!11405 Add and fix some description about bert and yolov3
From: @c_34 Reviewed-by: @oacjiewen,@wuxuejian Signed-off-by: @wuxuejian
This commit is contained in:
Gitee
commit
d5a977ca24
|
|
@ -1,26 +1,27 @@
|
|||
# Contents
|
||||
|
||||
- [YOLOv3-DarkNet53 Description](#yolov3-darknet53-description)
|
||||
- [Model Architecture](#model-architecture)
|
||||
- [Dataset](#dataset)
|
||||
- [Environment Requirements](#environment-requirements)
|
||||
- [Quick Start](#quick-start)
|
||||
- [Script Description](#script-description)
|
||||
- [Script and Sample Code](#script-and-sample-code)
|
||||
- [Script Parameters](#script-parameters)
|
||||
- [Training Process](#training-process)
|
||||
- [Training](#training)
|
||||
- [Distributed Training](#distributed-training)
|
||||
- [Evaluation Process](#evaluation-process)
|
||||
- [Evaluation](#evaluation)
|
||||
- [Model Description](#model-description)
|
||||
- [Performance](#performance)
|
||||
- [Evaluation Performance](#evaluation-performance)
|
||||
- [Inference Performance](#evaluation-performance)
|
||||
- [Description of Random Situation](#description-of-random-situation)
|
||||
- [ModelZoo Homepage](#modelzoo-homepage)
|
||||
- [Contents](#contents)
|
||||
- [YOLOv3-DarkNet53 Description](#yolov3-darknet53-description)
|
||||
- [Model Architecture](#model-architecture)
|
||||
- [Dataset](#dataset)
|
||||
- [Environment Requirements](#environment-requirements)
|
||||
- [Quick Start](#quick-start)
|
||||
- [Script Description](#script-description)
|
||||
- [Script and Sample Code](#script-and-sample-code)
|
||||
- [Script Parameters](#script-parameters)
|
||||
- [Training Process](#training-process)
|
||||
- [Training](#training)
|
||||
- [Distributed Training](#distributed-training)
|
||||
- [Evaluation Process](#evaluation-process)
|
||||
- [Evaluation](#evaluation)
|
||||
- [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)
|
||||
|
||||
# [YOLOv3-DarkNet53 Description](#contents)
|
||||
## [YOLOv3-DarkNet53 Description](#contents)
|
||||
|
||||
You only look once (YOLO) is a state-of-the-art, real-time object detection system. YOLOv3 is extremely fast and accurate.
|
||||
|
||||
|
|
@ -32,11 +33,11 @@ YOLOv3 uses a few tricks to improve training and increase performance, including
|
|||
[Paper](https://pjreddie.com/media/files/papers/YOLOv3.pdf): YOLOv3: An Incremental Improvement. Joseph Redmon, Ali Farhadi,
|
||||
University of Washington
|
||||
|
||||
# [Model Architecture](#contents)
|
||||
## [Model Architecture](#contents)
|
||||
|
||||
YOLOv3 use DarkNet53 for performing feature extraction, which is a hybrid approach between the network used in YOLOv2, Darknet-19, and that newfangled residual network stuff. DarkNet53 uses successive 3 × 3 and 1 × 1 convolutional layers and has some shortcut connections as well and is significantly larger. It has 53 convolutional layers.
|
||||
|
||||
# [Dataset](#contents)
|
||||
## [Dataset](#contents)
|
||||
|
||||
Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
|
||||
|
||||
|
|
@ -47,19 +48,19 @@ Dataset used: [COCO2014](https://cocodataset.org/#download)
|
|||
- Val：6GM, 40,504 images
|
||||
- Annotations: 241M, Train/Val annotations
|
||||
- Data format：zip files
|
||||
- Note：Data will be processed in yolo_dataset.py, and unzip files before uses it.
|
||||
- Note：Data will be processed in yolo_dataset.py, and unzip files before uses it.
|
||||
|
||||
# [Environment Requirements](#contents)
|
||||
## [Environment Requirements](#contents)
|
||||
|
||||
- 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](https://www.mindspore.cn/install/en)
|
||||
- For more information, please check the resources below：
|
||||
- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
|
||||
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
|
||||
- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
|
||||
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
|
||||
|
||||
# [Quick Start](#contents)
|
||||
## [Quick Start](#contents)
|
||||
|
||||
After installing MindSpore via the official website, you can start training and evaluation in as follows. If running on GPU, please add `--device_target=GPU` in the python command or use the "_gpu" shell script ("xxx_gpu.sh").
|
||||
|
||||
|
|
@ -101,9 +102,9 @@ python eval.py \
|
|||
sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
|
||||
```
|
||||
|
||||
# [Script Description](#contents)
|
||||
## [Script Description](#contents)
|
||||
|
||||
## [Script and Sample Code](#contents)
|
||||
### [Script and Sample Code](#contents)
|
||||
|
||||
```contents
|
||||
.
|
||||
|
|
@ -134,7 +135,7 @@ sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
|
|||
└─train.py # train net
|
||||
```
|
||||
|
||||
## [Script Parameters](#contents)
|
||||
### [Script Parameters](#contents)
|
||||
|
||||
```parameters
|
||||
Major parameters in train.py as follow.
