Merge master changes -> 7.1 (#547)

* Prefix logging messages with [TRT] (#497) * Add local build instructions (#498) * fix duplicate layer names bug (#446) (#467) Suppose we have a network with (not all distinct) layer names layer layer_1 layer When ImporterContext sees "layer", it sees it's not in mLayerNameCounts, and sets mLayerNameCounts["layer"] = 1 and adds a TRT layer with name "layer". It then sees "layer_1", concludes it's not in mLayerNameCounts, so it sets mLayerNameCounts["layer_1"] = 1 and adds a TRT layer with name "layer_1". NOW when it sees "layer", it sees that mLayerNameCounts["layer"] == 1, so we produce a "uniqueName" of "layer" + "_" + std::to_string(mLayerNameCounts["layer"] ), ie "layer_1", which is a name conflict for the TRT net. This change keeps track of all inserted names in a set and in the case of duplicates, tries suffix-appended modifications of the duplicated name by ever increasing integers until a name appears which has not been used. * Support Dynamic and 3D instanceNormalization (#515) * Add restrictions on multi-input convolutions (#521) * Update resize limitations in TensorRT (#538) Signed-off-by: Kevin Chen <[email protected]> Co-authored-by: Thomas Peters <[email protected]>
onnx · Oct 20, 2020 · 7eb49ff · 7eb49ff
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diff --git a/ImporterContext.hpp b/ImporterContext.hpp
@@ -26,11 +26,12 @@
 #include "onnx2trt_utils.hpp"
 
 #include <list>
+#include <set>
+#include <string>
 #include <unordered_map>
 
 namespace onnx2trt
 {
-
 class ImporterContext final : public IImporterContext
 {
     nvinfer1::INetworkDefinition* _network;
@@ -44,13 +45,17 @@ class ImporterContext final : public IImporterContext
     StringMap<float> mTensorRangeMins;
     StringMap<float> mTensorRangeMaxes;
     StringMap<nvinfer1::DataType> mLayerPrecisions;
-    StringMap<size_t>
-        mTensorNameCounts; // Keep track of how many times a tensor name shows up, to avoid duplicate naming in TRT.
-    StringMap<size_t>
-        mLayerNameCounts; // Keep track of how many times a tensor name shows up, to avoid duplicate naming in TRT.
-    std::unordered_set<std::string> mUnsupportedShapeTensors; // Container to hold output tensor names of layers that produce shape tensor outputs but do not natively support them.
-    StringMap<std::string> mLoopTensors; // Container to map subgraph tensors to their original outer graph names.
-    std::string mOnnxFileLocation; // Keep track of the directory of the parsed ONNX file
+    std::set<std::string> mTensorNames;                       // keep track of tensor names used so far,
+                                                              // to avoid duplicate naming in TRT.
+    std::set<std::string> mLayerNames;                        // keep track of layer names used so far,
+                                                              // to avoid duplicate naming in TRT.
+    int64_t mSuffixCounter = 0;                               // increasing suffix counter used to uniquify layer names.
+    std::unordered_set<std::string> mUnsupportedShapeTensors; // Container to hold any shape tensors that are
+                                                              // the output of layers that do not support
+                                                              // shape tensors.
+    StringMap<std::string> mLoopTensors;                      // Container to map subgraph tensors to
+                                                              // their original outer graph names.
+    std::string mOnnxFileLocation;                            // Keep track of the directory of the parsed ONNX file
 
 public:
     ImporterContext(nvinfer1::INetworkDefinition* network, nvinfer1::ILogger* logger)
@@ -98,13 +103,12 @@ class ImporterContext final : public IImporterContext
     {
         return mOnnxFileLocation;
     }
-    // This actually handles weights as well, but is named this way to be consistent with the tensors()
+    // This actually handles weights as well, but is named this way to be
+    // consistent with the tensors()
     virtual void registerTensor(TensorOrWeights tensor, const std::string& basename) override
     {
         // TRT requires unique tensor names.
-        const std::string uniqueName
-            = mTensorNameCounts[basename] ? (basename + "_" + std::to_string(mTensorNameCounts[basename])) : basename;
-        ++mTensorNameCounts[basename];
+        const std::string uniqueName = generateUniqueName(mTensorNames, basename);
 
         if (tensor)
         {
@@ -122,8 +126,9 @@ class ImporterContext final : public IImporterContext
                     convertINT64(reinterpret_cast<int64_t*>(weights.values), weights.shape, ctx), weights.shape};
             }
         }
-        // Overwrite previous tensors registered with the same name (this only happens when there are subgraphs,
-        // and in that case, overwriting is the desired behavior).
+        // Overwrite previous tensors registered with the same name (this only
+        // happens when there are subgraphs, and in that case, overwriting is the
+        // desired behavior).
         this->tensors()[basename] = std::move(tensor);
     }
 
