def eval_loader(self):
eval_data = Coco(
image_dir=self.eval_image_dir,
ann_file=self.eval_annotations,
preprocess=self._eval_preprocess(),
annotation_filter=self.eval_annotation_filter,
min_kp_anns=self.min_kp_anns if self.eval_annotation_filter else 0,
category_ids=[1] if self.eval_annotation_filter else [],
)
return torch.utils.data.DataLoader(eval_data,
batch_size=self.batch_size,
shuffle=False,
pin_memory=self.pin_memory,
num_workers=self.loader_workers,
drop_last=False,
collate_fn=collate_images_anns_meta)
def val_loader(self):
val_data = Coco(
image_dir=self.val_image_dir,
ann_file=self.val_annotations,
preprocess=self._preprocess(),
annotation_filter=True,
min_kp_anns=self.min_kp_anns,
category_ids=[1],
)
return torch.utils.data.DataLoader(
val_data,
batch_size=self.batch_size,
shuffle=False,
pin_memory=self.pin_memory,
num_workers=self.loader_workers,
drop_last=True,
collate_fn=openpifpaf.datasets.collate_images_targets_meta)
def train_loader(self):
train_data = Coco(
image_dir=self.train_image_dir,
ann_file=self.train_annotations,
preprocess=self._preprocess(),
annotation_filter=True,
min_kp_anns=self.min_kp_anns,
category_ids=[1],
)
return torch.utils.data.DataLoader(
train_data,
batch_size=self.batch_size,
shuffle=not self.debug and self.augmentation,
pin_memory=self.pin_memory,
num_workers=self.loader_workers,
drop_last=True,
collate_fn=collate_images_targets_meta)