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average-pooling-2d-reshape.cc
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// Copyright 2023 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 <cstddef>
#include <cstdint>
#include <limits>
#include <memory>
#include <vector>
#include <gtest/gtest.h>
#include "xnnpack.h"
#include "xnnpack/node-type.h"
#include "xnnpack/subgraph.h"
TEST(AveragePooling2DTestF32, Reshape)
{
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);
std::vector<size_t> dims{2, 3, 4, 5};
uint32_t input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, dims.size(), dims.data(), nullptr, 0,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t output_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, dims.size(), dims.data(), nullptr, 1,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
const size_t pooling_height = 2;
const size_t pooling_width = 2;
const size_t stride_height = 2;
const size_t stride_width = 2;
const float output_min = -std::numeric_limits<float>::infinity();
const float output_max = std::numeric_limits<float>::infinity();
ASSERT_EQ(xnn_status_success, xnn_define_average_pooling_2d(
subgraph, /*input_padding_top=*/0, /*input_padding_right=*/0, /*input_padding_bottom=*/0, /*input_padding_left=*/0, pooling_height,
pooling_width, stride_height, stride_width, output_min, output_max, input_id, output_id,
/*flags=*/0));
ASSERT_EQ(subgraph->num_nodes, 1);
struct xnn_node* node = &subgraph->nodes[0];
ASSERT_EQ(node->type, xnn_node_type_average_pooling_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_fp32);
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);
xnn_runtime_t runtime = nullptr;
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);
ASSERT_EQ(node->reshape(&runtime->opdata[0], subgraph->values, subgraph->num_values, /*threadpool=*/nullptr), xnn_status_success);
dims[0] = 7;
dims[3] = 9;
ASSERT_EQ(xnn_status_success, xnn_reshape_external_value(runtime, 0, dims.size(), dims.data()));
ASSERT_EQ(node->reshape(&runtime->opdata[0], runtime->values, runtime->num_values, /*threadpool=*/nullptr), xnn_status_reallocation_required);
const xnn_shape* output_shape = &runtime->values[node->outputs[0]].shape;
ASSERT_EQ(output_shape->dim[0], dims[0]);
ASSERT_EQ(output_shape->dim[1], dims[1] - 2);
ASSERT_EQ(output_shape->dim[2], dims[2] - 2);
ASSERT_EQ(output_shape->dim[3], dims[3]);
}
TEST(AveragePooling2DTestF32, ReshapeWithPadding)
{
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);
std::vector<size_t> dims{2, 3, 4, 5};
std::vector<size_t> output_dims{2, 3, 5, 5};
uint32_t input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, dims.size(), dims.data(), nullptr, 0,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t 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);
const size_t pooling_height = 2;
const size_t pooling_width = 2;
const size_t stride_height = 2;
const size_t stride_width = 2;
const float output_min = -std::numeric_limits<float>::infinity();
const float output_max = std::numeric_limits<float>::infinity();
ASSERT_EQ(xnn_status_success, xnn_define_average_pooling_2d(
subgraph, /*input_padding_top=*/3, /*input_padding_right=*/2, /*input_padding_bottom=*/1, /*input_padding_left=*/4, pooling_height,
pooling_width, stride_height, stride_width, output_min, output_max, input_id, output_id,
/*flags=*/0));
ASSERT_EQ(subgraph->num_nodes, 1);
struct xnn_node* node = &subgraph->nodes[0];
ASSERT_EQ(node->type, xnn_node_type_average_pooling_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_fp32);
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);
xnn_runtime_t runtime = nullptr;
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);
ASSERT_EQ(node->reshape(&runtime->opdata[0], subgraph->values, subgraph->num_values, /*threadpool=*/nullptr), xnn_status_success);
dims[0] = 2;
dims[1] = 2;
dims[2] = 8;
dims[3] = 17;
ASSERT_EQ(xnn_status_success, xnn_reshape_external_value(runtime, 0, dims.size(), dims.data()));
ASSERT_EQ(node->reshape(&runtime->opdata[0], runtime->values, runtime->num_values, /*threadpool=*/nullptr), xnn_status_reallocation_required);
const xnn_shape* output_shape = &runtime->values[node->outputs[0]].shape;
ASSERT_EQ(output_shape->dim[0], dims[0]);
ASSERT_EQ(output_shape->dim[1], 3);
ASSERT_EQ(output_shape->dim[2], 7);
ASSERT_EQ(output_shape->dim[3], dims[3]);
}