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/*******************************************************************************
* Copyright 2017-2018 Intel Corporation
*
* 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 <algorithm>
#include <cstdio>
#include <iostream>
#include <list>
#include <memory>
#include "gtest/gtest.h"
#include "ngraph/autodiff/adjoints.hpp"
#include "ngraph/file_util.hpp"
#include "ngraph/graph_util.hpp"
#include "ngraph/log.hpp"
#include "ngraph/ngraph.hpp"
#include "ngraph/op/batch_norm.hpp"
#include "ngraph/op/get_output_element.hpp"
#include "ngraph/op/parameter.hpp"
#include "ngraph/pass/manager.hpp"
#include "ngraph/pass/visualize_tree.hpp"
#include "ngraph/serializer.hpp"
#include "ngraph/util.hpp"
#include "nlohmann/json.hpp"
#include "util/all_close.hpp"
#include "util/autodiff/backprop_function.hpp"
#include "util/autodiff/numeric_compare.hpp"
#include "util/random.hpp"
#include "util/test_tools.hpp"
using namespace ngraph;
using namespace std;
//TODO: Move this test to backend_test.in.cpp once we have the INTERPRETER
// implementation for batchnorm
TEST(cpu_test, batchnorm_fprop_b1c2h2w2)
{
auto input_shape = Shape{1, 2, 2, 2};
auto input = make_shared<op::Parameter>(element::f32, input_shape);
auto mean_shape = Shape{2};
auto var_shape = Shape{2};
auto gamma_shape = Shape{2};
auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
auto beta_shape = Shape{2};
auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
double eps = 0.001;
auto shape_r = Shape{1, 2, 2, 2};
auto bn = make_shared<op::BatchNorm>(eps, gamma, beta, input);
auto output_rt = std::make_shared<op::GetOutputElement>(bn, 0);
auto mean_rt = std::make_shared<op::GetOutputElement>(bn, 1);
auto variance_rt = std::make_shared<op::GetOutputElement>(bn, 2);
auto f = make_shared<Function>(NodeVector{output_rt, mean_rt, variance_rt},
op::ParameterVector{input, gamma, beta});
auto backend = runtime::Backend::create("CPU");
// Create some tensors for input/output
auto _input = backend->create_tensor(element::f32, Shape{1, 2, 2, 2});
copy_data(_input,
vector<float>{0.54881352f,
0.71518934f,
0.60276335f,
0.54488319f,
0.42365479f,
0.64589411f,
0.4375872f,
0.89177299f});
auto _gamma = backend->create_tensor(element::f32, gamma_shape);
copy_data(_gamma, vector<float>{1.0f, 1.0f});
auto _beta = backend->create_tensor(element::f32, beta_shape);
copy_data(_beta, vector<float>{0.0f, 0.0f});
auto bn_output = backend->create_tensor(element::f32, shape_r);
auto result_mean = backend->create_tensor(element::f32, mean_shape);
auto result_variance = backend->create_tensor(element::f32, var_shape);
vector<float> expected_result{-0.71498716f,
1.48388731f,
-0.00196938f,
-0.76693159f,
-0.91316032f,
0.23943391f,
-0.84090298f,
1.51462936f};
vector<float> expected_mean{0.602912f, 0.599727f};
vector<float> expected_variance{0.00472505f, 0.0361782f};
backend->call(f, {bn_output, result_mean, result_variance}, {_input, _gamma, _beta});
EXPECT_TRUE(test::all_close(expected_result, read_vector<float>(bn_output)));
EXPECT_TRUE(test::all_close(expected_mean, read_vector<float>(result_mean)));
EXPECT_TRUE(test::all_close(expected_variance, read_vector<float>(result_variance)));
}
TEST(cpu_test, batchnorm_fprop_b2c2h2w1)
{
auto input_shape = Shape{2, 2, 2, 1};
auto input = make_shared<op::Parameter>(element::f32, input_shape);
auto mean_shape = Shape{2};
auto var_shape = Shape{2};
auto gamma_shape = Shape{2};
auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
auto beta_shape = Shape{2};
auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
double eps = 0.001;
