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!8635 Add SqueezeNet to model zoo 

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# Contents
- [SqueezeNet Description](#squeezenet-description)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Features](#features)
- [Mixed Precision](#mixed-precision)
- [Environment Requirements](#environment-requirements)
- [Quick Start](#quick-start)
- [Script Description](#script-description)
- [Script and Sample Code](#script-and-sample-code)
- [Script Parameters](#script-parameters)
- [Training Process](#training-process)
- [Evaluation Process](#evaluation-process)
- [Model Description](#model-description)
- [Performance](#performance)
- [Evaluation Performance](#evaluation-performance)
- [Inference Performance](#inference-performance)
- [How to use](#how-to-use)
- [Inference](#inference)
- [Continue Training on the Pretrained Model](#continue-training-on-the-pretrained-model)
- [Transfer Learning](#transfer-learning)
- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
# [SqueezeNet Description](#contents)
SqueezeNet is a lightweight and efficient CNN model proposed by Han et al., published in ICLR-2017. SqueezeNet has 50x fewer parameters than AlexNet, but the model performance (accuracy) is close to AlexNet.
These are examples of training SqueezeNet/SqueezeNet_Residual with CIFAR-10/ImageNet dataset in MindSpore. SqueezeNet_Residual adds residual operation on the basis of SqueezeNet, which can improve the accuracy of the model without increasing the amount of parameters.
[Paper](https://arxiv.org/abs/1602.07360): Forrest N. Iandola and Song Han and Matthew W. Moskewicz and Khalid Ashraf and William J. Dally and Kurt Keutzer. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
# [Model Architecture](#contents)
SqueezeNet is composed of fire modules. A fire module mainly includes two layers of convolution operations: one is the squeeze layer using a **1x1 convolution** kernel; the other is an expand layer using a mixture of **1x1** and **3x3 convolution** kernels.
# [Dataset](#contents)
Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
- Dataset size：175M，60,000 32*32 colorful images in 10 classes
- Train：146M，50,000 images
- Test：29M，10,000 images
- Data format：binary files
- Note：Data will be processed in src/dataset.py
Dataset used: [ImageNet2012](http://www.image-net.org/)
- Dataset size: 125G, 1250k colorful images in 1000 classes
- Train: 120G, 1200k images
- Test: 5G, 50k images
- Data format: RGB images.
- Note: Data will be processed in src/dataset.py
# [Features](#contents)
## Mixed Precision
The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) 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](#contents)
- 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](https://www.mindspore.cn/install/en)
- For more information, please check the resources below：
- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
# [Quick Start](#contents)
After installing MindSpore via the official website, you can start training and evaluation as follows:
- runing on Ascend
```
# distributed training
Usage: sh scripts/run_distribute_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
# standalone training
Usage: sh scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
# run evaluation example
Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
```
- running on GPU
```
# distributed training example
sh scripts/run_distribute_train_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
# standalone training example
sh scripts/run_standalone_train_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
# run evaluation example
sh scripts/run_eval_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
```
# [Script Description](#contents)
## [Script and Sample Code](#contents)
```
.
└── squeezenet
├── README.md
├── scripts
├── run_distribute_train.sh # launch ascend distributed training(8 pcs)
├── run_standalone_train.sh # launch ascend standalone training(1 pcs)
├── run_distribute_train_gpu.sh # launch gpu distributed training(8 pcs)
├── run_standalone_train_gpu.sh # launch gpu standalone training(1 pcs)
├── run_eval.sh # launch ascend evaluation
└── run_eval_gpu.sh # launch gpu evaluation
├── src
├── config.py # parameter configuration
├── dataset.py # data preprocessing
├── CrossEntropySmooth.py # loss definition for ImageNet dataset
├── lr_generator.py # generate learning rate for each step
└── squeezenet.py # squeezenet architecture, including squeezenet and squeezenet_residual
├── train.py # train net
├── eval.py # eval net
└── export.py # export checkpoint files into geir/onnx
```
## [Script Parameters](#contents)
Parameters for both training and evaluation can be set in config.py
- config for SqueezeNet, CIFAR-10 dataset
```py
"class_num": 10, # dataset class num
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 1e-4, # weight decay
"epoch_size": 120, # only valid for taining, which is always 1 for inference
"pretrain_epoch_size": 0, # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
"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 step
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint
"warmup_epochs": 5, # number of warmup epoch
"lr_decay_mode": "poly" # decay mode for generating learning rate
"lr_init": 0, # initial learning rate
"lr_end": 0, # final learning rate
"lr_max": 0.01, # maximum learning rate
```
- config for SqueezeNet, ImageNet dataset
```py
"class_num": 1000, # dataset class num
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 7e-5, # weight decay
"epoch_size": 200, # only valid for taining, which is always 1 for inference
"pretrain_epoch_size": 0, # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
"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 step
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "poly" # decay mode for generating learning rate
"use_label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"lr_init": 0, # initial learning rate
"lr_end": 0, # final learning rate
"lr_max": 0.01, # maximum learning rate
```
- config for SqueezeNet_Residual, CIFAR-10 dataset
```py
"class_num": 10, # dataset class num
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 1e-4, # weight decay
"epoch_size": 150, # only valid for taining, which is always 1 for inference
"pretrain_epoch_size": 0, # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
"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 step
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint
"warmup_epochs": 5, # number of warmup epoch
"lr_decay_mode": "linear" # decay mode for generating learning rate
"lr_init": 0, # initial learning rate
"lr_end": 0, # final learning rate
"lr_max": 0.01, # maximum learning rate
```
- config for SqueezeNet_Residual, ImageNet dataset
```py
"class_num": 1000, # dataset class num
"batch_size": 32, # batch size of input tensor
"loss_scale": 1024, # loss scale
"momentum": 0.9, # momentum
"weight_decay": 7e-5, # weight decay
"epoch_size": 300, # only valid for taining, which is always 1 for inference
"pretrain_epoch_size": 0, # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
"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 step
"keep_checkpoint_max": 10, # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./", # path to save checkpoint
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate
"use_label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"lr_init": 0, # initial learning rate
"lr_end": 0, # final learning rate
"lr_max": 0.01, # maximum learning rate
```
For more configuration details, please refer the script `config.py`.
