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/*******************************************************************************
* Copyright 2016-2018 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 "mkldnn_test_common.hpp"
#include "gtest/gtest.h"
#include "mkldnn.hpp"
namespace mkldnn {
template <typename data_t_src, typename data_t_wei,
typename data_t_acc, typename data_t_dst>
void compute_ref_conv_relu_fwd(const test_convolution_sizes_t &c,
const memory &src, const memory &weights, const memory &bias,
const memory &dst, bool w_bias, float negative_slope)
{
data_t_src *src_data = (data_t_src *)src.get_data_handle();
data_t_wei *weights_data = (data_t_wei *)weights.get_data_handle();
data_t_dst *bias_data
= (data_t_dst *)(w_bias ? bias.get_data_handle() : nullptr);
data_t_dst *dst_data = (data_t_dst *)dst.get_data_handle();
const memory::desc src_d = src.get_primitive_desc().desc();
const memory::desc weights_d = weights.get_primitive_desc().desc();
const memory::desc dst_d = dst.get_primitive_desc().desc();
size_t padded_ic = src_d.data.layout_desc.blocking.padding_dims[1];
size_t padded_oc = dst_d.data.layout_desc.blocking.padding_dims[1];
#pragma omp parallel for collapse(5) schedule(static)
for (int n = 0; n < c.mb; n++) {
for (int g = 0; g < c.ng; g++) {
for (int oc = 0; oc < c.oc / c.ng; oc++) {
for (int oh = 0; oh < c.oh; oh++) {
for (int ow = 0; ow < c.ow; ow++) {
size_t oidx = n * padded_oc * c.oh * c.ow
+ g * padded_oc / c.ng * c.oh * c.ow
+ oc * c.oh * c.ow + oh * c.ow + ow;
dst_data[map_index(dst_d, oidx)] = bias_data ?
bias_data[map_index(
bias.get_primitive_desc().desc(),
g * padded_oc / c.ng + oc)] :
data_t_dst{0};
for (int ic = 0; ic < c.ic / c.ng; ic++) {
for (int kh = 0; kh < c.kh; kh++) {
for (int kw = 0; kw < c.kw; kw++) {
int iw = ow * c.strw
- c.padw + kw * (1 + c.dilw);
int ih = oh * c.strh
- c.padh + kh * (1 + c.dilh);
if (iw < 0 || iw >= c.iw) continue;
if (ih < 0 || ih >= c.ih) continue;
size_t iidx = n * padded_ic * c.ih * c.iw
+ g * padded_ic / c.ng * c.ih * c.iw
+ ic * c.ih * c.iw + ih * c.iw + iw;
size_t widx = g * padded_oc / c.ng * padded_ic
/ c.ng * c.kh * c.kw
+ oc * padded_ic / c.ng * c.kh * c.kw
+ ic * c.kh * c.kw + kh * c.kw + kw;
dst_data[map_index(dst_d, oidx)]
+= src_data[map_index(src_d, iidx)]
* weights_data[map_index(
weights_d, widx)];
}
}
}
if (dst_data[map_index(dst_d, oidx)] < 0) {
dst_data[map_index(dst_d, oidx)] =
static_cast<data_t_dst>( negative_slope
* dst_data[map_index(dst_d, oidx)] );
}
}
}
}
}
}
}
template <typename data_t_src, typename data_t_wei,
typename data_t_acc, typename data_t_dst>
class convolution_relu_test
: public ::testing::TestWithParam<test_convolution_params_t> {
protected:
virtual void SetUp() {
auto p = ::testing::TestWithParam<test_convolution_params_t>::GetParam();
catch_expected_failures([=](){Test();}, p.expect_to_fail,
p.expected_status);
}
void Test() {
auto p = ::testing::TestWithParam<test_convolution_params_t>::GetParam();
ASSERT_TRUE(p.engine_kind == engine::kind::cpu);
ASSERT_EQ(p.aalgorithm, convolution_direct);
auto eng = engine(p.engine_kind, 0);
float negative_slope = p.relu_negative_slope;
memory::data_type data_type_src = data_traits<data_t_src>::data_type;
memory::data_type data_type_dst = data_traits<data_t_dst>::data_type;
memory::data_type data_type_wei = data_traits<data_t_wei>::data_type;
test_convolution_sizes_t cd = p.sizes;
auto c_src_desc = create_md({ cd.mb, cd.ic, cd.ih, cd.iw },
data_type_src, p.formats.src_format);
auto c_weights_desc = cd.ng > 1 ?
