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opencv/modules/dnn/test/test_layers.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#include <opencv2/core/ocl.hpp>
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
namespace opencv_test { namespace {
template<typename TString>
static String _tf(TString filename)
{
String basetestdir = getOpenCVExtraDir();
size_t len = basetestdir.size();
if(len > 0 && basetestdir[len-1] != '/' && basetestdir[len-1] != '\\')
return (basetestdir + "/dnn/layers") + filename;
return (basetestdir + "dnn/layers/") + filename;
}
void runLayer(Ptr<Layer> layer, std::vector<Mat> &inpBlobs, std::vector<Mat> &outBlobs)
{
size_t ninputs = inpBlobs.size();
std::vector<Mat> inp_(ninputs);
std::vector<Mat*> inp(ninputs);
std::vector<Mat> outp, intp;
std::vector<MatShape> inputs, outputs, internals;
for (size_t i = 0; i < ninputs; i++)
{
inp_[i] = inpBlobs[i].clone();
inp[i] = &inp_[i];
inputs.push_back(shape(inp_[i]));
}
layer->getMemoryShapes(inputs, 0, outputs, internals);
for (size_t i = 0; i < outputs.size(); i++)
{
outp.push_back(Mat(outputs[i], CV_32F));
}
for (size_t i = 0; i < internals.size(); i++)
{
intp.push_back(Mat(internals[i], CV_32F));
}
layer->finalize(inp, outp);
layer->forward(inp, outp, intp);
size_t noutputs = outp.size();
outBlobs.resize(noutputs);
for (size_t i = 0; i < noutputs; i++)
outBlobs[i] = outp[i];
}
class Test_Caffe_layers : public DNNTestLayer
{
public:
void testLayerUsingCaffeModels(const String& basename, bool useCaffeModel = false,
bool useCommonInputBlob = true, double l1 = 0.0,
double lInf = 0.0)
{
String prototxt = _tf(basename + ".prototxt");
String caffemodel = _tf(basename + ".caffemodel");
String inpfile = (useCommonInputBlob) ? _tf("blob.npy") : _tf(basename + ".input.npy");
String outfile = _tf(basename + ".npy");
Mat inp = blobFromNPY(inpfile);
Mat ref = blobFromNPY(outfile);
checkBackend(&inp, &ref);
Net net = readNetFromCaffe(prototxt, (useCaffeModel) ? caffemodel : String());
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp, "input");
Mat out = net.forward("output");
normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
}
};
TEST_P(Test_Caffe_layers, Softmax)
{
testLayerUsingCaffeModels("layer_softmax");
}
TEST_P(Test_Caffe_layers, LRN_spatial)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_lrn_spatial");
}
TEST_P(Test_Caffe_layers, LRN_channels)
{
testLayerUsingCaffeModels("layer_lrn_channels");
}
TEST_P(Test_Caffe_layers, Convolution)
{
testLayerUsingCaffeModels("layer_convolution", true);
}
TEST_P(Test_Caffe_layers, DeConvolution)
{
testLayerUsingCaffeModels("layer_deconvolution", true, false);
}
TEST_P(Test_Caffe_layers, InnerProduct)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
testLayerUsingCaffeModels("layer_inner_product", true);
}
TEST_P(Test_Caffe_layers, Pooling_max)
{
testLayerUsingCaffeModels("layer_pooling_max");
}
TEST_P(Test_Caffe_layers, Pooling_ave)
{
testLayerUsingCaffeModels("layer_pooling_ave");
}
TEST_P(Test_Caffe_layers, MVN)
{
testLayerUsingCaffeModels("layer_mvn");
}
void testReshape(const MatShape& inputShape, const MatShape& targetShape,
int axis = 0, int num_axes = -1,
MatShape mask = MatShape())
{
LayerParams params;
params.set("axis", axis);
params.set("num_axes", num_axes);
if (!mask.empty())
{
params.set("dim", DictValue::arrayInt<int*>(&mask[0], mask.size()));
}
Mat inp(inputShape.size(), &inputShape[0], CV_32F);
std::vector<Mat> inpVec(1, inp);
std::vector<Mat> outVec, intVec;
Ptr<Layer> rl = LayerFactory::createLayerInstance("Reshape", params);
runLayer(rl, inpVec, outVec);
Mat& out = outVec[0];
MatShape shape(out.size.p, out.size.p + out.dims);
EXPECT_EQ(shape, targetShape);
}
TEST(Layer_Test_Reshape, Accuracy)
{
{
int inp[] = {4, 3, 1, 2};
int out[] = {4, 3, 2};
testReshape(MatShape(inp, inp + 4), MatShape(out, out + 3), 2, 1);
