forked from mindspore-Ecosystem/mindspore
fix pretrained model in psenet network
This commit is contained in:
anzhengqi
parent
11cf74e6e8
commit
8d7f979bf4
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@ -2,6 +2,7 @@
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- [PSENet Description](#PSENet-description)
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- [Dataset](#dataset)
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- [Pretrained Model](#Pretrained-model)
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- [Features](#features)
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- [Mixed Precision](#mixed-precision)
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- [Environment Requirements](#environment-requirements)
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@ -15,6 +16,7 @@
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- [Distributed GPU Training](#distributed-gpu-training)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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- [Result](#result)
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- [Inference Process](#inference-process)
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- [Export MindIR](#export-mindir)
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- [Infer on Ascend310](#infer-on-ascend310)
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@ -48,6 +50,19 @@ Dataset used: [ICDAR2015](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalizatio
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A training set of 1000 images containing about 4500 readable words
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A testing set containing about 2000 readable words
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unzip dataset files and needn't transform to mindrecord.
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# [Pretrained Model](#contents)
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download pytorch pretrained model: [resnet50-19c8e357.pth](https://download.pytorch.org/models/resnet50-19c8e357.pth)
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transform pytorch model to mindspore model
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```shell
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cd src
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python psenet_model_torch2mindspore.py --torch_file=/path_to_model/resnet50-19c8e357.pth --output_path=../
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```
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# [Environment Requirements](#contents)
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- Hardware（Ascend or GPU）
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@ -61,34 +76,100 @@ A testing set containing about 2000 readable words
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- install [pyblind11](https://github.com/pybind/pybind11)
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- install [Opencv3.4](https://docs.opencv.org/3.4.9/)
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```shell
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# install pybind11
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pip install pybind11
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# install opencv3.4.9
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wget https://github.com/opencv/opencv/archive/3.4.9.zip
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unzip 3.4.9.zip
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cd opencv-3.4.9
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mkdir build
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cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_WEBP=OFF ..
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make -j4 # -j指定线程数，用户根据机器配置修改参数
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make install
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# export environment variables
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export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/usr/local/include
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib64
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```
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# [Quick Start](#contents)
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After installing MindSpore via the official website, you can start training and evaluation as follows:
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```python
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```shell
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# run distributed training example
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bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [PRED_TRAINED PATH] [TRAIN_ROOT_DIR]
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#download opencv library
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download pyblind11, opencv3.4
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#install pyblind11 opencv3.4
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setup pyblind11(install the library by the pip command)
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setup opencv3.4(compile source code install the library)
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#enter the path ,run Makefile to product file
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#enter the path ,run Makefile
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cd ./src/ETSNET/pse/;make
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#run test.py
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python test.py --ckpt pretrained_model.ckpt --TEST_ROOT_DIR [test root path]
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#download eval method from [here](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization).
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#click "My Methods" button,then download Evaluation Scripts
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#go to Evaluation Process for details
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download script.py
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# run evaluation example
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bash scripts/run_eval_ascend.sh
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```
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- running on ModelArts
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- If you want to train the model on modelarts, you can refer to the [official guidance document] of modelarts (https://support.huaweicloud.com/modelarts/)
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```python
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# Example of using distributed training on modelarts :
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# Data set storage method
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# ├── ICDAR2015 # dir
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# ├── train # train dir
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# ├── ic15 # train_dataset dir
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# ├── ch4_training_images
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# ├── ch4_training_localization_transcription_gt
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# ├── train_predtrained # predtrained dir
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# ├── eval # eval dir
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# ├── ic15 # eval dataset dir
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# ├── ch4_test_images
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# ├── challenge4_Test_Task1_GT
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# ├── checkpoint # ckpt files dir
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# (1) Choose either a (modify yaml file parameters) or b (modelArts create training job to modify parameters) 。
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# a. set "enable_modelarts=True" 。
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# set "run_distribute=True"
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# set "TRAIN_MODEL_SAVE_PATH=/cache/train/outputs_imagenet/"
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# set "TRAIN_ROOT_DIR=/cache/data/ic15/"
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# set "pre_trained=/cache/data/train_predtrained/pred file name" Without pre-training weights train_pretrained=""
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# b. add "enable_modelarts=True" Parameters are on the interface of modearts。
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# Set the parameters required by method a on the modelarts interface
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# Note: The path parameter does not need to be quoted
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# (2) Set the path of the network configuration file "_config_path=/The path of config in default_config.yaml/"
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# (3) Set the code path on the modelarts interface "/path/psenet"。
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# (4) Set the model's startup file on the modelarts interface "train.py" 。
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# (5) Set the data path of the model on the modelarts interface ".../ICDAR2015/train"(choices ICDAR2015/train Folder path) ,
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# The output path of the model "Output file path" and the log path of the model "Job log path" 。
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# (6) start trainning the model。
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# Example of using model inference on modelarts
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# (1) Place the trained model to the corresponding position of the bucket。
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# (2) chocie a or b。
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# a. set "enable_modelarts=True" 。
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# set "TEST_ROOT_DIR=/cache/data/ic15/"
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# set "ckpt=/cache/data/checkpoint/ckpt file"
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# b. Add "enable_modelarts=True" parameter on the interface of modearts。
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# Set the parameters required by method a on the modelarts interface
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# Note: The path parameter does not need to be quoted
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# (3) Set the path of the network configuration file "_config_path=/The path of config in default_config.yaml/"
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# (4) Set the code path on the modelarts interface "/path/psenet"。
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# (5) Set the model's startup file on the modelarts interface "eval.py" 。
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# (6) Set the data path of the model on the modelarts interface ".../ICDAR2015/eval"(choices ICDAR2015/eval Folder path) ,
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# The output path of the model "Output file path" and the log path of the model "Job log path" 。
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# (7) Start model inference。
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```
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# [Script Description](#contents)
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## [Script and Sample Code](#contents)
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@ -156,7 +237,7 @@ Major parameters in default_config.yaml are:
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Please follow the instructions in the link below: <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools>.
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```shell
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bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [PRED_TRAINED PATH] [TRAIN_ROOT_DIR]
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bash scripts/run_distribute_train.sh [RANK_FILE] [PRETRAINED_PATH] [TRAIN_ROOT_DIR]
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```
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rank_table_file which is specified by RANK_TABLE_FILE is needed when you are running a distribute task. You can generate it by using the [hccl_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
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@ -195,66 +276,27 @@ time: 2021-07-24 04:01:07, epoch: 90, step: 31, loss is 0.58495
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### run test code
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```test
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```shell
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python test.py --ckpt [CKPK_PATH] --TEST_ROOT_DIR [TEST_DATA_DIR]
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# click [Here](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization) to download evaluation scripts
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# choose My Methods -> Offline evaluation -> Evaluation Scripts
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# download data and put it in /path_to_data
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mkdir eval_ic15
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ln -s /path_to_data/script_test_ch4_t1_e1-1577983151.zip eval_ic15/script_test_ch4_t1_e1-1577983151.zip
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cd eval_ic15
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unzip script_test_ch4_t1_e1-1577983151.zip
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cd ..
