What is the shape of the C object corresponding to this TFLite output?

I have a YOLOv5 trained model converted to .tflite format having used this guide.

I use this code to print the input and output shape in python: “` interpreter = tf.lite.Interpreter( # model_path=”models/exported_resnet640.tflite”) # centernet_512x512 works correctly model_path=”models/yolov5_working.tflite”) # centernet_512x512 works correctly

interpreter.allocate_tensors()

Get input and output tensors.

input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() print(“======================================================”) print(input_details) print(“======================================================”)

print(output_details)

for detail in output_details: print(detail) print(” “) “` and the output looks like this:

“` [{‘name’: ‘input_1’, ‘index’: 0, ‘shape’: array([ 1, 480, 480, 3], dtype=int32), ‘shape_signature’: array([ 1, 480, 480, 3], dtype=int32), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}}]

{‘name’: ‘Identity’, ‘index’: 422, ‘shape’: array([ 1, 14175, 9], dtype=int32), ‘shape_signature’: array([ 1, 14175, 9], dtype=int32), ‘dtype’: <class ‘numpy.float32’>, ‘quantization’: (0.0, 0), ‘quantization_parameters’: {‘scales’: array([], dtype=float32), ‘zero_points’: array([], dtype=int32), ‘quantized_dimension’: 0}, ‘sparsity_parameters’: {}} After invoking the interpreter after giving some input, I get an output looking like this: Output: [[[0.01191081 0.01366316 0.02800988 … 0.1661754 0.31489396 0.4217688 ] [0.02396268 0.01650745 0.0442626 … 0.24655405 0.35853994 0.2839473 ] [0.04218047 0.01613732 0.0548977 … 0.13136038 0.25760946 0.5338376 ] … [0.82626414 0.9669814 0.4534862 … 0.18754318 0.11680853 0.18492043] [0.8983849 0.9680944 0.64181983 … 0.19781056 0.16431764 0.16926363] [0.9657682 0.9869368 0.5452545 … 0.13321301 0.12015155 0.15937251]]] “`

Using the Tensorflow Lite c_api.h, I am trying to get the same output in C, but I cannot understand how to create the object that get the data.

I have tried using a float*** with size 1 * 14715 * 9 * sizeof(float) and get the output like so: “` int number_of_detections = 14175; struct filedata o_boxes; float **box_coords = (float *)malloc(sizeof(float *) * 1);

box_coords[0] = (float **)malloc(sizeof(float *) * (int)number_of_detections); for (int i = 0; i < (int)number_of_detections; i++) { box_coords[0][i] = (float *)calloc(sizeof(float), 9); // box has 9 coordinates }

o_boxes.data = box_coords; o_boxes.size = 1 * (int)number_of_detections * 9 + 1;

const TfLiteTensor *output_tensor_boxes = TfLiteInterpreterGetOutputTensor(interpreter, 0); TfLiteTensorCopyToBuffer(output_tensor_boxes, o_boxes.data, o_boxes.size * sizeof(float));

box_coords = (float ***)&o_boxes.data;

for (int i = 0; i < o_boxes.size; i++) { for (int j = 0; j < 9; j++) { printf(“%f “, box_coords[0][i][j]); fflush(stdout); } printf(“n”); } where `struct filedata` is a simple struct: struct filedata { void *data; size_t size; }; “`

The result is some garbage big floats: 39688651931648.000000 0.000000 39805756899328.000000 0.000000 39807166185472.000000 0.000000 39807367512064.000000 0.000000 39807568838656.000000 and after the first iteration I get a Segmentation Fault.

How should I create/allocate my float array to get my data?