mbinkowski / deepspeechdistances Goto Github PK

INFO:tensorflow:Restoring parameters from /content/drive/My Drive/DeepSpeechDistances/checkpoint/ds2_large/model.ckpt-54800
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py in _do_call(self, fn, *args)
   1364     try:
-> 1365       return fn(*args)
   1366     except errors.OpError as e:

11 frames
InvalidArgumentError: No OpKernel was registered to support Op 'CudnnRNNCanonicalToParams' used by {{node ForwardPass/ds2_encoder/cudnn_gru/cudnn_gru/CudnnRNNCanonicalToParams}}with these attrs: [rnn_mode="gru", seed2=598, seed=0, dropout=0, num_params=60, input_mode="linear_input", T=DT_HALF, direction="bidirectional"]
Registered devices: [CPU, XLA_CPU]
Registered kernels:
  device='GPU'; T in [DT_DOUBLE]
  device='GPU'; T in [DT_FLOAT]
  device='GPU'; T in [DT_HALF]

	 [[ForwardPass/ds2_encoder/cudnn_gru/cudnn_gru/CudnnRNNCanonicalToParams]]

During handling of the above exception, another exception occurred:

InvalidArgumentError                      Traceback (most recent call last)
InvalidArgumentError: No OpKernel was registered to support Op 'CudnnRNNCanonicalToParams' used by node ForwardPass/ds2_encoder/cudnn_gru/cudnn_gru/CudnnRNNCanonicalToParams (defined at /tensorflow-1.15.2/python3.7/tensorflow_core/python/framework/ops.py:1748) with these attrs: [rnn_mode="gru", seed2=598, seed=0, dropout=0, num_params=60, input_mode="linear_input", T=DT_HALF, direction="bidirectional"]
Registered devices: [CPU, XLA_CPU]
Registered kernels:
  device='GPU'; T in [DT_DOUBLE]
  device='GPU'; T in [DT_FLOAT]
  device='GPU'; T in [DT_HALF]

	 [[ForwardPass/ds2_encoder/cudnn_gru/cudnn_gru/CudnnRNNCanonicalToParams]]

During handling of the above exception, another exception occurred:

InvalidArgumentError                      Traceback (most recent call last)
/tensorflow-1.15.2/python3.7/tensorflow_core/python/training/saver.py in restore(self, sess, save_path)
   1324       # We add a more reasonable error message here to help users (b/110263146)
   1325       raise _wrap_restore_error_with_msg(
-> 1326           err, "a mismatch between the current graph and the graph")
   1327 
   1328   @staticmethod

InvalidArgumentError: Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:

No OpKernel was registered to support Op 'CudnnRNNCanonicalToParams' used by node ForwardPass/ds2_encoder/cudnn_gru/cudnn_gru/CudnnRNNCanonicalToParams (defined at /tensorflow-1.15.2/python3.7/tensorflow_core/python/framework/ops.py:1748) with these attrs: [rnn_mode="gru", seed2=598, seed=0, dropout=0, num_params=60, input_mode="linear_input", T=DT_HALF, direction="bidirectional"]
Registered devices: [CPU, XLA_CPU]
Registered kernels:
  device='GPU'; T in [DT_DOUBLE]
  device='GPU'; T in [DT_FLOAT]
  device='GPU'; T in [DT_HALF]

	 [[ForwardPass/ds2_encoder/cudnn_gru/cudnn_gru/CudnnRNNCanonicalToParams]]```

reference_path = os.path.join(SAMPLE_PATH, 'ref', '*.wav')
eval_paths = [os.path.join(SAMPLE_PATH, f'noisy_{i+1}', '*.wav') for i
              in range(NUM_NOISE_LEVELS)]

evaluator = audio_distance.AudioDistance(
    load_path=os.path.join(PATH, 'checkpoint', 'ds2_large', 'model.ckpt-54800'),
    meta_path=os.path.join(PATH, 'checkpoint', 'collection-stripped-meta.meta'),
    required_sample_size=NUM_SPLITS * SAMPLES_PER_SPLIT,
    num_splits=NUM_SPLITS)

evaluator.load_real_data(reference_path)

NotImplementedError                       Traceback (most recent call last)
[<ipython-input-5-f43dcce18c53>](https://localhost:8080/#) in <module>()
      7     meta_path=os.path.join(PATH, 'checkpoint', 'collection-stripped-meta.meta'),
      8     required_sample_size=NUM_SPLITS * SAMPLES_PER_SPLIT,
----> 9     num_splits=NUM_SPLITS)
     10 
     11 evaluator.load_real_data(reference_path)