}
{
int inp[] = {1, 128, 4, 4};
int out[] = {1, 2048};
int mask[] = {-1, 2048};
testReshape(MatShape(inp, inp + 4), MatShape(out, out + 2), 0, -1,
MatShape(mask, mask + 2));
}
{
int inp[] = {1, 2, 3};
int out[] = {3, 1, 2};
int mask[] = {3, 1, 2};
testReshape(MatShape(inp, inp + 3), MatShape(out, out + 3), 0, -1,
MatShape(mask, mask + 3));
}
}
TEST_P(Test_Caffe_layers, BatchNorm)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_batch_norm", true);
testLayerUsingCaffeModels("layer_batch_norm_local_stats", true, false);
}
TEST_P(Test_Caffe_layers, ReLU)
{
testLayerUsingCaffeModels("layer_relu");
}
TEST_P(Test_Caffe_layers, Dropout)
{
testLayerUsingCaffeModels("layer_dropout");
}
TEST_P(Test_Caffe_layers, Concat)
{
testLayerUsingCaffeModels("layer_concat");
testLayerUsingCaffeModels("layer_concat_optim", true, false);
testLayerUsingCaffeModels("layer_concat_shared_input", true, false);
}
TEST_P(Test_Caffe_layers, Fused_Concat)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL))
throw SkipTestException("");
checkBackend();
// Test case
// input
// |
// v
// some_layer
// | |
// v v
// concat
Net net;
int interLayer;
{
LayerParams lp;
lp.type = "AbsVal";
lp.name = "someLayer";
interLayer = net.addLayerToPrev(lp.name, lp.type, lp);
}
{
LayerParams lp;
lp.set("axis", 1);
lp.type = "Concat";
lp.name = "testConcat";
int id = net.addLayer(lp.name, lp.type, lp);
net.connect(interLayer, 0, id, 0);
net.connect(interLayer, 0, id, 1);
}
int shape[] = {1, 2, 3, 4};
Mat input(4, shape, CV_32F);
randu(input, 0.0f, 1.0f); // [0, 1] to make AbsVal an identity transformation.
net.setInput(input);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
normAssert(slice(out, Range::all(), Range(0, 2), Range::all(), Range::all()), input, "", default_l1, default_lInf);
normAssert(slice(out, Range::all(), Range(2, 4), Range::all(), Range::all()), input, "", default_l1, default_lInf);
}
TEST_P(Test_Caffe_layers, Eltwise)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_eltwise");
}
TEST_P(Test_Caffe_layers, PReLU)
{
testLayerUsingCaffeModels("layer_prelu", true);
}
// TODO: fix an unstable test case
TEST_P(Test_Caffe_layers, layer_prelu_fc)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_prelu_fc", true, false);
}
//template<typename XMat>
//static void test_Layer_Concat()
//{
// Matx21f a(1.f, 1.f), b(2.f, 2.f), c(3.f, 3.f);
// std::vector<Blob> res(1), src = { Blob(XMat(a)), Blob(XMat(b)), Blob(XMat(c)) };
// Blob ref(XMat(Matx23f(1.f, 2.f, 3.f, 1.f, 2.f, 3.f)));
//
// runLayer(ConcatLayer::create(1), src, res);
// normAssert(ref, res[0]);
//}
//TEST(Layer_Concat, Accuracy)
//{
// test_Layer_Concat<Mat>());
//}
//OCL_TEST(Layer_Concat, Accuracy)
//{
// OCL_ON(test_Layer_Concat<Mat>());
// );
//}
TEST_P(Test_Caffe_layers, Reshape_Split_Slice)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
Net net = readNetFromCaffe(_tf("reshape_and_slice_routines.prototxt"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat input(6, 12, CV_32F);
RNG rng(0);
rng.fill(input, RNG::UNIFORM, -1, 1);
net.setInput(input, "input");
Mat output = net.forward("output");
normAssert(input, output, "", default_l1, default_lInf);
}
TEST_P(Test_Caffe_layers, Conv_Elu)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
{
if (!checkMyriadTarget())
throw SkipTestException("Myriad is not available/disabled in OpenCV");
}
Net net = readNetFromTensorflow(_tf("layer_elu_model.pb"));
ASSERT_FALSE(net.empty());
Mat inp = blobFromNPY(_tf("layer_elu_in.npy"));
Mat ref = blobFromNPY(_tf("layer_elu_out.npy"));
net.setInput(inp, "input");
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
class Layer_LSTM_Test : public ::testing::Test
{
public:
int numInp, numOut;
Mat Wh, Wx, b;
Ptr<LSTMLayer> layer;
std::vector<Mat> inputs, outputs;
Layer_LSTM_Test() {}
