cuDNN failed to initialize

[Al3n70rn]

Hi,

Thank for this very interesting package.

I have modified a little bit your notebook to use multiclass mask, everything seems to work properly but once I try to fit the model I have this error:

Training on 1 GPUs.
Epoch 1/10

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/indexed_slices.py:449: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/mask_loss/cond/gradients/mask_loss/cond/map/while_grad/gradients/mask_loss/cond/map/while/GatherNd_grad/Squeeze:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/mask_loss/cond/gradients/mask_loss/cond/map/while_grad/gradients/AddN_3:0", shape=(None, None), dtype=float32), dense_shape=Tensor("mask_loss/cond/map/while/gradient_tape/mask_loss/cond/gradients/mask_loss/cond/map/while_grad/gradients/mask_loss/cond/map/while/GatherNd_grad/Shape:0", shape=(2,), dtype=int64))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "shape. This may consume a large amount of memory." % value)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/indexed_slices.py:449: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/mask_loss/cond/gradients/mask_loss/cond/map/while_grad/gradients/mask_loss/cond/map/while/GatherNd_1_grad/Squeeze:0", shape=(None,), dtype=int64), values=Tensor("gradient_tape/mask_loss/cond/gradients/mask_loss/cond/map/while_grad/gradients/mask_loss/cond/map/while/transpose_grad/transpose:0", shape=(None, 28, 28, None), dtype=float32), dense_shape=Tensor("mask_loss/cond/map/while/gradient_tape/mask_loss/cond/gradients/mask_loss/cond/map/while_grad/gradients/mask_loss/cond/map/while/GatherNd_1_grad/Shape:0", shape=(4,), dtype=int64))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "shape. This may consume a large amount of memory." % value)

WARNING:tensorflow:Gradients do not exist for variables ['conv_0_semantic_upsample_2/kernel:0', 'conv_0_semantic_upsample_2/bias:0', 'conv_0_semantic_upsample_3/kernel:0', 'conv_0_semantic_upsample_3/bias:0', 'conv_0_semantic_upsample_4/kernel:0', 'conv_0_semantic_upsample_4/bias:0', 'conv_0_semantic_upsample_5/kernel:0', 'conv_0_semantic_upsample_5/bias:0', 'conv_0_semantic_upsample_6/kernel:0', 'conv_0_semantic_upsample_6/bias:0', 'conv_0_semantic_2/kernel:0', 'conv_0_semantic_2/bias:0', 'conv_0_semantic_3/kernel:0', 'conv_0_semantic_3/bias:0', 'conv_0_semantic_4/kernel:0', 'conv_0_semantic_4/bias:0', 'conv_0_semantic_5/kernel:0', 'conv_0_semantic_5/bias:0', 'conv_0_semantic_6/kernel:0', 'conv_0_semantic_6/bias:0', 'batch_normalization_0_semantic_2/gamma:0', 'batch_normalization_0_semantic_2/beta:0', 'batch_normalization_0_semantic_3/gamma:0', 'batch_normalization_0_semantic_3/beta:0', 'batch_normalization_0_semantic_4/gamma:0', 'batch_normalization_0_semantic_4/beta:0', 'batch_normalization_0_semantic_5/gamma:0', 'batch_normalization_0_semantic_5/beta:0', 'batch_normalization_0_semantic_6/gamma:0', 'batch_normalization_0_semantic_6/beta:0', 'conv_1_semantic_2/kernel:0', 'conv_1_semantic_2/bias:0', 'conv_1_semantic_3/kernel:0', 'conv_1_semantic_3/bias:0', 'conv_1_semantic_4/kernel:0', 'conv_1_semantic_4/bias:0', 'conv_1_semantic_5/kernel:0', 'conv_1_semantic_5/bias:0', 'conv_1_semantic_6/kernel:0', 'conv_1_semantic_6/bias:0'] when minimizing the loss.
WARNING:tensorflow:Gradients do not exist for variables ['conv_0_semantic_upsample_2/kernel:0', 'conv_0_semantic_upsample_2/bias:0', 'conv_0_semantic_upsample_3/kernel:0', 'conv_0_semantic_upsample_3/bias:0', 'conv_0_semantic_upsample_4/kernel:0', 'conv_0_semantic_upsample_4/bias:0', 'conv_0_semantic_upsample_5/kernel:0', 'conv_0_semantic_upsample_5/bias:0', 'conv_0_semantic_upsample_6/kernel:0', 'conv_0_semantic_upsample_6/bias:0', 'conv_0_semantic_2/kernel:0', 'conv_0_semantic_2/bias:0', 'conv_0_semantic_3/kernel:0', 'conv_0_semantic_3/bias:0', 'conv_0_semantic_4/kernel:0', 'conv_0_semantic_4/bias:0', 'conv_0_semantic_5/kernel:0', 'conv_0_semantic_5/bias:0', 'conv_0_semantic_6/kernel:0', 'conv_0_semantic_6/bias:0', 'batch_normalization_0_semantic_2/gamma:0', 'batch_normalization_0_semantic_2/beta:0', 'batch_normalization_0_semantic_3/gamma:0', 'batch_normalization_0_semantic_3/beta:0', 'batch_normalization_0_semantic_4/gamma:0', 'batch_normalization_0_semantic_4/beta:0', 'batch_normalization_0_semantic_5/gamma:0', 'batch_normalization_0_semantic_5/beta:0', 'batch_normalization_0_semantic_6/gamma:0', 'batch_normalization_0_semantic_6/beta:0', 'conv_1_semantic_2/kernel:0', 'conv_1_semantic_2/bias:0', 'conv_1_semantic_3/kernel:0', 'conv_1_semantic_3/bias:0', 'conv_1_semantic_4/kernel:0', 'conv_1_semantic_4/bias:0', 'conv_1_semantic_5/kernel:0', 'conv_1_semantic_5/bias:0', 'conv_1_semantic_6/kernel:0', 'conv_1_semantic_6/bias:0'] when minimizing the loss.

---------------------------------------------------------------------------

UnknownError                              Traceback (most recent call last)

<ipython-input-86-144b2c018829> in <module>()
     30     validation_data=val_data,
     31     validation_steps=val_data.y.shape[0] // batch_size,
---> 32     callbacks=train_callbacks)

6 frames

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

UnknownError:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
	 [[node resnet50_retinanetmask/conv1_conv/Conv2D (defined at <ipython-input-86-144b2c018829>:32) ]] [Op:__inference_train_function_125016]

Function call stack:
train_function

Do you have any clues on how to handle this issue?

Best regards,