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alalekalalek
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Merge pull request opencv#9287 from dkurt:tensorflow_unit_tests
2 parents 15aa0df + 3397931 commit 2f4a3e4

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4 files changed

+235
-40
lines changed

4 files changed

+235
-40
lines changed

‎modules/dnn/src/layers/convolution_layer.cpp

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -183,7 +183,7 @@ class ConvolutionLayerImpl : public BaseConvolutionLayerImpl
183183
}
184184
else
185185
{
186-
getConvPoolOutParams(Size(inpH, inpW), kernel, stride, padMode, out);
186+
getConvPoolOutParams(Size(inpW, inpH), kernel, stride, padMode, out);
187187
}
188188

189189
int ngroups = inpCn / blobs[0].size[1];

‎modules/dnn/src/layers/padding_layer.cpp

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -25,7 +25,7 @@ class PaddingLayerImpl : public PaddingLayer
2525
{
2626
setParamsFrom(params);
2727
paddingDim = params.get<int>("padding_dim");
28-
padding = abs(params.get<int>("padding"));
28+
padding = params.get<int>("padding");
2929
inputDims = params.get<int>("input_dims", 0);
3030
index = params.get<int>("index", 0);
3131
paddingValue = params.get<double>("value", 0);

‎modules/dnn/src/tensorflow/tf_importer.cpp

Lines changed: 179 additions & 38 deletions
Original file line numberDiff line numberDiff line change
@@ -559,21 +559,39 @@ void TFImporter::populateNet(Net dstNet)
559559
}
560560
else if (type == "BiasAdd" || type == "Add")
561561
{
562-
layerParams.blobs.resize(1);
563-
blobFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
562+
bool haveConst = false;
563+
for(int ii = 0; !haveConst && ii < layer.input_size(); ++ii)
564+
{
565+
Pin input = parsePin(layer.input(ii));
566+
haveConst = value_id.find(input.name) != value_id.end();
567+
}
568+
CV_Assert(!haveConst || layer.input_size() == 2);
564569

565-
int id = dstNet.addLayer(name, "Shift", layerParams);
566-
layer_id[name] = id;
570+
if (haveConst)
571+
{
572+
layerParams.blobs.resize(1);
573+
blobFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
567574

568-
// one input only
569-
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
570-
}
571-
else if (type == "Identity")
572-
{
573-
int id = dstNet.addLayer(name, "Identity", layerParams);
574-
layer_id[name] = id;
575+
int id = dstNet.addLayer(name, "Shift", layerParams);
576+
layer_id[name] = id;
575577

576-
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
578+
// one input only
579+
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
580+
}
581+
else
582+
{
583+
layerParams.set("operation", "sum");
584+
int id = dstNet.addLayer(name, "Eltwise", layerParams);
585+
layer_id[name] = id;
586+
587+
for (int ii = 0; ii < layer.input_size(); ii++)
588+
{
589+
Pin inp = parsePin(layer.input(ii));
590+
if (layer_id.find(inp.name) == layer_id.end())
591+
CV_Error(Error::StsError, "Input layer not found: " + inp.name);
592+
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii);
593+
}
594+
}
577595
}
578596
else if (type == "MatMul")
579597
{
@@ -624,13 +642,6 @@ void TFImporter::populateNet(Net dstNet)
624642
else if (type == "Const")
625643
{
626644
}
627-
else if (type == "Softmax")
628-
{
629-
int id = dstNet.addLayer(name, "Softmax", layerParams);
630-
layer_id[name] = id;
631-
632-
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
633-
}
634645
else if (type == "LRN")
635646
{
636647
if(hasLayerAttr(layer, "alpha")) {
@@ -653,37 +664,28 @@ void TFImporter::populateNet(Net dstNet)
653664

654665
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
655666
}
656-
else if (type == "Concat")
667+
else if (type == "Concat" || type == "ConcatV2")
657668
{
658-
int axis = getConstBlob(layer, value_id, 0).int_val().Get(0);
669+
int axisId = (type == "Concat" ? 0 : layer.input_size() - 1);
670+
int axis = getConstBlob(layer, value_id, axisId).int_val().Get(0);
659671
layerParams.set("axis", toNCHW[axis]);
660672

661673
int id = dstNet.addLayer(name, "Concat", layerParams);
662674
layer_id[name] = id;
663675

