--- /dev/null
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+// Copyright (C) 2018, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#ifdef HAVE_PROTOBUF
+
+#include "tf_graph_editor.hpp"
+
+namespace cv { namespace dnn {
+CV__DNN_EXPERIMENTAL_NS_BEGIN
+
+using ::google::protobuf::RepeatedField;
+using ::google::protobuf::MapPair;
+
+class Subgraph // Interface to match and replace TensorFlow subgraphs.
+{
+public:
+ // Add a node to be matched in the origin graph. Specify ids of nodes that
+ // are expected to be inputs. Returns id of a newly added node.
+ // TODO: Replace inputs to std::vector<int> in C++11
+ int addNodeToMatch(const std::string& op, int input_0 = -1, int input_1 = -1,
+ int input_2 = -1, int input_3 = -1)
+ {
+ int nodeInputs[] = {input_0, input_1, input_2, input_3};
+ int numInputs = 0;
+ for (int i = 0; i < 4; ++i)
+ {
+ CV_Assert(nodeInputs[i] < (int)nodes.size());
+ numInputs += (int)(nodeInputs[i] != -1);
+ }
+ nodes.push_back(op);
+ inputs.push_back(std::vector<int>(&nodeInputs[0], &nodeInputs[0] + numInputs));
+ return nodes.size() - 1;
+ }
+
+ // Specify resulting node. All the matched nodes in subgraph excluding
+ // input nodes will be fused into this single node.
+ // TODO: Replace inputs to std::vector<int> in C++11
+ void setFusedNode(const std::string& op, int input_0 = -1, int input_1 = -1,
+ int input_2 = -1, int input_3 = -1, int input_4 = -1,
+ int input_5 = -1)
+ {
+ int nodeInputs[] = {input_0, input_1, input_2, input_3, input_4, input_5};
+ int numInputs = 0;
+ for (int i = 0; i < 6; ++i)
+ {
+ CV_Assert(nodeInputs[i] < (int)nodes.size());
+ numInputs += (int)(nodeInputs[i] != -1);
+ }
+ fusedNodeInputs = std::vector<int>(&nodeInputs[0], &nodeInputs[0] + numInputs);
+
+ fusedNodeOp = op;
+ nodesToFuse.clear();
+ for (int i = 0; i < nodes.size(); ++i)
+ {
+ if (std::find(fusedNodeInputs.begin(), fusedNodeInputs.end(), i) == fusedNodeInputs.end())
+ nodesToFuse.push_back(i);
+ }
+ }
+
+ static const tensorflow::NodeDef& getInputNode(const tensorflow::GraphDef& net,
+ const tensorflow::NodeDef& node,
+ int inpId)
+ {
+ CV_Assert(inpId < node.input_size());
+ std::string name = node.input(inpId);
+ const int numNodes = net.node_size();
+ for (int i = 0; i < numNodes; ++i)
+ {
+ const tensorflow::NodeDef& node = net.node(i);
+ if (node.name() == name)
+ return node;
+ }
+ CV_Error(Error::StsParseError, "Input node with name " + name + " not found");
+ return net.node(0); // just return something
+ }
+
+ // Match TensorFlow subgraph starting from <nodeId> with a set of nodes to be fused.
+ // Returns true if nodes are matched and can be fused.
+ bool match(const tensorflow::GraphDef& net, int nodeId, int* numMatchedNodes)
+ {
+ *numMatchedNodes = 0;
+ int numNodes = net.node_size();
+ for (int i = 0; i < nodesToFuse.size(); ++i)
+ {
+ if (nodeId + i > numNodes - 1)
+ return false;
+
+ const tensorflow::NodeDef &node = net.node(nodeId + i);
+ if (node.op() != nodes[nodesToFuse[i]])
+ return false;
+
+ std::vector<int>& inputNodes = inputs[nodesToFuse[i]];
+ if (inputNodes.size() != node.input_size())
+ return false;
+ for (int j = 0; j < inputNodes.size(); ++j)
+ {
+ if (nodes[inputNodes[j]].empty()) // Unknown input node type.
+ continue;
+ const tensorflow::NodeDef& inpNode = getInputNode(net, node, j);
+ if (inpNode.op() != nodes[inputNodes[j]])
+ return false;
+ }
+
+ *numMatchedNodes += 1;
+ }
+ return true;
+ }
+
+ // Fuse matched subgraph.
