mirror of
https://github.com/Tencent/ncnn.git
synced 2026-10-18 21:54:39 +08:00
nihui
5ea98309e3
* harden layer parameter validation Original patches and author identities: https://github.com/Tencent/ncnn/pull/6912 (beilzx) https://github.com/Tencent/ncnn/pull/6919 (FluxMind-O) https://github.com/Tencent/ncnn/pull/6928 (lxcxjxhx) https://github.com/Tencent/ncnn/pull/6948 (ktz03) https://github.com/Tencent/ncnn/pull/6950 (ktz03) https://github.com/Tencent/ncnn/pull/6971 (LI) https://github.com/Tencent/ncnn/pull/6974 (LI) https://github.com/Tencent/ncnn/pull/6977 (LI) Co-authored-by: beilzx <2069360659@qq.com> Co-authored-by: FluxMind-O <3283918317@qq.com> Co-authored-by: lxcxjxhx <lxcxjxhx@users.noreply.github.com> Co-authored-by: ktz03 <2484593937@qq.com> Co-authored-by: LI <2484593937@qq.com>
534 lines
12 KiB
C++
534 lines
12 KiB
C++
// Copyright 2020 Tencent
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// SPDX-License-Identifier: BSD-3-Clause
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#include "testutil.h"
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#include "layer_type.h"
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#include <limits.h>
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static int packing_cpu_naive(const ncnn::Mat& a, ncnn::Mat& b, int out_elempack)
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{
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ncnn::ParamDict pd;
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pd.set(0, out_elempack);
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std::vector<ncnn::Mat> weights(0);
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ncnn::Option opt;
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opt.num_threads = 1;
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ncnn::Layer* op = ncnn::create_layer_naive("Packing");
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op->load_param(pd);
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ncnn::ModelBinFromMatArray mb(weights.data());
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op->load_model(mb);
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op->create_pipeline(opt);
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op->forward(a, b, opt);
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op->destroy_pipeline(opt);
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delete op;
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return 0;
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}
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static int test_packing_cpu_fp32(const ncnn::Mat& a, int in_elempack, int out_elempack)
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{
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ncnn::ParamDict pd;
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pd.set(0, out_elempack);
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std::vector<ncnn::Mat> weights(0);
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_vulkan_compute = false;
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opt.use_int8_inference = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_packing_layout = false;
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ncnn::Layer* op = ncnn::create_layer_cpu("Packing");
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op->load_param(pd);
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ncnn::ModelBinFromMatArray mb(weights.data());
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op->load_model(mb);
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op->create_pipeline(opt);
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ncnn::Mat ap;
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ncnn::convert_packing(a, ap, in_elempack, opt);
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ncnn::Mat b;
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packing_cpu_naive(ap, b, out_elempack);
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ncnn::Mat c;
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op->forward(ap, c, opt);
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op->destroy_pipeline(opt);
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delete op;
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if (CompareMat(b, c, 0.001) != 0)
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{
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fprintf(stderr, "test_packing_cpu_fp32 failed a.dims=%d a=(%d %d %d %d) in_elempack=%d out_elempack=%d\n", a.dims, a.w, a.h, a.d, a.c, in_elempack, out_elempack);
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return -1;
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}
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return 0;
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}
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static int test_packing_cpu_fp16(const ncnn::Mat& a, int in_elempack, int out_elempack)
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{
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ncnn::ParamDict pd;
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pd.set(0, out_elempack);
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std::vector<ncnn::Mat> weights(0);
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_vulkan_compute = false;
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opt.use_int8_inference = false;
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opt.use_fp16_storage = true;
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opt.use_fp16_arithmetic = true;
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opt.use_packing_layout = false;
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ncnn::Layer* op = ncnn::create_layer_cpu("Packing");
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if (!op->support_fp16_storage)
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{
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delete op;
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return 0;
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}
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op->load_param(pd);
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ncnn::ModelBinFromMatArray mb(weights.data());
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op->load_model(mb);
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op->create_pipeline(opt);
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ncnn::Mat a16;
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ncnn::cast_float32_to_float16(a, a16, opt);
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ncnn::Mat ap;
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ncnn::convert_packing(a16, ap, in_elempack, opt);
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ncnn::Mat b;
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packing_cpu_naive(ap, b, out_elempack);
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ncnn::Mat c;
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op->forward(ap, c, opt);
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op->destroy_pipeline(opt);
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delete op;
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ncnn::Mat c32;
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ncnn::cast_float16_to_float32(c, c32, opt);
