RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same about image-similarity-using-deep-ranking HOT 20 CLOSED

net.train()
for epoch in range(start_epoch, epochs + start_epoch):

    running_loss = 0.0
    for batch_idx, (data1, data2, data3) in enumerate(trainloader):

        if is_gpu:
            device = torch.device("cuda" if torch.cuda.is_avaliable() else "cpu")
            data1, data2, data3 = data1.to(device), data2.to(device), data3.to(device)
        # wrap in torch.autograd.Variable
        data1, data2, data3 = Variable(
            data1), Variable(data2), Variable(data3)

        # compute output and loss
        embedded_a, embedded_p, embedded_n = net(data1, data2, data3)
        loss = criterion(embedded_a, embedded_p, embedded_n)

Python 3.6.8 |Anaconda, Inc.| (default, Feb 21 2019, 18:30:04) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.is_available()
True
>>>

==> Initialize CUDA support for TripletNet model ...
==> Building new TripletNet model ...
==> Preparing Tiny ImageNet dataset ...
==> Start training ...
Traceback (most recent call last):
  File "main.py", line 111, in <module>
    main()
  File "main.py", line 107, in main
    testloader, args.start_epoch, args.epochs, args.is_gpu)
  File "D:\deep-ranking\model7\utils.py", line 99, in train
    device = torch.device('cuda:0' if torch.cuda.is_avaliable() else 'cpu')
AttributeError: module 'torch.cuda' has no attribute 'is_avaliable'

(deep-ranking) D:\deep-ranking\model7>python
Python 3.6.8 |Anaconda, Inc.| (default, Feb 21 2019, 18:30:04) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch.nn as nn
>>> torch.cuda.is_available()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'torch' is not defined
>>> import torch.cuda
>>> torch.cuda.is_available()
True
>>>

(deep-ranking) D:\deep-ranking\model7>python main.py
==> Initialize CUDA support for TripletNet model ...
==> Building new TripletNet model ...
==> Preparing Tiny ImageNet dataset ...
==> Start training ...
Training Epoch: 1 | Loss: 0.0
Training Epoch: 2 | Loss: 0.0
Training Epoch: 3 | Loss: 0.0

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
data1, data2, data3 = data1.to(device), data2.to(device), data3.to(device)

print("==> Start training ...")
net.train()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)