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Unit tests for TensorFlow importer

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This commit is contained in:
dkurt 2017-08-01 18:21:47 +03:00
parent 0bd357e7ec
commit 339793143c
4 changed files with 235 additions and 40 deletions
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2
modules/dnn/src/layers/convolution_layer.cpp
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@ -183,7 +183,7 @@ public:
}
else
{
getConvPoolOutParams(Size(inpH, inpW), kernel, stride, padMode, out);
getConvPoolOutParams(Size(inpW, inpH), kernel, stride, padMode, out);
}
int ngroups = inpCn / blobs[0].size[1];

2
modules/dnn/src/layers/padding_layer.cpp
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@ -25,7 +25,7 @@ public:
{
setParamsFrom(params);
paddingDim = params.get<int>("padding_dim");
padding = abs(params.get<int>("padding"));
padding = params.get<int>("padding");
inputDims = params.get<int>("input_dims", 0);
index = params.get<int>("index", 0);
paddingValue = params.get<double>("value", 0);

199
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -558,6 +558,16 @@ void TFImporter::populateNet(Net dstNet)
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else if (type == "BiasAdd" || type == "Add")
{
bool haveConst = false;
for(int ii = 0; !haveConst && ii < layer.input_size(); ++ii)
{
Pin input = parsePin(layer.input(ii));
haveConst = value_id.find(input.name) != value_id.end();
}
CV_Assert(!haveConst || layer.input_size() == 2);
if (haveConst)
{
layerParams.blobs.resize(1);
blobFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
@ -568,12 +578,20 @@ void TFImporter::populateNet(Net dstNet)
// one input only
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else if (type == "Identity")
else
{
int id = dstNet.addLayer(name, "Identity", layerParams);
layerParams.set("operation", "sum");
int id = dstNet.addLayer(name, "Eltwise", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
for (int ii = 0; ii < layer.input_size(); ii++)
{
Pin inp = parsePin(layer.input(ii));
if (layer_id.find(inp.name) == layer_id.end())
CV_Error(Error::StsError, "Input layer not found: " + inp.name);
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii);
}
}
}
else if (type == "MatMul")
{
@ -624,13 +642,6 @@ void TFImporter::populateNet(Net dstNet)
else if (type == "Const")
{
}
else if (type == "Softmax")
{
int id = dstNet.addLayer(name, "Softmax", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else if (type == "LRN")
{
if(hasLayerAttr(layer, "alpha")) {
@ -653,37 +664,28 @@ void TFImporter::populateNet(Net dstNet)
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else if (type == "Concat")
else if (type == "Concat" || type == "ConcatV2")
{
int axis = getConstBlob(layer, value_id, 0).int_val().Get(0);
int axisId = (type == "Concat" ? 0 : layer.input_size() - 1);
int axis = getConstBlob(layer, value_id, axisId).int_val().Get(0);
layerParams.set("axis", toNCHW[axis]);
int id = dstNet.addLayer(name, "Concat", layerParams);
layer_id[name] = id;
// input(0) is concat_dim
for (int ii = 1; ii < layer.input_size(); ii++)
int from = (type == "Concat" ? 1 : 0);
int to = (type == "Concat" ? layer.input_size() : layer.input_size() - 1);
// input(0) or input(n-1) is concat_dim
for (int ii = from; ii < to; ii++)
{
Pin inp = parsePin(layer.input(ii));
if (layer_id.find(inp.name) == layer_id.end())
CV_Error(Error::StsError, "Input layer not found: " + inp.name);
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii - 1);
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii - from);
}
}
else if (type == "Relu")
{
int id = dstNet.addLayer(name, "ReLU", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else if (type == "Elu")
{
int id = dstNet.addLayer(name, "ELU", layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else if (type == "MaxPool")
{
layerParams.set("pool", "max");
@ -736,6 +738,145 @@ void TFImporter::populateNet(Net dstNet)
// one input only
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
}
else if (type == "Mul")
{
bool haveConst = false;
for(int ii = 0; !haveConst && ii < layer.input_size(); ++ii)
{
Pin input = parsePin(layer.input(ii));
haveConst = value_id.find(input.name) != value_id.end();
}
CV_Assert(!haveConst || layer.input_size() == 2);
if (haveConst)
{
// Multiplication by constant.
CV_Assert(layer.input_size() == 2);
float scale = getConstBlob(layer, value_id).float_val()[0];
layerParams.set("scale", scale);
int id = dstNet.addLayer(name, "Power", layerParams);
layer_id[name] = id;
Pin inp0 = parsePin(layer.input(0));
if (layer_id.find(inp0.name) != layer_id.end())
// First operand is a constant.
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
else
connect(layer_id, dstNet, parsePin(layer.input(1)), id, 0);
}
else
{
layerParams.set("operation", "prod");
int id = dstNet.addLayer(name, "Eltwise", layerParams);
layer_id[name] = id;
for (int ii = 0; ii < layer.input_size(); ii++)
{
Pin inp = parsePin(layer.input(ii));
if (layer_id.find(inp.name) == layer_id.end())
CV_Error(Error::StsError, "Input layer not found: " + inp.name);
dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii);
}
}
}
else if (type == "Pad")
{
tensorflow::TensorProto paddings = getConstBlob(layer, value_id, 1);
MatShape shape;
blobShapeFromTensor(paddings, shape);
if (shape[0] != 4)
CV_Error(Error::StsError, "Expected NHWC data format");
// Copy tensor with paddings.
std::vector<int32_t> values(shape[0] * 2);
CV_Assert(sizeof(int32_t) * values.size() ==
paddings.tensor_content().size());
memcpy(&values[0], &paddings.tensor_content()[0],
paddings.tensor_content().size());
// Allow only one padding operation per layer.
bool padded = false;
for (int i = 0; i < values.size(); ++i)
{
if (values[i])
{
if (padded)
CV_Error(Error::StsError,
"Only single padding operation per layer is supported");
padded = true;
int axis = i / 2;
// Remap NHWC to NCHW.
// 0 -> 0
// 1 -> 2
// 2 -> 3
// 3 -> 1
if (axis != 0)
axis = axis % 3 + 1;
layerParams.set("padding_dim", axis);
if (i % 2) // Pad after
layerParams.set("padding", values[i]);
else // Pad before
layerParams.set("padding", -1 * values[i]);
int id = dstNet.addLayer(name, "Padding", layerParams);
layer_id[name] = id;
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
}
}
else if (type == "FusedBatchNorm")
{
// op: "FusedBatchNorm"
// input: "input"
// input: "BatchNorm/gamma"
// input: "BatchNorm/beta"
// input: "BatchNorm/moving_mean"
// input: "BatchNorm/moving_variance"
if (layer.input_size() != 5)
CV_Error(Error::StsNotImplemented,
"Expected gamma, beta, mean and std");
layerParams.blobs.resize(4);
// gamma
blobFromTensor(getConstBlob(layer, value_id, 1), layerParams.blobs[2]);
// beta
blobFromTensor(getConstBlob(layer, value_id, 2), layerParams.blobs[3]);
// mean
blobFromTensor(getConstBlob(layer, value_id, 3), layerParams.blobs[0]);
// std
blobFromTensor(getConstBlob(layer, value_id, 4), layerParams.blobs[1]);
if (hasLayerAttr(layer, "epsilon"))
layerParams.set("eps", getLayerAttr(layer, "epsilon").f());
layerParams.set("has_weight", true);
layerParams.set("has_bias", true);
int id = dstNet.addLayer(name, "BatchNorm", layerParams);
layer_id[name] = id;
// one input only
connect(layer_id, dstNet, parsePin(layer.input(0)), id, 0);
}
else if (type == "Abs" || type == "Tanh" || type == "Sigmoid" ||
type == "Relu" || type == "Elu" || type == "Softmax" ||
type == "Identity")
{
std::string dnnType = type;
if (type == "Abs") dnnType = "AbsVal";
else if (type == "Tanh") dnnType = "TanH";
else if (type == "Relu") dnnType = "ReLU";
else if (type == "Elu") dnnType = "ELU";
int id = dstNet.addLayer(name, dnnType, layerParams);
layer_id[name] = id;
connectToAllBlobs(layer_id, dstNet, parsePin(layer.input(0)), id, layer.input_size());
}
else
{
printLayerAttr(layer);

