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data_loader.py
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data_loader.py
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import h5py
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from PIL import Image
import numpy as np
import random
import torch
class MyDataset(Dataset):
def __init__(self, arg, data=None, target=None, imageSize=None, loadSize=None, transform=None):
self.data = data
self.target = target
self.imageSize = imageSize
self.loadSize = loadSize
self.arg = arg
def __getitem__(self, index):
x = self.data[index]
y = self.target[index]
x = x.transpose((2,0,1))
r = np.random.randint(0, self.arg.loadSize-self.imageSize)
r2 = np.random.randint(0, self.arg.loadSize-self.imageSize)
x = x[:, r:(self.imageSize+r), r2:(self.imageSize+r2)]
rotateNum = np.random.randint(0, 4)
for j in range(3):
for i in range(rotateNum):
x[j] = np.rot90(x[j])
if random.random() < 0.5:
x = np.fliplr(x).copy()
else:
x = np.array(x)
return torch.from_numpy(np.asarray(x)), torch.from_numpy(np.asarray(y))
def __len__(self):
return len(self.data)
class MyDataset_test(Dataset):
def __init__(self, data, label, transform=None):
self.data = data
self.label = label
def __getitem__(self, index):
x = self.data[index]
y = self.label[index]
x = x.transpose((2,0,1))
x = x[:, 16:240, 16:240]
return torch.from_numpy(np.asarray(x)), torch.from_numpy(np.asarray(y))
def __len__(self):
return len(self.data)
def get_data_loader_for_chosen(arg, chosen):
data_file = h5py.File(arg.train_file, 'r')['examples']
generators = []
for client in chosen:
train_set = MyDataset(arg, data_file[str(client)]['pixels'], data_file[str(client)]['label'], arg.fineSize, arg.loadSize)
train_set_loader = DataLoader(dataset=train_set, batch_size=arg.batch_size, shuffle=True, drop_last=True)
generators.append(iter(train_set_loader))
return generators
def get_data_loader_for_evaluation(arg):
data_file = h5py.File(arg.val_file, 'r')['examples']
test_set = MyDataset_test(data_file['0']['pixels'], data_file['0']['label'])
test_set_loader = DataLoader(dataset=test_set, batch_size=1, shuffle=True, drop_last=True)
return test_set_loader, len(data_file['0']['label'])