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data_loader.py
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data_loader.py
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import os
import numpy as np
import torch
from torch.utils.data.dataset import Dataset
from torch.utils.data.dataloader import DataLoader
from torchvision import transforms
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
class TextDataset(Dataset):
def __init__(self,data_dir,label_dir):
self.data_dir=data_dir
self.label_dir=label_dir
self.data = np.load(self.data_dir, allow_pickle=True)
print('shape of data:'+str(self.data.shape))
self.label=np.load(self.label_dir,allow_pickle=True)
def __len__(self):
data=np.load(self.data_dir,allow_pickle=True)
return len(data)
def __getitem__(self,idx):
return self.data[idx],self.label[idx]
if __name__=="__main__":
#see shape of data here
train_data=TextDataset('/content/drive/Shareddrives/CSE258/data/train_data50.npy','/content/drive/Shareddrives/CSE258/data/train_label50.npy')
train_loader=DataLoader(dataset=train_data,batch_size=32)
for data,label in train_loader:
#data = torch.from_numpy(data)
data.to(device=device, dtype=torch.float32)
print(label.shape)
print(data.shape)