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jikuai
7bdff6d26c
|
1 year ago | |
|---|---|---|
| ascend310_infer | 1 year ago | |
| config | 1 year ago | |
| scripts | 1 year ago | |
| src | 1 year ago | |
| README.md | 1 year ago | |
| README_CN.md | 1 year ago | |
| beit.py | 1 year ago | |
| beit_base_patch16_224.yaml | 1 year ago | |
| create_imagenet2012_label.py | 1 year ago | |
| eval.py | 1 year ago | |
| export.py | 1 year ago | |
| mindspore_hub_conf.py | 1 year ago | |
| postprocess.py | 1 year ago | |
| requirements.txt | 1 year ago | |
| train.py | 1 year ago | |
| train_beit.py | 1 year ago | |
| yacs.py | 1 year ago | |
README.md
Contents
- Vit Description
- Model Architecture
- Dataset
- Features
- Environment Requirements
- Quick Start
- Script Description
- Model Description
- Description of Random Situation
- ModelZoo Homepage
Vit Description
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.
Paper: Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. 2021.
Model Architecture
Specifically, the vit contains transformer encoder. The structure is patch_embeding + n transformer layer + head(FC for classification).
Dataset
Dataset used: ImageNet2012
- Dataset size 224*224 colorful images in 1000 classes
- Train：1,281,167 images
- Test： 50,000 images
- Data format：jpeg
- Note：Data will be processed in dataset.py
- Download the dataset, the directory structure is as follows:
└─dataset
├─train # train dataset, should be .tar file when running on clould
└─val # evaluate dataset
- Data format: RGB images.
- Note: Data will be processed in src/dataset.py
Features
Mixed Precision
The mixed precision training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
Environment Requirements
- Hardware（Ascend/GPU/CPU）
- Prepare hardware environment with Ascend/GPU/CPU processor.
- Framework
- For more information, please check the resources below：
Quick Start
After installing MindSpore via the official website, you can start training and evaluation as follows:
-
running on Ascend
# run training example CONFIG_PATH in ./config/*.yml or *.ymal python train.py --config_path=[CONFIG_PATH] > train.log 2>&1 & # run distributed training example cd scripts; bash run_train_distribute.sh [RANK_TABLE_FILE] [CONFIG_PATH] # run evaluation example cd scripts; bash run_eval.sh [RANK_TABLE_FILE] [CONFIG_PATH] # run inferenct example cd scripts; bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATASET] [DATA_PATH] [DEVICE_ID]For distributed training, a hccl configuration file(RANK_TABLE_FILE) with JSON format needs to be created in advance.
Please follow the instructions in the link below:
https://gitee.com/mindspore/models/tree/r1.5/utils/hccl_tools.
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ModelArts (If you want to run in modelarts, please check the official documentation of modelarts, and you can start training as follows)
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Train imagenet 8p on ModelArts
# (1) Add "config_path='/path_to_code/config/vit_patch32_imagenet2012_config_cloud.yml'" on the website UI interface. # (2) Perform a or b. # a. Set "enable_modelarts=1" on yml file. # Set "output_path" on yml file. # Set "data_path='/cache/data/ImageNet/'" on yml file. # Set other parameters on yml file you need. # b. Add "enable_modelarts=1" on the website UI interface. # Set "output_path" on yml file. # Set "data_path='/cache/data/ImageNet/'" on yml file. # Add other parameters on the website UI interface. # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.) # (4) Set the code directory to "/path/vit" on the website UI interface. # (5) Set the startup file to "train.py" on the website UI interface. # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (7) Create your job. -
Eval imagenet on ModelArts
# (1) Add "config_path='/path_to_code/config/vit_eval.yml'" on the website UI interface. # (2) Perform a or b. # a. Set "enable_modelarts=1" on yml file. # Set "output_path" on yml file. # Set "data_path='/cache/data/ImageNet/'" on yml file. # Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on yml file. # Set "load_path='/cache/checkpoint_path/model.ckpt'" on yml file. # Set other parameters on yml file you need. # b. Add "enable_modelarts=1" on the website UI interface. # Add "dataset_name=imagenet" on the website UI interface. # Add "val_data_path=/cache/data/ImageNet/val/" on the website UI interface. # Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface. # Add "load_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface. # Add other parameters on the website UI interface. # (3) Upload or copy your pretrained model to S3 bucket. # (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.) # (5) Set the code directory to "/path/vit" on the website UI interface. # (6) Set the startup file to "eval.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job. -
Export on ModelArts
# (1) Add "config_path='/path_to_code/config/vit_export.yml'" on the website UI interface. # (2) Perform a or b. # a. Set "enable_modelarts=1" on yml file. # Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on yml file. # Set "load_path='/cache/checkpoint_path/model.ckpt'" on yml file. # Set other parameters on yml file you need. # b. Add "enable_modelarts=1" on the website UI interface. # Add "checkpoint_url=s3://dir_to_trained_ckpt/" on the website UI interface. # Add "load_path=/cache/checkpoint_path/model.ckpt" on the website UI interface. # Add other parameters on the website UI interface. # (3) Upload or copy your trained model to S3 bucket. # (4) Set the code directory to "/path/vit" on the website UI interface. # (5) Set the startup file to "export.py" on the website UI interface. # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (7) Create your job.