void init(const MatShape &inpShape_, const MatShape &outShape_,
bool produceCellOutput, bool useTimestampDim)
{
numInp = total(inpShape_);
numOut = total(outShape_);
Wh = Mat::ones(4 * numOut, numOut, CV_32F);
Wx = Mat::ones(4 * numOut, numInp, CV_32F);
b = Mat::ones(4 * numOut, 1, CV_32F);
LayerParams lp;
lp.blobs.resize(3);
lp.blobs[0] = Wh;
lp.blobs[1] = Wx;
lp.blobs[2] = b;
lp.set<bool>("produce_cell_output", produceCellOutput);
lp.set<bool>("use_timestamp_dim", useTimestampDim);
layer = LSTMLayer::create(lp);
layer->setOutShape(outShape_);
}
};
TEST_F(Layer_LSTM_Test, get_set_test)
{
const int TN = 4;
MatShape inpShape = shape(5, 3, 2);
MatShape outShape = shape(3, 1, 2);
MatShape inpResShape = concat(shape(TN), inpShape);
MatShape outResShape = concat(shape(TN), outShape);
init(inpShape, outShape, true, false);
layer->setOutShape(outShape);
Mat C((int)outResShape.size(), &outResShape[0], CV_32F);
randu(C, -1., 1.);
Mat H = C.clone();
randu(H, -1., 1.);
Mat inp((int)inpResShape.size(), &inpResShape[0], CV_32F);
randu(inp, -1., 1.);
inputs.push_back(inp);
runLayer(layer, inputs, outputs);
EXPECT_EQ(2u, outputs.size());
print(outResShape, "outResShape");
print(shape(outputs[0]), "out0");
print(shape(outputs[0]), "out1");
EXPECT_EQ(outResShape, shape(outputs[0]));
EXPECT_EQ(outResShape, shape(outputs[1]));
EXPECT_EQ(0, layer->inputNameToIndex("x"));
EXPECT_EQ(0, layer->outputNameToIndex("h"));
EXPECT_EQ(1, layer->outputNameToIndex("c"));
}
TEST(Layer_LSTM_Test_Accuracy_with_, CaffeRecurrent)
{
LayerParams lp;
lp.blobs.resize(3);
lp.blobs[0] = blobFromNPY(_tf("lstm.prototxt.w_2.npy")); // Wh
lp.blobs[1] = blobFromNPY(_tf("lstm.prototxt.w_0.npy")); // Wx
lp.blobs[2] = blobFromNPY(_tf("lstm.prototxt.w_1.npy")); // bias
Ptr<LSTMLayer> layer = LSTMLayer::create(lp);
Mat inp = blobFromNPY(_tf("recurrent.input.npy"));
std::vector<Mat> inputs(1, inp), outputs;
runLayer(layer, inputs, outputs);
Mat h_t_reference = blobFromNPY(_tf("lstm.prototxt.h_1.npy"));
normAssert(h_t_reference, outputs[0]);
}
TEST(Layer_RNN_Test_Accuracy_with_, CaffeRecurrent)
{
Ptr<RNNLayer> layer = RNNLayer::create(LayerParams());
layer->setWeights(
blobFromNPY(_tf("rnn.prototxt.w_0.npy")),
blobFromNPY(_tf("rnn.prototxt.w_1.npy")),
blobFromNPY(_tf("rnn.prototxt.w_2.npy")),
blobFromNPY(_tf("rnn.prototxt.w_3.npy")),
blobFromNPY(_tf("rnn.prototxt.w_4.npy")) );
std::vector<Mat> output, input(1, blobFromNPY(_tf("recurrent.input.npy")));
runLayer(layer, input, output);
Mat h_ref = blobFromNPY(_tf("rnn.prototxt.h_1.npy"));
normAssert(h_ref, output[0]);
}
class Layer_RNN_Test : public ::testing::Test
{
public:
int nX, nH, nO, nT, nS;
Mat Whh, Wxh, bh, Who, bo;
Ptr<RNNLayer> layer;
std::vector<Mat> inputs, outputs;
Layer_RNN_Test()
{
nT = 3;
nS = 5;
nX = 31;
nH = 64;
nO = 100;
Whh = Mat::ones(nH, nH, CV_32F);
Wxh = Mat::ones(nH, nX, CV_32F);
bh = Mat::ones(nH, 1, CV_32F);
Who = Mat::ones(nO, nH, CV_32F);
bo = Mat::ones(nO, 1, CV_32F);
layer = RNNLayer::create(LayerParams());
layer->setProduceHiddenOutput(true);
layer->setWeights(Wxh, bh, Whh, Who, bo);
}
};
TEST_F(Layer_RNN_Test, get_set_test)
{
int sz[] = { nT, nS, 1, nX };
Mat inp(4, sz, CV_32F);
randu(inp, -1., 1.);
inputs.push_back(inp);
runLayer(layer, inputs, outputs);
EXPECT_EQ(outputs.size(), 2u);
EXPECT_EQ(shape(outputs[0]), shape(nT, nS, nO));
EXPECT_EQ(shape(outputs[1]), shape(nT, nS, nH));
}
TEST(Layer_Test_ROIPooling, Accuracy)
{
Net net = readNetFromCaffe(_tf("net_roi_pooling.prototxt"));
Mat inp = blobFromNPY(_tf("net_roi_pooling.input.npy"));
Mat rois = blobFromNPY(_tf("net_roi_pooling.rois.npy"));
Mat ref = blobFromNPY(_tf("net_roi_pooling.npy"));
net.setInput(inp, "input");
net.setInput(rois, "rois");
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();
normAssert(out, ref);
}
TEST_P(Test_Caffe_layers, FasterRCNN_Proposal)