@@ -133,9 +138,7 @@ class ImporterContext final : public IImporterContext
         if (layer)
         {
             const std::string name = basename.empty() ? layer->getName() : basename;
-            const std::string uniqueName
-                = mLayerNameCounts[name] ? (name + "_" + std::to_string(mLayerNameCounts[name])) : name;
-            ++mLayerNameCounts[name];
+            const std::string uniqueName = generateUniqueName(mLayerNames, basename);
 
             auto* ctx = this; // To enable logging.
             LOG_VERBOSE("Registering layer: " << name << " for ONNX node: " << basename);
@@ -225,6 +228,22 @@ class ImporterContext final : public IImporterContext
             return _opsets.at(domain);
         }
     }
+
+private:
+    std::string generateUniqueName(std::set<std::string>& namesSet, const std::string& basename)
+    {
+        std::string candidate = basename;
+
+        while (namesSet.find(candidate) != namesSet.end())
+        {
+            candidate = basename + "_" + std::to_string(mSuffixCounter);
+            ++mSuffixCounter;
+        }
+
+        namesSet.insert(candidate);
+
+        return candidate;
+    }
 };
 
 } // namespace onnx2trt
diff --git a/ModelImporter.cpp b/ModelImporter.cpp
@@ -374,7 +374,7 @@ bool ModelImporter::supportsModel(
     std::vector<size_t> topological_order;
     if (!toposort(model.graph().node(), &topological_order))
     {
-        cout << "Failed to sort model topologically, exiting ..." << endl;
+        LOG_ERROR("Failed to sort model topologically, exiting ...");
         return false;
     }
 
@@ -408,7 +408,7 @@ bool ModelImporter::supportsModel(
         }
         else
         {
-            std::cout << "Found unsupported node: " << tensorName << std::endl;
+            LOG_WARNING("Found unsupported node: " << tensorName);
             // This is not a supported node, reset newSubGraph
             newSubGraph = true;
             allSupported = false;

diff --git a/README.md b/README.md
@@ -43,10 +43,15 @@ Current supported ONNX operators are found in the [operator support matrix](oper
 
 ### Building
 
-For building on master, we recommend following the instructions on the [master branch of TensorRT](https://github.com/NVIDIA/TensorRT/) as there are new dependencies that were introduced to support these new features.
+For building on master, we recommend following the instructions on the [master branch of TensorRT](https://github.com/NVIDIA/TensorRT/) to take advatange of the latest plugin code required for importing certain operators.
 
-To build on older branches refer to their respective READMEs.
+To build only the ONNX-TensorRT parser, follow the following steps:
 
+    cd onnx-tensorrt
+    mkdir build && cd build
+    cmake .. -DTENSORRT_ROOT=<path_to_trt> && make -j
+    // Ensure that you update your LD_LIBRARY_PATH to pick up the location of the newly built library:
+    export LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH
 
 ## Executable usage
 

diff --git a/builtin_op_importers.cpp b/builtin_op_importers.cpp
@@ -430,7 +430,7 @@ DEFINE_BUILTIN_OP_IMPORTER(Conv)
     ASSERT(inputs.at(0).is_tensor(), ErrorCode::kUNSUPPORTED_NODE);
     if (inputs.at(1).is_tensor())
     {
-        ASSERT(inputs.at(1).is_tensor(), ErrorCode::kUNSUPPORTED_NODE);
+        ASSERT(ctx->network()->hasExplicitPrecision() && "TensorRT only supports multi-input conv for explicit precision QAT networks!", ErrorCode::kUNSUPPORTED_NODE);
         if (inputs.size() == 3)
         {
             ASSERT(inputs.at(2).is_weights(), ErrorCode::kUNSUPPORTED_NODE);
@@ -1613,7 +1613,8 @@ DEFINE_BUILTIN_OP_IMPORTER(InstanceNormalization)
     ASSERT(inputs.at(1).is_weights(), ErrorCode::kUNSUPPORTED_NODE);
     ASSERT(inputs.at(2).is_weights(), ErrorCode::kUNSUPPORTED_NODE);
     nvinfer1::ITensor* tensorPtr = &convertToTensor(inputs.at(0), ctx);
-    ASSERT(!isDynamic(tensorPtr->getDimensions()) && "InstanceNormalization does not support dynamic inputs!",
+    int nbDims = tensorPtr->getDimensions().nbDims;
+    ASSERT(nbDims >= 3 && nbDims <= 4 && "TensorRT only supports InstanceNormalization on 3D or 4D tensors!",
         ErrorCode::kUNSUPPORTED_NODE);
     auto scale_weights = inputs.at(1).weights();
     auto bias_weights = inputs.at(2).weights();
@@ -2535,9 +2536,11 @@ DEFINE_BUILTIN_OP_IMPORTER(Resize)
 {
     nvinfer1::ITensor& input = convertToTensor(inputs.at(0), ctx);
     // TRT does not support INT32 nor BOOL input types for this node
-    ASSERT(input.getType() != nvinfer1::DataType::kINT32 && input.getType() != nvinfer1::DataType::kBOOL, ErrorCode::kUNSUPPORTED_NODE);
+    ASSERT( (input.getType() != nvinfer1::DataType::kINT32
+                && input.getType() != nvinfer1::DataType::kBOOL)
+                && "This version of TensorRT does not support INT32 or BOOL input for the Resize operator.", ErrorCode::kUNSUPPORTED_NODE);
     int inputRank = input.getDimensions().nbDims;
-    ASSERT(inputRank > 0, ErrorCode::kUNSUPPORTED_NODE);
+    ASSERT( (inputRank > 0) && "The input tensor cannot be a scalar.", ErrorCode::kUNSUPPORTED_NODE);
     // Add resize layer
     nvinfer1::IResizeLayer* layer = ctx->network()->addResize(input);
     ctx->registerLayer(layer, node.name());
@@ -2562,22 +2565,57 @@ DEFINE_BUILTIN_OP_IMPORTER(Resize)
                 && "This version of TensorRT only supports floor nearest_mode!",
             ErrorCode::kUNSUPPORTED_NODE);
 
