From fc986f98e0c77800c35bf8c521b115e8a225f0a2 Mon Sep 17 00:00:00 2001 From: caojiewen Date: Fri, 9 Apr 2021 12:22:07 +0800 Subject: [PATCH] fixed the bad links --- model_zoo/research/cv/IPT/readme.md | 27 +++++++++++-------- model_zoo/research/cv/ManiDP/Readme.md | 8 +++--- model_zoo/research/cv/centernet_det/readme.md | 4 +-- model_zoo/research/cv/renas/Readme.md | 11 ++++---- 4 files changed, 26 insertions(+), 24 deletions(-) diff --git a/model_zoo/research/cv/IPT/readme.md b/model_zoo/research/cv/IPT/readme.md index ef85e368a99..b05482893c1 100755 --- a/model_zoo/research/cv/IPT/readme.md +++ b/model_zoo/research/cv/IPT/readme.md @@ -17,7 +17,9 @@ If you find our work useful in your research or publication, please cite our wor } ## Model architecture -### The overall network architecture of IPT is shown as below: + +### The overall network architecture of IPT is shown as below + ![architecture](./image/ipt.png) ## Dataset @@ -27,12 +29,9 @@ The benchmark datasets can be downloaded as follows: For super-resolution: Set5, - [Set14](https://sites.google.com/site/romanzeyde/research-interests), - [B100](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/), - -[Urban100](https://sites.google.com/site/jbhuang0604/publications/struct_sr). +Urban100. For denoising: @@ -47,11 +46,15 @@ The result images are converted into YCbCr color space. The PSNR is evaluated on ## Requirements ### Hardware (GPU) + > Prepare hardware environment with GPU. ### Framework + > [MindSpore](https://www.mindspore.cn/install/en) -### For more information, please check the resources below： + +### 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) @@ -61,7 +64,7 @@ The result images are converted into YCbCr color space. The PSNR is evaluated on ### Scripts and Sample Code -``` +```bash IPT ├── eval.py # inference entry ├── image @@ -95,23 +98,25 @@ IPT ## Evaluation ### Evaluation Process + > Inference example: > For SR x4: -``` +```bash python eval.py --dir_data ../../data/ --data_test Set14 --nochange --test_only --ext img --chop_new --scale 4 --pth_path ./model/IPT_sr4.ckpt ``` > Or one can run following script for all tasks. -``` +```bash sh scripts/run_eval.sh ``` ### Evaluation Result + The result are evaluated by the value of PSNR (Peak Signal-to-Noise Ratio), and the format is as following. -``` +```bash result: {"Mean psnr of Se5 x4 is 32.68"} ``` @@ -144,4 +149,4 @@ Derain results: ## ModeZoo Homepage -Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). \ No newline at end of file +Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). diff --git a/model_zoo/research/cv/ManiDP/Readme.md b/model_zoo/research/cv/ManiDP/Readme.md index 67010695b11..142eacfb283 100755 --- a/model_zoo/research/cv/ManiDP/Readme.md +++ b/model_zoo/research/cv/ManiDP/Readme.md @@ -37,18 +37,18 @@ Dataset used: [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) ## [Mixed Precision(Ascend)](#contents) -The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. +The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’. # [Environment Requirements](#contents) - Hardware（Ascend/GPU/CPU） - - Prepare hardware environment with Ascend、GPU or CPU 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. + - Prepare hardware environment with Ascend、GPU or CPU processor. - Framework - - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/) + - [MindSpore](https://www.mindspore.cn/install/en) - For more information, please check the resources below： - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) - - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html) + - [MindSpore API](https://www.mindspore.cn/doc/api_python/en/master/index.html) # [Script description](#contents) diff --git a/model_zoo/research/cv/centernet_det/readme.md b/model_zoo/research/cv/centernet_det/readme.md index 2bb6ec31c0c..34c5ea861de 100644 --- a/model_zoo/research/cv/centernet_det/readme.md +++ b/model_zoo/research/cv/centernet_det/readme.md @@ -76,10 +76,8 @@ Dataset used: [COCO2017](https://cocodataset.org/) # [Environment Requirements](#contents) - Hardware（Ascend） - - - Prepare hardware environment with Ascend 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. + - Prepare hardware environment with Ascend processor. - 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) diff --git a/model_zoo/research/cv/renas/Readme.md b/model_zoo/research/cv/renas/Readme.md index 8a1491d1615..6fa98e467e9 100755 --- a/model_zoo/research/cv/renas/Readme.md +++ b/model_zoo/research/cv/renas/Readme.md @@ -25,8 +25,7 @@ An effective and efficient architecture performance evaluation scheme is essenti # [Dataset](#contents) -- - Dataset used: [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) - +- Dataset used: [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) - Dataset size: 60000 colorful images in 10 classes - Train: 50000 images - Test: 10000 images @@ -37,18 +36,18 @@ An effective and efficient architecture performance evaluation scheme is essenti ## [Mixed Precision(Ascend)](#contents) -The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. +The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’. # [Environment Requirements](#contents) - Hardware（Ascend/GPU/CPU） - - Prepare hardware environment with Ascend、GPU or CPU 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. + - Prepare hardware environment with Ascend、GPU or CPU processor. - Framework - - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/) + - [MindSpore](https://www.mindspore.cn/install/en) - For more information, please check the resources below： - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) - - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html) + - [MindSpore API](https://www.mindspore.cn/doc/api_python/en/master/index.html) # [Script description](#contents)