forked from mindspore-Ecosystem/mindspore
!9023 add maskrcnn_mobilenetv1 network
From: @wang_hua_2019 Reviewed-by: Signed-off-by:
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# Contents
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- [MaskRCNN Description](#maskrcnn-description)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Environment Requirements](#environment-requirements)
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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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- [Training Script Parameters](#training-script-parameters)
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- [Parameters Configuration](#parameters-configuration)
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- [Training Process](#training-process)
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- [Training](#training)
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- [Distributed Training](#distributed-training)
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- [Training Result](#training-result)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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- [Evaluation Result](#evaluation-result)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Evaluation Performance](#evaluation-performance)
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- [Inference Performance](#inference-performance)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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# [MaskRCNN Description](#contents)
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MaskRCNN is a conceptually simple, flexible, and general framework for object instance segmentation. The approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in
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parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing to estimate human poses in the same framework.
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It shows top results in all three tracks of the COCO suite of challenges, including instance segmentation, boundingbox object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.
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# [Model Architecture](#contents)
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MaskRCNN is a two-stage target detection network. It extends FasterRCNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.This network uses a region proposal network (RPN), which can share the convolution features of the whole image with the detection network, so that the calculation of region proposal is almost cost free. The whole network further combines RPN and mask branch into a network by sharing the convolution features.
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This network uses MobileNetV1 as the backbone of the MaskRCNN network.
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[Paper](http://cn.arxiv.org/pdf/1703.06870v3): Kaiming He, Georgia Gkioxari, Piotr Dollar and Ross Girshick. "MaskRCNN"
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# [Dataset](#contents)
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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.
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- [COCO2017](https://cocodataset.org/) is a popular dataset with bounding-box and pixel-level stuff annotations. These annotations can be used for scene understanding tasks like semantic segmentation, object detection and image captioning. There are 118K/5K images for train/val.
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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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- Data format: image and json files
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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）
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- 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.
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- Framework
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- [MindSpore](https://gitee.com/mindspore/mindspore)
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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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- third-party libraries
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```bash
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pip install Cython
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pip install pycocotools
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pip install mmcv=0.2.14
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```
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# [Quick Start](#contents)
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1. Download the dataset COCO2017.
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2. Change the COCO_ROOT and other settings you need in `config.py`. The directory structure should look like the follows:
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```
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.
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└─cocodataset
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├─annotations
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├─instance_train2017.json
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└─instance_val2017.json
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├─val2017
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└─train2017
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```
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If you use your own dataset to train the network, **Select dataset to other when run script.**
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Create a txt file to store dataset information organized in the way as shown as following:
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```
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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 split by spaces. The first column is a relative path of image, followed by columns containing box and class information in 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), which can be set in `config.py`.
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3. Execute train script.
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After dataset preparation, you can start training as follows:
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```
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# distributed training
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sh run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT]
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# standalone training
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sh run_standalone_train.sh [PRETRAINED_CKPT]
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```
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Note:
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1. To speed up data preprocessing, MindSpore provide a data format named MindRecord, hence the first step is to generate MindRecord files based on COCO2017 dataset before training. The process of converting raw COCO2017 dataset to MindRecord format may take about 4 hours.
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2. For distributed training, a [hccl configuration file](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools) with JSON format needs to be created in advance.
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3. For large models like MaskRCNN, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
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4. Execute eval script.
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After training, you can start evaluation as follows:
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```bash
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# Evaluation
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sh run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
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```
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Note:
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1. VALIDATION_JSON_FILE is a label json file for evaluation.
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# [Script Description](#contents)
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## [Script and Sample Code](#contents)
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```shell
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.
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└─MaskRcnn
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├─README.md # README
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├─scripts # shell script
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├─run_standalone_train.sh # training in standalone mode(1pcs)
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├─run_distribute_train.sh # training in parallel mode(8 pcs)
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└─run_eval.sh # evaluation
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├─src
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├─maskrcnn_mobilenetv1
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├─__init__.py
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├─anchor_generator.py # generate base bounding box anchors
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├─bbox_assign_sample.py # filter positive and negative bbox for the first stage learning
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├─bbox_assign_sample_stage2.py # filter positive and negative bbox for the second stage learning
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├─mask_rcnn_mobilenetv1.py # main network architecture of maskrcnn
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├─fpn_neck.py # fpn network
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├─proposal_generator.py # generate proposals based on feature map
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├─rcnn_cls.py # rcnn bounding box regression branch
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├─rcnn_mask.py # rcnn mask branch
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├─mobilenetv1.py # backbone network
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├─roi_align.py # roi align network
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└─rpn.py # reagion proposal network
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├─config.py # network configuration
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├─dataset.py # dataset utils
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├─lr_schedule.py # leanring rate geneatore
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├─network_define.py # network define for maskrcnn
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└─util.py # routine operation
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├─mindspore_hub_conf.py # mindspore hub interface
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├─eval.py # evaluation scripts
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└─train.py # training scripts
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```
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## [Script Parameters](#contents)
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### [Training Script Parameters](#contents)
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```bash
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# distributed training
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Usage: sh run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]
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# standalone training
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Usage: sh run_standalone_train.sh [PRETRAINED_MODEL]
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```
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### [Parameters Configuration](#contents)
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```bash
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"img_width": 1280, # width of the input images
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"img_height": 768, # height of the input images
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# random threshold in data augmentation
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"keep_ratio": True,
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"flip_ratio": 0.5,
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"photo_ratio": 0.5,
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"expand_ratio": 1.0,
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"max_instance_count": 128, # max number of bbox for each image
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"mask_shape": (28, 28), # shape of mask in rcnn_mask
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# anchor
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"feature_shapes": [(192, 320), (96, 160), (48, 80), (24, 40), (12, 20)], # shape of fpn feaure maps