+ void replace(tensorflow::GraphDef& net, int nodeId, int* numReplacedNodes)
+ {
+ *numReplacedNodes = 0;
+
+ // Extract names of input nodes.
+ std::vector<std::string> inputsNames(fusedNodeInputs.size());
+ for (int i = 0; i < fusedNodeInputs.size(); ++i)
+ {
+ std::string inpName;
+ // Find input node name looking at inputs of fused nodes.
+ for (int j = 0; j < nodesToFuse.size() && inpName.empty(); ++j)
+ {
+ const tensorflow::NodeDef &node = net.node(nodeId + j);
+ std::vector<int>& inpIndices = inputs[nodesToFuse[j]];
+
+ CV_Assert(node.input_size() == inpIndices.size());
+ for (int k = 0; k < inpIndices.size(); ++k)
+ {
+ if (inpIndices[k] == fusedNodeInputs[i])
+ {
+ inpName = node.input(k);
+ break;
+ }
+ }
+ }
+ CV_Assert(!inpName.empty());
+ inputsNames[i] = inpName;
+ }
+
+ // Remove all nodes except the last one.
+ *numReplacedNodes = nodesToFuse.size() - 1;
+ net.mutable_node()->DeleteSubrange(nodeId, *numReplacedNodes);
+
+ // Modify the last node to be a fused one.
+ tensorflow::NodeDef* node = net.mutable_node(nodeId);
+ node->set_op(fusedNodeOp);
+ node->clear_input();
+ for (int i = 0; i < inputsNames.size(); ++i)
+ {
+ node->add_input(inputsNames[i]);
+ }
+
+ std::vector<tensorflow::NodeDef> inputNodes(inputsNames.size());
+ for (int i = 0; i < inputsNames.size(); ++i)
+ {
+ inputNodes[i] = getInputNode(net, *node, i);
+ }
+ finalize(net, node, inputNodes);
+ }
+
+ virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef*,
+ const std::vector<tensorflow::NodeDef>&) {}
+
+private:
+ std::vector<std::string> nodes; // Nodes to be matched in the origin graph.
+ std::vector<std::vector<int> > inputs; // Connections of an every node to it's inputs.
+
+ std::string fusedNodeOp; // Operation name of resulting fused node.
+ std::vector<int> nodesToFuse; // Set of nodes to be fused.
+ std::vector<int> fusedNodeInputs; // Inputs of fused node.
+};
+
+class BatchNormSubgraph : public Subgraph
+{
+public:
+ BatchNormSubgraph()
+ {
+ int input = addNodeToMatch("");
+ int epsilon = addNodeToMatch("Const");
+ int moving_variance = addNodeToMatch("Const");
+ int moving_mean = addNodeToMatch("Const");
+ int beta = addNodeToMatch("Const");
+ int gamma = addNodeToMatch("Const");
+ int add = addNodeToMatch("Add", moving_variance, epsilon);
+ int rsqrt = addNodeToMatch("Rsqrt", add);
+ int mul = addNodeToMatch("Mul", rsqrt, gamma);
+ int mul_1 = addNodeToMatch("Mul", input, mul);
+ int mul_2 = addNodeToMatch("Mul", moving_mean, mul);
+ int sub = addNodeToMatch("Sub", beta, mul_2);
+ addNodeToMatch("Add", mul_1, sub);
+
+ setFusedNode("FusedBatchNorm", input, gamma, beta, moving_mean, moving_variance, epsilon);
+ }
+
+ virtual void finalize(tensorflow::GraphDef&, tensorflow::NodeDef* fusedNode,
+ const std::vector<tensorflow::NodeDef>& inputNodes)
+ {
+ Mat epsMat = getTensorContent(inputNodes.back().attr().at("value").tensor());
+ CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1);
+
+ fusedNode->mutable_input()->ReleaseLast();
+ fusedNode->clear_attr();
+ tensorflow::AttrValue epsilon;
+ epsilon.set_f(epsMat.at<float>(0));
+ fusedNode->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("epsilon", epsilon));
+ }
+};
+
+class BatchNormNoGammaSubgraph : public Subgraph
+{
+public:
+ BatchNormNoGammaSubgraph()
+ {
+ int input = addNodeToMatch("");
+ int epsilon = addNodeToMatch("Const");
+ int moving_variance = addNodeToMatch("Const");
+ int moving_mean = addNodeToMatch("Const");
+ int beta = addNodeToMatch("Const");
+ int add = addNodeToMatch("Add", moving_variance, epsilon);
+ int rsqrt = addNodeToMatch("Rsqrt", add);
+ int mul = addNodeToMatch("Mul", input, rsqrt);
+ int mul_1 = addNodeToMatch("Mul", moving_mean, rsqrt);
+ int sub = addNodeToMatch("Sub", beta, mul_1);
+ addNodeToMatch("Add", mul, sub);
+
+ // There is a fake reference to beta that will be replaced to a new gamma tensor.
