forked from mindspore-Ecosystem/mindspore
!10396 Modify ssd_ghostnet network
From: @zhanghuiyao Reviewed-by: @linqingke Signed-off-by: @linqingke
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b9ee076347
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@ -195,7 +195,7 @@ For more configuration details, please refer the script `config.py`.
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```python
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# grep "AP" eval.log
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{'AP': 0.39830956300341397, 'Ap .5': 0.6658941566481336, 'AP .75': 0.396047897339743, 'AP (M)': 0.3075356543635785, 'AP (L)': 0.533772768618845, 'AR': 0.4519836272040302, 'AR .5': 0.693639798488665, 'AR .75': 0.4570214105793451, 'AR (M)': 0.32155148866429945, 'AR (L)': 0.6330360460795242}
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{'AP': 0.40250956300341397, 'Ap .5': 0.6658941566481336, 'AP .75': 0.396047897339743, 'AP (M)': 0.3075356543635785, 'AP (L)': 0.533772768618845, 'AR': 0.4519836272040302, 'AR .5': 0.693639798488665, 'AR .75': 0.4570214105793451, 'AR (M)': 0.32155148866429945, 'AR (L)': 0.6330360460795242}
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```
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@ -1,5 +1,5 @@
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# [SSD Description](#contents)
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SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape.Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.
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[Paper](https://arxiv.org/abs/1512.02325): Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg.European Conference on Computer Vision (ECCV), 2016 (In press).
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@ -9,24 +9,25 @@ SSD discretizes the output space of bounding boxes into a set of default boxes o
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The SSD approach is based on a feed-forward convolutional network that produces a fixed-size collection of bounding boxes and scores for the presence of object class instances in those boxes, followed by a non-maximum suppression step to produce the final detections. The early network layers are based on a standard architecture used for high quality image classification, which is called the base network. Then add auxiliary structure to the network to produce detections.
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# [Dataset](#contents)
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Dataset used: [COCO2017](<http://images.cocodataset.org/>)
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Dataset used: [COCO2017](<http://images.cocodataset.org/>)
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- Dataset size：19G
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- Train：18G，118000 images
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- Val：1G，5000 images
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- Annotations：241M，instances，captions，person_keypoints etc
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- Train：18G，118000 images
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- Val：1G，5000 images
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- Annotations：241M，instances，captions，person_keypoints etc
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- Data format：image and json files
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- Note：Data will be processed in dataset.py
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- Note：Data will be processed in dataset.py
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# [Environment Requirements](#contents)
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- Hardware（Ascend/GPU）
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- 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.
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- 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.
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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/training/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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- Install [MindSpore](https://www.mindspore.cn/install/en).
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@ -37,15 +38,16 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
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1. If coco dataset is used. **Select dataset to coco when run script.**
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Install Cython and pycocotool, and you can also install mmcv to process data.
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```
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```bash
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pip install Cython
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pip install pycocotools
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```
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And change the COCO_ROOT and other settings you need in `config.py`. The directory structure is as follows:
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```
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```python
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.
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└─cocodataset
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├─annotations
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@ -59,18 +61,18 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
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2. If your own dataset is used. **Select dataset to other when run script.**
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Organize the dataset infomation into a TXT file, each row in the file is as follows:
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```
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```python
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train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
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```
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Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `IMAGE_DIR`(dataset directory) and the relative path in `ANNO_PATH`(the TXT file path), `IMAGE_DIR` and `ANNO_PATH` are setting in `config.py`.
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Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `IMAGE_DIR`(dataset directory) and the relative path in `ANNO_PATH`(the TXT file path), `IMAGE_DIR` and `ANNO_PATH` are setting in `config.py`.
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# [Quick Start](#contents)
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After installing MindSpore via the official website, you can start training and evaluation on Ascend as follows:
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After installing MindSpore via the official website, you can start training and evaluation on Ascend as follows:
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```
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```bash
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# single npu training on Ascend
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python train.py
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@ -85,9 +87,9 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
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## [Script and Sample Code](#contents)
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```shell
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```python
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├── ssd_ghostnet
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├── ssd_ghostnet
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├── README.md ## readme file of ssd_ghostnet
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├── scripts
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└─ run_distribute_train_ghostnet.sh ## shell script for distributed on ascend
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@ -106,7 +108,7 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
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## [Script Parameters](#contents)
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```
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```python
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Major parameters in train.py and config_ghostnet_13x.py as follows:
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"device_num": 1 # Use device nums
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@ -129,39 +131,46 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
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```
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## [Training Process](#contents)
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### Training on Ascend
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To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
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- Distribute mode
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```
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```bash
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sh run_distribute_train_ghostnet.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
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```
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We need five or seven parameters for this scripts.
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- `DEVICE_NUM`: the device number for distributed train.
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- `EPOCH_NUM`: epoch num for distributed train.
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- `LR`: learning rate init value for distributed train.
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- `DATASET`：the dataset mode for distributed train.
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- `RANK_TABLE_FILE :` the path of [rank_table.json](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools), it is better to use absolute path.
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- `PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
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- `PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
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Training result will be stored in the current path, whose folder name begins with "LOG". Under this, you can find checkpoint file together with result like the followings in LOG4/log.txt.
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Training result will be stored in the current path, whose folder name begins with "LOG". Under this, you can find checkpoint file together with result like the followings in LOG4/log.txt.
