def extract_dense(self,
input_name,
dense_name,
scope_id,
scope_name="dense"):
self.scopes[scope_id] = scope_name
self.scopes[scope_id + 1] = "kernel"
kernel_name = self.generate_name(self.scopes, scope_id + 2)
kernel = self.get_tensor(kernel_name)
self.scopes[scope_id + 1] = "bias"
bias_name = self.generate_name(self.scopes, scope_id + 2)
bias = self.get_tensor(bias_name)
layer = caffe_net.LayerParameter(name=dense_name,
type='InnerProduct',
bottom=[input_name],
top=[dense_name])
num_output = len(kernel[0])
kernel = kernel.transpose((1, 0))
layer.inner_product_param(num_output, bias_term=bias is not None)
if bias is not None:
if len(bias) != num_output:
print("[ERROR] extract_dense failed")
exit(0)
layer.add_data(kernel, bias)
else:
layer.add_data(kernel)
self.caffe_model.add_layer(layer)
self.data_dict[dense_name] = Operators.fully_connect(
self.data_dict[input_name], kernel.transpose((1, 0)), bias,
dense_name)
return dense_name
def extract_dense(self,
input_name,
output_name,
scope_id,
scope_name="dense"):
if (isinstance(scope_name, str)):
layer_names = [scope_name, "kernel", "bias"]
elif (isinstance(scope_name, list)):
layer_names = scope_name
else:
print("[ERROR] unsupported dense scope_name")
exit(1)
kernel, bias = self.get_weights(scope_id, layer_names)
layer = caffe_net.LayerParameter(name=output_name,
type='InnerProduct',
bottom=[input_name],
top=[output_name])
num_output = len(kernel[0])
kernel = kernel.transpose((1, 0))
layer.inner_product_param(num_output, bias_term=bias is not None)
if bias is not None:
if len(bias) != num_output:
print("[ERROR] extract_dense failed")
exit(0)
layer.add_data(kernel, bias)
else:
layer.add_data(kernel)
self.caffe_model.add_layer(layer)
self.data_dict[output_name] = Operators.fully_connect(
self.data_dict[input_name], kernel.transpose((1, 0)), bias,
output_name)
return output_name
def generate(self, input = None):
#batch_seq_length = 16
input_text_name = "input_text"
input_text_shape = [self.batch, 16]
self.add_input(input_text_name, input_text_shape)
self.set_input(input)
#embedding
x = self.extract_embedding(input_text_name, 0, "emb_table", "embedding_lookup")
#bilstm
x = self.extract_lstm(x, None, "BiLSTM", 0, steps = -2, scope_name = ["BiLSTM/fw/lstm_cell", "BiLSTM/bw/lstm_cell"])
#FC
weight = self.get_weight("W")
bias = self.get_weight("b")
layer = caffe_net.LayerParameter("wb_fc_output", type='InnerProduct',
bottom=[x], top=["wb_fc_output"])
num_output = len(weight[0])
weight = weight.transpose((1,0))
layer.inner_product_param(num_output, bias_term=bias is not None)
if len(bias) != num_output:
print("[ERROR] extract_dense failed")
exit(0)
layer.add_data(weight, bias)
self.caffe_model.add_layer(layer)
self.data_dict["wb_fc_output"] = Operators.fully_connect(self.data_dict[x],
weight.transpose((1, 0)), bias,
"wb_fc_output")
x = "wb_fc_output"
#softmax
x = self.add_softmax(x, "softmax_output", -1)
#argmax
x = self.add_argmax(x, -1, "output")
self.save_caffe_model()
def extract_denses(self, input_name, output_names, output_nums, scope_id, scope_name="dense", share_index=0, share_num=1):
if (isinstance(scope_name, str)):
layer_names = [scope_name, "kernel", "bias"]
elif (isinstance(scope_name, list)):
layer_names = scope_name
else:
print("[ERROR] unsupported dense scope_name")
exit(1)
kernels, biases = self.get_weights(scope_id, layer_names)
if (share_num == 1):
last_sum = 0
for index in range(len(output_nums)):
kernel = kernels[:, last_sum:last_sum+output_nums[index]]
bias = None
if biases is not None:
bias = biases[last_sum:last_sum+output_nums[index]]
layer = caffe_net.LayerParameter(name=output_names[index], type='InnerProduct',
bottom=[input_name], top=[output_names[index]])
num_output = len(kernel[0])
kernel = kernel.transpose((1, 0))
layer.inner_product_param(num_output, bias_term=bias is not None)
if bias is not None:
if len(bias) != num_output:
print("[ERROR] extract_denses failed")
exit(0)
layer.add_data(kernel, bias)
else:
layer.add_data(kernel)
self.caffe_model.add_layer(layer)
self.data_dict[output_names[index]] = Operators.fully_connect(self.data_dict[input_name],
kernel.transpose((1, 0)), bias,
output_names[index])
last_sum = last_sum + output_nums[index]
if (last_sum != len(kernels[0])):
print("[ERROR] extract_denses failed")
exit(0)
else:
self.scopes[scope_id] = layer_names[0]
kernel_name = self.generate_name(self.scopes, scope_id+1) + "/kernel"
