242 lines
9.4 KiB
C++
242 lines
9.4 KiB
C++
// 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 <cstddef>
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#include <cstdint>
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#include <functional>
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#include <limits>
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#include <memory>
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#include <numeric>
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#include <random>
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#include <vector>
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#include <gtest/gtest.h>
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#include "xnnpack.h"
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#include "xnnpack/math.h"
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#include "xnnpack/node-type.h"
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#include "xnnpack/subgraph.h"
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#include "replicable_random_device.h"
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template <typename T>
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class GlobalAveragePooling2DTest : public ::testing::Test {
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protected:
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GlobalAveragePooling2DTest() {
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shape_dist = std::uniform_int_distribution<size_t>(3, XNN_MAX_TENSOR_DIMS);
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dim_dist = std::uniform_int_distribution<size_t>(1, 9);
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f32dist = std::uniform_real_distribution<float>();
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i8dist = std::uniform_int_distribution<int32_t>(
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std::numeric_limits<int8_t>::min(),
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std::numeric_limits<int8_t>::max());
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u8dist = std::uniform_int_distribution<int32_t>(
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std::numeric_limits<uint8_t>::min(),
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std::numeric_limits<uint8_t>::max());
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scale_dist = std::uniform_real_distribution<float>(0.1f, 5.0f);
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input_dims = RandomShape();
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output_dims = input_dims;
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output_dims[output_dims.size() - 3] = 1;
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output_dims[output_dims.size() - 2] = 1;
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batch_size = 1;
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for (size_t i = 0; i < input_dims.size() - 3; i++) {
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batch_size *= input_dims[i];
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}
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input_width =
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input_dims[input_dims.size() - 3] * input_dims[input_dims.size() - 2];
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channels = input_dims[input_dims.size() - 1];
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}
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std::vector<size_t> RandomShape()
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{
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std::vector<size_t> dims(shape_dist(rng));
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std::generate(dims.begin(), dims.end(), [&] { return dim_dist(rng); });
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return dims;
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}
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size_t NumElements(std::vector<size_t>& dims)
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{
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return std::accumulate(
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dims.begin(), dims.end(), size_t(1), std::multiplies<size_t>());
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}
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xnnpack::ReplicableRandomDevice rng;
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std::uniform_int_distribution<size_t> shape_dist;
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std::uniform_int_distribution<size_t> dim_dist;
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std::uniform_real_distribution<float> f32dist;
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std::uniform_real_distribution<float> scale_dist;
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std::uniform_int_distribution<int32_t> i8dist;
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std::uniform_int_distribution<int32_t> u8dist;
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float output_min = -std::numeric_limits<float>::infinity();
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float output_max = std::numeric_limits<float>::infinity();
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size_t batch_size;
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size_t input_width;
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size_t channels;
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std::vector<size_t> input_dims;
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std::vector<size_t> output_dims;
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};
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using GlobalAveragePooling2DTestQS8 = GlobalAveragePooling2DTest<int8_t>;
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using GlobalAveragePooling2DTestQU8 = GlobalAveragePooling2DTest<uint8_t>;
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using GlobalAveragePooling2DTestF16 = GlobalAveragePooling2DTest<xnn_float16>;
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using GlobalAveragePooling2DTestF32 = GlobalAveragePooling2DTest<float>;
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TEST_F(GlobalAveragePooling2DTestQS8, 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(2, /*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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uint32_t input_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_quantized_tensor_value(
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subgraph, xnn_datatype_qint8, 0, 1.0f, input_dims.size(), input_dims.data(), nullptr,
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/*external_id=*/0, /*flags=*/0, &input_id));
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ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
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uint32_t output_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_quantized_tensor_value(
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subgraph, xnn_datatype_qint8, 0, 1.0f, output_dims.size(), output_dims.data(), nullptr,
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/*external_id=*/1, /*flags=*/0, &output_id));
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ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_global_average_pooling_2d(subgraph, output_min, output_max, input_id, output_id, /*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_static_mean);
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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, 1);
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ASSERT_EQ(node->outputs[0], output_id);
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ASSERT_EQ(node->params.reduce.num_reduction_axes, 2);
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ASSERT_EQ(node->params.reduce.reduction_axes[0], input_dims.size() - 3);
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ASSERT_EQ(node->params.reduce.reduction_axes[1], input_dims.size() - 2);
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ASSERT_EQ(node->flags, 0);
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}
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TEST_F(GlobalAveragePooling2DTestQU8, 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(2, /*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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uint32_t input_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_quantized_tensor_value(
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subgraph, xnn_datatype_quint8, 0, 1.0f, input_dims.size(), input_dims.data(), nullptr,
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/*external_id=*/0, /*flags=*/0, &input_id));
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ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
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uint32_t output_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_quantized_tensor_value(
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subgraph, xnn_datatype_quint8, 0, 1.0f, output_dims.size(), output_dims.data(), nullptr,
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/*external_id=*/1, /*flags=*/0, &output_id));
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ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_global_average_pooling_2d(subgraph, output_min, output_max, input_id, output_id, /*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_static_mean);
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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, 1);
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ASSERT_EQ(node->outputs[0], output_id);
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ASSERT_EQ(node->params.reduce.num_reduction_axes, 2);
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ASSERT_EQ(node->params.reduce.reduction_axes[0], input_dims.size() - 3);
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ASSERT_EQ(node->params.reduce.reduction_axes[1], input_dims.size() - 2);
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ASSERT_EQ(node->flags, 0);
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}
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TEST_F(GlobalAveragePooling2DTestF16, 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(2, /*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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uint32_t 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_fp16, input_dims.size(), input_dims.data(), nullptr,
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/*external_id=*/0, /*flags=*/0, &input_id));
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ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
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uint32_t output_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_fp16, output_dims.size(), output_dims.data(), nullptr,
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/*external_id=*/1, /*flags=*/0, &output_id));
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ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_global_average_pooling_2d(subgraph, output_min, output_max, input_id, output_id, /*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_static_mean);
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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, 1);
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ASSERT_EQ(node->outputs[0], output_id);
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ASSERT_EQ(node->params.reduce.num_reduction_axes, 2);
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ASSERT_EQ(node->params.reduce.reduction_axes[0], input_dims.size() - 3);
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ASSERT_EQ(node->params.reduce.reduction_axes[1], input_dims.size() - 2);
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ASSERT_EQ(node->flags, 0);
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}
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TEST_F(GlobalAveragePooling2DTestF32, 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(2, /*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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uint32_t 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,
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/*external_id=*/0, /*flags=*/0, &input_id));
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ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
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uint32_t output_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,
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/*external_id=*/1, /*flags=*/0, &output_id));
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ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_global_average_pooling_2d(subgraph, output_min, output_max, input_id, output_id, /*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_static_mean);
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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, 1);
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ASSERT_EQ(node->outputs[0], output_id);
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ASSERT_EQ(node->params.reduce.num_reduction_axes, 2);
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ASSERT_EQ(node->params.reduce.reduction_axes[0], input_dims.size() - 3);
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ASSERT_EQ(node->params.reduce.reduction_axes[1], input_dims.size() - 2);
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ASSERT_EQ(node->flags, 0);
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}
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