def make_load(self, data, original):
if isinstance(original, Mapping):
feature_processors = {}
for key, val in six.iteritems(original):
feature_processors[key] = GraphConfig.from_dict(val)
return FeatureProcessorsConfig(feature_processors)
raise ValidationError("Feature processor is not valid for, `{}`".format(original))
def get_pipeline_config(mode):
return TFRecordImagePipelineConfig(
dynamic_pad=False,
data_files=train_data_file
if Modes.is_train(mode) else eval_data_file,
meta_data_file=meta_data_filename,
feature_processors=FeatureProcessorsConfig({
'image':
GraphConfig(input_layers=[['image', 0, 0]],
output_layers=[['image_out', 0, 0]],
layers=[
CastConfig(dtype='float32',
name='image_out',
inbound_nodes=[['image', 0, 0]])
])
}))
def test_graph_config(self):
config_dict = {
'input_layers': ['image'],
'output_layers': [['dense_0', 0, 0]],
'layers': [
{
'Conv2D': {
'filters': 64,
'strides': [1, 1],
'kernel_size': [2, 2],
'activation': 'relu',
'name': 'convolution_1',
}
},
{'Dense': {'units': 17, 'name': 'dense_0'}}
]
}
config = GraphConfig.from_dict(config_dict)
config_to_dict = config.to_dict()
assert_equal_graphs(config_dict, config_to_dict)
def from_config(cls, mode, features, labels, config): # pylint: disable=arguments-differ
"""Instantiates a Graph container from its config (output of `get_config()`).
Arguments:
mode:
features:
labels:
config: Model config dictionary.
Returns:
A model instance.
Raises:
ValueError: In case of improperly formatted config dict.
"""
# set the training mode
set_learning_phase(Modes.is_train(mode))
if not isinstance(config, GraphConfig):
config = GraphConfig.from_dict(config)
# layer instances created during
# the graph reconstruction process
created_layers = {}
# Create an input layer based on the defined inputs and features
for layer in config.input_layers:
layer_name, node_index, tensor_index = cls.get_node_data(layer)
if layer_name in features:
created_layers[layer_name] = InputLayer(
input_tensor=features[layer_name], name=layer_name)
elif isinstance(labels, Mapping) and layer_name in labels:
created_layers[layer_name] = InputLayer(
input_tensor=labels[layer_name], name=layer_name)
else:
raise ConfigurationError(
"Input `{}`is not found".format(layer_name))
def process_layer(layer):
"""Deserialize a layer, then call it on appropriate inputs.
Arguments:
layer_data: layer config dict.
Raises:
ValueError: In case of improperly formatted `layer_data` dict.
"""
layer_class = layer.IDENTIFIER
layer_name = layer.name
# Instantiate layer.
if layer_class in LAYERS:
created_layer = LAYERS[layer_class].from_config(layer)
elif layer_class in IMAGE_PROCESSORS:
created_layer = IMAGE_PROCESSORS[layer_class].from_config(
layer)
else:
raise ValueError(
"The layer `{}` is not supported.".format(layer_class))
created_layers[layer_name] = created_layer
# Gather layer inputs.
inbound_nodes_data = layer.inbound_nodes
input_tensors = []
for input_data in inbound_nodes_data:
in_layer_name, in_node_index, in_tensor_index = cls.get_node_data(
input_data)
if len(input_data) == 3:
kwargs = {}
elif len(input_data) == 4:
kwargs = input_data[3]
else:
raise ValueError('Improperly formatted model config.')
if in_layer_name not in created_layers:
raise ValueError('Missing layer: ' + in_layer_name)
inbound_layer = created_layers[in_layer_name]
inbound_node = inbound_layer.inbound_nodes[in_node_index]
input_tensors.append(
inbound_node.output_tensors[in_tensor_index])
# Call layer on its inputs, thus creating the node
# and building the layer if needed.
if input_tensors:
if len(input_tensors) == 1:
created_layer(input_tensors[0], **kwargs)
else:
created_layer(input_tensors, **kwargs)
for layer in config.layers:
process_layer(layer)
name = config.name
input_tensors = []
output_tensors = []
for layer_data in config.input_layers:
layer_name, node_index, tensor_index = cls.get_node_data(
layer_data)
assert layer_name in created_layers, "Layer `{}` not found".format(
layer_name)
layer = created_layers[layer_name]
layer_output_tensors = layer.inbound_nodes[
node_index].output_tensors
input_tensors.append(layer_output_tensors[tensor_index])
for layer_data in config.output_layers:
layer_name, node_index, tensor_index = cls.get_node_data(
layer_data)
assert layer_name in created_layers
layer = created_layers[layer_name]
layer_output_tensors = layer.inbound_nodes[
node_index].output_tensors
output_tensors.append(layer_output_tensors[tensor_index])
return cls(inputs=input_tensors, outputs=output_tensors, name=name)