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README.md
ResNet-50-THOR Example
Description
This is an example of training ResNet-50 V1.5 with ImageNet2012 dataset by second-order optimizer THOR. THOR is a novel approximate seond-order optimization method in MindSpore. With fewer iterations, THOR can finish ResNet-50 V1.5 training in 72 minutes to top-1 accuracy of 75.9% using 8 Ascend 910, which is much faster than SGD with Momentum.
Requirements
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Install MindSpore.
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Download the dataset ImageNet2012
Unzip the ImageNet2012 dataset to any path you want and the folder structure should include train and eval dataset as follows:
. ├── ilsvrc # train dataset └── ilsvrc_eval # infer dataset
Example structure
.
├── resnet_thor
├── README.md
├── src
├── crossentropy.py # CrossEntropy loss function
├── config.py # parameter configuration
├── resnet50.py # resnet50 backbone
├── dataset_helper.py # dataset help for minddata dataset
├── grad_reducer_thor.py # grad reducer for thor
├── model_thor.py # model
├── resnet_thor.py # resnet50_thor backone
├── thor.py # thor
├── thor_layer.py # thor layer
└── dataset_imagenet.py # data preprocessing
├── scripts
├── run_distribute_train.sh # launch distributed training(8 pcs)
└── run_eval.sh # launch infering
├── eval.py # infer script
└── train.py # train script
Parameter configuration
Parameters for both training and inference can be set in config.py.
"class_num": 1000, # dataset class number
"batch_size": 32, # batch size of input tensor
"loss_scale": 128, # loss scale
"momentum": 0.9, # momentum of THOR optimizer
"weight_decay": 5e-4, # weight decay
"epoch_size": 45, # 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_steps": 5004, # the step interval between two checkpoints. By default, the checkpoint will be saved every epoch
"keep_checkpoint_max": 20, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"frequency": 834, # the step interval to update second-order information matrix
Running the example
Train
Usage
# distributed training
Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [DEVICE_NUM]
Launch
# distributed training example(8 pcs)
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
About rank_table.json, you can refer to the distributed training tutorial.
Result
Training result will be stored in the example path, whose folder name begins with "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 4.4182425
epoch: 2 step: 5004, loss is 3.740064
epoch: 3 step: 5004, loss is 4.0546017
epoch: 4 step: 5004, loss is 3.7598825
epoch: 5 step: 5004, loss is 3.3744206
......
Infer
Usage
# infer
Usage: sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]
Launch
# infer with checkpoint
sh run_eval.sh dataset/ilsvrc_eval train_parallel0/resnet-42_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.
result: {'acc': 0.759503041} ckpt=train_parallel0/resnet-42_5004.ckpt