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dataset_utils.py
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"""
Dataset routines.
"""
__all__ = ['get_dataset_metainfo', 'get_train_data_source', 'get_val_data_source', 'get_test_data_source']
import tensorflow as tf
from .datasets.imagenet1k_cls_dataset import ImageNet1KMetaInfo
from .datasets.cub200_2011_cls_dataset import CUB200MetaInfo
from .datasets.cifar10_cls_dataset import CIFAR10MetaInfo
from .datasets.cifar100_cls_dataset import CIFAR100MetaInfo
from .datasets.svhn_cls_dataset import SVHNMetaInfo
from .datasets.voc_seg_dataset import VOCMetaInfo
from .datasets.ade20k_seg_dataset import ADE20KMetaInfo
from .datasets.cityscapes_seg_dataset import CityscapesMetaInfo
from .datasets.coco_seg_dataset import CocoSegMetaInfo
from .datasets.coco_hpe1_dataset import CocoHpe1MetaInfo
from .datasets.coco_hpe2_dataset import CocoHpe2MetaInfo
from .datasets.coco_hpe3_dataset import CocoHpe3MetaInfo
def get_dataset_metainfo(dataset_name):
"""
Get dataset metainfo by name of dataset.
Parameters
----------
dataset_name : str
Dataset name.
Returns
-------
DatasetMetaInfo
Dataset metainfo.
"""
dataset_metainfo_map = {
"ImageNet1K": ImageNet1KMetaInfo,
"CUB200_2011": CUB200MetaInfo,
"CIFAR10": CIFAR10MetaInfo,
"CIFAR100": CIFAR100MetaInfo,
"SVHN": SVHNMetaInfo,
"VOC": VOCMetaInfo,
"ADE20K": ADE20KMetaInfo,
"Cityscapes": CityscapesMetaInfo,
"CocoSeg": CocoSegMetaInfo,
"CocoHpe1": CocoHpe1MetaInfo,
"CocoHpe2": CocoHpe2MetaInfo,
"CocoHpe3": CocoHpe3MetaInfo,
}
if dataset_name in dataset_metainfo_map.keys():
return dataset_metainfo_map[dataset_name]()
else:
raise Exception("Unrecognized dataset: {}".format(dataset_name))
def get_train_data_source(ds_metainfo,
batch_size,
data_format="channels_last"):
"""
Get data source for training subset.
Parameters
----------
ds_metainfo : DatasetMetaInfo
Dataset metainfo.
batch_size : int
Batch size.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
Returns
-------
DataLoader
Data source.
int
Dataset size.
"""
data_generator = ds_metainfo.train_transform(
ds_metainfo=ds_metainfo,
data_format=data_format)
generator = ds_metainfo.train_generator(
data_generator=data_generator,
ds_metainfo=ds_metainfo,
batch_size=batch_size)
return tf.data.Dataset.from_generator(
generator=lambda: generator,
output_types=(tf.float32, tf.float32)),\
generator.n
def get_val_data_source(ds_metainfo,
batch_size,
data_format="channels_last"):
"""
Get data source for validation subset.
Parameters
----------
ds_metainfo : DatasetMetaInfo
Dataset metainfo.
batch_size : int
Batch size.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
Returns
-------
DataLoader
Data source.
int
Dataset size.
"""
data_generator = ds_metainfo.val_transform(
ds_metainfo=ds_metainfo,
data_format=data_format)
generator = ds_metainfo.val_generator(
data_generator=data_generator,
ds_metainfo=ds_metainfo,
batch_size=batch_size)
if hasattr(generator, "dataset"):
ds_metainfo.update_from_dataset(generator.dataset)
return tf.data.Dataset.from_generator(
generator=lambda: generator,
output_types=(tf.float32, tf.float32)),\
generator.n
def get_test_data_source(ds_metainfo,
batch_size,
data_format="channels_last"):
"""
Get data source for testing subset.
Parameters
----------
ds_metainfo : DatasetMetaInfo
Dataset metainfo.
batch_size : int
Batch size.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
Returns
-------
DataLoader
Data source.
int
Dataset size.
"""
data_generator = ds_metainfo.test_transform(
ds_metainfo=ds_metainfo,
data_format=data_format)
generator = ds_metainfo.test_generator(
data_generator=data_generator,
ds_metainfo=ds_metainfo,
batch_size=batch_size)
if hasattr(generator, "dataset"):
ds_metainfo.update_from_dataset(generator.dataset)
return tf.data.Dataset.from_generator(
generator=lambda: generator,
output_types=(tf.float32, tf.float32)),\
generator.n