def infer(use_cuda, inference_program, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=params_dirname, place=place)
# Setup inputs by creating 4 LoDTensors representing 4 words. Here each word
# is simply an index to look up for the corresponding word vector and hence
# the shape of word (base_shape) should be [1]. The length-based level of
# detail (lod) info of each LoDtensor should be [[1]] meaning there is only
# one lod_level and there is only one sequence of one word on this level.
# Note that lod info should be a list of lists.
lod = [[1]]
base_shape = [1]
# The range of random integers is [low, high]
first_word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=dict_size - 1)
second_word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=dict_size - 1)
third_word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=dict_size - 1)
fourth_word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=dict_size - 1)
result = inferencer.infer(
{
'firstw': first_word,
'secondw': second_word,
'thirdw': third_word,
'forthw': fourth_word
},
return_numpy=False)
print(np.array(result[0]))
def exp_sequence_expand():
# x = fluid.data(name='x', shape=[1], dtype='float32')
# y = fluid.data(name='y', shape=[1], dtype='float32', lod_level=1)
# out = layers.sequence_expand(x=x, y=y, ref_level=0)
x = fluid.data(name='x', shape=[4, 1], dtype='float32')
y = fluid.data(name='y', shape=[8, 1], dtype='float32', lod_level=1)
out = layers.sequence_expand(x=x, y=y, ref_level=0)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
np_data = np.array([[1], [2], [3], [4]]).astype('float32')
x_lod_tensor = fluid.create_lod_tensor(np_data, [[2, 2]], place)
print(x_lod_tensor)
y_lod_tensor = fluid.create_random_int_lodtensor([[2, 2], [3, 3, 1, 1]], [1],
place, low=0, high=1)
y_lod_tensor2 = fluid.create_random_int_lodtensor([[2, 2], [3, 3, 1, 1]], [9, 16], place, low=0, high=1)
print(y_lod_tensor)
print(y_lod_tensor2)
# lod: [[0, 2, 4][0, 3, 6, 7, 8]]
# dim: 8, 1
# layout: NCHW
# dtype: int64_t
# data: [0 0 1 1 1 1 1 0]
out_main = exe.run(fluid.default_main_program(),
feed={'x': x_lod_tensor, 'y': y_lod_tensor},
fetch_list=[out], return_numpy=False)
print(out_main[0])
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
# Setup inputs by creating 4 LoDTensors representing 4 words. Here each word
# is simply an index to look up for the corresponding word vector and hence
# the shape of word (base_shape) should be [1]. The recursive_sequence_lengths,
# which is length-based level of detail (lod) of each LoDTensor, should be [[1]]
# meaning there is only one level of detail and there is only one sequence of
# one word on this level.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[1]]
base_shape = [1]
# The range of random integers is [low, high]
first_word = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
second_word = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
third_word = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
fourth_word = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
assert feed_target_names[0] == 'firstw'
assert feed_target_names[1] == 'secondw'
assert feed_target_names[2] == 'thirdw'
assert feed_target_names[3] == 'forthw'
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: first_word,
feed_target_names[1]: second_word,
feed_target_names[2]: third_word,
feed_target_names[3]: fourth_word
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].recursive_sequence_lengths())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
# Setup inputs by creating 4 LoDTensors representing 4 words. Here each word
# is simply an index to look up for the corresponding word vector and hence
# the shape of word (base_shape) should be [1]. The length-based level of
# detail (lod) info of each LoDtensor should be [[1]] meaning there is only
# one lod_level and there is only one sequence of one word on this level.
# Note that lod info should be a list of lists.
lod = [[1]]
base_shape = [1]
# The range of random integers is [low, high]
first_word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=dict_size - 1)
second_word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=dict_size - 1)
third_word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=dict_size - 1)
fourth_word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=dict_size - 1)
assert feed_target_names[0] == 'firstw'
assert feed_target_names[1] == 'secondw'
assert feed_target_names[2] == 'thirdw'
assert feed_target_names[3] == 'forthw'
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: first_word,
feed_target_names[1]: second_word,
feed_target_names[2]: third_word,
feed_target_names[3]: fourth_word
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[4, 6]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for two sentences of
# length 4 and 6, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[4, 6]]
base_shape = [1]
# The range of random integers is [low, high]
word_data = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=1)
trg_word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'source_sequence'
assert feed_target_names[1] == 'target_sequence'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word_data,
feed_target_names[1]: trg_word,
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].recursive_sequence_lengths())
np_data = np.array(results[0])
print("Inference shape: ", np_data.shape)
print("Inference results: ", np_data)
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the length_based level of detail (lod) info is set to [[4, 6]],