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sh ./script/run_eval_ascend.sh
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python test.py --ckpt [CKPK PATH] --TEST_ROOT_DIR [TEST DATA DIR]
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```
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- running on ModelArts
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- If you want to train the model on modelarts, you can refer to the [official guidance document] of modelarts (https://support.huaweicloud.com/modelarts/)
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### [Result](#contents)
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```python
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# Example of using distributed training on modelarts :
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# Data set storage method
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# ├── ICDAR2015 # dir
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# ├── train # train dir
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# ├── ic15 # train_dataset dir
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# ├── ch4_training_images
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# ├── ch4_training_localization_transcription_gt
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# ├── train_predtrained # predtrained dir
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# ├── eval # eval dir
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# ├── ic15 # eval dataset dir
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# ├── ch4_test_images
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# ├── challenge4_Test_Task1_GT
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# ├── checkpoint # ckpt files dir
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# (1) Choose either a (modify yaml file parameters) or b (modelArts create training job to modify parameters) 。
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# a. set "enable_modelarts=True" 。
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# set "run_distribute=True"
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# set "TRAIN_MODEL_SAVE_PATH=/cache/train/outputs_imagenet/"
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# set "TRAIN_ROOT_DIR=/cache/data/ic15/"
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# set "pre_trained=/cache/data/train_predtrained/pred file name" Without pre-training weights train_pretrained=""
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# b. add "enable_modelarts=True" Parameters are on the interface of modearts。
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# Set the parameters required by method a on the modelarts interface
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# Note: The path parameter does not need to be quoted
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# (2) Set the path of the network configuration file "_config_path=/The path of config in default_config.yaml/"
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# (3) Set the code path on the modelarts interface "/path/psenet"。
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# (4) Set the model's startup file on the modelarts interface "train.py" 。
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# (5) Set the data path of the model on the modelarts interface ".../ICDAR2015/train"(choices ICDAR2015/train Folder path) ,
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# The output path of the model "Output file path" and the log path of the model "Job log path" 。
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# (6) start trainning the model。
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# Example of using model inference on modelarts
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# (1) Place the trained model to the corresponding position of the bucket。
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# (2) chocie a or b。
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# a. set "enable_modelarts=True" 。
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# set "TEST_ROOT_DIR=/cache/data/ic15/"
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# set "ckpt=/cache/data/checkpoint/ckpt file"
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# b. Add "enable_modelarts=True" parameter on the interface of modearts。
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# Set the parameters required by method a on the modelarts interface
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# Note: The path parameter does not need to be quoted
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# (3) Set the path of the network configuration file "_config_path=/The path of config in default_config.yaml/"
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# (4) Set the code path on the modelarts interface "/path/psenet"。
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# (5) Set the model's startup file on the modelarts interface "eval.py" 。
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# (6) Set the data path of the model on the modelarts interface ".../ICDAR2015/eval"(choices ICDAR2015/eval Folder path) ,
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# The output path of the model "Output file path" and the log path of the model "Job log path" 。
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# (7) Start model inference。
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```
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Calculated!{"precision": 0.8147966668299853，"recall"：0.8006740491092923，"hmean"：0.8076736279747451，"AP"：0}
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### Eval Script for ICDAR2015
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@ -342,8 +384,9 @@ The `res` folder is generated in the upper-level directory. For details about th
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| Loss Function | LossCallBack |
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| outputs | probability |
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| Loss | 0.35 |
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| Speed | 1pc: 444 ms/step; 8pcs: 446 ms/step |
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| Total time | 1pc: 75.48 h; 8pcs: 7.11 h |
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| Parameters | batch_size = 4 |
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| Speed | 1pc: 444 ms/step(fps: 9.0); 8pcs: 446 ms/step(fps: 71) |
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| Total time | 1pc: 75.48 h; 8pcs: 7.11 h |
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| Parameters (M) | 27.36 |
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| Checkpoint for Fine tuning | 109.44M (.ckpt file) |
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| Scripts | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/psenet> |
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@ -5,6 +5,7 @@
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- [PSENet示例](#psenet示例)
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- [概述](#概述)
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- [数据集](#数据集)
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- [预训练模型](#预训练模型)
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- [环境要求](#环境要求)
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- [快速入门](#快速入门)
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- [脚本说明](#脚本说明)
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@ -14,9 +15,7 @@
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- [分布式训练](#分布式训练)
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- [评估过程](#评估过程)
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- [运行测试代码](#运行测试代码)
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- [ICDAR2015评估脚本](#icdar2015评估脚本)
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- [用法](#用法)
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- [结果](#结果)
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- [结果](#结果)
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- [推理过程](#推理过程)
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- [导出MindIR](#导出mindir)
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- [在Ascend310执行推理](#在ascend310执行推理)
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@ -48,6 +47,21 @@
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训练集：包括约4500个可读单词的1000张图像。
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测试集：约2000个可读单词。
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下载得到的训练和推理数据解压后备用，不需要转为mindrecord数据
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# 预训练模型
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下载pytorch的预训练模型: [resnet50-19c8e357.pth](https://download.pytorch.org/models/resnet50-19c8e357.pth)
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将pytorch模型转为mindspore模型
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```shell
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cd src
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python psenet_model_torch2mindspore.py --torch_file=/path_to_model/resnet50-19c8e357.pth --output_path=../
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```
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执行完成，src的上层目录得到文件pretrained_model.ckpt文件，用于接下来的训练
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# 环境要求
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- 硬件：昇腾处理器（Ascend）
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@ -62,36 +76,100 @@
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- 安装[pyblind11](https://github.com/pybind/pybind11)
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- 安装[Opencv3.4](https://docs.opencv.org/3.4.9/)
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```shell
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# 使用pip安装pybind11
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pip install pybind11
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# 使用源码安装opencv3.4.9
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wget https://github.com/opencv/opencv/archive/3.4.9.zip
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unzip 3.4.9.zip
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cd opencv-3.4.9
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mkdir build
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cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_WEBP=OFF ..