664-
// input(0) is concat_dim
665-
for (int ii = 1; ii < layer.input_size(); ii++)
676+
677+
int from = (type == "Concat" ? 1 : 0);
678+
int to = (type == "Concat" ? layer.input_size() : layer.input_size() - 1);
679+
680+
// input(0) or input(n-1) is concat_dim
681+
for (int ii = from; ii < to; ii++)
666682
{
667683
Pin inp = parsePin(layer.input(ii));
668684
if (layer_id.find(inp.name) == layer_id.end())
669685
CV_Error(Error::StsError, "Input layer not found: " + inp.name);
670-
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii - 1);
686+
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii - from);
671687
}
672688
}
673-
else if (type == "Relu")
674-
{
675-
int id = dstNet.addLayer(name, "ReLU", layerParams);
676-
layer_id[name] = id;
677-
678-
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
679-
}
680-
else if (type == "Elu")
681-
{
682-
int id = dstNet.addLayer(name, "ELU", layerParams);
683-
layer_id[name] = id;
684-
685-
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
686-
}
687689
else if (type == "MaxPool")
688690
{
689691
layerParams.set("pool", "max");
@@ -736,6 +738,145 @@ void TFImporter::populateNet(Net dstNet)
736738
// one input only
737739
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
738740
}
741+
else if (type == "Mul")
742+
{
743+
bool haveConst = false;
744+
for(int ii = 0; !haveConst && ii < layer.input_size(); ++ii)
745+
{
746+
Pin input = parsePin(layer.input(ii));
747+
haveConst = value_id.find(input.name) != value_id.end();
748+
}
749+
CV_Assert(!haveConst || layer.input_size() == 2);
750+
751+
if (haveConst)
752+
{
753+
// Multiplication by constant.
754+
CV_Assert(layer.input_size() == 2);
755+
756+
float scale = getConstBlob(layer, value_id).float_val()[0];
757+
layerParams.set("scale", scale);
758+
759+
int id = dstNet.addLayer(name, "Power", layerParams);
760+
layer_id[name] = id;
761+
762+
Pin inp0 = parsePin(layer.input(0));
763+
if (layer_id.find(inp0.name) != layer_id.end())
764+
// First operand is a constant.
765+
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
766+
else
767+
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
768+
}
769+
else
770+
{
771+
layerParams.set("operation", "prod");
772+
int id = dstNet.addLayer(name, "Eltwise", layerParams);
773+
layer_id[name] = id;
774+
775+
for (int ii = 0; ii < layer.input_size(); ii++)
776+
{
777+
Pin inp = parsePin(layer.input(ii));
778+
if (layer_id.find(inp.name) == layer_id.end())
779+
CV_Error(Error::StsError, "Input layer not found: " + inp.name);
780+
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii);
781+
}
782+
}
783+
}
784+
else if (type == "Pad")
785+
{
786+
tensorflow::TensorProto paddings = getConstBlob(layer, value_id, 1);
787+
MatShape shape;
788+
blobShapeFromTensor(paddings, shape);
789+
if (shape[0] != 4)
790+
CV_Error(Error::StsError, "Expected NHWC data format");
791+
792+
// Copy tensor with paddings.
793+
std::vector<int32_t> values(shape[0] * 2);
794+
CV_Assert(sizeof(int32_t) * values.size() ==
795+
paddings.tensor_content().size());
796+
memcpy(&values[0], &paddings.tensor_content()[0],
797+
paddings.tensor_content().size());
798+
799+
// Allow only one padding operation per layer.
800+
bool padded = false;
801+
for (int i = 0; i < values.size(); ++i)
802+
{
803+
if (values[i])
804+
{
805+
if (padded)
806+
CV_Error(Error::StsError,
807+
"Only single padding operation per layer is supported");
808+
padded = true;
809+
810+
int axis = i / 2;
811+
// Remap NHWC to NCHW.
812+
// 0 -> 0
813+
// 1 -> 2
814+
// 2 -> 3
815+
// 3 -> 1
816+
if (axis != 0)
817+
axis = axis % 3 + 1;
818+
819+
layerParams.set("padding_dim", axis);
820+
if (i % 2) // Pad after
821+
layerParams.set("padding", values[i]);
822+
else // Pad before
823+
layerParams.set("padding", -1 * values[i]);
824+
825+
int id = dstNet.addLayer(name, "Padding", layerParams);
826+
layer_id[name] = id;
827+
828+
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
829+
}
830+
}
831+
}
832+
else if (type == "FusedBatchNorm")
833+
{
834+
// op: "FusedBatchNorm"
835+
// input: "input"
836+
// input: "BatchNorm/gamma"
837+
// input: "BatchNorm/beta"
838+
// input: "BatchNorm/moving_mean"
839+
// input: "BatchNorm/moving_variance"
840+
if (layer.input_size() != 5)
841+
CV_Error(Error::StsNotImplemented,
842+
"Expected gamma, beta, mean and std");
843+
844+
layerParams.blobs.resize(4);
845+
// gamma
846+
blobFromTensor(getConstBlob(layer, value_id, 1), layerParams.blobs[2]);
847+
// beta
848+
blobFromTensor(getConstBlob(layer, value_id, 2), layerParams.blobs[3]);
849+
// mean
850+
blobFromTensor(getConstBlob(layer, value_id, 3), layerParams.blobs[0]);
851+
// std
852+
blobFromTensor(getConstBlob(layer, value_id, 4), layerParams.blobs[1]);
853+
854+
if (hasLayerAttr(layer, "epsilon"))
855+
layerParams.set("eps", getLayerAttr(layer, "epsilon").f());
856+
857+
layerParams.set("has_weight", true);
858+
layerParams.set("has_bias", true);
859+
860+
int id = dstNet.addLayer(name, "BatchNorm", layerParams);
861+
layer_id[name] = id;
862+
863+
// one input only
864+
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
865+
}
866+
else if (type == "Abs" || type == "Tanh" || type == "Sigmoid" ||
867+
type == "Relu" || type == "Elu" || type == "Softmax" ||
868+
type == "Identity")
869+
{
870+
std::string dnnType = type;
871+
if (type == "Abs") dnnType = "AbsVal";
872+
else if (type == "Tanh") dnnType = "TanH";
873+
else if (type == "Relu") dnnType = "ReLU";
874+
else if (type == "Elu") dnnType = "ELU";
875+
876+
int id = dstNet.addLayer(name, dnnType, layerParams);
877+
layer_id[name] = id;
878+
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
879+
}
739880
else
740881
{
741882
printLayerAttr(layer);

