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ncnn is a high-performance neural network inference framework optimized for the mobile platform
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ncnn/examples/yolov4.cpp, yolov4->opt.use_vulkan_compute = true , detection is Error. #2108

Open lovemory opened 4 years ago

lovemory commented 4 years ago

Issue description: 47th row: yolov4->opt.use_vulkan_compute = true; detection is Error. 47th row : yolov4->opt.use_vulkan_compute = false; detection is OK.

cpp code copy from ncnn/examples/yolov4.cpp , and modify some lines to support input *.img code is : `// Tencent is pleased to support the open source community by making ncnn available. // // Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved. // // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except // in compliance with the License. You may obtain a copy of the License at // // https://opensource.org/licenses/BSD-3-Clause // // Unless required by applicable law or agreed to in writing, software distributed // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR // CONDITIONS OF ANY KIND, either express or implied. See the License for the // specific language governing permissions and limitations under the License.

include "net.h"

include <opencv2/core/core.hpp>

include <opencv2/highgui/highgui.hpp>

include <opencv2/imgproc/imgproc.hpp>

include

include

define NCNN_PROFILING

define YOLOV4_TINY //Using yolov4_tiny, if undef, using original yolov4

ifdef NCNN_PROFILING

include "benchmark.h"

endif

struct Object { cv::Rect_ rect; int label; float prob; };

static int init_yolov4(ncnn::Net yolov4, int target_size) { / --> Set the params you need for the ncnn inference <-- /

yolov4->opt.num_threads = 4; //You need to compile with libgomp for multi thread support

yolov4->opt.use_vulkan_compute = true; //You need to compile with libvulkan for gpu support

yolov4->opt.use_winograd_convolution = true;
yolov4->opt.use_sgemm_convolution = true;
yolov4->opt.use_fp16_packed = true;
yolov4->opt.use_fp16_storage = true;
yolov4->opt.use_fp16_arithmetic = true;
yolov4->opt.use_packing_layout = true;
yolov4->opt.use_shader_pack8 = false;
yolov4->opt.use_image_storage = false;

/* --> End of setting params <-- */
int ret = 0;

// original pretrained model from https://github.com/AlexeyAB/darknet
// the ncnn model https://drive.google.com/drive/folders/1YzILvh0SKQPS_lrb33dmGNq7aVTKPWS0?usp=sharing
// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models

ifdef YOLOV4_TINY

const char* yolov4_param = "yolov4-tiny-opt.param";
const char* yolov4_model = "yolov4-tiny-opt.bin";
*target_size = 416;

else

const char* yolov4_param = "yolov4-opt.param";
const char* yolov4_model = "yolov4-opt.bin";
*target_size = 608;

endif

ret = yolov4->load_param(yolov4_param);
if (ret != 0)
{
    return ret;
}

ret = yolov4->load_model(yolov4_model);
if (ret != 0)
{
    return ret;
}

return 0;

}

static int detect_yolov4(const cv::Mat& bgr, std::vector& objects, int target_size, ncnn::Net* yolov4) { int img_w = bgr.cols; int img_h = bgr.rows;

ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, bgr.cols, bgr.rows, target_size, target_size);
//ncnn::Mat in = ncnn::Mat::from_pixels(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, bgr.cols, bgr.rows);
const float mean_vals[3] = {0, 0, 0};
const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
in.substract_mean_normalize(mean_vals, norm_vals);

ncnn::Extractor ex = yolov4->create_extractor();

ex.input("data", in);

ncnn::Mat out;
ex.extract("output", out);

objects.clear();
fprintf(stdout,"out.h = %d\n", out.h);
for (int i = 0; i < out.h; i++)
{
    const float* values = out.row(i);

    Object object;
    object.label = values[0];
    object.prob = values[1];
    object.rect.x = values[2] * img_w;
    object.rect.y = values[3] * img_h;
    object.rect.width = values[4] * img_w - object.rect.x;
    object.rect.height = values[5] * img_h - object.rect.y;

    objects.push_back(object);
}

return 0;

