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98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
# copyright (c) 2022 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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"""
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This code is refer from:
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https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textdet/necks/fpn_unet.py
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"""
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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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class UpBlock(nn.Layer):
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def __init__(self, in_channels, out_channels):
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super().__init__()
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assert isinstance(in_channels, int)
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assert isinstance(out_channels, int)
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self.conv1x1 = nn.Conv2D(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0)
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self.conv3x3 = nn.Conv2D(
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in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.deconv = nn.Conv2DTranspose(
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out_channels, out_channels, kernel_size=4, stride=2, padding=1)
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def forward(self, x):
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x = F.relu(self.conv1x1(x))
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x = F.relu(self.conv3x3(x))
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x = self.deconv(x)
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return x
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class FPN_UNet(nn.Layer):
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def __init__(self, in_channels, out_channels):
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super().__init__()
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assert len(in_channels) == 4
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assert isinstance(out_channels, int)
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self.out_channels = out_channels
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blocks_out_channels = [out_channels] + [
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min(out_channels * 2**i, 256) for i in range(4)
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]
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blocks_in_channels = [blocks_out_channels[1]] + [
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in_channels[i] + blocks_out_channels[i + 2] for i in range(3)
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] + [in_channels[3]]
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self.up4 = nn.Conv2DTranspose(
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blocks_in_channels[4],
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blocks_out_channels[4],
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kernel_size=4,
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stride=2,
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padding=1)
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self.up_block3 = UpBlock(blocks_in_channels[3], blocks_out_channels[3])
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self.up_block2 = UpBlock(blocks_in_channels[2], blocks_out_channels[2])
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self.up_block1 = UpBlock(blocks_in_channels[1], blocks_out_channels[1])
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self.up_block0 = UpBlock(blocks_in_channels[0], blocks_out_channels[0])
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def forward(self, x):
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"""
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Args:
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x (list[Tensor] | tuple[Tensor]): A list of four tensors of shape
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:math:`(N, C_i, H_i, W_i)`, representing C2, C3, C4, C5
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features respectively. :math:`C_i` should matches the number in
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``in_channels``.
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Returns:
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Tensor: Shape :math:`(N, C, H, W)` where :math:`H=4H_0` and
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:math:`W=4W_0`.
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"""
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c2, c3, c4, c5 = x
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x = F.relu(self.up4(c5))
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x = paddle.concat([x, c4], axis=1)
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x = F.relu(self.up_block3(x))
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x = paddle.concat([x, c3], axis=1)
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x = F.relu(self.up_block2(x))
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x = paddle.concat([x, c2], axis=1)
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x = F.relu(self.up_block1(x))
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x = self.up_block0(x)
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return x
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