def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 4
# number of train data_loader workers
config_dict['num_train_workers'] = 4
# number of val data_loader workers
config_dict['num_val_workers'] = 4
# construct train data_loader
config_dict['train_dataset_path'] = './COCO_pack/coco_train2017.pkl'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = COCORandomDatasetSampler(
dataset=train_dataset,
batch_size=config_dict['batch_size'],
shuffle=True,
)
train_region_sampler = TypicalCOCOTrainingRegionSampler(
resize_shorter_range=(800, ), resize_longer_limit=1333, pad_divisor=32)
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=typical_coco_train_pipeline,
num_workers=config_dict['num_train_workers'])
# construct val data_loader
config_dict['val_dataset_path'] = './COCO_pack/coco_val2017.pkl'
val_dataset = Dataset(load_path=config_dict['val_dataset_path'])
val_dataset_sampler = RandomDatasetSampler(
dataset=val_dataset,
batch_size=config_dict['batch_size'],
shuffle=False,
ignore_last=False)
val_region_sampler = TypicalCOCOTrainingRegionSampler(
resize_shorter_range=(800, ), resize_longer_limit=1333, pad_divisor=32)
config_dict['val_data_loader'] = DataLoader(
dataset=val_dataset,
dataset_sampler=val_dataset_sampler,
region_sampler=val_region_sampler,
augmentation_pipeline=typical_coco_val_pipeline,
num_workers=config_dict['num_val_workers'])
# evaluator
# the evaluator should match the dataset
config_dict[
'val_annotation_path'] = '/home/yonghaohe/datasets/COCO/annotations/instances_val2017.json'
config_dict['evaluator'] = COCOEvaluator(
annotation_path=config_dict['val_annotation_path'],
label_indexes_to_category_ids=val_dataset.
meta_info['label_indexes_to_category_ids'])
def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 64
# number of train data_loader workers
config_dict['num_train_workers'] = 12
# number of val data_loader workers
config_dict['num_val_workers'] = 0
# construct train data_loader
config_dict['train_dataset_path'] = './WIDERFACE_pack/widerface_train.pkl'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = RandomWithNegDatasetSampler(
train_dataset,
batch_size=config_dict['batch_size'],
neg_ratio=0.2,
shuffle=True,
ignore_last=False)
train_region_sampler = RandomBBoxCropRegionSampler(crop_size=480,
resize_range=(0.5, 1.5),
resize_prob=0.5)
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=simple_widerface_train_pipeline,
num_workers=config_dict['num_train_workers'])
def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 12
# number of train data_loader workers
config_dict['num_train_workers'] = 6
# number of val data_loader workers
config_dict['num_val_workers'] = 0
# construct train data_loader
config_dict['train_dataset_path'] = './WIDERFACE_pack/widerface_train.pkl'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = RandomWithNegDatasetSampler(
train_dataset,
batch_size=config_dict['batch_size'],
neg_ratio=0.2,
shuffle=True,
ignore_last=False)
train_region_sampler = RandomBBoxCropWithRangeSelectionRegionSampler(
crop_size=480,
detection_ranges=config_dict['detection_scales'],
range_selection_probs=[1, 1, 1, 1, 1],
lock_threshold=30)
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=simple_widerface_train_pipeline,
num_workers=config_dict['num_train_workers'])
def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 16
# number of train data_loader workers
config_dict['num_train_workers'] = 4
# number of val data_loader workers
config_dict['num_val_workers'] = 0
# construct train data_loader
config_dict['train_dataset_path'] = 'xxxxxxxxx'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = RandomWithNegDatasetSampler(
train_dataset,
batch_size=config_dict['batch_size'],
neg_ratio=0.1,
shuffle=True,
ignore_last=False)
