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