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make -j4 # -j指定线程数，用户根据机器配置修改参数
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make install
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# opencv安装在/usr/local目录下，将该目录添加到环境变量中
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export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/usr/local/include
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib64
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```
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# 快速入门
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通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估：
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```python
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```shell
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# 分布式训练运行示例
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bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [PRED_TRAINED PATH] [TRAIN_ROOT_DIR]
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# 第一个参数为rank_table文件，第二个参数为生成的预训练模型，第三个参数为下载的训练数据集
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bash scripts/run_distribute_train.sh [RANK_FILE] [PRETRAINED_PATH] [TRAIN_ROOT_DIR]
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# 下载opencv库
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download pyblind11, opencv3.4
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# 安装pyblind11 opencv3.4
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setup pyblind11(install the library by the pip command)
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setup opencv3.4(compile source code install the library)
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# 单击[此处](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization)下载评估方法
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# 点击"我的方法"按钮，下载评估脚本
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# 输入路径，运行Makefile，找到产品文件
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# 进入路径，运行Makefile
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cd ./src/ETSNET/pse/;make clean&&make
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# 运行test.py
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python test.py --ckpt pretrained_model.ckpt --TEST_ROOT_DIR [test root path]
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python test.py --ckpt [CKPK_PATH] --TEST_ROOT_DIR [TEST_DATA_DIR]
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# 具体见评估过程
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download script.py
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# 运行评估示例
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bash scripts/run_eval_ascend.sh
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```
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- 如果要在modelarts上进行模型的训练，可以参考modelarts的[官方指导文档](https://support.huaweicloud.com/modelarts/) 开始进行模型的训练和推理，具体操作如下：
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```ModelArts
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# 在ModelArts上使用分布式训练示例:
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# 数据集存放方式
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# ├── ICDAR2015 # dir
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# ├── train # train dir
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# ├── ic15 # train_dataset dir
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# ├── ch4_training_images
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# ├── ch4_training_localization_transcription_gt
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# ├── train_predtrained # predtrained dir
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# ├── eval # eval dir
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# ├── ic15 # eval dataset dir
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# ├── ch4_test_images
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# ├── challenge4_Test_Task1_GT
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# ├── checkpoint # ckpt files dir
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# (1) 选择a(修改yaml文件参数)或者b(ModelArts创建训练作业修改参数)其中一种方式。
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# a. 设置 "enable_modelarts=True"
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# 设置 "run_distribute=True"
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# 设置 "TRAIN_MODEL_SAVE_PATH=/cache/train/outputs/"
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# 设置 "TRAIN_ROOT_DIR=/cache/data/ic15/"
|
||||
# 设置 "pre_trained=/cache/data/train_predtrained/pred file name" 如果没有预训练权重 pre_trained=""
|
||||
|
||||
# b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
|
||||
# 在modelarts的界面上设置方法a所需要的参数
|
||||
# 注意：路径参数不需要加引号
|
||||
|
||||
# (2)设置网络配置文件的路径 "_config_path=/The path of config in default_config.yaml/"
|
||||
# (3) 在modelarts的界面上设置代码的路径 "/path/psenet"。
|
||||
# (4) 在modelarts的界面上设置模型的启动文件 "train.py" 。
|
||||
# (5) 在modelarts的界面上设置模型的数据路径 ".../ICDAR2015/train"(选择ICDAR2015/train文件夹路径) ,
|
||||
# 模型的输出路径"Output file path" 和模型的日志路径 "Job log path" 。
|
||||
# (6) 开始模型的训练。
|
||||
|
||||
# 在modelarts上使用模型推理的示例
|
||||
# (1) 把训练好的模型地方到桶的对应位置。
|
||||
# (2) 选择a或者b其中一种方式。
|
||||
# a.设置 "enable_modelarts=True"
|
||||
# 设置 "TEST_ROOT_DIR=/cache/data/ic15"
|
||||
# 设置 "ckpt=/cache/data/checkpoint/ckpt file"
|
||||
|
||||
# b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
|
||||
# 在modelarts的界面上设置方法a所需要的参数
|
||||
# 注意：路径参数不需要加引号
|
||||
|
||||
# (3) 设置网络配置文件的路径 "_config_path=/The path of config in default_config.yaml/"
|
||||
