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Merge pull request #11650 from dkurt:dnn_default_backend

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This commit is contained in:
Vadim Pisarevsky
2018-06-06 09:30:39 +00:00
parent afaf5f940e b781ac7346
commit 3cbd2e2764
45 changed files with 253 additions and 199 deletions
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samples/dnn/classification.cpp
+7 -4
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@@ -21,12 +21,15 @@ const char* keys =
"{ height | | Preprocess input image by resizing to a specific height. }"
"{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
"{ backend | 0 | Choose one of computation backends: "
"0: default C++ backend, "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)}"
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default),"
"1: OpenCL }";
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }";
using namespace cv;
using namespace dnn;
samples/dnn/classification.py
+8 -5
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@@ -3,8 +3,8 @@ import argparse
import numpy as np
import sys
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL)
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)
parser = argparse.ArgumentParser(description='Use this script to run classification deep learning networks using OpenCV.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
@@ -32,13 +32,16 @@ parser.add_argument('--rgb', action='store_true',
help='Indicate that model works with RGB input images instead BGR ones.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: default C++ backend, "
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)" % backends)
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % 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' % targets)
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args = parser.parse_args()
# Load names of classes
samples/dnn/colorization.cpp
+1 -1
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@@ -107,7 +107,7 @@ int main(int argc, char **argv)
// run the L channel through the network
Mat inputBlob = blobFromImage(input);
net.setInput(inputBlob);
Mat result = net.forward("class8_ab");
Mat result = net.forward();
// retrieve the calculated a,b channels from the network output
Size siz(result.size[2], result.size[3]);
samples/dnn/colorization.py
+1 -1
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@@ -56,7 +56,7 @@ if __name__ == '__main__':
img_l_rs -= 50 # subtract 50 for mean-centering
net.setInput(cv.dnn.blobFromImage(img_l_rs))
ab_dec = net.forward('class8_ab')[0,:,:,:].transpose((1,2,0)) # this is our result
ab_dec = net.forward()[0,:,:,:].transpose((1,2,0)) # this is our result
(H_out,W_out) = ab_dec.shape[:2]
ab_dec_us = cv.resize(ab_dec, (W_orig, H_orig))
samples/dnn/fast_neural_style.py
+1
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@@ -14,6 +14,7 @@ parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of
args = parser.parse_args()
net = cv.dnn.readNetFromTorch(args.model)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV);
if args.input:
cap = cv.VideoCapture(args.input)
samples/dnn/mobilenet_ssd_accuracy.py
+1
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@@ -27,6 +27,7 @@ args = parser.parse_args()
### Get OpenCV predictions #####################################################
net = cv.dnn.readNetFromTensorflow(args.weights, args.prototxt)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV);
detections = []
for imgName in os.listdir(args.images):
samples/dnn/object_detection.cpp
+9 -5
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@@ -23,12 +23,16 @@ const char* keys =
"{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
"{ thr | .5 | Confidence threshold. }"
"{ backend | 0 | Choose one of computation backends: "
"0: default C++ backend, "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)}"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default),"
"1: OpenCL }";
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }";
using namespace cv;
using namespace dnn;
samples/dnn/object_detection.py
+8 -5
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@@ -3,8 +3,8 @@ import argparse
import sys
import numpy as np
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL)
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)
parser = argparse.ArgumentParser(description='Use this script to run object detection deep learning networks using OpenCV.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
@@ -33,13 +33,16 @@ parser.add_argument('--rgb', action='store_true',
parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: default C++ backend, "
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)" % backends)
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % 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' % targets)
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args = parser.parse_args()
# Load names of classes
samples/dnn/openpose.py
-5
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@@ -16,9 +16,6 @@ parser.add_argument('--dataset', help='Specify what kind of model was trained. '
parser.add_argument('--thr', default=0.1, type=float, help='Threshold value for pose parts heat map')
parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.')
parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.')
parser.add_argument('--inf_engine', action='store_true',
help='Enable Intel Inference Engine computational backend. '
'Check that plugins folder is in LD_LIBRARY_PATH environment variable')
args = parser.parse_args()
@@ -49,8 +46,6 @@ inWidth = args.width
inHeight = args.height
net = cv.dnn.readNetFromCaffe(args.proto, args.model)
if args.inf_engine:
net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
cap = cv.VideoCapture(args.input if args.input else 0)
samples/dnn/segmentation.cpp
+7 -4
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@@ -24,12 +24,15 @@ const char* keys =
"{ height | | Preprocess input image by resizing to a specific height. }"
"{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
"{ backend | 0 | Choose one of computation backends: "
"0: default C++ backend, "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)}"
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default),"
"1: OpenCL }";
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }";
using namespace cv;
using namespace dnn;
samples/dnn/segmentation.py
+8 -5
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@@ -3,8 +3,8 @@ import argparse
import numpy as np
import sys
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL)
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)
parser = argparse.ArgumentParser(description='Use this script to run semantic segmentation deep learning networks using OpenCV.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
@@ -34,13 +34,16 @@ parser.add_argument('--rgb', action='store_true',
help='Indicate that model works with RGB input images instead BGR ones.')
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: default C++ backend, "
"%d: automatically (by default), "
"%d: Halide language (http://halide-lang.org/), "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit)" % backends)
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: OpenCV implementation" % 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' % targets)
'%d: OpenCL, '
'%d: OpenCL fp16 (half-float precision), '
'%d: VPU' % targets)
args = parser.parse_args()
np.random.seed(324)