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Merge pull request #20175 from rogday:dnn_samples_cuda

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add cuda and vulkan backends to dnn samples
pull/20194/head
rogday 4 years ago committed by GitHub
parent de781b306f
commit 61359a5bd0
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13 changed files with 118 additions and 47 deletions
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  1. 9
      samples/dnn/classification.cpp
  2. 13
      samples/dnn/classification.py
  3. 9
      samples/dnn/dasiamrpn_tracker.cpp
  4. 9
      samples/dnn/human_parsing.cpp
  5. 15
      samples/dnn/human_parsing.py
  6. 9
      samples/dnn/object_detection.cpp
  7. 15
      samples/dnn/object_detection.py
  8. 6
      samples/dnn/person_reid.cpp
  9. 12
      samples/dnn/person_reid.py
  10. 9
      samples/dnn/segmentation.cpp
  11. 15
      samples/dnn/segmentation.py
  12. 29
      samples/dnn/siamrpnpp.py
  13. 15
      samples/dnn/virtual_try_on.py

9
samples/dnn/classification.cpp
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@ -22,12 +22,17 @@ std::string keys =
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA },"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }";
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }";
using namespace cv;
using namespace dnn;

13
samples/dnn/classification.py
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@ -7,9 +7,9 @@ from common import *
def get_args_parser(func_args):
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE,
cv.dnn.DNN_BACKEND_OPENCV)
cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD,
cv.dnn.DNN_TARGET_HDDL)
cv.dnn.DNN_TARGET_HDDL, cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'),
@ -32,14 +32,19 @@ def get_args_parser(func_args):
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
"%d: OpenCV implementation, "
"%d: VKCOM, "
"%d: CUDA" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: NCS2 VPU, '
'%d: HDDL VPU' % targets)
'%d: HDDL VPU, '
'%d: Vulkan, '
'%d: CUDA, '
'%d: CUDA fp16 (half-float preprocess)'% targets)
args, _ = parser.parse_known_args()
add_preproc_args(args.zoo, parser, 'classification')

9
samples/dnn/dasiamrpn_tracker.cpp
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@ -27,12 +27,17 @@ const char *keys =
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA },"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }"
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }"
;
static

9
samples/dnn/human_parsing.cpp
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@ -78,12 +78,17 @@ int main(int argc, char**argv)
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA }"
"{target t | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }"
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }"
);
if (argc == 1 || parser.has("help"))
{

15
samples/dnn/human_parsing.py
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@ -45,8 +45,10 @@ import numpy as np
import cv2 as cv
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD, cv.dnn.DNN_TARGET_HDDL)
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV,
cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD,
cv.dnn.DNN_TARGET_HDDL, cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
def preprocess(image):
@ -162,14 +164,19 @@ if __name__ == '__main__':
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
"%d: OpenCV implementation, "
"%d: VKCOM, "
"%d: CUDA"% backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: NCS2 VPU, '
'%d: HDDL VPU' % targets)
'%d: HDDL VPU, '
'%d: Vulkan, '
'%d: CUDA, '
'%d: CUDA fp16 (half-float preprocess)' % targets)
args, _ = parser.parse_known_args()
if not os.path.isfile(args.model):

9
samples/dnn/object_detection.cpp
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@ -27,12 +27,17 @@ std::string keys =
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }"
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }"
"{ async | 0 | Number of asynchronous forwards at the same time. "
"Choose 0 for synchronous mode }";

15
samples/dnn/object_detection.py
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@ -14,8 +14,10 @@ from tf_text_graph_common import readTextMessage
from tf_text_graph_ssd import createSSDGraph
from tf_text_graph_faster_rcnn import createFasterRCNNGraph
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD, cv.dnn.DNN_TARGET_HDDL)
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV,
cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD, cv.dnn.DNN_TARGET_HDDL,
cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'),
@ -35,14 +37,19 @@ parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DE
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
"%d: OpenCV implementation, "
"%d: VKCOM, "
"%d: CUDA" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: NCS2 VPU, '
'%d: HDDL VPU' % targets)
'%d: HDDL VPU, '
'%d: Vulkan, '
'%d: CUDA, '
'%d: CUDA fp16 (half-float preprocess)' % targets)
parser.add_argument('--async', type=int, default=0,
dest='asyncN',
help='Number of asynchronous forwards at the same time. '

6
samples/dnn/person_reid.cpp
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@ -36,13 +36,15 @@ const char* keys =
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation ,"
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA }"
"{target t | 0 | choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"6: CUDA ,"
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }";
namespace cv{

