From 2c558997c55eeaa1a9812179b92ecd3245ee983f Mon Sep 17 00:00:00 2001 From: caojian05 Date: Mon, 4 Jan 2021 15:35:25 +0800 Subject: [PATCH] deeplabv3 bugfix: 1. add CPU training usage in readme file; 2. add dataset list file genrate script. --- model_zoo/official/cv/deeplabv3/README.md | 46 +++++- model_zoo/official/cv/deeplabv3/README_CN.md | 44 +++++- .../cv/deeplabv3/src/data/get_dataset_lst.py | 142 ++++++++++++++++++ 3 files changed, 229 insertions(+), 3 deletions(-) create mode 100644 model_zoo/official/cv/deeplabv3/src/data/get_dataset_lst.py diff --git a/model_zoo/official/cv/deeplabv3/README.md b/model_zoo/official/cv/deeplabv3/README.md index e99e780764..39d4033a3b 100644 --- a/model_zoo/official/cv/deeplabv3/README.md +++ b/model_zoo/official/cv/deeplabv3/README.md @@ -50,6 +50,8 @@ JPEGImages/00004.jpg SegmentationClassGray/00004.png ...... ``` +You can also generate the list file automatically by run script: `python get_dataset_lst.py --data_root=/PATH/TO/DATA` + - Configure and run build_data.sh to convert dataset to mindrecords. Arguments in scripts/build_data.sh: ```shell @@ -164,10 +166,12 @@ run_eval_s8_multiscale_flip.sh ├── run_eval_s8_multiscale.sh # launch ascend evaluation with multiscale in s8 structure ├── run_eval_s8_multiscale_filp.sh # launch ascend evaluation with multiscale and filp in s8 structure ├── run_standalone_train.sh # launch ascend standalone training(1 pc) + ├── run_standalone_train_cpu.sh # launch CPU standalone training ├── src ├── data ├── dataset.py # mindrecord data generator ├── build_seg_data.py # data preprocessing + ├── get_dataset_lst.py # dataset list file generator ├── loss ├── loss.py # loss definition for deeplabv3 ├── nets @@ -189,6 +193,7 @@ Default configuration ```shell "data_file":"/PATH/TO/MINDRECORD_NAME" # dataset path +"device_target":Ascend # device target "train_epochs":300 # total epochs "batch_size":32 # batch size of input tensor "crop_size":513 # crop size @@ -238,7 +243,7 @@ For 8 devices training, training steps are as follows: 1. Train s16 with vocaug dataset, finetuning from resnet101 pretrained model, script is as follows: -```python +```shell # run_distribute_train_s16_r1.sh for((i=0;i<=$RANK_SIZE-1;i++)); do @@ -328,8 +333,34 @@ do done ``` +#### Running on CPU + +For CPU training, please config parameters, training script is as follows: + +```shell +# run_standalone_train_cpu.sh +python ${train_code_path}/train.py --data_file=/PATH/TO/MINDRECORD_NAME \ + --device_target=CPU \ + --train_dir=${train_path}/ckpt \ + --train_epochs=200 \ + --batch_size=32 \ + --crop_size=513 \ + --base_lr=0.015 \ + --lr_type=cos \ + --min_scale=0.5 \ + --max_scale=2.0 \ + --ignore_label=255 \ + --num_classes=21 \ + --model=deeplab_v3_s16 \ + --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL \ + --save_steps=1500 \ + --keep_checkpoint_max=200 >log 2>&1 & +``` + ### Result +#### Running on Ascend + - Training vocaug in s16 structure ```shell @@ -386,6 +417,17 @@ epoch time: 5962.164 ms, per step time: 542.015 ms ... ``` +#### Running on CPU + +- Training voctrain in s16 structure + +```bash +epoch: 1 step: 1, loss is 3.655448 +epoch: 2 step: 1, loss is 1.5531876 +epoch: 3 step: 1, loss is 1.5099041 +... +``` + ## [Evaluation Process](#contents) ### Usage @@ -438,7 +480,7 @@ Note: There OS is output stride, and MS is multiscale. # [Model Description](#contents) -## [Performance](#contents +## [Performance](#contents) ### Evaluation Performance diff --git a/model_zoo/official/cv/deeplabv3/README_CN.md b/model_zoo/official/cv/deeplabv3/README_CN.md index 07b18276b1..4a2c52c54f 100644 --- a/model_zoo/official/cv/deeplabv3/README_CN.md +++ b/model_zoo/official/cv/deeplabv3/README_CN.md @@ -63,6 +63,8 @@ Pascal VOC数据集和语义边界数据集（Semantic Boundaries Dataset，SBD ...... ``` +你也可以通过运行脚本：`python get_dataset_lst.py --data_root=/PATH/TO/DATA` 来自动生成数据清单文件。 + - 配置并运行build_data.sh，将数据集转换为MindRecords。scripts/build_data.sh中的参数： ``` @@ -177,10 +179,12 @@ run_eval_s8_multiscale_flip.sh ├── run_eval_s8_multiscale.sh # 使用多尺度s8结构启动Ascend评估 ├── run_eval_s8_multiscale_filp.sh # 使用多尺度和翻转s8结构启动Ascend评估 ├── run_standalone_train.sh # 启动Ascend单机训练（单卡） + ├── run_standalone_train_cpu.sh # 启动CPU单机训练 ├── src ├── data ├── dataset.py # 生成MindRecord数据 ├── build_seg_data.py # 数据预处理 + ├── get_dataset_lst.py # 生成数据清单文件 ├── loss ├── loss.py # DeepLabV3的损失定义 ├── nets @@ -202,6 +206,7 @@ run_eval_s8_multiscale_flip.sh ```bash "data_file":"/PATH/TO/MINDRECORD_NAME" # 数据集路径 +"device_target":Ascend # 训练后端类型 "train_epochs":300 # 总轮次数 "batch_size":32 # 输入张量的批次大小 "crop_size":513 # 裁剪大小 @@ -342,8 +347,34 @@ do done ``` +#### CPU环境运行 + +按以下样例配置训练参数，运行CPU训练脚本： + +```shell +# run_standalone_train_cpu.sh +python ${train_code_path}/train.py --data_file=/PATH/TO/MINDRECORD_NAME \ + --device_target=CPU \ + --train_dir=${train_path}/ckpt \ + --train_epochs=200 \ + --batch_size=32 \ + --crop_size=513 \ + --base_lr=0.015 \ + --lr_type=cos \ + --min_scale=0.5 \ + --max_scale=2.0 \ + --ignore_label=255 \ + --num_classes=21 \ + --model=deeplab_v3_s16 \ + --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL \ + --save_steps=1500 \ + --keep_checkpoint_max=200 >log 2>&1 & +``` + ### 结果 +#### Ascend处理器环境运行 + - 使用s16结构训练VOCaug ```bash @@ -400,6 +431,17 @@ Epoch time: 5962.164, per step time: 542.015 ... ``` +#### CPU环境运行 + +- 使用s16结构训练VOCtrain + +```bash +epoch: 1 step: 1, loss is 3.655448 +epoch: 2 step: 1, loss is 1.5531876 +epoch: 3 step: 1, loss is 1.5099041 +... +``` + ## 评估过程 ### 用法 @@ -470,7 +512,7 @@ python ${train_code_path}/eval.py --data_root=/PATH/TO/DATA \ | 损失 | 0.0065883575 | | 速度 | 31毫秒/步（单卡，s8）
234毫秒/步（8卡，s8） | | 微调检查点 | 443M （.ckpt文件） | -| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/office/cv/deeplabv3) | +| 脚本 | [链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeplabv3) | # 随机情况说明 diff --git a/model_zoo/official/cv/deeplabv3/src/data/get_dataset_lst.py b/model_zoo/official/cv/deeplabv3/src/data/get_dataset_lst.py new file mode 100644 index 0000000000..7d0cdb271c --- /dev/null +++ b/model_zoo/official/cv/deeplabv3/src/data/get_dataset_lst.py @@ -0,0 +1,142 @@ +import argparse +import os + +import numpy as np +import scipy.io +from PIL import Image + +parser = argparse.ArgumentParser('dataset list generator') +parser.add_argument("--data_dir", type=str, default='./', help='where dataset stored.') + +args, _ = parser.parse_known_args() + +data_dir = args.data_dir +print("Data dir is:", data_dir) + +# +VOC_IMG_DIR = os.path.join(data_dir, 'VOCdevkit/VOC2012/JPEGImages') +VOC_ANNO_DIR = os.path.join(data_dir, 'VOCdevkit/VOC2012/SegmentationClass') +VOC_ANNO_GRAY_DIR = os.path.join(data_dir, 'VOCdevkit/VOC2012/SegmentationClassGray') +VOC_TRAIN_TXT = os.path.join(data_dir, 'VOCdevkit/VOC2012/ImageSets/Segmentation/train.txt') +VOC_VAL_TXT = os.path.join(data_dir, 'VOCdevkit/VOC2012/ImageSets/Segmentation/val.txt') + +SBD_ANNO_DIR = os.path.join(data_dir, 'benchmark_RELEASE/dataset/cls') +SBD_IMG_DIR = os.path.join(data_dir, 'benchmark_RELEASE/dataset/img') +SBD_ANNO_PNG_DIR = os.path.join(data_dir, 'benchmark_RELEASE/dataset/cls_png') +SBD_ANNO_GRAY_DIR = os.path.join(data_dir, 'benchmark_RELEASE/dataset/cls_png_gray') +SBD_TRAIN_TXT = os.path.join(data_dir, 