未验证 提交 54ae8e45 编写于 作者: Q Qiao Longfei 提交者: GitHub

Merge pull request #10741 from jacquesqiao/inferencer-support-multi-gpu

Inferencer support parallel_executor
......@@ -12,11 +12,14 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import core
import executor
import framework
import io
import parallel_executor
import unique_name
from trainer import check_and_get_place
......@@ -24,40 +27,53 @@ __all__ = ['Inferencer', ]
class Inferencer(object):
def __init__(self, infer_func, param_path, place=None):
def __init__(self, infer_func, param_path, place=None, parallel=False):
"""
:param infer_func: a function that will return predict Variable
:param param_path: the path where the inference model is saved by fluid.io.save_params
:param place: place to do the inference
:param parallel: use parallel_executor to run the inference, it will use multi CPU/GPU.
"""
self.param_path = param_path
self.scope = core.Scope()
self.parallel = parallel
self.place = check_and_get_place(place)
self.inference_program = framework.Program()
with framework.program_guard(self.inference_program):
with unique_name.guard():
self.predict_var = infer_func()
self.exe = executor.Executor(check_and_get_place(place))
with executor.scope_guard(self.scope):
with self._prog_and_scope_guard():
# load params from param_path into scope
io.load_params(self.exe, param_path, self.inference_program)
io.load_params(executor.Executor(self.place), param_path)
if parallel:
with self._prog_and_scope_guard():
self.exe = parallel_executor.ParallelExecutor(
use_cuda=isinstance(self.place, core.CUDAPlace),
loss_name=self.predict_var.name)
else:
self.exe = executor.Executor(self.place)
def infer(self, inputs, return_numpy=True):
def infer(self, inputs):
"""
:param inputs: a map of {"input_name": input_var} that will be feed into the inference program
to get the predict value
:param return_numpy: if return numpy value for row tensor
: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)
with self._prog_and_scope_guard():
results = self.exe.run(feed=inputs,
fetch_list=[self.predict_var.name])
return results
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(main_program=self.inference_program):
with executor.scope_guard(self.scope):
yield
......@@ -94,7 +94,7 @@ def infer(use_cuda, inference_program, save_dirname=None):
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
print("infer results: ", results[0])
print("infer results: ", numpy.array(results[0]))
def main(use_cuda):
......
......@@ -112,7 +112,7 @@ def infer(use_cuda, inference_program, save_dirname=None):
results = inferencer.infer({'img': tensor_img})
print("infer results: ", results[0])
print("infer results: ", numpy.array(results[0]))
def main(use_cuda):
......
......@@ -93,7 +93,7 @@ def infer(use_cuda, inference_program, save_dirname=None):
results = inferencer.infer({'img': tensor_img})
print("infer results: ", results[0])
print("infer results: ", numpy.array(results[0]))
def main(use_cuda):
......
......@@ -127,14 +127,12 @@ def infer(use_cuda, inference_program, save_path):
third_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
fourth_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
result = inferencer.infer(
{
result = inferencer.infer({
'firstw': first_word,
'secondw': second_word,
'thirdw': third_word,
'forthw': fourth_word
},
return_numpy=False)
})
print(np.array(result[0]))
......
......@@ -12,18 +12,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import os
import core
import framework
import executor
import data_feeder
import contextlib
import executor
import framework
import io
import unique_name
import parallel_executor
# optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
import optimizer as opt_module
import parallel_executor
from transpiler import distribute_transpiler
__all__ = [
......
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