12
samples/dnn/person_reid.py
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@ -21,6 +21,7 @@ import cv2 as cv
backends = (cv.dnn.DNN_BACKEND_DEFAULT,
cv.dnn.DNN_BACKEND_INFERENCE_ENGINE,
cv.dnn.DNN_BACKEND_OPENCV,
cv.dnn.DNN_BACKEND_VKCOM,
cv.dnn.DNN_BACKEND_CUDA)
targets = (cv.dnn.DNN_TARGET_CPU,
@ -28,6 +29,7 @@ targets = (cv.dnn.DNN_TARGET_CPU,
cv.dnn.DNN_TARGET_OPENCL_FP16,
cv.dnn.DNN_TARGET_MYRIAD,
cv.dnn.DNN_TARGET_HDDL,
cv.dnn.DNN_TARGET_VULKAN,
cv.dnn.DNN_TARGET_CUDA,
cv.dnn.DNN_TARGET_CUDA_FP16)
@ -212,7 +214,8 @@ if __name__ == '__main__':
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation"
"%d: OpenCV implementation, "
"%d: VKCOM, "
"%d: CUDA backend"% backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
@ -220,9 +223,10 @@ if __name__ == '__main__':
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: NCS2 VPU, '
'%d: HDDL VPU'
'%d: CUDA,'
'%d: CUDA FP16,'
'%d: HDDL VPU, '
'%d: Vulkan, '
'%d: CUDA, '
'%d: CUDA FP16'
% targets)
args, _ = parser.parse_known_args()

9
samples/dnn/segmentation.cpp
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@ -21,12 +21,17 @@ std::string keys =
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }";
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }";
using namespace cv;
using namespace dnn;

15
samples/dnn/segmentation.py
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@ -5,8 +5,10 @@ import sys
from common import *
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD, cv.dnn.DNN_TARGET_HDDL)
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV,
cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD, cv.dnn.DNN_TARGET_HDDL,
cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'),
@ -22,14 +24,19 @@ parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DE
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
"%d: OpenCV implementation, "
"%d: VKCOM, "
"%d: CUDA"% backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: NCS2 VPU, '
'%d: HDDL VPU' % targets)
'%d: HDDL VPU, '
'%d: Vulkan, '
'%d: CUDA, '
'%d: CUDA fp16 (half-float preprocess)'% targets)
args, _ = parser.parse_known_args()
add_preproc_args(args.zoo, parser, 'segmentation')
parser = argparse.ArgumentParser(parents=[parser],

29
samples/dnn/siamrpnpp.py
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@ -327,9 +327,11 @@ def main():
""" Sample SiamRPN Tracker
"""
# Computation backends supported by layers
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV,
cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
# Target Devices for computation
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD,
cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
parser = argparse.ArgumentParser(description='Use this script to run SiamRPN++ Visual Tracker',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@ -338,17 +340,22 @@ def main():
parser.add_argument('--search_net', type=str, default='search_net.onnx', help='Path to part of SiamRPN++ ran on search frame.')
parser.add_argument('--rpn_head', type=str, default='rpn_head.onnx', help='Path to RPN Head ONNX model.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help='Select a computation backend: '
"%d: automatically (by default) "
"%d: Halide"
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)"
"%d: OpenCV Implementation" % backends)
help="Select a computation backend: "
"%d: automatically (by default), "
"%d: Halide, "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV Implementation, "
"%d: VKCOM, "
"%d: CUDA" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Select a target device: '
"%d: CPU target (by default)"
"%d: OpenCL"
"%d: OpenCL FP16"
"%d: Myriad" % targets)
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL FP16, '
'%d: Myriad, '
'%d: Vulkan, '
'%d: CUDA, '
'%d: CUDA fp16 (half-float preprocess)' % targets)
args, _ = parser.parse_known_args()
if args.input_video and not os.path.isfile(args.input_video):

15
samples/dnn/virtual_try_on.py
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@ -16,8 +16,10 @@ from numpy import linalg
from common import findFile
from human_parsing import parse_human
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD, cv.dnn.DNN_TARGET_HDDL)
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV,
cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD, cv.dnn.DNN_TARGET_HDDL,
cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
parser = argparse.ArgumentParser(description='Use this script to run virtial try-on using CP-VTON',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@ -33,14 +35,19 @@ parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DE
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % backends)
"%d: OpenCV implementation, "
"%d: VKCOM, "
"%d: CUDA" % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: NCS2 VPU, '
'%d: HDDL VPU' % targets)
'%d: HDDL VPU, '
'%d: Vulkan, '
'%d: CUDA, '
'%d: CUDA fp16 (half-float preprocess)'% targets)
args, _ = parser.parse_known_args()

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