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# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
import numpy as np
from torch.utils.data import DataLoader
from .NormalDataset import NormalDataset
# pytorch lightning related libs
import pytorch_lightning as pl
class NormalModule(pl.LightningDataModule):
def __init__(self, cfg):
super(NormalModule, self).__init__()
self.cfg = cfg
self.overfit = self.cfg.overfit
if self.overfit:
self.batch_size = 1
else:
self.batch_size = self.cfg.batch_size
self.data_size = {}
def prepare_data(self):
pass
@staticmethod
def worker_init_fn(worker_id):
np.random.seed(np.random.get_state()[1][0] + worker_id)
def setup(self, stage):
if stage == 'fit' or stage is None:
self.train_dataset = NormalDataset(cfg=self.cfg, split="train")
self.val_dataset = NormalDataset(cfg=self.cfg, split="val")
self.data_size = {
'train': len(self.train_dataset),
'val': len(self.val_dataset)
}
if stage == 'test' or stage is None:
self.test_dataset = NormalDataset(cfg=self.cfg, split="test")
def train_dataloader(self):
train_data_loader = DataLoader(self.train_dataset,
batch_size=self.batch_size,
shuffle=not self.overfit,
num_workers=self.cfg.num_threads,
pin_memory=True,
worker_init_fn=self.worker_init_fn)
return train_data_loader
def val_dataloader(self):
if self.overfit:
current_dataset = self.train_dataset
else:
current_dataset = self.val_dataset
val_data_loader = DataLoader(current_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.cfg.num_threads,
pin_memory=True)
return val_data_loader
def test_dataloader(self):
test_data_loader = DataLoader(self.test_dataset,
batch_size=1,
shuffle=False,
num_workers=self.cfg.num_threads,
pin_memory=True)
return test_data_loader
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