@@ -51,11 +51,11 @@ Parameters for both training and inference can be set in config.py.
...
@@ -51,11 +51,11 @@ Parameters for both training and inference can be set in config.py.
"save_checkpoint_steps": 195, # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
"save_checkpoint_steps": 195, # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint
"save_checkpoint_path": "./", # path to save checkpoint
"warmup_epochs": 5, # number of warmup epoch
"lr_decay_mode": "poly" # decay mode can be selected in steps, ploy and default
"lr_init": 0.01, # initial learning rate
"lr_init": 0.01, # initial learning rate
"lr_end": 0.00001, # final learning rate
"lr_end": 0.00001, # final learning rate
"lr_max": 0.1, # maximum learning rate
"lr_max": 0.1, # maximum learning rate
"warmup_epochs": 5, # number of warmup epoch
"lr_decay_mode": "poly" # decay mode can be selected in steps, ploy and default
```
```
## Running the example
## Running the example
...
@@ -65,7 +65,7 @@ Parameters for both training and inference can be set in config.py.
...
@@ -65,7 +65,7 @@ Parameters for both training and inference can be set in config.py.
#### Usage
#### Usage
```
```
# distribute training
# distributed training
Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
# standalone training
# standalone training
...
@@ -90,7 +90,7 @@ sh run_standalone_train.sh ~/cifar-10-batches-bin
...
@@ -90,7 +90,7 @@ sh run_standalone_train.sh ~/cifar-10-batches-bin
Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
Parameters for both training and inference can be set in config.py.
```
"class_num": 1001, # dataset class number
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum optimizer
"weight_decay": 1e-4, # weight decay
"epoch_size": 90, # only valid for taining, which is always 1 for inference
"buffer_size": 1000, # number of queue size in data preprocessing
"image_height": 224, # image height
"image_width": 224, # image width
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_epochs": 1, # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine", # decay mode for generating learning rate
"label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"lr_init": 0, # initial learning rate
"lr_max": 0.1, # maximum learning rate
```
## Running the example
### Train
#### Usage
```
# distributed training
Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
# standalone training
Usage: sh run_standalone_train.sh [DATASET_PATH]
```
#### Launch
```bash
# distributed training example(8 pcs)
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
# standalone training example(1 pcs)
sh run_standalone_train.sh dataset/ilsvrc
```
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
#### Result
Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
```
# distribute training result(8 pcs)
epoch: 1 step: 5004, loss is 4.8995576
epoch: 2 step: 5004, loss is 3.9235563
epoch: 3 step: 5004, loss is 3.833077
epoch: 4 step: 5004, loss is 3.2795618
epoch: 5 step: 5004, loss is 3.1978393
```
### Infer
#### Usage
```
# infer
Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
```
#### Launch
```bash
# infer with checkpoint
sh run_infer.sh dataset/ilsvrc_eval train_parallel0/resnet-90_5004.ckpt
```
> checkpoint can be produced in training process.
#### Result
Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