auto shape_r = Shape{2, 2, 2, 1};
auto bn = make_shared<op::BatchNorm>(eps, gamma, beta, input);
auto output_rt = std::make_shared<op::GetOutputElement>(bn, 0);
auto mean_rt = std::make_shared<op::GetOutputElement>(bn, 1);
auto variance_rt = std::make_shared<op::GetOutputElement>(bn, 2);
auto f = make_shared<Function>(NodeVector{output_rt, mean_rt, variance_rt},
op::ParameterVector{input, gamma, beta});
auto backend = runtime::Backend::create("CPU");
// Create some tensors for input/output
auto _input = backend->create_tensor(element::f32, Shape{2, 2, 2, 1});
copy_data(_input,
vector<float>{0.54881352f,
0.71518934f,
0.60276335f,
0.54488319f,
0.42365479f,
0.64589411f,
0.4375872f,
0.89177299f});
auto _gamma = backend->create_tensor(element::f32, gamma_shape);
copy_data(_gamma, vector<float>{1.0f, 1.0f});
auto _beta = backend->create_tensor(element::f32, beta_shape);
copy_data(_beta, vector<float>{0.0f, 0.0f});
auto bn_output = backend->create_tensor(element::f32, shape_r);
auto result_mean = backend->create_tensor(element::f32, mean_shape);
auto result_variance = backend->create_tensor(element::f32, var_shape);
vector<float> expected_result{
-0.30327f, 1.1561f, -0.0963782f, -0.434702f, -1.4011f, 0.548275f, -1.06187f, 1.59295f};
vector<float> expected_mean{0.583388f, 0.619252f};
vector<float> expected_variance{0.0119972f, 0.0282681f};
backend->call(f, {bn_output, result_mean, result_variance}, {_input, _gamma, _beta});
EXPECT_TRUE(test::all_close(expected_result, read_vector<float>(bn_output)));
EXPECT_TRUE(test::all_close(expected_mean, read_vector<float>(result_mean)));
EXPECT_TRUE(test::all_close(expected_variance, read_vector<float>(result_variance)));
}
class UnhandledOp : public ngraph::op::Abs
{
public:
UnhandledOp(const std::shared_ptr<Node>& arg)
: Abs(arg)
{
}
};
TEST(cpu_test, unhandled_op)
{
auto A = make_shared<op::Parameter>(element::f32, Shape{});
auto unhandled = make_shared<UnhandledOp>(A);
auto f = make_shared<Function>(unhandled, op::ParameterVector{A});
auto backend = runtime::Backend::create("CPU");
ASSERT_THROW(backend->compile(f), ngraph_error);
}
TEST(cpu_test, bn_bprop_n4c3h2w2)
{
auto input_shape = Shape{4, 3, 2, 2};
auto shape_mean = Shape{3};
auto input = make_shared<op::Parameter>(element::f32, input_shape);
auto mean_shape = Shape{3};
auto mean = make_shared<op::Parameter>(element::f32, mean_shape);
auto var_shape = Shape{3};
auto var = make_shared<op::Parameter>(element::f32, var_shape);
auto gamma_shape = Shape{3};
auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
auto beta_shape = Shape{3};
auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
double eps = 0.001;
auto shape_r = Shape{4, 3, 2, 2};
auto bn = make_shared<op::BatchNorm>(eps, gamma, beta, input);
auto bn_dx = make_shared<op::GetOutputElement>(bn, 0);
auto bn_dgamma = make_shared<op::GetOutputElement>(bn, 1);
auto bn_dbeta = make_shared<op::GetOutputElement>(bn, 2);
auto backend = runtime::Backend::create("CPU");
auto _input = backend->create_tensor(element::f32, input_shape);
vector<float> dataInput{
10.76331902f, 11.51178265f, 10.31018162f, 12.2993021f, 14.17626667f, 14.63498497f,
13.63494492f, 13.84248161f, 11.34602547f, 13.22014618f, 10.46686649f, 10.39842987f,
12.94806862f, 11.71670246f, 14.94438076f, 13.13236618f, 13.40889645f, 12.76128387f,
11.34430027f, 11.86629677f, 11.11464024f, 10.93221283f, 11.95324039f, 10.96581173f,
13.05455494f, 14.41404247f, 13.11169434f, 11.26559448f, 10.89965153f, 14.08202171f,
11.12685776f, 12.58428574f, 12.59247875f, 13.00187492f, 12.66310215f, 10.06655025f,
12.62048626f, 14.47942352f, 13.84950638f, 10.61425877f, 11.47936344f, 13.06011772f,
