342 lines
15 KiB
C++
342 lines
15 KiB
C++
// Copyright 2022 Google LLC
|
|
//
|
|
// This source code is licensed under the BSD-style license found in the
|
|
// LICENSE file in the root directory of this source tree.
|
|
|
|
#include <algorithm>
|
|
#include <array>
|
|
#include <cmath>
|
|
#include <cstddef>
|
|
#include <cstdint>
|
|
#include <memory>
|
|
#include <random>
|
|
#include <vector>
|
|
|
|
#include <gtest/gtest.h>
|
|
#include "xnnpack.h"
|
|
#include "xnnpack/buffer.h"
|
|
#include "xnnpack/common.h"
|
|
#include "xnnpack/math.h"
|
|
#include "xnnpack/node-type.h"
|
|
#include "xnnpack/operator-utils.h"
|
|
#include "xnnpack/operator.h"
|
|
#include "xnnpack/subgraph.h"
|
|
#include "replicable_random_device.h"
|
|
|
|
template <
|
|
typename InputType,
|
|
typename OutputType = InputType>
|
|
class AveragePoolingTest : public ::testing::Test {
|
|
protected:
|
|
AveragePoolingTest() {
|
|
input_size_dist = std::uniform_int_distribution<uint32_t>(10, 15);
|
|
pooling_size_dist = std::uniform_int_distribution<uint32_t>(1, 5);
|
|
stride_dist = std::uniform_int_distribution<uint32_t>(1, 2);
|
|
batch_size = input_size_dist(rng);
|
|
input_height = input_size_dist(rng);
|
|
input_width = input_size_dist(rng);
|
|
channels = input_size_dist(rng);
|
|
pooling_height = pooling_size_dist(rng);
|
|
pooling_width = pooling_size_dist(rng);
|
|
// Avoid padding == pooling dimension because it will result in NaNs and cause comparison to fail.
|
|
input_padding_top = std::uniform_int_distribution<uint32_t>(0, pooling_height - 1)(rng);
|
|
input_padding_right = std::uniform_int_distribution<uint32_t>(0, pooling_width - 1)(rng);
|
|
input_padding_bottom = std::uniform_int_distribution<uint32_t>(0, pooling_height - 1)(rng);
|
|
input_padding_left = std::uniform_int_distribution<uint32_t>(0, pooling_width - 1)(rng);
|
|
stride_height = stride_dist(rng);
|
|
stride_width = stride_dist(rng);
|
|
output_height = xnn_compute_convolution_output_dimension(
|
|
input_padding_top + input_height + input_padding_bottom, pooling_height, 1, stride_height);
|
|
output_width = xnn_compute_convolution_output_dimension(
|
|
input_padding_left + input_width + input_padding_right, pooling_width, 1, stride_width);
|
|
output_min = std::uniform_real_distribution<float>(-255.0f, 0.0f)(rng);
|
|
output_max = std::uniform_real_distribution<float>(0.1f, 255.0f)(rng);
|
|
input_dims = {batch_size, input_height, input_width, channels};
|
|
output_dims = {batch_size, output_height, output_width, channels};
|
|
input = xnnpack::Buffer<InputType>(XNN_EXTRA_BYTES / sizeof(InputType) + batch_size * input_height * input_width * channels);
|
|
operator_output = xnnpack::Buffer<OutputType>(batch_size * output_height * output_width * channels);
|
|
subgraph_output = xnnpack::Buffer<OutputType>(batch_size * output_height * output_width * channels);
|
|
}
|
|
|
|
xnnpack::ReplicableRandomDevice rng;
|
|
std::uniform_int_distribution<uint32_t> input_size_dist;
|
|
std::uniform_int_distribution<uint32_t> pooling_size_dist;
|
|
std::uniform_int_distribution<uint32_t> stride_dist;
|
|
uint32_t batch_size;
|
|
uint32_t input_height;
|
|
uint32_t input_width;
|
|
uint32_t channels;
|
|
uint32_t pooling_height;
|
|
uint32_t pooling_width;
|
|