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"anchor_scales": [8], # area of base anchor
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"anchor_ratios": [0.5, 1.0, 2.0], # ratio between width of height of base anchors
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"anchor_strides": [4, 8, 16, 32, 64], # stride size of each feature map levels
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"num_anchors": 3, # anchor number for each pixel
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# fpn
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"fpn_in_channels": [128, 256, 512, 1024], # in channel size for each layer
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"fpn_out_channels": 256, # out channel size for every layer
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"fpn_num_outs": 5, # out feature map size
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# rpn
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"rpn_in_channels": 256, # in channel size
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"rpn_feat_channels": 256, # feature out channel size
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"rpn_loss_cls_weight": 1.0, # weight of bbox classification in rpn loss
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"rpn_loss_reg_weight": 1.0, # weight of bbox regression in rpn loss
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"rpn_cls_out_channels": 1, # classification out channel size
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"rpn_target_means": [0., 0., 0., 0.], # bounding box decode/encode means
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"rpn_target_stds": [1.0, 1.0, 1.0, 1.0], # bounding box decode/encode stds
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# bbox_assign_sampler
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"neg_iou_thr": 0.3, # negative sample threshold after IOU
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"pos_iou_thr": 0.7, # positive sample threshold after IOU
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"min_pos_iou": 0.3, # minimal positive sample threshold after IOU
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"num_bboxes": 245520, # total bbox numner
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"num_gts": 128, # total ground truth number
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"num_expected_neg": 256, # negative sample number
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"num_expected_pos": 128, # positive sample number
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# proposal
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"activate_num_classes": 2, # class number in rpn classification
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"use_sigmoid_cls": True, # whethre use sigmoid as loss function in rpn classification
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# roi_alignj
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"roi_layer": dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2), # ROIAlign parameters
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"roi_align_out_channels": 256, # ROIAlign out channels size
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"roi_align_featmap_strides": [4, 8, 16, 32], # stride size for differnt level of ROIAling feature map
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"roi_align_finest_scale": 56, # finest scale ofr ROIAlign
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"roi_sample_num": 640, # sample number in ROIAling layer
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# bbox_assign_sampler_stage2 # bbox assign sample for the second stage, parameter meaning is similar with bbox_assign_sampler
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"neg_iou_thr_stage2": 0.5,
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"pos_iou_thr_stage2": 0.5,
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"min_pos_iou_stage2": 0.5,
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"num_bboxes_stage2": 2000,
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"num_expected_pos_stage2": 128,
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"num_expected_neg_stage2": 512,
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"num_expected_total_stage2": 512,
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# rcnn # rcnn parameter for the second stage, parameter meaning is similar with fpn
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"rcnn_num_layers": 2,
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"rcnn_in_channels": 256,
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"rcnn_fc_out_channels": 1024,
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"rcnn_mask_out_channels": 256,
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"rcnn_loss_cls_weight": 1,
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"rcnn_loss_reg_weight": 1,
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"rcnn_loss_mask_fb_weight": 1,
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"rcnn_target_means": [0., 0., 0., 0.],
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"rcnn_target_stds": [0.1, 0.1, 0.2, 0.2],
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# train proposal
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"rpn_proposal_nms_across_levels": False,
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"rpn_proposal_nms_pre": 2000, # proposal number before nms in rpn
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"rpn_proposal_nms_post": 2000, # proposal number after nms in rpn
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"rpn_proposal_max_num": 2000, # max proposal number in rpn
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"rpn_proposal_nms_thr": 0.7, # nms threshold for nms in rpn
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"rpn_proposal_min_bbox_size": 0, # min size of box in rpn
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# test proposal # part of parameters are similar with train proposal
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"rpn_nms_across_levels": False,
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"rpn_nms_pre": 1000,
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"rpn_nms_post": 1000,
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"rpn_max_num": 1000,
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"rpn_nms_thr": 0.7,
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"rpn_min_bbox_min_size": 0,
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"test_score_thr": 0.05, # score threshold
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"test_iou_thr": 0.5, # IOU threshold
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"test_max_per_img": 100, # max number of instance
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"test_batch_size": 2, # batch size
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"rpn_head_loss_type": "CrossEntropyLoss", # loss type in rpn
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"rpn_head_use_sigmoid": True, # whether use sigmoid or not in rpn
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"rpn_head_weight": 1.0, # rpn head weight in loss
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"mask_thr_binary": 0.5, # mask threshold for in rcnn
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# LR
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"base_lr": 0.02, # base learning rate
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"base_step": 58633, # bsae step in lr generator
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"total_epoch": 13, # total epoch in lr generator
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"warmup_step": 500, # warmp up step in lr generator
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"warmup_mode": "linear", # warmp up mode
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"warmup_ratio": 1/3.0, # warpm up ratio
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"sgd_momentum": 0.9, # momentum in optimizer
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# train
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"batch_size": 2,
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"loss_scale": 1,
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"momentum": 0.91,
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"weight_decay": 1e-4,
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"pretrain_epoch_size": 0, # pretrained epoch size
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"epoch_size": 12, # total epoch size
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"save_checkpoint": True, # whether save checkpoint or not
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"save_checkpoint_epochs": 1, # save checkpoint interval
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"keep_checkpoint_max": 12, # max number of saved checkpoint
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"save_checkpoint_path": "./", # path of checkpoint
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"mindrecord_dir": "/home/maskrcnn/MindRecord_COCO2017_Train", # path of mindrecord
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"coco_root": "/home/maskrcnn/", # path of coco root dateset
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"train_data_type": "train2017", # name of train dataset
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"val_data_type": "val2017", # name of evaluation dataset
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"instance_set": "annotations/instances_{}.json", # name of annotation
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"coco_classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
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'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
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'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
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'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
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'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
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'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
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'kite', 'baseball bat', 'baseball glove', 'skateboard',
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'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
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'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
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'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
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'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
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'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
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'refrigerator', 'book', 'clock', 'vase', 'scissors',
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'teddy bear', 'hair drier', 'toothbrush'),
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"num_classes": 81
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```
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## [Training Process](#contents)
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- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
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### [Training](#content)
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- Run `run_standalone_train.sh` for non-distributed training of MaskRCNN model.
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```bash
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# standalone training
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sh run_standalone_train.sh [PRETRAINED_MODEL]
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```
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### [Distributed Training](#content)
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- Run `run_distribute_train.sh` for distributed training of Mask model.
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```bash
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sh run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]
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```
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> hccl.json which is specified by RANK_TABLE_FILE is needed when you are running a distribute task. You can generate it by using the [hccl_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
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> As for PRETRAINED_MODEL, if not set, the model will be trained from the very beginning. Ready-made pretrained_models are not available now. Stay tuned.
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> This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
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### [Training Result](#content)
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Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the followings in loss_rankid.log.