+ setFusedNode("FusedBatchNorm", input, beta, beta, moving_mean, moving_variance, epsilon);
+ }
+
+ virtual void finalize(tensorflow::GraphDef& net, tensorflow::NodeDef* fusedNode,
+ const std::vector<tensorflow::NodeDef>& inputNodes)
+ {
+ Mat epsMat = getTensorContent(inputNodes.back().attr().at("value").tensor());
+ CV_Assert(epsMat.total() == 1, epsMat.type() == CV_32FC1);
+
+ fusedNode->mutable_input()->ReleaseLast();
+ fusedNode->clear_attr();
+ tensorflow::AttrValue epsilon;
+ epsilon.set_f(epsMat.at<float>(0));
+ fusedNode->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("epsilon", epsilon));
+
+ tensorflow::NodeDef* gamma = net.add_node();
+ gamma->set_op("Const");
+ gamma->set_name(fusedNode->name() + "/gamma");
+ // Just put a single value to recognize this node as Const.
+ gamma->mutable_attr()->insert(MapPair<std::string, tensorflow::AttrValue>("value", epsilon));
+ fusedNode->set_input(1, gamma->name());
+ }
+};
+
+// tf.contrib.layers.flatten
+class FlattenSubgraph : public Subgraph
+{
+public:
+ FlattenSubgraph()
+ {
+ int input = addNodeToMatch("");
+ int shape = addNodeToMatch("Const");
+ int stack = addNodeToMatch("Const");
+ int stack_1 = addNodeToMatch("Const");
+ int stack_2 = addNodeToMatch("Const");
+ int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
+ int shape_pack = addNodeToMatch("Const");
+ int pack = addNodeToMatch("Pack", strided_slice, shape_pack);
+ addNodeToMatch("Reshape", input, pack);
+
+ setFusedNode("Flatten", input);
+ }
+};
+
+// tf.contrib.layers.flatten in case of unknown batch size
+class FlattenShapeSubgraph : public Subgraph
+{
+public:
+ FlattenShapeSubgraph()
+ {
+ int input = addNodeToMatch("");
+ int shape = addNodeToMatch("Shape", input);
+ int stack = addNodeToMatch("Const");
+ int stack_1 = addNodeToMatch("Const");
+ int stack_2 = addNodeToMatch("Const");
+ int strided_slice = addNodeToMatch("StridedSlice", shape, stack, stack_1, stack_2);
+ int shape_pack = addNodeToMatch("Const");
+ int pack = addNodeToMatch("Pack", strided_slice, shape_pack);
+ addNodeToMatch("Reshape", input, pack);
+
+ setFusedNode("Flatten", input);
+ }
+};
+
+void simplifySubgraphs(tensorflow::GraphDef& net)
+{
+ std::vector<Ptr<Subgraph> > subgraphs;
+ subgraphs.push_back(Ptr<Subgraph>(new BatchNormSubgraph()));
+ subgraphs.push_back(Ptr<Subgraph>(new BatchNormNoGammaSubgraph()));
+ subgraphs.push_back(Ptr<Subgraph>(new FlattenSubgraph()));
+ subgraphs.push_back(Ptr<Subgraph>(new FlattenShapeSubgraph()));
+
+ int numNodes = net.node_size();
+ int numMatchedNodes, numReplacedNodes;
+ for (int i = 0; i < numNodes; ++i)
+ {
+ for (int j = 0; j < subgraphs.size(); ++j)
+ {
+ if (subgraphs[j]->match(net, i, &numMatchedNodes))
+ {
+ subgraphs[j]->replace(net, i, &numReplacedNodes);
