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49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
# copyright (c) 2020 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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from paddle import nn
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import paddle
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class MTB(nn.Layer):
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def __init__(self, cnn_num, in_channels):
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super(MTB, self).__init__()
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self.block = nn.Sequential()
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self.out_channels = in_channels
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self.cnn_num = cnn_num
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if self.cnn_num == 2:
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for i in range(self.cnn_num):
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self.block.add_sublayer(
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'conv_{}'.format(i),
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nn.Conv2D(
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in_channels=in_channels
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if i == 0 else 32 * (2**(i - 1)),
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out_channels=32 * (2**i),
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kernel_size=3,
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stride=2,
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padding=1))
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self.block.add_sublayer('relu_{}'.format(i), nn.ReLU())
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self.block.add_sublayer('bn_{}'.format(i),
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nn.BatchNorm2D(32 * (2**i)))
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def forward(self, images):
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x = self.block(images)
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if self.cnn_num == 2:
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# (b, w, h, c)
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x = paddle.transpose(x, [0, 3, 2, 1])
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x_shape = paddle.shape(x)
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x = paddle.reshape(
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x, [x_shape[0], x_shape[1], x_shape[2] * x_shape[3]])
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return x
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