{
if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
Net net = readNetFromCaffe(_tf("net_faster_rcnn_proposal.prototxt"));
Mat scores = blobFromNPY(_tf("net_faster_rcnn_proposal.scores.npy"));
Mat deltas = blobFromNPY(_tf("net_faster_rcnn_proposal.deltas.npy"));
Mat imInfo = (Mat_<float>(1, 3) << 600, 800, 1.6f);
net.setInput(scores, "rpn_cls_prob_reshape");
net.setInput(deltas, "rpn_bbox_pred");
net.setInput(imInfo, "im_info");
std::vector<Mat> outs;
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.forward(outs, "output");
for (int i = 0; i < 2; ++i)
{
Mat ref = blobFromNPY(_tf(i == 0 ? "net_faster_rcnn_proposal.out_rois.npy" :
"net_faster_rcnn_proposal.out_scores.npy"));
const int numDets = ref.size[0];
EXPECT_LE(numDets, outs[i].size[0]);
normAssert(outs[i].rowRange(0, numDets), ref);
if (numDets < outs[i].size[0])
EXPECT_EQ(countNonZero(outs[i].rowRange(numDets, outs[i].size[0])), 0);
}
}
typedef testing::TestWithParam<tuple<Vec4i, Vec2i, bool> > Scale_untrainable;
TEST_P(Scale_untrainable, Accuracy)
{
Vec4i inpShapeVec = get<0>(GetParam());
int axis = get<1>(GetParam())[0];
int weightsDims = get<1>(GetParam())[1];
bool testFusion = get<2>(GetParam());
const int inpShape[] = {inpShapeVec[0], inpShapeVec[1], inpShapeVec[2], inpShapeVec[3]};
// Create a network with two inputs. Scale layer multiplies a first input to
// a second one. See http://caffe.berkeleyvision.org/tutorial/layers/scale.html
Net net;
// Check that this version of Scale layer won't be fused with Convolution layer.
if (testFusion)
{
LayerParams lp;
lp.set("kernel_size", 1);
lp.set("num_output", 3);
lp.set("group", 3);
lp.set("bias_term", false);
lp.type = "Convolution";
lp.name = "testConv";
std::vector<int> weightsShape(4);
weightsShape[0] = 3; // #outChannels
weightsShape[1] = 1; // #inpChannels / group
weightsShape[2] = 1; // height
weightsShape[3] = 1; // width
Mat weights(weightsShape, CV_32F);
weights.setTo(1);
lp.blobs.push_back(weights);
net.addLayerToPrev(lp.name, lp.type, lp);
}
LayerParams lp;
lp.type = "Scale";
lp.name = "testLayer";
lp.set("axis", axis);
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 1, id, 1);
Mat input(4, inpShape, CV_32F);
Mat weights(weightsDims, &inpShape[axis], CV_32F);
randu(input, -1, 1);
randu(weights, -1, 1);
std::vector<String> inpNames(2);
inpNames[0] = "scale_input";
inpNames[1] = "scale_weights";
net.setInputsNames(inpNames);
net.setInput(input, inpNames[0]);
net.setInput(weights, inpNames[1]);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();
Mat ref(input.dims, input.size, CV_32F);
float* inpData = (float*)input.data;
float* refData = (float*)ref.data;
float* weightsData = (float*)weights.data;
int spatialSize = 1;
for (int i = axis + weightsDims; i < 4; ++i)
spatialSize *= inpShape[i];
for (int i = 0; i < ref.total(); ++i)
{
float w = weightsData[(i / spatialSize) % weights.total()];
refData[i] = inpData[i] * w;
}
normAssert(out, ref);
}
INSTANTIATE_TEST_CASE_P(Layer_Test, Scale_untrainable, Combine(
/*input size*/ Values(Vec4i(2, 3, 4, 5)),
/*axis, #dims*/ Values(Vec2i(0, 1), Vec2i(0, 2), Vec2i(0, 3), Vec2i(0, 4),
Vec2i(1, 1), Vec2i(1, 2), Vec2i(1, 3),
Vec2i(2, 1), Vec2i(2, 2),
Vec2i(3, 1)),
/*conv fusion*/ testing::Bool()
));
typedef testing::TestWithParam<tuple<Vec4i, Vec4i, int, int, int> > Crop;
TEST_P(Crop, Accuracy)
{
Vec4i inpShapeVec = get<0>(GetParam());
Vec4i sizShapeVec = get<1>(GetParam());
int axis = get<2>(GetParam());
int numOffsets = get<3>(GetParam());
int offsetVal = get<4>(GetParam());
const int inpShape[] = {inpShapeVec[0], inpShapeVec[1], inpShapeVec[2], inpShapeVec[3]};
const int sizShape[] = {sizShapeVec[0], sizShapeVec[1], sizShapeVec[2], sizShapeVec[3]};
// Create a network with two inputs. Crop layer crops a first input to
// the size of a second one.