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```bash
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# distribute training result(8p)
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2123 epoch: 1 step: 7393 ,rpn_loss: 0.24854, rcnn_loss: 1.04492, rpn_cls_loss: 0.19238, rpn_reg_loss: 0.05603, rcnn_cls_loss: 0.47510, rcnn_reg_loss: 0.16919, rcnn_mask_loss: 0.39990, total_loss: 1.29346
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3973 epoch: 2 step: 7393 ,rpn_loss: 0.02769, rcnn_loss: 0.51367, rpn_cls_loss: 0.01746, rpn_reg_loss: 0.01023, rcnn_cls_loss: 0.24255, rcnn_reg_loss: 0.05630, rcnn_mask_loss: 0.21484, total_loss: 0.54137
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5820 epoch: 3 step: 7393 ,rpn_loss: 0.06665, rcnn_loss: 1.00391, rpn_cls_loss: 0.04999, rpn_reg_loss: 0.01663, rcnn_cls_loss: 0.44458, rcnn_reg_loss: 0.17700, rcnn_mask_loss: 0.38232, total_loss: 1.07056
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7665 epoch: 4 step: 7393 ,rpn_loss: 0.14612, rcnn_loss: 0.56885, rpn_cls_loss: 0.06186, rpn_reg_loss: 0.08429, rcnn_cls_loss: 0.21228, rcnn_reg_loss: 0.08105, rcnn_mask_loss: 0.27539, total_loss: 0.71497
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...
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16885 epoch: 9 step: 7393 ,rpn_loss: 0.07977, rcnn_loss: 0.85840, rpn_cls_loss: 0.04395, rpn_reg_loss: 0.03583, rcnn_cls_loss: 0.37598, rcnn_reg_loss: 0.11450, rcnn_mask_loss: 0.36816, total_loss: 0.93817
|
||||
18727 epoch: 10 step: 7393 ,rpn_loss: 0.02379, rcnn_loss: 1.20508, rpn_cls_loss: 0.01431, rpn_reg_loss: 0.00947, rcnn_cls_loss: 0.32178, rcnn_reg_loss: 0.18872, rcnn_mask_loss: 0.69434, total_loss: 1.22887
|
||||
20570 epoch: 11 step: 7393 ,rpn_loss: 0.03967, rcnn_loss: 1.07422, rpn_cls_loss: 0.01508, rpn_reg_loss: 0.02461, rcnn_cls_loss: 0.28687, rcnn_reg_loss: 0.15027, rcnn_mask_loss: 0.63770, total_loss: 1.11389
|
||||
22411 epoch: 12 step: 7393 ,rpn_loss: 0.02937, rcnn_loss: 0.85449, rpn_cls_loss: 0.01704, rpn_reg_loss: 0.01234, rcnn_cls_loss: 0.20667, rcnn_reg_loss: 0.12439, rcnn_mask_loss: 0.52344, total_loss: 0.88387
|
||||
```
|
||||
|
||||
## [Evaluation Process](#contents)
|
||||
|
||||
### [Evaluation](#content)
|
||||
|
||||
- Run `run_eval.sh` for evaluation.
|
||||
|
||||
```bash
|
||||
# infer
|
||||
sh run_eval.sh [VALIDATION_ANN_FILE_JSON] [CHECKPOINT_PATH]
|
||||
```
|
||||
|
||||
> As for the COCO2017 dataset, VALIDATION_ANN_FILE_JSON is refer to the annotations/instances_val2017.json in the dataset directory.
|
||||
> checkpoint can be produced and saved in training process, whose folder name begins with "train/checkpoint" or "train_parallel*/checkpoint".
|
||||
|
||||
### [Evaluation result](#content)
|
||||
|
||||
Inference result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.
|
||||
|
||||
```bash
|
||||
Evaluate annotation type *bbox*
|
||||
Accumulating evaluation results...
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.227
|
||||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.398
|
||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.232
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.240
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.283
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.239
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.390
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.411
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.270
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.440
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.501
|
||||
|
||||
Evaluate annotation type *segm*
|
||||
Accumulating evaluation results...
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.176
|
||||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.339
|
||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.166
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.089
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.185
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.254
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.193
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.292
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.302
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.179
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.320
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.388
|
||||
```
|
||||
|
||||
# Model Description
|
||||
|
||||
## Performance
|
||||
|
||||
### Evaluation Performance
|
||||
|
||||
| Parameters | Ascend |
|
||||
| -------------------------- | ----------------------------------------------------------- |
|
||||
| Model Version | V1 |
|
||||
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G |
|
||||
| uploaded Date | 12/01/2020 (month/day/year) |
|
||||
| MindSpore Version | 1.0.0 |
|
||||
| Dataset | COCO2017 |
|
||||
| Training Parameters | epoch=12, batch_size = 2 |
|
||||
| Optimizer | SGD |
|
||||
| Loss Function | Softmax Cross Entropy, Sigmoid Cross Entropy, SmoothL1Loss |
|
||||
| Output | Probability |
|
||||
| Loss | 0.88387 |
|
||||
| Speed | 8pcs: 249 ms/step |
|
||||
| Total time | 8pcs: 6.23 hours |
|
||||
| Scripts | [maskrcnn script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/maskrcnn_mobilenetv1) |
|
||||
|
||||
### Inference Performance
|
||||
|
||||
| Parameters | Ascend |
|
||||
| ------------------- | --------------------------- |
|
||||
| Model Version | V1 |
|
||||
| Resource | Ascend 910 |
|
||||
| Uploaded Date | 12/01/2020 (month/day/year) |
|
||||
| MindSpore Version | 1.0.0 |
|
||||
| Dataset | COCO2017 |
|
||||
| batch_size | 2 |
|
||||
| outputs | mAP |
|
||||
| Accuracy | IoU=0.50:0.95 (BoundingBox 22.7%, Mask 17.6%) |
|
||||
| Model for inference | 107M (.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).
|
||||
|
|
@ -0,0 +1,134 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# less required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
"""Evaluation for MaskRcnn"""
|
||||
import os
|
||||
import argparse
|
||||
import time
|
||||
import numpy as np
|
||||
from pycocotools.coco import COCO
|
||||
from mindspore import context, Tensor
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
from mindspore.common import set_seed
|
||||
|
||||
from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
|
||||
from src.config import config
|
||||
from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
|
||||
from src.util import coco_eval, bbox2result_1image, results2json, get_seg_masks
|
||||
|
||||
set_seed(1)
|
||||
|
||||
parser = argparse.ArgumentParser(description="MaskRcnn evaluation")
|
||||
parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
|
||||
parser.add_argument("--ann_file", type=str, default="val.json", help="Ann file, default is val.json.")
