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modify resnet50-quant readme

tags/v1.1.0
yuchaojie 5 years ago
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      model_zoo/official/cv/resnet50_quant/README.md
  2. +2
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      model_zoo/official/nlp/transformer/README.md

model_zoo/official/cv/resnet50_quant/Readme.md → model_zoo/official/cv/resnet50_quant/README.md View File

@@ -1,6 +1,6 @@
# Contents

- [resnet50 Description](#resnet50-description)
- [ResNet50 Description](#resnet50-description)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Features](#features)
@@ -18,15 +18,15 @@
- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)

# [resnet50 Description](#contents)
# [ResNet50 Description](#contents)

ResNet-50 is a convolutional neural network that is 50 layers deep, which can classify ImageNet image to 1000 object categories with 76% accuracy.

[Paper](https://arxiv.org/abs/1512.03385) Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun."Deep Residual Learning for Image Recognition." He, Kaiming , et al. "Deep Residual Learning for Image Recognition." IEEE Conference on Computer Vision & Pattern Recognition IEEE Computer Society, 2016.
[Paper](https://arxiv.org/abs/1512.03385): Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun."Deep Residual Learning for Image Recognition." He, Kaiming , et al. "Deep Residual Learning for Image Recognition." IEEE Conference on Computer Vision & Pattern Recognition IEEE Computer Society, 2016.

This is the quantitative network of Resnet50.
This is the quantitative network of ResNet50.

# [Model architecture](#contents)
# [Model Architecture](#contents)

The overall network architecture of Resnet50 is show below:

@@ -34,13 +34,20 @@ The overall network architecture of Resnet50 is show below:

# [Dataset](#contents)

Dataset used: [imagenet](http://www.image-net.org/)
Dataset used: [ImageNet2012](http://www.image-net.org/)

- Dataset size: ~125G, 1.2W colorful images in 1000 classes
- Train: 120G, 1.2W images
- Test: 5G, 50000 images
- Data format: RGB images.
- Note: Data will be processed in src/dataset.py
- Dataset size 224*224 colorful images in 1000 classes
- Train：1,281,167 images
- Test： 50,000 images
- Data format：jpeg
- Note：Data will be processed in dataset.py
- Download the dataset, the directory structure is as follows:

```
└─dataset
├─ilsvrc # train dataset
└─validation_preprocess # evaluate dataset
```


# [Features](#contents)
@@ -70,9 +77,10 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
├── Readme.md # descriptions about Resnet50-Quant
├── scripts
│ ├──run_train.sh # shell script for train on Ascend
│ ├──run_infer.sh # shell script for evaluation on Ascend
├── model
│ ├──resnet_quant.py # define the network model of resnet50-quant
│ ├──run_infer.sh # shell script for evaluation on Ascend
├── models
│ ├──resnet_quant.py # define the network model of resnet50-quant
│ ├──resnet_quant_manual.py # define the manually quantized network model of resnet50-quant
├── src
│ ├──config.py # parameter configuration
│ ├──dataset.py # creating dataset
@@ -91,24 +99,24 @@ Parameters for both training and evaluation can be set in config.py
- config for Resnet50-quant, ImageNet2012 dataset

```python
'class_num': 10 # the number of classes in the dataset
'batch_size': 32 # training batch size
'loss_scale': 1024 # the initial loss_scale value
'momentum': 0.9 # momentum
'weight_decay': 1e-4 # weight decay value
'epoch_size': 120 # total training epochs
'class_num': 10 # the number of classes in the dataset
'batch_size': 32 # training batch size
'loss_scale': 1024 # the initial loss_scale value
'momentum': 0.9 # momentum
'weight_decay': 1e-4 # weight decay value
'epoch_size': 120 # total training epochs
'pretrained_epoch_size': 90 # pretraining epochs of resnet50, which is unquantative network of resnet50_quant
'data_load_mode': 'mindata' # the style of loading data into device
'save_checkpoint':True # whether save checkpoint file after training finish
'save_checkpoint_epochs': 1 # the step from which start to save checkpoint file.
'keep_checkpoint_max': 50 # only keep the last keep_checkpoint_max checkpoint
'data_load_mode': 'mindata' # the style of loading data into device
'save_checkpoint':True # whether save checkpoint file after training finish
'save_checkpoint_epochs': 1 # the step from which start to save checkpoint file.
'keep_checkpoint_max': 50 # only keep the last keep_checkpoint_max checkpoint
'save_checkpoint_path': './' # the absolute full path to save the checkpoint file
"warmup_epochs": 0 # number of warmup epochs
'lr_decay_mode': "cosine" #learning rate decay mode, including steps, steps_decay, cosine or liner
'use_label_smooth': True #whether use label smooth
'label_smooth_factor': 0.1 #label smooth factor
'lr_init': 0 # initial learning rate
'lr_max': 0.005 # the max learning rate
"warmup_epochs": 0 # number of warmup epochs
'lr_decay_mode': "cosine" # learning rate decay mode, including steps, steps_decay, cosine or liner
'use_label_smooth': True # whether use label smooth
'label_smooth_factor': 0.1 # label smooth factor
'lr_init': 0 # initial learning rate
'lr_max': 0.005 # the max learning rate
```

