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,
}
# 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
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return train_generator, validation_generator
def create_generators(batch_size, phi, is_text_detect, is_detect_quadrangle,
rand_transf_augm, train_ann_path, val_ann_path,
train_class_path, val_class_path):
"""
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': batch_size,
'phi': phi,
'detect_text': is_text_detect,
'detect_quadrangle': is_detect_quadrangle
}
# create random transform generator for augmenting training data
if rand_transf_augm:
misc_effect = MiscEffect()
visual_effect = VisualEffect()
else:
misc_effect = None
visual_effect = None
from generators.csv_ import CSVGenerator
train_generator = CSVGenerator(train_ann_path,
train_class_path,
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args)
if val_ann_path:
validation_generator = CSVGenerator(val_ann_path,
val_class_path,
shuffle_groups=False,
**common_args)
else:
validation_generator = None
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,
image_extension=".png",
**common_args
)
validation_generator = PascalVocGenerator(
args.pascal_path,
'val',
skip_difficult=True,
shuffle_groups=False,
image_extension=".png",
**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
ckp_model_dir = "2020-08-04 (B1 MND WBiFPN LLR)"
ckp_model_file = "csv_298_0.0389_0.6384.h5"
quad_angle_arg = False
txt_detect_arg = False
phi = 1
weighted_bifpn = True
common_args = {
"batch_size": 1,
"phi": phi,
}
test_generator = CSVGenerator(
path_test_csv,
path_classes_csv,
base_dir=paths_base_dir,
detect_quadrangle=quad_angle_arg,
detect_text=txt_detect_arg,
)
model_path = ckp_base_path + ckp_model_dir + "/" + ckp_model_file
input_shape = (test_generator.image_size, test_generator.image_size)
print(input_shape)
anchors = test_generator.anchors
num_classes = test_generator.num_classes()
model, prediction_model = efficientdet(phi=phi,
num_classes=num_classes,
weighted_bifpn=weighted_bifpn)
prediction_model.load_weights(model_path, by_name=True)
average_precisions = evaluate(test_generator,
prediction_model,
visualize=False)
# image, annotations = rotate(image, annotations, prob=self.rotate_prob, border_value=self.border_value)
image, annotations = flipx(image, annotations, prob=self.flip_prob)
image, annotations = crop(image, annotations, prob=self.crop_prob)
image, annotations = translate(image,
annotations,
prob=self.translate_prob,
border_value=self.border_value)
return image, annotations
if __name__ == '__main__':
from generators.csv_ import CSVGenerator
train_generator = CSVGenerator('datasets/ic15/train.csv',
'datasets/ic15/classes.csv',
detect_text=True,
batch_size=1,
phi=5,
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'].astype(np.int32)
quadrangles = annotations['quadrangles'].astype(np.int32)
for box in boxes:
cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]),
(0, 0, 255), 1)
cv2.drawContours(image, quadrangles, -1, (0, 255, 255), 1)
src_image = image.copy()
# cv2.namedWindow('src_image', cv2.WINDOW_NORMAL)
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
from generators.utils import affine_transform, get_affine_transform
from utils.image import read_image_bgr, preprocess_image, resize_image
import os.path as osp
DATA_SUFFIX = '_datamap.png'
RESULT_PATH = "result/"
PROCESS_PATH = "process/"
model_path = 'checkpoints/csv.h5'
colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
score_threshold = 0.5
flip_test = False
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
generator = CSVGenerator(
'data/annotations.csv',
'data/classes.csv',
'data',
)
num_classes = generator.num_classes()
classes = list(generator.classes.keys())
model, prediction_model, debug_model = centernet(num_classes=num_classes,
nms=True,
flip_test=flip_test,
freeze_bn=False,
score_threshold=score_threshold)
prediction_model.load_weights(model_path, by_name=True, skip_mismatch=True)