uint32_t output_height;
|
|
uint32_t output_width;
|
|
uint32_t stride_height;
|
|
uint32_t stride_width;
|
|
std::array<size_t, 4> input_dims;
|
|
std::array<size_t, 4> output_dims;
|
|
uint32_t input_padding_top;
|
|
uint32_t input_padding_right;
|
|
uint32_t input_padding_bottom;
|
|
uint32_t input_padding_left;
|
|
float output_min;
|
|
float output_max;
|
|
|
|
uint32_t input_id;
|
|
uint32_t output_id;
|
|
|
|
xnnpack::Buffer<InputType> input;
|
|
xnnpack::Buffer<OutputType> operator_output;
|
|
xnnpack::Buffer<OutputType> subgraph_output;
|
|
};
|
|
|
|
using AveragePoolingTestF16 = AveragePoolingTest<xnn_float16>;
|
|
using AveragePoolingTestF32 = AveragePoolingTest<float>;
|
|
|
|
TEST_F(AveragePoolingTestF16, define)
|
|
{
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
|
|
|
|
xnn_subgraph_t subgraph = nullptr;
|
|
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/2, /*flags=*/0, &subgraph));
|
|
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
|
|
|
|
input_id = XNN_INVALID_NODE_ID;
|
|
ASSERT_EQ(
|
|
xnn_status_success, xnn_define_tensor_value(
|
|
subgraph, xnn_datatype_fp16, input_dims.size(), input_dims.data(), nullptr, 0,
|
|
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
|
|
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
|
|
|
|
output_id = XNN_INVALID_NODE_ID;
|
|
ASSERT_EQ(
|
|
xnn_status_success, xnn_define_tensor_value(
|
|
subgraph, xnn_datatype_fp16, output_dims.size(), output_dims.data(), nullptr, 1,
|
|
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
|
|
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
|
|
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_define_average_pooling_2d(
|
|
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height,
|
|
pooling_width, stride_height, stride_width, output_min, output_max, input_id, output_id,
|
|
/*flags=*/0));
|
|
|
|
ASSERT_EQ(subgraph->num_nodes, 1);
|
|
const struct xnn_node* node = &subgraph->nodes[0];
|
|
ASSERT_EQ(node->type, xnn_node_type_average_pooling_2d);
|
|
|
|
ASSERT_EQ(node->params.pooling_2d.padding_top, input_padding_top);
|
|
ASSERT_EQ(node->params.pooling_2d.padding_right, input_padding_right);
|
|
ASSERT_EQ(node->params.pooling_2d.padding_bottom, input_padding_bottom);
|
|
ASSERT_EQ(node->params.pooling_2d.padding_left, input_padding_left);
|
|
ASSERT_EQ(node->params.pooling_2d.pooling_height, pooling_height);
|
|
ASSERT_EQ(node->params.pooling_2d.pooling_width, pooling_width);
|
|
ASSERT_EQ(node->params.pooling_2d.stride_height, stride_height);
|
|
ASSERT_EQ(node->params.pooling_2d.stride_width, stride_width);
|
|
ASSERT_EQ(node->activation.output_min, output_min);
|
|
ASSERT_EQ(node->activation.output_max, output_max);
|
|
ASSERT_EQ(node->num_inputs, 1);
|
|
ASSERT_EQ(node->inputs[0], input_id);
|
|
ASSERT_EQ(node->num_outputs, 1);
|
|
ASSERT_EQ(node->outputs[0], output_id);
|
|
ASSERT_EQ(node->flags, 0);
|
|
}
|
|
|
|
TEST_F(AveragePoolingTestF32, define)
|
|
{
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
|
|
|
|
xnn_subgraph_t subgraph = nullptr;
|
|
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/2, /*flags=*/0, &subgraph));
|
|
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
|
|
|
|
input_id = XNN_INVALID_NODE_ID;
|
|
ASSERT_EQ(
|
|
