Torchvision FasterRCNN Bounding box error

vision
6

@ptrblck as per your suggestions I tried with DataLoader but the result was the same.
The code snippet below creates a custom dataset object which takes in images and targets of type torch cuda float .

class CustomDataset(torch.utils.data.Dataset):

    def __init__(self,xtr,ytr):

        self.xtr = xtr
        self.ytr = ytr

    def __getitem__(self,idx):

        img = self.xtr[idx]
        tar = self.ytr[idx]

        return img,tar

    def __len__(self):

        return len(self.xtr)
    

def collate_fn(batch):
    return list(zip(*batch))

The dataset sample is pretty small. I was trying with a single image. I am facing some problems with num_workers as it is throwing me Cuda Runtime error so decided to skip it. The rest of the code is given below.

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False,num_classes=4)
model.to(device)
optimizer = optim.Adam(model.parameters(),lr=0.000001)

## trying with one image only
x_train = images[0:1]   
y_train = labels[0:1]

## Converts numpy to TorchCudaFloat 
x_train,y_train = from_numpy_to_tensor(x_train,y_train)

## DataLoader object 
dataset = CustomDataset(x_train,y_train)
dataloader = DataLoader(dataset,batch_size=1,collate_fn=collate_fn)


## Iterations
for i in range(20):
    print("Iter No:",i)
    for xtr,ytr in dataloader:
        #print("Iter No:",i)
        #optimizer.zero_grad()
        ytr = list(ytr)
        #print("In train before:")
        print(ytr)
        print(xtr)
        output = model(xtr,ytr)

At the start of the iteration my targets are something like this:-

[{'boxes': tensor([[ 311.9933, 1013.7640,  719.6339, 1142.7417],
        [ 308.1646,  928.4176,  739.4443,  961.6580],
        [ 308.7562,  830.7968,  740.0359,  864.0373],
        [ 305.8657,  680.8315,  763.0424,  708.1243],
        [ 300.3259,  439.0691,  790.4506,  523.2395],
        [ 306.6932,  248.2031,  741.7596,  458.5648]], device='cuda:0'), 'labels': tensor([4, 3, 3, 3, 2, 1], device='cuda:0')}]

And at the end of the 20th iteration the targets have changed to:-

[{'boxes': tensor([[117.7318, 383.4572, 271.5563, 432.2433],
        [116.2870, 351.1749, 279.0319, 363.7481],
        [116.5102, 314.2498, 279.2552, 326.8230],
        [115.4195, 257.5252, 287.9367, 267.8488],
        [113.3290, 166.0784, 298.2794, 197.9159],
        [115.7318,  93.8831, 279.9056, 173.4527]], device='cuda:0'), 'labels': tensor([4, 3, 3, 3, 2, 1], device='cuda:0')}]

As you can see the value of the targets are getting altered by a huge marging just in 20 iterations in just a simple forward pass.
@ptrblck Is this a bug? Do I have to open an issue in that case?

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