103 lines
3.9 KiB
Bash
103 lines
3.9 KiB
Bash
# Copyright (c) 2025 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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source $(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/utils.sh
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init
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function hybrid_paddlex() {
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# PaddleX test
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export DEVICE=($(echo $HIP_VISIBLE_DEVICES | tr "," "\n"))
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export DCU_DEVICES=`echo $HIP_VISIBLE_DEVICES`
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unset HIP_VISIBLE_DEVICES
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git clone --depth=1000 https://gitee.com/paddlepaddle/PaddleX.git
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cd PaddleX
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pip install -e .[base]
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paddlex --install PaddleClas
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paddlex --install PaddleSeg
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wget -q https://paddle-model-ecology.bj.bcebos.com/paddlex/data/cls_flowers_examples.tar -P ./dataset
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tar -xf ./dataset/cls_flowers_examples.tar -C ./dataset/
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wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/seg_optic_examples.tar -P ./dataset
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tar -xf ./dataset/seg_optic_examples.tar -C ./dataset/
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# train Reset50
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echo "Start Reset50"
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python main.py -c paddlex/configs/modules/image_classification/ResNet50.yaml \
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-o Global.mode=train \
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-o Global.dataset_dir=./dataset/cls_flowers_examples \
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-o Global.output=resnet50_output \
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-o Global.device="dcu:${DCU_DEVICES}" \
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-o Train.epochs_iters=2
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# inference Reset50
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python main.py -c paddlex/configs/modules/image_classification/ResNet50.yaml \
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-o Global.mode=predict \
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-o Predict.model_dir="./resnet50_output/best_model/inference" \
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-o Global.device="dcu:${DEVICE[0]}"
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# inference Reset50 with cinn
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python main.py -c paddlex/configs/modules/image_classification/ResNet50.yaml \
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-o Global.mode=predict \
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-o Predict.model_dir="./resnet50_output/best_model/inference" \
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-o Global.device="dcu:${DEVICE[0]}" \
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-o Predict.kernel_option.enable_cinn=True
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echo "End Reset50"
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echo "Start DeepLabv3+"
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# train DeepLabv3+
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python main.py -c paddlex/configs/modules/semantic_segmentation/Deeplabv3_Plus-R50.yaml \
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-o Global.mode=train \
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-o Global.dataset_dir=./dataset/seg_optic_examples \
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-o Global.output=deeplabv3p_output \
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-o Global.device="dcu:${DCU_DEVICES}" \
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-o Train.epochs_iters=2
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# inference DeepLabv3+
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python main.py -c paddlex/configs/modules/semantic_segmentation/Deeplabv3_Plus-R50.yaml \
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-o Global.mode=predict \
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-o Predict.model_dir="./deeplabv3p_output/best_model/inference" \
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-o Global.device="dcu:${DEVICE[0]}"
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echo "End DeepLabv3+"
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}
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function main(){
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cd ${PADDLE_ROOT}/build
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pip install hypothesis
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/opt/py310/bin/pip install -r ${PADDLE_ROOT}/python/unittest_py/requirements.txt
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/opt/py310/bin/pip install safetensors
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if ls ${PADDLE_ROOT}/build/python/dist/*whl >/dev/null 2>&1; then
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pip install ${PADDLE_ROOT}/build/python/dist/*whl
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fi
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if ls ${PADDLE_ROOT}/dist/*whl >/dev/null 2>&1; then
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pip install ${PADDLE_ROOT}/dist/*whl
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fi
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cp ${PADDLE_ROOT}/build/test/legacy_test/testsuite.py ${PADDLE_ROOT}/build/python
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cp -r ${PADDLE_ROOT}/build/test/white_list ${PADDLE_ROOT}/build/python
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run_hybrid_ci=${1:-"false"}
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ut_total_startTime_s=`date +%s`
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parallel_test_base_gpu_test
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ut_total_endTime_s=`date +%s`
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echo "TestCases Total Time: $[ $ut_total_endTime_s - $ut_total_startTime_s ]s"
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echo "ipipe_log_param_TestCases_Total_Time: $[ $ut_total_endTime_s - $ut_total_startTime_s ]s" >> ${PADDLE_ROOT}/build/build_summary.txt
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if [[ "$IF_DCU" == "ON" ]]; then
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hybrid_paddlex
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fi
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}
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main
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