} static int frame_count_index= 0; static int draw_objects(const cv::Mat& bgr, const std::vector& objects, int is_streaming) { static const char* class_names[] = {"background", "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" };

cv::Mat image = bgr.clone();

for (size_t i = 0; i < objects.size(); i++)
{
    const Object& obj = objects[i];

    fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
            obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);

    cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0));

    char text[256];
    sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);

    int baseLine = 0;
    cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

    int x = obj.rect.x;
    int y = obj.rect.y - label_size.height - baseLine;
    if (y < 0)
        y = 0;
    if (x + label_size.width > image.cols)
        x = image.cols - label_size.width;

    cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
                  cv::Scalar(255, 255, 255), -1);

    cv::putText(image, text, cv::Point(x, y + label_size.height),
                cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
}

cv::imwrite("ncnn_output.jpg",image );

char savefilename[200]="img_fcw_04_yolo.bmp" ;
if (1)
{
    frame_count_index++;
    memset(savefilename, 0, sizeof(savefilename));
    sprintf (savefilename, "result_image/img_fcw_04_yolo_%05d.jpg", frame_count_index);
    cv::imwrite(savefilename,image );
}

return 0;

}

int main(int argc, char** argv) { cv::Mat frame; std::vector objects;

ncnn::Net yolov4;

const char* devicepath;

int target_size = 0;
int is_streaming = 0;

if (argc < 2)
{
    fprintf(stderr, "Usage: %s [v4l inpude device or image]\n", argv[0]);
    return -1;
}

devicepath = argv[1];

ifdef NCNN_PROFILING

double t_load_start = ncnn::get_current_time();

endif

int ret = init_yolov4(&yolov4, &target_size); //We load model and param first!
if (ret != 0)
{
    fprintf(stderr, "Failed to load model or param, error %d", ret);
    return -1;
}

ifdef NCNN_PROFILING

double t_load_end = ncnn::get_current_time();
fprintf(stdout, "NCNN Init time %.02lfms\n", t_load_end - t_load_start);

endif

int i = 0;
for (i = 1; i < argc; i++)
{
    fprintf(stdout, "file: %s\t", argv[i]);
    fprintf(stdout, "i = %d start\n", i);
    frame = cv::imread(argv[i], 1);
    if (frame.empty())
    {
        fprintf(stderr, "Failed to read image %s.\n", argv[1]);
        return -1;
    }
    // cv::Rect myROI(780, 420, 416, 416);
    // cv::Mat croppedImage = frame(myROI);

ifdef NCNN_PROFILING

    double t_detect_start = ncnn::get_current_time();

endif

    detect_yolov4(frame, objects, target_size, &yolov4); //Create an extractor and run detection

ifdef NCNN_PROFILING

    double t_detect_end = ncnn::get_current_time();
    fprintf(stdout, "NCNN detection time %.02lfms\n", t_detect_end - t_detect_start);

endif

ifdef NCNN_PROFILING

    double t_draw_start = ncnn::get_current_time();

endif

    draw_objects(frame, objects, is_streaming); //Draw detection results on opencv image

ifdef NCNN_PROFILING

    double t_draw_end = ncnn::get_current_time();
    fprintf(stdout, "NCNN OpenCV draw result time %.02lfms\n", t_draw_end - t_draw_start);

endif

    fprintf(stdout, "file: %s\t", argv[i]);
    fprintf(stdout, "i = %d end\n\n", i);
}

return 0;