13.63069057f, 12.31748772f, 13.84555244f, 10.95815468f, 12.78933334f, 12.75389099f};
copy_data(_input, dataInput);
auto _mean = backend->create_tensor(element::f32, mean_shape);
copy_data(_mean, vector<float>{12.56472874f, 12.80312157f, 11.81676865f});
auto _var = backend->create_tensor(element::f32, var_shape);
copy_data(_var, vector<float>{1.94557643f, 1.32772446f, 1.28163588f});
auto _gamma = backend->create_tensor(element::f32, gamma_shape);
copy_data(_gamma, vector<float>{2.0f, 2.0f, 2.0f});
auto _beta = backend->create_tensor(element::f32, beta_shape);
copy_data(_beta, vector<float>{1.0f, 1.0f, 1.0f});
auto result = backend->create_tensor(element::f32, shape_r);
shared_ptr<runtime::TensorView> _delta = backend->create_tensor(element::f32, shape_r);
vector<float> deltaData(shape_size(shape_r), 20.0f);
copy_data(_delta, deltaData);
auto f = make_shared<Function>(NodeVector{bn_dx, bn_dgamma, bn_dbeta},
op::ParameterVector{mean, var, input, gamma, beta});
auto C = std::make_shared<op::Parameter>(element::f32, shape_r);
auto zero = ngraph::make_zero(bn_dgamma->get_element_type(), bn_dgamma->get_shape());
ngraph::autodiff::Adjoints adjoints(NodeVector{bn_dx, bn_dgamma, bn_dbeta},
NodeVector{C, zero, zero});
auto dinput = adjoints.backprop_node(input);
auto dgamma = adjoints.backprop_node(gamma);
auto dbeta = adjoints.backprop_node(beta);
auto df = make_shared<Function>(NodeVector{dinput, dgamma, dbeta},
op::ParameterVector{mean, var, input, gamma, beta, C});
//roundtrip serialization
string js = serialize(df, 4);
istringstream in(js);
df = deserialize(in);
shared_ptr<runtime::TensorView> _dinput = backend->create_tensor(element::f32, shape_r);
shared_ptr<runtime::TensorView> _dgamma = backend->create_tensor(element::f32, gamma_shape);
shared_ptr<runtime::TensorView> _dbeta = backend->create_tensor(element::f32, beta_shape);
backend->call(df, {_dinput, _dgamma, _dbeta}, {_mean, _var, _input, _gamma, _beta, _delta});
vector<float> expected_input{
8.17051607e-06f, 4.77576657e-06f, 1.02257760e-05f, 1.20387525e-06f, -1.73868522e-06f,
3.84632768e-06f, -1.07932050e-05f, -2.57458956e-06f, -2.22166714e-06f, -8.38779043e-06f,
-2.48082982e-06f, 5.89238360e-06f, -2.52895109e-07f, -8.68433445e-06f, -5.82726737e-06f,
8.84659658e-06f, 3.03944108e-05f, 4.05480879e-05f, 1.84123158e-05f, 2.30061178e-05f,
1.34087590e-05f, -9.26072571e-07f, -3.22908454e-05f, -2.07365116e-05f, -4.21330941e-05f,
2.83083100e-05f, -3.71039101e-05f, -4.84390640e-06f, -2.93012376e-05f, 5.68858087e-06f,
1.83181458e-05f, -1.07494506e-05f, -2.32429103e-06f, 6.92914809e-06f, -6.66512321e-06f,
-7.00302840e-06f, -3.46675184e-06f, -4.36748381e-06f, 6.73822226e-07f, -4.20158993e-06f,
3.83005061e-06f, 5.85143729e-06f, 4.17875243e-06f, -8.64167783e-06f, 1.00170803e-05f,
-4.23939666e-06f, 4.80201680e-06f, 4.62702078e-06f};
ASSERT_TRUE(ngraph::test::all_close(read_vector<float>(_dinput), expected_input, 1e-3f, 1e-4f));
vector<float> expected_dgamma{7.06315041e-05f, -2.35289335e-04f, -5.06639481e-05f};
ASSERT_TRUE(
ngraph::test::all_close(read_vector<float>(_dgamma), expected_dgamma, 1e-2f, 1e-3f));
vector<float> expected_dbeta{320.f, 320.f, 320.f};
ASSERT_TRUE(ngraph::test::all_close(read_vector<float>(_dbeta), expected_dbeta, 1e-4f, 1e-8f));
}
TEST(cpu_test, batchnorm_fprop_inference_b2c2h2w1)
{
auto input_shape = Shape{2, 2, 2, 1};
auto input = make_shared<op::Parameter>(element::f32, input_shape);
auto mean_shape = Shape{2};
auto mean = make_shared<op::Parameter>(element::f32, mean_shape);
auto var_shape = Shape{2};
auto var = make_shared<op::Parameter>(element::f32, var_shape);
