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Can't run Onset Frames Transcription

See original GitHub issue

I’m not sure if I’m doing something wrong, but I can’t figure out how to run onsets_frames_transcription_transcribe. Any help would be very much appreciated!

I’ve tried pip install magenta and the full Development Environment.

Then I tried to run Onset Frames Transcription as follows:

MODEL_DIR=train
onsets_frames_transcription_transcribe --model_dir="${CHECKPOINT_DIR}" /Users/klt/Desktop/tmp/p.wav

I get this output:

/Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/librosa/__init__.py:35: FutureWarning: You are using librosa with Python 2. Please note that librosa 0.7 will be the last version to support Python 2, after which it will require Python 3 or later.
  FutureWarning)
WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py:12: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

W1123 20:41:19.349154 4733580736 module_wrapper.py:139] From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py:12: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:136: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.
    
W1123 20:41:20.726474 4733580736 deprecation.py:323] From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:136: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.
    
WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:626: output_shapes (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.data.get_output_shapes(dataset)`.
W1123 20:41:27.954725 4733580736 deprecation.py:323] From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:626: output_shapes (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.data.get_output_shapes(dataset)`.
WARNING:tensorflow:Using temporary folder as model directory: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpolFdbX
W1123 20:41:27.996778 4733580736 estimator.py:1821] Using temporary folder as model directory: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpolFdbX
INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': None, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x14141c990>, '_model_dir': '/var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpolFdbX', '_protocol': None, '_save_checkpoints_steps': 300, '_keep_checkpoint_every_n_hours': 1, '_service': None, '_num_ps_replicas': 0, '_tpu_config': TPUConfig(iterations_per_loop=300, num_shards=None, num_cores_per_replica=None, per_host_input_for_training=2, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_tf_random_seed': None, '_save_summary_steps': 300, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_cluster': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': None, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_global_id_in_cluster': 0, '_master': ''}
I1123 20:41:28.003739 4733580736 estimator.py:212] Using config: {'_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': None, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x14141c990>, '_model_dir': '/var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpolFdbX', '_protocol': None, '_save_checkpoints_steps': 300, '_keep_checkpoint_every_n_hours': 1, '_service': None, '_num_ps_replicas': 0, '_tpu_config': TPUConfig(iterations_per_loop=300, num_shards=None, num_cores_per_replica=None, per_host_input_for_training=2, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_tf_random_seed': None, '_save_summary_steps': 300, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_cluster': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': None, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_global_id_in_cluster': 0, '_master': ''}
INFO:tensorflow:_TPUContext: eval_on_tpu False
I1123 20:41:28.022785 4733580736 tpu_context.py:220] _TPUContext: eval_on_tpu False
WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/onsets_frames_transcription_transcribe.py:103: make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.
W1123 20:41:28.025648 4733580736 deprecation.py:323] From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/onsets_frames_transcription_transcribe.py:103: make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.
2019-11-23 20:41:28.055441: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-23 20:41:28.102833: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9c4d6bd0b0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2019-11-23 20:41:28.102980: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
INFO:tensorflow:Starting transcription for /Users/klt/Desktop/tmp/p.wav...
I1123 20:41:28.128268 4733580736 onsets_frames_transcription_transcribe.py:113] Starting transcription for /Users/klt/Desktop/tmp/p.wav...
INFO:tensorflow:Processing file...
I1123 20:41:28.128619 4733580736 onsets_frames_transcription_transcribe.py:119] Processing file...
INFO:tensorflow:Running inference...
I1123 20:41:32.646573 4733580736 onsets_frames_transcription_transcribe.py:129] Running inference...
INFO:tensorflow:Could not find trained model in model_dir: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpolFdbX, running initialization to predict.
I1123 20:41:32.646955 4733580736 estimator.py:615] Could not find trained model in model_dir: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpolFdbX, running initialization to predict.
WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
W1123 20:41:32.658987 4733580736 deprecation.py:506] From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Bus error: 10
(venv) (base) MacBook-Pro:onsets_frames_transcription klt$ MODEL_DIR="train"
(venv) (base) MacBook-Pro:onsets_frames_transcription klt$ onsets_frames_transcription_transcribe --model_dir="${CHECKPOINT_DIR}" "/Users/klt/Desktop/tmp/p.wav"
/Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/librosa/__init__.py:35: FutureWarning: You are using librosa with Python 2. Please note that librosa 0.7 will be the last version to support Python 2, after which it will require Python 3 or later.
  FutureWarning)
WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py:12: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

W1123 20:42:42.978538 4656154048 module_wrapper.py:139] From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py:12: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:136: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.
    
