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82 lines
2.6 KiB
82 lines
2.6 KiB
3 years ago
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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import os
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import cv2
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import numpy as np
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from PIL import Image
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import paddle
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class BasePredictor(object):
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def __init__(self):
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pass
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def build_inference_model(self):
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if paddle.in_dynamic_mode():
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# todo self.model = build_model(self.cfg)
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pass
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else:
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place = paddle.get_device()
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self.exe = paddle.static.Executor(place)
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file_names = os.listdir(self.weight_path)
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for file_name in file_names:
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if file_name.find('model') > -1:
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model_file = file_name
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elif file_name.find('param') > -1:
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param_file = file_name
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self.program, self.feed_names, self.fetch_targets = paddle.static.load_inference_model(
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self.weight_path,
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executor=self.exe,
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model_filename=model_file,
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params_filename=param_file)
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def base_forward(self, inputs):
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if paddle.in_dynamic_mode():
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out = self.model(inputs)
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else:
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feed_dict = {}
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if isinstance(inputs, dict):
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feed_dict = inputs
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elif isinstance(inputs, (list, tuple)):
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for i, feed_name in enumerate(self.feed_names):
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feed_dict[feed_name] = inputs[i]
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else:
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feed_dict[self.feed_names[0]] = inputs
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out = self.exe.run(self.program,
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fetch_list=self.fetch_targets,
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feed=feed_dict)
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return out
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def is_image(self, input):
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try:
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if isinstance(input, (np.ndarray, Image.Image)):
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return True
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elif isinstance(input, str):
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if not os.path.isfile(input):
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raise ValueError('input must be a file')
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img = Image.open(input)
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_ = img.size
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return True
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else:
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return False
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except:
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return False
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def run(self):
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raise NotImplementedError
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