From 983d6f16a7d1ed73ceccb74cb3f8a5419cf3fefb Mon Sep 17 00:00:00 2001 From: CaoJian Date: Mon, 24 Aug 2020 16:19:16 +0800 Subject: [PATCH] model_zoo README.md format change for googlenet --- model_zoo/official/cv/googlenet/README.md | 495 ++++++++++++++-------- 1 file changed, 325 insertions(+), 170 deletions(-) diff --git a/model_zoo/official/cv/googlenet/README.md b/model_zoo/official/cv/googlenet/README.md index 8cfbc52c4..ae7d7a4b0 100644 --- a/model_zoo/official/cv/googlenet/README.md +++ b/model_zoo/official/cv/googlenet/README.md @@ -48,8 +48,7 @@ Dataset used: [CIFAR-10]() - Train：146M，50,000 images - Test：29.3M，10,000 images - Data format：binary files - - Note：Data will be processed in dataset.py - + - Note：Data will be processed in src/dataset.py # [Features](#contents) @@ -66,7 +65,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil - Hardware（Ascend/GPU） - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. - Framework - - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/) + - [MindSpore](https://www.mindspore.cn/install/en) - For more information, please check the resources below： - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html) @@ -77,16 +76,45 @@ 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: -```python -# run training example -python train.py > train.log 2>&1 & +- runing on Ascend -# run distributed training example -Ascend: sh scripts/run_train.sh rank_table.json OR GPU: sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7 + ```python + # run training example + python train.py > train.log 2>&1 & + + # run distributed training example + sh scripts/run_train.sh rank_table.json + + # run evaluation example + python eval.py > eval.log 2>&1 & + OR + sh run_eval.sh + ``` + + For distributed training, a hccl configuration file with JSON format needs to be created in advance. + + Please follow the instructions in the link below: + + https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools. + +- running on GPU + + For running on GPU, please change `device_target` from `Ascend` to `GPU` in configuration file src/config.py + + ```python + # run training example + export CUDA_VISIBLE_DEVICES=0 + python train.py > train.log 2>&1 & + + # run distributed training example + sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7 + + # run evaluation example + python eval.py --checkpoint_path=[CHECKPOINT_PATH] > eval.log 2>&1 & + OR + sh run_eval_gpu.sh [CHECKPOINT_PATH] + ``` -# run evaluation example -python eval.py > eval.log 2>&1 & OR Ascend: sh run_eval.sh OR GPU: sh run_eval_gpu.sh -``` @@ -100,8 +128,10 @@ python eval.py > eval.log 2>&1 & OR Ascend: sh run_eval.sh OR GPU: sh run_eval ├── googlenet ├── README.md // descriptions about googlenet ├── scripts - │ ├──run_train.sh // shell script for distributed - │ ├──run_eval.sh // shell script for evaluation + │ ├──run_train.sh // shell script for distributed on Ascend + │ ├──run_train_gpu.sh // shell script for distributed on GPU + │ ├──run_eval.sh // shell script for evaluation on Ascend + │ ├──run_eval_gpu.sh // shell script for evaluation on GPU ├── src │ ├──dataset.py // creating dataset │ ├──googlenet.py // googlenet architecture @@ -113,98 +143,153 @@ python eval.py > eval.log 2>&1 & OR Ascend: sh run_eval.sh OR GPU: sh run_eval ## [Script Parameters](#contents) -```python -Major parameters in train.py and config.py are: - ---data_path: The absolute full path to the train and evaluation datasets. ---epoch_size: Total training epochs. ---batch_size: Training batch size. ---lr_init: Initial learning rate. ---num_classes: The number of classes in the training set. ---weight_decay: Weight decay value. ---image_height: Image height used as input to the model. ---image_width: Image width used as input the model. ---pre_trained: Whether training from scratch or training based on the - pre-trained model.Optional values are True, False. ---device_target: Device where the code will be implemented. Optional values - are "Ascend", "GPU". ---device_id: Device ID used to train or evaluate the dataset. Ignore it - when you use run_train.sh for distributed training. ---checkpoint_path: The absolute full path to the checkpoint file saved - after training. ---onnx_filename: File name of the onnx model used in export.py. ---air_filename: File name of the air model used in export.py. -``` +Parameters for both training and evaluation can be set in config.py + +- config for GoogleNet, CIFAR-10 dataset + + ```python + 'pre_trained': 'False' # whether training based on the pre-trained model + 'nump_classes': 10 # the number of classes in the dataset + 'lr_init': 0.1 # initial learning rate + 'batch_size': 