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747451d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | # /*---------------------------------------------------------------------------------------------
# * Copyright (c) 2022 STMicroelectronics.
# * All rights reserved.
# *
# * This software is licensed under terms that can be found in the LICENSE file in
# * the root directory of this software component.
# * If no LICENSE file comes with this software, it is provided AS-IS.
# *--------------------------------------------------------------------------------------------*/
# Import necessary libraries
import os
import sys
from pathlib import Path
import warnings
import sklearn
import mlflow
from hydra.core.hydra_config import HydraConfig
from omegaconf import DictConfig
from typing import Tuple, Optional, List, Dict
import numpy as np
# Suppress warnings and TensorFlow logs
warnings.filterwarnings("ignore")
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import onnxruntime
import tensorflow as tf
import tqdm
from sklearn.metrics import accuracy_score, confusion_matrix
# Import utility functions
from image_classification.tf.src.utils import ai_runner_invoke
from common.evaluation import model_is_quantized, predict_onnx_batch
from common.utils import (
tf_dataset_to_np_array, display_figures, ai_runner_interp, ai_interp_input_quant,
ai_interp_outputs_dequant, plot_confusion_matrix, torch_dataset_to_np_array
) # Common utilities for evaluation and visualization
# Define a class for evaluating ONNX models
class ONNXModelEvaluator:
"""
A class to evaluate ONNX models.
Args:
cfg (DictConfig): Configuration object for evaluation.
model (object): The ONNX model to evaluate.
dataloaders (dict): Dictionary containing datasets for testing and validation.
"""
def __init__(self, cfg: DictConfig, model: object,
dataloaders: dict = None):
self.cfg = cfg
self.input_model = model
self.test_ds = dataloaders['test']
self.valid_ds = dataloaders['valid']
self.output_dir = HydraConfig.get().runtime.output_dir
self.class_names = cfg.dataset.class_names
self.display_figures = cfg.general.display_figures
input_chpos = getattr(cfg.evaluation, 'input_chpos', 'chlast') if hasattr(cfg, 'evaluation') else 'chlast'
if self.cfg.model.framework == "tf":
# Dataloader is channel last with TF
if input_chpos=="chfirst" or self._get_target() == 'host':
self.nchw = True
else:
self.nchw = False
else:
# Dataloader is already channel first with Torch
if input_chpos=="chfirst":
self.nchw = False
else:
self.nchw = True
self.eval_ds = None
self.name_ds = None
def _prepare_evaluation(self):
"""
Prepares the evaluation process by selecting the appropriate dataset.
"""
# Use the test dataset if available; otherwise, use the validation dataset
if self.test_ds:
self.eval_ds = self.test_ds
self.name_ds = "test_set"
else:
self.eval_ds = self.valid_ds
self.name_ds = "validation_set"
def _ensure_output_dir(self):
"""
Ensures that the output directory exists.
"""
if not os.path.isdir(self.output_dir):
os.makedirs(self.output_dir)
def _get_target(self):
"""
Retrieves the evaluation target from the configuration.
Returns:
str: the target on which evaluation will be done, by default 'host'
"""
if self.cfg.evaluation and self.cfg.evaluation.target:
return self.cfg.evaluation.target
return "host"
def _get_model_type(self):
"""
Determines whether the model is quantized or float.
Returns:
str: 'quantized' or 'float' depending on model quantization status
"""
return 'quantized' if model_is_quantized(self.input_model.model_path) else 'float'
def _get_ai_runner_interpreter(self, target):
"""
Retrieves the AI runner interpreter for the specified target.
Args:
target (str): target on which evaluation is to be performed
Returns: ai_runner interpreter correctly parametrized
"""
name_model = os.path.basename(self.input_model.model_path)
return ai_runner_interp(target, name_model)
def _predict(self, ai_runner_interpreter, model_type, target):
"""
Runs predictions on the evaluation dataset.
Args:
ai_runner_interpreter: AI runner interpreter for inference.
model_type (str): Type of the model ('float' or 'quantized').
target (str): Evaluation target.
Returns:
Tuple[np.ndarray, np.ndarray]: Predicted labels and input labels.
"""
if model_type == 'float' or target == 'host':
# Use ONNX runtime for predictions
prd_labels, input_labels = predict_onnx_batch(sess=self.input_model,
data=self.eval_ds,
nchw=self.nchw)
prd_labels = prd_labels.argmax(axis=1)
elif target in ['stedgeai_host', 'stedgeai_n6', 'stedgeai_h7p'] and model_type == 'quantized':
# Use AI runner for predictions
prd_labels = []
if self.cfg.model.framework == "tf":
# Convert the tf dataset to NumPy array as dataloader was based on TF framework
input_samples, input_labels = tf_dataset_to_np_array(input_ds=self.eval_ds,
nchw=self.nchw)
else: #if self.cfg.model.framework == "torch":
input_samples, input_labels = torch_dataset_to_np_array(input_loader=self.eval_ds,
nchw=self.nchw)
for i in tqdm.tqdm(range(input_samples.shape[0])):
data = ai_interp_input_quant(ai_runner_interpreter, input_samples[i][None], '.onnx')
prd_label = ai_runner_invoke(data, ai_runner_interpreter)
prd_label = ai_interp_outputs_dequant(ai_runner_interpreter, [prd_label])[0]
prd_label = prd_label.argmax(axis=1)
prd_labels.append(prd_label)
prd_labels = np.array(prd_labels, dtype=np.float32)
else:
raise TypeError("Only supported targets are \"host\", \"stedgeai_host\" or \"stedgeai_n6\". "
"Check the \"evaluation\" section of your configuration file.")
return prd_labels, input_labels
def _run_evaluate(self):
"""
Runs the evaluation process and computes metrics.
Returns:
Tuple[float, np.ndarray]: Accuracy and confusion matrix.
"""
self._ensure_output_dir() # Ensure the output directory exists
target = self._get_target() # Get the evaluation target
model_type = self._get_model_type() # Determine the model type
ai_runner_interpreter = self._get_ai_runner_interpreter(target=target) # Get the AI runner interpreter
# Run predictions
prd_labels, input_labels = self._predict(ai_runner_interpreter, model_type, target)
accuracy = round(accuracy_score(input_labels, prd_labels) * 100, 2)
print(f'[INFO] : Evaluation accuracy on {self.name_ds}: {accuracy} %')
# Log evaluation results to a file
log_file_name = f"{self.output_dir}/stm32ai_main.log"
with open(log_file_name, 'a', encoding='utf-8') as f:
f.write(f'{model_type} onnx model\nEvaluation accuracy: {accuracy} %\n')
# Compute the confusion matrix
cm = confusion_matrix(input_labels, prd_labels)
acc_metric_name = f"int_acc_{self.name_ds}" if model_type == 'quantized' else f"float_acc_{self.name_ds}"
mlflow.log_metric(acc_metric_name, accuracy)
# Plot and display the confusion matrix if enabled
if self.display_figures:
model_name = f'{model_type}_onnx_model_{self.name_ds}'
plot_confusion_matrix(
cm=cm,
class_names=self.class_names,
model_name=model_name,
title=f'{model_name}\naccuracy: {accuracy}',
output_dir=self.output_dir
)
display_figures(self.cfg)
return accuracy, cm
def evaluate(self):
"""
Executes the full evaluation process.
Returns:
float: Accuracy of the model on the evaluation dataset.
"""
self._prepare_evaluation() # Prepare the evaluation process
acc, cm = self._run_evaluate() # Run the evaluation
print('[INFO] : Evaluation complete.')
return acc # Return the accuracy
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