TensorFlow Lite (TFLite) Python Inference Example with Quantization

quantized-inference-example.py

# TFLite quantized inference example
#
# Based on:
# https://www.tensorflow.org/lite/performance/post_training_integer_quant
# https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Tensor.QuantizationParams

import numpy as np
import tensorflow as tf

# Location of tflite model file (float32 or int8 quantized)
model_path = "my-model-file.lite"

# Processed features (copy from Edge Impulse project)
features = [
  # <COPY FEATURES HERE!>
]
  
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=model_path)

# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Allocate tensors
interpreter.allocate_tensors()

# Print the input and output details of the model
print()
print("Input details:")
print(input_details)
print()
print("Output details:")
print(output_details)
print()

# Convert features to NumPy array
np_features = np.array(features)

# If the expected input type is int8 (quantized model), rescale data
input_type = input_details[0]['dtype']
if input_type == np.int8:
    input_scale, input_zero_point = input_details[0]['quantization']
    print("Input scale:", input_scale)
    print("Input zero point:", input_zero_point)
    print()
    np_features = (np_features / input_scale) + input_zero_point
    np_features = np.around(np_features)
    
# Convert features to NumPy array of expected type
np_features = np_features.astype(input_type)

# Add dimension to input sample (TFLite model expects (# samples, data))
np_features = np.expand_dims(np_features, axis=0)

# Create input tensor out of raw features
interpreter.set_tensor(input_details[0]['index'], np_features)

# Run inference
interpreter.invoke()

# output_details[0]['index'] = the index which provides the input
output = interpreter.get_tensor(output_details[0]['index'])

# If the output type is int8 (quantized model), rescale data
output_type = output_details[0]['dtype']
if output_type == np.int8:
    output_scale, output_zero_point = output_details[0]['quantization']
    print("Raw output scores:", output)
    print("Output scale:", output_scale)
    print("Output zero point:", output_zero_point)
    print()
    output = output_scale * (output.astype(np.float32) - output_zero_point)

# Print the results of inference
print("Inference output:", output)

hey Shawn , insaaf from india as i am working currently on yolov8 model and trynna get into the android application ,feels difficulty in interpreting the output of my yolov8 pytorch model into tflite model Here ill be attaching the input and ouput of tesnor details:
input
[{'dtype': <class 'numpy.float32'>,
'index': 0,
'name': 'inputs_0',
'quantization': (0.0, 0),
'quantization_parameters': {'quantized_dimension': 0,
'scales': array([], dtype=float32),
'zero_points': array([], dtype=int32)},
'shape': array([ 1, 640, 640, 3], dtype=int32),
'shape_signature': array([ 1, 640, 640, 3], dtype=int32),
'sparsity_parameters': {}}]
output
[{'dtype': <class 'numpy.float32'>,
'index': 542,
'name': 'Identity',
'quantization': (0.0, 0),
'quantization_parameters': {'quantized_dimension': 0,
'scales': array([], dtype=float32),
'zero_points': array([], dtype=int32)},
'shape': array([ 1, 22, 8400], dtype=int32),
'shape_signature': array([ 1, 22, 8400], dtype=int32),
'sparsity_parameters': {}}]
pls help me out in solving this problem i dont where im doing mistake
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