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Merge pull request #13694 from dkurt:dnn_ie_async

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Asynchronous API from Intel's Inference Engine (#13694)

* Add forwardAsync for asynchronous mode from Intel's Inference Engine

* Python test for forwardAsync

* Replace Future_Mat to AsyncMat

* Shadow AsyncMat

* Isolate InferRequest callback

* Manage exceptions in Async API of IE
This commit is contained in:
Dmitry Kurtaev
2019-04-19 21:01:19 +03:00
committed by Alexander Alekhin
parent 3abae3c511
commit a5c92c2029
8 changed files with 503 additions and 82 deletions
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modules/dnn/misc/python/test/test_dnn.py
+88 -52
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@@ -5,8 +5,8 @@ import numpy as np
from tests_common import NewOpenCVTests, unittest
def normAssert(test, a, b, lInf=1e-5):
test.assertLess(np.max(np.abs(a - b)), lInf)
def normAssert(test, a, b, msg=None, lInf=1e-5):
test.assertLess(np.max(np.abs(a - b)), lInf, msg)
def inter_area(box1, box2):
x_min, x_max = max(box1[0], box2[0]), min(box1[2], box2[2])
@@ -53,53 +53,6 @@ def normAssertDetections(test, ref, out, confThreshold=0.0, scores_diff=1e-5, bo
if errMsg:
test.fail(errMsg)
# Returns a simple one-layer network created from Caffe's format
def getSimpleNet():
prototxt = """
name: "simpleNet"
input: "data"
layer {
type: "Identity"
name: "testLayer"
top: "testLayer"
bottom: "data"
}
"""
return cv.dnn.readNetFromCaffe(bytearray(prototxt, 'utf8'))
def testBackendAndTarget(backend, target):
net = getSimpleNet()
net.setPreferableBackend(backend)
net.setPreferableTarget(target)
inp = np.random.standard_normal([1, 2, 3, 4]).astype(np.float32)
try:
net.setInput(inp)
net.forward()
except BaseException as e:
return False
return True
haveInfEngine = testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU)
dnnBackendsAndTargets = [
[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
]
if haveInfEngine:
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU])
if testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD):
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD])
if cv.ocl.haveOpenCL() and cv.ocl.useOpenCL():
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL])
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL_FP16])
if haveInfEngine and cv.ocl_Device.getDefault().isIntel():
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL])
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
def printParams(backend, target):
backendNames = {
cv.dnn.DNN_BACKEND_OPENCV: 'OCV',
@@ -116,8 +69,44 @@ def printParams(backend, target):
class dnn_test(NewOpenCVTests):
def __init__(self, *args, **kwargs):
super(dnn_test, self).__init__(*args, **kwargs)
self.dnnBackendsAndTargets = [
[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
]
if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU):
self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU])
if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD):
self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD])
if cv.ocl.haveOpenCL() and cv.ocl.useOpenCL():
self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL])
self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL_FP16])
if cv.ocl_Device.getDefault().isIntel():
if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL):
self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL])
if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16):
self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
def find_dnn_file(self, filename, required=True):
return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd())], required=required)
return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd()),
os.environ['OPENCV_TEST_DATA_PATH']],
required=required)
def checkIETarget(self, backend, target):
proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt', required=True)
model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel', required=True)
net = cv.dnn.readNet(proto, model)
net.setPreferableBackend(backend)
net.setPreferableTarget(target)
inp = np.random.standard_normal([1, 2, 10, 11]).astype(np.float32)
try:
net.setInput(inp)
net.forward()
except BaseException as e:
return False
return True
def test_blobFromImage(self):
np.random.seed(324)
@@ -148,7 +137,7 @@ class dnn_test(NewOpenCVTests):
def test_face_detection(self):
testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt2', required=testdata_required)
proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt', required=testdata_required)
model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel', required=testdata_required)
if proto is None or model is None:
raise unittest.SkipTest("Missing DNN test files (dnn/opencv_face_detector.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
@@ -164,7 +153,7 @@ class dnn_test(NewOpenCVTests):
[0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801]]
print('\n')
for backend, target in dnnBackendsAndTargets:
for backend, target in self.dnnBackendsAndTargets:
printParams(backend, target)
net = cv.dnn.readNet(proto, model)
@@ -178,5 +167,52 @@ class dnn_test(NewOpenCVTests):
normAssertDetections(self, ref, out, 0.5, scoresDiff, iouDiff)
def test_async(self):
timeout = 5000 # in milliseconds
testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt', required=testdata_required)
model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel', required=testdata_required)
if proto is None or model is None:
raise unittest.SkipTest("Missing DNN test files (dnn/layers/layer_convolution.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
print('\n')
for backend, target in self.dnnBackendsAndTargets:
if backend != cv.dnn.DNN_BACKEND_INFERENCE_ENGINE:
continue
printParams(backend, target)
netSync = cv.dnn.readNet(proto, model)
netSync.setPreferableBackend(backend)
netSync.setPreferableTarget(target)
netAsync = cv.dnn.readNet(proto, model)
netAsync.setPreferableBackend(backend)
netAsync.setPreferableTarget(target)
# Generate inputs
numInputs = 10
inputs = []
for _ in range(numInputs):
inputs.append(np.random.standard_normal([2, 6, 75, 113]).astype(np.float32))
# Run synchronously
refs = []
for i in range(numInputs):
netSync.setInput(inputs[i])
refs.append(netSync.forward())
# Run asynchronously. To make test more robust, process inputs in the reversed order.
outs = []
for i in reversed(range(numInputs)):
netAsync.setInput(inputs[i])
outs.insert(0, netAsync.forwardAsync())
for i in reversed(range(numInputs)):
if outs[i].wait_for(timeout) == 1:
self.fail("Timeout")
normAssert(self, refs[i], outs[i].get(), 'Index: %d' % i, 1e-10)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()