128 # training batch size + 'epoch_size': 125 # total training epochs + 'momentum': 0.9 # momentum + 'weight_decay': 5e-4 # weight decay value + 'buffer_size': 10 # buffer size + 'image_height': 224 # image height used as input to the model + 'image_width': 224 # image width used as input to the model + 'data_path': './cifar10' # absolute full path to the train and evaluation datasets + 'device_target': 'Ascend' # device running the program + 'device_id': 4 # device ID used to train or evaluate the dataset. Ignore it when you use run_train.sh for distributed training + 'keep_checkpoint_max': 10 # only keep the last keep_checkpoint_max checkpoint + 'checkpoint_path': './train_googlenet_cifar10-125_390.ckpt' # the absolute full path to save the checkpoint file + 'onnx_filename': 'googlenet.onnx' # file name of the onnx model used in export.py + 'geir_filename': 'googlenet.geir' # file name of the geir model used in export.py + ``` ## [Training Process](#contents) ### Training -``` -python train.py > 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: +- running on Ascend + + ``` + python train.py > 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: + + ``` + # grep "loss is " train.log + epoch: 1 step: 390, loss is 1.4842823 + epcoh: 2 step: 390, loss is 1.0897788 + ... + ``` + + The model checkpoint will be saved in the current directory. + +- running on GPU + + ``` + export CUDA_VISIBLE_DEVICES=0 + python train.py > 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 folder `./ckpt_0/` by default. -``` -# grep "loss is " train.log -epoch: 1 step: 390, loss is 1.4842823 -epcoh: 2 step: 390, loss is 1.0897788 -... -``` - -The model checkpoint will be saved in the current directory. ### Distributed Training -``` -Ascend: sh scripts/run_train.sh rank_table.json OR GPU: sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7 -``` - -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: - -``` -# grep "result: " train_parallel*/log -train_parallel0/log:epoch: 1 step: 48, loss is 1.4302931 -train_parallel0/log:epcoh: 2 step: 48, loss is 1.4023874 -... -train_parallel1/log:epoch: 1 step: 48, loss is 1.3458025 -train_parallel1/log:epcoh: 2 step: 48, loss is 1.3729336 -... -... -``` +- running on Ascend + + ``` + sh scripts/run_train.sh rank_table.json + ``` + + 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: + + ``` + # grep "result: " train_parallel*/log + train_parallel0/log:epoch: 1 step: 48, loss is 1.4302931 + train_parallel0/log:epcoh: 2 step: 48, loss is 1.4023874 + ... + train_parallel1/log:epoch: 1 step: 48, loss is 1.3458025 + train_parallel1/log:epcoh: 2 step: 48, loss is 1.3729336 + ... + ... + ``` + +- running on GPU + + ``` + sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7 + ``` + + The above shell script will run distribute training in the background. You can view the results through the file `train/train.log`. ## [Evaluation Process](#contents) ### Evaluation -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/googlenet/train_googlenet_cifar10-125_390.ckpt". - -``` -python eval.py > eval.log 2>&1 & -OR -Ascned: sh scripts/run_eval.sh -OR -GPU: sh scripts/run_eval_gpu.sh -``` - -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: " eval.log -accuracy: {'acc': 0.934} -``` - -Note that for evaluation after distributed training, please set the checkpoint_path to be the last saved checkpoint file such as "username/googlenet/train_parallel0/train_googlenet_cifar10-125_48.ckpt". The accuracy of the test dataset will be as follows: - -``` -# grep "accuracy: " dist.eval.log -accuracy: {'acc': 0.9217} -``` +- evaluation on CIFAR-10 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/googlenet/train_googlenet_cifar10-125_390.ckpt". + + ``` + python eval.py > eval.log 2>&1 & + OR + sh scripts/run_eval.sh + ``` + + 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: " eval.log + accuracy: {'acc': 0.934} + ``` + + Note that for evaluation after distributed training, please set the checkpoint_path to be the last saved checkpoint file such as "username/googlenet/train_parallel0/train_googlenet_cifar10-125_48.ckpt". The accuracy of the test dataset will be as follows: + + ``` + # grep "accuracy: " dist.eval.log + accuracy: {'acc': 0.9217} + ``` + +- evaluation on CIFAR-10 dataset when running on GPU + + 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/googlenet/train/ckpt_0/train_googlenet_cifar10-125_390.ckpt". + + ``` + python eval.py --checkpoint_path=[CHECKPOINT_PATH] > eval.log 2>&1 & + ``` + + 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: " eval.log + accuracy: {'acc': 0.930} + ``` + + OR, + + ``` + sh scripts/run_eval_gpu.sh [CHECKPOINT_PATH] + ``` + + The above python command will run in the background. You can view the results through the file "eval/eval.log". The accuracy of the test dataset will be as follows: + + ``` + # grep "accuracy: " eval/eval.log + accuracy: {'acc': 0.930} + ``` + + # [Model Description](#contents) @@ -212,100 +297,170 @@ accuracy: {'acc': 0.9217} ### Evaluation Performance -| Parameters | GoogleNet | -| -------------------------- | ----------------------------------------------------------- | -| Model Version | Inception V1 | -| Resource | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G | -| uploaded Date | 06/09/2020 (month/day/year) | -| MindSpore Version | 0.3.0-alpha | -| Dataset | CIFAR-10 | -| Training Parameters | epoch=125, steps=390, batch_size = 128, lr=0.1 | -| Optimizer | SGD | -| Loss Function | Softmax Cross Entropy | -| outputs | probability | -| Loss | 0.0016 | -| Speed | 1pc: 79 ms/step; 8pcs: 82 ms/step | -| Total time | 1pc: 63.85 mins; 8pcs: 11.28 mins | -| Parameters (M) | 13.0 | -| Checkpoint for Fine tuning | 43.07M (.ckpt file) | -| Model for inference | 21.50M (.onnx file), 21.60M(.air file) | -| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet | +| Parameters | Ascend | GPU | +| -------------------------- | ----------------------------------------------------------- | ---------------------- | +| Model Version | Inception V1 | Inception V1 | +| Resource | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G | NV SMX2 V100-32G | +| uploaded Date | 06/09/2020 (month/day/year) | 08/20/2020 | +| MindSpore Version | 0.2.0-alpha | 0.6.0-alpha | +| Dataset | CIFAR-10 | CIFAR-10 | +| Training Parameters | epoch=125, steps=390, batch_size = 128, lr=0.1 | epoch=125, steps=390, batch_size=128, lr=0.1 | +| Optimizer | SGD | SGD | +| Loss Function | Softmax Cross Entropy | Softmax Cross Entropy | +| outputs | probability | probobility | +| Loss | 0.0016 | 0.0016 | +| Speed | 1pc: 79 ms/step; 8pcs: 82 ms/step | 1pc: 150 ms/step; 8pcs: 164 ms/step | +| Total time | 1pc: 63.85 mins; 8pcs: 11.28 mins | 1pc: 126.87 mins; 8pcs: 21.65 mins | +| Parameters (M) | 13.0 | 13.0 | +| Checkpoint for Fine tuning | 43.07M (.ckpt file) | 43.07M (.ckpt file) | +| Model for inference | 21.50M (.onnx file), 21.60M(.air file) | | +| Scripts | [googlenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet) | [googlenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/googlenet) | ### Inference Performance -| Parameters | GoogleNet | -| ------------------- | --------------------------- | -| Model Version | Inception V1 | -| Resource | Ascend 910 | -| Uploaded Date | 06/09/2020 (month/day/year) | -| MindSpore Version | 0.2.0-alpha | -| Dataset | CIFAR-10, 10,000 images | -| batch_size | 128 | -| outputs | probability | -| Accuracy | 1pc: 93.4%; 8pcs: 92.17% | -| Model for inference | 21.50M (.onnx file) | +| Parameters | Ascend | GPU | +| ------------------- | --------------------------- | --------------------------- | +| Model Version | Inception V1 | Inception V1 | +| Resource | Ascend 910 | GPU | +| Uploaded Date | 06/09/2020 (month/day/year) | 08/20/2020 (month/day/year) | +| MindSpore Version | 0.2.0-alpha | 0.6.0-alpha | +| Dataset | CIFAR-10, 10,000 images | CIFAR-10, 10,000 images | +| batch_size | 128 | 128 | +| outputs | probability | probability | +| Accuracy | 1pc: 93.4%; 8pcs: 92.17% | 1pc: 93%, 8pcs: 92.89% | +| Model for inference | 21.50M (.onnx file) | | ## [How to use](#contents) ### 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](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/network_migration.html). Following the steps below, this is a simple example: -``` -# Load unseen dataset for inference -dataset = dataset.create_dataset(cfg.data_path, 1, False) - -# Define model -net = GoogleNet(num_classes=cfg.num_classes) -opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, - cfg.momentum, weight_decay=cfg.weight_decay) -loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', - is_grad=False) -model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}) - -# Load pre-trained model -param_dict = load_checkpoint(cfg.checkpoint_path) -load_param_into_net(net, param_dict) -net.set_train(False) - -# Make predictions on the unseen dataset -acc = model.eval(dataset) -print("accuracy: ", acc) -``` +- Running on Ascend + + ``` + # Set context + context.set_context(mode=context.GRAPH_HOME, device_target=cfg.device_target) + context.set_context(device_id=cfg.device_id) + + # Load unseen dataset for inference + dataset = dataset.create_dataset(cfg.data_path, 1, False) + + # Define model + net = GoogleNet(num_classes=cfg.num_classes) + opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, + cfg.momentum, weight_decay=cfg.weight_decay) + loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', + is_grad=False) + model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}) + + # Load pre-trained model + param_dict = load_checkpoint(cfg.checkpoint_path) + load_param_into_net(net, param_dict) + net.set_train(False) + + # Make predictions on the unseen dataset + acc = model.eval(dataset) + print("accuracy: ", acc) + ``` + +- Running on GPU: + + ``` + # Set context + context.set_context(mode=context.GRAPH_HOME, device_target="GPU") + + # Load unseen dataset for inference + dataset = dataset.create_dataset(cfg.data_path, 1, False) + + # Define model + net = GoogleNet(num_classes=cfg.num_classes) + opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, + cfg.momentum, weight_decay=cfg.weight_decay) + loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', + is_grad=False) + model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}) + + # Load pre-trained model + param_dict = load_checkpoint(args_opt.checkpoint_path) + 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 -``` -# Load dataset -dataset = create_dataset(cfg.data_path, cfg.epoch_size) -batch_num = dataset.get_dataset_size() - -# Define model -net = GoogleNet(num_classes=cfg.num_classes) -# Continue training if set pre_trained to be True -if cfg.pre_trained: - param_dict = load_checkpoint(cfg.checkpoint_path) - load_param_into_net(net, param_dict) -lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, - steps_per_epoch=batch_num) -opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), - Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay) -loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False) -model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}, - amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None) - -# Set callbacks -config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, - keep_checkpoint_max=cfg.keep_checkpoint_max) -time_cb = TimeMonitor(data_size=batch_num) -ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory="./", - config=config_ck) -loss_cb = LossMonitor() - -# Start training -model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb]) -print("train success") -``` +- running on Ascend + + ``` + # Load dataset + dataset = create_dataset(cfg.data_path, 1) + batch_num = dataset.get_dataset_size() + + # Define model + net = GoogleNet(num_classes=cfg.num_classes) + # Continue training if set pre_trained to be True + if cfg.pre_trained: + param_dict = load_checkpoint(cfg.checkpoint_path) + load_param_into_net(net, param_dict) + lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, + steps_per_epoch=batch_num) + opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), + Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay) + loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False) + model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}, + amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None) + + # Set callbacks + config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, + keep_checkpoint_max=cfg.keep_checkpoint_max) + time_cb = TimeMonitor(data_size=batch_num) + ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory="./", + config=config_ck) + loss_cb = LossMonitor() + + # Start training + model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb]) + print("train success") + ``` + +- running on GPU + + ``` + # Load dataset + dataset = create_dataset(cfg.data_path, 1) + batch_num = dataset.get_dataset_size() + + # Define model + net = GoogleNet(num_classes=cfg.num_classes) + # Continue training if set pre_trained to be True + if cfg.pre_trained: + param_dict = load_checkpoint(cfg.checkpoint_path) + load_param_into_net(net, param_dict) + lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, + steps_per_epoch=batch_num) + opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), + Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay) + loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False) + model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'}, + amp_level="O2", keep_batchnorm_fp32=True, loss_scale_manager=None) + + # Set callbacks + config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, + keep_checkpoint_max=cfg.keep_checkpoint_max) + time_cb = TimeMonitor(data_size=batch_num) + ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory="./ckpt_" + str(get_rank()) + "/", + config=config_ck) + loss_cb = LossMonitor() + + # Start training + model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb]) + print("train success") + ``` ### Transfer Learning To be added. -- GitLab