TF 1.13 says: "The two structures don't have the same nested structure."

[lazydroid]

Inference works perfectly with the pre-trained model.

However, when I try to train, I get the following error. Looks like things have changed from TF1.4 to TF 1.13 and I'd be glad if you tell how to fix this?

Traceback (most recent call last):
  File "./main.py", line 118, in <module>
    trainOp=createUpdateOp()
  File "./main.py", line 105, in createUpdateOp
    grads = optimizer.compute_gradients(totalLoss, var_list=net.getVariables())
  File "/home/lenik/tfenv/local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.py", line 512, in compute_gradients
    colocate_gradients_with_ops=colocate_gradients_with_ops)
  File "/home/lenik/tfenv/local/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py", line 664, in gradients
    unconnected_gradients)
  File "/home/lenik/tfenv/local/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py", line 965, in _GradientsHelper
    lambda: grad_fn(op, *out_grads))
  File "/home/lenik/tfenv/local/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py", line 420, in _MaybeCompile
    return grad_fn()  # Exit early
  File "/home/lenik/tfenv/local/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py", line 965, in <lambda>
    lambda: grad_fn(op, *out_grads))
  File "/home/lenik/tfenv/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_grad.py", line 84, in _SwitchGrad
    return merge(grad, name="cond_grad")[0], None
  File "/home/lenik/tfenv/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 406, in merge
    nest.assert_same_structure(inputs[0], v, expand_composites=True)
  File "/home/lenik/tfenv/local/lib/python2.7/site-packages/tensorflow/python/util/nest.py", line 249, in assert_same_structure
    % (str(e), str1, str2))
ValueError: The two structures don't have the same nested structure.

First structure: type=IndexedSlices str=IndexedSlices(indices=Tensor("optimizer/gradients/RPNloss/cond/calcRPNLoss/calcAllRPNLosses/cond_1/getNegativeLosses/GatherNd/Switch_grad/cond_grad/range:0", shape=(?,), dtype=int32), values=Tensor("optimizer/gradients/zeros_5:0", shape=(?,), dtype=float32), dense_shape=Tensor("optimizer/gradients/RPNloss/cond/calcRPNLoss/calcAllRPNLosses/cond_1/getNegativeLosses/GatherNd/Switch_grad/cond_grad/Shape:0", shape=(1,), dtype=int32))

Second structure: type=IndexedSlices str=IndexedSlices(indices=Tensor("optimizer/gradients/RPNloss/cond/calcRPNLoss/calcAllRPNLosses/cond_1/getNegativeLosses/GatherNd_grad/Squeeze:0", shape=(?,), dtype=int32), values=IndexedSlices(indices=Tensor("optimizer/gradients/RPNloss/cond/calcRPNLoss/calcAllRPNLosses/cond_1/Merge_grad/cond_grad/Switch_1:1", shape=(?,), dtype=int32), values=Tensor("optimizer/gradients/RPNloss/cond/calcRPNLoss/calcAllRPNLosses/cond_1/Merge_grad/tuple/control_dependency_1:0", shape=(?,), dtype=float32), dense_shape=Tensor("optimizer/gradients/RPNloss/cond/calcRPNLoss/calcAllRPNLosses/cond_1/Merge_grad/cond_grad/Switch_2:1", shape=(1,), dtype=int32)), dense_shape=Tensor("optimizer/gradients/RPNloss/cond/calcRPNLoss/calcAllRPNLosses/cond_1/getNegativeLosses/GatherNd_grad/Shape:0", shape=(1,), dtype=int32))