+ numNodes -= numReplacedNodes;
+ break;
+ }
+ }
+ }
+}
+
+void RemoveIdentityOps(tensorflow::GraphDef& net)
+{
+ typedef std::map<String, String> IdentityOpsMap;
+ IdentityOpsMap identity_ops;
+
+ std::vector<int> identity_ops_idx;
+
+ int layersCount = net.node_size();
+ for (int li = 0; li < layersCount; li++)
+ {
+ const tensorflow::NodeDef &layer = net.node(li);
+ String type = layer.op();
+
+ if (type == "Identity" || type == "Dropout") {
+ identity_ops_idx.push_back(li);
+ identity_ops[layer.name()] = layer.input(0);
+ }
+ }
+
+ for (int li = 0; li < layersCount; li++)
+ {
+ tensorflow::NodeDef* layer = net.mutable_node(li);
+ for (int input_id = 0; input_id < layer->input_size(); input_id++) {
+ String input_op_name = layer->input(input_id);
+ IdentityOpsMap::iterator it = identity_ops.find(input_op_name);
+
+ if (it != identity_ops.end()) {
+ layer->set_input(input_id, it->second);
+ }
+ }
+ }
+
+ std::sort(identity_ops_idx.begin(), identity_ops_idx.end());
+
+ int removed_nodes = 0;
+ for(size_t i = 0; i < identity_ops_idx.size(); i++) {
+ int start_id = identity_ops_idx[i] - removed_nodes;
+ net.mutable_node()->DeleteSubrange(start_id, 1);
+ removed_nodes++;
+ }
+}
+
+Mat getTensorContent(const tensorflow::TensorProto &tensor)
+{
+ std::string content = tensor.tensor_content();
+ switch (tensor.dtype())
+ {
+ case tensorflow::DT_FLOAT:
+ {
+ if (!content.empty())
+ return Mat(1, content.size() / sizeof(float), CV_32FC1, (void*)content.c_str()).clone();
+ else
+ {
+ const RepeatedField<float>& field = tensor.float_val();
+ CV_Assert(!field.empty());
+ return Mat(1, field.size(), CV_32FC1, (void*)field.data()).clone();
+ }
+ }
+ case tensorflow::DT_DOUBLE:
+ {
+ if (!content.empty())
+ return Mat(1, content.size() / sizeof(double), CV_64FC1, (void*)content.c_str()).clone();
+ else
+ {
+ const RepeatedField<double>& field = tensor.double_val();
+ CV_Assert(!field.empty());
+ return Mat(1, field.size(), CV_64FC1, (void*)field.data()).clone();
+ }
+ }
+ case tensorflow::DT_INT32:
+ {
+ if (!content.empty())
+ return Mat(1, content.size() / sizeof(int32_t), CV_32SC1, (void*)content.c_str()).clone();
+ else
+ {
+ const RepeatedField<int32_t>& field = tensor.int_val();
+ CV_Assert(!field.empty());
+ return Mat(1, field.size(), CV_32SC1, (void*)field.data()).clone();
+ }
+ }
+ case tensorflow::DT_HALF:
+ {
+ Mat halfs;
+ if (!content.empty())
+ {
+ static const int kHalfSize = 2;
+ halfs = Mat(1, content.size() / kHalfSize, CV_16UC1, (void*)content.c_str());
+ }
+ else
+ {
+ const RepeatedField<int32_t>& field = tensor.half_val();
+ CV_Assert(!field.empty());
+ Mat ints(1, field.size(), CV_32SC1, (void*)field.data());
+ ints.convertTo(halfs, CV_16UC1);
+ }
+ // Reinterpret as a signed shorts just for a convertFp16 call.
