def extract_embedding_kernel(self, input_name, scope_id, tensorflow_weight_name, output_name):
self.scopes[scope_id] = tensorflow_weight_name
weight_name = self.generate_name(self.scopes, scope_id+1)
weight = self.get_tensor(weight_name)
layer = caffe_net.LayerParameter(name=output_name, type='Embed',
bottom=[input_name], top=[output_name])
layer.add_data(weight)
self.embedding_dim = len(weight[0])
layer.embed_param(len(weight), self.embedding_dim)
self.caffe_model.add_layer(layer)
self.data_dict[output_name] = Operators.embedding(self.data_dict[input_name], weight, output_name)
def add_embedding(self, input_name, weight_name, output_name, transpose=False):
layer = caffe_net.LayerParameter(name=output_name, type='Embed',
bottom=[input_name,weight_name], top=[output_name])
weight = self.data_dict[weight_name]
if transpose:
input_dim = weight.shape[-1]
embedding_dim = weight.shape[-2]
else:
input_dim = weight.shape[-2]
embedding_dim = weight.shape[-1]
layer.embed_param(input_dim, embedding_dim, transpose)
self.caffe_model.add_layer(layer)
self.data_dict[output_name] = Operators.embedding(self.data_dict[input_name], weight, transpose, output_name)
return output_name