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if (CompareMat(b, c32, 0.001) != 0)
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{
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fprintf(stderr, "test_packing_cpu_fp16 failed a.dims=%d a=(%d %d %d %d) in_elempack=%d out_elempack=%d\n", a.dims, a.w, a.h, a.d, a.c, in_elempack, out_elempack);
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return -1;
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}
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return 0;
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}
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static int test_packing_cpu_int8(const ncnn::Mat& a, int in_elempack, int out_elempack)
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{
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ncnn::ParamDict pd;
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pd.set(0, out_elempack);
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std::vector<ncnn::Mat> weights(0);
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_vulkan_compute = false;
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opt.use_int8_inference = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_packing_layout = false;
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ncnn::Layer* op = ncnn::create_layer_cpu("Packing");
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op->load_param(pd);
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ncnn::ModelBinFromMatArray mb(weights.data());
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op->load_model(mb);
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op->create_pipeline(opt);
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ncnn::Mat a8;
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if (a.dims == 1) a8 = RandomS8Mat(a.w);
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if (a.dims == 2) a8 = RandomS8Mat(a.w, a.h);
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if (a.dims == 3) a8 = RandomS8Mat(a.w, a.h, a.c);
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if (a.dims == 4) a8 = RandomS8Mat(a.w, a.h, a.d, a.c);
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ncnn::Mat ap;
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ncnn::convert_packing(a8, ap, in_elempack, opt);
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ncnn::Mat b;
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packing_cpu_naive(ap, b, out_elempack);
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ncnn::Mat c;
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op->forward(ap, c, opt);
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op->destroy_pipeline(opt);
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delete op;
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ncnn::Mat b32;
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ncnn::cast_int8_to_float32(b, b32, opt);
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ncnn::Mat c32;
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ncnn::cast_int8_to_float32(c, c32, opt);
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if (CompareMat(b32, c32, 0.001) != 0)
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{
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fprintf(stderr, "test_packing_cpu_int8 failed a.dims=%d a=(%d %d %d %d) in_elempack=%d out_elempack=%d\n", a.dims, a.w, a.h, a.d, a.c, in_elempack, out_elempack);
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return -1;
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}
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return 0;
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}
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static int test_packing_cpu(const ncnn::Mat& a, int in_elempack, int out_elempack)
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{
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return 0
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|| test_packing_cpu_fp32(a, in_elempack, out_elempack)
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|| test_packing_cpu_fp16(a, in_elempack, out_elempack)
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|| test_packing_cpu_int8(a, in_elempack, out_elempack);
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}
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#if NCNN_VULKAN
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static int test_packing_gpu(const ncnn::Mat& a, int in_elempack, int out_elempack, int cast_type)
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{
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ncnn::ParamDict pd;
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pd.set(0, out_elempack);
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pd.set(2, cast_type); // cast_type_from
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pd.set(3, cast_type); // cast_type_to
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std::vector<ncnn::Mat> weights(0);
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_vulkan_compute = true;
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opt.use_int8_inference = false;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_bf16_packed = cast_type == 5; // bfloat16
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opt.use_bf16_storage = false;
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opt.use_int8_storage = false;
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opt.use_int8_arithmetic = false;
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opt.use_packing_layout = true;
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ncnn::VulkanDevice* vkdev = ncnn::get_gpu_device();
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ncnn::VkAllocator* blob_vkallocator = vkdev->acquire_blob_allocator();
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ncnn::VkAllocator* staging_vkallocator = vkdev->acquire_staging_allocator();
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opt.blob_vkallocator = blob_vkallocator;
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opt.workspace_vkallocator = blob_vkallocator;
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opt.staging_vkallocator = staging_vkallocator;
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if (!vkdev->info.support_fp16_packed()) opt.use_fp16_packed = false;
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if (!vkdev->info.support_fp16_storage()) opt.use_fp16_storage = false;
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ncnn::Layer* op = ncnn::create_layer_vulkan("Packing");
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op->vkdev = vkdev;
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op->load_param(pd);
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ncnn::ModelBinFromMatArray mb(weights.data());
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op->load_model(mb);
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op->create_pipeline(opt);
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ncnn::Mat a_cast = a;
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if (cast_type == 5)
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ncnn::cast_float32_to_bfloat16(a, a_cast, opt);