+ Mat halfsSigned(halfs.size(), CV_16SC1, halfs.data);
+ Mat floats(halfs.size(), CV_32FC1);
+ convertFp16(halfsSigned, floats);
+ return floats;
+ }
+ case tensorflow::DT_QUINT8:
+ {
+ CV_Assert(!content.empty());
+ return Mat(1, content.size(), CV_8UC1, (void*)content.c_str()).clone();
+ }
+ default:
+ CV_Error(Error::StsError, "Tensor's data type is not supported");
+ break;
+ }
+ return Mat();
+}
+
+CV__DNN_EXPERIMENTAL_NS_END
+}} // namespace dnn, namespace cv
+
+#endif // HAVE_PROTOBUF
#include <google/protobuf/text_format.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include "tf_io.hpp"
+#include "tf_graph_editor.hpp"
#endif
namespace cv {
}
}
-static Mat getTensorContent(const tensorflow::TensorProto &tensor)
-{
- std::string content = tensor.tensor_content();
- switch (tensor.dtype())
- {
- case tensorflow::DT_FLOAT:
- {
- if (!content.empty())
- return Mat(1, content.size() / sizeof(float), CV_32FC1, (void*)content.c_str()).clone();
- else
- {
- const RepeatedField<float>& field = tensor.float_val();
- CV_Assert(!field.empty());
- return Mat(1, field.size(), CV_32FC1, (void*)field.data()).clone();
- }
- }
- case tensorflow::DT_DOUBLE:
- {
- if (!content.empty())
- return Mat(1, content.size() / sizeof(double), CV_64FC1, (void*)content.c_str()).clone();
- else
- {
- const RepeatedField<double>& field = tensor.double_val();
- CV_Assert(!field.empty());
- return Mat(1, field.size(), CV_64FC1, (void*)field.data()).clone();
- }
- }
- case tensorflow::DT_INT32:
- {
- if (!content.empty())
- return Mat(1, content.size() / sizeof(int32_t), CV_32SC1, (void*)content.c_str()).clone();
- else
- {
- const RepeatedField<int32_t>& field = tensor.int_val();
- CV_Assert(!field.empty());
- return Mat(1, field.size(), CV_32SC1, (void*)field.data()).clone();
- }
- }
- case tensorflow::DT_HALF:
- {
- Mat halfs;
- if (!content.empty())
- {
- static const int kHalfSize = 2;
- halfs = Mat(1, content.size() / kHalfSize, CV_16UC1, (void*)content.c_str());
- }
- else
- {
- const RepeatedField<int32_t>& field = tensor.half_val();
- CV_Assert(!field.empty());
- Mat ints(1, field.size(), CV_32SC1, (void*)field.data());
- ints.convertTo(halfs, CV_16UC1);
- }
- // Reinterpret as a signed shorts just for a convertFp16 call.
- Mat halfsSigned(halfs.size(), CV_16SC1, halfs.data);
- Mat floats(halfs.size(), CV_32FC1);
- convertFp16(halfsSigned, floats);
- return floats;
- }
- case tensorflow::DT_QUINT8:
- {
- CV_Assert(!content.empty());
- return Mat(1, content.size(), CV_8UC1, (void*)content.c_str()).clone();
- }
- default:
- CV_Error(Error::StsError, "Tensor's data type is not supported");
- break;
- }
- return Mat();
-}
-
template <typename T>
void parseTensor(const tensorflow::TensorProto &tensor, Mat &dstBlob)
{
layerParams.set("pad_mode", getLayerAttr(layer, "padding").s());
}
-void RemoveIdentityOps(tensorflow::GraphDef& net) {
- typedef std::map<String, String> IdentityOpsMap;
- IdentityOpsMap identity_ops;
-
- std::vector<int> identity_ops_idx;
-
- int layersCount = net.node_size();
- for (int li = 0; li < layersCount; li++)
- {
- const tensorflow::NodeDef &layer = net.node(li);
- String type = layer.op();
-
- if (type == "Identity" || type == "Dropout") {
- identity_ops_idx.push_back(li);
- identity_ops[layer.name()] = layer.input(0);
- }
- }
-
- for (int li = 0; li < layersCount; li++)
- {
- tensorflow::NodeDef* layer = net.mutable_node(li);
- for (int input_id = 0; input_id < layer->input_size(); input_id++) {