## [Training Process](#contents)
### Usage
#### Running on Ascend
```
# distributed training
Usage: sh scripts/run_distribute_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
# standalone training
Usage: sh scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
```
For distributed training, a hccl configuration file with JSON format needs to be created in advance.
Please follow the instructions in the link [hccl_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
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.
#### Running on GPU
```
# distributed training example
sh scripts/run_distribute_train_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
# standalone training example
sh scripts/run_standalone_train_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
```
### Result
- Training SqueezeNet with CIFAR-10 dataset
```
# standalone training result
epoch: 1 step 1562, loss is 1.7103254795074463
epoch: 2 step 1562, loss is 2.06101131439209
epoch: 3 step 1562, loss is 1.5594401359558105
epoch: 4 step 1562, loss is 1.4127278327941895
epoch: 5 step 1562, loss is 1.2140142917633057
...
```
- Training SqueezeNet with ImageNet dataset
```
# distribute training result(8 pcs)
epoch: 1 step 5004, loss is 5.716324329376221
epoch: 2 step 5004, loss is 5.350603103637695
epoch: 3 step 5004, loss is 4.580031394958496
epoch: 4 step 5004, loss is 4.784664154052734
epoch: 5 step 5004, loss is 4.136358261108398
...
```
- Training SqueezeNet_Residual with CIFAR-10 dataset
```
# standalone training result
epoch: 1 step 1562, loss is 2.298271656036377
epoch: 2 step 1562, loss is 2.2728664875030518
epoch: 3 step 1562, loss is 1.9493038654327393
epoch: 4 step 1562, loss is 1.7553865909576416
epoch: 5 step 1562, loss is 1.3370063304901123
...
```
- Training SqueezeNet_Residual with ImageNet dataset
```
# distribute training result(8 pcs)
epoch: 1 step 5004, loss is 6.802495002746582
epoch: 2 step 5004, loss is 6.386072158813477
epoch: 3 step 5004, loss is 5.513605117797852
epoch: 4 step 5004, loss is 5.312961101531982
epoch: 5 step 5004, loss is 4.888848304748535
...
```
## [Evaluation Process](#contents)
### Usage
#### Running on Ascend
```
# evaluation
Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
```
```
# evaluation example
sh scripts/run_eval.sh squeezenet cifar10 0 ~/cifar-10-verify-bin train/squeezenet_cifar10-120_1562.ckpt
```
checkpoint can be produced in training process.
#### Running on GPU
```
sh scripts/run_eval_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
```
### Result
Evaluation result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.
- Evaluating SqueezeNet with CIFAR-10 dataset
```
result: {'top_1_accuracy': 0.8896233974358975, 'top_5_accuracy': 0.9965945512820513}
```
- Evaluating SqueezeNet with ImageNet dataset
```
result: {'top_1_accuracy': 0.5851472471190781, 'top_5_accuracy': 0.8105393725992317}
```
- Evaluating SqueezeNet_Residual with CIFAR-10 dataset
```
result: {'top_1_accuracy': 0.9077524038461539, 'top_5_accuracy': 0.9969951923076923}
```
- Evaluating SqueezeNet_Residual with ImageNet dataset
```
result: {'top_1_accuracy': 0.6094950384122919, 'top_5_accuracy': 0.826324423815621}
```
# [Model Description](#contents)
## [Performance](#contents)
### Evaluation Performance
#### SqueezeNet on CIFAR-10
| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | SqueezeNet |
| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G |
| uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | CIFAR-10 |
| Training Parameters | epoch=120, steps=195, batch_size=32, lr=0.01 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 0.0496 |
| Speed | 1pc: 16.7 ms/step; 8pcs: 17.0 ms/step |
| Total time | 1pc: 55.5 mins; 8pcs: 15.0 mins |
| Parameters (M) | 4.8 |
| Checkpoint for Fine tuning | 6.4M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
#### SqueezeNet on ImageNet
| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | SqueezeNet |
| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G |
| uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| Training Parameters | epoch=200, steps=5004, batch_size=32, lr=0.01 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 2.9150 |
| Speed | 8pcs: 19.9 ms/step |
| Total time | 8pcs: 5.2 hours |
| Parameters (M) | 4.8 |
| Checkpoint for Fine tuning | 13.3M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
#### SqueezeNet_Residual on CIFAR-10
| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | SqueezeNet_Residual |
| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G |
| uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | CIFAR-10 |
| Training Parameters | epoch=150, steps=195, batch_size=32, lr=0.01 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 0.0641 |
| Speed | 1pc: 16.9 ms/step; 8pcs: 17.3 ms/step |
| Total time | 1pc: 68.6 mins; 8pcs: 20.9 mins |
| Parameters (M) | 4.8 |
| Checkpoint for Fine tuning | 6.5M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
#### SqueezeNet_Residual on ImageNet
| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | SqueezeNet_Residual |
| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G |
| uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| Training Parameters | epoch=300, steps=5004, batch_size=32, lr=0.01 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 2.9040 |
| Speed | 8pcs: 20.2 ms/step |
| Total time | 8pcs: 8.0 hours |
| Parameters (M) | 4.8 |