# which has only one lod level. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for two sentences of
# length 4 and 6, respectively.
# Note that lod info should be a list of lists.
lod = [[4, 6]]
base_shape = [1]
# The range of random integers is [low, high]
word_data = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=1)
trg_word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'source_sequence'
assert feed_target_names[1] == 'target_sequence'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word_data,
feed_target_names[1]: trg_word,
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference shape: ", np_data.shape)
print("Inference results: ", np_data)
def infer(use_cuda, inference_program, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
inferencer = Inferencer(infer_func=partial(inference_program, word_dict),
param_path=params_dirname,
place=place)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
tensor_words = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=len(word_dict) - 1)
results = inferencer.infer({'words': tensor_words})
print("infer results: ", results)
def infer(use_cuda, inference_program, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
inferencer = fluid.Inferencer(
infer_func=partial(inference_program, word_dict),
param_path=params_dirname,
place=place)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
# which has only one lod level. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that lod info should be a list of lists.
lod = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
tensor_words = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=len(word_dict) - 1)
results = inferencer.infer({'words': tensor_words})
print("infer results: ", results)
def infer(self, use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [[3, 4, 2]]
base_shape = [1]
word = fluid.create_random_int_lodtensor(lod,
base_shape,
place,
low=0,
high=self.word_dict_len -
1)
assert feed_target_names[0] == 'word_data'
results = exe.run(inference_program,
feed={feed_target_names[0]: word},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
def infer(use_cuda, inference_program, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = Inferencer(infer_func=inference_program,
param_path=params_dirname,
place=place)
# Setup inputs by creating 4 LoDTensors representing 4 words. Here each word
# is simply an index to look up for the corresponding word vector and hence
# the shape of word (base_shape) should be [1]. The recursive_sequence_lengths,
# which is length-based level of detail (lod) of each LoDTensor, should be [[1]]
# meaning there is only one level of detail and there is only one sequence of
# one word on this level.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[1]]
base_shape = [1]
# The range of random integers is [low, high]
first_word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=dict_size - 1)
second_word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=dict_size - 1)
third_word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=dict_size - 1)
fourth_word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=dict_size - 1)
result = inferencer.infer(
{
'firstw': first_word,
'secondw': second_word,
'thirdw': third_word,
'forthw': fourth_word
},
return_numpy=False)
print(np.array(result[0]))
def infer(use_cuda, inference_program, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(
inference_program, param_path=params_dirname, place=place)
# Setup inputs by creating LoDTensors to represent sequences of words.
# Here each word is the basic element of these LoDTensors and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
# which has only one lod level. Then the created LoDTensors will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that lod info should be a list of lists.
lod = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_n2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_n1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_0 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_p1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_p2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
pred = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=PRED_DICT_LEN - 1)
mark = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=MARK_DICT_LEN - 1)
results = inferencer.infer(
{
'word_data': word,
'ctx_n2_data': ctx_n2,
'ctx_n1_data': ctx_n1,
'ctx_0_data': ctx_0,
'ctx_p1_data': ctx_p1,
'ctx_p2_data': ctx_p2,
'verb_data': pred,
'mark_data': mark
},
return_numpy=False)
print("infer results: ", np.array(results[0]))
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
# Setup inputs by creating 4 LoDTensors representing 4 words. Here each word
# is simply an index to look up for the corresponding word vector and hence
# the shape of word (base_shape) should be [1]. The recursive_sequence_lengths,
# which is length-based level of detail (lod) of each LoDTensor, should be [[1]]
# meaning there is only one level of detail and there is only one sequence of
# one word on this level.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[1]]
base_shape = [1]
# The range of random integers is [low, high]
first_word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=dict_size - 1)
