def create_generators(args):
"""
Create generators for training and validation.
Args
args: parseargs object containing configuration for generators.
preprocess_image: Function that preprocesses an image for the network.
"""
common_args = {
'batch_size': args.batch_size,
'input_size': args.input_size,
}
# create random transform generator for augmenting training data
if args.random_transform:
misc_effect = MiscEffect(border_value=0)
visual_effect = VisualEffect()
else:
misc_effect = None
visual_effect = None
if args.dataset_type == 'coco':
from generators.coco import CocoGenerator
train_generator = CocoGenerator(args.coco_path,
'train',
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args)
validation_generator = CocoGenerator(args.coco_path,
'val',
shuffle_groups=False,
**common_args)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return train_generator, validation_generator
def create_generators(args):
"""
Create generators for training and validation.
Args
args: parseargs object containing configuration for generators.
preprocess_image: Function that preprocesses an image for the network.
"""
common_args = {
'batch_size': args.batch_size,
'phi': args.phi,
'detect_text': args.detect_text,
'detect_quadrangle': args.detect_quadrangle
}
# create random transform generator for augmenting training data
if args.random_transform:
misc_effect = MiscEffect()
visual_effect = VisualEffect()
else:
misc_effect = None
visual_effect = None
if args.dataset_type == 'pascal':
from generators.pascal import PascalVocGenerator
train_generator = PascalVocGenerator(args.pascal_path,
'trainval',
skip_difficult=True,
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args)
validation_generator = PascalVocGenerator(args.pascal_path,
'val',
skip_difficult=True,
shuffle_groups=False,
**common_args)
elif args.dataset_type == 'csv':
from generators.csv_ import CSVGenerator
train_generator = CSVGenerator(args.annotations_path,
args.classes_path,
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args)
if args.val_annotations_path:
validation_generator = CSVGenerator(args.val_annotations_path,
args.classes_path,
shuffle_groups=False,
**common_args)
else:
validation_generator = None
elif args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from generators.coco import CocoGenerator
train_generator = CocoGenerator(args.coco_path,
'train2017',
misc_effect=misc_effect,
visual_effect=visual_effect,
group_method='random',
**common_args)
validation_generator = CocoGenerator(args.coco_path,
'val2017',
shuffle_groups=False,
**common_args)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return train_generator, validation_generator
tag = '{}. {}'.format(index + 1, coco_tag[index])
tf.summary.scalar(tag, result, epoch)
if __name__ == '__main__':
from model import efficientdet
import os
from generators.coco import CocoGenerator
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
phi = 2
weighted_bifpn = True
model_path = 'efficientdet-d2.h5'
common_args = {
'batch_size': 1,
'phi': phi,
}
test_generator = CocoGenerator('datasets/coco',
'test-dev2017',
shuffle_groups=False,
**common_args)
num_classes = test_generator.num_classes()
model, prediction_model = efficientdet(phi=phi,
num_classes=num_classes,
weighted_bifpn=weighted_bifpn,
score_threshold=0.01)
prediction_model.load_weights(model_path, by_name=True)
evaluate(test_generator, prediction_model, threshold=0.01)
if __name__ == '__main__':
# from generators.pascal import PascalVocGenerator
#
# train_generator = PascalVocGenerator(
# 'datasets/VOC0712',
# 'trainval',
# skip_difficult=True,
# batch_size=1,
# shuffle_groups=False
# )
from generators.coco import CocoGenerator
train_generator = CocoGenerator(
'/home/adam/.keras/datasets/coco/2017_118_5',
'train2017',
batch_size=1,
shuffle_groups=False
)
misc_effect = MiscEffect()
for i in range(train_generator.size()):
image = train_generator.load_image(i)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
annotations = train_generator.load_annotations(i)
boxes = annotations['bboxes']
for box in boxes.astype(np.int32):
cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (0, 0, 255), 2)
src_image = image.copy()
# cv2.namedWindow('src_image', cv2.WINDOW_NORMAL)
cv2.imshow('src_image', src_image)
image, boxes = misc_effect(image, boxes)
# image, boxes = multi_scale(image, boxes)
if coco_eval_stats is not None and self.tensorboard is not None and self.tensorboard.writer is not None:
import tensorflow as tf