| Checkpoint for Fine tuning | 15.3M (.ckpt file) |
| Scripts | [squeezenet script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/squeezenet) |
### Inference Performance
#### SqueezeNet on CIFAR-10
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | SqueezeNet |
| Resource | Ascend 910 |
| Uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | CIFAR-10 |
| batch_size | 32 |
| outputs | probability |
| Accuracy | 1pc: 89.0%; 8pcs: 84.4% |
#### SqueezeNet on ImageNet
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | SqueezeNet |
| Resource | Ascend 910 |
| Uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| batch_size | 32 |
| outputs | probability |
| Accuracy | 8pcs: 58.5%(TOP1), 81.1%(TOP5) |
#### SqueezeNet_Residual on CIFAR-10
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | SqueezeNet_Residual |
| Resource | Ascend 910 |
| Uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | CIFAR-10 |
| batch_size | 32 |
| outputs | probability |
| Accuracy | 1pc: 90.8%; 8pcs: 87.4% |
#### SqueezeNet_Residual on ImageNet
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | SqueezeNet_Residual |
| Resource | Ascend 910 |
| Uploaded Date | 11/06/2020 (month/day/year) |
| MindSpore Version | 1.0.0 |
| Dataset | ImageNet |
| batch_size | 32 |
| outputs | probability |
| Accuracy | 8pcs: 60.9%(TOP1), 82.6%(TOP5) |
## [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/training/en/master/advanced_use/migrate_3rd_scripts.html). Following the steps below, this is a simple example:
- Running on Ascend
```
# Set context
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target='Ascend',
device_id=device_id)
# Load unseen dataset for inference
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=False,
batch_size=config.batch_size,
target='Ascend')
# Define model
net = squeezenet(num_classes=config.class_num)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
model = Model(net,
loss_fn=loss,
metrics={'top_1_accuracy', 'top_5_accuracy'})
# 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)
```
- Running on GPU:
```
# Set context
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target='GPU',
device_id=device_id)
# Load unseen dataset for inference
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=False,
batch_size=config.batch_size,
target='GPU')
# Define model
net = squeezenet(num_classes=config.class_num)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
model = Model(net,
loss_fn=loss,
metrics={'top_1_accuracy', 'top_5_accuracy'})
# 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
- running on Ascend
```
# Load dataset
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=True,
repeat_num=1,
batch_size=config.batch_size,
target='Ascend')
step_size = dataset.get_dataset_size()
# define net
net = squeezenet(num_classes=config.class_num)
# load checkpoint
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(net, param_dict)
# init lr
lr = get_lr(lr_init=config.lr_init,
lr_end=config.lr_end,
lr_max=config.lr_max,
total_epochs=config.epoch_size,
warmup_epochs=config.warmup_epochs,
pretrain_epochs=config.pretrain_epoch_size,
steps_per_epoch=step_size,
lr_decay_mode=config.lr_decay_mode)
lr = Tensor(lr)
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
loss_scale = FixedLossScaleManager(config.loss_scale,
drop_overflow_update=False)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
lr,
config.momentum,
config.weight_decay,
config.loss_scale,
use_nesterov=True)
model = Model(net,
loss_fn=loss,
optimizer=opt,
loss_scale_manager=loss_scale,
metrics={'acc'},
amp_level="O2",
keep_batchnorm_fp32=False)
# Set callbacks
config_ck = CheckpointConfig(
save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
keep_checkpoint_max=config.keep_checkpoint_max)
time_cb = TimeMonitor(data_size=step_size)
ckpt_cb = ModelCheckpoint(prefix=args_opt.net + '_' + args_opt.dataset,
directory=ckpt_save_dir,
config=config_ck)
loss_cb = LossMonitor()
# Start training
model.train(config.epoch_size - config.pretrain_epoch_size, dataset,
callbacks=[time_cb, ckpt_cb, loss_cb])
print("train success")
```
- running on GPU
```
# Load dataset
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=True,
repeat_num=1,
batch_size=config.batch_size,
target='Ascend')
step_size = dataset.get_dataset_size()
# define net
net = squeezenet(num_classes=config.class_num)
# load checkpoint
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(net, param_dict)
# init lr
lr = get_lr(lr_init=config.lr_init,
lr_end=config.lr_end,
lr_max=config.lr_max,
total_epochs=config.epoch_size,
warmup_epochs=config.warmup_epochs,
pretrain_epochs=config.pretrain_epoch_size,
steps_per_epoch=step_size,
lr_decay_mode=config.lr_decay_mode)
lr = Tensor(lr)
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
lr,
config.momentum,
config.weight_decay,
use_nesterov=True)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
# Set callbacks
config_ck = CheckpointConfig(
save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
keep_checkpoint_max=config.keep_checkpoint_max)
time_cb = TimeMonitor(data_size=step_size)
ckpt_cb = ModelCheckpoint(prefix=args_opt.net + '_' + args_opt.dataset,
directory=ckpt_save_dir,
config=config_ck)
loss_cb = LossMonitor()
# Start training
model.train(config.epoch_size - config.pretrain_epoch_size, dataset,
callbacks=[time_cb, ckpt_cb, loss_cb])
print("train success")
```
### Transfer Learning
To be added.
