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!7588 fix typos in README 

Merge pull request !7588 from gengdongjie/master
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mindspore-ci-bot 2020-10-22 10:47:44 +08:00 committed by Gitee
parent 733ad9cf2a 7cdc647afa
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84
model_zoo/official/cv/maskrcnn/README.md
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@ -19,8 +19,8 @@
- [Evaluation Result](#evaluation-result)
- [Model Description](#model-description)
- [Performance](#performance)
- [Training Performance](#training-performance)
- [Evaluation Performance](#evaluation-performance)
- [Inference Performance](#inference-performance)
- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
@ -280,7 +280,7 @@ Usage: sh run_standalone_train.sh [PRETRAINED_MODEL]
"save_checkpoint": True, # whether save checkpoint or not
"save_checkpoint_epochs": 1, # save checkpoint interval
"keep_checkpoint_max": 12, # max number of saved checkpoint
"save_checkpoint_path": "./checkpoint", # path of checkpoint
"save_checkpoint_path": "./", # path of checkpoint
"mindrecord_dir": "/home/maskrcnn/MindRecord_COCO2017_Train", # path of mindrecord
"coco_root": "/home/maskrcnn/", # path of coco root dateset
@ -336,13 +336,13 @@ Training result will be stored in the example path, whose folder name begins wit
```
# distribute training result(8p)
epoch: 1 step: 7393 ,rpn_loss: 0.10626, rcnn_loss: 0.81592, rpn_cls_loss: 0.05862, rpn_reg_loss: 0.04761, rcnn_cls_loss: 0.32642, rcnn_reg_loss: 0.15503, rcnn_mask_loss: 0.33447, total_loss: 0.92218
epoch: 2 step: 7393 ,rpn_loss: 0.00911, rcnn_loss: 0.34082, rpn_cls_loss: 0.00341, rpn_reg_loss: 0.00571, rcnn_cls_loss: 0.07440, rcnn_reg_loss: 0.05872, rcnn_mask_loss: 0.20764, total_loss: 0.34993
epoch: 3 step: 7393 ,rpn_loss: 0.02087, rcnn_loss: 0.98633, rpn_cls_loss: 0.00665, rpn_reg_loss: 0.01422, rcnn_cls_loss: 0.35913, rcnn_reg_loss: 0.21375, rcnn_mask_loss: 0.41382, total_loss: 1.00720
epoch: 1 step: 7393 ,rpn_loss: 0.05716, rcnn_loss: 0.81152, rpn_cls_loss: 0.04828, rpn_reg_loss: 0.00889, rcnn_cls_loss: 0.28784, rcnn_reg_loss: 0.17590, rcnn_mask_loss: 0.34790, total_loss: 0.86868
epoch: 2 step: 7393 ,rpn_loss: 0.00434, rcnn_loss: 0.36572, rpn_cls_loss: 0.00339, rpn_reg_loss: 0.00095, rcnn_cls_loss: 0.08240, rcnn_reg_loss: 0.05554, rcnn_mask_loss: 0.22778, total_loss: 0.37006
epoch: 3 step: 7393 ,rpn_loss: 0.00996, rcnn_loss: 0.83789, rpn_cls_loss: 0.00701, rpn_reg_loss: 0.00294, rcnn_cls_loss: 0.39478, rcnn_reg_loss: 0.14917, rcnn_mask_loss: 0.29370, total_loss: 0.84785
...
epoch: 10 step: 7393 ,rpn_loss: 0.02122, rcnn_loss: 0.55176, rpn_cls_loss: 0.00620, rpn_reg_loss: 0.01503, rcnn_cls_loss: 0.12708, rcnn_reg_loss: 0.10254, rcnn_mask_loss: 0.32227, total_loss: 0.57298
epoch: 11 step: 7393 ,rpn_loss: 0.03772, rcnn_loss: 0.60791, rpn_cls_loss: 0.03058, rpn_reg_loss: 0.00713, rcnn_cls_loss: 0.23987, rcnn_reg_loss: 0.11743, rcnn_mask_loss: 0.25049, total_loss: 0.64563
epoch: 12 step: 7393 ,rpn_loss: 0.06482, rcnn_loss: 0.47681, rpn_cls_loss: 0.04770, rpn_reg_loss: 0.01709, rcnn_cls_loss: 0.16492, rcnn_reg_loss: 0.04990, rcnn_mask_loss: 0.26196, total_loss: 0.54163
epoch: 10 step: 7393 ,rpn_loss: 0.00667, rcnn_loss: 0.65625, rpn_cls_loss: 0.00536, rpn_reg_loss: 0.00131, rcnn_cls_loss: 0.17590, rcnn_reg_loss: 0.16199, rcnn_mask_loss: 0.31812, total_loss: 0.66292
epoch: 11 step: 7393 ,rpn_loss: 0.02003, rcnn_loss: 0.52051, rpn_cls_loss: 0.01761, rpn_reg_loss: 0.00241, rcnn_cls_loss: 0.16028, rcnn_reg_loss: 0.08411, rcnn_mask_loss: 0.27588, total_loss: 0.54054
epoch: 12 step: 7393 ,rpn_loss: 0.00547, rcnn_loss: 0.39258, rpn_cls_loss: 0.00285, rpn_reg_loss: 0.00262, rcnn_cls_loss: 0.08002, rcnn_reg_loss: 0.04990, rcnn_mask_loss: 0.26245, total_loss: 0.39804
```
## [Evaluation Process](#contents)
@ -364,39 +364,39 @@ Inference result will be stored in the example path, whose folder name is "eval"
```
Evaluate annotation type *bbox*
Accumulating evaluation results...