-        // Note both asymmetric and align_corners resize modes go through the same import path in TRT:
-        if (transformationMode == "asymmetric" || transformationMode == "align_corners")
-        {
-            layer->setAlignCorners(true);
-        }
-
         // The existence of a fourth input means a shape was passed as the resize parameter
+        // For ONNX resize with the "sizes", TensorRT's resize maps to ONNX's in the following ways:
+        // Nearest:
+        //     alignCorners = 0: ASYMMETRIC
+        //     alignCorners = 1: ALIGN_CORNERS
+        // Linear:
+        //     alignCorners = 0: HALF_PIXEL
+        //     alignCorners = 1: ALIGN_CORNERS
         if (inputs.size() == 4)
         {
+            if (transformationMode == "align_corners")
+            {
+                layer->setAlignCorners(true);
+            }
+            if (mode == "nearest")
+            {
+                ASSERT((transformationMode == "asymmetric" || transformationMode == "align_corners") && "TensorRT only supports asymmetric and align_corners transformation modes for nearest neighbor resizes when sizes are provided!", ErrorCode::kUNSUPPORTED_NODE);
+            }
+            else if (mode == "linear")
+            {
+                ASSERT((transformationMode == "half_pixel" || transformationMode == "pytorch_half_pixel" || transformationMode == "align_corners") && "TensorRT only supports half_pixel, pytorch_half_pixel, and align_corners transofmration modes for linear resizes when sizes are provided!", ErrorCode::kUNSUPPORTED_NODE);
+            }
             auto* resizeShape = &convertToTensor(inputs.at(3), ctx);
             layer->setInput(1, *resizeShape);
             layer->setResizeMode(resizeMode);
             RETURN_FIRST_OUTPUT(layer);
         }
+        // For ONNX resize with "scales", TensorRT's resize maps to ONNX's in the following ways:
+        // Nearest:
+        //    alignCorners = 0: ASYMMETRIC
+        //    alignCorners = 1: ASYMMETRIC
+        // Linear:
+        //    alignCorners = 0: HALF_PIXEL
+        //    alignCorners = 1: ASYMMETRIC
+        else
+        {
+            if (mode == "nearest")
+            {
+                ASSERT(transformationMode == "asymmetric" && "TensorRT only supports asymmetric tranformation mode for nearest neighbor resizes when scales are provided!",ErrorCode::kUNSUPPORTED_NODE);
+            }
+            else if (mode == "linear")
+            {
+                ASSERT((transformationMode == "asymmetric" || transformationMode == "pytorch_half_pixel" || transformationMode == "half_pixel") && "TensorRT only supports half pixel, pytorch half_pixel, and asymmetric tranformation mode for linear resizes when scales are provided!", ErrorCode::kUNSUPPORTED_NODE);
+                if (transformationMode == "asymmetric")
+                {
+                    layer->setAlignCorners(true);
+                }
+            }
+        }
     }
-    // For opset 10 resize, the only supported mode is asymmetric resize, which is mapped to TRT's alignCorners.
+    // For opset 10 resize, the only supported mode is asymmetric resize with scales.
     else
     {
         transformationMode = "asymmetric";

diff --git a/onnx2trt_utils.hpp b/onnx2trt_utils.hpp
@@ -39,7 +39,7 @@
     do                                                                                                                 \
     {                                                                                                                  \
         std::stringstream ss{};                                                                                        \
-        ss << __FILENAME__ << ":" << __LINE__ << ": " << msg;                                                          \
+        ss << "[TRT]" << __FILENAME__ << ":" << __LINE__ << ": " << msg;                                               \
         ctx->logger().log(severity, ss.str().c_str());                                                                 \
     } while (0)