# [Description of Random Situation](#contents)
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
# [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

95
model_zoo/official/cv/squeezenet/eval.py Executable file
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""eval squeezenet."""
import os
import argparse
from mindspore import context
from mindspore.common import set_seed
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.CrossEntropySmooth import CrossEntropySmooth
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
help='Model.')
parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
args_opt = parser.parse_args()
set_seed(1)
if args_opt.net == "squeezenet":
from src.squeezenet import SqueezeNet as squeezenet
if args_opt.dataset == "cifar10":
from src.config import config1 as config
from src.dataset import create_dataset_cifar as create_dataset
else:
from src.config import config2 as config
from src.dataset import create_dataset_imagenet as create_dataset
else:
from src.squeezenet import SqueezeNet_Residual as squeezenet
if args_opt.dataset == "cifar10":
from src.config import config3 as config
from src.dataset import create_dataset_cifar as create_dataset
else:
from src.config import config4 as config
from src.dataset import create_dataset_imagenet as create_dataset
if __name__ == '__main__':
target = args_opt.device_target
# init context
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target=target,
device_id=device_id)
# create dataset
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=False,
batch_size=config.batch_size,
target=target)
step_size = dataset.get_dataset_size()
# define net
net = squeezenet(num_classes=config.class_num)
# load checkpoint
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
# define loss
if args_opt.dataset == "imagenet":
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropySmooth(sparse=True,
reduction='mean',
smooth_factor=config.label_smooth_factor,
num_classes=config.class_num)
else:
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
# define model
model = Model(net,
loss_fn=loss,
metrics={'top_1_accuracy', 'top_5_accuracy'})
# eval model
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)

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model_zoo/official/cv/squeezenet/export.py Executable file
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
##############export checkpoint file into air and onnx models#################
python export.py --net squeezenet --dataset cifar10 --checkpoint_path squeezenet_cifar10-120_1562.ckpt
"""
import argparse
import numpy as np
from mindspore import Tensor
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
help='Model.')
parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
args_opt = parser.parse_args()
if args_opt.net == "squeezenet":
from src.squeezenet import SqueezeNet as squeezenet
else:
from src.squeezenet import SqueezeNet_Residual as squeezenet
if args_opt.dataset == "cifar10":
num_classes = 10
else:
num_classes = 1000
onnx_filename = args_opt.net + '_' + args_opt.dataset + '.onnx'
air_filename = args_opt.net + '_' + args_opt.dataset + '.air'
net = squeezenet(num_classes=num_classes)
assert args_opt.checkpoint_path is not None, "checkpoint_path is None."
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
input_arr = Tensor(np.zeros([1, 3, 227, 227], np.float32))
export(net, input_arr, file_name=onnx_filename, file_format="ONNX")
export(net, input_arr, file_name=air_filename, file_format="AIR")

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model_zoo/official/cv/squeezenet/scripts/run_distribute_train.sh Executable file
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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ $# != 4 ] && [ $# != 5 ]
then
echo "Usage: sh scripts/run_distribute_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
then
echo "error: the selected net is neither squeezenet nor squeezenet_residual"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $3)
PATH2=$(get_real_path $4)
if [ $# == 5 ]
then
PATH3=$(get_real_path $5)
fi
if [ ! -f $PATH1 ]
then
echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
exit 1
fi
if [ ! -d $PATH2 ]
then
echo "error: DATASET_PATH=$PATH2 is not a directory"
exit 1
fi
if [ $# == 5 ] && [ ! -f $PATH3 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH3 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=8
export RANK_SIZE=8
export RANK_TABLE_FILE=$PATH1
export SERVER_ID=0
rank_start=$((DEVICE_NUM * SERVER_ID))
for((i=0; i<${DEVICE_NUM}; i++))
do
export DEVICE_ID=${i}
export RANK_ID=$((rank_start + i))
rm -rf ./train_parallel$i
mkdir ./train_parallel$i
cp ./train.py ./train_parallel$i
cp -r ./src ./train_parallel$i
cd ./train_parallel$i || exit
echo "start training for rank $RANK_ID, device $DEVICE_ID"
env > env.log
if [ $# == 4 ]
then
python train.py --net=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
fi
if [ $# == 5 ]
then
python train.py --net=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log &
fi
cd ..
