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tests_training_and_inference.py
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tests_training_and_inference.py
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import argparse
import json
from axelerate import setup_training, setup_evaluation
import tensorflow.keras.backend as K
from termcolor import colored
import traceback
import time
def configs(network_type):
classifier = {
"model" : {
"type": "Classifier",
"architecture": "Tiny Yolo",
"input_size": [224,224],
"fully-connected": [],
"labels": [],
"dropout" : 0.5
},
"weights" : {
"full": "",
"backend": None,
"save_bottleneck": True
},
"train" : {
"actual_epoch": 5,
"train_image_folder": "sample_datasets/classifier/imgs",
"train_times": 1,
"valid_image_folder": "sample_datasets/classifier/imgs_validation",
"valid_times": 1,
"valid_metric": "accuracy",
"batch_size": 2,
"learning_rate": 1e-4,
"saved_folder": "classifier",
"first_trainable_layer": "",
"augmentation": True
},
"converter" : {
"type": []
}
}
detector = {
"model":{
"type": "Detector",
"architecture": "MobileNet7_5",
"input_size": [240, 320],
"anchors": [[[0.51424575, 0.54116074], [0.29523918, 0.45838044], [0.21371929, 0.21518053]]],
"labels": ["aeroplane", "person", "diningtable"," bottle", "bird", "bus", "boat", "cow", "sheep", "train"],
"obj_thresh" : 0.7,
"iou_thresh" : 0.3,
"coord_scale" : 0.5,
"object_scale" : 5.0,
"no_object_scale" : 0.5
},
"weights" : {
"full": "",
"backend": None
},
"train" : {
"actual_epoch": 5,
"train_image_folder": "sample_datasets/detector/imgs",
"train_annot_folder": "sample_datasets/detector/anns",
"train_times": 1,
"valid_image_folder": "sample_datasets/detector/imgs_validation",
"valid_annot_folder": "sample_datasets/detector/anns_validation",
"valid_times": 1,
"valid_metric": "recall",
"batch_size": 2,
"learning_rate": 1e-4,
"saved_folder": "detector",
"first_trainable_layer": "",
"augmentation": True,
"is_only_detect" : False
},
"converter" : {
"type": []
}
}
segnet = {
"model" : {
"type": "SegNet",
"architecture": "MobileNet5_0",
"input_size": [224,224],
"n_classes" : 20
},
"weights" : {
"full": "",
"backend": None
},
"train" : {
"actual_epoch": 5,
"train_image_folder": "sample_datasets/segmentation/imgs",
"train_annot_folder": "sample_datasets/segmentation/anns",
"train_times": 4,
"valid_image_folder": "sample_datasets/segmentation/imgs_validation",
"valid_annot_folder": "sample_datasets/segmentation/anns_validation",
"valid_times": 4,
"valid_metric": "loss",
"batch_size": 2,
"learning_rate": 1e-4,
"saved_folder": "segment",
"first_trainable_layer": "",
"ignore_zero_class": False,
"augmentation": True
},
"converter" : {
"type": []
}
}
dict = {'all':[classifier,detector,segnet],'classifier':[classifier],'detector':[detector],'segnet':[segnet]}
return dict[network_type]
argparser = argparse.ArgumentParser(description='Test axelerate on sample datasets')
argparser.add_argument(
'-t',
'--type',
default="all",
help='type of network to test:classifier,detector,segnet or all')
argparser.add_argument(
'-a',
'--arch',
type=bool,
default=False,
help='test all architectures?')
argparser.add_argument(
'-c',
'--conv',
type=bool,
default=False,
help='test all converters?')
args = argparser.parse_args()
archs = ['MobileNet7_5']
converters = [""]
errors = []
if args.arch:
archs = ['Full Yolo', 'Tiny Yolo', 'MobileNet1_0', 'MobileNet7_5', 'MobileNet5_0', 'MobileNet2_5', 'SqueezeNet', 'NASNetMobile', 'ResNet50', 'DenseNet121']
if args.conv:
converters = ['k210', 'tflite_fullint', 'tflite_dynamic', 'edgetpu', 'openvino', 'onnx']
for item in configs(args.type):
for arch in archs:
for converter in converters:
try:
item['model']['architecture'] = arch
item['converter']['type'] = converter
print(json.dumps(item, indent=4, sort_keys=False))
model_path = setup_training(config_dict=item)
K.clear_session()
setup_evaluation(item, model_path)
except Exception as e:
traceback.print_exc()
print(colored(str(e), 'red'))
time.sleep(2)
errors.append(item['model']['type'] + " " + arch + " " + converter + " " + str(e))
for error in errors:
print(error)