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.376
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.598
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.405
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.239
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.414
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.475
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.378
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.602
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.407
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.242
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.417
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.480
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.500
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.528
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.371
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.572
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.653
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.497
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.524
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.363
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.567
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.647
Evaluate annotation type *segm*
Accumulating evaluation results...
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.326
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.553
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.344
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.557
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.351
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.169
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.462
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.278
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.426
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.445
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.294
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.484
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.558
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.365
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.480
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.433
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.451
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.285
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.490
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.586
```
# Model Description
## Performance
### Training Performance
### Evaluation Performance
| Parameters | MaskRCNN |
| -------------------------- | ----------------------------------------------------------- |
@ -407,14 +407,18 @@ Accumulating evaluation results...
| Dataset | COCO2017 |
| Training Parameters | epoch=12, batch_size = 2 |
| Optimizer | SGD |
| Loss Function | Softmax Cross Entropy ,Sigmoid Cross Entropy,SmoothL1Loss |
| Speed | 1pc: 250 ms/step; 8pcs: 260 ms/step |
| Total time | 1pc: 52 hours; 8pcs: 6.6 hours |
| Parameters (M) | 280 |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/maskrcnn |
| Loss Function | Softmax Cross Entropy, Sigmoid Cross Entropy, SmoothL1Loss |
| Output | Probability |
| Loss | 0.39804 |
| Speed | 1pc: 193 ms/step; 8pcs: 207 ms/step |
| Total time | 1pc: 46 hours; 8pcs: 5.38 hours |
| Parameters (M) | 84.8 |
| Checkpoint for Fine tuning | 85M(.ckpt file) |
| Model for inference | 571M(.air file) |
| Scripts | [maskrcnn script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/maskrcnn) |
### Evaluation Performance
### Inference Performance
| Parameters | MaskRCNN |
| ------------------- | --------------------------- |
@ -425,12 +429,12 @@ Accumulating evaluation results...
| Dataset | COCO2017 |
| batch_size | 2 |
| outputs | mAP |
| Accuracy | IoU=0.50:0.95 32.4% |
| Model for inference | 254M (.ckpt file) |
| Accuracy | IoU=0.50:0.95 (BoundingBox 37.0%, Mask 33.5) |
| Model for inference | 170M (.ckpt file) |
# [Description of Random Situation](#contents)
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py for weight initialization.
# [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

1
model_zoo/official/cv/maskrcnn/scripts/run_distribute_train.sh
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@ -46,6 +46,7 @@ exit 1
fi
ulimit -u unlimited
export HCCL_CONNECT_TIMEOUT=600
export DEVICE_NUM=8
export RANK_SIZE=8
export RANK_TABLE_FILE=$PATH1

1
model_zoo/official/cv/maskrcnn/src/config.py
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@ -126,7 +126,6 @@ config = ed({
"warmup_step": 500,
"warmup_mode": "linear",
"warmup_ratio": 1/3.0,
"sgd_step": [8, 11],
"sgd_momentum": 0.9,
# train

75
model_zoo/official/cv/resnet/README.md
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@ -15,6 +15,7 @@
- [Model Description](#model-description)
- [Performance](#performance)
- [Evaluation Performance](#evaluation-performance)
- [Inference Performance](#inference-performance)
- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
@ -136,9 +137,11 @@ sh run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [C
├── src
├── config.py # parameter configuration
├── dataset.py # data preprocessing
├── CrossEntropySmooth.py # loss definition for ImageNet2012 dataset
├── CrossEntropySmooth.py # loss definition for ImageNet2012 dataset
├── lr_generator.py # generate learning rate for each step
└── resnet.py # resnet backbone, including resnet50 and resnet101 and se-resnet50
├── export.py # export model for inference
├── mindspore_hub_conf.py # mindspore hub interface
├── eval.py # eval net
└── train.py # train net
```
@ -184,7 +187,7 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "Linear", # decay mode for generating learning rate
"use_label_smooth": True, # label smooth
"use_label_smooth": True, # label smooth
"label_smooth_factor": 0.1, # label smooth factor
"lr_init": 0, # initial learning rate
"lr_max": 0.8, # maximum learning rate
@ -207,7 +210,7 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 0, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate
"use_label_smooth": True, # label_smooth
"use_label_smooth": True, # label_smooth
"label_smooth_factor": 0.1, # label_smooth_factor
"lr": 0.1 # base learning rate
```
@ -229,7 +232,7 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
"warmup_epochs": 3, # number of warmup epoch
"lr_decay_mode": "cosine" # decay mode for generating learning rate
"use_label_smooth": True, # label_smooth
"use_label_smooth": True, # label_smooth
"label_smooth_factor": 0.1, # label_smooth_factor
"lr_init": 0.0, # initial learning rate
"lr_max": 0.3, # maximum learning rate
@ -313,18 +316,13 @@ epoch: 5 step: 5004, loss is 3.1978393
- Training ResNet101 with ImageNet2012 dataset
```
# distribute training result(8p)
# distribute training result(8 pcs)
epoch: 1 step: 5004, loss is 4.805483
epoch: 2 step: 5004, loss is 3.2121816
epoch: 3 step: 5004, loss is 3.429647
epoch: 4 step: 5004, loss is 3.3667371
epoch: 5 step: 5004, loss is 3.1718972
...
