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* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs Co-authored-by: Jason <jiangjiajun@baidu.com>
161 lines
6.5 KiB
Python
161 lines
6.5 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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import fastdeploy as fd
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from fastdeploy import ModelFormat
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import cv2
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import os
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import numpy as np
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import runtime_config as rc
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import pickle
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def test_segmentation_ppliteseg():
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pp_liteseg_model_url = "https://bj.bcebos.com/fastdeploy/tests/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_test.tgz"
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fd.download_and_decompress(pp_liteseg_model_url, "resources")
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model_path = "./resources/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_test"
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# 配置runtime,加载模型
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runtime_option = fd.RuntimeOption()
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model_file = os.path.join(model_path, "model.pdmodel")
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params_file = os.path.join(model_path, "model.pdiparams")
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config_file = os.path.join(model_path, "deploy.yaml")
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image_file_1 = os.path.join(model_path, "cityscapes_demo_1.png")
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image_file_2 = os.path.join(model_path, "cityscapes_demo_2.png")
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result_file_1 = os.path.join(model_path, "ppliteseg_result1.pkl")
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result_file_2 = os.path.join(model_path, "ppliteseg_result2.pkl")
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model = fd.vision.segmentation.PaddleSegModel(
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model_file, params_file, config_file, runtime_option=rc.test_option)
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model.postprocessor.store_score_map = True
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im1 = cv2.imread(image_file_1)
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im2 = cv2.imread(image_file_2)
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with open(result_file_1, "rb") as f:
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expect1 = pickle.load(f)
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with open(result_file_2, "rb") as f:
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expect2 = pickle.load(f)
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for i in range(3):
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# test single predict
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result1 = model.predict(im1)
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result2 = model.predict(im2)
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diff_label_map_1 = np.fabs(
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np.array(result1.label_map) - np.array(expect1["label_map"]))
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diff_label_map_2 = np.fabs(
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np.array(result2.label_map) - np.array(expect2["label_map"]))
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diff_score_map_1 = np.fabs(
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np.array(result1.score_map) - np.array(expect1["score_map"]))
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diff_score_map_2 = np.fabs(
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np.array(result2.score_map) - np.array(expect2["score_map"]))
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thres = 1e-05
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assert diff_label_map_1.max(
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) < thres, "The label_map diff is %f, which is bigger than %f" % (
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diff_label_map_1.max(), thres)
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assert diff_score_map_1.max(
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) < thres, "The score map diff is %f, which is bigger than %f" % (
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diff_score_map_1.max(), thres)
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assert diff_label_map_2.max(
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) < thres, "The label_map diff is %f, which is bigger than %f" % (
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diff_label_map_2.max(), thres)
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assert diff_score_map_2.max(
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) < thres, "The score map diff is %f, which is bigger than %f" % (
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diff_score_map_2.max(), thres)
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print("Single image No diff")
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# test batch predict
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results = model.batch_predict([im1, im2])
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result1 = results[0]
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result2 = results[1]
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diff_label_map_1 = np.fabs(
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np.array(result1.label_map) - np.array(expect1["label_map"]))
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diff_label_map_2 = np.fabs(
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np.array(result2.label_map) - np.array(expect2["label_map"]))
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diff_score_map_1 = np.fabs(
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np.array(result1.score_map) - np.array(expect1["score_map"]))
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diff_score_map_2 = np.fabs(
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np.array(result2.score_map) - np.array(expect2["score_map"]))
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thres = 1e-05
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assert diff_label_map_1.max(
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) < thres, "The label_map diff is %f, which is bigger than %f" % (
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diff_label_map_1.max(), thres)
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assert diff_score_map_1.max(
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) < thres, "The score map diff is %f, which is bigger than %f" % (
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diff_score_map_1.max(), thres)
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assert diff_label_map_2.max(
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) < thres, "The label_map diff is %f, which is bigger than %f" % (
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diff_label_map_2.max(), thres)
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assert diff_score_map_2.max(
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) < thres, "The score map diff is %f, which is bigger than %f" % (
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diff_score_map_2.max(), thres)
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print("Batch images No diff")
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def test_segmentation_ppliteseg_runtime():
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pp_liteseg_model_url = "https://bj.bcebos.com/fastdeploy/tests/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_test.tgz"
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fd.download_and_decompress(pp_liteseg_model_url, "resources")
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model_path = "./resources/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_test"
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# 配置runtime,加载模型
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runtime_option = fd.RuntimeOption()
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model_file = os.path.join(model_path, "model.pdmodel")
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params_file = os.path.join(model_path, "model.pdiparams")
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config_file = os.path.join(model_path, "deploy.yaml")
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image_file_1 = os.path.join(model_path, "cityscapes_demo_1.png")
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result_file_1 = os.path.join(model_path, "ppliteseg_result1.pkl")
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preprocessor = fd.vision.segmentation.PaddleSegPreprocessor(config_file)
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postprocessor = fd.vision.segmentation.PaddleSegPostprocessor(config_file)
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postprocessor.store_score_map = True
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rc.test_option.set_model_path(
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model_file, params_file, model_format=ModelFormat.PADDLE)
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rc.test_option.use_paddle_backend()
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runtime = fd.Runtime(rc.test_option)
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with open(result_file_1, "rb") as f:
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expect1 = pickle.load(f)
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im1 = cv2.imread(image_file_1)
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print(image_file_1)
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for i in range(3):
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# test runtime
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input_tensors, ims_info = preprocessor.run([im1])
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output_tensors = runtime.infer({"x": input_tensors[0]})
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results = postprocessor.run(output_tensors, ims_info)
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result1 = results[0]
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diff_label_map_1 = np.fabs(
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np.array(result1.label_map) - np.array(expect1["label_map"]))
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diff_score_map_1 = np.fabs(
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np.array(result1.score_map) - np.array(expect1["score_map"]))
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thres = 1e-05
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assert diff_label_map_1.max(
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) < thres, "The label_map diff is %f, which is bigger than %f" % (
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diff_label_map_1.max(), thres)
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assert diff_score_map_1.max(
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) < thres, "The score map diff is %f, which is bigger than %f" % (
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diff_score_map_1.max(), thres)
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print("Runtime images No diff")
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if __name__ == "__main__":
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test_segmentation_ppliteseg()
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test_segmentation_ppliteseg_runtime()
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