# (4) 在modelarts的界面上设置代码的路径 "/path/psenet"。
|
||||
# (5) 在modelarts的界面上设置模型的启动文件 "eval.py" 。
|
||||
# (6) 在modelarts的界面上设置模型的数据路径 "../ICDAR2015/eval"(选择ICDAR2015/eval文件夹路径) ,
|
||||
# 模型的输出路径"Output file path" 和模型的日志路径 "Job log path" 。
|
||||
# (7) 开始模型的推理。
|
||||
```
|
||||
|
||||
## 脚本说明
|
||||
|
||||
## 脚本和样例代码
|
||||
|
|
@ -153,7 +231,8 @@ bash scripts/run_eval_ascend.sh
|
|||
请遵循链接中的说明：[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools)
|
||||
|
||||
```shell
|
||||
bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [PRED_TRAINED PATH] [TRAIN_ROOT_DIR]
|
||||
# 第一个参数为rank_table文件，第二个参数为生成的预训练模型，第三个参数为下载的训练数据集
|
||||
bash scripts/run_distribute_train.sh [RANK_FILE] [PRETRAINED_PATH] [TRAIN_ROOT_DIR]
|
||||
```
|
||||
|
||||
上述shell脚本将在后台运行分布训练。可以通过`device[X]/test_*.log`文件查看结果。
|
||||
|
|
@ -173,81 +252,24 @@ device_1/log:epcoh： 2, step: 40，loss is 0.76629
|
|||
|
||||
### 运行测试代码
|
||||
|
||||
```test
|
||||
python test.py --ckpt [CKPK PATH] --TEST_ROOT_DIR [TEST DATA DIR]
|
||||
|
||||
```
|
||||
|
||||
- 如果要在modelarts上进行模型的训练，可以参考modelarts的[官方指导文档](https://support.huaweicloud.com/modelarts/) 开始进行模型的训练和推理，具体操作如下：
|
||||
|
||||
```ModelArts
|
||||
# 在ModelArts上使用分布式训练示例:
|
||||
# 数据集存放方式
|
||||
|
||||
# ├── ICDAR2015 # dir
|
||||
# ├── train # train dir
|
||||
# ├── ic15 # train_dataset dir
|
||||
# ├── ch4_training_images
|
||||
# ├── ch4_training_localization_transcription_gt
|
||||
# ├── train_predtrained # predtrained dir
|
||||
# ├── eval # eval dir
|
||||
# ├── ic15 # eval dataset dir
|
||||
# ├── ch4_test_images
|
||||
# ├── challenge4_Test_Task1_GT
|
||||
# ├── checkpoint # ckpt files dir
|
||||
|
||||
# (1) 选择a(修改yaml文件参数)或者b(ModelArts创建训练作业修改参数)其中一种方式。
|
||||
# a. 设置 "enable_modelarts=True"
|
||||
# 设置 "run_distribute=True"
|
||||
# 设置 "TRAIN_MODEL_SAVE_PATH=/cache/train/outputs/"
|
||||
# 设置 "TRAIN_ROOT_DIR=/cache/data/ic15/"
|
||||
# 设置 "pre_trained=/cache/data/train_predtrained/pred file name" 如果没有预训练权重 pre_trained=""
|
||||
|
||||
# b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
|
||||
# 在modelarts的界面上设置方法a所需要的参数
|
||||
# 注意：路径参数不需要加引号
|
||||
|
||||
# (2)设置网络配置文件的路径 "_config_path=/The path of config in default_config.yaml/"
|
||||
# (3) 在modelarts的界面上设置代码的路径 "/path/psenet"。
|
||||
# (4) 在modelarts的界面上设置模型的启动文件 "train.py" 。
|
||||
# (5) 在modelarts的界面上设置模型的数据路径 ".../ICDAR2015/train"(选择ICDAR2015/train文件夹路径) ,
|
||||
# 模型的输出路径"Output file path" 和模型的日志路径 "Job log path" 。
|
||||
# (6) 开始模型的训练。
|
||||
|
||||
# 在modelarts上使用模型推理的示例
|
||||
# (1) 把训练好的模型地方到桶的对应位置。
|
||||
# (2) 选择a或者b其中一种方式。
|
||||
# a.设置 "enable_modelarts=True"
|
||||
# 设置 "TEST_ROOT_DIR=/cache/data/ic15"
|
||||
# 设置 "ckpt=/cache/data/checkpoint/ckpt file"
|
||||
|
||||
# b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
|
||||
# 在modelarts的界面上设置方法a所需要的参数
|
||||
# 注意：路径参数不需要加引号
|
||||
|
||||
# (3) 设置网络配置文件的路径 "_config_path=/The path of config in default_config.yaml/"
|
||||
# (4) 在modelarts的界面上设置代码的路径 "/path/psenet"。
|
||||
# (5) 在modelarts的界面上设置模型的启动文件 "eval.py" 。
|
||||
# (6) 在modelarts的界面上设置模型的数据路径 "../ICDAR2015/eval"(选择ICDAR2015/eval文件夹路径) ,
|
||||
# 模型的输出路径"Output file path" 和模型的日志路径 "Job log path" 。
|
||||
# (7) 开始模型的推理。
|
||||
```
|
||||
|
||||
### ICDAR2015评估脚本
|
||||
|
||||
#### 用法
|
||||
|
||||
第一步：单击[此处](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization)下载评估方法。
|
||||
|
||||
第二步：单击"我的方法"按钮，下载评估脚本。
|
||||
|
||||
第三步：建议将评估方法根符号链接到$MINDSPORE/model_zoo/psenet/eval_ic15/。如果您的文件夹结构不同，您可能需要更改评估脚本文件中的相应路径。
|
||||
|
||||
```shell
|
||||
bash ./script/run_eval_ascend.sh.sh
|
||||
# 第一个参数为训练得到的模型文件，第二个参数为下载得到的推理数据集
|
||||
python test.py --ckpt [CKPK_PATH] --TEST_ROOT_DIR [TEST_DATA_DIR]
|
||||
|
||||
# 单击[此处](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization)下载评估方法
|
||||
# 点击"My Methods"按钮，选择Offline evaluation -> Evaluation Scripts
|
||||
# 下载完成后，将数据放在/path_to_data路径
|
||||
mkdir eval_ic15
|
||||
ln -s /path_to_data/script_test_ch4_t1_e1-1577983151.zip eval_ic15/script_test_ch4_t1_e1-1577983151.zip
|
||||
|
||||
cd eval_ic15
|
||||
unzip script_test_ch4_t1_e1-1577983151.zip
|
||||
cd ..
|
||||
|
||||
bash ./script/run_eval_ascend.sh
|
||||
```
|
||||
|
||||
#### 结果
|
||||
### 结果
|
||||
|
||||
Calculated!{"precision": 0.8147966668299853，"recall"：0.8006740491092923，"hmean"：0.8076736279747451，"AP"：0}
|
||||
|
||||
|
|
@ -317,7 +339,8 @@ bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
|
|||
| 损失函数 | LossCallBack |
|
||||
| 输出 | 概率 |
|
||||
| 损失 | 0.35 |
|
||||
| 速度 | 1卡：444毫秒/步；8卡：446毫秒/步
|
||||
| 训练参数 | batch_size = 4 |
|
||||
| 速度 | 1卡：444毫秒/步(fps: 9.0)；8卡：446毫秒/步(fps: 71) |
|
||||
| 总时间 | 1卡：75.48小时；8卡：7.11小时|
|
||||
| 参数(M) | 27.36 |
|
||||
| 微调检查点 | 109.44M （.ckpt file） |
|
||||
|
|
|
|||
|
|
@ -2,3 +2,5 @@ numpy
|
|||
opencv-python
|
||||
pillow
|
||||
pyyaml
|
||||
Polygon3
|
||||
pyclipper
|
||||
|
|
|
|||
|
|
@ -13,8 +13,7 @@
|
|||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
mindspore_home = ${MINDSPORE_HOME}
|
||||
CXXFLAGS = -I include -I ${mindspore_home}/model_zoo/official/cv/psenet -std=c++11 -O3
|
||||
CXXFLAGS = -std=c++11 -O3
|
||||
CXX_SOURCES = adaptor.cpp
|
||||
opencv_home = ${OPENCV_HOME}
|
||||
OPENCV = -I$(opencv_home)/include -L$(opencv_home)/lib64 -lopencv_superres -lopencv_ml -lopencv_objdetect \
|
||||
|
|
|
|||
|
|
@ -13,7 +13,6 @@
|
|||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
#include "src/ETSNET/pse/adaptor.h"
|
||||
#include <pybind11/pybind11.h>
|
||||
#include <pybind11/numpy.h>
|
||||
#include <pybind11/stl.h>
|
||||
|
|
@ -26,6 +25,7 @@
|
|||
#include <opencv2/core/core.hpp>
|
||||
#include <opencv2/highgui/highgui.hpp>
|
||||
#include <opencv2/imgproc/imgproc.hpp>
|
||||
#include "./adaptor.h"
|
||||
|
||||
using std::vector;
|
||||
using std::queue;
|
||||
|
|
|
|||
|
|
@ -0,0 +1,326 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""transform pytorch checkpoint to mindspore checkpoint"""
|
||||
import os
|
||||
import argparse
|
||||
|
||||
import torch
|
||||
from mindspore import Tensor
|
||||
from mindspore.train.serialization import save_checkpoint
|
||||
|
||||
parser = argparse.ArgumentParser(description="transform pytorch checkpoint to mindspore checkpoint")
|
||||
parser.add_argument("--torch_file", type=str, required=True, help="input pytorch checkpoint filename")
|
||||
parser.add_argument("--output_path", type=str, required=True, help="output mindspore checkpoint path")
|
||||
args = parser.parse_args()
|
||||
|
||||
weights_dict = {
|
||||
"feature_extractor.conv1.weight": "conv1.weight",
|
||||
"feature_extractor.bn1.moving_mean": "bn1.running_mean",
|
||||
"feature_extractor.bn1.moving_variance": "bn1.running_var",
|
||||
"feature_extractor.bn1.gamma": "bn1.weight",
|
||||
"feature_extractor.bn1.beta": "bn1.bias",
|
||||
|
||||
"feature_extractor.layer1.0.conv1.weight": "layer1.0.conv1.weight",
|
||||
"feature_extractor.layer1.0.bn1.moving_mean": "layer1.0.bn1.running_mean",
|
||||
"feature_extractor.layer1.0.bn1.moving_variance": "layer1.0.bn1.running_var",
|
||||
"feature_extractor.layer1.0.bn1.gamma": "layer1.0.bn1.weight",
|
||||
"feature_extractor.layer1.0.bn1.beta": "layer1.0.bn1.bias",
|
||||
"feature_extractor.layer1.0.conv2.weight": "layer1.0.conv2.weight",
|
||||
"feature_extractor.layer1.0.bn2.moving_mean": "layer1.0.bn2.running_mean",
|
||||
"feature_extractor.layer1.0.bn2.moving_variance": "layer1.0.bn2.running_var",
|
||||
"feature_extractor.layer1.0.bn2.gamma": "layer1.0.bn2.weight",
|
||||
"feature_extractor.layer1.0.bn2.beta": "layer1.0.bn2.bias",
|
||||
"feature_extractor.layer1.0.conv3.weight": "layer1.0.conv3.weight",
|
||||
"feature_extractor.layer1.0.bn3.moving_mean": "layer1.0.bn3.running_mean",
|
||||
"feature_extractor.layer1.0.bn3.moving_variance": "layer1.0.bn3.running_var",
|
||||
"feature_extractor.layer1.0.bn3.gamma": "layer1.0.bn3.weight",
|
||||
"feature_extractor.layer1.0.bn3.beta": "layer1.0.bn3.bias",
|
||||
"feature_extractor.layer1.0.conv_down_sample.weight": "layer1.0.downsample.0.weight",
|
||||
"feature_extractor.layer1.0.bn_down_sample.moving_mean": "layer1.0.downsample.1.running_mean",
|
||||
"feature_extractor.layer1.0.bn_down_sample.moving_variance": "layer1.0.downsample.1.running_var",
|
||||
"feature_extractor.layer1.0.bn_down_sample.gamma": "layer1.0.downsample.1.weight",
|
||||
"feature_extractor.layer1.0.bn_down_sample.beta": "layer1.0.downsample.1.bias",
|
||||
|
||||
"feature_extractor.layer1.1.conv1.weight": "layer1.1.conv1.weight",
|
||||
"feature_extractor.layer1.1.bn1.moving_mean": "layer1.1.bn1.running_mean",
|
||||
"feature_extractor.layer1.1.bn1.moving_variance": "layer1.1.bn1.running_var",
|
||||
"feature_extractor.layer1.1.bn1.gamma": "layer1.1.bn1.weight",
|
||||
"feature_extractor.layer1.1.bn1.beta": "layer1.1.bn1.bias",
|
||||
"feature_extractor.layer1.1.conv2.weight": "layer1.1.conv2.weight",
|
||||
"feature_extractor.layer1.1.bn2.moving_mean": "layer1.1.bn2.running_mean",
|
||||
"feature_extractor.layer1.1.bn2.moving_variance": "layer1.1.bn2.running_var",
|
||||
"feature_extractor.layer1.1.bn2.gamma": "layer1.1.bn2.weight",
|
||||
"feature_extractor.layer1.1.bn2.beta": "layer1.1.bn2.bias",
|
||||
"feature_extractor.layer1.1.conv3.weight": "layer1.1.conv3.weight",
|
||||
"feature_extractor.layer1.1.bn3.moving_mean": "layer1.1.bn3.running_mean",
|
||||
"feature_extractor.layer1.1.bn3.moving_variance": "layer1.1.bn3.running_var",
|
||||
"feature_extractor.layer1.1.bn3.gamma": "layer1.1.bn3.weight",
|
||||
"feature_extractor.layer1.1.bn3.beta": "layer1.1.bn3.bias",
|
||||
|
||||
"feature_extractor.layer1.2.conv1.weight": "layer1.2.conv1.weight",
|
||||
"feature_extractor.layer1.2.bn1.moving_mean": "layer1.2.bn1.running_mean",
|
||||
"feature_extractor.layer1.2.bn1.moving_variance": "layer1.2.bn1.running_var",
|
||||
"feature_extractor.layer1.2.bn1.gamma": "layer1.2.bn1.weight",
|
||||
"feature_extractor.layer1.2.bn1.beta": "layer1.2.bn1.bias",
|
||||
"feature_extractor.layer1.2.conv2.weight": "layer1.2.conv2.weight",
|
||||
"feature_extractor.layer1.2.bn2.moving_mean": "layer1.2.bn2.running_mean",
|
||||
"feature_extractor.layer1.2.bn2.moving_variance": "layer1.2.bn2.running_var",
|
||||
"feature_extractor.layer1.2.bn2.gamma": "layer1.2.bn2.weight",
|
||||
"feature_extractor.layer1.2.bn2.beta": "layer1.2.bn2.bias",
|
||||
"feature_extractor.layer1.2.conv3.weight": "layer1.2.conv3.weight",
|
||||
"feature_extractor.layer1.2.bn3.moving_mean": "layer1.2.bn3.running_mean",
|
||||
"feature_extractor.layer1.2.bn3.moving_variance": "layer1.2.bn3.running_var",
|
||||
"feature_extractor.layer1.2.bn3.gamma": "layer1.2.bn3.weight",
|
||||
"feature_extractor.layer1.2.bn3.beta": "layer1.2.bn3.bias",
|
||||
|
||||
"feature_extractor.layer2.0.conv1.weight": "layer2.0.conv1.weight",
|
||||
"feature_extractor.layer2.0.bn1.moving_mean": "layer2.0.bn1.running_mean",
|
||||
"feature_extractor.layer2.0.bn1.moving_variance": "layer2.0.bn1.running_var",
|
||||
"feature_extractor.layer2.0.bn1.gamma": "layer2.0.bn1.weight",
|
||||
"feature_extractor.layer2.0.bn1.beta": "layer2.0.bn1.bias",
|
||||
"feature_extractor.layer2.0.conv2.weight": "layer2.0.conv2.weight",
|
||||
"feature_extractor.layer2.0.bn2.moving_mean": "layer2.0.bn2.running_mean",
|
||||
"feature_extractor.layer2.0.bn2.moving_variance": "layer2.0.bn2.running_var",
|
||||
"feature_extractor.layer2.0.bn2.gamma": "layer2.0.bn2.weight",
|
||||
"feature_extractor.layer2.0.bn2.beta": "layer2.0.bn2.bias",
|
||||
"feature_extractor.layer2.0.conv3.weight": "layer2.0.conv3.weight",
|
||||
"feature_extractor.layer2.0.bn3.moving_mean": "layer2.0.bn3.running_mean",
|
||||
"feature_extractor.layer2.0.bn3.moving_variance": "layer2.0.bn3.running_var",
|
||||
"feature_extractor.layer2.0.bn3.gamma": "layer2.0.bn3.weight",
|
||||
"feature_extractor.layer2.0.bn3.beta": "layer2.0.bn3.bias",
|
||||
"feature_extractor.layer2.0.conv_down_sample.weight": "layer2.0.downsample.0.weight",
|
||||
"feature_extractor.layer2.0.bn_down_sample.moving_mean": "layer2.0.downsample.1.running_mean",
|
||||
"feature_extractor.layer2.0.bn_down_sample.moving_variance": "layer2.0.downsample.1.running_var",
|
||||
"feature_extractor.layer2.0.bn_down_sample.gamma": "layer2.0.downsample.1.weight",
|
||||
"feature_extractor.layer2.0.bn_down_sample.beta": "layer2.0.downsample.1.bias",
|
||||
|
||||
"feature_extractor.layer2.1.conv1.weight": "layer2.1.conv1.weight",
|
||||
"feature_extractor.layer2.1.bn1.moving_mean": "layer2.1.bn1.running_mean",
|
||||
"feature_extractor.layer2.1.bn1.moving_variance": "layer2.1.bn1.running_var",
|
||||
"feature_extractor.layer2.1.bn1.gamma": "layer2.1.bn1.weight",
|
||||
"feature_extractor.layer2.1.bn1.beta": "layer2.1.bn1.bias",
|
||||
"feature_extractor.layer2.1.conv2.weight": "layer2.1.conv2.weight",
|
||||
"feature_extractor.layer2.1.bn2.moving_mean": "layer2.1.bn2.running_mean",
|
||||
"feature_extractor.layer2.1.bn2.moving_variance": "layer2.1.bn2.running_var",
|
||||
"feature_extractor.layer2.1.bn2.gamma": "layer2.1.bn2.weight",
|
||||
"feature_extractor.layer2.1.bn2.beta": "layer2.1.bn2.bias",
|
||||
"feature_extractor.layer2.1.conv3.weight": "layer2.1.conv3.weight",
|
||||
"feature_extractor.layer2.1.bn3.moving_mean": "layer2.1.bn3.running_mean",
|
||||
"feature_extractor.layer2.1.bn3.moving_variance": "layer2.1.bn3.running_var",
|
||||
"feature_extractor.layer2.1.bn3.gamma": "layer2.1.bn3.weight",
|
||||
"feature_extractor.layer2.1.bn3.beta": "layer2.1.bn3.bias",
|
||||
|
||||
"feature_extractor.layer2.2.conv1.weight": "layer2.2.conv1.weight",
|
||||
"feature_extractor.layer2.2.bn1.moving_mean": "layer2.2.bn1.running_mean",
|
||||
"feature_extractor.layer2.2.bn1.moving_variance": "layer2.2.bn1.running_var",
|
||||
"feature_extractor.layer2.2.bn1.gamma": "layer2.2.bn1.weight",
|
||||
"feature_extractor.layer2.2.bn1.beta": "layer2.2.bn1.bias",
|
||||
"feature_extractor.layer2.2.conv2.weight": "layer2.2.conv2.weight",
|
||||
"feature_extractor.layer2.2.bn2.moving_mean": "layer2.2.bn2.running_mean",
|
||||
"feature_extractor.layer2.2.bn2.moving_variance": "layer2.2.bn2.running_var",
|
||||
"feature_extractor.layer2.2.bn2.gamma": "layer2.2.bn2.weight",
|
||||
"feature_extractor.layer2.2.bn2.beta": "layer2.2.bn2.bias",
|
||||
"feature_extractor.layer2.2.conv3.weight": "layer2.2.conv3.weight",
|
||||
"feature_extractor.layer2.2.bn3.moving_mean": "layer2.2.bn3.running_mean",
|
||||
"feature_extractor.layer2.2.bn3.moving_variance": "layer2.2.bn3.running_var",
|
||||
"feature_extractor.layer2.2.bn3.gamma": "layer2.2.bn3.weight",
|
||||
"feature_extractor.layer2.2.bn3.beta": "layer2.2.bn3.bias",
|
||||
|
||||
"feature_extractor.layer2.3.conv1.weight": "layer2.3.conv1.weight",
|
||||
"feature_extractor.layer2.3.bn1.moving_mean": "layer2.3.bn1.running_mean",
|
||||
"feature_extractor.layer2.3.bn1.moving_variance": "layer2.3.bn1.running_var",
|
||||
"feature_extractor.layer2.3.bn1.gamma": "layer2.3.bn1.weight",
|
||||
"feature_extractor.layer2.3.bn1.beta": "layer2.3.bn1.bias",
|
||||
"feature_extractor.layer2.3.conv2.weight": "layer2.3.conv2.weight",
|
||||
"feature_extractor.layer2.3.bn2.moving_mean": "layer2.3.bn2.running_mean",
|
||||
"feature_extractor.layer2.3.bn2.moving_variance": "layer2.3.bn2.running_var",
|
||||
"feature_extractor.layer2.3.bn2.gamma": "layer2.3.bn2.weight",
|
||||
"feature_extractor.layer2.3.bn2.beta": "layer2.3.bn2.bias",
|
||||
"feature_extractor.layer2.3.conv3.weight": "layer2.3.conv3.weight",
|
||||
"feature_extractor.layer2.3.bn3.moving_mean": "layer2.3.bn3.running_mean",
|
||||
"feature_extractor.layer2.3.bn3.moving_variance": "layer2.3.bn3.running_var",
|
||||
"feature_extractor.layer2.3.bn3.gamma": "layer2.3.bn3.weight",
|
||||
"feature_extractor.layer2.3.bn3.beta": "layer2.3.bn3.bias",
|
||||
|
||||
"feature_extractor.layer3.0.conv1.weight": "layer3.0.conv1.weight",
|
||||
"feature_extractor.layer3.0.bn1.moving_mean": "layer3.0.bn1.running_mean",
|
||||
"feature_extractor.layer3.0.bn1.moving_variance": "layer3.0.bn1.running_var",
|
||||
"feature_extractor.layer3.0.bn1.gamma": "layer3.0.bn1.weight",
|
||||
"feature_extractor.layer3.0.bn1.beta": "layer3.0.bn1.bias",
|
||||
"feature_extractor.layer3.0.conv2.weight": "layer3.0.conv2.weight",
|
||||
"feature_extractor.layer3.0.bn2.moving_mean": "layer3.0.bn2.running_mean",
|
||||
"feature_extractor.layer3.0.bn2.moving_variance": "layer3.0.bn2.running_var",
|
||||
"feature_extractor.layer3.0.bn2.gamma": "layer3.0.bn2.weight",
|
||||
"feature_extractor.layer3.0.bn2.beta": "layer3.0.bn2.bias",
|
||||
"feature_extractor.layer3.0.conv3.weight": "layer3.0.conv3.weight",
|
||||
"feature_extractor.layer3.0.bn3.moving_mean": "layer3.0.bn3.running_mean",
|
||||
"feature_extractor.layer3.0.bn3.moving_variance": "layer3.0.bn3.running_var",
|
||||
"feature_extractor.layer3.0.bn3.gamma": "layer3.0.bn3.weight",
|
||||