second_word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=dict_size - 1)
third_word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=dict_size - 1)
fourth_word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=dict_size - 1)
assert feed_target_names[0] == 'firstw'
assert feed_target_names[1] == 'secondw'
assert feed_target_names[2] == 'thirdw'
assert feed_target_names[3] == 'forthw'
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: first_word,
feed_target_names[1]: second_word,
feed_target_names[2]: third_word,
feed_target_names[3]: fourth_word
},
fetch_list=fetch_targets,
return_numpy=False)
def to_infer_tensor(lod_tensor):
infer_tensor = fluid.core.PaddleTensor()
infer_tensor.lod = lod_tensor.lod()
infer_tensor.data = fluid.core.PaddleBuf(np.array(lod_tensor))
infer_tensor.shape = lod_tensor.shape()
infer_tensor.dtype = fluid.core.PaddleDType.INT64
return infer_tensor
infer_inputs = [first_word, second_word, third_word, fourth_word]
infer_inputs = [to_infer_tensor(t) for t in infer_inputs]
infer_config = fluid.core.NativeConfig()
infer_config.model_dir = 'word2vec.inference.model'
infer_config.use_gpu = use_cuda
if use_cuda:
infer_config.device = 0
infer_config.fraction_of_gpu_memory = 0.15
compiled_program = fluid.compiler.CompiledProgram(inference_program)
compiled_program.with_inference_optimize(infer_config)
assert compiled_program._is_inference is True
infer_outputs = exe.run(compiled_program, feed=infer_inputs)
np_data = np.array(results[0])
infer_out = infer_outputs[0].data.float_data()
for a, b in zip(np_data[0], infer_out):
assert np.isclose(a, b), "a: {}, b: {}".format(a, b)
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be fed
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# Setup inputs by creating LoDTensors to represent sequences of words.
# Here each word is the basic element of these LoDTensors and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
# which has only one lod level. Then the created LoDTensors will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that lod info should be a list of lists.
lod = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
word = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
pred = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=pred_dict_len - 1)
ctx_n2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_n1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_0 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p1 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p2 = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
mark = fluid.create_random_int_lodtensor(
lod, base_shape, place, low=0, high=mark_dict_len - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'word_data'
assert feed_target_names[1] == 'verb_data'
assert feed_target_names[2] == 'ctx_n2_data'
assert feed_target_names[3] == 'ctx_n1_data'
assert feed_target_names[4] == 'ctx_0_data'
assert feed_target_names[5] == 'ctx_p1_data'
assert feed_target_names[6] == 'ctx_p2_data'
assert feed_target_names[7] == 'mark_data'
results = exe.run(
inference_program,
feed={
feed_target_names[0]: word,
feed_target_names[1]: pred,
feed_target_names[2]: ctx_n2,
feed_target_names[3]: ctx_n1,
feed_target_names[4]: ctx_0,
feed_target_names[5]: ctx_p1,
feed_target_names[6]: ctx_p2,
feed_target_names[7]: mark
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
def infer(use_cuda, inference_program, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = fluid.Inferencer(inference_program,
param_path=params_dirname,
place=place)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
word = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=WORD_DICT_LEN - 1)
ctx_n2 = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=WORD_DICT_LEN - 1)
ctx_n1 = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=WORD_DICT_LEN - 1)
ctx_0 = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=WORD_DICT_LEN - 1)
ctx_p1 = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=WORD_DICT_LEN - 1)
ctx_p2 = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=WORD_DICT_LEN - 1)
pred = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=PRED_DICT_LEN - 1)
mark = fluid.create_random_int_lodtensor(recursive_seq_lens,
base_shape,
place,
low=0,
high=MARK_DICT_LEN - 1)
results = inferencer.infer(
{
'word_data': word,
'ctx_n2_data': ctx_n2,
'ctx_n1_data': ctx_n1,
'ctx_0_data': ctx_0,
'ctx_p1_data': ctx_p1,
'ctx_p2_data': ctx_p2,
'verb_data': pred,
'mark_data': mark
},
return_numpy=False)
print("infer results: ", np.array(results[0]).shape)
batch_id = batch_id + 1
use_cuda = False #在cpu上进行预测
save_dirname = "label_semantic_roles.inference.model" #调用训练好的模型进行预测
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
lod = [[3, 4, 2]]
base_shape = [1]
# 构造假数据作为输入，整数随机数的范围是[low, high]
word = fluid.create_random_int_lodtensor(lod,
base_shape,
place,
low=0,
high=word_dict_len - 1)
pred = fluid.create_random_int_lodtensor(lod,
base_shape,
place,
low=0,
high=pred_dict_len - 1)
ctx_n2 = fluid.create_random_int_lodtensor(lod,
base_shape,
place,
low=0,
high=word_dict_len - 1)
ctx_n1 = fluid.create_random_int_lodtensor(lod,
base_shape,
place,