// See http://caffe.berkeleyvision.org/tutorial/layers/crop.html
Net net;
LayerParams lp;
lp.name = "testCrop";
lp.type = "Crop";
lp.set("axis", axis);
if (numOffsets > 0)
{
std::vector<int> offsets(numOffsets, offsetVal);
lp.set("offset", DictValue::arrayInt<int*>(&offsets[0], offsets.size()));
}
else
offsetVal = 0;
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 1, id, 1);
Mat inpImage(4, inpShape, CV_32F);
Mat sizImage(4, sizShape, CV_32F);
randu(inpImage, -1, 1);
randu(sizImage, -1, 1);
std::vector<String> inpNames(2);
inpNames[0] = "cropImage";
inpNames[1] = "sizImage";
net.setInputsNames(inpNames);
net.setInput(inpImage, inpNames[0]);
net.setInput(sizImage, inpNames[1]);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
// There are a few conditions that represent invalid input to the crop
// layer, so in those cases we want to verify an exception is thrown.
bool shouldThrowException = false;
if (numOffsets > 1 && numOffsets != 4 - axis)
shouldThrowException = true;
else
for (int i = axis; i < 4; i++)
if (sizShape[i] + offsetVal > inpShape[i])
shouldThrowException = true;
Mat out;
if (shouldThrowException)
{
ASSERT_ANY_THROW(out = net.forward());
return;
}
else
out = net.forward();
// Finally, compare the cropped output blob from the DNN layer (out)
// to a reference blob (ref) that we compute here.
std::vector<Range> crop_range;
crop_range.resize(4, Range::all());
for (int i = axis; i < 4; i++)
crop_range[i] = Range(offsetVal, sizShape[i] + offsetVal);
Mat ref(sizImage.dims, sizImage.size, CV_32F);
inpImage(&crop_range[0]).copyTo(ref);
normAssert(out, ref);
}
INSTANTIATE_TEST_CASE_P(Layer_Test, Crop, Combine(
/*input blob shape*/ Values(Vec4i(1, 3, 20, 30)),
/*cropsize blob shape*/ Values(Vec4i(1, 3, 10, 12)),
/*start axis*/ Values(0, 1, 2),
/*number of offsets*/ Values(0, 1, 2, 4),
/*offset value*/ Values(3, 4)
));
// Check that by default average pooling layer should not count zero padded values
// into the normalization area.
TEST_P(Test_Caffe_layers, Average_pooling_kernel_area)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
LayerParams lp;
lp.name = "testAvePool";
lp.type = "Pooling";
lp.set("kernel_size", 2);
lp.set("stride", 2);
lp.set("pool", "AVE");
Net net;
net.addLayerToPrev(lp.name, lp.type, lp);
// 1 2 | 3
// 4 5 | 6
// ----+--
// 7 8 | 9
Mat inp = (Mat_<float>(3, 3) << 1, 2, 3, 4, 5, 6, 7, 8, 9);
Mat ref = (Mat_<float>(2, 2) << (1 + 2 + 4 + 5) / 4.f, (3 + 6) / 2.f, (7 + 8) / 2.f, 9);
Mat tmp = blobFromImage(inp);
net.setInput(blobFromImage(inp));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
normAssert(out, blobFromImage(ref));
}
// Test PriorBoxLayer in case of no aspect ratios (just squared proposals).
TEST_P(Test_Caffe_layers, PriorBox_squares)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE ||
(backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)))
throw SkipTestException("");
LayerParams lp;
lp.name = "testPriorBox";
lp.type = "PriorBox";
lp.set("min_size", 2);
lp.set("flip", true);
lp.set("clip", true);
float variance[] = {0.1f, 0.1f, 0.2f, 0.2f};
float aspectRatios[] = {1.0f}; // That should be ignored.
lp.set("variance", DictValue::arrayReal<float*>(&variance[0], 4));
lp.set("aspect_ratio", DictValue::arrayReal<float*>(&aspectRatios[0], 1));
Net net;
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 1); // The second input is an input image. Shapes are used for boxes normalization.
Mat inp(1, 2, CV_32F);
randu(inp, -1, 1);
net.setInput(blobFromImage(inp));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
Mat ref = (Mat_<float>(4, 4) << 0.0, 0.0, 0.75, 1.0,
0.25, 0.0, 1.0, 1.0,
0.1f, 0.1f, 0.2f, 0.2f,
0.1f, 0.1f, 0.2f, 0.2f);
normAssert(out.reshape(1, 4), ref);
}
typedef TestWithParam<tuple<int, int> > Layer_Test_DWconv_Prelu;
TEST_P(Layer_Test_DWconv_Prelu, Accuracy)
{
// Test case
// input img size 3x16x16 value all 1
// |
// v
// dw_conv weight[0]=-1 weight[1]=-2 weight[2]=-3 bias={1,2,3}