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## [Evaluation Process](#contents)
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### Evaluation on Ascend
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```
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python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.ckpt
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```bash
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python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.ckpt
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```
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# [Model Description](#contents)
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## [Performance](#contents)
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### Evaluation Performance
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@ -177,7 +186,6 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
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| Loss Function | Sigmoid Cross Entropy,SmoothL1Loss |
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| Total time | 8pcs: 12hours |
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### Inference Performance
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| Parameters | Ascend |
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@ -19,7 +19,7 @@ import os
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import argparse
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import time
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import numpy as np
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from mindspore import context, Tensor
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.ssd_ghostnet import SSD300, ssd_ghostnet
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from src.dataset import create_ssd_dataset, data_to_mindrecord_byte_image, voc_data_to_mindrecord
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@ -47,11 +47,11 @@ def ssd_eval(dataset_path, ckpt_path):
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print("total images num: ", total)
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print("Processing, please wait a moment.")
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for data in ds.create_dict_iterator():
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img_id = data['img_id']
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img_id = data['img_id'].asnumpy()
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img_np = data['image']
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image_shape = data['image_shape']
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image_shape = data['image_shape'].asnumpy()
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output = net(Tensor(img_np))
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output = net(img_np)
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for batch_idx in range(img_np.shape[0]):
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pred_data.append({"boxes": output[0].asnumpy()[batch_idx],
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"box_scores": output[1].asnumpy()[batch_idx],
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@ -24,7 +24,7 @@ import numpy as np
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import cv2
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import mindspore.dataset as de
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import mindspore.dataset.transforms.vision.c_transforms as C
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import mindspore.dataset.vision.c_transforms as C2
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from mindspore.mindrecord import FileWriter
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from .config_ghostnet_13x import config
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from .box_utils import jaccard_numpy, ssd_bboxes_encode
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@ -397,12 +397,12 @@ def create_ssd_dataset(mindrecord_file, batch_size=32, repeat_num=10, device_num
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"""Create SSD dataset with MindDataset."""
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ds = de.MindDataset(mindrecord_file, columns_list=["img_id", "image", "annotation"], num_shards=device_num,
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shard_id=rank, num_parallel_workers=num_parallel_workers, shuffle=is_training)
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decode = C.Decode()
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decode = C2.Decode()
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ds = ds.map(input_columns=["image"], operations=decode)
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change_swap_op = C.HWC2CHW()
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normalize_op = C.Normalize(
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change_swap_op = C2.HWC2CHW()
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normalize_op = C2.Normalize(
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mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])
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color_adjust_op = C.RandomColorAdjust(
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color_adjust_op = C2.RandomColorAdjust(
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brightness=0.4, contrast=0.4, saturation=0.4)
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compose_map_func = (lambda img_id, image, annotation: preprocess_fn(
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img_id, image, annotation, is_training))
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@ -413,7 +413,7 @@ def create_ssd_dataset(mindrecord_file, batch_size=32, repeat_num=10, device_num
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output_columns = ["img_id", "image", "image_shape"]
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trans = [normalize_op, change_swap_op]
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ds = ds.map(input_columns=["img_id", "image", "annotation"],
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output_columns=output_columns, columns_order=output_columns,
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output_columns=output_columns, column_order=output_columns,
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operations=compose_map_func, python_multiprocessing=is_training,
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num_parallel_workers=num_parallel_workers)
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ds = ds.map(input_columns=["image"], operations=trans, python_multiprocessing=is_training,
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@ -25,16 +25,16 @@ def init_net_param(network, initialize_mode='TruncatedNormal'):
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if 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
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np.random.seed(seed=1)
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if initialize_mode == 'TruncatedNormal':
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p.set_parameter_data(initializer(
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p.set_data(initializer(
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TruncatedNormal(), p.data.shape, p.data.dtype))
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else:
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p.set_parameter_data(
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initialize_mode, p.data.shape, p.data.dtype)
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p.set_data(
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initialize_mode, p.data.shape)
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def load_backbone_params(network, param_dict):
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"""Init the parameters from pre-train model, default is mobilenetv2."""
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for _, param in net.parameters_and_names():
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for _, param in network.parameters_and_names():
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param_name = param.name.replace('network.backbone.', '')
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name_split = param_name.split('.')
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if 'features_1' in param_name:
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@ -118,13 +118,11 @@ class DepthwiseConv(nn.Cell):
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stride=stride, pad_mode=pad_mode, pad=pad)
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self.bias_add = P.BiasAdd()
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weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
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self.weight = Parameter(initializer(
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'ones', weight_shape))
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self.weight = Parameter(initializer('ones', weight_shape), name="weight")
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if has_bias:
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bias_shape = [channel_multiplier * in_planes]
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self.bias = Parameter(initializer(
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'zeros', bias_shape))
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self.bias = Parameter(initializer('zeros', bias_shape), name="bias")
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else:
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self.bias = None
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@ -624,15 +622,15 @@ class TrainingWrapper(nn.Cell):
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self.network = network
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self.weights = ms.ParameterTuple(network.trainable_params())
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self.optimizer = optimizer
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self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
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self.grad = C.GradOperation(get_by_list=True, sens_param=True)
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self.sens = sens
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self.reducer_flag = False
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self.grad_reducer = None
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self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
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if self.parallel_mode in [ms.ParallelMode.DATA_PARALLEL, ms.ParallelMode.HYBRID_PARALLEL]:
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if self.parallel_mode in [context.ParallelMode.DATA_PARALLEL, context.ParallelMode.HYBRID_PARALLEL]:
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self.reducer_flag = True
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if self.reducer_flag:
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mean = context.get_auto_parallel_context("mirror_mean")
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mean = context.get_auto_parallel_context("gradients_mean")
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if auto_parallel_context().get_device_num_is_set():
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degree = context.get_auto_parallel_context("device_num")
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else:
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@ -70,7 +70,7 @@ def main():
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if args_opt.distribute:
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device_num = args_opt.device_num
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
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device_num=device_num)
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init()
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rank = args_opt.device_id % device_num
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