54
modules/dnn/test/test_tf_importer.cpp
View File

@ -71,4 +71,58 @@ TEST(Test_TensorFlow, inception_accuracy)
normAssert(ref, out);
}
static std::string path(const std::string& file)
{
return findDataFile("dnn/tensorflow/" + file, false);
}
static void runTensorFlowNet(const std::string& prefix)
{
std::string netPath = path(prefix + "_net.pb");
std::string inpPath = path(prefix + "_in.npy");
std::string outPath = path(prefix + "_out.npy");
Net net = readNetFromTensorflow(netPath);
cv::Mat input = blobFromNPY(inpPath);
cv::Mat target = blobFromNPY(outPath);
net.setInput(input);
cv::Mat output = net.forward();
normAssert(target, output);
}
TEST(Test_TensorFlow, single_conv)
{
runTensorFlowNet("single_conv");
}
TEST(Test_TensorFlow, padding)
{
runTensorFlowNet("padding_same");
runTensorFlowNet("padding_valid");
}
TEST(Test_TensorFlow, eltwise_add_mul)
{
runTensorFlowNet("eltwise_add_mul");
}
TEST(Test_TensorFlow, pad_and_concat)
{
runTensorFlowNet("pad_and_concat");
}
TEST(Test_TensorFlow, fused_batch_norm)
{
runTensorFlowNet("fused_batch_norm");
}
TEST(Test_TensorFlow, pooling)
{
runTensorFlowNet("max_pool_even");
runTensorFlowNet("max_pool_odd_valid");
runTensorFlowNet("max_pool_odd_same");
}
}