|
||||
parser.add_argument("--checkpoint_path", type=str, required=True, help="Checkpoint file path.")
|
||||
parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
|
||||
|
||||
def MaskRcnn_eval(dataset_path, ckpt_path, ann_file):
|
||||
"""MaskRcnn evaluation."""
|
||||
ds = create_maskrcnn_dataset(dataset_path, batch_size=config.test_batch_size, is_training=False)
|
||||
|
||||
net = Mask_Rcnn_Mobilenetv1(config)
|
||||
param_dict = load_checkpoint(ckpt_path)
|
||||
load_param_into_net(net, param_dict)
|
||||
net.set_train(False)
|
||||
|
||||
eval_iter = 0
|
||||
total = ds.get_dataset_size()
|
||||
outputs = []
|
||||
dataset_coco = COCO(ann_file)
|
||||
|
||||
print("\n========================================\n")
|
||||
print("total images num: ", total)
|
||||
print("Processing, please wait a moment.")
|
||||
max_num = 128
|
||||
|
||||
for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):
|
||||
img_data = data['image']
|
||||
img_metas = data['image_shape']
|
||||
gt_bboxes = data['box']
|
||||
gt_labels = data['label']
|
||||
gt_num = data['valid_num']
|
||||
gt_mask = data["mask"]
|
||||
|
||||
start = time.time()
|
||||
# run net
|
||||
output = net(Tensor(img_data), Tensor(img_metas), Tensor(gt_bboxes), Tensor(gt_labels), Tensor(gt_num),
|
||||
Tensor(gt_mask))
|
||||
end = time.time()
|
||||
print("Iter {} cost time {}".format(eval_iter, end - start))
|
||||
|
||||
# output
|
||||
all_bbox = output[0]
|
||||
all_label = output[1]
|
||||
all_mask = output[2]
|
||||
all_mask_fb = output[3]
|
||||
|
||||
for j in range(config.test_batch_size):
|
||||
all_bbox_squee = np.squeeze(all_bbox.asnumpy()[j, :, :])
|
||||
all_label_squee = np.squeeze(all_label.asnumpy()[j, :, :])
|
||||
all_mask_squee = np.squeeze(all_mask.asnumpy()[j, :, :])
|
||||
all_mask_fb_squee = np.squeeze(all_mask_fb.asnumpy()[j, :, :, :])
|
||||
|
||||
all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
|
||||
all_labels_tmp_mask = all_label_squee[all_mask_squee]
|
||||
all_mask_fb_tmp_mask = all_mask_fb_squee[all_mask_squee, :, :]
|
||||
|
||||
if all_bboxes_tmp_mask.shape[0] > max_num:
|
||||
inds = np.argsort(-all_bboxes_tmp_mask[:, -1])
|
||||
inds = inds[:max_num]
|
||||
all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]
|
||||
all_labels_tmp_mask = all_labels_tmp_mask[inds]
|
||||
all_mask_fb_tmp_mask = all_mask_fb_tmp_mask[inds]
|
||||
|
||||
|
||||
bbox_results = bbox2result_1image(all_bboxes_tmp_mask, all_labels_tmp_mask, config.num_classes)
|
||||
segm_results = get_seg_masks(all_mask_fb_tmp_mask, all_bboxes_tmp_mask, all_labels_tmp_mask, img_metas[j],
|
||||
True, config.num_classes)
|
||||
outputs.append((bbox_results, segm_results))
|
||||
|
||||
eval_iter = eval_iter + 1
|
||||
|
||||
eval_types = ["bbox", "segm"]
|
||||
result_files = results2json(dataset_coco, outputs, "./results.pkl")
|
||||
coco_eval(result_files, eval_types, dataset_coco, single_result=False)
|
||||
|
||||
if __name__ == '__main__':
|
||||
prefix = "MaskRcnn_eval.mindrecord"
|
||||
mindrecord_dir = config.mindrecord_dir
|
||||
mindrecord_file = os.path.join(mindrecord_dir, prefix)
|
||||
if not os.path.exists(mindrecord_file):
|
||||
if not os.path.isdir(mindrecord_dir):
|
||||
os.makedirs(mindrecord_dir)
|
||||
if args_opt.dataset == "coco":
|
||||
if os.path.isdir(config.coco_root):
|
||||
print("Create Mindrecord.")
|
||||
data_to_mindrecord_byte_image("coco", False, prefix, file_num=1)
|
||||
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
|
||||
else:
|
||||
print("coco_root not exits.")
|
||||
else:
|
||||
if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
|
||||
print("Create Mindrecord.")
|
||||
data_to_mindrecord_byte_image("other", False, prefix, file_num=1)
|
||||
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
|
||||
else:
|
||||
print("IMAGE_DIR or ANNO_PATH not exits.")
|
||||
|
||||
print("Start Eval!")
|
||||
MaskRcnn_eval(mindrecord_file, args_opt.checkpoint_path, args_opt.ann_file)
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""hub config."""
