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modify resnet50-quant readme
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# Contents
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- [resnet50 Description](#resnet50-description)
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- [ResNet50 Description](#resnet50-description)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Features](#features)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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# [resnet50 Description](#contents)
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# [ResNet50 Description](#contents)
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ResNet-50 is a convolutional neural network that is 50 layers deep, which can classify ImageNet image to 1000 object categories with 76% accuracy.
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[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.
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[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.
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This is the quantitative network of Resnet50.
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This is the quantitative network of ResNet50.
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# [Model architecture](#contents)
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# [Model Architecture](#contents)
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The overall network architecture of Resnet50 is show below:
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@ -34,13 +34,20 @@ The overall network architecture of Resnet50 is show below:
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# [Dataset](#contents)
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Dataset used: [imagenet](http://www.image-net.org/)
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Dataset used: [ImageNet2012](http://www.image-net.org/)
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- Dataset size: ~125G, 1.2W colorful images in 1000 classes
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- Train: 120G, 1.2W images
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- Test: 5G, 50000 images
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- Data format: RGB images.
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- Note: Data will be processed in src/dataset.py
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- Dataset size 224*224 colorful images in 1000 classes
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- Train：1,281,167 images
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- Test： 50,000 images
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- Data format：jpeg
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- Note：Data will be processed in dataset.py
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- Download the dataset, the directory structure is as follows:
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```
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└─dataset
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├─ilsvrc # train dataset
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└─validation_preprocess # evaluate dataset
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```
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# [Features](#contents)
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@ -70,9 +77,10 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
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├── Readme.md # descriptions about Resnet50-Quant
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├── scripts
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│ ├──run_train.sh # shell script for train on Ascend
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│ ├──run_infer.sh # shell script for evaluation on Ascend
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├── model
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│ ├──resnet_quant.py # define the network model of resnet50-quant
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│ ├──run_infer.sh # shell script for evaluation on Ascend
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├── models
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│ ├──resnet_quant.py # define the network model of resnet50-quant
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│ ├──resnet_quant_manual.py # define the manually quantized network model of resnet50-quant
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├── src
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│ ├──config.py # parameter configuration
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│ ├──dataset.py # creating dataset
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- config for Resnet50-quant, ImageNet2012 dataset
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```python
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'class_num': 10 # the number of classes in the dataset
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'batch_size': 32 # training batch size
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'loss_scale': 1024 # the initial loss_scale value
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'momentum': 0.9 # momentum
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'weight_decay': 1e-4 # weight decay value
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'epoch_size': 120 # total training epochs
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'class_num': 10 # the number of classes in the dataset
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'batch_size': 32 # training batch size
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'loss_scale': 1024 # the initial loss_scale value
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'momentum': 0.9 # momentum
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'weight_decay': 1e-4 # weight decay value
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'epoch_size': 120 # total training epochs
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'pretrained_epoch_size': 90 # pretraining epochs of resnet50, which is unquantative network of resnet50_quant
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'data_load_mode': 'mindata' # the style of loading data into device
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'save_checkpoint':True # whether save checkpoint file after training finish
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'save_checkpoint_epochs': 1 # the step from which start to save checkpoint file.
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'keep_checkpoint_max': 50 # only keep the last keep_checkpoint_max checkpoint
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'data_load_mode': 'mindata' # the style of loading data into device
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'save_checkpoint':True # whether save checkpoint file after training finish
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'save_checkpoint_epochs': 1 # the step from which start to save checkpoint file.
