alexandrosstergiou / keras-depthwiseconv3d Goto Github PK

 load_model('model.h5',
              custom_objects={'DepthwiseConv3D': DepthwiseConv3D})

if (self.groups > self.input_dim):
           raise ValueError('The number of groups cannot exceed the number of channels')

from DepthwiseConv3D import DepthwiseConv3D as DepthwiseConv3D

input1 = Input((128,128,128,1))

x = DepthwiseConv3D(3, padding='same', dilation_rate=(2,2,2))(input1)

model=Model(input1,x)

volume=np.zeros((1,128,128,128,1))

model.predict(volume)

----> 1 model.predict(volume)

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1011         max_queue_size=max_queue_size,
   1012         workers=workers,
-> 1013         use_multiprocessing=use_multiprocessing)
   1014 
   1015   def reset_metrics(self):

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in predict(self, model, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    496         model, ModeKeys.PREDICT, x=x, batch_size=batch_size, verbose=verbose,
    497         steps=steps, callbacks=callbacks, max_queue_size=max_queue_size,
--> 498         workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
    499 
    500 

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    473               mode=mode,
    474               training_context=training_context,
--> 475               total_epochs=1)
    476           cbks.make_logs(model, epoch_logs, result, mode)
    477 

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
    126         step=step, mode=mode, size=current_batch_size) as batch_logs:
    127       try:
--> 128         batch_outs = execution_function(iterator)
    129       except (StopIteration, errors.OutOfRangeError):
    130         # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
     96     # `numpy` translates Tensors to values in Eager mode.
     97     return nest.map_structure(_non_none_constant_value,
---> 98                               distributed_function(input_fn))
     99 
    100   return execution_function

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
    566         xla_context.Exit()
    567     else:
--> 568       result = self._call(*args, **kwds)
    569 
    570     if tracing_count == self._get_tracing_count():

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
    636               *args, **kwds)
    637       # If we did not create any variables the trace we have is good enough.
--> 638       return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds)  # pylint: disable=protected-access
    639 
    640     def fn_with_cond(*inner_args, **inner_kwds):

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
   1609          if isinstance(t, (ops.Tensor,
   1610                            resource_variable_ops.BaseResourceVariable))),
-> 1611         self.captured_inputs)
   1612 
   1613   def _call_flat(self, args, captured_inputs, cancellation_manager=None):

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1690       # No tape is watching; skip to running the function.
   1691       return self._build_call_outputs(self._inference_function.call(
-> 1692           ctx, args, cancellation_manager=cancellation_manager))
   1693     forward_backward = self._select_forward_and_backward_functions(
   1694         args,

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
    543               inputs=args,
    544               attrs=("executor_type", executor_type, "config_proto", config),
--> 545               ctx=ctx)
    546         else:
    547           outputs = execute.execute_with_cancellation(

~\Miniconda3\envs\nnseries\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     65     else:
     66       message = e.message
---> 67     six.raise_from(core._status_to_exception(e.code, message), None)
     68   except TypeError as e:
     69     keras_symbolic_tensors = [

~\Miniconda3\envs\nnseries\lib\site-packages\six.py in raise_from(value, from_value)

NotFoundError:  No algorithm worked!
	 [[node model_8/depthwise_conv3d_2/Conv3D (defined at C:\Users\PC\DepthwiseConv3D.py:244) ]] [Op:__inference_distributed_function_959]

Function call stack:
distributed_function