/
vocdetection_datamodule.py
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/
vocdetection_datamodule.py
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import torch
import torchvision.transforms as T
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from pl_bolts.utils.warnings import warn_missing_pkg
try:
from torchvision.datasets import VOCDetection
except ModuleNotFoundError:
warn_missing_pkg('torchvision') # pragma: no-cover
_TORCHVISION_AVAILABLE = False
else:
_TORCHVISION_AVAILABLE = True
class Compose(object):
"""
Like `torchvision.transforms.compose` but works for (image, target)
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, target):
for t in self.transforms:
image, target = t(image, target)
return image, target
def _collate_fn(batch):
return tuple(zip(*batch))
CLASSES = (
"__background__ ",
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor",
)
def _prepare_voc_instance(image, target):
"""
Prepares VOC dataset into appropriate target for fasterrcnn
https://github.com/pytorch/vision/issues/1097#issuecomment-508917489
"""
anno = target["annotation"]
h, w = anno["size"]["height"], anno["size"]["width"]
boxes = []
classes = []
area = []
iscrowd = []
objects = anno["object"]
if not isinstance(objects, list):
objects = [objects]
for obj in objects:
bbox = obj["bndbox"]
bbox = [int(bbox[n]) - 1 for n in ["xmin", "ymin", "xmax", "ymax"]]
boxes.append(bbox)
classes.append(CLASSES.index(obj["name"]))
iscrowd.append(int(obj["difficult"]))
area.append((bbox[2] - bbox[0]) * (bbox[3] - bbox[1]))
boxes = torch.as_tensor(boxes, dtype=torch.float32)
classes = torch.as_tensor(classes)
area = torch.as_tensor(area)
iscrowd = torch.as_tensor(iscrowd)
image_id = anno["filename"][5:-4]
image_id = torch.as_tensor([int(image_id)])
target = {}
target["boxes"] = boxes
target["labels"] = classes
target["image_id"] = image_id
# for conversion to coco api
target["area"] = area
target["iscrowd"] = iscrowd
return image, target
class VOCDetectionDataModule(LightningDataModule):
"""
TODO(teddykoker) docstring
"""
name = "vocdetection"
def __init__(
self,
data_dir: str,
year: str = "2012",
num_workers: int = 16,
normalize: bool = False,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
if not _TORCHVISION_AVAILABLE:
raise ModuleNotFoundError( # pragma: no-cover
'You want to use VOC dataset loaded from `torchvision` which is not installed yet.'
)
self.year = year
self.data_dir = data_dir
self.num_workers = num_workers
self.normalize = normalize
@property
def num_classes(self):
"""
Return:
21
"""
return 21
def prepare_data(self):
"""
Saves VOCDetection files to data_dir
"""
VOCDetection(self.data_dir, year=self.year, image_set="train", download=True)
VOCDetection(self.data_dir, year=self.year, image_set="val", download=True)
def train_dataloader(self, batch_size=1, transforms=None):
"""
VOCDetection train set uses the `train` subset
Args:
batch_size: size of batch
transforms: custom transforms
"""
t = [_prepare_voc_instance]
transforms = transforms or self.train_transforms or self._default_transforms()
if transforms is not None:
t.append(transforms)
transforms = Compose(t)
dataset = VOCDetection(
self.data_dir, year=self.year, image_set="train", transforms=transforms
)
loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=True,
num_workers=self.num_workers,
pin_memory=True,
collate_fn=_collate_fn,
)
return loader
def val_dataloader(self, batch_size=1, transforms=None):
"""
VOCDetection val set uses the `val` subset
Args:
batch_size: size of batch
transforms: custom transforms
"""
t = [_prepare_voc_instance]
transforms = transforms or self.val_transforms or self._default_transforms()
if transforms is not None:
t.append(transforms)
transforms = Compose(t)
dataset = VOCDetection(
self.data_dir, year=self.year, image_set="val", transforms=transforms
)
loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
num_workers=self.num_workers,
pin_memory=True,
collate_fn=_collate_fn,
)
return loader
def _default_transforms(self):
if self.normalize:
return (
lambda image, target: (
T.Compose(
[
T.ToTensor(),
T.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)(image),
target,
),
)
return lambda image, target: (T.ToTensor()(image), target)