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fix word missing in readme.txt and checkpoint directory

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pull/3490/head
yangyongjie 5 years ago
parent 7fbed0ce94
commit 8056e18f84
3 changed files with 31 additions and 31 deletions
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56
model_zoo/official/cv/warpctc/README.md
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@ -28,7 +28,7 @@ These is an example of training Warpctc with self-generated captcha image datase
```shell
.
└──warpct
└──warpctc
├── README.md
├── script
├── run_distribute_train.sh # launch distributed training in Ascend(8 pcs)
@ -55,18 +55,18 @@ These is an example of training Warpctc with self-generated captcha image datase
Parameters for both training and evaluation can be set in config.py.
```
"max_captcha_digits": 4, # max number of digits in each
"captcha_width": 160, # width of captcha images
"captcha_height": 64, # height of capthca images
"batch_size": 64, # batch size of input tensor
"epoch_size": 30, # only valid for taining, which is always 1 for inference
"hidden_size": 512, # hidden size in LSTM layers
"learning_rate": 0.01, # initial learning rate
"momentum": 0.9 # momentum of SGD optimizer
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_steps": 98, # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
"keep_checkpoint_max": 30, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint
"max_captcha_digits": 4, # max number of digits in each
"captcha_width": 160, # width of captcha images
"captcha_height": 64, # height of capthca images
"batch_size": 64, # batch size of input tensor
"epoch_size": 30, # only valid for taining, which is always 1 for inference
"hidden_size": 512, # hidden size in LSTM layers
"learning_rate": 0.01, # initial learning rate
"momentum": 0.9 # momentum of SGD optimizer
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_steps": 97, # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
"keep_checkpoint_max": 30, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./checkpoint", # path to save checkpoint
```
## Running the example
@ -77,13 +77,13 @@ Parameters for both training and evaluation can be set in config.py.
```
# distributed training in Ascend
Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
Usage: bash run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
# distributed training in GPU
Usage: sh run_distribute_train_for_gpu.sh [RANK_SIZE] [DATASET_PATH]
Usage: bash run_distribute_train_for_gpu.sh [RANK_SIZE] [DATASET_PATH]
# standalone training
Usage: sh run_standalone_train.sh [DATASET_PATH] [PLATFORM]
Usage: bash run_standalone_train.sh [DATASET_PATH] [PLATFORM]
```
@ -91,16 +91,16 @@ Usage: sh run_standalone_train.sh [DATASET_PATH] [PLATFORM]
```
# distribute training example in Ascend
sh run_distribute_train.sh rank_table.json ../data/train
bash run_distribute_train.sh rank_table.json ../data/train
# distribute training example in GPU
sh run_distribute_train.sh 8 ../data/train
bash run_distribute_train_for_gpu.sh 8 ../data/train
# standalone training example in Ascend
sh run_standalone_train.sh ../data/train Ascend
bash run_standalone_train.sh ../data/train Ascend
# standalone training example in GPU
sh run_standalone_train.sh ../data/train GPU
bash run_standalone_train.sh ../data/train GPU
```
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
@ -111,11 +111,11 @@ Training result will be stored in folder `scripts`, whose name begins with "trai
```
# distribute training result(8 pcs)
Epoch: [ 1/ 30], step: [ 98/ 98], loss: [0.5853/0.5853], time: [376813.7944]
Epoch: [ 2/ 30], step: [ 98/ 98], loss: [0.4007/0.4007], time: [75882.0951]
Epoch: [ 3/ 30], step: [ 98/ 98], loss: [0.0921/0.0921], time: [75150.9385]
Epoch: [ 4/ 30], step: [ 98/ 98], loss: [0.1472/0.1472], time: [75135.0193]
Epoch: [ 5/ 30], step: [ 98/ 98], loss: [0.0186/0.0186], time: [75199.5809]
Epoch: [ 1/ 30], step: [ 97/ 97], loss: [0.5853/0.5853], time: [376813.7944]
Epoch: [ 2/ 30], step: [ 97/ 97], loss: [0.4007/0.4007], time: [75882.0951]
Epoch: [ 3/ 30], step: [ 97/ 97], loss: [0.0921/0.0921], time: [75150.9385]
Epoch: [ 4/ 30], step: [ 97/ 97], loss: [0.1472/0.1472], time: [75135.0193]
Epoch: [ 5/ 30], step: [ 97/ 97], loss: [0.0186/0.0186], time: [75199.5809]
...
```
@ -126,17 +126,17 @@ Epoch: [ 5/ 30], step: [ 98/ 98], loss: [0.0186/0.0186], time: [75199.5809]
```
# evaluation
Usage: sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [PLATFORM]
Usage: bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [PLATFORM]
```
#### Launch
```
# evaluation example in Ascend
sh run_eval.sh ../data/test warpctc-30-98.ckpt Ascend
bash run_eval.sh ../data/test warpctc-30-97.ckpt Ascend
# evaluation example in GPU
sh run_eval.sh ../data/test warpctc-30-98.ckpt GPU
bash run_eval.sh ../data/test warpctc-30-97.ckpt GPU
```
> checkpoint can be produced in training process.

4
model_zoo/official/cv/warpctc/src/config.py
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@ -25,7 +25,7 @@ config = EasyDict({
"learning_rate": 0.01,
"momentum": 0.9,
"save_checkpoint": True,
"save_checkpoint_steps": 98,
"save_checkpoint_steps": 97,
"keep_checkpoint_max": 30,
"save_checkpoint_path": "./",
"save_checkpoint_path": "./checkpoint",
})

2
model_zoo/official/cv/warpctc/train.py
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@ -101,6 +101,6 @@ if __name__ == '__main__':
if cf.save_checkpoint:
config_ck = CheckpointConfig(save_checkpoint_steps=cf.save_checkpoint_steps,
keep_checkpoint_max=cf.keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix="warpctc", directory=cf.save_checkpoint_path, config=config_ck)
ckpt_cb = ModelCheckpoint(prefix="warpctc", directory=cf.save_checkpoint_path + str(rank), config=config_ck)
callbacks.append(ckpt_cb)
model.train(cf.epoch_size, dataset, callbacks=callbacks)

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