#include "test_precomp.hpp"
#include "npy_blob.hpp"
+#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
+
namespace opencv_test
{
net = readNetFromTensorflow(model);
ASSERT_FALSE(net.empty());
}
+ net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
net = readNetFromTensorflow(model);
ASSERT_FALSE(net.empty());
}
+ net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat sample = imread(_tf("grace_hopper_227.png"));
ASSERT_TRUE(!sample.empty());
ASSERT_FALSE(net.empty());
- net.setPreferableBackend(DNN_BACKEND_DEFAULT);
+ net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
cv::Mat input = blobFromNPY(inpPath);
runTensorFlowNet("atrous_conv2d_valid", targetId);
runTensorFlowNet("atrous_conv2d_same", targetId);
runTensorFlowNet("depthwise_conv2d", targetId);
+ runTensorFlowNet("keras_atrous_conv2d_same", targetId);
}
TEST_P(Test_TensorFlow_layers, padding)
runTensorFlowNet("eltwise_add_mul", GetParam());
}
-TEST_P(Test_TensorFlow_layers, pad_and_concat)
+TEST_P(Test_TensorFlow_layers, concat)
{
runTensorFlowNet("pad_and_concat", GetParam());
+ runTensorFlowNet("concat_axis_1", GetParam());
}
TEST_P(Test_TensorFlow_layers, batch_norm)
TEST_P(Test_TensorFlow_layers, pooling)
{
int targetId = GetParam();
+ cv::ocl::Device d = cv::ocl::Device::getDefault();
+ bool loosenFlag = targetId == DNN_TARGET_OPENCL && d.isIntel() && d.type() == cv::ocl::Device::TYPE_CPU;
runTensorFlowNet("max_pool_even", targetId);
runTensorFlowNet("max_pool_odd_valid", targetId);
runTensorFlowNet("ave_pool_same", targetId);
- runTensorFlowNet("max_pool_odd_same", targetId);
+ runTensorFlowNet("max_pool_odd_same", targetId, false, loosenFlag ? 3e-5 : 1e-5, loosenFlag ? 3e-4 : 1e-4);
runTensorFlowNet("reduce_mean", targetId); // an average pooling over all spatial dimensions.
}
runTensorFlowNet("deconvolution_stride_2_same", targetId);
runTensorFlowNet("deconvolution_adj_pad_valid", targetId);
runTensorFlowNet("deconvolution_adj_pad_same", targetId);
+ runTensorFlowNet("keras_deconv_valid", targetId);
+ runTensorFlowNet("keras_deconv_same", targetId);
}
TEST_P(Test_TensorFlow_layers, matmul)
runTensorFlowNet("unfused_flatten_unknown_batch", targetId);
}
+TEST_P(Test_TensorFlow_layers, l2_normalize)
+{
+ int targetId = GetParam();
+ runTensorFlowNet("l2_normalize", targetId);
+ runTensorFlowNet("l2_normalize_3d", targetId);
+}
+
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, availableDnnTargets());
typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_nets;
}
Net net = readNetFromTensorflow(netPath, netConfig);
-
+ net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(GetParam());
net.setInput(inp);
normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
- normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2);
+ normAssertDetections(target[2], output[2], "", 0.2);
}
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
Mat img = imread(findDataFile("dnn/street.png", false));
Mat blob = blobFromImage(img, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false);
+ net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(GetParam());
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
- out = out.reshape(1, out.total() / 7);
+ Mat ref = (Mat_<float>(5, 7) << 0, 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
+ 0, 3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
+ 0, 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
+ 0, 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
+ 0, 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
+ normAssertDetections(ref, out, "", 0.5);
+}
- Mat detections;
- for (int i = 0; i < out.rows; ++i)
- {
- if (out.at<float>(i, 2) > 0.5)
- detections.push_back(out.row(i).colRange(1, 7));
- }
+TEST_P(Test_TensorFlow_nets, Inception_v2_Faster_RCNN)
+{
+ std::string proto = findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
+ std::string model = findDataFile("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb", false);
+
+ Net net = readNetFromTensorflow(model, proto);
+ net.setPreferableBackend(DNN_BACKEND_OPENCV);
+ Mat img = imread(findDataFile("dnn/dog416.png", false));
+ Mat blob = blobFromImage(img, 1.0f / 127.5, Size(800, 600), Scalar(127.5, 127.5, 127.5), true, false);
- Mat ref = (Mat_<float>(5, 6) << 1, 0.90176028, 0.19872092, 0.36311883, 0.26461923, 0.63498729,
- 3, 0.93569964, 0.64865261, 0.45906419, 0.80675775, 0.65708131,
- 3, 0.75838411, 0.44668293, 0.45907149, 0.49459291, 0.52197015,
- 10, 0.95932811, 0.38349164, 0.32528657, 0.40387636, 0.39165527,
- 10, 0.93973452, 0.66561931, 0.37841269, 0.68074018, 0.42907384);
- normAssert(detections, ref);
+ net.setInput(blob);
+ Mat out = net.forward();
+
+ Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/faster_rcnn_inception_v2_coco_2018_01_28.detection_out.npy"));
+ normAssertDetections(ref, out, "", 0.3);
}
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
+ net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(GetParam());
net.setInput(blob);
Mat out = net.forward();
// References are from test for Caffe model.