-
Script Description
Script and Sample Code
├── model_zoo
├── README.md // descriptions about all the models
├── vit
├── README.md // descriptions about vit
├── ascend310_infer // application for 310 inference
├── scripts
│ ├──run_train_distribute.sh // shell script for distributed on Ascend
│ ├──run_train_standalone.sh // shell script for single node on Ascend
│ ├──run_eval.sh // shell script for evaluation on Ascend
│ ├──run_infer_310.sh // shell script for 310 inference
├── src
│ ├──autoaugment.py // autoaugment for data processing
│ ├──callback.py // logging callback
│ ├──cross_entropy.py // ce loss
│ ├──dataset.py // creating dataset
│ ├──eval_engine.py // eval code
│ ├──logging.py // logging engine
│ ├──lr_generator.py // lr schedule
│ ├──metric.py // metric for eval
│ ├──optimizer.py // user defined optimizer
│ ├──vit.py // model architecture
│ ├──model_utils // cloud depending files, all model zoo shares the same files, not recommend user changing
├── config
│ ├──vit_eval.yml // parameter configuration for eval
│ ├──vit_export.yml // parameter configuration for export
│ ├──vit_patch32_imagenet2012_config.yml // parameter configuration for 8P training
│ ├──vit_patch32_imagenet2012_config_cloud.yml // parameter configuration for 8P training on cloud
│ ├──vit_patch32_imagenet2012_config_standalone.yml // parameter configuration for 1P training
├── train.py // training script
├── eval.py // evaluation script
├── postprogress.py // post process for 310 inference
├── export.py // export checkpoint files into air/mindir
├── create_imagenet2012_label.py // create label for 310 inference
├── requirements.txt // requirements pip list
├── mindspore_hub_conf.py // The mindspore_hub_conf file required for the operation of the hub warehouse
Script Parameters
Parameters for both training and evaluation can be set in config.py
-
config for vit, ImageNet dataset
enable_modelarts: 1 # train on cloud or not # Url for modelarts data_url: "" # S3 dataset path train_url: "" # S3 output path checkpoint_url: "" # S3 pretrain model path output_path: "/cache/train" # output cache, copy to train_url data_path: "/cache/datasets/imagenet" # dataset cache(real path on cloud), copy from data_url load_path: "/cache/model/vit_base_patch32.ckpt" # model cache, copy from checkpoint_url # train datasets dataset_path: '/cache/datasets/imagenet/train' # training dataset train_image_size: 224 # image height and weight used as input to the model interpolation: 'BILINEAR' # dataset interpolation crop_min: 0.05 # random crop min value batch_size: 256 # batch size for train train_num_workers: 14 # parallel work number # eval datasets eval_path: '/cache/datasets/imagenet/val' # eval dataset eval_image_size: 224 # image height and weight used as input to the model eval_batch_size: 256 # batch size for eval eval_interval: 1 # eval interval eval_offset: -1 # eval offset eval_num_workers: 12 # parallel work number # network backbone: 'vit_base_patch32' # backbone type class_num: 1001 # class number, imagenet is 1000+1 vit_config_path: 'src.vit.VitConfig' #vit config path, for advanced user to design transformer based new architecture pretrained: '' # pre-trained model path, '' means not use pre-trained model # lr lr_decay_mode: 'cosine' # lr decay type, support cosine, exp... detail see lr_generator.py lr_init: 0.0 # start lr(epoch 0) lr_max: 0.00355 # max lr lr_min: 0.0 # min lr (max epoch) max_epoch: 300 # max epoch warmup_epochs: 40 # warmup epoch # optimizer