epoch: 67 step: 5004, loss is 2.2768745
epoch: 68 step: 5004, loss is 1.7223864
epoch: 69 step: 5004, loss is 2.0665488
epoch: 70 step: 5004, loss is 1.8717369
...
```
- Training SE-ResNet50 with ImageNet2012 dataset
@ -457,7 +455,7 @@ result: {'top_5_accuracy': 0.9342589628681178, 'top_1_accuracy': 0.7680657810499
| -------------------------- | ------------------------------------------------------------------------ |
| Model Version | SE-ResNet50 |
| Resource | Ascend 910，CPU 2.60GHz 192cores，Memory 755G |
| uploaded Date | 08/16/2020 (month/day/year) ； |
| uploaded Date | 08/16/2020 (month/day/year) |
| MindSpore Version | 0.7.0-alpha |
| Dataset | ImageNet2012 |
| Training Parameters | epoch=24, steps per epoch=5004, batch_size = 32 |
@ -471,6 +469,61 @@ result: {'top_5_accuracy': 0.9342589628681178, 'top_1_accuracy': 0.7680657810499
| Checkpoint for Fine tuning | 215.9M (.ckpt file) |
| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnet) |
### Inference Performance
#### ResNet50 on CIFAR-10
| Parameters | Ascend | GPU |
| ------------------- | --------------------------- | --------------------------- |
| Model Version | ResNet50-v1.5 | ResNet50-v1.5 |
| Resource | Ascend 910 | GPU |
| Uploaded Date | 04/01/2020 (month/day/year) | 08/01/2020 (month/day/year) |
| MindSpore Version | 0.1.0-alpha | 0.6.0-alpha |
| Dataset | CIFAR-10 | CIFAR-10 |
| batch_size | 32 | 32 |
| outputs | probability | probability |
| Accuracy | 91.44% | 91.37% |
| Model for inference | 91M (.air file) | |
#### ResNet50 on ImageNet2012
| Parameters | Ascend | GPU |
| ------------------- | --------------------------- | --------------------------- |
| Model Version | ResNet50-v1.5 | ResNet50-v1.5 |
| Resource | Ascend 910 | GPU |
| Uploaded Date | 04/01/2020 (month/day/year) | 08/01/2020 (month/day/year) |
| MindSpore Version | 0.1.0-alpha | 0.6.0-alpha |
| Dataset | ImageNet2012 | ImageNet2012 |
| batch_size | 256 | 32 |
| outputs | probability | probability |
| Accuracy | 76.70% | 76.74% |
| Model for inference | 98M (.air file) | |
#### ResNet101 on ImageNet2012
| Parameters | Ascend | GPU |
| ------------------- | --------------------------- | --------------------------- |
| Model Version | ResNet101 | ResNet101 |
| Resource | Ascend 910 | GPU |
| Uploaded Date | 04/01/2020 (month/day/year) | 08/01/2020 (month/day/year) |
| MindSpore Version | 0.1.0-alpha | 0.6.0-alpha |
| Dataset | ImageNet2012 | ImageNet2012 |
| batch_size | 32 | 32 |
| outputs | probability | probability |
| Accuracy | 78.53% | 78.64% |
| Model for inference | 171M (.air file) | |
#### SE-ResNet50 on ImageNet2012
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | SE-ResNet50 |
| Resource | Ascend 910 |
| Uploaded Date | 08/16/2020 (month/day/year) |
| MindSpore Version | 0.7.0-alpha |
| Dataset | ImageNet2012 |
| batch_size | 32 |
| outputs | probability |
| Accuracy | 76.80% |
| Model for inference | 109M (.air file) |
# [Description of Random Situation](#contents)
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.