auto gamma_shape = Shape{2};
auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
auto beta_shape = Shape{2};
auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
double eps = 0.001;
auto shape_r = Shape{2, 2, 2, 1};
auto bn = make_shared<op::BatchNorm>(eps, gamma, beta, input, mean, var);
auto f = make_shared<Function>(bn, op::ParameterVector{input, gamma, beta, mean, var});
auto backend = runtime::Backend::create("CPU");
// Create some tensors for input/output
auto _input = backend->create_tensor(element::f32, Shape{2, 2, 2, 1});
copy_data(_input,
vector<float>{0.54881352f,
0.71518934f,
0.60276335f,
0.54488319f,
0.42365479f,
0.64589411f,
0.4375872f,
0.89177299f});
auto _gamma = backend->create_tensor(element::f32, gamma_shape);
copy_data(_gamma, vector<float>{1.0f, 1.0f});
auto _beta = backend->create_tensor(element::f32, beta_shape);
copy_data(_beta, vector<float>{0.0f, 0.0f});
auto _mean = backend->create_tensor(element::f32, mean_shape);
copy_data(_mean, vector<float>{0.583388f, 0.619252f});
auto _var = backend->create_tensor(element::f32, var_shape);
copy_data(_var, vector<float>{0.0119972f, 0.0282681f});
auto bn_output = backend->create_tensor(element::f32, shape_r);
auto result_mean = backend->create_tensor(element::f32, mean_shape);
auto result_variance = backend->create_tensor(element::f32, var_shape);
vector<float> expected_result{
-0.30327f, 1.1561f, -0.0963782f, -0.434702f, -1.4011f, 0.548275f, -1.06187f, 1.59295f};
backend->call(f, {bn_output}, {_input, _gamma, _beta, _mean, _var});
ASSERT_TRUE(
ngraph::test::all_close(expected_result, read_vector<float>(bn_output), 1e-3f, 1e-4f));
}
TEST(cpu_test, batchnorm_fprop_globalstats_b2c2w2h1)
{
auto input_shape = Shape{2, 2, 2, 1};
auto input = make_shared<op::Parameter>(element::f32, input_shape);
auto mean_shape = Shape{2};
auto mean = make_shared<op::Parameter>(element::f32, mean_shape);
auto var_shape = Shape{2};
auto var = make_shared<op::Parameter>(element::f32, var_shape);
auto gamma_shape = Shape{2};
auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
auto beta_shape = Shape{2};
auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
double eps = 0.001;
auto shape_r = Shape{2, 2, 2, 1};
auto bn = make_shared<op::BatchNorm>(eps, gamma, beta, input, mean, var, true);
auto f = make_shared<Function>(bn, op::ParameterVector{gamma, beta, input, mean, var});
auto backend = runtime::Backend::create("CPU");
// Create some tensors for input/output
auto _input = backend->create_tensor(element::f32, Shape{2, 2, 2, 1});
copy_data(_input,
vector<float>{0.54881352f,
0.71518934f,
0.60276335f,
0.54488319f,
0.42365479f,
0.64589411f,
0.4375872f,
0.89177299f});
auto _gamma = backend->create_tensor(element::f32, gamma_shape);
copy_data(_gamma, vector<float>{1.0f, 1.0f});
auto _beta = backend->create_tensor(element::f32, beta_shape);
copy_data(_beta, vector<float>{0.0f, 0.0f});
auto _mean = backend->create_tensor(element::f32, mean_shape);
copy_data(_mean, vector<float>{0.583388f, 0.619252f});
auto _var = backend->create_tensor(element::f32, var_shape);
copy_data(_var, vector<float>{0.0119972f, 0.0282681f});
auto bn_output = backend->create_tensor(element::f32, shape_r);
auto result_mean = backend->create_tensor(element::f32, mean_shape);
auto result_variance = backend->create_tensor(element::f32, var_shape);
vector<float> expected_result{
-0.30327f, 1.1561f, -0.0963782f, -0.434702f, -1.4011f, 0.548275f, -1.06187f, 1.59295f};
backend->call(f, {bn_output}, {_gamma, _beta, _input, _mean, _var});
ASSERT_TRUE(
ngraph::test::all_close(expected_result, read_vector<float>(bn_output), 1e-3f, 1e-4f));
}