## [Training process](#contents)
@@ -120,7 +128,7 @@ Parameters for both training and evaluation can be set in config.py

```
# training example
Ascend: bash run_train.sh Ascend ~/hccl_4p_0123_x.x.x.x.json ~/imagenet/train/
Ascend: bash run_train.sh Ascend ~/hccl.json ~/imagenet/train/ ~/pretrained_ckeckpoint
```

### Result
@@ -165,41 +173,39 @@ result: {'acc': 0.76576314102564111}

## [Performance](#contents)

### Training Performance

| Parameters | Resnet50 |
| -------------------------- | ---------------------------------------------------------- |
| Model Version | V1 |
| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G |
| uploaded Date | 06/06/2020 |
| MindSpore Version | 0.3.0 |
| Dataset | ImageNet |
| Training Parameters | src/config.py |
| Optimizer | Momentum |
| Loss Function | SoftmaxCrossEntropy |
| outputs | ckpt file |
| Loss | 1.8 |
| Accuracy | |
| Total time | 16h |
| Params (M) | batch_size=32, epoch=30 |
| Checkpoint for Fine tuning | |
| Model for inference | |

#### Evaluation Performance

| Parameters | Resnet50 |
| -------------------------- | ----------------------------- |
| Model Version | V1 |
| Resource | Ascend 910 |
| uploaded Date | 06/06/2020 |
| MindSpore Version | 0.3.0 |
| Dataset | ImageNet, 1.2W |
| batch_size | 130(8P) |
| outputs | probability |
| Accuracy | ACC1[76.57%] ACC5[92.90%] |
| Speed | 5ms/step |
| Total time | 5min |
| Model for inference | |
### Evaluation Performance

| Parameters | Ascend |
| -------------------------- | ----------------------------------------------------------- |
| Model Version | ResNet50 V1.5 |
| Resource | Ascend 910, CPU 2.60GHz, 56cores, Memory 314G |
| uploaded Date | 06/06/2020 (month/day/year) |
| MindSpore Version | 0.3.0-alpha |
| Dataset | ImageNet |
| Training Parameters | epoch=30(with pretrained) or 120, steps per epoch=5004, batch_size=32 |
| Optimizer | Momentum |
| Loss Function | Softmax Cross Entropy |
| outputs | probability |
| Loss | 1.8 |
| Speed | 8pcs: 407 ms/step |
| Total time | 8pcs: 17 hours(30 epochs with pretrained) |
| Parameters (M) | 25.5 |
| Checkpoint for Fine tuning | 197M (.ckpt file) |
| Scripts | [resnet50-quant script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet50_quant) |

#### Inference Performance

| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | ResNet50 V1.5 |
| Resource | Ascend 910, CPU 2.60GHz, 56cores, Memory 314G |
| Uploaded Date | 06/06/2020 (month/day/year) |
| MindSpore Version | 0.3.0-alpha |
| Dataset | ImageNet |
| batch_size | 32 |
| outputs | probability |
| Accuracy | ACC1[76.57%] ACC5[92.90%] |
| Model for inference | 197M (.ckpt file) |

# [Description of Random Situation](#contents)


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model_zoo/official/nlp/transformer/README.md View File

@@ -233,7 +233,7 @@ Parameters for learning rate:

### Training Performance

| Parameters | Transformer |
| Parameters | Ascend |
| -------------------------- | -------------------------------------------------------------- |
| Resource | Ascend 910 |
| uploaded Date | 06/09/2020 (month/day/year) |
@@ -252,7 +252,7 @@ Parameters for learning rate:

### Evaluation Performance

| Parameters | GoogleNet |
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Resource | Ascend 910 |
| Uploaded Date | 06/09/2020 (month/day/year) |


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