done

85
model_zoo/official/cv/squeezenet/scripts/run_distribute_train_gpu.sh Executable file
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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ $# != 3 ] && [ $# != 4 ]
then
echo "Usage: sh scripts/run_distribute_train_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
then
echo "error: the selected net is neither squeezenet nor squeezenet_residual"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $3)
if [ $# == 4 ]
then
PATH2=$(get_real_path $4)
fi
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ $# == 5 ] && [ ! -f $PATH2 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=8
export RANK_SIZE=8
rm -rf ./train_parallel
mkdir ./train_parallel
cp ./train.py ./train_parallel
cp -r ./src ./train_parallel
cd ./train_parallel || exit
if [ $# == 3 ]
then
mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
python train.py --net=$1 --dataset=$2 --run_distribute=True \
--device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 &> log &
fi
if [ $# == 4 ]
then
mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
python train.py --net=$1 --dataset=$2 --run_distribute=True \
--device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
fi

76
model_zoo/official/cv/squeezenet/scripts/run_eval.sh Executable file
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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ $# != 5 ]
then
echo "Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]"
exit 1
fi
if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
then
echo "error: the selected net is neither squeezenet nor squeezenet_residual"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $4)
PATH2=$(get_real_path $5)
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ ! -f $PATH2 ]
then
echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=$3
export RANK_SIZE=$DEVICE_NUM
export RANK_ID=0
if [ -d "eval" ];
then
rm -rf ./eval
fi
mkdir ./eval
cp ./eval.py ./eval
cp -r ./src ./eval
cd ./eval || exit
env > env.log
echo "start evaluation for device $DEVICE_ID"
python eval.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
cd ..

76
model_zoo/official/cv/squeezenet/scripts/run_eval_gpu.sh Executable file
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@ -0,0 +1,76 @@
#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ $# != 5 ]
then
echo "Usage: sh scripts/run_eval_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]"
exit 1
fi
if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
then
echo "error: the selected net is neither squeezenet nor squeezenet_residual"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $4)
PATH2=$(get_real_path $5)
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ ! -f $PATH2 ]
then
echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=$3
export RANK_SIZE=$DEVICE_NUM
export RANK_ID=0
if [ -d "eval" ];
then
rm -rf ./eval
fi
mkdir ./eval
cp ./eval.py ./eval
cp -r ./src ./eval
cd ./eval || exit
env > env.log
echo "start evaluation for device $DEVICE_ID"
python eval.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --checkpoint_path=$PATH2 --device_target="GPU" &> log &
cd ..

87
model_zoo/official/cv/squeezenet/scripts/run_standalone_train.sh Executable file
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@ -0,0 +1,87 @@
#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ $# != 4 ] && [ $# != 5 ]
then
echo "Usage: sh scripts/run_standalone_train.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
then
echo "error: the selected net is neither squeezenet nor squeezenet_residual"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $4)
if [ $# == 5 ]
then
PATH2=$(get_real_path $5)
fi
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ $# == 5 ] && [ ! -f $PATH2 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=$3
export RANK_ID=0
export RANK_SIZE=1
if [ -d "train" ];
then
rm -rf ./train
fi
mkdir ./train
cp ./train.py ./train
cp -r ./src ./train
cd ./train || exit
echo "start training for device $DEVICE_ID"
env > env.log
if [ $# == 4 ]
then
python train.py --net=$1 --dataset=$2 --dataset_path=$PATH1 &> log &
fi
if [ $# == 5 ]
then
python train.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
fi
cd ..

87
model_zoo/official/cv/squeezenet/scripts/run_standalone_train_gpu.sh Executable file
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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ $# != 4 ] && [ $# != 5 ]
then
echo "Usage: sh scripts/run_standalone_train_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
exit 1
fi
if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
then
echo "error: the selected net is neither squeezenet nor squeezenet_residual"
exit 1
fi
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH1=$(get_real_path $4)
if [ $# == 5 ]
then
PATH2=$(get_real_path $5)
fi
if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi
if [ $# == 5 ] && [ ! -f $PATH2 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=$3
export RANK_ID=0
export RANK_SIZE=1
if [ -d "train" ];
then
rm -rf ./train
fi
mkdir ./train
cp ./train.py ./train
cp -r ./src ./train
cd ./train || exit
echo "start training for device $DEVICE_ID"
env > env.log
if [ $# == 4 ]
then
python train.py --net=$1 --dataset=$2 --device_target="GPU" --dataset_path=$PATH1 &> log &
fi
if [ $# == 5 ]
then
python train.py --net=$1 --dataset=$2 --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
fi
cd ..

38
model_zoo/official/cv/squeezenet/src/CrossEntropySmooth.py Executable file
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""define loss function for network"""
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.nn.loss.loss import _Loss
from mindspore.ops import functional as F
from mindspore.ops import operations as P
class CrossEntropySmooth(_Loss):
"""CrossEntropy"""
def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
super(CrossEntropySmooth, self).__init__()
self.onehot = P.OneHot()
self.sparse = sparse
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
self.ce = nn.SoftmaxCrossEntropyWithLogits(reduction=reduction)
def construct(self, logit, label):
if self.sparse:
label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
loss = self.ce(logit, label)
return loss

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model_zoo/official/cv/squeezenet/src/config.py Executable file
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
network config setting, will be used in train.py and eval.py
"""
from easydict import EasyDict as ed
# config for squeezenet, cifar10
config1 = ed({
"class_num": 10,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 120,
"pretrain_epoch_size": 0,
"save_checkpoint": True,
"save_checkpoint_epochs": 1,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 5,
"lr_decay_mode": "poly",
"lr_init": 0,
"lr_end": 0,
"lr_max": 0.01
})
# config for squeezenet, imagenet
config2 = ed({
"class_num": 1000,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 7e-5,
"epoch_size": 200,
"pretrain_epoch_size": 0,
"save_checkpoint": True,
"save_checkpoint_epochs": 1,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 0,
"lr_decay_mode": "poly",
"use_label_smooth": True,
"label_smooth_factor": 0.1,
"lr_init": 0,
"lr_end": 0,
"lr_max": 0.01
})
# config for squeezenet_residual, cifar10
config3 = ed({
"class_num": 10,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 150,
"pretrain_epoch_size": 0,
"save_checkpoint": True,
"save_checkpoint_epochs": 1,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 5,
"lr_decay_mode": "linear",
"lr_init": 0,
"lr_end": 0,
"lr_max": 0.01
})
# config for squeezenet_residual, imagenet
config4 = ed({
"class_num": 1000,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 7e-5,
"epoch_size": 300,
"pretrain_epoch_size": 0,
"save_checkpoint": True,
"save_checkpoint_epochs": 1,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"warmup_epochs": 0,
"lr_decay_mode": "cosine",
"use_label_smooth": True,
"label_smooth_factor": 0.1,
"lr_init": 0,
"lr_end": 0,
"lr_max": 0.01
})

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
create train or eval dataset.
