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inception.h
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inception.h
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/* Copyright 2017 Stanford, NVIDIA
*
* 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.
*/
#include "model.h"
Tensor InceptionA(CnnModel &model, Tensor input, int pool_features)
{
Tensor t1 = model.add_conv_layer(input, 64, 1, 1, 1, 1, 0, 0);
Tensor t2 = model.add_conv_layer(input, 48, 1, 1, 1, 1, 0, 0);
t2 = model.add_conv_layer(t2, 64, 5, 5, 1, 1, 2, 2);
Tensor t3 = model.add_conv_layer(input, 64, 1, 1, 1, 1, 0, 0);
t3 = model.add_conv_layer(t3, 96, 3, 3, 1, 1, 1, 1);
t3 = model.add_conv_layer(t3, 96, 3, 3, 1, 1, 1, 1);
Tensor t4 = model.add_pool_layer(input, 3, 3, 1, 1, 1, 1, POOL2D_AVG);
t4 = model.add_conv_layer(t4, pool_features, 1, 1, 1, 1, 0, 0);
Tensor concat[4];
concat[0] = t1; concat[1] = t2; concat[2] = t3; concat[3] = t4;
Tensor output = model.add_concat_layer(4, concat);
return output;
}
Tensor InceptionB(CnnModel &model, Tensor input)
{
Tensor t1 = model.add_conv_layer(input, 384, 3, 3, 2, 2, 0, 0);
Tensor t2 = model.add_conv_layer(input, 64, 1, 1, 1, 1, 0, 0);
t2 = model.add_conv_layer(t2, 96, 3, 3, 1, 1, 1, 1);
t2 = model.add_conv_layer(t2, 96, 3, 3, 2, 2, 0, 0);
Tensor t3 = model.add_pool_layer(input, 3, 3, 2, 2, 0, 0);
Tensor concat[3];
concat[0] = t1; concat[1] = t2; concat[2] = t3;
Tensor output = model.add_concat_layer(3, concat);
return output;
}
Tensor InceptionC(CnnModel &model, Tensor input, int channels)
{
Tensor t1 = model.add_conv_layer(input, 192, 1, 1, 1, 1, 0, 0);
Tensor t2 = model.add_conv_layer(input, channels, 1, 1, 1, 1, 0, 0);
t2 = model.add_conv_layer(t2, channels, 1, 7, 1, 1, 0, 3);
t2 = model.add_conv_layer(t2, 192, 7, 1, 1, 1, 3, 0);
Tensor t3 = model.add_conv_layer(input, channels, 1, 1, 1, 1, 0, 0);
t3 = model.add_conv_layer(t3, channels, 7, 1, 1, 1, 3, 0);
t3 = model.add_conv_layer(t3, channels, 1, 7, 1, 1, 0, 3);
t3 = model.add_conv_layer(t3, channels, 7, 1, 1, 1, 3, 0);
t3 = model.add_conv_layer(t3, 192, 1, 7, 1, 1, 0, 3);
Tensor t4 = model.add_pool_layer(input, 3, 3, 1, 1, 1, 1, POOL2D_AVG);
t4 = model.add_conv_layer(t4, 192, 1, 1, 1, 1, 0, 0);
Tensor concat[4];
concat[0] = t1; concat[1] = t2; concat[2] = t3; concat[3] = t4;
Tensor output = model.add_concat_layer(4, concat);
return output;
}
Tensor InceptionD(CnnModel &model, Tensor input)
{
Tensor t1 = model.add_conv_layer(input, 192, 1, 1, 1, 1, 0, 0);
t1 = model.add_conv_layer(t1, 320, 3, 3, 2, 2, 0, 0);
Tensor t2 = model.add_conv_layer(input, 192, 1, 1, 1, 1, 0, 0);
t2 = model.add_conv_layer(t2, 192, 1, 7, 1, 1, 0, 3);
t2 = model.add_conv_layer(t2, 192, 7, 1, 1, 1, 3, 0);
t2 = model.add_conv_layer(t2, 192, 3, 3, 2, 2, 0, 0);
Tensor t3 = model.add_pool_layer(input, 3, 3, 2, 2, 0, 0);
Tensor concat[3];
concat[0] = t1; concat[1] = t2; concat[2] = t3;
Tensor output = model.add_concat_layer(3, concat);
return output;
}
Tensor InceptionE(CnnModel &model, Tensor input)
{
Tensor t1 = model.add_conv_layer(input, 320, 1, 1, 1, 1, 0, 0);
Tensor t2i = model.add_conv_layer(input, 384, 1, 1, 1, 1, 0, 0);
Tensor t2 = model.add_conv_layer(t2i, 384, 1, 3, 1, 1, 0, 1);
Tensor t3 = model.add_conv_layer(t2i, 384, 3, 1, 1, 1, 1, 0);
Tensor t3i = model.add_conv_layer(input, 448, 1, 1, 1, 1, 0, 0);
t3i = model.add_conv_layer(t3i, 384, 3, 3, 1, 1, 1, 1);
Tensor t4 = model.add_conv_layer(t3i, 384, 1, 3, 1, 1, 0, 1);
Tensor t5 = model.add_conv_layer(t3i, 384, 3, 1, 1, 1, 1, 0);
Tensor t6 = model.add_pool_layer(input, 3, 3, 1, 1, 1, 1, POOL2D_AVG);
t6 = model.add_conv_layer(t6, 192, 1, 1, 1, 1, 0, 0);
Tensor concat[6];
concat[0] = t1; concat[1] = t2; concat[2] = t3;
concat[3] = t4; concat[4] = t5; concat[5] = t6;
Tensor output = model.add_concat_layer(6, concat);
return output;
}
Tensor DenseBlock(CnnModel &model, Tensor input, int numLayers, int growthRate)
{
Tensor t, last = input;
for (int i = 0; i < numLayers; i++) {
t = model.add_bn_layer(last, true/*relu*/);
t = model.add_conv_layer(t, 4 * growthRate, 1, 1, 1, 1, 0, 0, false/*relu*/);
t = model.add_bn_layer(t, true/*relu*/);
t = model.add_conv_layer(t, growthRate, 3, 3, 1, 1, 1, 1, false/*relu*/);
Tensor concat[2];
concat[0] = last; concat[1] = t;
last = model.add_concat_layer(2, concat);
}
return last;
}
Tensor Transition(CnnModel &model, Tensor input, int outputSize)
{
Tensor t = model.add_conv_layer(input, outputSize, 1, 1, 1, 1, 0, 0);
t = model.add_pool_layer(t, 2, 2, 2, 2, 0, 0, POOL2D_AVG);
return t;
}
Tensor BottleneckBlock(CnnModel &model, Tensor input, int outChannels,
int bnChannels, int stride)
{
Tensor t = model.add_conv_layer(input, bnChannels, 1, 1, 1, 1, 0, 0);
//t = model.add_bn_layer(t);
t = model.add_conv_layer(t, bnChannels, 3, 3, stride, stride, 1, 1);
//t = model.add_bn_layer(t);
t = model.add_conv_layer(t, outChannels, 1, 1, 1, 1, 0, 0);
//t = model.add_bn_layer(t);
return t;
}