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ncnn::Mat ap;
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ncnn::convert_packing(a_cast, ap, in_elempack, opt);
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ncnn::Mat b;
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packing_cpu_naive(ap, b, out_elempack);
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ncnn::Mat d;
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// forward
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ncnn::VkCompute cmd(vkdev);
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// upload
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ncnn::VkMat a_gpu;
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cmd.record_clone(ap, a_gpu, opt);
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ncnn::VkMat d_gpu;
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op->forward(a_gpu, d_gpu, cmd, opt);
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// download
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cmd.record_clone(d_gpu, d, opt);
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cmd.submit_and_wait();
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op->destroy_pipeline(opt);
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delete op;
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vkdev->reclaim_blob_allocator(blob_vkallocator);
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vkdev->reclaim_staging_allocator(staging_vkallocator);
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if (cast_type == 5)
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{
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ncnn::Mat b32;
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ncnn::Mat d32;
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ncnn::cast_bfloat16_to_float32(b, b32, opt);
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ncnn::cast_bfloat16_to_float32(d, d32, opt);
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b = b32;
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d = d32;
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}
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if (CompareMat(b, d, cast_type == 5 ? 0.f : 0.001f) != 0)
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{
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fprintf(stderr, "test_packing_gpu failed a.dims=%d a=(%d %d %d %d) in_elempack=%d out_elempack=%d cast_type=%d\n", a.dims, a.w, a.h, a.d, a.c, in_elempack, out_elempack, cast_type);
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return -1;
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}
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return 0;
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}
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static int test_packing_gpu_int8(const ncnn::Mat& a, int in_elempack, int out_elempack)
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{
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ncnn::ParamDict pd;
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pd.set(0, out_elempack);
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pd.set(2, 4); // cast_type_from
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pd.set(3, 4); // cast_type_to
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std::vector<ncnn::Mat> weights(0);
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ncnn::Option opt;
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opt.num_threads = 1;
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opt.use_vulkan_compute = true;
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opt.use_int8_inference = false;
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opt.use_fp16_packed = false;
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opt.use_fp16_storage = false;
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opt.use_fp16_arithmetic = false;
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opt.use_int8_storage = false;
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opt.use_int8_arithmetic = false;
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opt.use_packing_layout = true;
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ncnn::VulkanDevice* vkdev = ncnn::get_gpu_device();
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ncnn::VkAllocator* blob_vkallocator = vkdev->acquire_blob_allocator();
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ncnn::VkAllocator* staging_vkallocator = vkdev->acquire_staging_allocator();
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opt.blob_vkallocator = blob_vkallocator;
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opt.workspace_vkallocator = blob_vkallocator;
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opt.staging_vkallocator = staging_vkallocator;
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if (!vkdev->info.support_int8_packed()) opt.use_int8_packed = false;
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if (!vkdev->info.support_int8_storage()) opt.use_int8_storage = false;
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ncnn::Layer* op = ncnn::create_layer_vulkan("Packing");
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op->vkdev = vkdev;
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op->load_param(pd);
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ncnn::ModelBinFromMatArray mb(weights.data());
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op->load_model(mb);
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op->create_pipeline(opt);
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ncnn::Mat a8;
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if (a.dims == 1) a8 = RandomS8Mat(a.w);
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if (a.dims == 2) a8 = RandomS8Mat(a.w, a.h);
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if (a.dims == 3) a8 = RandomS8Mat(a.w, a.h, a.c);
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if (a.dims == 4) a8 = RandomS8Mat(a.w, a.h, a.d, a.c);
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ncnn::Mat ap;
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ncnn::convert_packing(a8, ap, in_elempack, opt);
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ncnn::Mat b;
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packing_cpu_naive(ap, b, out_elempack);
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ncnn::Mat c;
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// forward
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ncnn::VkCompute cmd(vkdev);
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// upload
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ncnn::VkMat a_gpu;
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cmd.record_clone(ap, a_gpu, opt);
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ncnn::VkMat c_gpu;
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op->forward(a_gpu, c_gpu, cmd, opt);
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// download
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cmd.record_clone(c_gpu, c, opt);
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cmd.submit_and_wait();
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op->destroy_pipeline(opt);
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delete op;
