def convert(torch_model, class_map, description="Neural Network Model"): """ Convert a torch model to PMML @model. The torch model object @class_map. A map in the form {class_id: class_name} @description. A short description of the model Returns a DeepNeuralNetwork object which can be exported to PMML """ pmml = DeepNetwork(description=description, class_map=class_map) pmml.torch_model = torch_model pmml.model_name = str(torch_model) layers = serialize_layers(torch_model, '') # Attach the first layer previous_layer = convert_input() pmml._append_layer(previous_layer) # Attach the following layers flattened = False for identifier, layer in layers: print(layer.__class__) if layer.__class__ in CONVERSIONS: if layer.__class__ == nn.Linear and not flattened: previous_layer = convert_flatten(previous_layer) pmml._append_layer(previous_layer) flattened = True converter = CONVERSIONS[layer.__class__] previous_layer = converter(identifier, layer, previous_layer) pmml._append_layer(previous_layer) elif layer.__class__ == Bottleneck: pmml, previous_layer = convert_bottleneck(pmml, identifier, layer, previous_layer) else: pass # raise ValueError("Unknown layer type:", layer.__class__) return pmml
def convert(keras_model, class_map, description="Neural Network Model"): """ Convert a keras model to PMML @model. The keras model object @class_map. A map in the form {class_id: class_name} @description. A short description of the model Returns a DeepNeuralNetwork object which can be exported to PMML """ pmml = DeepNetwork(description=description, class_map=class_map) pmml.keras_model = keras_model pmml.model_name = keras_model.name config = keras_model.get_config() for layer in config['layers']: layer_class = layer['class_name'] layer_config = layer['config'] layer_inbound_nodes = layer['inbound_nodes'] # Input if layer_class is "InputLayer": pmml._append_layer( InputLayer(name=layer_config['name'], input_size=layer_config['batch_input_shape'][1:])) # Conv2D elif layer_class is "Conv2D": pmml._append_layer( Conv2D( name=layer_config['name'], channels=layer_config['filters'], kernel_size=layer_config['kernel_size'], dilation_rate=layer_config['dilation_rate'], use_bias=layer_config['use_bias'], activation=layer_config['activation'], strides=layer_config['strides'], padding=layer_config['padding'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) # DepthwiseConv2D elif layer_class is "DepthwiseConv2D": pmml._append_layer( DepthwiseConv2D( name=layer_config['name'], kernel_size=layer_config['kernel_size'], depth_multiplier=layer_config['depth_multiplier'], use_bias=layer_config['use_bias'], activation=layer_config['activation'], strides=layer_config['strides'], padding=layer_config['padding'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) # MaxPooling elif layer_class is "MaxPooling2D": pmml._append_layer( MaxPooling2D( name=layer_config['name'], pool_size=layer_config['pool_size'], strides=layer_config['strides'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) elif layer_class is "AveragePooling2D": pmml._append_layer( AveragePooling2D( name=layer_config['name'], pool_size=layer_config['pool_size'], strides=layer_config['strides'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) elif layer_class is "GlobalAveragePooling2D": pmml._append_layer( GlobalAveragePooling2D( name=layer_config['name'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) # Flatten elif layer_class is "Flatten": pmml._append_layer( Flatten( name=layer_config['name'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) # Dense elif layer_class is "Dense": pmml._append_layer( Dense( name=layer_config['name'], channels=layer_config['units'], use_bias=layer_config['use_bias'], activation=layer_config['activation'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) # Zero padding layer elif layer_class is "ZeroPadding2D": pmml._append_layer( ZeroPadding2D( name=layer_config['name'], padding=layer_config['padding'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) # Reshape layer elif layer_class is "Reshape": pmml._append_layer( Reshape( name=layer_config['name'], target_shape=layer_config['target_shape'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) elif layer_class is "Dropout": pmml._append_layer( Dropout( name=layer_config['name'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) # Batch Normalization elif layer_class is "BatchNormalization": pmml._append_layer( BatchNormalization( name=layer_config['name'], axis=layer_config['axis'], momentum=layer_config['momentum'], epsilon=layer_config['epsilon'], center=layer_config['center'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) elif layer_class is "Add": pmml._append_layer( Merge(name=layer_config['name'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes))) elif layer_class is "Subtract": pmml._append_layer( Merge(name=layer_config['name'], operator='subtract', inbound_nodes=get_inbound_nodes(layer_inbound_nodes))) elif layer_class is "Dot": pmml._append_layer( Merge(name=layer_config['name'], operator='dot', inbound_nodes=get_inbound_nodes(layer_inbound_nodes))) elif layer_class is "Concatenate": pmml._append_layer( Merge(name=layer_config['name'], axis=layer_config['axis'], operator='concatenate', inbound_nodes=get_inbound_nodes(layer_inbound_nodes))) elif layer_class is "Activation": pmml._append_layer( Activation( name=layer_config['name'], activation=layer_config['activation'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) elif layer_class is "ReLU": pmml._append_layer( Activation( name=layer_config['name'], activation='relu', threshold=layer_config['threshold'], max_value=layer_config['max_value'], negative_slope=layer_config['negative_slope'], inbound_nodes=get_inbound_nodes(layer_inbound_nodes), )) # Unknown layer else: raise ValueError("Unknown layer type:", layer_class) return pmml