W1123 20:42:44.601079 4656154048 deprecation.py:323] From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:136: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.
    
WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:626: output_shapes (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.data.get_output_shapes(dataset)`.
W1123 20:42:49.091053 4656154048 deprecation.py:323] From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:626: output_shapes (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.data.get_output_shapes(dataset)`.
WARNING:tensorflow:Using temporary folder as model directory: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpLReVyx
W1123 20:42:49.123639 4656154048 estimator.py:1821] Using temporary folder as model directory: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpLReVyx
INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': None, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x13b54bd90>, '_model_dir': '/var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpLReVyx', '_protocol': None, '_save_checkpoints_steps': 300, '_keep_checkpoint_every_n_hours': 1, '_service': None, '_num_ps_replicas': 0, '_tpu_config': TPUConfig(iterations_per_loop=300, num_shards=None, num_cores_per_replica=None, per_host_input_for_training=2, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_tf_random_seed': None, '_save_summary_steps': 300, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_cluster': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': None, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_global_id_in_cluster': 0, '_master': ''}
I1123 20:42:49.126346 4656154048 estimator.py:212] Using config: {'_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': None, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x13b54bd90>, '_model_dir': '/var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpLReVyx', '_protocol': None, '_save_checkpoints_steps': 300, '_keep_checkpoint_every_n_hours': 1, '_service': None, '_num_ps_replicas': 0, '_tpu_config': TPUConfig(iterations_per_loop=300, num_shards=None, num_cores_per_replica=None, per_host_input_for_training=2, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_tf_random_seed': None, '_save_summary_steps': 300, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_cluster': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': None, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_global_id_in_cluster': 0, '_master': ''}
INFO:tensorflow:_TPUContext: eval_on_tpu False
I1123 20:42:49.128591 4656154048 tpu_context.py:220] _TPUContext: eval_on_tpu False
WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/onsets_frames_transcription_transcribe.py:103: make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.
W1123 20:42:49.129336 4656154048 deprecation.py:323] From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/onsets_frames_transcription_transcribe.py:103: make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.
2019-11-23 20:42:49.145057: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-23 20:42:49.196234: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fe191151f50 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2019-11-23 20:42:49.196301: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
INFO:tensorflow:Starting transcription for /Users/klt/Desktop/tmp/p.wav...
I1123 20:42:49.234787 4656154048 onsets_frames_transcription_transcribe.py:113] Starting transcription for /Users/klt/Desktop/tmp/p.wav...
INFO:tensorflow:Processing file...
I1123 20:42:49.238156 4656154048 onsets_frames_transcription_transcribe.py:119] Processing file...
INFO:tensorflow:Running inference...
I1123 20:42:51.931320 4656154048 onsets_frames_transcription_transcribe.py:129] Running inference...
INFO:tensorflow:Could not find trained model in model_dir: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpLReVyx, running initialization to predict.
I1123 20:42:51.931879 4656154048 estimator.py:615] Could not find trained model in model_dir: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpLReVyx, running initialization to predict.
WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
W1123 20:42:51.936925 4656154048 deprecation.py:506] From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Bus error: 10
(venv) (base) MacBook-Pro:onsets_frames_transcription klt$ onsets_frames_transcription_transcribe --model_dir="${CHECKPOINT_DIR}" /Users/klt/Desktop/tmp/p.wav
/Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/librosa/__init__.py:35: FutureWarning: You are using librosa with Python 2. Please note that librosa 0.7 will be the last version to support Python 2, after which it will require Python 3 or later.
  FutureWarning)
WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py:12: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

W1123 20:43:46.547178 4500542912 module_wrapper.py:139] From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py:12: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:136: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.
    
W1123 20:43:48.238924 4500542912 deprecation.py:323] From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:136: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.
    
WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:626: output_shapes (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.data.get_output_shapes(dataset)`.
W1123 20:43:52.166743 4500542912 deprecation.py:323] From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/data.py:626: output_shapes (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.data.get_output_shapes(dataset)`.
WARNING:tensorflow:Using temporary folder as model directory: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpP9UtYs
W1123 20:43:52.207689 4500542912 estimator.py:1821] Using temporary folder as model directory: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpP9UtYs
INFO:tensorflow:Using config: {'_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': None, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x135b4f650>, '_model_dir': '/var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpP9UtYs', '_protocol': None, '_save_checkpoints_steps': 300, '_keep_checkpoint_every_n_hours': 1, '_service': None, '_num_ps_replicas': 0, '_tpu_config': TPUConfig(iterations_per_loop=300, num_shards=None, num_cores_per_replica=None, per_host_input_for_training=2, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_tf_random_seed': None, '_save_summary_steps': 300, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_cluster': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': None, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_global_id_in_cluster': 0, '_master': ''}
I1123 20:43:52.208148 4500542912 estimator.py:212] Using config: {'_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': None, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x135b4f650>, '_model_dir': '/var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpP9UtYs', '_protocol': None, '_save_checkpoints_steps': 300, '_keep_checkpoint_every_n_hours': 1, '_service': None, '_num_ps_replicas': 0, '_tpu_config': TPUConfig(iterations_per_loop=300, num_shards=None, num_cores_per_replica=None, per_host_input_for_training=2, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_tf_random_seed': None, '_save_summary_steps': 300, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_cluster': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': None, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_global_id_in_cluster': 0, '_master': ''}
INFO:tensorflow:_TPUContext: eval_on_tpu False
I1123 20:43:52.209754 4500542912 tpu_context.py:220] _TPUContext: eval_on_tpu False
WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/onsets_frames_transcription_transcribe.py:103: make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.
W1123 20:43:52.210043 4500542912 deprecation.py:323] From /Users/klt/code/phd/magenta-master/magenta/models/onsets_frames_transcription/onsets_frames_transcription_transcribe.py:103: make_initializable_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_initializable_iterator(dataset)`.
2019-11-23 20:43:52.221619: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-23 20:43:52.251399: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb7beeaf4c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2019-11-23 20:43:52.251438: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
INFO:tensorflow:Starting transcription for /Users/klt/Desktop/tmp/p.wav...
I1123 20:43:52.303266 4500542912 onsets_frames_transcription_transcribe.py:113] Starting transcription for /Users/klt/Desktop/tmp/p.wav...
INFO:tensorflow:Processing file...
I1123 20:43:52.306233 4500542912 onsets_frames_transcription_transcribe.py:119] Processing file...
INFO:tensorflow:Running inference...
I1123 20:43:54.843364 4500542912 onsets_frames_transcription_transcribe.py:129] Running inference...
INFO:tensorflow:Could not find trained model in model_dir: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpP9UtYs, running initialization to predict.
I1123 20:43:54.843811 4500542912 estimator.py:615] Could not find trained model in model_dir: /var/folders/35/xkv0vz050bn4_qxdn4dclz700000gn/T/tmpP9UtYs, running initialization to predict.
WARNING:tensorflow:From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
W1123 20:43:54.848026 4500542912 deprecation.py:506] From /Users/klt/code/phd/magenta-master/venv/lib/python2.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Bus error: 10

As far as I can tell the problem happens at

prediction_list = list(
            estimator.predict(
                input_fn,
                checkpoint_path=checkpoint_path,
                yield_single_examples=False))

Issue Analytics

  • State:closed
  • Created 4 years ago
  • Comments:11

github_iconTop GitHub Comments

1reaction
cghawthornecommented, Apr 6, 2020

Sorry for the confusion, the use_cudnn hparam does not apply to the drum model. It was written to be trained on TPUs, so it doesn’t have any cudnn-specific code.

The bus error indicates that TensorFlow is trying to use instructions your CPU doesn’t support. You’ll need to either use a different computer or compile TensorFlow specifically for your machine.

0reactions
eupstoncommented, Apr 6, 2020

I’m getting Bus 10 error as well and when I try to add the hparam above I run into: ValueError: Unknown hyperparameter type for use_cudnn

my arguments are: python onsets_frames_transcription_transcribe.py --model_dir=e-gmd_checkpoint/ --config="drums" --hparams=use_cudnn=false danny_test.wav

tensorflow version is: 1.15.2

any ideas?

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