|
||||
from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
|
||||
from src.config import config
|
||||
|
||||
def create_network(name, *args, **kwargs):
|
||||
if name == "maskrcnn_mobilenetv1":
|
||||
return Mask_Rcnn_Mobilenetv1(config=config)
|
||||
raise NotImplementedError(f"{name} is not implemented in the repo")
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 2 ]
|
||||
then
|
||||
echo "Usage: sh run_train.sh [RANK_TABLE_FILE] [PRETRAINED_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
PATH1=$(get_real_path $1)
|
||||
PATH2=$2
|
||||
|
||||
echo $PATH1
|
||||
echo $PATH2
|
||||
|
||||
if [ ! -f $PATH1 ]
|
||||
then
|
||||
echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
ulimit -u unlimited
|
||||
export HCCL_CONNECT_TIMEOUT=600
|
||||
export DEVICE_NUM=8
|
||||
export RANK_SIZE=8
|
||||
export RANK_TABLE_FILE=$PATH1
|
||||
|
||||
echo 3 > /proc/sys/vm/drop_caches
|
||||
|
||||
cpus=`cat /proc/cpuinfo| grep "processor"| wc -l`
|
||||
avg=`expr $cpus \/ $RANK_SIZE`
|
||||
gap=`expr $avg \- 1`
|
||||
|
||||
for((i=0; i<${DEVICE_NUM}; i++))
|
||||
do
|
||||
start=`expr $i \* $avg`
|
||||
end=`expr $start \+ $gap`
|
||||
cmdopt=$start"-"$end
|
||||
|
||||
export DEVICE_ID=$i
|
||||
export RANK_ID=$i
|
||||
rm -rf ./train_parallel$i
|
||||
mkdir ./train_parallel$i
|
||||
cp ../*.py ./train_parallel$i
|
||||
cp *.sh ./train_parallel$i
|
||||
cp -r ../src ./train_parallel$i
|
||||
cd ./train_parallel$i || exit
|
||||
echo "start training for rank $RANK_ID, device $DEVICE_ID"
|
||||
env > env.log
|
||||
taskset -c $cmdopt python train.py --do_train=True --device_id=$i --rank_id=$i --run_distribute=True --device_num=$DEVICE_NUM \
|
||||
--pre_trained=$PATH2 &> log &
|
||||
cd ..
|
||||
done
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 2 ]
|
||||
then
|
||||
echo "Usage: sh run_eval.sh [ANN_FILE] [CHECKPOINT_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
PATH1=$(get_real_path $1)
|
||||
PATH2=$(get_real_path $2)
|
||||
echo $PATH1
|
||||
echo $PATH2
|
||||
|
||||
if [ ! -f $PATH1 ]
|
||||
then
|
||||
echo "error: ANN_FILE=$PATH1 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ ! -f $PATH2 ]
|
||||
then
|
||||
echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
ulimit -u unlimited
|
||||
export DEVICE_NUM=1
|
||||
export RANK_SIZE=$DEVICE_NUM
|
||||
export DEVICE_ID=0
|
||||
export RANK_ID=0
|
||||
|
||||
if [ -d "eval" ];
|
||||
then
|
||||
rm -rf ./eval
|
||||
fi
|
||||
mkdir ./eval
|
||||
cp ../*.py ./eval
|
||||
cp *.sh ./eval
|
||||
cp -r ../src ./eval
|
||||
cd ./eval || exit
|
||||
env > env.log
|
||||
echo "start eval for device $DEVICE_ID"
|
||||
python eval.py --device_id=$DEVICE_ID --ann_file=$PATH1 --checkpoint_path=$PATH2 &> log &
|
||||
cd ..
|
||||
|
|
@ -0,0 +1,57 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 1 ]
|
||||
then
|
||||
echo "Usage: sh run_standalone_train.sh [PRETRAINED_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
PATH1=$(get_real_path $1)
|
||||
echo $PATH1
|
||||
|
||||
if [ ! -f $PATH1 ]
|
||||
then
|
||||
echo "error: PRETRAINED_PATH=$PATH1 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
ulimit -u unlimited
|
||||
export DEVICE_NUM=1
|
||||
export DEVICE_ID=0
|
||||
export RANK_ID=0
|
||||
export RANK_SIZE=1
|
||||
|
||||
if [ -d "train" ];
|
||||
then
|
||||
rm -rf ./train
|
||||
fi
|
||||
mkdir ./train
|
||||
cp ../*.py ./train
|
||||
cp *.sh ./train
|
||||
cp -r ../src ./train
|
||||
cd ./train || exit
|
||||
echo "start training for device $DEVICE_ID"
|
||||
env > env.log
|
||||
python train.py --do_train=True --device_id=$DEVICE_ID --pre_trained=$PATH1 &> log &
|
||||
cd ..
|
||||
|
|
@ -0,0 +1,159 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#" :===========================================================================
|
||||
"""
|
||||
network config setting, will be used in train.py and eval.py
|
||||
"""
|
||||
from easydict import EasyDict as ed
|
||||
|
||||
config = ed({
|
||||
"img_width": 1280,
|
||||
"img_height": 768,
|
||||
"keep_ratio": True,
|
||||
"flip_ratio": 0.5,
|
||||
"photo_ratio": 0.5,
|
||||
"expand_ratio": 1.0,
|
||||
|
||||
"max_instance_count": 128,
|
||||
"mask_shape": (28, 28),
|
||||
|
||||
# anchor
|
||||
"feature_shapes": [(192, 320), (96, 160), (48, 80), (24, 40), (12, 20)],
|
||||
"anchor_scales": [8],
|
||||
"anchor_ratios": [0.5, 1.0, 2.0],
|
||||
"anchor_strides": [4, 8, 16, 32, 64],
|
||||
"num_anchors": 3,
|
||||
|
||||
# fpn
|
||||
"fpn_in_channels": [128, 256, 512, 1024],
|
||||
"fpn_out_channels": 256,
|
||||
"fpn_num_outs": 5,
|
||||
|
||||
# rpn
|
||||
"rpn_in_channels": 256,
|
||||
"rpn_feat_channels": 256,
|
||||
"rpn_loss_cls_weight": 1.0,
|
||||
"rpn_loss_reg_weight": 1.0,
|
||||
"rpn_cls_out_channels": 1,
|
||||
"rpn_target_means": [0., 0., 0., 0.],
|
||||
"rpn_target_stds": [1.0, 1.0, 1.0, 1.0],
|
||||
|
||||
# bbox_assign_sampler
|
||||
"neg_iou_thr": 0.3,
|
||||
"pos_iou_thr": 0.7,
|
||||
"min_pos_iou": 0.3,
|
||||
"num_bboxes": 245520,
|
||||
"num_gts": 128,
|
||||
"num_expected_neg": 256,
|
||||
"num_expected_pos": 128,
|
||||
|
||||
# proposal
|
||||
"activate_num_classes": 2,
|
||||
"use_sigmoid_cls": True,
|
||||
|
||||
# roi_align
|
||||
"roi_layer": dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2),
|
||||
"roi_align_out_channels": 256,
|
||||
"roi_align_featmap_strides": [4, 8, 16, 32],
|
||||