xnn_status_success, xnn_define_tensor_value(
|
|
subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr, 0,
|
|
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
|
|
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
|
|
|
|
output_id = XNN_INVALID_NODE_ID;
|
|
ASSERT_EQ(
|
|
xnn_status_success, xnn_define_tensor_value(
|
|
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, 1,
|
|
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
|
|
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
|
|
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_define_average_pooling_2d(
|
|
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height,
|
|
pooling_width, stride_height, stride_width, output_min, output_max, input_id, output_id,
|
|
/*flags=*/0));
|
|
|
|
ASSERT_EQ(subgraph->num_nodes, 1);
|
|
const struct xnn_node* node = &subgraph->nodes[0];
|
|
ASSERT_EQ(node->type, xnn_node_type_average_pooling_2d);
|
|
|
|
ASSERT_EQ(node->params.pooling_2d.padding_top, input_padding_top);
|
|
ASSERT_EQ(node->params.pooling_2d.padding_right, input_padding_right);
|
|
ASSERT_EQ(node->params.pooling_2d.padding_bottom, input_padding_bottom);
|
|
ASSERT_EQ(node->params.pooling_2d.padding_left, input_padding_left);
|
|
ASSERT_EQ(node->params.pooling_2d.pooling_height, pooling_height);
|
|
ASSERT_EQ(node->params.pooling_2d.pooling_width, pooling_width);
|
|
ASSERT_EQ(node->params.pooling_2d.stride_height, stride_height);
|
|
ASSERT_EQ(node->params.pooling_2d.stride_width, stride_width);
|
|
ASSERT_EQ(node->activation.output_min, output_min);
|
|
ASSERT_EQ(node->activation.output_max, output_max);
|
|
ASSERT_EQ(node->num_inputs, 1);
|
|
ASSERT_EQ(node->inputs[0], input_id);
|
|
ASSERT_EQ(node->num_outputs, 1);
|
|
ASSERT_EQ(node->outputs[0], output_id);
|
|
ASSERT_EQ(node->flags, 0);
|
|
}
|
|
|
|
TEST_F(AveragePoolingTestF16, matches_operator_api)
|
|
{
|
|
std::uniform_real_distribution<float> f32dist;
|
|
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
|
|
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
|
|
|
|
// Call operator API.
|
|
xnn_operator_t op = nullptr;
|
|
const xnn_status status = xnn_create_average_pooling2d_nhwc_f16(
|
|
input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height, pooling_width,
|
|
stride_height, stride_width, output_min, output_max, /*flags=*/0, &op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, op);
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_op(op, xnn_delete_operator);
|
|
|
|
size_t workspace_size = 0;
|
|
size_t workspace_alignment = 0;
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_reshape_average_pooling2d_nhwc_f16(
|
|
op, batch_size, input_height, input_width,
|
|
channels, /*input_pixel_stride=*/channels, /*output_pixel_stride=*/channels,
|
|
&workspace_size, &workspace_alignment,
|
|
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
|
|
/*threadpool=*/nullptr));
|
|
ASSERT_LE(workspace_alignment, XNN_ALLOCATION_ALIGNMENT);
|
|
|
|
xnnpack::Buffer<char, XNN_ALLOCATION_ALIGNMENT> workspace(workspace_size);
|
|
ASSERT_EQ(xnn_status_success, xnn_setup_average_pooling2d_nhwc_f16(op, workspace.data(), input.data(), operator_output.data()));
|
|
|
|
ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));
|
|
|
|
// Call subgraph API.
