def context(self, trainable=False):
"""
Get the input ,output and program of the pretrained emotion_detection_textcnn
Args:
trainable(bool): whether fine-tune the pretrained parameters of emotion_detection_textcnn or not
Returns:
inputs(dict): the input variables of emotion_detection_textcnn (words)
outputs(dict): the output variables of emotion_detection_textcnn (the sentiment prediction results)
main_program(Program): the main_program of lac with pretrained prameters
"""
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
# word seq data
data = fluid.layers.data(name="words",
shape=[1],
dtype="int64",
lod_level=1)
data_name = data.name
pred, fc = textcnn_net(data, 240465)
pred_name = pred.name
fc_name = fc.name
prefix_name = "@HUB_{}@".format(self.name)
add_vars_prefix(program=main_program, prefix=prefix_name)
for param in main_program.global_block().iter_parameters():
param.trainable = trainable
place = fluid.CPUPlace()
exe = fluid.Executor(place)
# load the emotion_detection_textcnn pretrained model
def if_exist(var):
return os.path.exists(
os.path.join(self.pretrained_model_path, var.name))
fluid.io.load_vars(exe,
self.pretrained_model_path,
predicate=if_exist)
inputs = {
"words":
main_program.global_block().vars[prefix_name + data_name]
}
outputs = {
"class_probs":
main_program.global_block().vars[prefix_name + pred_name],
"sentence_feature":
main_program.global_block().vars[prefix_name + fc_name]
}
return inputs, outputs, main_program
def context(self, trainable=False, max_seq_len=128, num_slots=1):
"""
Get the input ,output and program of the pretrained emotion_detection_textcnn
Args:
trainable(bool): Whether fine-tune the pretrained parameters of emotion_detection_textcnn or not.
max_seq_len (int): It will limit the total sequence returned so that it has a maximum length.
num_slots(int): It's number of data inputted to the model, selectted as following options:
- 1(default): There's only one data to be feeded in the model, e.g. the module is used for text classification task.
- 2: There are two data to be feeded in the model, e.g. the module is used for text matching task (point-wise).
- 3: There are three data to be feeded in the model, e.g. the module is used for text matching task (pair-wise).
Returns:
inputs(dict): the input variables of emotion_detection_textcnn (words)
outputs(dict): the output variables of input words (word embeddings and label probilities);
the sentence embedding and sequence length of the first input text.
main_program(Program): the main_program of emotion_detection_textcnn with pretrained prameters
"""
assert num_slots >= 1 and num_slots <= 3, "num_slots must be 1, 2, or 3, but the input is %d" % num_slots
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
text_1 = fluid.layers.data(
name="text",
shape=[-1, max_seq_len, 1],
dtype="int64",
lod_level=0)
seq_len = fluid.layers.data(
name="seq_len", shape=[1], dtype='int64', lod_level=0)
seq_len_used = fluid.layers.squeeze(seq_len, axes=[1])
# Add embedding layer.
w_param_attrs = fluid.ParamAttr(
name="embedding_0.w_0",
initializer=fluid.initializer.TruncatedNormal(scale=0.02),
trainable=trainable)
dict_dim = 240466
emb_1 = fluid.layers.embedding(
input=text_1,
size=[dict_dim, 128],
is_sparse=True,
padding_idx=dict_dim - 1,
dtype='float32',
param_attr=w_param_attrs)
emb_1_name = emb_1.name
data_list = [text_1]
emb_name_list = [emb_1_name]
# Add lstm layer.
pred, fc = textcnn_net(emb_1, seq_len_used)
pred_name = pred.name
fc_name = fc.name
if num_slots > 1:
text_2 = fluid.data(
name='text_2',
shape=[-1, max_seq_len],
dtype='int64',
lod_level=0)
emb_2 = fluid.embedding(
input=text_2,
size=[dict_dim, 128],
is_sparse=True,
padding_idx=dict_dim - 1,
dtype='float32',
param_attr=w_param_attrs)
emb_2_name = emb_2.name
data_list.append(text_2)
emb_name_list.append(emb_2_name)
if num_slots > 2:
text_3 = fluid.data(
name='text_3',
shape=[-1, max_seq_len],
dtype='int64',
lod_level=0)
emb_3 = fluid.embedding(
input=text_3,
size=[dict_dim, 128],
is_sparse=True,
padding_idx=dict_dim - 1,
dtype='float32',
param_attr=w_param_attrs)
emb_3_name = emb_3.name
data_list.append(text_3)
emb_name_list.append(emb_3_name)
variable_names = filter(
lambda v: v not in ['text', 'text_2', 'text_3', "seq_len"],
list(main_program.global_block().vars.keys()))
prefix_name = "@HUB_{}@".format(self.name)
add_vars_prefix(
program=main_program, prefix=prefix_name, vars=variable_names)
for param in main_program.global_block().iter_parameters():
param.trainable = trainable
place = fluid.CPUPlace()
exe = fluid.Executor(place)
# Load the emotion_detection_textcnn pretrained model.
def if_exist(var):
return os.path.exists(
os.path.join(self.pretrained_model_path, var.name))
fluid.io.load_vars(
exe, self.pretrained_model_path, predicate=if_exist)
inputs = {'seq_len': seq_len}
outputs = {
"class_probs":
main_program.global_block().vars[prefix_name + pred_name],
"sentence_feature":
main_program.global_block().vars[prefix_name + fc_name]
}
for index, data in enumerate(data_list):
if index == 0:
inputs['text'] = data
outputs['emb'] = main_program.global_block().vars[
prefix_name + emb_name_list[0]]
else:
inputs['text_%s' % (index + 1)] = data
outputs['emb_%s' % (index + 1)] = main_program.global_block(
).vars[prefix_name + emb_name_list[index]]
return inputs, outputs, main_program