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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 | # /*---------------------------------------------------------------------------------------------
# * 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 os
import warnings
import numpy as np
import tqdm
import mlflow
import tensorflow as tf
import matplotlib.pyplot as plt
from hydra.core.hydra_config import HydraConfig
from omegaconf import DictConfig
from timeit import default_timer as timer
from datetime import timedelta
from tabulate import tabulate
from pathlib import Path
from object_detection.tf.src.postprocessing import get_detections
from object_detection.tf.src.utils import ObjectDetectionMetricsData, calculate_objdet_metrics, calculate_average_metrics
from common.utils import log_to_file, count_h5_parameters
class KerasModelEvaluator:
"""
A class to evaluate Keras object detection models.
Args:
cfg (DictConfig): Configuration object for evaluation.
model (tf.keras.Model): The Keras model to evaluate.
dataloaders (dict): Dictionary containing datasets for testing and validation.
"""
def __init__(self, cfg: DictConfig, model: tf.keras.Model,
dataloaders: dict = None):
self.cfg = cfg
self.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
self.eval_ds = None
self.name_ds = None
def _prepare_evaluation(self):
"""
Prepares the evaluation process by selecting the appropriate 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 _display_objdet_metrics(self, metrics, class_names):
table = []
classes = list(metrics.keys())
for c in sorted(classes):
table.append([
class_names[c],
round(100 * metrics[c].pre, 1),
round(100 * metrics[c].rec, 1),
round(100 * metrics[c].ap, 1)])
print()
headers = ["Class name", "Precision %", " Recall %", " AP % "]
print()
print(tabulate(table, headers=headers, tablefmt="pipe", numalign="center"))
mpre, mrec, mAP = calculate_average_metrics(metrics)
print("\nAverages over classes %:")
print("-----------------------")
print(" Mean precision: {:.1f}".format(100 * mpre))
print(" Mean recall: {:.1f}".format(100 * mrec))
print(" Mean AP (mAP): {:.1f}".format(100 * mAP))
def _plot_precision_versus_recall(self, metrics, class_names, plots_dir):
"""
Plot the precision versus recall curves. AP values are the areas under these curves.
"""
if os.path.exists(plots_dir):
import shutil
shutil.rmtree(plots_dir)
os.makedirs(plots_dir)
for c in list(metrics.keys()):
figure = plt.figure(figsize=(10, 10))
plt.xlabel("recall")
plt.ylabel("interpolated precision")
plt.title("Class '{}' (AP = {:.2f})".
format(class_names[c], metrics[c].ap * 100))
plt.plot(metrics[c].interpolated_precision, metrics[c].interpolated_recall)
plt.grid()
plt.savefig(f"{plots_dir}/{class_names[c]}.png")
plt.close(figure)
def _run_evaluate(self):
"""
Runs the evaluation process and computes metrics.
Returns:
dict: Dictionary of evaluation metrics for each class.
"""
# Count the number of parameters in the model and log them
count_h5_parameters(output_dir=self.output_dir,
model=self.model)
tf.print(f'[INFO] : Evaluating the Keras object detection model using {self.name_ds}...')
input_shape = self.model.input_shape[1:] #self.model.input.shape[1:3]
dataset_size = sum([x.shape[0] for x, _ in self.eval_ds])
exmpl, _ = iter(self.eval_ds).next()
batch_size = exmpl.shape[0]
_, labels = iter(self.eval_ds).next()
num_labels = int(tf.shape(labels)[1])
cpp = self.cfg.postprocessing
metrics_data = None
num_detections = 0
start_time = timer()
for i, data in enumerate(tqdm.tqdm(self.eval_ds)):
images, gt_labels = data
image_size = tf.shape(images)[1:3]
predictions = self.model(images)
boxes, scores = get_detections(self.cfg, predictions, image_size)
if i == 0:
num_detections = boxes.shape[1]
metrics_data = ObjectDetectionMetricsData(
num_labels, cpp.max_detection_boxes, len(self.class_names),
num_detections, dataset_size, batch_size
)
metrics_data.add_data(gt_labels, boxes, scores)
metrics_data.update_batch_index(i, cpp.confidence_thresh, cpp.NMS_thresh, image_size)
end_time = timer()
eval_run_time = int(end_time - start_time)
print("Evaluation run time: " + str(timedelta(seconds=eval_run_time)))
groundtruths, detections = metrics_data.get_data()
metrics = calculate_objdet_metrics(groundtruths, detections, cpp.IoU_eval_thresh)
self._display_objdet_metrics(metrics, self.class_names)
log_to_file(self.output_dir, f"Keras object detection model dataset used: {self.cfg.dataset.dataset_name}")
mpre, mrec, mAP = calculate_average_metrics(metrics)
model_type = "float"
log_to_file(self.output_dir, "{}_model_mpre: {:.1f}".format(model_type, 100 * mpre))
log_to_file(self.output_dir, "{}_model_mrec: {:.1f}".format(model_type, 100 * mrec))
log_to_file(self.output_dir, "{}_model_map: {:.1f}".format(model_type, 100 * mAP))
# Log metrics in mlflow
mlflow.log_metric(f"{model_type}_model_mpre", round(100 * mpre, 2))
mlflow.log_metric(f"{model_type}_model_mrec", round(100 * mrec, 2))
mlflow.log_metric(f"{model_type}_model_mAP", round(100 * mAP, 2))
if self.cfg.postprocessing.plot_metrics:
print("\nPlotting precision versus recall curves")
plots_dir = os.path.join(self.output_dir, "precision_vs_recall_curves", os.path.basename(getattr(self.model, "model_path", "keras_model")))
print("Plots directory:", plots_dir)
self._plot_precision_versus_recall(metrics, self.class_names, plots_dir)
print('[INFO] : Evaluation complete.')
return metrics
def evaluate(self):
"""
Executes the full evaluation process.
Returns:
dict: Dictionary of evaluation metrics for each class.
"""
self._prepare_evaluation()
return self._run_evaluate()
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