for f in os.listdir(PROCESS_PATH):
image, annotations = flipx(image, annotations, prob=self.flip_prob)
image, annotations = crop(image, annotations, prob=self.crop_prob)
image, annotations = translate(image,
annotations,
prob=self.translate_prob,
border_value=self.border_value)
return image, annotations
if __name__ == '__main__':
from generators.csv_ import CSVGenerator
train_generator = CSVGenerator('../generators/val.csv',
'../generators/csv_class_file.csv',
'../generators/csv_property_file.csv',
base_dir='/Users/yanyan/data',
detect_text=False,
batch_size=1,
phi=3,
shuffle_groups=False)
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)
image, annotations = crop(image, annotations)
boxes = annotations['bboxes'].astype(np.int32)
labels = annotations['labels']
# quadrangles = annotations['quadrangles'].astype(np.int32)
for box, label in zip(boxes, labels):
cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]),
def load_efficient_det(config, LOCAL_ANNOTATIONS_PATH, LOCAL_ROOT_PATH,
LOCAL_CLASSES_PATH, LOCAL_VALIDATIONS_PATH,
LOCAL_LOGS_PATH, LOCAL_SNAPSHOTS_PATH):
common_args = {
'phi': config['phi'],
'detect_text': config['detect_text'],
'detect_quadrangle': config['detect_quadrangle']
}
# create random transform generator for augmenting training data
if config['random_transform']:
misc_effect = MiscEffect()
visual_effect = VisualEffect()
else:
misc_effect = None
visual_effect = None
annotations_df = pd.read_csv(LOCAL_ANNOTATIONS_PATH, header=None)
# stratified sampling
N = int(len(annotations_df) * 0.15)
evaluation_df = annotations_df.groupby(
5, group_keys=False).apply(lambda x: x.sample(
int(np.rint(N * len(x) / len(annotations_df))))).sample(frac=1)
evaluation_path = f'{LOCAL_ROOT_PATH}/evaluation.csv'
evaluation_df.to_csv(evaluation_path, index=False, header=None)
config['steps_per_epoch'] = annotations_df.iloc[:, 0].nunique(
) / config['batch_size']
train_generator = CSVGenerator(LOCAL_ANNOTATIONS_PATH,
LOCAL_CLASSES_PATH,
batch_size=config['batch_size'],
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args)
if config['train_evaluation']:
evaluation_generator = CSVGenerator(evaluation_path,
LOCAL_CLASSES_PATH,
batch_size=config['batch_size'],
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args)
else:
evaluation_generator = None
if config['validation']:
validation_generator = CSVGenerator(LOCAL_VALIDATIONS_PATH,
LOCAL_CLASSES_PATH,
batch_size=config['batch_size'],
misc_effect=misc_effect,
visual_effect=visual_effect,
**common_args)
else:
validation_generator = None
num_classes = train_generator.num_classes()
num_anchors = train_generator.num_anchors
model, prediction_model = efficientdet(
config['phi'],
num_classes=num_classes,
num_anchors=num_anchors,
weighted_bifpn=config['weighted_bifpn'],
freeze_bn=config['freeze_bn'],
detect_quadrangle=config['detect_quadrangle'])
# freeze backbone layers
if config['freeze_backbone']:
# 227, 329, 329, 374, 464, 566, 656
for i in range(1, [227, 329, 329, 374, 464, 566, 656][config['phi']]):
model.layers[i].trainable = False
# optionally choose specific GPU
gpu = config['gpu']
device = gpu.split(':')[0]
if gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = device
if gpu and len(gpu.split(':')) > 1:
gpus = gpu.split(':')[1]
model = tf.keras.utils.multi_gpu_model(model,
gpus=list(
map(int, gpus.split(','))))
if config['snapshot'] == 'imagenet':
model_name = 'efficientnet-b{}'.format(config['phi'])
file_name = '{}_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5'.format(
model_name)
file_hash = WEIGHTS_HASHES[model_name][1]
weights_path = tf.keras.utils.get_file(file_name,
BASE_WEIGHTS_PATH + file_name,
cache_subdir='models',
file_hash=file_hash)
model.load_weights(weights_path, by_name=True)
elif config['snapshot']:
print('Loading model, this may take a second...')
model.load_weights(config['snapshot'], by_name=True)
return (model, prediction_model, train_generator, evaluation_generator,
validation_generator, config)