9 frames
[/content/drive/My Drive/DeepSpeechDistances/audio_distance.py](https://localhost:8080/#) in __init__(self, load_path, meta_path, keep_features, required_sample_size, num_splits, do_kdsd, do_conditional_dsds, sample_freq)
     81                  tf.split(self.eval_features, num_splits))
     82 
---> 83     dists = [frechet_dist(ref, ev) for ref, ev in zipped]
     84     self.dists = [(tf.reduce_mean(dists), tf.math.reduce_std(dists))]
     85     if self.do_kdsd:

[/content/drive/My Drive/DeepSpeechDistances/audio_distance.py](https://localhost:8080/#) in <listcomp>(.0)
     81                  tf.split(self.eval_features, num_splits))
     82 
---> 83     dists = [frechet_dist(ref, ev) for ref, ev in zipped]
     84     self.dists = [(tf.reduce_mean(dists), tf.math.reduce_std(dists))]
     85     if self.do_kdsd:

[/tensorflow-1.15.2/python3.7/tensorflow_gan/python/eval/classifier_metrics.py](https://localhost:8080/#) in frechet_classifier_distance_from_activations(activations1, activations2)
    791   """
    792   return _frechet_classifier_distance_from_activations_helper(
--> 793       activations1, activations2, streaming=False)
    794 
    795 

[/tensorflow-1.15.2/python3.7/tensorflow_gan/python/eval/classifier_metrics.py](https://localhost:8080/#) in _frechet_classifier_distance_from_activations_helper(activations1, activations2, streaming)
    714     num_examples_real = tf.cast(tf.shape(input=activations1)[0], tf.float64)
    715     sigma = (num_examples_real / (num_examples_real - 1) *
--> 716              tfp.stats.covariance(activations1),)
    717     # Calculate the unbiased covariance matrix of second activations.
    718     num_examples_generated = tf.cast(

[/tensorflow-1.15.2/python3.7/tensorflow_probability/python/stats/sample_stats.py](https://localhost:8080/#) in covariance(x, y, sample_axis, event_axis, keepdims, name)
    436                 # batch of covariance.
    437                 event_shape**2,
--> 438                 tf.ones([sample_ndims], tf.int32)),
    439             0))
    440     # Permuting by the argsort inverts the permutation, making

[/tensorflow-1.15.2/python3.7/tensorflow_core/python/ops/array_ops.py](https://localhost:8080/#) in ones(shape, dtype, name)
   2558         # Create a constant if it won't be very big. Otherwise create a fill op
   2559         # to prevent serialized GraphDefs from becoming too large.
-> 2560         output = _constant_if_small(one, shape, dtype, name)
   2561         if output is not None:
   2562           return output

[/tensorflow-1.15.2/python3.7/tensorflow_core/python/ops/array_ops.py](https://localhost:8080/#) in _constant_if_small(value, shape, dtype, name)
   2293 def _constant_if_small(value, shape, dtype, name):
   2294   try:
-> 2295     if np.prod(shape) < 1000:
   2296       return constant(value, shape=shape, dtype=dtype, name=name)
   2297   except TypeError:

<__array_function__ internals> in prod(*args, **kwargs)

[/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py](https://localhost:8080/#) in prod(a, axis, dtype, out, keepdims, initial, where)
   3050     array([1, 2, 6])
   3051     >>> a = np.array([[1, 2, 3], [4, 5, 6]])
-> 3052     >>> np.cumprod(a, dtype=float) # specify type of output
   3053     array([   1.,    2.,    6.,   24.,  120.,  720.])
   3054 

[/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py](https://localhost:8080/#) in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
     84             else:
     85                 return reduction(axis=axis, out=out, **passkwargs)
---> 86 
     87     return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
     88 

[/tensorflow-1.15.2/python3.7/tensorflow_core/python/framework/ops.py](https://localhost:8080/#) in __array__(self)
    734   def __array__(self):
    735     raise NotImplementedError("Cannot convert a symbolic Tensor ({}) to a numpy"
--> 736                               " array.".format(self.name))
    737 
    738   def __len__(self):

NotImplementedError: Cannot convert a symbolic Tensor (covariance/Size_2:0) to a numpy array.