|
|
xnn_subgraph_t subgraph = nullptr;
|
|
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/2, /*flags=*/0, &subgraph));
|
|
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
|
|
input_id = XNN_INVALID_NODE_ID;
|
|
ASSERT_EQ(
|
|
xnn_status_success, xnn_define_tensor_value(
|
|
subgraph, xnn_datatype_fp16, input_dims.size(), input_dims.data(), nullptr, /*external_id=*/0,
|
|
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
|
|
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
|
|
|
|
output_id = XNN_INVALID_NODE_ID;
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_define_tensor_value(
|
|
subgraph, xnn_datatype_fp16, output_dims.size(), output_dims.data(), nullptr, /*external_id=*/1,
|
|
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
|
|
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
|
|
|
|
xnn_runtime_t runtime = nullptr;
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_define_average_pooling_2d(
|
|
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height,
|
|
pooling_width, stride_height, stride_width, output_min, output_max, input_id, output_id,
|
|
/*flags=*/0));
|
|
ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
|
|
ASSERT_NE(nullptr, runtime);
|
|
std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);
|
|
std::array<xnn_external_value, 2> external = {
|
|
xnn_external_value{input_id, input.data()}, xnn_external_value{output_id, subgraph_output.data()}};
|
|
ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
|
|
ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));
|
|
|
|
ASSERT_EQ(subgraph_output, operator_output);
|
|
}
|
|
|
|
TEST_F(AveragePoolingTestF32, matches_operator_api)
|
|
{
|
|
std::uniform_real_distribution<float> f32dist;
|
|
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
|
|
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
|
|
|
|
// Call operator API.
|
|
xnn_operator_t op = nullptr;
|
|
const xnn_status status = xnn_create_average_pooling2d_nhwc_f32(
|
|
input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height, pooling_width,
|
|
stride_height, stride_width, output_min, output_max, /*flags=*/0, &op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, op);
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_op(op, xnn_delete_operator);
|
|
|
|
size_t workspace_size = 0;
|
|
size_t workspace_alignment = 0;
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_reshape_average_pooling2d_nhwc_f32(
|
|
op, batch_size, input_height, input_width,
|
|
channels, /*input_pixel_stride=*/channels, /*output_pixel_stride=*/channels,
|
|
&workspace_size, &workspace_alignment,
|
|
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
|
|
/*threadpool=*/nullptr));
|
|
ASSERT_LE(workspace_alignment, XNN_ALLOCATION_ALIGNMENT);
|
|
|
|
xnnpack::Buffer<char, XNN_ALLOCATION_ALIGNMENT> workspace(workspace_size);
|
|
ASSERT_EQ(xnn_status_success, xnn_setup_average_pooling2d_nhwc_f32(op, workspace.data(), input.data(), operator_output.data()));
|
|
|
|
ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));
|
|
|
|
// Call subgraph API.
|
|
xnn_subgraph_t subgraph = nullptr;
|
|
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/2, /*flags=*/0, &subgraph));
|
|
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
|
|
input_id = XNN_INVALID_NODE_ID;
|
|
ASSERT_EQ(
|
|
xnn_status_success, xnn_define_tensor_value(
|
|
subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr, /*external_id=*/0,
|
|
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
|
|
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
|
|
|
|
output_id = XNN_INVALID_NODE_ID;
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_define_tensor_value(
|
|
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, /*external_id=*/1,
|
|
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
|
|
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
|
|
|
|
xnn_runtime_t runtime = nullptr;
|
|
ASSERT_EQ(
|
|
xnn_status_success,
|
|
xnn_define_average_pooling_2d(
|
|
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height,
|
|
pooling_width, stride_height, stride_width, output_min, output_max, input_id, output_id,
|
|
/*flags=*/0));
|
|
ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
|
|
ASSERT_NE(nullptr, runtime);
|
|
std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);
|
|
std::array<xnn_external_value, 2> external = {
|
|
xnn_external_value{input_id, input.data()}, xnn_external_value{output_id, subgraph_output.data()}};
|
|
ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
|
|
ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));
|
|
|
|
ASSERT_EQ(subgraph_output, operator_output);
|
|
}
|