|
||||
|
|
@ -197,9 +198,9 @@ optional arguments:
|
|||
Resize rate for multi-scale training. Default: None
|
||||
```
|
||||
|
||||
## [Training Process](#contents)
|
||||
### [Training Process](#contents)
|
||||
|
||||
### Training
|
||||
#### Training
|
||||
|
||||
```command
|
||||
python train.py \
|
||||
|
|
@ -230,7 +231,7 @@ After training, you'll get some checkpoint files under the outputs folder by def
|
|||
|
||||
The model checkpoint will be saved in outputs directory.
|
||||
|
||||
### Distributed Training
|
||||
#### Distributed Training
|
||||
|
||||
For Ascend device, distributed training example(8p) by shell script
|
||||
|
||||
|
|
@ -261,9 +262,9 @@ epoch[319], iter[102300], loss:31.952403, 496.02 imgs/sec, lr:2.409552052995423e
|
|||
...
|
||||
```
|
||||
|
||||
## [Evaluation Process](#contents)
|
||||
### [Evaluation Process](#contents)
|
||||
|
||||
### Evaluation
|
||||
#### Evaluation
|
||||
|
||||
Before running the command below. If running on GPU, please add `--device_target=GPU` in the python command or use the "_gpu" shell script ("xxx_gpu.sh").
|
||||
|
||||
|
|
@ -278,6 +279,8 @@ sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
|
|||
|
||||
The above python command will run in the background. You can view the results through the file "log.txt". The mAP of the test dataset will be as follows:
|
||||
|
||||
This the standard format from `pycocotools`, you can refer to [cocodataset](https://cocodataset.org/#detection-eval) for more detail.
|
||||
|
||||
```eval log
|
||||
# log.txt
|
||||
=============coco eval reulst=========
|
||||
|
|
@ -295,11 +298,11 @@ The above python command will run in the background. You can view the results th
|
|||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551
|
||||
```
|
||||
|
||||
# [Model Description](#contents)
|
||||
## [Model Description](#contents)
|
||||
|
||||
## [Performance](#contents)
|
||||
### [Performance](#contents)
|
||||
|
||||
### Evaluation Performance
|
||||
#### Evaluation Performance
|
||||
|
||||
| Parameters | YOLO |YOLO |
|
||||
| -------------------------- | ----------------------------------------------------------- |------------------------------------------------------------ |
|
||||
|
|
@ -319,7 +322,7 @@ The above python command will run in the background. You can view the results th
|
|||
| Checkpoint for Fine tuning | 474M (.ckpt file) | 474M (.ckpt file) |
|
||||
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53 | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53 |
|
||||
|
||||
### Inference Performance
|
||||
#### Inference Performance
|
||||
|
||||
| Parameters | YOLO |YOLO |
|
||||
| ------------------- | --------------------------- |------------------------------|
|
||||
|
|
@ -333,10 +336,10 @@ The above python command will run in the background. You can view the results th
|
|||
| Accuracy | 8pcs: 31.1% | 8pcs: 29.7%~30.3% (shape=416)|
|
||||
| Model for inference | 474M (.ckpt file) | 474M (.ckpt file) |
|
||||
|
||||
# [Description of Random Situation](#contents)
|
||||
## [Description of Random Situation](#contents)
|
||||
|
||||
There are random seeds in distributed_sampler.py, transforms.py, yolo_dataset.py files.
|
||||
|
||||
# [ModelZoo Homepage](#contents)
|
||||
## [ModelZoo Homepage](#contents)
|
||||
|
||||
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
|
||||
|
|
|
|||
|
|
@ -12,8 +12,8 @@
|
|||
- [Pre-Training](#pre-training)
|
||||
- [Fine-Tuning and Evaluation](#fine-tuning-and-evaluation)
|
||||
- [Options and Parameters](#options-and-parameters)
|
||||
- [Options:](#options)
|
||||
- [Parameters:](#parameters)
|
||||
- [Options](#options)
|
||||
- [Parameters](#parameters)
|
||||
- [Training Process](#training-process)
|
||||
- [Training](#training)
|
||||
- [Running on Ascend](#running-on-ascend)
|
||||
|
|
@ -380,7 +380,8 @@ config for lossscale and etc.
|
|||
```text
|
||||
Parameters for dataset and network (Pre-Training/Fine-Tuning/Evaluation):
|
||||
seq_length length of input sequence: N, default is 128
|
||||
vocab_size size of each embedding vector: N, must be consistant with the dataset you use. Default is 21136
|
||||
vocab_size size of each embedding vector: N, must be consistant with the dataset you use. Default is 21128.
|
||||
Usually, we use 21128 for CN vocabs and 30522 for EN vocabs according to the origin paper.
|
||||
hidden_size size of bert encoder layers: N, default is 768
|
||||
num_hidden_layers number of hidden layers: N, default is 12
|
||||
num_attention_heads number of attention heads: N, default is 12
|
||||
|
|
|
|||
|
|
@ -50,6 +50,16 @@ def _tensor_grad_overflow(grad):
|
|||
class BertFinetuneCell(nn.Cell):
|
||||
"""
|
||||
Especifically defined for finetuning where only four inputs tensor are needed.
|
||||
|
||||
Append an optimizer to the training network after that the construct
|
||||
function can be called to create the backward graph.
|
||||
|
||||
Different from the builtin loss_scale wrapper cell, we apply grad_clip before the optimization.
|
||||
|
||||
Args:
|
||||
network (Cell): The training network. Note that loss function should have been added.
|
||||
optimizer (Optimizer): Optimizer for updating the weights.
|
||||
scale_update_cell (Cell): Cell to do the loss scale. Default: None.
|
||||
"""
|
||||
def __init__(self, network, optimizer, scale_update_cell=None):
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue