214 lines
9.2 KiB
C++
214 lines
9.2 KiB
C++
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// Copyright 2022 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#include <algorithm>
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#include <array>
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#include <cstddef>
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#include <cstdint>
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#include <limits>
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#include <memory>
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#include <random>
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#include <xnnpack.h>
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#include <xnnpack/node-type.h>
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#include <xnnpack/operator.h>
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#include <xnnpack/subgraph.h>
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#include <gtest/gtest.h>
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namespace {
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inline size_t compute_output_dimension(size_t padded_input_dimension, size_t kernel_dimension)
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{
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return padded_input_dimension / kernel_dimension;
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}
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} // namespace
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class ArgmaxPoolingTestF32 : public ::testing::Test {
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protected:
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ArgmaxPoolingTestF32()
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{
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random_device = std::unique_ptr<std::random_device>(new std::random_device());
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rng = std::mt19937((*random_device)());
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input_size_dist = std::uniform_int_distribution<uint32_t>(10, 15);
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pooling_size_dist = std::uniform_int_distribution<uint32_t>(2, 5);
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batch_size = input_size_dist(rng);
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input_height = input_size_dist(rng);
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input_width = input_size_dist(rng);
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channels = input_size_dist(rng);
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pooling_height = pooling_size_dist(rng);
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pooling_width = pooling_size_dist(rng);
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input_padding_top = input_size_dist(rng);
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input_padding_right = input_size_dist(rng);
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input_padding_bottom = input_size_dist(rng);
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input_padding_left = input_size_dist(rng);
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output_height = compute_output_dimension(input_height + input_padding_top + input_padding_bottom, pooling_height);
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output_width = compute_output_dimension(input_width + input_padding_left + input_padding_right, pooling_width);
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input_dims = {batch_size, input_height, input_width, channels};
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output_dims = {batch_size, output_height, output_width, channels};
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input = std::vector<float>(XNN_EXTRA_BYTES / sizeof(float) + batch_size * input_height * input_width * channels);
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operator_output = std::vector<float>(batch_size * output_height * output_width * channels);
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operator_output_index = std::vector<uint32_t>(batch_size * output_height * output_width * channels);
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subgraph_output = std::vector<float>(batch_size * output_height * output_width * channels);
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subgraph_output_index = std::vector<uint32_t>(batch_size * output_height * output_width * channels);
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}
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std::unique_ptr<std::random_device> random_device;
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std::mt19937 rng;
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std::uniform_int_distribution<uint32_t> input_size_dist;
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std::uniform_int_distribution<uint32_t> pooling_size_dist;
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uint32_t batch_size;
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uint32_t input_height;
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uint32_t input_width;
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uint32_t channels;
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uint32_t pooling_height;
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uint32_t pooling_width;
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uint32_t output_height;
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uint32_t output_width;
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std::array<size_t, 4> input_dims;
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std::array<size_t, 4> output_dims;
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uint32_t input_padding_top;
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uint32_t input_padding_right;
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uint32_t input_padding_bottom;
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uint32_t input_padding_left;
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uint32_t input_id;
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uint32_t output_value_id;
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uint32_t output_index_id;
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std::vector<float> input;
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std::vector<float> operator_output;
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std::vector<uint32_t> operator_output_index;
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std::vector<float> subgraph_output;
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std::vector<uint32_t> subgraph_output_index;
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};
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TEST_F(ArgmaxPoolingTestF32, define)
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{
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ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
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xnn_subgraph_t subgraph = nullptr;
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ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/3, /*flags=*/0, &subgraph));
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std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
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input_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_tensor_value(
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subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr, 0,
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/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
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ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
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output_value_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_tensor_value(
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subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, 1,
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/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_value_id));
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ASSERT_NE(output_value_id, XNN_INVALID_NODE_ID);
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output_index_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_tensor_value(
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subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, 2,
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/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_index_id));
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ASSERT_NE(output_index_id, XNN_INVALID_NODE_ID);
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ASSERT_EQ(
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xnn_status_success, xnn_define_argmax_pooling_2d(
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subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left,
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pooling_height, pooling_width, input_id, output_value_id, output_index_id,
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/*flags=*/0));
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ASSERT_EQ(subgraph->num_nodes, 1);
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const struct xnn_node* node = &subgraph->nodes[0];
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ASSERT_EQ(node->type, xnn_node_type_argmax_pooling_2d);
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ASSERT_EQ(node->compute_type, xnn_compute_type_fp32);
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ASSERT_EQ(node->params.pooling_2d.padding_top, input_padding_top);
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ASSERT_EQ(node->params.pooling_2d.padding_right, input_padding_right);
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ASSERT_EQ(node->params.pooling_2d.padding_bottom, input_padding_bottom);
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ASSERT_EQ(node->params.pooling_2d.padding_left, input_padding_left);
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ASSERT_EQ(node->params.pooling_2d.pooling_height, pooling_height);
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ASSERT_EQ(node->params.pooling_2d.pooling_width, pooling_width);
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ASSERT_EQ(node->num_inputs, 1);
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ASSERT_EQ(node->inputs[0], input_id);
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ASSERT_EQ(node->num_outputs, 2);
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ASSERT_EQ(node->outputs[0], output_value_id);
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ASSERT_EQ(node->outputs[1], output_index_id);
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ASSERT_EQ(node->flags, 0);
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}
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TEST_F(ArgmaxPoolingTestF32, matches_operator_api)
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{
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std::uniform_real_distribution<float> f32dist(-255.0f, 255.0f);
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
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std::fill(operator_output.begin(), operator_output.end(), nanf(""));
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std::fill(subgraph_output.begin(), subgraph_output.end(), nanf(""));
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ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
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// Call operator API.
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xnn_operator_t op = nullptr;
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const xnn_status status = xnn_create_argmax_pooling2d_nhwc_f32(
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input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, pooling_height, pooling_width,
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channels, channels, channels, /*flags=*/0, &op);
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if (status == xnn_status_unsupported_hardware) {
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GTEST_SKIP();
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}
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ASSERT_EQ(xnn_status_success, status);
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ASSERT_NE(nullptr, op);
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_op(op, xnn_delete_operator);
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ASSERT_EQ(
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xnn_status_success, xnn_setup_argmax_pooling2d_nhwc_f32(
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op, batch_size, input_height, input_width, input.data(), operator_output.data(),
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operator_output_index.data(), /*threadpool=*/nullptr));
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ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));
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// Call subgraph API.
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xnn_subgraph_t subgraph = nullptr;
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ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/3, /*flags=*/0, &subgraph));
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std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
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input_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_tensor_value(
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subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr, /*external_id=*/0,
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/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
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ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
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output_value_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_tensor_value(
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subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, /*external_id=*/1,
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/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_value_id));
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ASSERT_NE(output_value_id, XNN_INVALID_NODE_ID);
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output_index_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_tensor_value(
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subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr, /*external_id=*/2,
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/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_index_id));
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ASSERT_NE(output_index_id, XNN_INVALID_NODE_ID);
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xnn_runtime_t runtime = nullptr;
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ASSERT_EQ(
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xnn_status_success, xnn_define_argmax_pooling_2d(
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subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left,
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pooling_height, pooling_width, input_id, output_value_id, output_index_id,
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/*flags=*/0));
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ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
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ASSERT_NE(nullptr, runtime);
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std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);
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std::array<xnn_external_value, 3> external = {
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xnn_external_value{input_id, input.data()}, xnn_external_value{output_value_id, subgraph_output.data()},
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xnn_external_value{output_index_id, subgraph_output_index.data()}};
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ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
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ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));
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ASSERT_EQ(subgraph_output, operator_output);
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}
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