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/*
Copyright (c) 2013, Taiga Nomi
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the <organization> nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#pragma once
#include "gtest/gtest.h"
#include "testhelper.h"
#include "tiny_dnn/tiny_dnn.h"
namespace tiny_dnn {
/*
TEST(deconvolutional, setup_tiny) {
deconvolutional_layer<sigmoid> l(2, 2, 3, 1, 2,
padding::valid, true, 1, 1, backend_t::tiny_dnn);
EXPECT_EQ(l.parallelize(), true); // if layer can be parallelized
EXPECT_EQ(l.in_channels(), 3); // num of input tensors
EXPECT_EQ(l.out_channels(), 2); // num of output tensors
EXPECT_EQ(l.in_data_size(), 4); // size of input tensors
EXPECT_EQ(l.out_data_size(), 32); // size of output tensors
EXPECT_EQ(l.in_data_shape().size(), 1); // number of inputs shapes
EXPECT_EQ(l.out_data_shape().size(), 1); // num of output shapes
EXPECT_EQ(l.weights().size(), 2); // the wieghts vector size
EXPECT_EQ(l.weights_grads().size(), 2); // the wieghts vector size
EXPECT_EQ(l.inputs().size(), 3); // num of input edges
EXPECT_EQ(l.outputs().size(), 2); // num of outpus edges
EXPECT_EQ(l.in_types().size(), 3); // num of input data types
EXPECT_EQ(l.out_types().size(), 2); // num of output data types
EXPECT_EQ(l.fan_in_size(), 9); // num of incoming connections
EXPECT_EQ(l.fan_out_size(), 18); // num of outgoing connections
EXPECT_STREQ(l.layer_type().c_str(), "deconv"); // string with layer type
EXPECT_TRUE(l.backend_type() == backend_t::tiny_dnn);
}
#ifdef CNN_USE_NNPACK
TEST(deconvolutional, setup_nnp) {
deconvolutional_layer<sigmoid> l(2, 2, 3, 1, 2,
padding::valid, true, 1, 1, backend_t::nnpack);
EXPECT_EQ(l.parallelize(), true); // if layer can be parallelized
EXPECT_EQ(l.in_channels(), 3); // num of input tensors
EXPECT_EQ(l.out_channels(), 2); // num of output tensors
EXPECT_EQ(l.in_data_size(), 4); // size of input tensors
EXPECT_EQ(l.out_data_size(), 32); // size of output tensors
EXPECT_EQ(l.in_data_shape().size(), 1); // number of inputs shapes
EXPECT_EQ(l.out_data_shape().size(), 1); // num of output shapes
EXPECT_EQ(l.weights().size(), 2); // the wieghts vector size
EXPECT_EQ(l.weights_grads().size(), 2); // the wieghts vector size
EXPECT_EQ(l.inputs().size(), 3); // num of input edges
EXPECT_EQ(l.outputs().size(), 2); // num of outpus edges
EXPECT_EQ(l.in_types().size(), 3); // num of input data types
EXPECT_EQ(l.out_types().size(), 2); // num of output data types
EXPECT_EQ(l.fan_in_size(), 9); // num of incoming connections
EXPECT_EQ(l.fan_out_size(), 18); // num of outgoing connections
EXPECT_STREQ(l.layer_type().c_str(), "deconv"); // string with layer type
EXPECT_TRUE(l.backend_type() == backend_t::nnpack);
}
#endif
TEST(deconvolutional, fprop) {
typedef network<sequential> CNN;
CNN nn;
deconvolutional_layer<sigmoid> l(2, 2, 3, 1, 2);
// layer::forward_propagation expects tensors, even if we feed only one input at a time
auto create_simple_tensor = [](size_t vector_size) {
return tensor_t(1, vec_t(vector_size));
};
// create simple tensors that wrap the payload vectors of the correct size
tensor_t in_tensor = create_simple_tensor(4)
, out_tensor = create_simple_tensor(32)
, a_tensor = create_simple_tensor(32)
, weight_tensor = create_simple_tensor(18)
, bias_tensor = create_simple_tensor(2);
// short-hand references to the payload vectors
vec_t &in = in_tensor[0]
, &out = out_tensor[0]
, &weight = weight_tensor[0];
ASSERT_EQ(l.in_shape()[1].size(), 18); // weight
uniform_rand(in.begin(), in.end(), -1.0, 1.0);
std::vector<tensor_t*> in_data, out_data;
in_data.push_back(&in_tensor);
in_data.push_back(&weight_tensor);
in_data.push_back(&bias_tensor);
out_data.push_back(&out_tensor);
out_data.push_back(&a_tensor);
l.setup(false);
{
l.forward_propagation(in_data, out_data);
for (auto o: out)
EXPECT_DOUBLE_EQ(o, (tiny_dnn::float_t)0.5);
}
weight[0] = 0.3; weight[1] = 0.1; weight[2] = 0.2;
weight[3] = 0.0; weight[4] =-0.1; weight[5] =-0.1;
weight[6] = 0.05; weight[7] =-0.2; weight[8] = 0.05;
weight[9] = 0.0; weight[10] =-0.1; weight[11] = 0.1;
weight[12] = 0.1; weight[13] =-0.2; weight[14] = 0.3;
weight[15] = 0.2; weight[16] =-0.3; weight[17] = 0.2;
in[0] = 3; in[1] = 2;
in[2] = 3; in[3] = 0;
{
l.forward_propagation(in_data, out_data);
EXPECT_NEAR(0.7109495, out[0], 1E-5);
EXPECT_NEAR(0.7109495, out[1], 1E-5);
EXPECT_NEAR(0.6899745, out[2], 1E-5);
EXPECT_NEAR(0.5986877, out[3], 1E-5);
EXPECT_NEAR(0.7109495, out[4], 1E-5);
EXPECT_NEAR(0.5000000, out[5], 1E-5);
EXPECT_NEAR(0.5249792, out[6], 1E-5);
EXPECT_NEAR(0.4501660, out[7], 1E-5);
EXPECT_NEAR(0.5374298, out[8], 1E-5);
EXPECT_NEAR(0.3100255, out[9], 1E-5);
EXPECT_NEAR(0.3658644, out[10], 1E-5);
EXPECT_NEAR(0.5249791, out[11], 1E-5);
EXPECT_NEAR(0.5374298, out[12], 1E-5);
EXPECT_NEAR(0.3543437, out[13], 1E-5);
EXPECT_NEAR(0.5374298, out[14], 1E-5);
EXPECT_NEAR(0.5000000, out[15], 1E-5);
}
}
TEST(deconvolutional, fprop2) {
typedef network<sequential> CNN;
CNN nn;
deconvolutional_layer<sigmoid> l(2, 2, 3, 1, 2, padding::same);
auto create_simple_tensor = [](size_t vector_size) {
return tensor_t(1, vec_t(vector_size));
};
tensor_t in_tensor = create_simple_tensor(4)
, out_tensor = create_simple_tensor(32)
, a_tensor = create_simple_tensor(32)
, weight_tensor = create_simple_tensor(18)
, bias_tensor = create_simple_tensor(2);
// short-hand references to the payload vectors
vec_t &in = in_tensor[0]
, &out = out_tensor[0]
, &weight = weight_tensor[0];
ASSERT_EQ(l.in_shape()[1].size(), 18); // weight
uniform_rand(in.begin(), in.end(), -1.0, 1.0);
std::vector<tensor_t*> in_data, out_data;
in_data.push_back(&in_tensor);
in_data.push_back(&weight_tensor);
in_data.push_back(&bias_tensor);
out_data.push_back(&out_tensor);
out_data.push_back(&a_tensor);
l.setup(false);
{
l.forward_propagation(in_data, out_data);
for (auto o: out)
EXPECT_DOUBLE_EQ(o, (tiny_dnn::float_t)0.5);
}
weight[0] = 0.3; weight[1] = 0.1; weight[2] = 0.2;
weight[3] = 0.0; weight[4] =-0.1; weight[5] =-0.1;
weight[6] = 0.05; weight[7] =-0.2; weight[8] = 0.05;
weight[9] = 0.0; weight[10] =-0.1; weight[11] = 0.1;
weight[12] = 0.1; weight[13] =-0.2; weight[14] = 0.3;
weight[15] = 0.2; weight[16] =-0.3; weight[17] = 0.2;
in[0] = 3; in[1] = 2;
in[2] = 3; in[3] = 0;
{
l.forward_propagation(in_data, out_data);
EXPECT_NEAR(0.5000000, out[0], 1E-5);
EXPECT_NEAR(0.5249792, out[1], 1E-5);
EXPECT_NEAR(0.3100255, out[2], 1E-5);
EXPECT_NEAR(0.3658644, out[3], 1E-5);
}
}
TEST(deconvolutional, gradient_check) { // tanh - mse
network<sequential> nn;
nn << deconvolutional_layer<tan_h>(2, 2, 3, 1, 1);
const auto test_data = generate_gradient_check_data(nn.in_data_size());
nn.init_weight();
EXPECT_TRUE(nn.gradient_check<mse>(test_data.first, test_data.second, epsilon<float_t>(), GRAD_CHECK_ALL));
}
TEST(deconvolutional, gradient_check2) { // sigmoid - mse
network<sequential> nn;
nn << deconvolutional_layer<sigmoid>(2, 2, 3, 1, 1);
const auto test_data = generate_gradient_check_data(nn.in_data_size());
nn.init_weight();
EXPECT_TRUE(nn.gradient_check<mse>(test_data.first, test_data.second, epsilon<float_t>(), GRAD_CHECK_ALL));
}
TEST(deconvolutional, gradient_check3) { // rectified - mse
network<sequential> nn;
nn << deconvolutional_layer<rectified_linear>(2, 2, 3, 1, 1);
const auto test_data = generate_gradient_check_data(nn.in_data_size());
nn.init_weight();
EXPECT_TRUE(nn.gradient_check<mse>(test_data.first, test_data.second, epsilon<float_t>(), GRAD_CHECK_ALL));
}
TEST(deconvolutional, gradient_check4) { // identity - mse
network<sequential> nn;
nn << deconvolutional_layer<identity>(2, 2, 3, 1, 1);
const auto test_data = generate_gradient_check_data(nn.in_data_size());
nn.init_weight();
EXPECT_TRUE(nn.gradient_check<mse>(test_data.first, test_data.second, epsilon<float_t>(), GRAD_CHECK_ALL));
}
TEST(deconvolutional, gradient_check5) { // sigmoid - cross-entropy
network<sequential> nn;
nn << deconvolutional_layer<sigmoid>(2, 2, 3, 1, 1);
const auto test_data = generate_gradient_check_data(nn.in_data_size());
nn.init_weight();
EXPECT_TRUE(nn.gradient_check<cross_entropy>(test_data.first, test_data.second, epsilon<float_t>(), GRAD_CHECK_ALL));
}
TEST(deconvolutional, read_write)
{
deconvolutional_layer<tan_h> l1(2, 2, 3, 1, 1);
deconvolutional_layer<tan_h> l2(2, 2, 3, 1, 1);
l1.init_weight();
l2.init_weight();
serialization_test(l1, l2);
}
TEST(deconvolutional, read_write2) {
#define O true
#define X false
static const bool connection[] = {
O, X, X, X, O, O,
O, O, X, X, X, O,
O, O, O, X, X, X
};
#undef O
#undef X
deconvolutional_layer<tan_h> layer1(14, 14, 5, 3, 6, connection_table(connection, 3, 6));
deconvolutional_layer<tan_h> layer2(14, 14, 5, 3, 6, connection_table(connection, 3, 6));
layer1.init_weight();
layer2.init_weight();
serialization_test(layer1, layer2);
}
*/
} // namespace tiny-dnn
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