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ValueError: Dilated convolutions not yet supported #51

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kmader opened this Issue Oct 29, 2017 · 2 comments

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kmader commented Oct 29, 2017

Dilated convolutions would be helpful for wavenet and similar architectures

INFO:plaidml:b'Opening device "intel(r)_iris(tm)_graphics_550.0'
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-aa075ef71a9c> in <module>()
----> 1 full_seg_model = load_model('dilate_full_lesion_model.h5', compile = False)

/Users/mader/anaconda/lib/python3.5/site-packages/keras/models.py in load_model(filepath, custom_objects, compile)
    237             raise ValueError('No model found in config file.')
    238         model_config = json.loads(model_config.decode('utf-8'))
--> 239         model = model_from_config(model_config, custom_objects=custom_objects)
    240 
    241         # set weights

/Users/mader/anaconda/lib/python3.5/site-packages/keras/models.py in model_from_config(config, custom_objects)
    311                         'Maybe you meant to use '
    312                         '`Sequential.from_config(config)`?')
--> 313     return layer_module.deserialize(config, custom_objects=custom_objects)
    314 
    315 

/Users/mader/anaconda/lib/python3.5/site-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
     52                                     module_objects=globs,
     53                                     custom_objects=custom_objects,
---> 54                                     printable_module_name='layer')

/Users/mader/anaconda/lib/python3.5/site-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    137                 return cls.from_config(config['config'],
    138                                        custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 139                                                            list(custom_objects.items())))
    140             with CustomObjectScope(custom_objects):
    141                 return cls.from_config(config['config'])

/Users/mader/anaconda/lib/python3.5/site-packages/keras/engine/topology.py in from_config(cls, config, custom_objects)
   2495                 if layer in unprocessed_nodes:
   2496                     for node_data in unprocessed_nodes.pop(layer):
-> 2497                         process_node(layer, node_data)
   2498 
   2499         name = config.get('name')

/Users/mader/anaconda/lib/python3.5/site-packages/keras/engine/topology.py in process_node(layer, node_data)
   2452             if input_tensors:
   2453                 if len(input_tensors) == 1:
-> 2454                     layer(input_tensors[0], **kwargs)
   2455                 else:
   2456                     layer(input_tensors, **kwargs)

/Users/mader/anaconda/lib/python3.5/site-packages/keras/engine/topology.py in __call__(self, inputs, **kwargs)
    600 
    601             # Actually call the layer, collecting output(s), mask(s), and shape(s).
--> 602             output = self.call(inputs, **kwargs)
    603             output_mask = self.compute_mask(inputs, previous_mask)
    604 

/Users/mader/anaconda/lib/python3.5/site-packages/keras/layers/convolutional.py in call(self, inputs)
    162                 padding=self.padding,
    163                 data_format=self.data_format,
--> 164                 dilation_rate=self.dilation_rate)
    165         if self.rank == 3:
    166             outputs = K.conv3d(

/Users/mader/anaconda/lib/python3.5/site-packages/plaidml/keras/backend.py in conv2d(x, kernel, strides, padding, dilation_rate, data_format, force_winograd)
   1509                            kernel.shape[3] > 4)):
   1510         return _winograd(x, kernel, padding=padding)
-> 1511     return conv(x, kernel, strides, padding, data_format, dilation_rate)
   1512 
   1513 

/Users/mader/anaconda/lib/python3.5/site-packages/plaidml/keras/backend.py in conv(x, kernel, strides, padding, data_format, dilation_rate, channelwise)
   1296     for entry in dilation_rate:
   1297         if entry != 1:
-> 1298             raise ValueError("Dilated convolutions not yet supported")
   1299     if kernel.ndim != rank + 2:
   1300         raise ValueError("Convolution kernel shape inconsistent with input shape: " +

ValueError: Dilated convolutions not yet supported

@tzerrell tzerrell self-assigned this Oct 30, 2017

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tzerrell Oct 30, 2017

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Thanks for the report, I'll work on adding this support.

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tzerrell commented Oct 30, 2017

Thanks for the report, I'll work on adding this support.

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tzerrell Nov 3, 2017

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Dilated convolutions are now available on master and in the latest pips (version 0.1.2).

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tzerrell commented Nov 3, 2017

Dilated convolutions are now available on master and in the latest pips (version 0.1.2).

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