// |
// v
// prelu weight={1,2,3}
// |
// v
// output out size 3x14x14 if right: out[0]=-8 out[0]=-32 out[0]=-72
// but current opencv output: out[0]=-24 out[0]=-48 out[0]=-72
const int num_input = get<0>(GetParam()); //inpChannels
const int group = 3; //outChannels=group when group>1
const int num_output = get<1>(GetParam());
const int kernel_depth = num_input/group;
CV_Assert(num_output >= group, num_output % group == 0, num_input % group == 0);
Net net;
//layer 1: dwconv
LayerParams lp;
lp.name = "dwconv";
lp.type = "Convolution";
lp.set("kernel_size", 3);
lp.set("num_output", num_output);
lp.set("pad", 0);
lp.set("group", group);
lp.set("stride", 1);
lp.set("engine", "CAFFE");
lp.set("bias_term", "true");
std::vector<int> weightsShape(4);
weightsShape[0] = num_output; // #outChannels
weightsShape[1] = kernel_depth; // #inpChannels / group
weightsShape[2] = 3; // height
weightsShape[3] = 3; // width
Mat weights(weightsShape, CV_32F, Scalar(1));
//assign weights
for (int i = 0; i < weightsShape[0]; ++i)
{
for (int j = 0; j < weightsShape[1]; ++j)
{
for (int k = 0; k < weightsShape[2]; ++k)
{
for (int l = 0; l < weightsShape[3]; ++l)
{
weights.ptr<float>(i, j, k)[l]=-1*(i+1);
}
}
}
}
lp.blobs.push_back(weights);
//assign bias
Mat bias(1, num_output, CV_32F, Scalar(1));
for (int i = 0; i < 1; ++i)
{
for (int j = 0; j < num_output; ++j)
{
bias.ptr<float>(i)[j]=j+1;
}
}
lp.blobs.push_back(bias);
net.addLayerToPrev(lp.name, lp.type, lp);
//layer 2: prelu
LayerParams lpr;
lpr.name = "dw_relu";
lpr.type = "PReLU";
Mat weightsp(1, num_output, CV_32F, Scalar(1));
//assign weights
for (int i = 0; i < 1; ++i)
{
for (int j = 0; j < num_output; ++j)
{
weightsp.ptr<float>(i)[j]=j+1;
}
}
lpr.blobs.push_back(weightsp);
net.addLayerToPrev(lpr.name, lpr.type, lpr);
int shape[] = {1, num_input, 16, 16};
Mat in_blob(4, &shape[0], CV_32FC1, Scalar(1));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setInput(in_blob);
Mat out = net.forward();
//assign target
std::vector<int> outShape(4);
outShape[0] = 1;
outShape[1] = num_output; // outChannels
outShape[2] = 14; // height
outShape[3] = 14; // width
Mat target(outShape, CV_32F, Scalar(1));
for (int i = 0; i < outShape[0]; ++i)
{
for (int j = 0; j < outShape[1]; ++j)
{
for (int k = 0; k < outShape[2]; ++k)
{
for (int l = 0; l < outShape[3]; ++l)
{
target.ptr<float>(i, j, k)[l]=(-9*kernel_depth*(j+1)+j+1)*(j+1);
}
}
}
}
normAssert(out, target);
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_DWconv_Prelu, Combine(Values(3, 6), Values(3, 6)));
#ifdef HAVE_INF_ENGINE
// Using Intel's Model Optimizer generate .xml and .bin files:
// ./ModelOptimizer -w /path/to/caffemodel -d /path/to/prototxt \
// -p FP32 -i -b ${batch_size} -o /path/to/output/folder
TEST(Layer_Test_Convolution_DLDT, Accuracy)
{
Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
Mat inp = blobFromNPY(_tf("blob.npy"));
netDefault.setInput(inp);
netDefault.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat outDefault = netDefault.forward();
net.setInput(inp);
Mat out = net.forward();
normAssert(outDefault, out);
std::vector<int> outLayers = net.getUnconnectedOutLayers();
ASSERT_EQ(net.getLayer(outLayers[0])->name, "output_merge");
ASSERT_EQ(net.getLayer(outLayers[0])->type, "Concat");
}
// 1. Create a .prototxt file with the following network:
// layer {
// type: "Input" name: "data" top: "data"
// input_param { shape { dim: 1 dim: 2 dim: 3 } }
// }
// layer {
// type: "Input" name: "second_input" top: "second_input"
// input_param { shape { dim: 1 dim: 2 dim: 3 } }
// }
// layer {
// type: "Eltwise" name: "output" top: "output"
// bottom: "data" bottom: "second_input"
// eltwise_param { operation: SUM }
// }
//
// 2. Create a .caffemodel file using Caffe:
//
// import caffe
// net = caffe.Net('/path/to/prototxt', caffe.TEST)
// net.save('/path/to/caffemodel')
//
// 3. Convert using ModelOptimizer.
