mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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129 lines
4.5 KiB
Python
129 lines
4.5 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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import logging
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from . import c_lib_wrap as C
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class Runtime:
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def __init__(self, runtime_option):
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self._runtime = C.Runtime()
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assert self._runtime.init(
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runtime_option._option), "Initialize Runtime Failed!"
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def infer(self, data):
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assert isinstance(data, dict), "The input data should be type of dict."
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return self._runtime.infer(data)
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def num_inputs(self):
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return self._runtime.num_inputs()
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def num_outputs(self):
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return self._runtime.num_outputs()
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def get_input_info(self, index):
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assert isinstance(
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index, int), "The input parameter index should be type of int."
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assert index < self.num_inputs(
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), "The input parameter index:{} should less than number of inputs:{}.".format(
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index, self.num_inputs)
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return self._runtime.get_input_info(index)
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def get_output_info(self, index):
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assert isinstance(
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index, int), "The input parameter index should be type of int."
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assert index < self.num_outputs(
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), "The input parameter index:{} should less than number of outputs:{}.".format(
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index, self.num_outputs)
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return self._runtime.get_output_info(index)
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class RuntimeOption:
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def __init__(self):
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self._option = C.RuntimeOption()
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def set_model_path(self, model_path, params_path="",
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model_format="paddle"):
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return self._option.set_model_path(model_path, params_path,
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model_format)
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def use_gpu(self, device_id=0):
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return self._option.use_gpu(device_id)
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def use_cpu(self):
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return self._option.use_cpu()
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def set_cpu_thread_num(self, thread_num=8):
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return self._option.set_cpu_thread_num(thread_num)
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def use_paddle_backend(self):
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return self._option.use_paddle_backend()
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def use_ort_backend(self):
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return self._option.use_ort_backend()
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def use_trt_backend(self):
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return self._option.use_trt_backend()
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def enable_paddle_mkldnn(self):
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return self._option.enable_paddle_mkldnn()
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def disable_paddle_mkldnn(self):
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return self._option.disable_paddle_mkldnn()
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def enable_paddle_log_info(self):
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return self._option.enable_paddle_log_info()
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def disable_paddle_log_info(self):
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return self._option.disable_paddle_log_info()
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def set_paddle_mkldnn_cache_size(self, cache_size):
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return self._option.set_paddle_mkldnn_cache_size(cache_size)
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def set_trt_input_shape(self,
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tensor_name,
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min_shape,
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opt_shape=None,
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max_shape=None):
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if opt_shape is None and max_shape is None:
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opt_shape = min_shape
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max_shape = min_shape
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else:
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assert opt_shape is not None and max_shape is not None, "Set min_shape only, or set min_shape, opt_shape, max_shape both."
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return self._option.set_trt_input_shape(tensor_name, min_shape,
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opt_shape, max_shape)
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def set_trt_cache_file(self, cache_file_path):
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return self._option.set_trt_cache_file(cache_file_path)
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def enable_trt_fp16(self):
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return self._option.enable_trt_fp16()
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def disable_trt_fp16(self):
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return self._option.disable_trt_fp16()
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def __repr__(self):
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attrs = dir(self._option)
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message = "RuntimeOption(\n"
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for attr in attrs:
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if attr.startswith("__"):
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continue
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if hasattr(getattr(self._option, attr), "__call__"):
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continue
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message += " {} : {}\t\n".format(attr,
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getattr(self._option, attr))
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message.strip("\n")
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message += ")"
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return message
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