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ncnn::Mat b32;
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ncnn::cast_int8_to_float32(b, b32, opt);
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ncnn::Mat c32;
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ncnn::cast_int8_to_float32(c, c32, opt);
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if (CompareMat(b32, c32, 0.001) != 0)
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{
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fprintf(stderr, "test_packing_gpu_int8 failed a.dims=%d a=(%d %d %d %d) in_elempack=%d out_elempack=%d\n", a.dims, a.w, a.h, a.d, a.c, in_elempack, out_elempack);
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return -1;
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}
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return 0;
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}
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static int test_packing_gpu(const ncnn::Mat& a, int in_elempack, int out_elempack)
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{
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return 0
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|| test_packing_gpu(a, in_elempack, out_elempack, 1)
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|| test_packing_gpu(a, in_elempack, out_elempack, 5)
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|| test_packing_gpu_int8(a, in_elempack, out_elempack);
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}
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#endif
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static int test_packing_cpu(const ncnn::Mat& a)
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{
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return 0
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|| test_packing_cpu(a, 1, 1)
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|| test_packing_cpu(a, 4, 4)
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|| test_packing_cpu(a, 4, 8)
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|| test_packing_cpu(a, 1, 4)
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|| test_packing_cpu(a, 4, 1)
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|| test_packing_cpu(a, 1, 8)
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|| test_packing_cpu(a, 8, 1)
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|| test_packing_cpu(a, 4, 8)
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|| test_packing_cpu(a, 8, 4)
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|| test_packing_cpu(a, 1, 16)
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|| test_packing_cpu(a, 16, 1)
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|| test_packing_cpu(a, 4, 16)
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|| test_packing_cpu(a, 16, 4)
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|| test_packing_cpu(a, 8, 16)
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|| test_packing_cpu(a, 16, 8);
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}
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#if NCNN_VULKAN
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static int test_packing_gpu(const ncnn::Mat& a)
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{
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return 0
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|| test_packing_gpu(a, 1, 1)
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|| test_packing_gpu(a, 4, 4)
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|| test_packing_gpu(a, 1, 4)
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|| test_packing_gpu(a, 4, 1);
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}
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#endif // NCNN_VULKAN
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static int test_packing_0()
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{
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ncnn::Mat a = RandomMat(9, 7, 10, 16);
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ncnn::Mat b = RandomMat(9, 7, 10, 3);
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return 0
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|| test_packing_cpu(a)
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|| test_packing_cpu(b)
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#if NCNN_VULKAN
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|| test_packing_gpu(a)
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#endif
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;
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}
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static int test_packing_1()
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{
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ncnn::Mat a = RandomMat(9, 10, 16);
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ncnn::Mat b = RandomMat(9, 10, 3);
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return 0
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|| test_packing_cpu(a)
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|| test_packing_cpu(b)
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#if NCNN_VULKAN
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|| test_packing_gpu(a)
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#endif
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;
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}
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static int test_packing_2()
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{
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ncnn::Mat a = RandomMat(19, 16);
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return 0
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|| test_packing_cpu(a)
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#if NCNN_VULKAN
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|| test_packing_gpu(a)
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#endif
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;
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}
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static int test_packing_3()
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{
|
|
ncnn::Mat a = RandomMat(80);
|
|
|
|
return 0
|
|
|| test_packing_cpu(a)
|
|
#if NCNN_VULKAN
|
|
|| test_packing_gpu(a)
|
|
#endif
|
|
;
|
|
}
|
|
|
|
#if NCNN_VALIDATION
|
|
static int test_packing_load_param()
|
|
{
|
|
ncnn::ParamDict base;
|
|
if (test_layer_param(ncnn::LayerType::Packing, base, 0) != 0)
|
|
return -1;
|
|
|
|
for (int i = 1; i <= 16; i++)
|
|
{
|
|
if (test_layer_param(ncnn::LayerType::Packing, base, 0, i, 0) != 0)
|
|
return -1;
|
|
}
|
|
|
|
const int invalid[] = {0, -1, INT_MIN};
|
|
for (int i = 0; i < 3; i++)
|
|
{
|
|
if (test_layer_param(ncnn::LayerType::Packing, base, 0, invalid[i], -1) != 0)
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
#endif // NCNN_VALIDATION
|
|
|
|
int main()
|
|
{
|
|
SRAND(7767517);
|
|
|
|
return 0
|
|
|| test_packing_0()
|
|
|| test_packing_1()
|
|
|| test_packing_2()
|
|
|| test_packing_3()
|
|
#if NCNN_VALIDATION
|
|
|| test_packing_load_param()
|
|
#endif // NCNN_VALIDATION
|
|
;
|
|
}
|