train_region_sampler = RandomBBoxCropRegionSampler(crop_size=512,
resize_range=(0.5, 1.5))
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=simple_widerface_train_pipeline,
num_workers=config_dict['num_train_workers'])
# construct val data_loader
config_dict['val_dataset_path'] = 'xxxxxxxxxx'
val_dataset = Dataset(load_path=config_dict['val_dataset_path'])
val_dataset_sampler = RandomDatasetSampler(
dataset=val_dataset,
batch_size=config_dict['batch_size'],
shuffle=False,
ignore_last=False)
val_region_sampler = IdleRegionSampler()
config_dict['val_data_loader'] = DataLoader(
dataset=val_dataset,
dataset_sampler=val_dataset_sampler,
region_sampler=val_region_sampler,
augmentation_pipeline=simple_widerface_val_pipeline,
num_workers=config_dict['num_val_workers'])
def prepare_data_pipeline():
# batch size
config_dict['batch_size'] = 4
# number of train data_loader workers
config_dict['num_train_workers'] = 4
# number of val data_loader workers
config_dict['num_val_workers'] = 4
# construct train data_loader
config_dict['train_dataset_path'] = './debug_data/train.pkl'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = RandomWithNegDatasetSampler(
train_dataset,
batch_size=config_dict['batch_size'],
neg_ratio=0.2,
shuffle=True,
ignore_last=False)
train_region_sampler = RandomBBoxCropRegionSampler(crop_size=640,
resize_range=(0.5, 1.5),
resize_prob=0.5)
config_dict['train_data_loader'] = DataLoader(
dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=train_pipeline,
num_workers=config_dict['num_train_workers'])
# construct val data_loader
# config_dict['val_dataset_path'] = './debug_data/train.pkl'
# val_dataset = Dataset(load_path=config_dict['val_dataset_path'])
# val_dataset_sampler = RandomDatasetSampler(dataset=val_dataset,
# batch_size=config_dict['batch_size'],
# shuffle=False,
# ignore_last=False)
# val_region_sampler = IdleRegionSampler()
# config_dict['val_data_loader'] = DataLoader(dataset=val_dataset,
# dataset_sampler=val_dataset_sampler,
# region_sampler=val_region_sampler,
# augmentation_pipeline=val_pipeline,
# num_workers=config_dict['num_val_workers'])
# evaluator
# the evaluator should match the dataset
# validation interval in epochs
config_dict['val_interval'] = 0
# config_dict['val_annotation_path'] = './debug_data/annotations/instances_train2017.json'
config_dict[
'evaluator'] = None # COCOEvaluator(annotation_path=config_dict['val_annotation_path'],
# number of train data_loader workers
config_dict['num_train_workers'] = 4
# number of val data_loader workers
config_dict['num_val_workers'] = 2
# construct train data_loader
config_dict['train_dataset_path'] = 'xxxxxxxxx'
train_dataset = Dataset(load_path=config_dict['train_dataset_path'])
train_dataset_sampler = COCORandomDatasetSampler(dataset=train_dataset,
batch_size=config_dict['batch_size'],
shuffle=True, )
train_region_sampler = TypicalCOCOTrainingRegionSampler(resize_shorter_range=(800,), resize_longer_limit=1333, pad_divisor=32)
config_dict['train_data_loader'] = DataLoader(dataset=train_dataset,
dataset_sampler=train_dataset_sampler,
region_sampler=train_region_sampler,
augmentation_pipeline=typical_coco_train_pipeline,
num_workers=config_dict['num_train_workers'])
# construct val data_loader
config_dict['val_dataset_path'] = 'xxxxxxxxxx'
val_dataset = Dataset(load_path=config_dict['val_dataset_path'])
val_dataset_sampler = RandomDatasetSampler(dataset=val_dataset,
batch_size=config_dict['batch_size'],
shuffle=False,
ignore_last=False)
val_region_sampler = TypicalCOCOTrainingRegionSampler(resize_shorter_range=(800,), resize_longer_limit=1333, pad_divisor=32)
config_dict['val_data_loader'] = DataLoader(dataset=val_dataset,
dataset_sampler=val_dataset_sampler,
region_sampler=val_region_sampler,
augmentation_pipeline=typical_coco_val_pipeline,