"""
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
from mindspore.communication.management import init, get_rank, get_group_size
def create_dataset_cifar(dataset_path,
do_train,
repeat_num=1,
batch_size=32,
target="Ascend"):
"""
create a train or evaluate cifar10 dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
target(str): the device target. Default: Ascend
Returns:
dataset
"""
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
init()
rank_id = get_rank()
device_num = get_group_size()
if device_num == 1:
ds = de.Cifar10Dataset(dataset_path,
num_parallel_workers=8,
shuffle=True)
else:
ds = de.Cifar10Dataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=device_num,
shard_id=rank_id)
# define map operations
if do_train:
trans = [
C.RandomCrop((32, 32), (4, 4, 4, 4)),
C.RandomHorizontalFlip(prob=0.5),
C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
C.Resize((227, 227)),
C.Rescale(1.0 / 255.0, 0.0),
C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
C.CutOut(112),
C.HWC2CHW()
]
else:
trans = [
C.Resize((227, 227)),
C.Rescale(1.0 / 255.0, 0.0),
C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(operations=type_cast_op,
input_columns="label",
num_parallel_workers=8)
ds = ds.map(operations=trans,
input_columns="image",
num_parallel_workers=8)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
def create_dataset_imagenet(dataset_path,
do_train,
repeat_num=1,
batch_size=32,
target="Ascend"):
"""
create a train or eval imagenet dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
target(str): the device target. Default: Ascend
Returns:
dataset
"""
if target == "Ascend":
device_num, rank_id = _get_rank_info()
else:
init()
rank_id = get_rank()
device_num = get_group_size()
if device_num == 1:
ds = de.ImageFolderDataset(dataset_path,
num_parallel_workers=8,
shuffle=True)
else:
ds = de.ImageFolderDataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=device_num,
shard_id=rank_id)
image_size = 227
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
# define map operations
if do_train:
trans = [
C.RandomCropDecodeResize(image_size,
scale=(0.08, 1.0),
ratio=(0.75, 1.333)),
C.RandomHorizontalFlip(prob=0.5),
C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
C.Normalize(mean=mean, std=std),
C.CutOut(112),
C.HWC2CHW()
]
else:
trans = [
C.Decode(),
C.Resize((256, 256)),
C.CenterCrop(image_size),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(operations=type_cast_op,
input_columns="label",
num_parallel_workers=8)
ds = ds.map(operations=trans,
input_columns="image",
num_parallel_workers=8)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
def _get_rank_info():
"""
get rank size and rank id
"""
rank_size = int(os.environ.get("RANK_SIZE", 1))
if rank_size > 1:
rank_size = get_group_size()
rank_id = get_rank()
else:
rank_size = 1
rank_id = 0
return rank_size, rank_id

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""learning rate generator"""
import math
import numpy as np
def get_lr(lr_init, lr_end, lr_max, total_epochs, warmup_epochs,
pretrain_epochs, steps_per_epoch, lr_decay_mode):
"""
generate learning rate array
Args:
lr_init(float): init learning rate
lr_end(float): end learning rate
lr_max(float): max learning rate
total_epochs(int): total epoch of training
warmup_epochs(int): number of warmup epochs
pretrain_epochs(int): number of pretrain epochs
steps_per_epoch(int): steps of one epoch
lr_decay_mode(string): learning rate decay mode,
including steps, poly, linear or cosine
Returns:
np.array, learning rate array
"""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
warmup_steps = steps_per_epoch * warmup_epochs
pretrain_steps = steps_per_epoch * pretrain_epochs
decay_steps = total_steps - warmup_steps
if lr_decay_mode == 'steps':
decay_epoch_index = [
0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps
]
for i in range(total_steps):
if i < decay_epoch_index[0]:
lr = lr_max
elif i < decay_epoch_index[1]:
lr = lr_max * 0.1
elif i < decay_epoch_index[2]:
lr = lr_max * 0.01
else:
lr = lr_max * 0.001
lr_each_step.append(lr)
elif lr_decay_mode == 'poly':
for i in range(total_steps):
if i < warmup_steps:
lr = linear_warmup_lr(i, warmup_steps, lr_max, lr_init)
else:
base = (1.0 - (i - warmup_steps) / decay_steps)
lr = lr_max * base * base
lr_each_step.append(lr)
elif lr_decay_mode == 'linear':
for i in range(total_steps):
if i < warmup_steps:
lr = linear_warmup_lr(i, warmup_steps, lr_max, lr_init)
else:
lr = lr_max - (lr_max - lr_end) * (i -
warmup_steps) / decay_steps
lr_each_step.append(lr)
elif lr_decay_mode == 'cosine':
for i in range(total_steps):
if i < warmup_steps:
lr = linear_warmup_lr(i, warmup_steps, lr_max, lr_init)
else:
linear_decay = (total_steps - i) / decay_steps
cosine_decay = 0.5 * (
1 + math.cos(math.pi * 2 * 0.47 *
(i - warmup_steps) / decay_steps))
decayed = linear_decay * cosine_decay + 0.00001
lr = lr_max * decayed
lr_each_step.append(lr)
else:
raise NotImplementedError(
'Learning rate decay mode [{:s}] cannot be recognized'.format(
lr_decay_mode))
lr_each_step = np.array(lr_each_step).astype(np.float32)
learning_rate = lr_each_step[pretrain_steps:]
return learning_rate
def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
lr_inc = (base_lr - init_lr) / warmup_steps
lr = init_lr + lr_inc * current_step
return lr

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Squeezenet."""
import mindspore.nn as nn
from mindspore.common import initializer as weight_init
from mindspore.ops import operations as P
class Fire(nn.Cell):
def __init__(self, inplanes, squeeze_planes, expand1x1_planes,
expand3x3_planes):
super(Fire, self).__init__()
self.inplanes = inplanes
self.squeeze = nn.Conv2d(inplanes,
squeeze_planes,
kernel_size=1,
has_bias=True)
self.squeeze_activation = nn.ReLU()
self.expand1x1 = nn.Conv2d(squeeze_planes,
expand1x1_planes,
kernel_size=1,
has_bias=True)
self.expand1x1_activation = nn.ReLU()
self.expand3x3 = nn.Conv2d(squeeze_planes,
expand3x3_planes,
kernel_size=3,
pad_mode='same',
has_bias=True)
self.expand3x3_activation = nn.ReLU()
self.concat = P.Concat(axis=1)
def construct(self, x):
x = self.squeeze_activation(self.squeeze(x))
return self.concat((self.expand1x1_activation(self.expand1x1(x)),
self.expand3x3_activation(self.expand3x3(x))))
class SqueezeNet(nn.Cell):
r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
accuracy with 50x fewer parameters and <0.5MB model size"
<https://arxiv.org/abs/1602.07360>`_ paper.
Get SqueezeNet neural network.
Args:
num_classes (int): Class number.
Returns:
Cell, cell instance of SqueezeNet neural network.
Examples:
>>> net = SqueezeNet(10)
"""
def __init__(self, num_classes=10):
super(SqueezeNet, self).__init__()
self.features = nn.SequentialCell([
nn.Conv2d(3,
96,
kernel_size=7,
stride=2,
pad_mode='valid',
has_bias=True),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2),
Fire(96, 16, 64, 64),
Fire(128, 16, 64, 64),
Fire(128, 32, 128, 128),
nn.MaxPool2d(kernel_size=3, stride=2),
Fire(256, 32, 128, 128),
Fire(256, 48, 192, 192),
Fire(384, 48, 192, 192),
Fire(384, 64, 256, 256),
nn.MaxPool2d(kernel_size=3, stride=2),
Fire(512, 64, 256, 256),
])
# Final convolution is initialized differently from the rest
self.final_conv = nn.Conv2d(512,
num_classes,
kernel_size=1,
has_bias=True)
self.dropout = nn.Dropout(keep_prob=0.5)
self.relu = nn.ReLU()
self.mean = P.ReduceMean(keep_dims=True)
self.flatten = nn.Flatten()
self.custom_init_weight()
def custom_init_weight(self):
"""
Init the weight of Conv2d in the net.
"""
for _, cell in self.cells_and_names():
if isinstance(cell, nn.Conv2d):
if cell is self.final_conv:
cell.weight.set_data(
weight_init.initializer('normal', cell.weight.shape,
cell.weight.dtype))
else:
cell.weight.set_data(
weight_init.initializer('he_uniform',
cell.weight.shape,
cell.weight.dtype))
if cell.bias is not None:
cell.bias.set_data(
weight_init.initializer('zeros', cell.bias.shape,
cell.bias.dtype))
def construct(self, x):
x = self.features(x)
x = self.dropout(x)
x = self.final_conv(x)
x = self.relu(x)
x = self.mean(x, (2, 3))
x = self.flatten(x)
return x
class SqueezeNet_Residual(nn.Cell):
r"""SqueezeNet with simple bypass model architecture from the `"SqueezeNet:
AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
<https://arxiv.org/abs/1602.07360>`_ paper.
Get SqueezeNet with simple bypass neural network.
Args:
num_classes (int): Class number.
Returns:
Cell, cell instance of SqueezeNet with simple bypass neural network.
Examples:
>>> net = SqueezeNet_Residual(10)
"""
def __init__(self, num_classes=10):
super(SqueezeNet_Residual, self).__init__()
self.conv1 = nn.Conv2d(3,
96,
kernel_size=7,
stride=2,
pad_mode='valid',
has_bias=True)
self.fire2 = Fire(96, 16, 64, 64)
self.fire3 = Fire(128, 16, 64, 64)
self.fire4 = Fire(128, 32, 128, 128)
self.fire5 = Fire(256, 32, 128, 128)
self.fire6 = Fire(256, 48, 192, 192)
self.fire7 = Fire(384, 48, 192, 192)
self.fire8 = Fire(384, 64, 256, 256)
self.fire9 = Fire(512, 64, 256, 256)
# Final convolution is initialized differently from the rest
self.conv10 = nn.Conv2d(512, num_classes, kernel_size=1, has_bias=True)
self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2)
self.add = P.TensorAdd()
self.dropout = nn.Dropout(keep_prob=0.5)
self.mean = P.ReduceMean(keep_dims=True)
self.flatten = nn.Flatten()
self.custom_init_weight()
def custom_init_weight(self):
"""
Init the weight of Conv2d in the net.
"""
for _, cell in self.cells_and_names():
if isinstance(cell, nn.Conv2d):
if cell is self.conv10:
cell.weight.set_data(
weight_init.initializer('normal', cell.weight.shape,
cell.weight.dtype))
else:
cell.weight.set_data(
weight_init.initializer('xavier_uniform',
cell.weight.shape,
cell.weight.dtype))
if cell.bias is not None:
cell.bias.set_data(
weight_init.initializer('zeros', cell.bias.shape,
cell.bias.dtype))
def construct(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.fire2(x)
x = self.add(x, self.fire3(x))
x = self.fire4(x)
x = self.max_pool2d(x)
x = self.add(x, self.fire5(x))
x = self.fire6(x)
x = self.add(x, self.fire7(x))
x = self.fire8(x)
x = self.max_pool2d(x)
x = self.add(x, self.fire9(x))
x = self.dropout(x)
x = self.conv10(x)
x = self.relu(x)
x = self.mean(x, (2, 3))
x = self.flatten(x)
return x

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""train squeezenet."""
import os
import argparse
from mindspore import context
from mindspore import Tensor
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.model import Model
from mindspore.context import ParallelMode
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.common import set_seed
from src.lr_generator import get_lr
from src.CrossEntropySmooth import CrossEntropySmooth
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
help='Model.')
parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
args_opt = parser.parse_args()
set_seed(1)
if args_opt.net == "squeezenet":
from src.squeezenet import SqueezeNet as squeezenet
if args_opt.dataset == "cifar10":
from src.config import config1 as config
from src.dataset import create_dataset_cifar as create_dataset
else:
from src.config import config2 as config
from src.dataset import create_dataset_imagenet as create_dataset
else:
from src.squeezenet import SqueezeNet_Residual as squeezenet
if args_opt.dataset == "cifar10":
from src.config import config3 as config
from src.dataset import create_dataset_cifar as create_dataset
else:
from src.config import config4 as config
from src.dataset import create_dataset_imagenet as create_dataset
if __name__ == '__main__':
target = args_opt.device_target
ckpt_save_dir = config.save_checkpoint_path
# init context
context.set_context(mode=context.GRAPH_MODE,
device_target=target)
if args_opt.run_distribute:
if target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(device_id=device_id,
enable_auto_mixed_precision=True)
context.set_auto_parallel_context(
device_num=args_opt.device_num,
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
init()
# GPU target
else:
init()
context.set_auto_parallel_context(
device_num=get_group_size(),
parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(
get_rank()) + "/"
# create dataset
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=True,
repeat_num=1,
batch_size=config.batch_size,
target=target)
step_size = dataset.get_dataset_size()
# define net
net = squeezenet(num_classes=config.class_num)
# load checkpoint
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(net, param_dict)
# init lr
lr = get_lr(lr_init=config.lr_init,
lr_end=config.lr_end,
lr_max=config.lr_max,
total_epochs=config.epoch_size,
warmup_epochs=config.warmup_epochs,
pretrain_epochs=config.pretrain_epoch_size,
steps_per_epoch=step_size,
lr_decay_mode=config.lr_decay_mode)
lr = Tensor(lr)
# define loss
if args_opt.dataset == "imagenet":
if not config.use_label_smooth:
config.label_smooth_factor = 0.0
loss = CrossEntropySmooth(sparse=True,
reduction='mean',
smooth_factor=config.label_smooth_factor,
num_classes=config.class_num)
else:
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
# define opt, model
if target == "Ascend":
loss_scale = FixedLossScaleManager(config.loss_scale,
drop_overflow_update=False)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
lr,
config.momentum,
config.weight_decay,
config.loss_scale,
use_nesterov=True)
model = Model(net,
loss_fn=loss,
optimizer=opt,
loss_scale_manager=loss_scale,
metrics={'acc'},
amp_level="O2",
keep_batchnorm_fp32=False)
else:
# GPU target
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
lr,
config.momentum,
config.weight_decay,
use_nesterov=True)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
# define callbacks
time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
if config.save_checkpoint:
config_ck = CheckpointConfig(
save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
keep_checkpoint_max=config.keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix=args_opt.net + '_' + args_opt.dataset,
directory=ckpt_save_dir,
config=config_ck)
cb += [ckpt_cb]
# train model
model.train(config.epoch_size - config.pretrain_epoch_size,
dataset,
callbacks=cb)