opt: 'adamw' # optimizer type beta1: 0.9 # adam beta beta2: 0.999 # adam beta weight_decay: 0.05 # weight decay no_weight_decay_filter: "beta,bias" # which type of weight not use weight decay gc_flag: 0 # use gc or not, not support for user defined opt, support for system defined opt # loss, some parameter also used in datasets loss_scale: 1024 # amp loss scale use_label_smooth: 1 # use label smooth or not label_smooth_factor: 0.1 #label smooth factor mixup: 0.2 # use mixup or not autoaugment: 1 # use autoaugment or not loss_name: "ce_smooth_mixup" #loss type, detail see cross_entropy.py # ckpt save_checkpoint: 1 # save .ckpt(training result) or not save_checkpoint_epochs: 8 # when to save .ckpt keep_checkpoint_max: 3 # max keep ckpt save_checkpoint_path: './outputs' # save path # profiler open_profiler: 0 # do profiling or not. if use profile, you'd better set a small dataset as training dataset and set max_epoch=1
For more configuration details, please refer the script train.py, eval.py, export.py and config/*.yml.
Training Process
Training
-
running on Ascend
python train.py --config_path=[CONFIG_PATH] > train.log 2>&1 &The python command above will run in the background, you can view the results through the file
train.log.After training, you'll get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
# vim log 2021-08-05 15:17:12:INFO:compile time used=143.16s 2021-08-05 15:34:41:INFO:epoch[0], epoch time: 1048.72s, per step time: 0.2096s, loss=6.738676, lr=0.000011, fps=1221.51 2021-08-05 15:52:03:INFO:epoch[1], epoch time: 1041.90s, per step time: 0.2082s, loss=6.381927, lr=0.000022, fps=1229.51 ...The model checkpoint will be saved in the train directory.
Distributed Training
-
running on Ascend
cd scripts bash run_train_distribute.sh [RANK_TABLE_FILE] [CONFIG_PATH]The above shell script will run distribute training in the background. You can view the results through the file
train_parallel[X]/log. The loss value will be achieved as follows:# vim train_parallel0/log # fps depend on cpu processing ability, data processing take times 2021-08-05 20:15:16:INFO:compile time used=191.77s 2021-08-05 20:17:46:INFO:epoch[0], epoch time: 149.10s, per step time: 0.2386s, loss=6.729037, lr=0.000089, fps=8584.97, accuracy=0.014940, eval_cost=1.58 2021-08-05 20:20:11:INFO:epoch[1], epoch time: 143.44s, per step time: 0.2295s, loss=6.786729, lr=0.000177, fps=8923.72, accuracy=0.047000, eval_cost=1.27 ... 2021-08-06 08:18:18:INFO:epoch[299], epoch time: 143.19s, per step time: 0.2291s, loss=2.718115, lr=0.000000, fps=8939.29, accuracy=0.741800, eval_cost=1.28 2021-08-06 08:18:20:INFO:training time used=43384.70s 2021-08-06 08:18:20:INFO:last_metric[0.74206] 2021-08-06 08:18:20:INFO:ip[*.*.*.*], mean_fps[8930.40]
Evaluation Process
Evaluation
-
evaluation on imagenet dataset when running on Ascend
Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/vit/vit_base_patch32.ckpt".
cd scripts bash run_eval.sh [RANK_TABLE_FILE] [CONFIG_PATH]The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:
# grep "accuracy=" eval0/log accuracy=0.741260Note that for evaluation after distributed training, please choose the checkpoint_path to be the saved checkpoint file such as "username/vit/train_parallel0/outputs/vit_base_patch32-288_625.ckpt". The accuracy of the test dataset will be as follows:
# grep "accuracy=" eval0/log accuracy=0.741260
Export Process
Export
Before export model, you must modify the config file, config/export.yml.
