From 9a511d05d4d7d86c679a9838061e6012c2f35cd8 Mon Sep 17 00:00:00 2001 From: guoqi Date: Sat, 10 Oct 2020 15:41:48 +0800 Subject: [PATCH] fix some typos in resnet readme file, and update the performance to 256 batchsize. --- model_zoo/official/cv/resnet/README.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/model_zoo/official/cv/resnet/README.md b/model_zoo/official/cv/resnet/README.md index 726e5c6f62..e7b1c8f0bc 100644 --- a/model_zoo/official/cv/resnet/README.md +++ b/model_zoo/official/cv/resnet/README.md @@ -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: -- Runing on Ascend +- Running on Ascend ``` # distributed training 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] ``` -- Runing on GPU +- Running on GPU ``` # distributed training example 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 ├── README.md - ├── script + ├── scripts ├── run_distribute_train.sh # launch ascend distributed training(8 pcs) ├── run_parameter_server_train.sh # launch ascend parameter server training(8 pcs) ├── run_eval.sh # launch ascend evaluation @@ -136,7 +136,7 @@ sh run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [C ├── src ├── config.py # parameter configuration ├── 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 └── resnet.py # resnet backbone, including resnet50 and resnet101 and se-resnet50 ├── 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 -"batch_size": 32, # batch size of input tensor +"batch_size": 256, # batch size of input tensor "loss_scale": 1024, # loss scale "momentum": 0.9, # momentum optimizer "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 "warmup_epochs": 0, # number of warmup epoch "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 "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 ``` @@ -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 "warmup_epochs": 0, # number of warmup epoch "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 "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 "warmup_epochs": 3, # number of warmup epoch "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 "lr_init": 0.0, # initial 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) | MindSpore Version | 0.1.0-alpha |0.6.0-alpha | | 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| | Loss Function | Softmax Cross Entropy |Softmax Cross Entropy | | outputs | probability | probability | | Loss | 1.8464266 | 1.9023 | -| Speed | 18.4ms/step（8pcs） |67.1ms/step（8pcs）| -| Total time | 139 mins | 500 mins| +| Speed | 118ms/step（8pcs） |67.1ms/step（8pcs）| +| Total time | 114 mins | 500 mins| | Parameters (M) | 25.5 | 25.5 | | 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) |