bias_name = self.generate_name(self.scopes, scope_id+1) + "/bias"
if (share_index == 0):
self.add_weight(kernel_name, weight=kernels)
if (biases is not None):
self.add_weight(bias_name, weight=biases)
tmp_name = self.add_matmul(input_name, kernel_name, self.generate_name(self.scopes, scope_id+1)+"/matmul"+str(share_index))
if (biases is not None):
tmp_name = self.add_sum([tmp_name, bias_name], self.generate_name(self.scopes, scope_id+1)+"/sum"+str(share_index))
slice_point = []
last_sum = 0
for i in range(len(output_nums)-1):
last_sum = last_sum + output_nums[i]
slice_point.append(last_sum)
shape_len = len(self.get_tensor_shape(self.generate_name(self.scopes, scope_id+1)+"/matmul"+str(share_index)))
self.add_slice(tmp_name, output_names, shape_len-1, slice_point)
return output_names
def extract_embeddings(self, word_input_name, position_input_name,
token_input_name):
# embedding block
self.scopes[1] = "embeddings"
# word embedding
word_embedding_name_1 = "we_1"
self.extract_embedding(word_input_name, 2, "word_embeddings",
word_embedding_name_1)
self.scopes[2] = "word_embeddings_2"
weight_name = self.generate_name(self.scopes, 3)
weight = self.get_tensor(weight_name)
word_embedding_name_2 = "we_2"
layer = caffe_net.LayerParameter(name=word_embedding_name_2,
type='InnerProduct',
bottom=[word_embedding_name_1],
top=[word_embedding_name_2])
num_output = len(weight[0])
layer.inner_product_param(num_output, bias_term=False)
layer.add_data(weight)
self.caffe_model.add_layer(layer)
self.data_dict[word_embedding_name_2] = Operators.fully_connect(
self.data_dict[word_embedding_name_1], weight, None,
word_embedding_name_2)
# position embedding
position_embedding_name = "pe"
self.extract_embedding(position_input_name, 2, "position_embeddings",
position_embedding_name)
# token type embedding
token_type_embedding_name = "tte"
self.extract_embedding(token_input_name, 2, "token_type_embeddings",
token_type_embedding_name)
# eltwise
sum_name = "embedding_sum"
self.add_sum([
word_embedding_name_2, position_embedding_name,
token_type_embedding_name
], sum_name)
# layer norm
layer_norm_name = "embedding_ln"
self.extract_layer_norm(sum_name, layer_norm_name, 2)
return layer_norm_name
def extract_dense_prefix(self, input_name, dense_name, weight_name_prefix):
kernel_name = weight_name_prefix + "weight"
bias_name = weight_name_prefix + "bias"
kernel = self.get_weight(kernel_name)
bias = self.get_weight(bias_name)
layer = caffe_net.LayerParameter(name=dense_name,
type='InnerProduct',
bottom=[input_name],
top=[dense_name])
num_output = len(kernel)
layer.inner_product_param(num_output, bias_term=bias is not None)
if bias is not None:
if len(bias) != num_output:
print("[ERROR] extract extract_dense_prefix")
layer.add_data(kernel, bias)
else:
layer.add_data(kernel)
self.caffe_model.add_layer(layer)
self.data_dict[dense_name] = Operators.fully_connect(
self.data_dict[input_name], kernel.transpose((1, 0)), bias,
dense_name)
def extract_dense(self, input_name, output_name, scope_id, scope_name="dense", share_index=0, share_num=1):
if (isinstance(scope_name, str)):
layer_names = [scope_name, "kernel", "bias"]
elif (isinstance(scope_name, list)):
layer_names = scope_name
else:
print("[ERROR] unsupported dense scope_name")
exit(1)
kernel, bias = self.get_weights(scope_id, layer_names)
if (share_num == 1):
layer = caffe_net.LayerParameter(name=output_name, type='InnerProduct',
bottom=[input_name], top=[output_name])
num_output = len(kernel[0])
kernel = kernel.transpose((1, 0))
layer.inner_product_param(num_output, bias_term=bias is not None)
if bias is not None:
if len(bias) != num_output:
print("[ERROR] extract_dense failed")
exit(0)
layer.add_data(kernel, bias)
else:
layer.add_data(kernel)
self.caffe_model.add_layer(layer)
self.data_dict[output_name] = Operators.fully_connect(self.data_dict[input_name],
kernel.transpose((1, 0)), bias,
output_name)
else:
self.scopes[scope_id] = layer_names[0]
kernel_name = self.generate_name(self.scopes, scope_id+1) + "/kernel"
bias_name = self.generate_name(self.scopes, scope_id+1) + "/bias"
if (share_index == 0):
self.add_weight(kernel_name, weight=kernel)
if (bias is not None):
self.add_weight(bias_name, weight=bias)
tmp_name = self.add_matmul(input_name, kernel_name, output_name+"/matmul"+str(share_index))
if (bias is not None):
self.add_sum([tmp_name, bias_name], output_name)
return output_name