"feature_extractor.layer3.0.bn3.beta": "layer3.0.bn3.bias",
|
||||
"feature_extractor.layer3.0.conv_down_sample.weight": "layer3.0.downsample.0.weight",
|
||||
"feature_extractor.layer3.0.bn_down_sample.moving_mean": "layer3.0.downsample.1.running_mean",
|
||||
"feature_extractor.layer3.0.bn_down_sample.moving_variance": "layer3.0.downsample.1.running_var",
|
||||
"feature_extractor.layer3.0.bn_down_sample.gamma": "layer3.0.downsample.1.weight",
|
||||
"feature_extractor.layer3.0.bn_down_sample.beta": "layer3.0.downsample.1.bias",
|
||||
|
||||
"feature_extractor.layer3.1.conv1.weight": "layer3.1.conv1.weight",
|
||||
"feature_extractor.layer3.1.bn1.moving_mean": "layer3.1.bn1.running_mean",
|
||||
"feature_extractor.layer3.1.bn1.moving_variance": "layer3.1.bn1.running_var",
|
||||
"feature_extractor.layer3.1.bn1.gamma": "layer3.1.bn1.weight",
|
||||
"feature_extractor.layer3.1.bn1.beta": "layer3.1.bn1.bias",
|
||||
"feature_extractor.layer3.1.conv2.weight": "layer3.1.conv2.weight",
|
||||
"feature_extractor.layer3.1.bn2.moving_mean": "layer3.1.bn2.running_mean",
|
||||
"feature_extractor.layer3.1.bn2.moving_variance": "layer3.1.bn2.running_var",
|
||||
"feature_extractor.layer3.1.bn2.gamma": "layer3.1.bn2.weight",
|
||||
"feature_extractor.layer3.1.bn2.beta": "layer3.1.bn2.bias",
|
||||
"feature_extractor.layer3.1.conv3.weight": "layer3.1.conv3.weight",
|
||||
"feature_extractor.layer3.1.bn3.moving_mean": "layer3.1.bn3.running_mean",
|
||||
"feature_extractor.layer3.1.bn3.moving_variance": "layer3.1.bn3.running_var",
|
||||
"feature_extractor.layer3.1.bn3.gamma": "layer3.1.bn3.weight",
|
||||
"feature_extractor.layer3.1.bn3.beta": "layer3.1.bn3.bias",
|
||||
|
||||
"feature_extractor.layer3.2.conv1.weight": "layer3.2.conv1.weight",
|
||||
"feature_extractor.layer3.2.bn1.moving_mean": "layer3.2.bn1.running_mean",
|
||||
"feature_extractor.layer3.2.bn1.moving_variance": "layer3.2.bn1.running_var",
|
||||
"feature_extractor.layer3.2.bn1.gamma": "layer3.2.bn1.weight",
|
||||
"feature_extractor.layer3.2.bn1.beta": "layer3.2.bn1.bias",
|
||||
"feature_extractor.layer3.2.conv2.weight": "layer3.2.conv2.weight",
|
||||
"feature_extractor.layer3.2.bn2.moving_mean": "layer3.2.bn2.running_mean",
|
||||
"feature_extractor.layer3.2.bn2.moving_variance": "layer3.2.bn2.running_var",
|
||||
"feature_extractor.layer3.2.bn2.gamma": "layer3.2.bn2.weight",
|
||||
"feature_extractor.layer3.2.bn2.beta": "layer3.2.bn2.bias",
|
||||
"feature_extractor.layer3.2.conv3.weight": "layer3.2.conv3.weight",
|
||||
"feature_extractor.layer3.2.bn3.moving_mean": "layer3.2.bn3.running_mean",
|
||||
"feature_extractor.layer3.2.bn3.moving_variance": "layer3.2.bn3.running_var",
|
||||
"feature_extractor.layer3.2.bn3.gamma": "layer3.2.bn3.weight",
|
||||
"feature_extractor.layer3.2.bn3.beta": "layer3.2.bn3.bias",
|
||||
|
||||
"feature_extractor.layer3.3.conv1.weight": "layer3.3.conv1.weight",
|
||||
"feature_extractor.layer3.3.bn1.moving_mean": "layer3.3.bn1.running_mean",
|
||||
"feature_extractor.layer3.3.bn1.moving_variance": "layer3.3.bn1.running_var",
|
||||
"feature_extractor.layer3.3.bn1.gamma": "layer3.3.bn1.weight",
|
||||
"feature_extractor.layer3.3.bn1.beta": "layer3.3.bn1.bias",
|
||||
"feature_extractor.layer3.3.conv2.weight": "layer3.3.conv2.weight",
|
||||
"feature_extractor.layer3.3.bn2.moving_mean": "layer3.3.bn2.running_mean",
|
||||
"feature_extractor.layer3.3.bn2.moving_variance": "layer3.3.bn2.running_var",
|
||||
"feature_extractor.layer3.3.bn2.gamma": "layer3.3.bn2.weight",
|
||||
"feature_extractor.layer3.3.bn2.beta": "layer3.3.bn2.bias",
|
||||
"feature_extractor.layer3.3.conv3.weight": "layer3.3.conv3.weight",
|
||||
"feature_extractor.layer3.3.bn3.moving_mean": "layer3.3.bn3.running_mean",
|
||||
"feature_extractor.layer3.3.bn3.moving_variance": "layer3.3.bn3.running_var",
|
||||
"feature_extractor.layer3.3.bn3.gamma": "layer3.3.bn3.weight",
|
||||
"feature_extractor.layer3.3.bn3.beta": "layer3.3.bn3.bias",
|
||||
|
||||
"feature_extractor.layer3.4.conv1.weight": "layer3.4.conv1.weight",
|
||||
"feature_extractor.layer3.4.bn1.moving_mean": "layer3.4.bn1.running_mean",
|
||||
"feature_extractor.layer3.4.bn1.moving_variance": "layer3.4.bn1.running_var",
|
||||
"feature_extractor.layer3.4.bn1.gamma": "layer3.4.bn1.weight",
|
||||
"feature_extractor.layer3.4.bn1.beta": "layer3.4.bn1.bias",
|
||||
"feature_extractor.layer3.4.conv2.weight": "layer3.4.conv2.weight",
|
||||
"feature_extractor.layer3.4.bn2.moving_mean": "layer3.4.bn2.running_mean",
|
||||
"feature_extractor.layer3.4.bn2.moving_variance": "layer3.4.bn2.running_var",
|
||||
"feature_extractor.layer3.4.bn2.gamma": "layer3.4.bn2.weight",
|
||||
"feature_extractor.layer3.4.bn2.beta": "layer3.4.bn2.bias",
|
||||
"feature_extractor.layer3.4.conv3.weight": "layer3.4.conv3.weight",
|
||||
"feature_extractor.layer3.4.bn3.moving_mean": "layer3.4.bn3.running_mean",
|
||||
"feature_extractor.layer3.4.bn3.moving_variance": "layer3.4.bn3.running_var",
|
||||
"feature_extractor.layer3.4.bn3.gamma": "layer3.4.bn3.weight",
|
||||
"feature_extractor.layer3.4.bn3.beta": "layer3.4.bn3.bias",
|
||||
|
||||
"feature_extractor.layer3.5.conv1.weight": "layer3.5.conv1.weight",
|
||||
"feature_extractor.layer3.5.bn1.moving_mean": "layer3.5.bn1.running_mean",
|
||||
"feature_extractor.layer3.5.bn1.moving_variance": "layer3.5.bn1.running_var",
|
||||
"feature_extractor.layer3.5.bn1.gamma": "layer3.5.bn1.weight",
|
||||
"feature_extractor.layer3.5.bn1.beta": "layer3.5.bn1.bias",
|
||||
"feature_extractor.layer3.5.conv2.weight": "layer3.5.conv2.weight",
|
||||
