2.1.0-rc2 aarch64

Author: PINTO0309

so/2.1.0-rc2/download_ubuntu1910_aarch64_libtensorflowlite.so.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1PeayNKafP9OSyFMD_FMc1hjTXD0Bmu6B" > /dev/null
+CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
+curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1PeayNKafP9OSyFMD_FMc1hjTXD0Bmu6B" -o libtensorflowlite.so
+
+echo Download finished.

test/enet/build/.gitkeep

@@ -23,7 +23,7 @@
 int main()
 {
     /* read input image data */
-    cv::Mat image = cv::imread(RESOURCE_DIR"test.jpg", 1);
+    cv::Mat image = cv::imread(RESOURCE_DIR"test.jpg", 1), prepimage;
     cv::imshow("InputImage", image);
 
     // Initialize Colormap.
@@ -32,9 +32,10 @@ int main()
     // Initialize Threads.
     auto num_of_threads = 4;
 
-    cv::resize(image, image, cv::Size(480, 360));
-    cv::imshow("InputImage for CNN", image);
-    image.convertTo(image, CV_32FC1, 1.0 / 255);
+    cv::resize(image, prepimage, cv::Size(480, 360));
+    prepimage.convertTo(prepimage, CV_32FC1, 1.0 / 255);
+    cv::imshow("InputImage for CNN", prepimage);
+    cv::waitKey(0);
 
     // TFLite
     std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromFile(MODEL_FILENAME);
@@ -74,7 +75,8 @@ int main()
     // Set data to input tensor
     printf("=== MEM copy ===\n");
     float* input = interpreter->typed_input_tensor<float>(0);
-    memcpy(input, image.data, sizeof(float) * input_array_size);
+    //memcpy(input, prepimage.reshape(0, 1).data, sizeof(float) * input_array_size);
+    memcpy(input, prepimage.data, sizeof(float) * input_array_size);
 
     // Run inference
     printf("=== Pre-invoke ===\n");
@@ -88,8 +90,15 @@ int main()
     std::vector<float> output_data;
     const auto& output_indices = interpreter->outputs();
     const int num_outputs = output_indices.size();
+    std::cout << "num_outputs: " << num_outputs << std::endl;
+    //Inputs: 1040
+    //num_outputs: 2
+    //output_indices[0]: 716 ENet/fullconv/BiasAdd
+    //output_indices[1]: 726 ENet/logits_to_softmax
+    std::cout << "output_indices[0]: " << output_indices[0] << std::endl;
+    std::cout << "output_indices[1]: " << output_indices[1] << std::endl;
     int out_idx = 0;
-    for (int i = 0; i < num_outputs; ++i)
+    /*for (int i = 0; i < num_outputs; ++i)
     {
         const auto* out_tensor = interpreter->tensor(output_indices[i]);
         assert(out_tensor != nullptr);
@@ -100,16 +109,37 @@ int main()
         {
             output_data[out_idx++] = output[j];
         }
+    }*/
+    const auto* out_tensor = interpreter->tensor(output_indices[1]);
+    assert(out_tensor != nullptr);
+    const int num_values = out_tensor->bytes / sizeof(float);
+    output_data.resize(out_idx + num_values);
+    const float* output = interpreter->typed_output_tensor<float>(1);
+    for (int i = 0; i < num_values; ++i)
+    {
+        output_data[out_idx++] = output[i];
     }
 
+    //std::ostringstream output1_string_stream;
+    //std::copy(output_data.begin(), output_data.end(), std::ostream_iterator<int>(output1_string_stream, " "));
+    //std::cout << "output1 value: " << output1_string_stream.str() << std::endl;
+
+    std::ostringstream output2_string_stream;
+    std::copy(output_data.begin(), output_data.end(), std::ostream_iterator<int>(output2_string_stream, " "));
+    std::cout << "output2 value: " << output2_string_stream.str() << std::endl;
+
     // Create segmantation map.
-    cv::Mat seg_im(cv::Size(input_tensor_shape[1], input_tensor_shape[2]), CV_8UC3);
+    printf("=== Create segmantation map start ===\n");
+    cv::Mat seg_im(cv::Size(input_tensor_shape[1], input_tensor_shape[2]), CV_32FC1);
+    printf("=== Create segmantation map 1 ===\n");
     LabelToColorMap(output_data, *color_map.get(), seg_im);
+    printf("=== Create segmantation map end ===\n");
 
     // output tensor size => camera resolution
     cv::resize(seg_im, seg_im, cv::Size(480, 360));
-    seg_im = (image / 2) + (seg_im / 2);
+    //seg_im = (image / 2) + (seg_im / 2);
 
+    cv::imshow("Segmentation Image", seg_im);
     cv::waitKey(0);
     return 0;
 }