} **Android.mk is:** LOCAL_PATH := $(call my-dir) $(warning "the value of LOCAL_PATH is $(LOCAL_PATH)")

include $(CLEAR_VARS) LOCAL_MODULE := opencv-core-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/arm64-v8a/libopencv_core.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-imgcodecs-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/arm64-v8a/libopencv_imgcodecs.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-imgproc-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/arm64-v8a/libopencv_imgproc.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-ittnotify-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/libittnotify.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-tbb-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/libtbb.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := webp-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/liblibwebp.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-IlmImf-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/libIlmImf.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-jpeg-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/liblibjpeg-turbo.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-jasper-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/liblibjasper.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-png-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/liblibpng.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-tiff-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/liblibtiff.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-tegra-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/libtegra_hal.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-dnn-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/arm64-v8a/libopencv_dnn.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-protobuf-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/3rdparty/libs/arm64-v8a/liblibprotobuf.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := opencv-ximgproc-prebuilt LOCAL_SRC_FILES := ../../../../extern/lib/opencv_4.4.0_opencl/arm64-v8a/libopencv_ximgproc.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := libncnn-prebuilt LOCAL_SRC_FILES := ../../../../extern/ncnn/arm64-v8a/libncnn.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := glslang-prebuilt LOCAL_SRC_FILES :=../../../../extern/ncnn/arm64-v8a/libglslang.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := SPIRV-prebuilt LOCAL_SRC_FILES := ../../../../extern/ncnn/arm64-v8a/libSPIRV.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := OGLCompiler-prebuilt LOCAL_SRC_FILES := ../../../../extern/ncnn/arm64-v8a/libOGLCompiler.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := OSDependent-prebuilt LOCAL_SRC_FILES := ../../../../extern/ncnn/arm64-v8a/libOSDependent.a include $(PREBUILT_STATIC_LIBRARY)

include $(CLEAR_VARS) LOCAL_MODULE := ncnn_yolov4tiny_test LOCAL_CFLAGS := -Werror -Wno-write-strings -Dsupportneon -DDEBUG -D_DEBUGPROCTIME -DFCW_ENABLE_YOLO4TINY LOCAL_LDFLAGS := -pie -fPIE

LOCAL_CFLAGS += -fopenmp LOCAL_CPPFLAGS += -fopenmp LOCAL_LDFLAGS += -fopenmp

LOCAL_SRC_FILES += ../adas_vehicle_det_ncnn_yolov4.cpp LOCAL_C_INCLUDES += ${LOCAL_PATH}/../../../../extern/include/opencv_4.4.0 LOCAL_C_INCLUDES += ${LOCAL_PATH}/../../../../extern/ncnn/include/ncnn

LOCAL_LDLIBS := -lz -llog

LOCAL_LDLIBS := -lz -llog -ljnigraphics -lvulkan -landroid CXXFLAGS := -D_GLIBCXX_DEBUG -O2 LOCAL_STATIC_LIBRARIES += opencv-dnn-prebuilt LOCAL_STATIC_LIBRARIES += opencv-imgcodecs-prebuilt LOCAL_STATIC_LIBRARIES += opencv-ximgproc-prebuilt LOCAL_STATIC_LIBRARIES += opencv-imgproc-prebuilt LOCAL_STATIC_LIBRARIES += opencv-core-prebuilt LOCAL_STATIC_LIBRARIES += opencv-protobuf-prebuilt LOCAL_STATIC_LIBRARIES += opencv-ittnotify-prebuilt LOCAL_STATIC_LIBRARIES += opencv-tbb-prebuilt LOCAL_STATIC_LIBRARIES += opencv-IlmImf-prebuilt LOCAL_STATIC_LIBRARIES += opencv-jasper-prebuilt LOCAL_STATIC_LIBRARIES += opencv-jpeg-prebuilt LOCAL_STATIC_LIBRARIES += opencv-png-prebuilt LOCAL_STATIC_LIBRARIES += opencv-tiff-prebuilt LOCAL_STATIC_LIBRARIES += opencv-tegra-prebuilt LOCAL_STATIC_LIBRARIES += webp-prebuilt LOCAL_STATIC_LIBRARIES += libncnn-prebuilt LOCAL_STATIC_LIBRARIES += glslang-prebuilt LOCAL_STATIC_LIBRARIES += SPIRV-prebuilt LOCAL_STATIC_LIBRARIES += OGLCompiler-prebuilt LOCAL_STATIC_LIBRARIES += OSDependent-prebuilt include $(BUILD_EXECUTABLE)

