[Simple-tseter-of-pytorch-code] #python #pytorch

test_pytorch_init_test.py

import numpy as np
import torch
from models import miracle_net
from config import Config
opt=Config()
net = miracle_net.MiracleNet(opt)
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
class Template(Dataset):
    def __init__(self):
        super(Template, self).__init__()
        self.x = np.random.rand(64, 2, 41, 9)
        self.y = np.ones(64).astype(np.int64)
    def __len__(self):
        return 64
    def __getitem__(self, index):
        inputs, label = self.x[index], self.y[index]
        return torch.from_numpy(inputs).float(), torch.from_numpy(np.array(label)).long()
test_loader = DataLoader(dataset=Template()
                         , batch_size=32, shuffle=True,
                         num_workers=0, drop_last=False)
train_loader = DataLoader(dataset=Template()
                          , batch_size=32, shuffle=True,
                                  num_workers=0, drop_last=False)