"feature_extractor.layer3.5.bn2.moving_mean": "layer3.5.bn2.running_mean",
|
||||
"feature_extractor.layer3.5.bn2.moving_variance": "layer3.5.bn2.running_var",
|
||||
"feature_extractor.layer3.5.bn2.gamma": "layer3.5.bn2.weight",
|
||||
"feature_extractor.layer3.5.bn2.beta": "layer3.5.bn2.bias",
|
||||
"feature_extractor.layer3.5.conv3.weight": "layer3.5.conv3.weight",
|
||||
"feature_extractor.layer3.5.bn3.moving_mean": "layer3.5.bn3.running_mean",
|
||||
"feature_extractor.layer3.5.bn3.moving_variance": "layer3.5.bn3.running_var",
|
||||
"feature_extractor.layer3.5.bn3.gamma": "layer3.5.bn3.weight",
|
||||
"feature_extractor.layer3.5.bn3.beta": "layer3.5.bn3.bias",
|
||||
|
||||
"feature_extractor.layer4.0.conv1.weight": "layer4.0.conv1.weight",
|
||||
"feature_extractor.layer4.0.bn1.moving_mean": "layer4.0.bn1.running_mean",
|
||||
"feature_extractor.layer4.0.bn1.moving_variance": "layer4.0.bn1.running_var",
|
||||
"feature_extractor.layer4.0.bn1.gamma": "layer4.0.bn1.weight",
|
||||
"feature_extractor.layer4.0.bn1.beta": "layer4.0.bn1.bias",
|
||||
"feature_extractor.layer4.0.conv2.weight": "layer4.0.conv2.weight",
|
||||
"feature_extractor.layer4.0.bn2.moving_mean": "layer4.0.bn2.running_mean",
|
||||
"feature_extractor.layer4.0.bn2.moving_variance": "layer4.0.bn2.running_var",
|
||||
"feature_extractor.layer4.0.bn2.gamma": "layer4.0.bn2.weight",
|
||||
"feature_extractor.layer4.0.bn2.beta": "layer4.0.bn2.bias",
|
||||
"feature_extractor.layer4.0.conv3.weight": "layer4.0.conv3.weight",
|
||||
"feature_extractor.layer4.0.bn3.moving_mean": "layer4.0.bn3.running_mean",
|
||||
"feature_extractor.layer4.0.bn3.moving_variance": "layer4.0.bn3.running_var",
|
||||
"feature_extractor.layer4.0.bn3.gamma": "layer4.0.bn3.weight",
|
||||
"feature_extractor.layer4.0.bn3.beta": "layer4.0.bn3.bias",
|
||||
"feature_extractor.layer4.0.conv_down_sample.weight": "layer4.0.downsample.0.weight",
|
||||
"feature_extractor.layer4.0.bn_down_sample.moving_mean": "layer4.0.downsample.1.running_mean",
|
||||
"feature_extractor.layer4.0.bn_down_sample.moving_variance": "layer4.0.downsample.1.running_var",
|
||||
"feature_extractor.layer4.0.bn_down_sample.gamma": "layer4.0.downsample.1.weight",
|
||||
"feature_extractor.layer4.0.bn_down_sample.beta": "layer4.0.downsample.1.bias",
|
||||
|
||||
"feature_extractor.layer4.1.conv1.weight": "layer4.1.conv1.weight",
|
||||
"feature_extractor.layer4.1.bn1.moving_mean": "layer4.1.bn1.running_mean",
|
||||
"feature_extractor.layer4.1.bn1.moving_variance": "layer4.1.bn1.running_var",
|
||||
"feature_extractor.layer4.1.bn1.gamma": "layer4.1.bn1.weight",
|
||||
"feature_extractor.layer4.1.bn1.beta": "layer4.1.bn1.bias",
|
||||
"feature_extractor.layer4.1.conv2.weight": "layer4.1.conv2.weight",
|
||||
"feature_extractor.layer4.1.bn2.moving_mean": "layer4.1.bn2.running_mean",
|
||||
"feature_extractor.layer4.1.bn2.moving_variance": "layer4.1.bn2.running_var",
|
||||
"feature_extractor.layer4.1.bn2.gamma": "layer4.1.bn2.weight",
|
||||
"feature_extractor.layer4.1.bn2.beta": "layer4.1.bn2.bias",
|
||||
"feature_extractor.layer4.1.conv3.weight": "layer4.1.conv3.weight",
|
||||
"feature_extractor.layer4.1.bn3.moving_mean": "layer4.1.bn3.running_mean",
|
||||
"feature_extractor.layer4.1.bn3.moving_variance": "layer4.1.bn3.running_var",
|
||||
"feature_extractor.layer4.1.bn3.gamma": "layer4.1.bn3.weight",
|
||||
"feature_extractor.layer4.1.bn3.beta": "layer4.1.bn3.bias",
|
||||
|
||||
"feature_extractor.layer4.2.conv1.weight": "layer4.2.conv1.weight",
|
||||
"feature_extractor.layer4.2.bn1.moving_mean": "layer4.2.bn1.running_mean",
|
||||
"feature_extractor.layer4.2.bn1.moving_variance": "layer4.2.bn1.running_var",
|
||||
"feature_extractor.layer4.2.bn1.gamma": "layer4.2.bn1.weight",
|
||||
"feature_extractor.layer4.2.bn1.beta": "layer4.2.bn1.bias",
|
||||
"feature_extractor.layer4.2.conv2.weight": "layer4.2.conv2.weight",
|
||||
"feature_extractor.layer4.2.bn2.moving_mean": "layer4.2.bn2.running_mean",
|
||||
"feature_extractor.layer4.2.bn2.moving_variance": "layer4.2.bn2.running_var",
|
||||
"feature_extractor.layer4.2.bn2.gamma": "layer4.2.bn2.weight",
|
||||
"feature_extractor.layer4.2.bn2.beta": "layer4.2.bn2.bias",
|
||||
"feature_extractor.layer4.2.conv3.weight": "layer4.2.conv3.weight",
|
||||
"feature_extractor.layer4.2.bn3.moving_mean": "layer4.2.bn3.running_mean",
|
||||
"feature_extractor.layer4.2.bn3.moving_variance": "layer4.2.bn3.running_var",
|
||||
"feature_extractor.layer4.2.bn3.gamma": "layer4.2.bn3.weight",
|
||||
"feature_extractor.layer4.2.bn3.beta": "layer4.2.bn3.bias",
|
||||
}
|
||||
|
||||
torch_param_dict = torch.load(args.torch_file)
|
||||
ms_params = []
|
||||
|
||||
for ms_name, torch_name in weights_dict.items():
|
||||
if torch_name not in torch_param_dict.keys():
|
||||
print("param {} not in pytorch checkpoint".format(torch_name))
|
||||
continue
|
||||
torch_value = torch_param_dict[torch_name]
|
||||
np_value = torch_value.data.numpy()
|
||||
each_param = dict()
|
||||
each_param["name"] = ms_name
|
||||
each_param["data"] = Tensor(np_value)
|
||||
ms_params.append(each_param)
|
||||
|
||||
save_checkpoint(ms_params, os.path.join(args.output_path, "pretrained_model.ckpt"))
|
||||
|
|
@ -100,7 +100,7 @@ def train():
|
|||
|
||||
if config.pre_trained:
|
||||
param_dict = load_checkpoint(config.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
load_param_into_net(net, param_dict, strict_load=True)
|
||||
print('Load Pretrained parameters done!')
|
||||
|
||||
criterion = DiceLoss(batch_size=config.TRAIN_BATCH_SIZE)
|
||||
|
|
|
|||
Loading…
Reference in New Issue