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model_zoo README.md format change for googlenet

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CaoJian 2020-08-24 16:19:16 +08:00
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@ -48,8 +48,7 @@ Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
- 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:
Parameters for both training and evaluation can be set in config.py
--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.
```
- 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 &
```
- running on Ascend
The python command above will run in the background, you can view the results through the file `train.log`.
```
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.
After training, you'll get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
- running on GPU
```
# grep "loss is " train.log
epoch: 1 step: 390, loss is 1.4842823
epcoh: 2 step: 390, loss is 1.0897788
...
```
```
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.
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
```
- running on Ascend
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:
```
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
...
...
```
```
# 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".
- evaluation on CIFAR-10 dataset when running on Ascend
```
python eval.py > eval.log 2>&1 &
OR
Ascned: sh scripts/run_eval.sh
OR
GPU: sh scripts/run_eval_gpu.sh
```
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}
```
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:
- evaluation on CIFAR-10 dataset when running on GPU
```
# grep "accuracy: " eval.log
accuracy: {'acc': 0.934}
```
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}
```
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:
OR,
```
# grep "accuracy: " dist.eval.log
accuracy: {'acc': 0.9217}
```
```
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)
- Running on Ascend
# 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'})
```
# 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)
```
# Load pre-trained model
param_dict = load_checkpoint(cfg.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
- Running on GPU:
# Make predictions on the unseen dataset
acc = model.eval(dataset)
print("accuracy: ", acc)
```
```
# 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()
- running on Ascend
# 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)
```
# 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")
```
# 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()
- running on GPU
# Start training
model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
print("train success")
```
```
# 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.