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eval.py
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import os
import sys
import argparse
import numpy as np
import cv2 as cv
from datasets import DATASETS
if "PYTHONPATH" in os.environ:
root_dir = os.environ["PYTHONPATH"]
else:
root_dir = os.path.join("..", "..")
sys.path.append(root_dir)
from models import MODELS
parser = argparse.ArgumentParser("Evaluation with OpenCV on different models in the zoo.")
parser.add_argument("--model", "-m", type=str, required=True, help="model name")
parser.add_argument("--dataset", "-d", type=str, required=True, help="Dataset name")
parser.add_argument("--dataset_root", "-dr", type=str, required=True, help="Root directory of given dataset")
args = parser.parse_args()
models = dict(
mobilenetv1=dict(
name="MobileNet",
topic="image_classification",
modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr.onnx"),
topK=5,
loadLabel=False),
mobilenetv1_q=dict(
name="MobileNet",
topic="image_classification",
modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr_int8.onnx"),
topK=5,
loadLabel=False),
mobilenetv1_bq=dict(
name="MobileNet",
topic="image_classification",
modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv1_2022apr_int8bq.onnx"),
topK=5,
loadLabel=False),
mobilenetv2=dict(
name="MobileNet",
topic="image_classification",
modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr.onnx"),
topK=5,
loadLabel=False),
mobilenetv2_q=dict(
name="MobileNet",
topic="image_classification",
modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr_int8.onnx"),
topK=5,
loadLabel=False),
mobilenetv2_bq=dict(
name="MobileNet",
topic="image_classification",
modelPath=os.path.join(root_dir, "models/image_classification_mobilenet/image_classification_mobilenetv2_2022apr_int8bq.onnx"),
topK=5,
loadLabel=False),
ppresnet=dict(
name="PPResNet",
topic="image_classification",
modelPath=os.path.join(root_dir, "models/image_classification_ppresnet/image_classification_ppresnet50_2022jan.onnx"),
topK=5,
loadLabel=False),
ppresnet_q=dict(
name="PPResNet",
topic="image_classification",
modelPath=os.path.join(root_dir, "models/image_classification_ppresnet/image_classification_ppresnet50_2022jan_int8.onnx"),
topK=5,
loadLabel=False),
ppresnet_bq=dict(
name="PPResNet",
topic="image_classification",
modelPath=os.path.join(root_dir, "models/image_classification_ppresnet/image_classification_ppresnet50_2022jan_int8bq.onnx"),
topK=5,
loadLabel=False),
yunet=dict(
name="YuNet",
topic="face_detection",
modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2023mar.onnx"),
topK=5000,
confThreshold=0.3,
nmsThreshold=0.45),
yunet_q=dict(
name="YuNet",
topic="face_detection",
modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2023mar_int8.onnx"),
topK=5000,
confThreshold=0.3,
nmsThreshold=0.45),
yunet_bq=dict(
name="YuNet",
topic="face_detection",
modelPath=os.path.join(root_dir, "models/face_detection_yunet/face_detection_yunet_2023mar_int8bq.onnx"),
topK=5000,
confThreshold=0.3,
nmsThreshold=0.45),
sface=dict(
name="SFace",
topic="face_recognition",
modelPath=os.path.join(root_dir, "models/face_recognition_sface/face_recognition_sface_2021dec.onnx")),
sface_q=dict(
name="SFace",
topic="face_recognition",
modelPath=os.path.join(root_dir, "models/face_recognition_sface/face_recognition_sface_2021dec_int8.onnx")),
sface_bq=dict(
name="SFace",
topic="face_recognition",
modelPath=os.path.join(root_dir, "models/face_recognition_sface/face_recognition_sface_2021dec_int8bq.onnx")),
crnn_en=dict(
name="CRNN",
topic="text_recognition",
modelPath=os.path.join(root_dir, "models/text_recognition_crnn/text_recognition_CRNN_EN_2021sep.onnx")),
crnn_en_q=dict(
name="CRNN",
topic="text_recognition",
modelPath=os.path.join(root_dir, "models/text_recognition_crnn/text_recognition_CRNN_EN_2022oct_int8.onnx")),
pphumanseg=dict(
name="PPHumanSeg",
topic="human_segmentation",
modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2023mar.onnx")),
pphumanseg_q=dict(
name="PPHumanSeg",
topic="human_segmentation",
modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2023mar_int8.onnx")),
pphumanseg_bq=dict(
name="PPHumanSeg",
topic="human_segmentation",
modelPath=os.path.join(root_dir, "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg_2023mar_int8bq.onnx")),
)
datasets = dict(
imagenet=dict(
name="ImageNet",
topic="image_classification",
size=224),
widerface=dict(
name="WIDERFace",
topic="face_detection"),
lfw=dict(
name="LFW",
topic="face_recognition",
target_size=112),
icdar=dict(
name="ICDAR",
topic="text_recognition"),
iiit5k=dict(
name="IIIT5K",
topic="text_recognition"),
mini_supervisely=dict(
name="MiniSupervisely",
topic="human_segmentation"),
)
def main(args):
# Instantiate model
model_key = args.model.lower()
assert model_key in models
model_name = models[model_key].pop("name")
model_topic = models[model_key].pop("topic")
model_handler, _ = MODELS.get(model_name)
model = model_handler(**models[model_key])
# Instantiate dataset
dataset_key = args.dataset.lower()
assert dataset_key in datasets
dataset_name = datasets[dataset_key].pop("name")
dataset_topic = datasets[dataset_key].pop("topic")
dataset = DATASETS.get(dataset_name)(root=args.dataset_root, **datasets[dataset_key])
# Check if model_topic matches dataset_topic
assert model_topic == dataset_topic
# Run evaluation
dataset.eval(model)
dataset.print_result()
if __name__ == "__main__":
main(args)