
| assert len(in_channels) == 4 | |
| assert isinstance(out_channels, int) | |
| self.out_channels = out_channels | |
| blocks_out_channels = [out_channels] + [ | |
| min(out_channels * 2**i, 256) for i in range(4) | |
| ] | |
| blocks_in_channels = [blocks_out_channels[1]] + [ | |
| in_channels[i] + blocks_out_channels[i + 2] for i in range(3) | |
| ] + [in_channels[3]] | |
| self.up4 = nn.Conv2DTranspose( | |
| blocks_in_channels[4], | |
| blocks_out_channels[4], | |
| kernel_size=4, | |
| stride=2, | |
| padding=1) | |
| self.up_block3 = UpBlock(blocks_in_channels[3], blocks_out_channels[3]) | |
| self.up_block2 = UpBlock(blocks_in_channels[2], blocks_out_channels[2]) | |
| self.up_block1 = UpBlock(blocks_in_channels[1], blocks_out_channels[1]) | |
| self.up_block0 = UpBlock(blocks_in_channels[0], blocks_out_channels[0]) | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x (list[Tensor] | tuple[Tensor]): A list of four tensors of shape | |
| :math:`(N, C_i, H_i, W_i)`, representing C2, C3, C4, C5 | |
| features respectively. :math:`C_i` should matches the number in | |
| ``in_channels``. | |
| Returns: | |
| Tensor: Shape :math:`(N, C, H, W)` where :math:`H=4H_0` and | |
| :math:`W=4W_0`. | |
| """ | |
| c2, c3, c4, c5 = x | |
| x = F.relu(self.up4(c5)) | |
| x = paddle.concat([x, c4], axis=1) | |
| x = F.relu(self.up_block3(x)) | |
| x = paddle.concat([x, c3], axis=1) | |
| x = F.relu(self.up_block2(x)) | |
| x = paddle.concat([x, c2], axis=1) | |
| x = F.relu(self.up_block1(x)) | |
| x = self.up_block0(x) | |
| return x | |