def infer(self, inputs, return_numpy=True):
"""
Do Inference for Inputs
Args:
inputs (map): a map of {"input_name": input_var} that will be feed into the inference program
return_numpy (bool): transform return value into numpy or not
Returns:
Tensor or Numpy: the predict value of the inference model for the inputs
Examples:
.. code-block:: python
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
"""
if not isinstance(inputs, dict):
raise ValueError(
"inputs should be a map of {'input_name': input_var}")
with executor.scope_guard(self.scope):
results = self.exe.run(self.inference_program,
feed=inputs,
fetch_list=[self.predict_var],
return_numpy=return_numpy)
return results
def _test_by_executor(self, reader, feed_order, fetch_list):
with executor.scope_guard(self.scope):
feed_var_list = build_feed_var_list(self.test_program, feed_order)
feeder = data_feeder.DataFeeder(
feed_list=feed_var_list, place=self.place)
exe = executor.Executor(self.place)
accumulated = len(fetch_list) * [0]
count = 0
for data in reader():
outs = exe.run(program=self.test_program,
feed=feeder.feed(data),
fetch_list=fetch_list)
accumulated = [x[0] + x[1][0] for x in zip(accumulated, outs)]
count += 1
return [x / count for x in accumulated]
def _test_by_executor(self, reader, feed_order, fetch_list):
with executor.scope_guard(self.scope):
feed_var_list = build_feed_var_list(self.test_program, feed_order)
feeder = data_feeder.DataFeeder(
feed_list=feed_var_list, place=self.place)
exe = executor.Executor(self.place)
accumulated = len(fetch_list) * [0]
count = 0
for data in reader():
outs = exe.run(program=self.test_program,
feed=feeder.feed(data),
fetch_list=fetch_list)
accumulated = [x[0] + x[1][0] for x in zip(accumulated, outs)]
count += 1
return [x / count for x in accumulated]
def infer(self, inputs, return_numpy=True):
"""
:param inputs: a map of {"input_name": input_var} that will be feed into the inference program
to get the predict value
:return: the predict value of the inference model
"""
if not isinstance(inputs, dict):
raise ValueError(
"inputs should be a map of {'input_name': input_var}")
with executor.scope_guard(self.scope):
results = self.exe.run(self.inference_program,
feed=inputs,
fetch_list=[self.predict_var],
return_numpy=return_numpy)
return results
def infer(self, inputs, return_numpy=True):
"""
:param inputs: a map of {"input_name": input_var} that will be feed into the inference program
to get the predict value
:return: the predict value of the inference model
"""
if not isinstance(inputs, dict):
raise ValueError(
"inputs should be a map of {'input_name': input_var}")
with executor.scope_guard(self.scope):
results = self.exe.run(self.inference_program,
feed=inputs,
fetch_list=[self.predict_var],
return_numpy=return_numpy)
return results
def _prog_and_scope_guard(self):
with framework.program_guard(
main_program=self.train_program,
startup_program=self.startup_program):
with executor.scope_guard(self.scope):
yield
def _prog_and_scope_guard(self):
with framework.program_guard(main_program=self.inference_program):
with executor.scope_guard(self.scope):
yield
def _prog_and_scope_guard(self):
with framework.program_guard(main_program=self.inference_program):
with executor.scope_guard(self.scope):
yield
def _prog_and_scope_guard(self):
with framework.program_guard(
main_program=self.train_program,
startup_program=self.startup_program):
with executor.scope_guard(self.scope):
yield