From 8114352c2a2e43b5a38e3352d4ec7879a2418fbc Mon Sep 17 00:00:00 2001 From: yuchaojie Date: Mon, 19 Oct 2020 20:26:49 +0800 Subject: [PATCH] modify resnet50-quant readme --- .../resnet50_quant/{Readme.md => README.md} | 132 +++++++++--------- model_zoo/official/nlp/transformer/README.md | 4 +- 2 files changed, 71 insertions(+), 65 deletions(-) rename model_zoo/official/cv/resnet50_quant/{Readme.md => README.md} (55%) diff --git a/model_zoo/official/cv/resnet50_quant/Readme.md b/model_zoo/official/cv/resnet50_quant/README.md similarity index 55% rename from model_zoo/official/cv/resnet50_quant/Readme.md rename to model_zoo/official/cv/resnet50_quant/README.md index 0b405d3bab7..517005f9d10 100644 --- a/model_zoo/official/cv/resnet50_quant/Readme.md +++ b/model_zoo/official/cv/resnet50_quant/README.md @@ -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 +### Evaluation 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 | | +| 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) | -#### Evaluation Performance +#### Inference 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 | | +| 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) diff --git a/model_zoo/official/nlp/transformer/README.md b/model_zoo/official/nlp/transformer/README.md index a45f2f84ec5..1c347968898 100644 --- a/model_zoo/official/nlp/transformer/README.md +++ b/model_zoo/official/nlp/transformer/README.md @@ -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) |