def val_dataloader(self): if self.data_config.dataset_name == "normal_dataset": transform = self._3d_augmenation("valid") else: transform = None if self.use_ddp: sampler = torch.utils.data.distributed.DistributedSampler( self.valid_dataset, shuffle=False) valid_loader = DataLoader( self.valid_dataset, batch_size=self.test_config.batch_size, num_workers=self.cpu_count, pin_memory=True, sampler=sampler, gpu_transforms=transform, ) else: sampler = torch.utils.data.sampler.SequentialSampler( self.valid_dataset) valid_loader = DataLoader( self.valid_dataset, batch_size=self.test_config.batch_size, pin_memory=True, sampler=sampler, num_workers=self.cpu_count, gpu_transforms=transform, ) return valid_loader
def train_dataloader(self): if self.data_config.dataset_name == "normal_dataset": transform = self._3d_augmenation("train") else: transform = None if self.use_ddp: sampler = torch.utils.data.distributed.DistributedSampler( self.train_dataset, shuffle=True) train_loader = DataLoader( self.train_dataset, batch_size=self.train_config.batch_size, num_workers=self.cpu_count, pin_memory=True, sampler=sampler, drop_last=True, gpu_transforms=transform, ) else: sampler = torch.utils.data.sampler.RandomSampler( self.train_dataset) train_loader = DataLoader( self.train_dataset, batch_size=self.train_config.batch_size, pin_memory=True, sampler=sampler, drop_last=True, num_workers=self.cpu_count, gpu_transforms=transform, ) return train_loader
def train_dataloader(self): transforms_augment_cpu = [] transforms_augment = [] #transforms_augment_cpu.append(rtr.intensity.RandomAddValue(UniformParameter(-0.2, 0.2))) #cpu_transforms = Compose(transforms_augment_cpu) keys = ('data', 'label') # transforms_augment.append(rtr.GaussianNoise(0., 0.05)) transforms_augment.append(rtr.Rot90(dims=(0, 1, 2), keys=keys)) transforms_augment.append(rtr.Mirror(dims=(0, 1, 2), keys=keys)) #transforms_augment.append(ElasticDeformer3d(32, 4, keys=keys, # interp_mode={ 'data': 'linear', 'label': 'nearest' })) #transforms_augment.append(rtr.BaseAffine( # scale=UniformParameter(0.95, 1.05), # rotation=UniformParameter(-45, 45), degree=True, # translation=UniformParameter(-0.05, 0.05), # keys=('data', 'label'), # interpolation_mode='nearest')) gpu_transforms = Compose(transforms_augment) return DataLoader(self.train_dataset, batch_size=self.hparams.batch_size, num_workers=self.hparams.num_loader_workers, shuffle=True, #batch_transforms=cpu_transforms, gpu_transforms=gpu_transforms, pin_memory=True)
def test_progressive_resize_integration(self): sizes = [1, 3, 6] scheduler = SizeStepScheduler([1, 2], [1, 3, 6]) trafo = ProgressiveResize(scheduler) dset = [self.batch_dict] * 10 loader = DataLoader(dset, num_workers=4, batch_transforms=trafo) data_shape = [tuple(i["data"].shape) for i in loader] self.assertIn((1, 1, 1, 1, 1), data_shape) # self.assertIn((1, 1, 3, 3, 3), data_shape) self.assertIn((1, 1, 6, 6, 6), data_shape)
def val_dataloader(self): gpu_transforms = [] keys = ('data', 'label') gpu_transforms.append(rtr.Rot90(dims=(0, 1, 2), keys=keys)) gpu_transforms.append(rtr.Mirror(dims=(0, 1, 2), keys=keys)) gpu_transforms = Compose(gpu_transforms) return DataLoader(self.val_dataset, batch_size=2 * self.hparams.batch_size, num_workers=self.hparams.num_loader_workers, shuffle=False, gpu_transforms=gpu_transforms, pin_memory=True)
def train_dataloader(self): keys = ('data', 'label') transforms_augment = [] transforms_augment.append(rtr.Rot90(dims=(0, 1, 2), keys=keys)) transforms_augment.append(rtr.Mirror(dims=(0, 1, 2), keys=keys)) #transforms_augment.append(ElasticDeformer3d(32, 4, keys=keys, # interp_mode={ 'data': 'linear', 'label': 'nearest' })) gpu_transforms = Compose(transforms_augment) return DataLoader(self.train_dataset, batch_size=self.hparams.batch_size, num_workers=self.hparams.num_loader_workers, shuffle=True, gpu_transforms=gpu_transforms, pin_memory=True)
def test_dataloader(self): if self.data_config.dataset_name == "normal_dataset": transform = self._3d_augmenation("test") else: transform = None sampler = torch.utils.data.sampler.SequentialSampler(self.test_dataset) test_loader = DataLoader( self.test_dataset, batch_size=self.test_config.batch_size, pin_memory=True, sampler=sampler, num_workers=self.cpu_count, gpu_transforms=transform, ) return test_loader
def val_dataloader(self): gpu_transforms = [] gpu_transforms.append(rtr.Rot90(dims=(0, 1, 2), keys=('data', 'label'))) gpu_transforms.append(rtr.Mirror(dims=(0, 1, 2), keys=('data', 'label'))) gpu_transforms = Compose(gpu_transforms) # batch_transforms = [] # batch_transforms.append(BatchRandomCrop(self.hparams.crop_size, bs=1, dist=0, keys=('data', 'label'))) # batch_transforms = Compose(batch_transforms) return DataLoader(self.val_dataset, batch_size=2 * self.hparams.batch_size, num_workers=self.hparams.num_loader_workers, shuffle=False, # batch_transforms=batch_transforms, gpu_transforms=gpu_transforms, pin_memory=True)