"roi_align_finest_scale": 56,
|
||||
"roi_sample_num": 640,
|
||||
|
||||
# bbox_assign_sampler_stage2
|
||||
"neg_iou_thr_stage2": 0.5,
|
||||
"pos_iou_thr_stage2": 0.5,
|
||||
"min_pos_iou_stage2": 0.5,
|
||||
"num_bboxes_stage2": 2000,
|
||||
"num_expected_pos_stage2": 128,
|
||||
"num_expected_neg_stage2": 512,
|
||||
"num_expected_total_stage2": 512,
|
||||
|
||||
# rcnn
|
||||
"rcnn_num_layers": 2,
|
||||
"rcnn_in_channels": 256,
|
||||
"rcnn_fc_out_channels": 1024,
|
||||
"rcnn_mask_out_channels": 256,
|
||||
"rcnn_loss_cls_weight": 1,
|
||||
"rcnn_loss_reg_weight": 1,
|
||||
"rcnn_loss_mask_fb_weight": 1,
|
||||
"rcnn_target_means": [0., 0., 0., 0.],
|
||||
"rcnn_target_stds": [0.1, 0.1, 0.2, 0.2],
|
||||
|
||||
# train proposal
|
||||
"rpn_proposal_nms_across_levels": False,
|
||||
"rpn_proposal_nms_pre": 2000,
|
||||
"rpn_proposal_nms_post": 2000,
|
||||
"rpn_proposal_max_num": 2000,
|
||||
"rpn_proposal_nms_thr": 0.7,
|
||||
"rpn_proposal_min_bbox_size": 0,
|
||||
|
||||
# test proposal
|
||||
"rpn_nms_across_levels": False,
|
||||
"rpn_nms_pre": 1000,
|
||||
"rpn_nms_post": 1000,
|
||||
"rpn_max_num": 1000,
|
||||
"rpn_nms_thr": 0.7,
|
||||
"rpn_min_bbox_min_size": 0,
|
||||
"test_score_thr": 0.05,
|
||||
"test_iou_thr": 0.5,
|
||||
"test_max_per_img": 100,
|
||||
"test_batch_size": 2,
|
||||
|
||||
"rpn_head_loss_type": "CrossEntropyLoss",
|
||||
"rpn_head_use_sigmoid": True,
|
||||
"rpn_head_weight": 1.0,
|
||||
"mask_thr_binary": 0.5,
|
||||
|
||||
# LR
|
||||
"base_lr": 0.04,
|
||||
"base_step": 58633,
|
||||
"total_epoch": 13,
|
||||
"warmup_step": 500,
|
||||
"warmup_mode": "linear",
|
||||
"warmup_ratio": 1/3.0,
|
||||
"sgd_momentum": 0.9,
|
||||
|
||||
# train
|
||||
"batch_size": 2,
|
||||
"loss_scale": 1,
|
||||
"momentum": 0.91,
|
||||
"weight_decay": 1e-4,
|
||||
"pretrain_epoch_size": 0,
|
||||
"epoch_size": 12,
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 1,
|
||||
"keep_checkpoint_max": 12,
|
||||
"save_checkpoint_path": "./",
|
||||
|
||||
"mindrecord_dir": "/home/mask_rcnn/MindRecord_COCO2017_Train",
|
||||
"coco_root": "/home/mask_rcnn/coco2017/",
|
||||
"train_data_type": "train2017",
|
||||
"val_data_type": "val2017",
|
||||
"instance_set": "annotations/instances_{}.json",
|
||||
"coco_classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
||||
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
||||
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
||||
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
|
||||
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
|
||||
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
||||
'kite', 'baseball bat', 'baseball glove', 'skateboard',
|
||||
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
|
||||
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
|
||||
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
|
||||
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
||||
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
|
||||
'refrigerator', 'book', 'clock', 'vase', 'scissors',
|
||||
'teddy bear', 'hair drier', 'toothbrush'),
|
||||
"num_classes": 81
|
||||
})
|
||||
|
|
@ -0,0 +1,529 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
"""MaskRcnn dataset"""
|
||||
from __future__ import division
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
from numpy import random
|
||||
|
||||
import mmcv
|
||||
import mindspore.dataset as de
|
||||
import mindspore.dataset.vision.c_transforms as C
|
||||
from mindspore.mindrecord import FileWriter
|
||||
from src.config import config
|
||||
import cv2
|
||||
|
||||
def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
|
||||
"""Calculate the ious between each bbox of bboxes1 and bboxes2.
|
||||
|
||||
Args:
|
||||
bboxes1(ndarray): shape (n, 4)
|
||||
bboxes2(ndarray): shape (k, 4)
|
||||
mode(str): iou (intersection over union) or iof (intersection
|
||||
over foreground)
|
||||
|
||||
Returns:
|
||||
ious(ndarray): shape (n, k)
|
||||
"""
|
||||
|
||||
assert mode in ['iou', 'iof']
|
||||
|
||||
bboxes1 = bboxes1.astype(np.float32)
|
||||
bboxes2 = bboxes2.astype(np.float32)
|
||||
rows = bboxes1.shape[0]
|
||||
cols = bboxes2.shape[0]
|
||||
ious = np.zeros((rows, cols), dtype=np.float32)
|
||||
if rows * cols == 0:
|
||||
return ious
|
||||
exchange = False
|
||||
if bboxes1.shape[0] > bboxes2.shape[0]:
|
||||
bboxes1, bboxes2 = bboxes2, bboxes1
|
||||
ious = np.zeros((cols, rows), dtype=np.float32)
|
||||
exchange = True
|
||||
area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (bboxes1[:, 3] - bboxes1[:, 1] + 1)
|
||||
area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (bboxes2[:, 3] - bboxes2[:, 1] + 1)
|
||||
for i in range(bboxes1.shape[0]):
|
||||
x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])
|
||||
y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])
|
||||
x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])
|
||||
y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])
|
||||
overlap = np.maximum(x_end - x_start + 1, 0) * np.maximum(
|
||||
y_end - y_start + 1, 0)
|
||||
if mode == 'iou':
|
||||
union = area1[i] + area2 - overlap
|
||||
else:
|
||||
union = area1[i] if not exchange else area2
|
||||
ious[i, :] = overlap / union
|
||||
if exchange:
|
||||
ious = ious.T
|
||||
return ious
|
||||
|
||||
class PhotoMetricDistortion:
|
||||
"""Photo Metric Distortion"""
|
||||
