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81 lines
2.6 KiB
81 lines
2.6 KiB
# 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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