AssertionError: Inputs should be in interval (0.0, 1.0)

[danielmimimi]

Hello,

I just downloaded the code and let the jupyter notebook run inside the docker.
Unfortunately during the first training step an error occured - see following description

I used version 0.4

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
/tmp/ipykernel_3252/1474236369.py in 
      1 for epoch in range(1, conf.epochs + 1):
----> 2     train_epoch(model, train_loader, conf.device, optimizer, f'Epoch {epoch}/{conf.epochs}', scaler, scheduler)
      3     if epoch % 10 == 0:
      4         show_results(model, test_loader, conf.device)

/tmp/ipykernel_3252/3876317219.py in train_epoch(model, data_loader, device, optimizer, desc, scaler, scheduler, progress)
      6         optimizer.zero_grad()
      7         with autocast(scaler is not None):
----> 8             outputs: dict = model(batch['inputs'], targets=batch)
      9         loss = outputs['loss']
     10         if progress:

/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1192         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194             return forward_call(*input, **kwargs)
   1195         # Do not call functions when jit is used
   1196         full_backward_hooks, non_full_backward_hooks = [], []

/opt/conda/lib/python3.7/site-packages/celldetection/models/cpn.py in forward(self, inputs, targets, nms, **kwargs)
    550 
    551         # Core
...
--> 643                 inputs <= self.assert_range[1]), f'Inputs should be in interval {self.assert_range}'
    644         if self.transform is not None:
    645             inputs = self.transform(inputs)

AssertionError: Inputs should be in interval (0.0, 1.0)

I thought I let you know, thanks

Daniel

[danielmimimi]

I switched to version 3.1 and got the following error :

RuntimeError: cuDNN error: CUDNN_STATUS_INTERNAL_ERROR
You can try to repro this exception using the following code snippet. If that doesn't trigger the error, please include your original repro script when reporting this issue.

import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.allow_tf32 = True
data = torch.randn([8, 32, 256, 256], dtype=torch.half, device='cuda', requires_grad=True).to(memory_format=torch.channels_last)
net = torch.nn.Conv2d(32, 12, kernel_size=[1, 1], padding=[0, 0], stride=[1, 1], dilation=[1, 1], groups=1)
net = net.cuda().half().to(memory_format=torch.channels_last)
out = net(data)
out.backward(torch.randn_like(out))
torch.cuda.synchronize()

ConvolutionParams 
    data_type = CUDNN_DATA_HALF
    padding = [0, 0, 0]
    stride = [1, 1, 0]
    dilation = [1, 1, 0]
    groups = 1
    deterministic = false
    allow_tf32 = true
input: TensorDescriptor 0x55636ac53f60
    type = CUDNN_DATA_HALF
    nbDims = 4
    dimA = 8, 32, 256, 256, 
    strideA = 2097152, 1, 8192, 32, 
output: TensorDescriptor 0x55636ac4a9a0
    type = CUDNN_DATA_HALF
    nbDims = 4
    dimA = 8, 12, 256, 256, 
    strideA = 786432, 1, 3072, 12, 
weight: FilterDescriptor 0x55636ac62780
    type = CUDNN_DATA_HALF
    tensor_format = CUDNN_TENSOR_NHWC
    nbDims = 4
    dimA = 12, 32, 1, 1, 
Pointer addresses: 
    input: 0xe4e000000
    output: 0xe49000000
    weight: 0xe055ffa00

I tried your code snipped to reproduce the error but it was working. I will attach my Dockerfile and Code.
Downloads.zip

[neymar-jr]

Same problem. Did you fix it?

[ericup]

Hi, it should work now with the newest version from GitHub.
This Notebook was fixed before, the others should now also be up to date.

For context: A while ago we included the data normalization in the networks. Since then all models expect input data to be in the range [0, 1]. To modify the normalization, you can adapt inputs_mean and inputs_std.