def __init__(self,
|
||||
brightness_delta=32,
|
||||
contrast_range=(0.5, 1.5),
|
||||
saturation_range=(0.5, 1.5),
|
||||
hue_delta=18):
|
||||
self.brightness_delta = brightness_delta
|
||||
self.contrast_lower, self.contrast_upper = contrast_range
|
||||
self.saturation_lower, self.saturation_upper = saturation_range
|
||||
self.hue_delta = hue_delta
|
||||
|
||||
def __call__(self, img, boxes, labels):
|
||||
# random brightness
|
||||
img = img.astype('float32')
|
||||
|
||||
if random.randint(2):
|
||||
delta = random.uniform(-self.brightness_delta,
|
||||
self.brightness_delta)
|
||||
img += delta
|
||||
|
||||
# mode == 0 --> do random contrast first
|
||||
# mode == 1 --> do random contrast last
|
||||
mode = random.randint(2)
|
||||
if mode == 1:
|
||||
if random.randint(2):
|
||||
alpha = random.uniform(self.contrast_lower,
|
||||
self.contrast_upper)
|
||||
img *= alpha
|
||||
|
||||
# convert color from BGR to HSV
|
||||
img = mmcv.bgr2hsv(img)
|
||||
|
||||
# random saturation
|
||||
if random.randint(2):
|
||||
img[..., 1] *= random.uniform(self.saturation_lower,
|
||||
self.saturation_upper)
|
||||
|
||||
# random hue
|
||||
if random.randint(2):
|
||||
img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
|
||||
img[..., 0][img[..., 0] > 360] -= 360
|
||||
img[..., 0][img[..., 0] < 0] += 360
|
||||
|
||||
# convert color from HSV to BGR
|
||||
img = mmcv.hsv2bgr(img)
|
||||
|
||||
# random contrast
|
||||
if mode == 0:
|
||||
if random.randint(2):
|
||||
alpha = random.uniform(self.contrast_lower,
|
||||
self.contrast_upper)
|
||||
img *= alpha
|
||||
|
||||
# randomly swap channels
|
||||
if random.randint(2):
|
||||
img = img[..., random.permutation(3)]
|
||||
|
||||
return img, boxes, labels
|
||||
|
||||
class Expand:
|
||||
"""expand image"""
|
||||
def __init__(self, mean=(0, 0, 0), to_rgb=True, ratio_range=(1, 4)):
|
||||
if to_rgb:
|
||||
self.mean = mean[::-1]
|
||||
else:
|
||||
self.mean = mean
|
||||
self.min_ratio, self.max_ratio = ratio_range
|
||||
|
||||
def __call__(self, img, boxes, labels, mask):
|
||||
if random.randint(2):
|
||||
return img, boxes, labels, mask
|
||||
|
||||
h, w, c = img.shape
|
||||
ratio = random.uniform(self.min_ratio, self.max_ratio)
|
||||
expand_img = np.full((int(h * ratio), int(w * ratio), c),
|
||||
self.mean).astype(img.dtype)
|
||||
left = int(random.uniform(0, w * ratio - w))
|
||||
top = int(random.uniform(0, h * ratio - h))
|
||||
expand_img[top:top + h, left:left + w] = img
|
||||
img = expand_img
|
||||
boxes += np.tile((left, top), 2)
|
||||
|
||||
mask_count, mask_h, mask_w = mask.shape
|
||||
expand_mask = np.zeros((mask_count, int(mask_h * ratio), int(mask_w * ratio))).astype(mask.dtype)
|
||||
expand_mask[:, top:top + h, left:left + w] = mask
|
||||
mask = expand_mask
|
||||
|
||||
return img, boxes, labels, mask
|
||||
|
||||
def rescale_column(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""rescale operation for image"""
|
||||
img_data, scale_factor = mmcv.imrescale(img, (config.img_width, config.img_height), return_scale=True)
|
||||
if img_data.shape[0] > config.img_height:
|
||||
img_data, scale_factor2 = mmcv.imrescale(img_data, (config.img_height, config.img_height), return_scale=True)
|
||||
scale_factor = scale_factor*scale_factor2
|
||||
|
||||
gt_bboxes = gt_bboxes * scale_factor
|
||||
gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_data.shape[1] - 1)
|
||||
gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_data.shape[0] - 1)
|
||||
|
||||
gt_mask_data = np.array([
|
||||
mmcv.imrescale(mask, scale_factor, interpolation='nearest')
|
||||
for mask in gt_mask
|
||||
])
|
||||
|
||||
pad_h = config.img_height - img_data.shape[0]
|
||||
pad_w = config.img_width - img_data.shape[1]
|
||||
assert ((pad_h >= 0) and (pad_w >= 0))
|
||||
|
||||
pad_img_data = np.zeros((config.img_height, config.img_width, 3)).astype(img_data.dtype)
|
||||
pad_img_data[0:img_data.shape[0], 0:img_data.shape[1], :] = img_data
|
||||
|
||||
mask_count, mask_h, mask_w = gt_mask_data.shape
|
||||
pad_mask = np.zeros((mask_count, config.img_height, config.img_width)).astype(gt_mask_data.dtype)
|
||||
pad_mask[:, 0:mask_h, 0:mask_w] = gt_mask_data
|
||||
|
||||
img_shape = (config.img_height, config.img_width, 1.0)
|
||||
img_shape = np.asarray(img_shape, dtype=np.float32)
|
||||
|
||||
return (pad_img_data, img_shape, gt_bboxes, gt_label, gt_num, pad_mask)
|
||||
|
||||
def rescale_column_test(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""rescale operation for image of eval"""
|
||||
img_data, scale_factor = mmcv.imrescale(img, (config.img_width, config.img_height), return_scale=True)
|
||||
if img_data.shape[0] > config.img_height:
|
||||
img_data, scale_factor2 = mmcv.imrescale(img_data, (config.img_height, config.img_height), return_scale=True)
|
||||
scale_factor = scale_factor*scale_factor2
|
||||
|
||||
pad_h = config.img_height - img_data.shape[0]
|
||||
pad_w = config.img_width - img_data.shape[1]
|
||||
assert ((pad_h >= 0) and (pad_w >= 0))
|
||||
|
||||
pad_img_data = np.zeros((config.img_height, config.img_width, 3)).astype(img_data.dtype)
|
||||
pad_img_data[0:img_data.shape[0], 0:img_data.shape[1], :] = img_data
|
||||
|
||||
img_shape = np.append(img_shape, (scale_factor, scale_factor))
|
||||
img_shape = np.asarray(img_shape, dtype=np.float32)
|
||||
|
||||
