forked from mindspore-Ecosystem/mindspore
replaced the model name 'music auto tagging' as 'fcn-4'
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# Contents
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- [Music Auto Tagging Description](#fcn-4-description)
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- [FCN-4 Description](#fcn-4-description)
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- [Model Architecture](#model-architecture)
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- [Features](#features)
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- [Mixed Precision](#mixed-precision)
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- [Environment Requirements](#environment-requirements)
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- [Quick Start](#quick-start)
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- [Quick Start](#quick-start)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Script Parameters](#script-parameters)
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- [Evaluation Performance](#evaluation-performance)
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- [ModelZoo Homepage](#modelzoo-homepage)
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## [FCN-4 Description](#contents)
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# [Music Auto Tagging Description](#contents)
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This repository provides a script and recipe to train the Music Auto Tagging model to achieve state-of-the-art accuracy.
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This repository provides a script and recipe to train the FCN-4 model to achieve state-of-the-art accuracy.
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[Paper](https://arxiv.org/abs/1606.00298): `"Keunwoo Choi, George Fazekas, and Mark Sandler, “Automatic tagging using deep convolutional neural networks,” in International Society of Music Information Retrieval Conference. ISMIR, 2016."
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## [Model Architecture](#contents)
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# [Model Architecture](#contents)
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FCN-4 is a convolutional neural network architecture, its name FCN-4 comes from the fact that it has 4 layers. Its layers consists of Convolutional layers, Max Pooling layers, Activation layers, Fully connected layers.
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Music Auto Tagging is a convolutional neural network architecture, its name Music Auto Tagging comes from the fact that it has 4 layers. Its layers consists of Convolutional layers, Max Pooling layers, Activation layers, Fully connected layers.
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## [Features](#contents)
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# [Features](#contents)
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### Mixed Precision
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## Mixed Precision
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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.
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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.
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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’.
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# [Environment Requirements](#contents)
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## [Environment Requirements](#contents)
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- Hardware（Ascend
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- 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.
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- 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.
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- Framework
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- [MindSpore](https://www.mindspore.cn/install/en)
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- [MindSpore](https://www.mindspore.cn/install/en)
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- For more information, please check the resources below：
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- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
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- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
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- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
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- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
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## [Quick Start](#contents)
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# [Quick Start](#contents)
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After installing MindSpore via the official website, you can start training and evaluation as follows:
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After installing MindSpore via the official website, you can start training and evaluation as follows:
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### 1. Download and preprocess the dataset
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@ -66,11 +61,13 @@ After installing MindSpore via the official website, you can start training and
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### 3. Train
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after having your dataset, first convert the audio clip into mindrecord dataset by using the following codes
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```shell
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python pre_process_data.py --device_id 0
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```
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Then, you can start training the model by using the following codes
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```shell
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SLOG_PRINT_TO_STDOUT=1 python train.py --device_id 0
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```
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### 4. Test
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Then you can test your model
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```shell
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SLOG_PRINT_TO_STDOUT=1 python eval.py --device_id 0
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```
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# [Script Description](#contents)
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## [Script Description](#contents)
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## [Script and Sample Code](#contents)
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### [Script and Sample Code](#contents)
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```
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```shell
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├── model_zoo
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├── README.md // descriptions about all the models
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├── music_auto_tagging
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├── music_auto_tagging
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├── README.md // descriptions about googlenet
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├── scripts
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├── scripts
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│ ├──run_train.sh // shell script for distributed on Ascend
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│ ├──run_eval.sh // shell script for evaluation on Ascend
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│ ├──run_process_data.sh // shell script for convert audio clips to mindrecord
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@ -102,19 +100,19 @@ SLOG_PRINT_TO_STDOUT=1 python eval.py --device_id 0
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│ ├──config.py // parameter configuration
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│ ├──loss.py // loss function
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│ ├──tag.txt // tag for each number
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├── train.py // training script
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├── eval.py // evaluation script
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├── export.py // export model in air format
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├── train.py // training script
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├── eval.py // evaluation script
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├── export.py // export model in air format
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```
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## [Script Parameters](#contents)
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### [Script Parameters](#contents)
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Parameters for both training and evaluation can be set in config.py
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- config for Music Auto tagging
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- config for FCN-4
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```python
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'num_classes': 50 # number of tagging classes
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'num_consumer': 4 # file number for mindrecord
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'get_npy': 1 # mode for converting to npy, default 1 in this case
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'device_id': 0 # device ID used to train or evaluate the dataset. Ignore it when you use run_train.sh for distributed training
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'mr_path': '/dev/data/Music_Tagger_Data/fea/' # path to mindrecord
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'mr_name': ['train', 'val'] # mindrecord name
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'pre_trained': False # whether training based on the pre-trained model
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'lr': 0.0005 # learning rate
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'batch_size': 32 # training batch size
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@ -135,8 +133,8 @@ Parameters for both training and evaluation can be set in config.py
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'loss_scale': 1024.0 # loss scale
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'num_consumer': 4 # file number for mindrecord
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'mixed_precision': False # if use mix precision calculation
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'train_filename': 'train.mindrecord0' # file name of the train mindrecord data
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'val_filename': 'val.mindrecord0' # file name of the evaluation mindrecord data
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'train_filename': 'train.mindrecord0' # file name of the train mindrecord data
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'val_filename': 'val.mindrecord0' # file name of the evaluation mindrecord data
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'data_dir': '/dev/data/Music_Tagger_Data/fea/' # directory of mindrecord data
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'device_target': 'Ascend' # device running the program
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'device_id': 0, # device ID used to train or evaluate the dataset. Ignore it when you use run_train.sh for distributed training
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'model_name': 'MusicTagger_3-50_543.ckpt', # checkpoint name
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```
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### [Training Process](#contents)
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## [Training Process](#contents)
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### Training
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#### Training
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- running on Ascend
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```shell
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python train.py > train.log 2>&1 &
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```
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python train.py > train.log 2>&1 &
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```
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The python command above will run in the background, you can view the results through the file `train.log`.
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After training, you'll get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
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```
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```shell
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# grep "loss is " train.log
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epoch: 1 step: 100, loss is 0.23264095
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epoch: 1 step: 200, loss is 0.2013525
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...
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```
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The model checkpoint will be saved in the set directory.
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## [Evaluation Process](#contents)
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The model checkpoint will be saved in the set directory.
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### Evaluation
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### [Evaluation Process](#contents)
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#### Evaluation
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# [Model Description](#contents)
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## [Performance](#contents)
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## [Model Description](#contents)
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### Evaluation Performance
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### [Performance](#contents)
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#### Evaluation Performance
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| Parameters | Ascend |
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| -------------------------- | ----------------------------------------------------------- |
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@ -195,9 +192,8 @@ Parameters for both training and evaluation can be set in config.py
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| Speed | 1pc: 160 samples/sec; |
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| Total time | 1pc: 20 mins; |
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| Checkpoint for Fine tuning | 198.73M(.ckpt file) |
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| Scripts | [music_auto_tagging script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/audio/music_auto_tagging) |
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| Scripts | [music_auto_tagging script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/audio/fcn-4) |
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## [ModelZoo Homepage](#contents)
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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