summary = tf.Summary()
for index, result in enumerate(coco_eval_stats):
summary_value = summary.value.add()
summary_value.simple_value = result
summary_value.tag = '{}. {}'.format(index + 1, coco_tag[index])
self.tensorboard.writer.add_summary(summary, epoch)
logs[coco_tag[index]] = result
if __name__ == '__main__':
dataset_dir = '/home/adam/.keras/datasets/coco/2017_118_5'
test_generator = CocoGenerator(
anchors_path='yolo_anchors.txt',
data_dir=dataset_dir,
set_name='test-dev2017',
shuffle_groups=False,
)
input_shape = (416, 416)
model, prediction_model = yolo_body(test_generator.anchors, num_classes=80)
model.load_weights('checkpoints/yolov3_weights.h5', by_name=True)
coco_eval_stats = evaluate(test_generator, model)
coco_tag = [
'AP @[ IoU=0.50:0.95 | area= all | maxDets=100 ]',
'AP @[ IoU=0.50 | area= all | maxDets=100 ]',
'AP @[ IoU=0.75 | area= all | maxDets=100 ]',
'AP @[ IoU=0.50:0.95 | area= small | maxDets=100 ]',
'AP @[ IoU=0.50:0.95 | area=medium | maxDets=100 ]',
'AP @[ IoU=0.50:0.95 | area= large | maxDets=100 ]',
'AR @[ IoU=0.50:0.95 | area= all | maxDets= 1 ]',
'AR @[ IoU=0.50:0.95 | area= all | maxDets= 10 ]',
tag = '{}. {}'.format(index + 1, coco_tag[index])
tf.summary.scalar(tag, result, epoch)
if __name__ == '__main__':
from model import efficientdet
import os
from generators.coco import CocoGenerator
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
phi = 1
weighted_bifpn = True
model_path = 'checkpoints/colab_efficientdet.h5'
common_args = {
'batch_size': 1,
'phi': phi,
}
test_generator = CocoGenerator('G:\datasets\COCO',
'train2017',
shuffle_groups=False,
**common_args)
num_classes = test_generator.num_classes()
model, prediction_model = efficientdet(phi=phi,
num_classes=num_classes,
weighted_bifpn=weighted_bifpn,
score_threshold=0.01)
prediction_model.load_weights(model_path, by_name=True)
evaluate(test_generator, prediction_model, threshold=0.01)
def create_generators(args):
"""
Create generators for training and validation.
Args
args: parseargs object containing configuration for generators.
preprocess_image: Function that preprocesses an image for the network.
"""
common_args = {
'batch_size': args.batch_size,
'input_size': args.input_size,
}
# create random transform generator for augmenting training data
if args.random_transform:
misc_effect = MiscEffect(border_value=0)
visual_effect = VisualEffect()
else:
misc_effect = None
visual_effect = None
if args.dataset_type == 'pascal':
from generators.pascal import PascalVocGenerator
train_generator = PascalVocGenerator(
args.pascal_path,
'trainval',
skip_difficult=True,
multi_scale=args.multi_scale,
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args
)
validation_generator = PascalVocGenerator(
'/opt/train_data/VOC_data/VOCdevkit/VOC2007/',
'test',
skip_difficult=True,
shuffle_groups=False,
**common_args
)
elif args.dataset_type == 'csv':
from generators.csv_ import CSVGenerator
train_generator = CSVGenerator(
args.annotations_path,
args.classes_path,
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args
)
if args.val_annotations_path:
validation_generator = CSVGenerator(
args.val_annotations_path,
args.classes_path,
shuffle_groups=False,
**common_args
)
else:
validation_generator = None
elif args.dataset_type == 'coco':
from generators.coco import CocoGenerator
train_generator = CocoGenerator(
args.coco_path,
'train2017',
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args
)
validation_generator = CocoGenerator(
args.coco_path,
'val2017',
shuffle_groups=False,
**common_args
)
else:
raise ValueError('Invalid data type received: {}'.format(args.dataset_type))
return train_generator, validation_generator
self.tensorboard.writer.add_summary(summary, epoch)
logs[coco_tag[index]] = result
if __name__ == '__main__':
from model import efficientdet
import os
from generators.coco import CocoGenerator
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
phi = 0
weighted_bifpn = True
common_args = {
'batch_size': 1,
'phi': phi,
}
test_generator = CocoGenerator('/home/adam/workspace/datasets/coco',
'val2017',
shuffle_groups=False,
**common_args)
model_path = 'checkpoints/2019-12-06/coco_14_1.6369_1.6223.h5'
num_classes = test_generator.num_classes()
model, prediction_model = efficientdet(phi=phi,
num_classes=num_classes,
weighted_bifpn=weighted_bifpn,
score_threshold=0.01)
prediction_model.load_weights(model_path, by_name=True)
evaluate(test_generator, prediction_model, threshold=0.01)
