here is the code where i am getting input

def validate(val_loader, model, criterion):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    bin_op.binarization()
    for i, (input, target) in enumerate(val_loader):
        target = target.cuda(async=True)
        with torch.no_grad():
            input_var = torch.autograd.Variable(input)
            target_var = torch.autograd.Variable(target)
            print(input_var)
 

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)```

* this is how my input sample look like,I am getting **floating point** as input altough it is in encoded in **uint8**

* So i round it off to nearest integer as to get 8 bit value

print(input_var)

```27.325001 13.325003 -56.674995 -62.674999 -66.675003 -70.675003
    -64.675003 -57.674995 -18.675001 18.325003 -2.675003 3.324996
    12.325003 16.325003 25.325001 -14.675002 1.324996 -12.675002
    -27.674999 -38.674999 -23.675001 -23.675001 -37.674999 -16.675001
    57.324997 -46.674999 -72.675003 -74.675003 -78.675003 -102.674995
    -98.675003 -81.675003 19.325003 104.324989 101.324989 125.324989
    125.324989 120.324989 125.324989 128.324982 127.324989 128.324982
    125.324989 121.324989 123.324989 126.324989 126.324989 108.324989
    110.324989 128.324982 128.324982 129.324982 129.324982 129.324982
    129.324982 127.324989 129.324982 127.324989 122.324989 125.324989
    129.324982 129.324982 127.324989 127.324989 129.324982 128.324982
    129.324982 129.324982 130.324982 130.324982 128.324982 127.324989
    128.324982 126.324989 128.324982 129.324982 128.324982 128.324982
    127.324989 128.324982 128.324982 130.324982 129.324982 130.324982
    130.324982 131.324982 131.324982 129.324982 129.324982 129.324982
    129.324982 129.324982 129.324982 129.324982 127.324989 128.324982
    128.324982 117.324989 14.325003 15.325003 21.325001 51.324997
    -5.675003 -97.675003 -96.675003 -94.675003 -93.675003 -81.675003
    60.324997 121.324989 119.324989 119.324989 121.324989 125.324989
    125.324989 120.324989 119.324989 127.324989 128.324982 127.324989
    118.324989 2.324996 -84.675003 -90.675003 -95.675003 -96.675003
    -97.675003 -68.675003 33.325001 -0.675003 59.324997 103.324989```