- String input_op_name = layer->input(input_id);
- IdentityOpsMap::iterator it = identity_ops.find(input_op_name);
-
- if (it != identity_ops.end()) {
- layer->set_input(input_id, it->second);
- }
- }
- }
-
- std::sort(identity_ops_idx.begin(), identity_ops_idx.end());
-
- int removed_nodes = 0;
- for(size_t i = 0; i < identity_ops_idx.size(); i++) {
- int start_id = identity_ops_idx[i] - removed_nodes;
- net.mutable_node()->DeleteSubrange(start_id, 1);
- removed_nodes++;
- }
-}
-
Pin parsePin(const std::string &name)
{
Pin pin(name);
RemoveIdentityOps(netBin);
RemoveIdentityOps(netTxt);
+ if (!netTxt.ByteSize())
+ simplifySubgraphs(netBin);
+
std::set<String> layers_to_ignore;
tensorflow::GraphDef& net = netTxt.ByteSize() != 0 ? netTxt : netBin;
connect(layer_id, dstNet, inpId, id, 0);
data_layouts[name] = DATA_LAYOUT_UNKNOWN;
}
- else if (type == "Flatten")
+ else if (type == "Flatten" || type == "Squeeze")
{
Pin inpId = parsePin(layer.input(0));
- if (data_layouts[layer.input(0)] == DATA_LAYOUT_NHWC)
+ int inpLayout = data_layouts[layer.input(0)];
+ if (type == "Squeeze")
+ {
+ CV_Assert(hasLayerAttr(layer, "squeeze_dims"));
+ const tensorflow::AttrValue& dims = getLayerAttr(layer, "squeeze_dims");
+ if (inpLayout == DATA_LAYOUT_NHWC)
+ {
+ if (dims.list().i_size() != 2 || dims.list().i(0) != 1 || dims.list().i(1) != 2)
+ CV_Error(Error::StsNotImplemented, "Unsupported squeeze configuration");
+ }
+ else if (inpLayout == DATA_LAYOUT_NCHW)
+ {
+ if (dims.list().i_size() != 2 || dims.list().i(0) != 2 || dims.list().i(1) != 3)
+ CV_Error(Error::StsNotImplemented, "Unsupported squeeze configuration");
+ }
+ else
+ CV_Error(Error::StsNotImplemented, "Unsupported squeeze configuration");
+ }
+ if (inpLayout == DATA_LAYOUT_NHWC)
{
LayerParams permLP;
int order[] = {0, 2, 3, 1}; // From OpenCV's NCHW to NHWC.
bool isTraining = hasLayerAttr(layer, "is_training") && getLayerAttr(layer, "is_training").b();
- layerParams.blobs.resize(4);
- Mat gamma, beta, mean, std;
- blobFromTensor(getConstBlob(layer, value_id, 1), gamma);
- blobFromTensor(getConstBlob(layer, value_id, 2), beta);
+ layerParams.blobs.resize(2);
+
+ const tensorflow::TensorProto& gammaTensor = getConstBlob(layer, value_id, 1);
+ if (!gammaTensor.tensor_content().empty())
+ {
+ layerParams.blobs.resize(layerParams.blobs.size() + 1);
+ layerParams.set("has_weight", true);
+ blobFromTensor(gammaTensor, layerParams.blobs.back());
+ }
+ else
+ layerParams.set("has_weight", false);
+
+ const tensorflow::TensorProto& betaTensor = getConstBlob(layer, value_id, 2);
+ if (!betaTensor.tensor_content().empty())
+ {
+ layerParams.blobs.resize(layerParams.blobs.size() + 1);
+ layerParams.set("has_bias", true);
+ blobFromTensor(betaTensor, layerParams.blobs.back());
+ }
+ else
+ layerParams.set("has_bias", false);
+
+ Mat mean, std;
if (isTraining)
{
- mean = Mat::zeros(1, beta.total(), CV_32F);
- std = Mat::ones(1, beta.total(), CV_32F);
+ if (layerParams.blobs.size() == 2)
+ CV_Error(Error::StsNotImplemented, "Cannot determine number "
+ "of parameters for batch normalization layer.");
+ mean = Mat::zeros(1, layerParams.blobs[3].total(), CV_32F);
+ std = Mat::ones(1, layerParams.blobs[3].total(), CV_32F);
// Add an extra layer: Mean-Variance normalization
LayerParams mvnParams;
}
layerParams.blobs[0] = mean;
layerParams.blobs[1] = std;
- layerParams.blobs[2] = gamma;
- layerParams.blobs[3] = beta;
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;
projects / platform / upstream / opencv.git / commitdiff