create_md({ cd.ng, cd.oc / cd.ng, cd.ic / cd.ng, cd.kh, cd.kw },
data_type_wei, p.formats.weights_format) :
create_md({ cd.oc, cd.ic, cd.kh, cd.kw },
data_type_wei, p.formats.weights_format);
auto c_dst_desc = create_md({ cd.mb, cd.oc, cd.oh, cd.ow },
data_type_dst, p.formats.dst_format);
auto c_src = memory({c_src_desc, eng});
auto c_weights = memory({c_weights_desc, eng});
auto c_dst = memory({c_dst_desc, eng});
auto dst_ref = memory({c_dst_desc, eng});
fill_data<data_t_src>(c_src.get_primitive_desc().get_size()
/ sizeof(data_t_src), (data_t_src *)c_src.get_data_handle());
// TODO: Temporary workaround for testing of convolution + relu
if (cd.mb) {
data_t_src *src_data = (data_t_src *)c_src.get_data_handle();
const int mb_chunk = static_cast<int>(
(c_src.get_primitive_desc().get_size() / sizeof(data_t_src))
/ cd.mb );
for (int i = 0; i < cd.mb * mb_chunk; ++i) {
if ((i / mb_chunk) % 2) src_data[i] *= (data_t_src)-1.;
}
}
fill_data<data_t_wei>(
c_weights.get_primitive_desc().get_size()
/ sizeof(data_t_wei),(data_t_wei *)c_weights.get_data_handle());
fill_data<data_t_dst>(
c_dst.get_primitive_desc().get_size()
/ sizeof(data_t_dst),(data_t_dst *)c_dst.get_data_handle());
bool with_bias = p.formats.bias_format != memory::format::format_undef;
auto c_bias_desc = with_bias ?
create_md({ cd.oc }, data_type_dst, p.formats.bias_format) :
create_md({}, data_type_dst, p.formats.bias_format);
auto c_bias = memory({c_bias_desc, eng});
if (with_bias) {
fill_data<data_t_dst>(
c_bias.get_primitive_desc().get_size() / sizeof(data_t_dst),
(data_t_dst *)c_bias.get_data_handle(), 1., true);
}
check_zero_tail<data_t_src>(1, c_src);
check_zero_tail<data_t_wei>(1, c_weights);
check_zero_tail<data_t_dst>(1, c_dst);
std::vector<int> padR = {
right_padding(cd.ih, cd.oh, cd.kh, cd.padh, cd.strh, cd.dilh),
right_padding(cd.iw, cd.ow, cd.kw, cd.padw, cd.strw, cd.dilw)
};
auto conv_desc = with_bias
? convolution_forward::desc(prop_kind::forward_scoring,
p.aalgorithm, c_src_desc, c_weights_desc, c_bias_desc,
c_dst_desc, { cd.strh, cd.strw }, { cd.dilh, cd.dilw },
{ cd.padh, cd.padw }, padR, padding_kind::zero)
: convolution_forward::desc(prop_kind::forward_scoring,
p.aalgorithm, c_src_desc, c_weights_desc, c_dst_desc,
{ cd.strh, cd.strw }, { cd.dilh, cd.dilw },
{ cd.padh, cd.padw }, padR, padding_kind::zero);
auto conv_relu_desc =
convolution_relu_forward::desc(conv_desc, negative_slope);
auto conv_primitive_desc =
convolution_relu_forward::primitive_desc(conv_relu_desc, eng);
auto conv = with_bias
? convolution_relu_forward(conv_primitive_desc,
c_src, c_weights, c_bias, c_dst)
: convolution_relu_forward(conv_primitive_desc,
c_src, c_weights, c_dst);
std::vector<primitive> pipeline;
pipeline.push_back(conv);
stream(stream::kind::lazy).submit(pipeline).wait();
compute_ref_conv_relu_fwd<data_t_src, data_t_wei, data_t_wei,
data_t_dst>(cd, c_src, c_weights, c_bias, dst_ref, with_bias,
negative_slope);
check_zero_tail<data_t_dst>(1, dst_ref);
compare_data<data_t_dst>(dst_ref, c_dst);
check_zero_tail<data_t_dst>(0, c_dst);
}
};
}
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