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!7153 fix some typos in resnet readme file

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Merge pull request !7153 from guoqi/master
pull/7153/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent f5b481d776 9a511d05d4
commit 7126e316bc
1 changed files with 12 additions and 12 deletions
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model_zoo/official/cv/resnet/README.md
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@ -91,7 +91,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
After installing MindSpore via the official website, you can start training and evaluation as follows: After installing MindSpore via the official website, you can start training and evaluation as follows:
- Runing on Ascend - Running on Ascend
``` ```
# distributed training # distributed training
Usage: sh run_distribute_train.sh [resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional) Usage: sh run_distribute_train.sh [resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
@ -104,7 +104,7 @@ Usage: sh run_standalone_train.sh [resnet50|resnet101|se-resnet50] [cifar10|imag
Usage: sh run_eval.sh [resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH] Usage: sh run_eval.sh [resnet50|resnet101|se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
``` ```
- Runing on GPU - Running on GPU
``` ```
# distributed training example # distributed training example
sh run_distribute_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional) sh run_distribute_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
@ -124,7 +124,7 @@ sh run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [C
. .
└──resnet └──resnet
├── README.md ├── README.md
├── script ├── scripts
├── run_distribute_train.sh # launch ascend distributed training(8 pcs) ├── run_distribute_train.sh # launch ascend distributed training(8 pcs)
├── run_parameter_server_train.sh # launch ascend parameter server training(8 pcs) ├── run_parameter_server_train.sh # launch ascend parameter server training(8 pcs)
├── run_eval.sh # launch ascend evaluation ├── run_eval.sh # launch ascend evaluation
@ -136,7 +136,7 @@ sh run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [C
├── src ├── src
├── config.py # parameter configuration ├── config.py # parameter configuration
├── dataset.py # data preprocessing ├── dataset.py # data preprocessing
├── crossentropy.py # loss definition for ImageNet2012 dataset ├── CrossEntropySmooth.py # loss definition for ImageNet2012 dataset
├── lr_generator.py # generate learning rate for each step ├── lr_generator.py # generate learning rate for each step
└── resnet.py # resnet backbone, including resnet50 and resnet101 and se-resnet50 └── resnet.py # resnet backbone, including resnet50 and resnet101 and se-resnet50
├── eval.py # eval net ├── eval.py # eval net
@ -172,7 +172,7 @@ Parameters for both training and evaluation can be set in config.py.
``` ```
"class_num": 1001, # dataset class number "class_num": 1001, # dataset class number
"batch_size": 32, # batch size of input tensor "batch_size": 256, # batch size of input tensor
"loss_scale": 1024, # loss scale "loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum optimizer "momentum": 0.9, # momentum optimizer
"weight_decay": 1e-4, # weight decay "weight_decay": 1e-4, # weight decay
@ -184,10 +184,10 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path "save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch "warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "Linear", # decay mode for generating learning rate "lr_decay_mode": "Linear", # decay mode for generating learning rate
"label_smooth": True, # label smooth "use_label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor "label_smooth_factor": 0.1, # label smooth factor
"lr_init": 0, # initial learning rate "lr_init": 0, # initial learning rate
"lr_max": 0.1, # maximum learning rate "lr_max": 0.8, # maximum learning rate
"lr_end": 0.0, # minimum learning rate "lr_end": 0.0, # minimum learning rate
``` ```
@ -207,7 +207,7 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path "save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch "warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate "lr_decay_mode": "cosine" # decay mode for generating learning rate
"label_smooth": 1, # label_smooth "use_label_smooth": True, # label_smooth
"label_smooth_factor": 0.1, # label_smooth_factor "label_smooth_factor": 0.1, # label_smooth_factor
"lr": 0.1 # base learning rate "lr": 0.1 # base learning rate
``` ```
@ -229,7 +229,7 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path "save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 3, # number of warmup epoch "warmup_epochs": 3, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate "lr_decay_mode": "cosine" # decay mode for generating learning rate
"label_smooth": True, # label_smooth "use_label_smooth": True, # label_smooth
"label_smooth_factor": 0.1, # label_smooth_factor "label_smooth_factor": 0.1, # label_smooth_factor
"lr_init": 0.0, # initial learning rate "lr_init": 0.0, # initial learning rate
"lr_max": 0.3, # maximum learning rate "lr_max": 0.3, # maximum learning rate
@ -421,13 +421,13 @@ result: {'top_5_accuracy': 0.9342589628681178, 'top_1_accuracy': 0.7680657810499
| uploaded Date | 04/01/2020 (month/day/year) ； | 08/01/2020 (month/day/year) | uploaded Date | 04/01/2020 (month/day/year) ； | 08/01/2020 (month/day/year)
| MindSpore Version | 0.1.0-alpha |0.6.0-alpha | | MindSpore Version | 0.1.0-alpha |0.6.0-alpha |
| Dataset | ImageNet2012 | ImageNet2012| | Dataset | ImageNet2012 | ImageNet2012|
| Training Parameters | epoch=90, steps per epoch=5004, batch_size = 32 |epoch=90, steps per epoch=5004, batch_size = 32 | | Training Parameters | epoch=90, steps per epoch=626, batch_size = 256 |epoch=90, steps per epoch=5004, batch_size = 32 |
| Optimizer | Momentum |Momentum| | Optimizer | Momentum |Momentum|
| Loss Function | Softmax Cross Entropy |Softmax Cross Entropy | | Loss Function | Softmax Cross Entropy |Softmax Cross Entropy |
| outputs | probability | probability | | outputs | probability | probability |
| Loss | 1.8464266 | 1.9023 | | Loss | 1.8464266 | 1.9023 |
| Speed | 18.4ms/step（8pcs） |67.1ms/step（8pcs）| | Speed | 118ms/step（8pcs） |67.1ms/step（8pcs）|
| Total time | 139 mins | 500 mins| | Total time | 114 mins | 500 mins|
| Parameters (M) | 25.5 | 25.5 | | Parameters (M) | 25.5 | 25.5 |
| Checkpoint for Fine tuning | 197M (.ckpt file) |197M (.ckpt file) | | Checkpoint for Fine tuning | 197M (.ckpt file) |197M (.ckpt file) |
| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) | | Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |

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