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# * 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 shutil
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.models import model_family
from object_detection.tf.src.utils import ai_runner_invoke
from object_detection.tf.src.utils import ObjectDetectionMetricsData, calculate_objdet_metrics, calculate_average_metrics
from common.utils import (
ai_runner_interp, ai_interp_input_quant, ai_interp_outputs_dequant, log_to_file, display_figures
) # Common utilities for evaluation and visualization
class TFLiteQuantizedModelEvaluator:
"""
A class to evaluate TensorFlow Lite (TFLite) quantized object detection models.
Args:
cfg (DictConfig): Configuration object for evaluation.
model (object): The quantized TFLite Interpreter object.
dataloaders (dict): Dictionary containing datasets for testing and validation.
"""
def __init__(self, cfg: DictConfig, model: object,
dataloaders: dict = None):
self.cfg = cfg
self.quantized_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.
"""
# 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 _get_target(self):
if self.cfg.evaluation and self.cfg.evaluation.target:
return self.cfg.evaluation.target
return "host"
def _get_interpreter(self, target):
name_model = os.path.basename(self.quantized_model.model_path)
return ai_runner_interp(target, name_model)
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.
"""
# Create the directory where plots will be saved
if os.path.exists(plots_dir):
shutil.rmtree(plots_dir)
os.makedirs(plots_dir)
for c in list(metrics.keys()):
# Plot the precision versus recall curve
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()
# Save the plot in the plots directory
plt.savefig(f"{plots_dir}/{class_names[c]}.png")
plt.close(figure)
def _run_evaluate(self):
"""
Runs the evaluation process and computes metrics.
Returns:
float: Accuracy of the quantized model on the evaluation dataset.
"""
tf.print(f'[INFO] : Evaluating the quantized object detection model using {self.name_ds}...')
target = self._get_target()
ai_runner_interpreter = self._get_interpreter(target=target)
input_details = self.quantized_model.get_input_details()[0]
output_details = self.quantized_model.get_output_details()
model_batch_size = input_details['shape_signature'][0]
if model_batch_size != 1 and target == 'host':
batch_size = 64
else:
batch_size = 1
input_shape = tuple(input_details['shape'][1:])
image_size = input_shape[:2]
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
predictions_all = []
images_full = []
start_time = timer()
for i, data in enumerate(tqdm.tqdm(self.eval_ds)):
images, gt_labels = data
batch_size = int(tf.shape(images)[0])
input_index = input_details['index']
tensor_shape = (batch_size,) + input_shape
self.quantized_model.resize_tensor_input(input_index, tensor_shape)
self.quantized_model.allocate_tensors()
scale, zero_point = input_details['quantization']
images_quant = images / scale + zero_point
input_dtype = input_details['dtype']
images_quant = tf.cast(images_quant, input_dtype)
images_quant = tf.clip_by_value(images_quant, np.iinfo(input_dtype).min, np.iinfo(input_dtype).max)
if "evaluation" in self.cfg and self.cfg.evaluation:
if "gen_npy_input" in self.cfg.evaluation and self.cfg.evaluation.gen_npy_input==True:
images_full.append(images)
if target == 'host':
self.quantized_model.set_tensor(input_index, images_quant)
self.quantized_model.invoke()
elif target in ['stedgeai_host', 'stedgeai_n6', 'stedgeai_h7p']:
data_quant = ai_interp_input_quant(ai_runner_interpreter, images.numpy(), '.tflite')
predictions = ai_runner_invoke(data_quant, ai_runner_interpreter)
predictions = ai_interp_outputs_dequant(ai_runner_interpreter, predictions)
else:
raise RuntimeError(f"Unsupported target: {target}")
if model_family(self.cfg.model.model_type) in ["ssd", "st_yoloxn"]:
if target == 'host':
# Model outputs are scores, boxes and anchors.
predictions = (self.quantized_model.get_tensor(output_details[0]['index']),
self.quantized_model.get_tensor(output_details[1]['index']),
self.quantized_model.get_tensor(output_details[2]['index']))
else:
if target == 'host':
predictions = self.quantized_model.get_tensor(output_details[0]['index'])
elif target in ['stedgeai_host', 'stedgeai_n6', 'stedgeai_h7p']:
predictions = predictions[0]
if "evaluation" in self.cfg and self.cfg.evaluation:
if "gen_npy_output" in self.cfg.evaluation and self.cfg.evaluation.gen_npy_output==True:
predictions_all.append(predictions)
# The TFLITE version of yolov8 has channel-first outputs
if model_family(self.cfg.model.model_type) in ["yolov8n"]:
predictions = tf.transpose(predictions, perm=[0, 2, 1])
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)
# Saves evaluation dataset in a .npy
if "evaluation" in self.cfg and self.cfg.evaluation:
if "gen_npy_input" in self.cfg.evaluation and self.cfg.evaluation.gen_npy_input==True:
if "npy_in_name" in self.cfg.evaluation and self.cfg.evaluation.npy_in_name:
npy_in_name = self.cfg.evaluation.npy_in_name
else:
npy_in_name = "unknown_npy_in_name"
images_full = np.concatenate(images_full, axis=0)
np.save(os.path.join(self.output_dir, f"{npy_in_name}.npy"), images_full)
# Saves model output in a .npy
if "evaluation" in self.cfg and self.cfg.evaluation:
if "gen_npy_output" in self.cfg.evaluation and self.cfg.evaluation.gen_npy_output==True:
if "npy_out_name" in self.cfg.evaluation and self.cfg.evaluation.npy_out_name:
npy_out_name = self.cfg.evaluation.npy_out_name
else:
npy_out_name = "unknown_npy_out_name"
predictions_all = np.concatenate(predictions_all, axis=0)
np.save(os.path.join(self.output_dir, f"{npy_out_name}.npy"), predictions_all)
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"Quantized TFLite object detection model dataset used: {self.cfg.dataset.dataset_name}")
mpre, mrec, mAP = calculate_average_metrics(metrics)
model_type = "quantized"
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(self.quantized_model.model_path))
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() # Prepare the evaluation process
return self._run_evaluate()
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