‎modules/dnn/test/test_tf_importer.cpp

Lines changed: 54 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -71,4 +71,58 @@ TEST(Test_TensorFlow, inception_accuracy)
7171
normAssert(ref, out);
7272
}
7373

74+
static std::string path(const std::string& file)
75+
{
76+
return findDataFile("dnn/tensorflow/" + file, false);
77+
}
78+
79+
static void runTensorFlowNet(const std::string& prefix)
80+
{
81+
std::string netPath = path(prefix + "_net.pb");
82+
std::string inpPath = path(prefix + "_in.npy");
83+
std::string outPath = path(prefix + "_out.npy");
84+
85+
Net net = readNetFromTensorflow(netPath);
86+
87+
cv::Mat input = blobFromNPY(inpPath);
88+
cv::Mat target = blobFromNPY(outPath);
89+
90+
net.setInput(input);
91+
cv::Mat output = net.forward();
92+
normAssert(target, output);
93+
}
94+
95+
TEST(Test_TensorFlow, single_conv)
96+
{
97+
runTensorFlowNet("single_conv");
98+
}
99+
100+
TEST(Test_TensorFlow, padding)
101+
{
102+
runTensorFlowNet("padding_same");
103+
runTensorFlowNet("padding_valid");
104+
}
105+
106+
TEST(Test_TensorFlow, eltwise_add_mul)
107+
{
108+
runTensorFlowNet("eltwise_add_mul");
109+
}
110+
111+
TEST(Test_TensorFlow, pad_and_concat)
112+
{
113+
runTensorFlowNet("pad_and_concat");
114+
}
115+
116+
TEST(Test_TensorFlow, fused_batch_norm)
117+
{
118+
runTensorFlowNet("fused_batch_norm");
119+
}
120+
121+
TEST(Test_TensorFlow, pooling)
122+
{
123+
runTensorFlowNet("max_pool_even");
124+
runTensorFlowNet("max_pool_odd_valid");
125+
runTensorFlowNet("max_pool_odd_same");
126+
}
127+
74128
}

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