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'keep_checkpoint_max': 50 # only keep the last keep_checkpoint_max checkpoint
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'save_checkpoint_path': './' # the absolute full path to save the checkpoint file
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"warmup_epochs": 0 # number of warmup epochs
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'lr_decay_mode': "cosine" #learning rate decay mode, including steps, steps_decay, cosine or liner
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'use_label_smooth': True #whether use label smooth
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'label_smooth_factor': 0.1 #label smooth factor
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'lr_init': 0 # initial learning rate
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'lr_max': 0.005 # the max learning rate
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"warmup_epochs": 0 # number of warmup epochs
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'lr_decay_mode': "cosine" # learning rate decay mode, including steps, steps_decay, cosine or liner
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'use_label_smooth': True # whether use label smooth
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'label_smooth_factor': 0.1 # label smooth factor
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'lr_init': 0 # initial learning rate
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'lr_max': 0.005 # the max learning rate
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```
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## [Training process](#contents)
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@ -120,7 +128,7 @@ Parameters for both training and evaluation can be set in config.py
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```
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# training example
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Ascend: bash run_train.sh Ascend ~/hccl_4p_0123_x.x.x.x.json ~/imagenet/train/
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Ascend: bash run_train.sh Ascend ~/hccl.json ~/imagenet/train/ ~/pretrained_ckeckpoint
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```
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### Result
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@ -165,41 +173,39 @@ result: {'acc': 0.76576314102564111}
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## [Performance](#contents)
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### Training Performance
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### Evaluation Performance
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| Parameters | Resnet50 |
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| -------------------------- | ---------------------------------------------------------- |
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| Model Version | V1 |
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| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G |
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| uploaded Date | 06/06/2020 |
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| MindSpore Version | 0.3.0 |
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| Dataset | ImageNet |
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| Training Parameters | src/config.py |
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| Optimizer | Momentum |
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| Loss Function | SoftmaxCrossEntropy |
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| outputs | ckpt file |
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| Loss | 1.8 |
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| Accuracy | |
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| Total time | 16h |
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| Params (M) | batch_size=32, epoch=30 |
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| Checkpoint for Fine tuning | |
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| Model for inference | |
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| Parameters | Ascend |
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| -------------------------- | ----------------------------------------------------------- |
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| Model Version | ResNet50 V1.5 |
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| Resource | Ascend 910, CPU 2.60GHz, 56cores, Memory 314G |
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| uploaded Date | 06/06/2020 (month/day/year) |
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| MindSpore Version | 0.3.0-alpha |
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| Dataset | ImageNet |
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| Training Parameters | epoch=30(with pretrained) or 120, steps per epoch=5004, batch_size=32 |
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| Optimizer | Momentum |
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| Loss Function | Softmax Cross Entropy |
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| outputs | probability |
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| Loss | 1.8 |
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| Speed | 8pcs: 407 ms/step |
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| Total time | 8pcs: 17 hours(30 epochs with pretrained) |
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| Parameters (M) | 25.5 |
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| Checkpoint for Fine tuning | 197M (.ckpt file) |
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| Scripts | [resnet50-quant script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet50_quant) |
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#### Evaluation Performance
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#### Inference Performance
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| Parameters | Resnet50 |
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| -------------------------- | ----------------------------- |
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| Model Version | V1 |
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| Resource | Ascend 910 |
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| uploaded Date | 06/06/2020 |
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| MindSpore Version | 0.3.0 |
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| Dataset | ImageNet, 1.2W |
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| batch_size | 130(8P) |
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| outputs | probability |
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| Accuracy | ACC1[76.57%] ACC5[92.90%] |
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| Speed | 5ms/step |
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| Total time | 5min |
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| Model for inference | |
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| Parameters | Ascend |
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| ------------------- | --------------------------- |
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| Model Version | ResNet50 V1.5 |
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| Resource | Ascend 910, CPU 2.60GHz, 56cores, Memory 314G |
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| Uploaded Date | 06/06/2020 (month/day/year) |
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| MindSpore Version | 0.3.0-alpha |
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| Dataset | ImageNet |
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| batch_size | 32 |
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| outputs | probability |
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| Accuracy | ACC1[76.57%] ACC5[92.90%] |
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| Model for inference | 197M (.ckpt file) |
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# [Description of Random Situation](#contents)
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@ -233,7 +233,7 @@ Parameters for learning rate:
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### Training Performance
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| Parameters | Transformer |
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| Parameters | Ascend |
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| -------------------------- | -------------------------------------------------------------- |
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| Resource | Ascend 910 |
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| uploaded Date | 06/09/2020 (month/day/year) |
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@ -252,7 +252,7 @@ Parameters for learning rate:
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### Evaluation Performance
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| Parameters | GoogleNet |
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| Parameters | Ascend |
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| ------------------- | --------------------------- |
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| Resource | Ascend 910 |
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| Uploaded Date | 06/09/2020 (month/day/year) |
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