Added non_square

Support for non square YOLO models.

non_square/nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp

@@ -0,0 +1,209 @@
+/*
+ * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the "Software"),
+ * to deal in the Software without restriction, including without limitation
+ * the rights to use, copy, modify, merge, publish, distribute, sublicense,
+ * and/or sell copies of the Software, and to permit persons to whom the
+ * Software is furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+ * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+ * DEALINGS IN THE SOFTWARE.
+
+ * Edited by Marcos Luciano
+ * https://www.github.com/marcoslucianops
+ */
+
+#include "yoloPlugins.h"
+#include "NvInferPlugin.h"
+#include <cassert>
+#include <iostream>
+#include <memory>
+
+int kNUM_CLASSES;
+float kBETA_NMS;
+std::vector<float> kANCHORS;
+std::vector<std::vector<int>> kMASK;
+
+namespace {
+template <typename T>
+void write(char*& buffer, const T& val)
+{
+    *reinterpret_cast<T*>(buffer) = val;
+    buffer += sizeof(T);
+}
+
+template <typename T>
+void read(const char*& buffer, T& val)
+{
+    val = *reinterpret_cast<const T*>(buffer);
+    buffer += sizeof(T);
+}
+}
+
+cudaError_t cudaYoloLayer (
+    const void* input, void* output, const uint& batchSize,
+    const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
+    const uint& numBBoxes, uint64_t outputSize, cudaStream_t stream, const uint modelCoords, const float modelScale, const uint modelType);
+
+YoloLayer::YoloLayer (const void* data, size_t length)
+{
+    const char *d = static_cast<const char*>(data);
+    read(d, m_NumBoxes);
+    read(d, m_NumClasses);
+    read(d, m_GridSizeX);
+    read(d, m_GridSizeY);
+    read(d, m_OutputSize);
+
+    read(d, m_type);
+    read(d, m_new_coords);
+    read(d, m_scale_x_y);
+    read(d, m_beta_nms);
+    uint anchorsSize;
+    read(d, anchorsSize);
+    for (uint i = 0; i < anchorsSize; i++) {
+        float result;
+        read(d, result);
+        m_Anchors.push_back(result);
+    }
+    uint maskSize;
+    read(d, maskSize);
+    for (uint i = 0; i < maskSize; i++) {
+        uint nMask;
+        read(d, nMask);
+        std::vector<int> pMask;
+        for (uint f = 0; f < nMask; f++) {
+            int result;
+            read(d, result);
+            pMask.push_back(result);
+        }
+        m_Mask.push_back(pMask);
+    }
+    kNUM_CLASSES = m_NumClasses;
+    kBETA_NMS = m_beta_nms;
+    kANCHORS = m_Anchors;
+    kMASK = m_Mask;
+};
+
+YoloLayer::YoloLayer (
+    const uint& numBoxes, const uint& numClasses, const uint& gridSizeX, const uint& gridSizeY, const uint model_type, const uint new_coords, const float scale_x_y, const float beta_nms, const std::vector<float> anchors, std::vector<std::vector<int>> mask) :
+    m_NumBoxes(numBoxes),
+    m_NumClasses(numClasses),
+    m_GridSizeX(gridSizeX),
+    m_GridSizeY(gridSizeY),
+    m_type(model_type),
+    m_new_coords(new_coords),
+    m_scale_x_y(scale_x_y),
+    m_beta_nms(beta_nms),
+    m_Anchors(anchors),
+    m_Mask(mask)
+{
+    assert(m_NumBoxes > 0);
+    assert(m_NumClasses > 0);
+    assert(m_GridSizeX > 0);
+    assert(m_GridSizeY > 0);
+    m_OutputSize = m_GridSizeX * m_GridSizeY * (m_NumBoxes * (4 + 1 + m_NumClasses));
+};
+
+nvinfer1::Dims
+YoloLayer::getOutputDimensions(
+    int index, const nvinfer1::Dims* inputs, int nbInputDims)
+{
+    assert(index == 0);
+    assert(nbInputDims == 1);
+    return inputs[0];
+}
+
+bool YoloLayer::supportsFormat (
+    nvinfer1::DataType type, nvinfer1::PluginFormat format) const {
+    return (type == nvinfer1::DataType::kFLOAT &&
+            format == nvinfer1::PluginFormat::kNCHW);
+}
+
+void
+YoloLayer::configureWithFormat (
+    const nvinfer1::Dims* inputDims, int nbInputs,
+    const nvinfer1::Dims* outputDims, int nbOutputs,
+    nvinfer1::DataType type, nvinfer1::PluginFormat format, int maxBatchSize)
+{
+    assert(nbInputs == 1);
+    assert (format == nvinfer1::PluginFormat::kNCHW);
+    assert(inputDims != nullptr);
+}
+
+int YoloLayer::enqueue(
+    int batchSize, const void* const* inputs, void** outputs, void* workspace,
+    cudaStream_t stream)
+{
+    CHECK(cudaYoloLayer(
+              inputs[0], outputs[0], batchSize, m_GridSizeX, m_GridSizeY, m_NumClasses, m_NumBoxes,
+              m_OutputSize, stream, m_new_coords, m_scale_x_y, m_type));
+    return 0;
+}
+
+size_t YoloLayer::getSerializationSize() const
+{
+    int anchorsSum = 1;
+    for (uint i = 0; i < m_Anchors.size(); i++) {
+        anchorsSum += 1;
+    }
+    int maskSum = 1;
+    for (uint i = 0; i < m_Mask.size(); i++) {
+        maskSum += 1;
+        for (uint f = 0; f < m_Mask[i].size(); f++) {
+            maskSum += 1;
+        }
+    }
+
+    return sizeof(m_NumBoxes) + sizeof(m_NumClasses) + sizeof(m_GridSizeX) + sizeof(m_GridSizeY) + sizeof(m_OutputSize) + sizeof(m_type)
+            + sizeof(m_new_coords) + sizeof(m_scale_x_y) + sizeof(m_beta_nms) + anchorsSum * sizeof(float) + maskSum * sizeof(int);
+}
+
+void YoloLayer::serialize(void* buffer) const
+{
+    char *d = static_cast<char*>(buffer);
+    write(d, m_NumBoxes);
+    write(d, m_NumClasses);
+    write(d, m_GridSizeX);
+    write(d, m_GridSizeY);
+    write(d, m_OutputSize);
+
+    write(d, m_type);
+    write(d, m_new_coords);
+    write(d, m_scale_x_y);
+    write(d, m_beta_nms);
+    uint anchorsSize = m_Anchors.size();
+    write(d, anchorsSize);
+    for (uint i = 0; i < anchorsSize; i++) {
+        write(d, m_Anchors[i]);
+    }
+    uint maskSize = m_Mask.size();
+    write(d, maskSize);
+    for (uint i = 0; i < maskSize; i++) {
+        uint pMaskSize = m_Mask[i].size();
+        write(d, pMaskSize);
+        for (uint f = 0; f < pMaskSize; f++) {
+            write(d, m_Mask[i][f]);
+        }
+    }
+    kNUM_CLASSES = m_NumClasses;
+    kBETA_NMS = m_beta_nms;
+    kANCHORS = m_Anchors;
+    kMASK = m_Mask;
+}
+
+nvinfer1::IPluginV2* YoloLayer::clone() const
+{
+    return new YoloLayer (m_NumBoxes, m_NumClasses, m_GridSizeX, m_GridSizeY, m_type, m_new_coords, m_scale_x_y, m_beta_nms, m_Anchors, m_Mask);
+}
+
+REGISTER_TENSORRT_PLUGIN(YoloLayerPluginCreator);