Application.mk is: APP_ABI := arm64-v8a APP_STL := c++_shared APP_CPPFLAGS := -frtti -fexceptions APP_PLATFORM := android-24

test output:

47th row : yolov4->opt.use_vulkan_compute = false; detection is OK.

mercury:/data/local/tmp/200811120236 $ ./ncnn_yolov4tiny_test input_image/input_img_00001.jpg NCNN Init time 6174.19ms file: input_image/input_img_00001.jpg i = 1 start out.h = 1 NCNN detection time 650.85ms 8 = 0.41767 at 1249.40 19.08 651.86 x 740.22 NCNN OpenCV draw result time 308.95ms file: input_image/input_img_00001.jpg i = 1 end

47th row: yolov4->opt.use_vulkan_compute = true; detection is Error.

mercury:/data/local/tmp/200811120236 $ ./ncnn_yolov4tiny_test input_image/input_img_00001.jpg [0 Adreno (TM) 506] queueC=0[3] queueG=0[3] queueT=0[3] [0 Adreno (TM) 506] bugsbn1=1 buglbia=0 bugcopc=0 bugihfa=0 [0 Adreno (TM) 506] fp16p=1 fp16s=0 fp16a=0 int8s=0 int8a=0 NCNN Init time 11329.38ms file: input_image/input_img_00001.jpg i = 1 start out.h = 1160 NCNN detection time 1696.75ms 1 = 0.33333 at 915.46 738.17 162.94 x 143.67 1 = 0.33333 at 841.61 738.17 162.94 x 143.67 1 = 0.33333 at 1063.15 738.17 162.94 x 143.67 1 = 0.33333 at 989.30 738.17 162.94 x 143.67 1 = 0.33333 at 767.76 738.17 162.94 x 143.67 1 = 0.33333 at 546.22 738.17 162.94 x 143.67 1 = 0.33333 at 472.38 738.17 162.94 x 143.67 1 = 0.33333 at 693.92 738.17 162.94 x 143.67 1 = 0.33333 at 620.07 738.17 162.94 x 143.67 1 = 0.33333 at 1136.99 738.17 162.94 x 143.67 1 = 0.33333 at 1653.92 738.17 162.94 x 143.67 1 = 0.33333 at 1580.07 738.17 162.94 x 143.67 1 = 0.33333 at 1801.61 738.17 162.94 x 143.67 1 = 0.33333 at 1727.76 738.17 162.94 x 143.67 1 = 0.33333 at 1506.22 738.17 162.94 x 143.67 1 = 0.33333 at 1284.69 738.17 162.94 x 143.67 1 = 0.33333 at 1210.84 738.17 162.94 x 143.67 1 = 0.33333 at 1432.38 738.17 162.94 x 143.67 1 = 0.33333 at 1358.53 738.17 162.94 x 143.67

`

lovemory commented 4 years ago

device information:

<< Vulkan 设备 - Adreno (TM) 506 >> 设备名称: Adreno (TM) 506 设备类型: 集成 GPU 设备 UUID: 25-E7-14-03-43-51-00-00-00-00-00-06-00-05-00-00 设备 ID: 00005143-05000600 内存大小: 2896740 KB 最大 1D 图像大小: 16384 最大 2D 图像大小: 16384 x 16384 最大 3D 图像大小: 2048 x 2048 x 2048 最大 Cube Image Size: 16384 x 16384 最大 Image Layers: 2048 最大 Texel Buffer Elements: 65536 最大 Uniform Buffer Range: 65536 Max Storage Buffer Range: 2147483647 Max Push Constants Size: 128 字节 Max Memory Allocation Count: 4096 Max Sampler Allocation Count: 4000 Buffer Image Granularity: 1 字节 Max Bound Descriptor Sets: 4 Max Per-Stage Descriptor Samplers: 16 Max Per-Stage Descriptor Uniform Buffers: 14 Max Per-Stage Descriptor Storage Buffers: 24 Max Per-Stage Descriptor Sampled Images: 128 Max Per-Stage Descriptor Storage Images: 4 Max Per-Stage Descriptor Input Attachments: 8 Max Per-Stage Resources: 158 Max Descriptor Set Samplers: 96 Max Descriptor Set Uniform Buffers: 84 Max Descriptor Set Dynamic Uniform Buffers: 8 Max Descriptor Set Storage Buffers: 24 Max Descriptor Set Dynamic Storage Buffers: 4 Max Descriptor Set Sampled Images: 768 Max Descriptor Set Storage Images: 24 Max Descriptor Set Input Attachments: 8 Max Vertex Input Attributes: 32 Max Vertex Input Bindings: 32 MaxVertex Input Attribute Offset: 4096 Max Vertex Input Binding Stride: 2048 Max Vertex Output Components: 128 Max Fragment Input Components: 128 Max Fragment Output Attachments: 8 Max Fragment DualSrc Attachments: 1 Max Fragment Combined Output Resources: 72 Max Compute Shared Memory Size: 32 KB Max Compute Work Group Count: X: 65535, Y: 65535, Z: 65535 Max Compute Work Group Invocations: 512 Max Compute Work Group Size: X: 1024, Y: 1024, Z: 64 Subpixel Precision Bits: 4 Subtexel Precision Bits: 8 Mipmap Precision Bits: 8 Max Draw Indexed Index Value: 4294967295 Max Draw Indirect Count: 4294967295 Max Sampler LOD Bias: 15.996094 Max Sampler Anisotropy: 16.000000 Max Viewports: 1 Max Viewport Size: 16384 x 16384 Viewport Bounds Range: -32768.000000 ... 32767.000000 Min Memory Map Alignment: 64 字节 Min Texel Buffer Offset Alignment: 64 字节 Min Uniform Buffer Offset Alignment: 64 字节 Min Storage Buffer Offset Alignment: 64 字节 Min / Max Texel Offset: -8 / 7 Min / Max Texel Gather Offset: -32 / 31 Min / Max Interpolation Offset: -0.500000 / 0.437500 Subpixel Interpolation Offset Bits: 4 Max Framebuffer Size: 16384 x 16384 Max Framebuffer Layers: 2048 Framebuffer Color Sample Counts: 0x00000007 Framebuffer Depth Sample Counts: 0x00000007 Framebuffer Stencil Sample Counts: 0x00000007 Framebuffer No Attachments Sample Counts: 0x00000007 Max Color Attachments: 8 Sampled Image Color Sample Counts: 0x00000007 Sampled Image Integer Sample Counts: 0x00000007 Sampled Image Depth Sample Counts: 0x00000007 Sampled Image Stencil Sample Counts: 0x00000007 Storage Image Sample Counts: 0x00000001 Max Sample Mask Words: 1 Timestamp Period: 52.083332 ns Max Clip Distances: 8 Max Cull Distances: 8 Max Combined Clip and Cull Distances: 8 Discrete Queue Priorities: 3 Point Size Range: 1.000000 ... 1023.000000 Line Width Range: 1.000000 ... 