'benchmark_RELEASE/dataset/train.txt') +SBD_VAL_TXT = os.path.join(data_dir, 'benchmark_RELEASE/dataset/val.txt') + +VOC_TRAIN_LST_TXT = os.path.join(data_dir, 'voc_train_lst.txt') +VOC_VAL_LST_TXT = os.path.join(data_dir, 'voc_val_lst.txt') +VOC_AUG_TRAIN_LST_TXT = os.path.join(data_dir, 'vocaug_train_lst.txt') + + +def __get_data_list(data_list_file): + with open(data_list_file, mode='r') as f: + return f.readlines() + + +def conv_voc_colorpng_to_graypng(): + if not os.path.exists(VOC_ANNO_GRAY_DIR): + os.makedirs(VOC_ANNO_GRAY_DIR) + + for ann in os.listdir(VOC_ANNO_DIR): + ann_im = Image.open(os.path.join(VOC_ANNO_DIR, ann)) + ann_im = Image.fromarray(np.array(ann_im)) + ann_im.save(os.path.join(VOC_ANNO_GRAY_DIR, ann)) + + +def __gen_palette(cls_nums=256): + palette = np.zeros((cls_nums, 3), dtype=np.uint8) + for i in range(cls_nums): + lbl = i + j = 0 + while lbl: + palette[i, 0] |= (((lbl >> 0) & 1) << (7 - j)) + palette[i, 1] |= (((lbl >> 1) & 1) << (7 - j)) + palette[i, 2] |= (((lbl >> 2) & 1) << (7 - j)) + lbl >>= 3 + j += 1 + return palette.flatten() + + +def conv_sbd_mat_to_png(): + if not os.path.exists(SBD_ANNO_PNG_DIR): + os.makedirs(SBD_ANNO_PNG_DIR) + if not os.path.exists(SBD_ANNO_GRAY_DIR): + os.makedirs(SBD_ANNO_GRAY_DIR) + + palette = __gen_palette() + for an in os.listdir(SBD_ANNO_DIR): + img_id = an[:-4] + mat = scipy.io.loadmat(os.path.join(SBD_ANNO_DIR, an)) + anno = mat['GTcls'][0]['Segmentation'][0].astype(np.uint8) + anno_png = Image.fromarray(anno) + # save to gray png + anno_png.save(os.path.join(SBD_ANNO_GRAY_DIR, img_id + '.png')) + # save to color png use palette + anno_png.putpalette(palette) + anno_png.save(os.path.join(SBD_ANNO_PNG_DIR, img_id + '.png')) + + +def create_voc_train_lst_txt(): + voc_train_data_lst = __get_data_list(VOC_TRAIN_TXT) + with open(VOC_TRAIN_LST_TXT, mode='w') as f: + for id_ in voc_train_data_lst: + id_ = id_.strip() + img_ = os.path.join(VOC_IMG_DIR, id_ + '.jpg') + anno_ = os.path.join(VOC_ANNO_GRAY_DIR, id_ + '.png') + f.write(img_ + ' ' + anno_ + '\n') + + +def create_voc_val_lst_txt(): + voc_val_data_lst = __get_data_list(VOC_VAL_TXT) + with open(VOC_VAL_LST_TXT, mode='w') as f: + for id_ in voc_val_data_lst: + id_ = id_.strip() + img_ = os.path.join(VOC_IMG_DIR, id_ + '.jpg') + anno_ = os.path.join(VOC_ANNO_GRAY_DIR, id_ + '.png') + f.write(img_ + ' ' + anno_ + '\n') + + +def create_voc_train_aug_lst_txt(): + voc_train_data_lst = __get_data_list(VOC_TRAIN_TXT) + voc_val_data_lst = __get_data_list(VOC_VAL_TXT) + + sbd_train_data_lst = __get_data_list(SBD_TRAIN_TXT) + sbd_val_data_lst = __get_data_list(SBD_VAL_TXT) + + with open(VOC_AUG_TRAIN_LST_TXT, mode='w') as f: + for id_ in sbd_train_data_lst + sbd_val_data_lst: + if id_ in voc_train_data_lst + voc_val_data_lst: + continue + id_ = id_.strip() + img_ = os.path.join(SBD_ANNO_DIR, id_ + '.jpg') + anno_ = os.path.join(SBD_ANNO_GRAY_DIR, id_ + '.png') + f.write(img_ + ' ' + anno_ + '\n') + + for id_ in voc_train_data_lst: + id_ = id_.strip() + img_ = os.path.join(VOC_IMG_DIR, id_ + '.jpg') + anno_ = os.path.join(VOC_ANNO_GRAY_DIR, id_ + '.png') + f.write(img_ + ' ' + anno_ + '\n') + + +if __name__ == '__main__': + print('converting voc color png to gray png ...') + conv_voc_colorpng_to_graypng() + print('converting done.') + + create_voc_train_lst_txt() + print('generating voc train list success.') + + create_voc_val_lst_txt() + print('generating voc val list success.') + + print('converting sbd annotations to png ...') + conv_sbd_mat_to_png() + print('converting done') + + create_voc_train_aug_lst_txt() + print('generating voc train aug list success.')