TEST(Test_DLDT, two_inputs)
{
Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
int inpSize[] = {1, 2, 3};
Mat firstInp(3, &inpSize[0], CV_32F);
Mat secondInp(3, &inpSize[0], CV_32F);
randu(firstInp, -1, 1);
randu(secondInp, -1, 1);
net.setInput(firstInp, "data");
net.setInput(secondInp, "second_input");
Mat out = net.forward();
normAssert(out, firstInp + secondInp);
}
class UnsupportedLayer : public Layer
{
public:
UnsupportedLayer(const LayerParams &params) {}
static Ptr<Layer> create(const LayerParams& params)
{
return Ptr<Layer>(new UnsupportedLayer(params));
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV;
}
virtual void forward(std::vector<cv::Mat*> &inputs, std::vector<cv::Mat> &outputs, std::vector<cv::Mat> &internals) CV_OVERRIDE {}
virtual void forward(cv::InputArrayOfArrays inputs, cv::OutputArrayOfArrays outputs, cv::OutputArrayOfArrays internals) CV_OVERRIDE {}
};
TEST(Test_DLDT, fused_output)
{
static const int kNumChannels = 3;
CV_DNN_REGISTER_LAYER_CLASS(Unsupported, UnsupportedLayer);
Net net;
{
LayerParams lp;
lp.set("kernel_size", 1);
lp.set("num_output", 3);
lp.set("bias_term", false);
lp.type = "Convolution";
lp.name = "testConv";
lp.blobs.push_back(Mat({kNumChannels, 1, 1, 1}, CV_32F, Scalar(1)));
net.addLayerToPrev(lp.name, lp.type, lp);
}
{
LayerParams lp;
lp.set("bias_term", false);
lp.type = "Scale";
lp.name = "testScale";
lp.blobs.push_back(Mat({kNumChannels}, CV_32F, Scalar(1)));
net.addLayerToPrev(lp.name, lp.type, lp);
}
{
LayerParams lp;
net.addLayerToPrev("unsupported_layer", "Unsupported", lp);
}
net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
net.setInput(Mat({1, 1, 1, 1}, CV_32FC1, Scalar(1)));
ASSERT_NO_THROW(net.forward());
LayerFactory::unregisterLayer("Unsupported");
}
TEST(Test_DLDT, multiple_networks)
{
Net nets[2];
for (int i = 0; i < 2; ++i)
{
nets[i].setInputsNames(std::vector<String>(1, format("input_%d", i)));
LayerParams lp;
lp.set("kernel_size", 1);
lp.set("num_output", 1);
lp.set("bias_term", false);
lp.type = "Convolution";
lp.name = format("testConv_%d", i);
lp.blobs.push_back(Mat({1, 1, 1, 1}, CV_32F, Scalar(1 + i)));
nets[i].addLayerToPrev(lp.name, lp.type, lp);
nets[i].setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
nets[i].setInput(Mat({1, 1, 1, 1}, CV_32FC1, Scalar(1)));
}
Mat out_1 = nets[0].forward();
Mat out_2 = nets[1].forward();
// After the second model is initialized we try to receive an output from the first network again.
out_1 = nets[0].forward();
normAssert(2 * out_1, out_2);
}
#endif // HAVE_INF_ENGINE
// Test a custom layer.
class CustomInterpLayer CV_FINAL : public Layer
{
public:
CustomInterpLayer(const LayerParams &params) : Layer(params)
{
zoomFactor = params.get<int>("zoom_factor", 0);
outWidth = params.get<int>("width", 0);
outHeight = params.get<int>("height", 0);
}
static Ptr<Layer> create(LayerParams& params)
{
return Ptr<Layer>(new CustomInterpLayer(params));
}
virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
const int requiredOutputs,
std::vector<std::vector<int> > &outputs,
std::vector<std::vector<int> > &internals) const CV_OVERRIDE
{
const int batchSize = inputs[0][0];
const int numChannels = inputs[0][1];
const int inpHeight = inputs[0][2];
const int inpWidth = inputs[0][3];
std::vector<int> outShape(4);
outShape[0] = batchSize;
outShape[1] = numChannels;
outShape[2] = outHeight != 0 ? outHeight : (inpHeight + (inpHeight - 1) * (zoomFactor - 1));
outShape[3] = outWidth != 0 ? outWidth : (inpWidth + (inpWidth - 1) * (zoomFactor - 1));
outputs.assign(1, outShape);
return false;
}
virtual void finalize(const std::vector<Mat*>& inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
if (!outWidth && !outHeight)
{
outHeight = outputs[0].size[2];
outWidth = outputs[0].size[3];
}
}
// Implementation of this custom layer is based on https://github.com/cdmh/deeplab-public/blob/master/src/caffe/layers/interp_layer.cpp
virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat>& internals) CV_OVERRIDE
{
Mat& inp = *inputs[0];
Mat& out = outputs[0];
const float* inpData = (float*)inp.data;
float* outData = (float*)out.data;
const int batchSize = inp.size[0];
const int numChannels = inp.size[1];
const int inpHeight = inp.size[2];
const int inpWidth = inp.size[3];
const float rheight = (outHeight > 1) ? static_cast<float>(inpHeight - 1) / (outHeight - 1) : 0.f;
const float rwidth = (outWidth > 1) ? static_cast<float>(inpWidth - 1) / (outWidth - 1) : 0.f;
for (int h2 = 0; h2 < outHeight; ++h2)
{
const float h1r = rheight * h2;
const int h1 = h1r;
const int h1p = (h1 < inpHeight - 1) ? 1 : 0;
const float h1lambda = h1r - h1;
const float h0lambda = 1.f - h1lambda;
for (int w2 = 0; w2 < outWidth; ++w2)
{
const float w1r = rwidth * w2;
const int w1 = w1r;
const int w1p = (w1 < inpWidth - 1) ? 1 : 0;
const float w1lambda = w1r - w1;
const float w0lambda = 1.f - w1lambda;
const float* pos1 = inpData + h1 * inpWidth + w1;
float* pos2 = outData + h2 * outWidth + w2;
for (int c = 0; c < batchSize * numChannels; ++c)
{
pos2[0] =
h0lambda * (w0lambda * pos1[0] + w1lambda * pos1[w1p]) +
h1lambda * (w0lambda * pos1[h1p * inpWidth] + w1lambda * pos1[h1p * inpWidth + w1p]);
pos1 += inpWidth * inpHeight;
pos2 += outWidth * outHeight;
}
}
}
}
void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
Layer::forward_fallback(inputs, outputs, internals);
}
private:
int outWidth, outHeight, zoomFactor;
};
#ifndef OPENCV_DNN_EXTERNAL_PROTOBUF
TEST_P(Test_Caffe_layers, Interp)
#else
TEST_P(Test_Caffe_layers, DISABLED_Interp) // requires patched protobuf (available in OpenCV source tree only)
#endif
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
// Test a cusom layer.