- Mat ref = (Mat_<float>(6, 5) << 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
- 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
- 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
- 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
- 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
- 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
- normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref, "", 2.8e-4, 3.4e-3);
+ Mat ref = (Mat_<float>(6, 7) << 0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631,
+ 0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168,
+ 0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290,
+ 0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477,
+ 0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
+ 0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
+ normAssertDetections(ref, out, "", 0.9, 3.4e-3, 1e-2);
+}
+
+// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
+// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
+// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
+// sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
+// feed_dict={'input_images:0': inp})
+// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
+// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
+// np.save('east_text_detection.scores.npy', scores)
+// np.save('east_text_detection.geometry.npy', geometry)
+TEST_P(Test_TensorFlow_nets, EAST_text_detection)
+{
+ std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
+ std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
+ std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy", false);
+ std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy", false);
+
+ Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));
+
+ net.setPreferableTarget(GetParam());
+
+ Mat img = imread(imgPath);
+ Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false);
+ net.setInput(inp);
+
+ std::vector<Mat> outs;
+ std::vector<String> outNames(2);
+ outNames[0] = "feature_fusion/Conv_7/Sigmoid";
+ outNames[1] = "feature_fusion/concat_3";
+ net.forward(outs, outNames);
+
+ Mat scores = outs[0];
+ Mat geometry = outs[1];
+
+ normAssert(scores, blobFromNPY(refScoresPath), "scores");
+ normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", 1e-4, 3e-3);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_nets, availableDnnTargets());
-TEST(Test_TensorFlow, defun)
+typedef testing::TestWithParam<DNNTarget> Test_TensorFlow_fp16;
+
+TEST_P(Test_TensorFlow_fp16, tests)
{
- runTensorFlowNet("defun_dropout");
+ int targetId = GetParam();
+ const float l1 = 7e-4;
+ const float lInf = 1e-2;
+ runTensorFlowNet("fp16_single_conv", targetId, false, l1, lInf);
+ runTensorFlowNet("fp16_deconvolution", targetId, false, l1, lInf);
+ runTensorFlowNet("fp16_max_pool_odd_same", targetId, false, l1, lInf);
+ runTensorFlowNet("fp16_padding_valid", targetId, false, l1, lInf);
+ runTensorFlowNet("fp16_eltwise_add_mul", targetId, false, l1, lInf);
+ runTensorFlowNet("fp16_max_pool_odd_valid", targetId, false, l1, lInf);
+ runTensorFlowNet("fp16_pad_and_concat", targetId, false, l1, lInf);
+ runTensorFlowNet("fp16_max_pool_even", targetId, false, l1, lInf);
+ runTensorFlowNet("fp16_padding_same", targetId, false, l1, lInf);
}
-TEST(Test_TensorFlow, fp16)
+INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_fp16,
+ Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
+
+TEST(Test_TensorFlow, defun)
{
- const float l1 = 1e-3;
- const float lInf = 1e-2;
- runTensorFlowNet("fp16_single_conv", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_deconvolution", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_max_pool_odd_same", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_padding_valid", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_eltwise_add_mul", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_max_pool_odd_valid", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_pad_and_concat", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_max_pool_even", DNN_TARGET_CPU, false, l1, lInf);
- runTensorFlowNet("fp16_padding_same", DNN_TARGET_CPU, false, l1, lInf);
+ runTensorFlowNet("defun_dropout");
}
TEST(Test_TensorFlow, quantized)
TEST(Test_TensorFlow, relu6)
{
runTensorFlowNet("keras_relu6");
+ runTensorFlowNet("keras_relu6", DNN_TARGET_CPU, /*hasText*/ true);
}
TEST(Test_TensorFlow, keras_mobilenet_head)
runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true, l1, lInf, true);
}
+TEST(Test_TensorFlow, resize_bilinear)
+{
+ runTensorFlowNet("resize_bilinear");
+ runTensorFlowNet("resize_bilinear_factor");
+}
+
+TEST(Test_TensorFlow, two_inputs)
+{
+ Net net = readNet(path("two_inputs_net.pbtxt"));
+ net.setPreferableBackend(DNN_BACKEND_OPENCV);
+
+ Mat firstInput(2, 3, CV_32FC1), secondInput(2, 3, CV_32FC1);
+ randu(firstInput, -1, 1);
+ randu(secondInput, -1, 1);
+
+ net.setInput(firstInput, "first_input");
+ net.setInput(secondInput, "second_input");
+ Mat out = net.forward();
+
+ normAssert(out, firstInput + secondInput);
+}
+
}
projects / platform / upstream / opencv.git / blobdiff