1.000000 Point Size Granularity: 0.062500 Optimal Buffer Copy Offset Alignment: 64 字节 Optimal Buffer Copy Row Pitch Alignment: 64 字节 Non-Coherent Atom Size: 1 字节 API 版本: 1.1.66 Vulkan 库: /system/lib64/libvulkan.so Alpha To One: 支持 Anisotropic Filtering: 支持 ASTC LDR Texture Compression: 支持 BC Texture Compression: 不支持 Depth Bias Clamping: 支持 Depth Bounds Tests: 不支持 Depth Clamping: 支持 Draw Indirect First Instance: 不支持 Dual Source Blend Operations: 支持 ETC2 and EAC Texture Compression: 支持 Fragment Stores and Atomics: 支持 Full Draw Index Uint32: 支持 Geometry Shader: 不支持 Image Cube Array: 支持 Independent Blend: 支持 Inherited Queries: 支持 Large Points: 支持 Logic Operations: 不支持 Multi-Draw Indirect: 支持 Multi Viewport: 不支持 Occlusion Query Precise: 不支持 Pipeline Statistics Query: 不支持 Point and Wireframe Fill Modes: 支持 Robust Buffer Access: 支持 Sample Rate Shading: 支持 Shader Clip Distance: 支持 Shader Cull Distance: 支持 Shader Float64: 不支持 Shader Image Gather Extended: 支持 Shader Int16: 支持 Shader Int64: 不支持 Shader Resource Min LOD: 不支持 Shader Resource Residency: 不支持 Shader Sampled Image Array Dynamic Indexing: 支持 Shader Storage Buffer Array Dynamic Indexing: 支持 Shader Storage Image Array Dynamic Indexing: 支持 Shader Storage Image Extended Formats: 不支持 Shader Storage Image Multisample: 不支持 Shader Storage Image Read Without Format: 不支持 Shader Storage Image Write Without Format: 支持 Shader Tesselation and Geometry Point Size: 不支持 Shader Uniform Buffer Array Dynamic Indexing: 支持 Sparse Binding: 不支持 Sparse Residency 2 Samples: 不支持 Sparse Residency 4 Samples: 不支持 Sparse Residency 8 Samples: 不支持 Sparse Residency 16 Samples: 不支持 Sparse Residency Aliased: 不支持 Sparse Residency Aligned Mip Size: 否 Sparse Residency Buffer: 不支持 Sparse Residency Image 2D: 不支持 Sparse Residency Image 3D: 不支持 Sparse Residency Non-Resident Strict: 否 Sparse Residency Standard 2D Block Shape: 否 Sparse Residency Standard 2D Multisample Block Shape: 否 Sparse Residency Standard 3D Block Shape: 否 Standard Sample Locations: 是 Strict Line Rasterization: 是 Tesselation Shader: 不支持 Timestamps on All Graphics and Compute Queues: 支持 Variable Multisample Rate: 不支持 Vertex Pipeline Stores and Atomics: 支持 Wide Lines: 不支持 设备扩展: VK_KHR_incremental_present VK_KHR_shared_presentable_image VK_GOOGLE_display_timing VK_KHR_swapchain VK_KHR_maintenance1 VK_KHR_maintenance2 VK_KHR_maintenance3 VK_KHR_multiview VK_KHR_variable_pointers VK_KHR_storage_buffer_storage_class VK_KHR_relaxed_block_layout VK_KHR_get_memory_requirements2 VK_KHR_dedicated_allocation VK_KHR_external_memory VK_KHR_external_memory_fd VK_KHR_external_semaphore VK_KHR_external_semaphore_fd VK_KHR_external_fence VK_KHR_external_fence_fd VK_KHR_sampler_ycbcr_conversion VK_KHR_bind_memory2 VK_KHR_shader_draw_parameters VK_KHR_push_descriptor VK_KHR_descriptor_update_template VK_KHR_sampler_mirror_clamp_to_edge VK_ANDROID_external_memory_android_hardware_buffer VK_KHR_device_group VK_EXT_sampler_filter_minmax 实例扩展: VK_KHR_surface VK_KHR_android_surface VK_EXT_swapchain_colorspace VK_KHR_get_surface_capabilities2 VK_EXT_debug_report VK_KHR_get_physical_device_properties2 VK_KHR_external_memory_capabilities VK_KHR_external_semaphore_capabilities VK_KHR_external_fence_capabilities VK_KHR_device_group_creation