CV_DNN_REGISTER_LAYER_CLASS(Interp, CustomInterpLayer);
try
{
testLayerUsingCaffeModels("layer_interp", false, false);
}
catch (...)
{
LayerFactory::unregisterLayer("Interp");
throw;
}
LayerFactory::unregisterLayer("Interp");
// Test an implemented layer.
testLayerUsingCaffeModels("layer_interp", false, false);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_Caffe_layers, dnnBackendsAndTargets());
TEST(Layer_Test_PoolingIndices, Accuracy)
{
Net net;
LayerParams lp;
lp.set("pool", "max");
lp.set("kernel_w", 2);
lp.set("kernel_h", 2);
lp.set("stride_w", 2);
lp.set("stride_h", 2);
lp.set("pad_w", 0);
lp.set("pad_h", 0);
lp.name = "testLayer.name"; // This test also checks that OpenCV lets use names with dots.
lp.type = "Pooling";
net.addLayerToPrev(lp.name, lp.type, lp);
Mat inp(10, 10, CV_8U);
randu(inp, 0, 255);
Mat maxValues(5, 5, CV_32F, Scalar(-1)), indices(5, 5, CV_32F, Scalar(-1));
for (int y = 0; y < 10; ++y)
{
int dstY = y / 2;
for (int x = 0; x < 10; ++x)
{
int dstX = x / 2;
uint8_t val = inp.at<uint8_t>(y, x);
if ((float)inp.at<uint8_t>(y, x) > maxValues.at<float>(dstY, dstX))
{
maxValues.at<float>(dstY, dstX) = val;
indices.at<float>(dstY, dstX) = y * 10 + x;
}
}
}
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setInput(blobFromImage(inp));
std::vector<Mat> outputs;
net.forward(outputs, lp.name);
normAssert(maxValues, outputs[0].reshape(1, 5));
normAssert(indices, outputs[1].reshape(1, 5));
}
typedef testing::TestWithParam<tuple<Vec4i, int> > Layer_Test_ShuffleChannel;
TEST_P(Layer_Test_ShuffleChannel, Accuracy)
{
Vec4i inpShapeVec = get<0>(GetParam());
int group = get<1>(GetParam());
ASSERT_EQ(inpShapeVec[1] % group, 0);
const int groupSize = inpShapeVec[1] / group;
Net net;
LayerParams lp;
lp.set("group", group);
lp.type = "ShuffleChannel";
lp.name = "testLayer";
net.addLayerToPrev(lp.name, lp.type, lp);
const int inpShape[] = {inpShapeVec[0], inpShapeVec[1], inpShapeVec[2], inpShapeVec[3]};
Mat inp(4, inpShape, CV_32F);
randu(inp, 0, 255);
net.setInput(inp);
Mat out = net.forward();
for (int n = 0; n < inpShapeVec[0]; ++n)
{
for (int c = 0; c < inpShapeVec[1]; ++c)
{
Mat outChannel = getPlane(out, n, c);
Mat inpChannel = getPlane(inp, n, groupSize * (c % group) + c / group);
normAssert(outChannel, inpChannel);
}
}
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_ShuffleChannel, Combine(
/*input shape*/ Values(Vec4i(1, 6, 5, 7), Vec4i(3, 12, 1, 4)),
/*group*/ Values(1, 2, 3, 6)
));
// Check if relu is not fused to convolution if we requested it's output
TEST(Layer_Test_Convolution, relu_fusion)
{
Net net;
{
LayerParams lp;
lp.set("kernel_size", 1);
lp.set("num_output", 1);
lp.set("bias_term", false);
lp.type = "Convolution";
lp.name = "testConv";
int weightsShape[] = {1, 1, 1, 1};
Mat weights(4, &weightsShape[0], CV_32F, Scalar(1));
lp.blobs.push_back(weights);
net.addLayerToPrev(lp.name, lp.type, lp);
}
{
LayerParams lp;
lp.type = "ReLU";
lp.name = "testReLU";
net.addLayerToPrev(lp.name, lp.type, lp);
}
int sz[] = {1, 1, 2, 3};
Mat input(4, &sz[0], CV_32F);
randu(input, -1.0, -0.1);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat output = net.forward("testConv");
normAssert(input, output);
}
}} // namespace