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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 | |