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Commit f056c0f1 authored by Dmitry Kurtaev's avatar Dmitry Kurtaev
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UINT8 face detection network using Intel's Inference Engine backend

parent 8b2122e1
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Showing with 80 additions and 27 deletions
modules/dnn/misc/quantize_face_detector.py
View file @ f056c0f1
......@@ -104,8 +104,12 @@ def l2norm(x, name):
inp = tf.placeholder(dtype, [1, 300, 300, 3], 'data')
data_bn = batch_norm(inp, 'data_bn')
data_scale = scale(data_bn, 'data_scale')
data_scale = tf.pad(data_scale, [[0, 0], [3, 3], [3, 3], [0, 0]])
# Instead of tf.pad we use tf.space_to_batch_nd layers which override convolution's padding strategy to explicit numbers
# data_scale = tf.pad(data_scale, [[0, 0], [3, 3], [3, 3], [0, 0]])
data_scale = tf.space_to_batch_nd(data_scale, [1, 1], [[3, 3], [3, 3]], name='Pad')
conv1_h = conv(data_scale, stride=2, pad='VALID', name='conv1_h')
conv1_bn_h = batch_norm(conv1_h, 'conv1_bn_h')
conv1_scale_h = scale(conv1_bn_h, 'conv1_scale_h')
conv1_relu = tf.nn.relu(conv1_scale_h)
......@@ -133,8 +137,11 @@ layer_128_1_sum = layer_128_1_conv2 + layer_128_1_conv_expand_h
layer_256_1_bn1 = batch_norm(layer_128_1_sum, 'layer_256_1_bn1')
layer_256_1_scale1 = scale(layer_256_1_bn1, 'layer_256_1_scale1')
layer_256_1_relu1 = tf.nn.relu(layer_256_1_scale1)
layer_256_1_conv1 = tf.pad(layer_256_1_relu1, [[0, 0], [1, 1], [1, 1], [0, 0]])
# layer_256_1_conv1 = tf.pad(layer_256_1_relu1, [[0, 0], [1, 1], [1, 1], [0, 0]])
layer_256_1_conv1 = tf.space_to_batch_nd(layer_256_1_relu1, [1, 1], [[1, 1], [1, 1]], name='Pad_1')
layer_256_1_conv1 = conv(layer_256_1_conv1, stride=2, pad='VALID', name='layer_256_1_conv1')
layer_256_1_bn2 = batch_norm(layer_256_1_conv1, 'layer_256_1_bn2')
layer_256_1_scale2 = scale(layer_256_1_bn2, 'layer_256_1_scale2')
layer_256_1_relu2 = tf.nn.relu(layer_256_1_scale2)
......@@ -160,8 +167,11 @@ fc7 = tf.nn.relu(last_scale_h, name='last_relu')
conv6_1_h = conv(fc7, 'conv6_1_h', activ=tf.nn.relu)
conv6_2_h = conv(conv6_1_h, stride=2, name='conv6_2_h', activ=tf.nn.relu)
conv7_1_h = conv(conv6_2_h, 'conv7_1_h', activ=tf.nn.relu)
conv7_2_h = tf.pad(conv7_1_h, [[0, 0], [1, 1], [1, 1], [0, 0]])
# conv7_2_h = tf.pad(conv7_1_h, [[0, 0], [1, 1], [1, 1], [0, 0]])
conv7_2_h = tf.space_to_batch_nd(conv7_1_h, [1, 1], [[1, 1], [1, 1]], name='Pad_2')
conv7_2_h = conv(conv7_2_h, stride=2, pad='VALID', name='conv7_2_h', activ=tf.nn.relu)
conv8_1_h = conv(conv7_2_h, pad='SAME', name='conv8_1_h', activ=tf.nn.relu)
conv8_2_h = conv(conv8_1_h, pad='SAME', name='conv8_2_h', activ=tf.nn.relu)
conv9_1_h = conv(conv8_2_h, 'conv9_1_h', activ=tf.nn.relu)
......@@ -201,6 +211,7 @@ with tf.Session() as sess:
inputData = np.random.standard_normal([1, 3, 300, 300]).astype(np.float32)
cvNet.setInput(inputData)
cvNet.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
outDNN = cvNet.forward(out_nodes)
outTF = sess.run([mbox_loc, mbox_conf_flatten], feed_dict={inp: inputData.transpose(0, 2, 3, 1)})
......@@ -254,7 +265,7 @@ for i in reversed(range(len(graph_def.node))):
del graph_def.node[i]
for attr in ['T', 'data_format', 'Tshape', 'N', 'Tidx', 'Tdim',
'use_cudnn_on_gpu', 'Index', 'Tperm', 'is_training',
'Tpaddings']:
'Tpaddings', 'Tblock_shape', 'Tcrops']:
if attr in graph_def.node[i].attr:
del graph_def.node[i].attr[attr]
......