The config items you should modify are batch_size and ckpt_file/pretrained.
python export.py --config_path=[CONFIG_PATH]
Inference Process
Inference
Before performing inference, we need to export model first. Air model can only be exported in Ascend 910 environment, mindir model can be exported in any environment.
Current batch_ Size can only be set to 1.
-
inference on imagenet dataset when running on Ascend
Before running the command below, you should modify the config file. The items you should modify are batch_size and val_data_path.
Inference result will be stored in the example path, you can find result like the followings in acc.log.
# Ascend310 inference cd scripts bash run_infer_310.sh [MINDIR_PATH] [NET_TYPE] [DATASET] [DATA_PATH] [DEVICE_ID] Total data: 50000, top1 accuracy: 0.74084, top5 accuracy: 0.91026
Model Description
Performance
Evaluation Performance
Vit on imagenet 1200k images
| Parameters | Ascend |
|---|---|
| Model Version | Vit |
| Resource | Ascend 910; CPU 2.60GHz, 56cores; Memory 314G; OS Euler2.8 |
| uploaded Date | 08/30/2021 (month/day/year) |
| MindSpore Version | 1.3.0 |
| Dataset | 1200k images |
| Training Parameters | epoch=300, steps=625*300, batch_size=256, lr=0.00355 |
| Optimizer | Adamw |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 1.0 |
| Speed | 1pc: 180 ms/step; 8pcs: 185 ms/step |
| Total time | 8pcs: 11 hours |
| Parameters (M) | 86.0 |
| Checkpoint for Fine tuning | 1000M (.ckpt file) |
| Scripts | vit script |
Inference Performance
Vit on 1200k images
| Parameters | Ascend |
|---|---|
| Model Version | Vit |
| Resource | Ascend 910; OS Euler2.8 |
| Uploaded Date | 08/30/2021 (month/day/year) |
| MindSpore Version | 1.3.0 |
| Dataset | 1200k images |
| batch_size | 256 |
| outputs | probability |
| Accuracy | 8pcs: 73.5%-74.6% |
How to use
Inference
If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this Link. Following the steps below, this is a simple example:
-
Running on Ascend
# get args from cfg and get parameter by args args.loss_scale = ... lrs = ... ... # Set context context.set_context(mode=context.GRAPH_HOME, device_target=args.device_target) context.set_context(device_id=args.device_id) # Load unseen dataset for inference dataset = dataset.create_dataset(args.data_path, 1, False) # Define model net = ViT(args.vit_config) opt = AdamW(filter(lambda x: x.requires_grad, net.get_parameters()), lrs, args.beta1, args.beta2, loss_scale=args.loss_scale, weight_decay=cfg.weight_decay) loss = CrossEntropySmoothMixup(smooth_factor=args.label_smooth_factor, num_classes=args.class_num) model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}) # Load pre-trained model param_dict = load_checkpoint(args.pretrained) load_param_into_net(net, param_dict) net.set_train(False) # Make predictions on the unseen dataset acc = model.eval(dataset) print("accuracy: ", acc)
Continue Training on the Pretrained Model
-
running on Ascend
# get args from cfg and get parameter by args args.loss_scale = ... lrs = ... ... # Load dataset dataset = create_dataset(cfg.data_path, 1) batch_num = dataset.get_dataset_size() # Define model net = ViT(args.vit_config) # Continue training if set pre_trained to be True if cfg.pretrained != '': param_dict = load_checkpoint(cfg.pretrained) load_param_into_net(net, param_dict) # Define model opt = AdamW(filter(lambda x: x.requires_grad, net.get_parameters()), lrs, args.beta1, args.beta2, loss_scale=args.loss_scale, weight_decay=cfg.weight_decay) loss = CrossEntropySmoothMixup(smooth_factor=args.label_smooth_factor, num_classes=args.class_num) model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}) # Start training epoch_size = args.max_epoch step_size = dataset.get_dataset_size() # Set callbacks state_cb = StateMonitor(data_size=step_size, tot_batch_size=args.batch_size * device_num, lrs=lrs, eval_interval=args.eval_interval, eval_offset=args.eval_offset, eval_engine=eval_engine, logger=args.logger.info) cb = [state_cb, ] model.train(epoch_size, dataset, callbacks=cb, sink_size=step_size) print("train success")
Description of Random Situation
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
ModelZoo Homepage
Please check the official homepage.
测试vit
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