modules/dnn/src/layers/normalize_bbox_layer.cpp
View file @ f056c0f1
......@@ -63,9 +63,18 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() &&
pnorm == 2 && !blobs.empty();
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
{
if (pnorm != 2)
return false;
if (!blobs.empty())
return true;
if (preferableTarget == DNN_TARGET_MYRIAD)
return !acrossSpatial;
return startAxis == 1 && (!acrossSpatial || endAxis > 1);
}
else
return backendId == DNN_BACKEND_OPENCV;
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
......@@ -80,6 +89,14 @@ public:
return true;
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
CV_Assert(inputs.size() == 1);
endAxis = endAxis == -1 ? (inputs[0]->dims - 1) : endAxis;
startAxis = startAxis == -1 ? (inputs[0]->dims - 1) : startAxis;
acrossSpatial = (startAxis == 1 && endAxis == inputs[0]->dims - 1);
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
......@@ -240,24 +257,52 @@ public:
}
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "Normalize";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
CV_Assert(!blobs.empty());
ieLayer->params["eps"] = format("%f", epsilon);
ieLayer->params["across_spatial"] = acrossSpatial ? "1" : "0";
ieLayer->params["channel_shared"] = blobs[0].total() == 1 ? "1" : "0";
if (input->dims.size() == 4)
{
const int numChannels = input->dims[2]; // NOTE: input->dims are reversed (whcn)
const size_t numChannels = blobs[0].total();
ieLayer->blobs["weights"] = wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
lp.type = "Normalize";
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
if (blobs.empty())
{
auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
InferenceEngine::Layout::C,
{numChannels});
weights->allocate();
std::vector<float> ones(numChannels, 1);
weights->set(ones);
ieLayer->blobs["weights"] = weights;
ieLayer->params["channel_shared"] = "0";
}
else
{
CV_Assert(numChannels == blobs[0].total());
ieLayer->blobs["weights"] = wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C);
ieLayer->params["channel_shared"] = blobs[0].total() == 1 ? "1" : "0";
}
ieLayer->params["eps"] = format("%f", epsilon);
ieLayer->params["across_spatial"] = acrossSpatial ? "1" : "0";
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
else
{
InferenceEngine::LayerParams lp;
lp.name = name;
lp.type = "GRN";
lp.precision = InferenceEngine::Precision::FP32;
std::shared_ptr<InferenceEngine::CNNLayer> ieLayer(new InferenceEngine::CNNLayer(lp));
ieLayer->params["bias"] = format("%f", epsilon);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
}
#endif // HAVE_INF_ENGINE
return Ptr<BackendNode>();
}
......
modules/dnn/src/tensorflow/tf_importer.cpp
View file @ f056c0f1
......@@ -529,7 +529,8 @@ const tensorflow::TensorProto& TFImporter::getConstBlob(const tensorflow::NodeDe
Pin kernel_inp = parsePin(layer.input(input_blob_index));
if (const_layers.find(kernel_inp.name) == const_layers.end())
CV_Error(Error::StsError, "Const kernel input not found");
CV_Error(Error::StsError, "Input [" + layer.input(input_blob_index) +
"] for node [" + layer.name() + "] not found");
if (kernel_inp.blobIndex != 0)
CV_Error(Error::StsError, "Unsupported kernel input");
......@@ -867,13 +868,13 @@ void TFImporter::populateNet(Net dstNet)
layerParams.set("num_output", layerParams.blobs[0].size[0]);
setStrides(layerParams, layer);
setPadding(layerParams, layer);
if (!layerParams.has("pad_w") && !layerParams.has("pad_h"))
setPadding(layerParams, layer);
// The final node of dilated convolution subgraph.
next_layers = getNextLayers(net, name, "BatchToSpaceND");
if (!next_layers.empty())
{
layerParams.set("pad_mode", ""); // We use padding values.
CV_Assert(next_layers.size() == 1);
ExcludeLayer(net, next_layers[0].second, 0, false);
layers_to_ignore.insert(next_layers[0].first);
......
modules/dnn/test/test_tf_importer.cpp
View file @ f056c0f1
......@@ -239,7 +239,7 @@ TEST_P(Test_TensorFlow_layers, l2_normalize)
// TODO: fix it and add to l2_normalize
TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
runTensorFlowNet("l2_normalize_3d");
}
......@@ -360,10 +360,6 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
{
checkBackend();
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD))
throw SkipTestException("");
std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
......@@ -386,7 +382,7 @@ TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 3.4e-3;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.017 : 1e-2;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.024 : 1e-2;
normAssertDetections(ref, out, "", 0.9, scoreDiff, iouDiff);
}
......
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