return (pad_img_data, img_shape, gt_bboxes, gt_label, gt_num, gt_mask)
|
||||
|
||||
def resize_column(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""resize operation for image"""
|
||||
img_data = img
|
||||
img_data, w_scale, h_scale = mmcv.imresize(
|
||||
img_data, (config.img_width, config.img_height), return_scale=True)
|
||||
scale_factor = np.array(
|
||||
[w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
|
||||
img_shape = (config.img_height, config.img_width, 1.0)
|
||||
img_shape = np.asarray(img_shape, dtype=np.float32)
|
||||
|
||||
gt_bboxes = gt_bboxes * scale_factor
|
||||
gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1) # x1, x2 [0, W-1]
|
||||
gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1) # y1, y2 [0, H-1]
|
||||
|
||||
gt_mask_data = np.array([
|
||||
mmcv.imresize(mask, (config.img_width, config.img_height), interpolation='nearest')
|
||||
for mask in gt_mask
|
||||
])
|
||||
return (img_data, img_shape, gt_bboxes, gt_label, gt_num, gt_mask_data)
|
||||
|
||||
def resize_column_test(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""resize operation for image of eval"""
|
||||
img_data = img
|
||||
img_data, w_scale, h_scale = mmcv.imresize(
|
||||
img_data, (config.img_width, config.img_height), return_scale=True)
|
||||
img_shape = np.append(img_shape, (h_scale, w_scale))
|
||||
img_shape = np.asarray(img_shape, dtype=np.float32)
|
||||
|
||||
return (img_data, img_shape, gt_bboxes, gt_label, gt_num, gt_mask)
|
||||
|
||||
def impad_to_multiple_column(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""impad operation for image"""
|
||||
img_data = mmcv.impad(img, (config.img_height, config.img_width))
|
||||
img_data = img_data.astype(np.float32)
|
||||
return (img_data, img_shape, gt_bboxes, gt_label, gt_num, gt_mask)
|
||||
|
||||
def imnormalize_column(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""imnormalize operation for image"""
|
||||
img_data = mmcv.imnormalize(img, [123.675, 116.28, 103.53], [58.395, 57.12, 57.375], True)
|
||||
img_data = img_data.astype(np.float32)
|
||||
return (img_data, img_shape, gt_bboxes, gt_label, gt_num, gt_mask)
|
||||
|
||||
def flip_column(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""flip operation for image"""
|
||||
img_data = img
|
||||
img_data = mmcv.imflip(img_data)
|
||||
flipped = gt_bboxes.copy()
|
||||
_, w, _ = img_data.shape
|
||||
|
||||
flipped[..., 0::4] = w - gt_bboxes[..., 2::4] - 1 # x1 = W-x2-1
|
||||
flipped[..., 2::4] = w - gt_bboxes[..., 0::4] - 1 # x2 = W-x1-1
|
||||
|
||||
gt_mask_data = np.array([mask[:, ::-1] for mask in gt_mask])
|
||||
|
||||
return (img_data, img_shape, flipped, gt_label, gt_num, gt_mask_data)
|
||||
|
||||
def transpose_column(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""transpose operation for image"""
|
||||
img_data = img.transpose(2, 0, 1).copy()
|
||||
img_data = img_data.astype(np.float16)
|
||||
img_shape = img_shape.astype(np.float16)
|
||||
gt_bboxes = gt_bboxes.astype(np.float16)
|
||||
gt_label = gt_label.astype(np.int32)
|
||||
gt_num = gt_num.astype(np.bool)
|
||||
gt_mask_data = gt_mask.astype(np.bool)
|
||||
|
||||
return (img_data, img_shape, gt_bboxes, gt_label, gt_num, gt_mask_data)
|
||||
|
||||
def photo_crop_column(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""photo crop operation for image"""
|
||||
random_photo = PhotoMetricDistortion()
|
||||
img_data, gt_bboxes, gt_label = random_photo(img, gt_bboxes, gt_label)
|
||||
|
||||
return (img_data, img_shape, gt_bboxes, gt_label, gt_num, gt_mask)
|
||||
|
||||
def expand_column(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask):
|
||||
"""expand operation for image"""
|
||||
expand = Expand()
|
||||
img, gt_bboxes, gt_label, gt_mask = expand(img, gt_bboxes, gt_label, gt_mask)
|
||||
|
||||
return (img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask)
|
||||
|
||||
def pad_to_max(img, img_shape, gt_bboxes, gt_label, gt_num, gt_mask, instance_count):
|
||||
pad_max_number = config.max_instance_count
|
||||
gt_box_new = np.pad(gt_bboxes, ((0, pad_max_number - instance_count), (0, 0)), mode="constant", constant_values=0)
|
||||
gt_label_new = np.pad(gt_label, ((0, pad_max_number - instance_count)), mode="constant", constant_values=-1)
|
||||
gt_iscrowd_new = np.pad(gt_num, ((0, pad_max_number - instance_count)), mode="constant", constant_values=1)
|
||||
gt_iscrowd_new_revert = ~(gt_iscrowd_new.astype(np.bool))
|
||||
|
||||
return img, img_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert, gt_mask
|
||||
|
||||
def preprocess_fn(image, box, mask, mask_shape, is_training):
|
||||
"""Preprocess function for dataset."""
|
||||
def _infer_data(image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert,
|
||||
gt_mask_new, instance_count):
|
||||
image_shape = image_shape[:2]
|
||||
input_data = image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert, gt_mask_new
|
||||
|
||||
if config.keep_ratio:
|
||||
input_data = rescale_column_test(*input_data)
|
||||
else:
|
||||
input_data = resize_column_test(*input_data)
|
||||
input_data = imnormalize_column(*input_data)
|
||||
|
||||
input_data = pad_to_max(*input_data, instance_count)
|
||||
output_data = transpose_column(*input_data)
|
||||
return output_data
|
||||
|
||||
def _data_aug(image, box, mask, mask_shape, is_training):
|
||||
"""Data augmentation function."""
|
||||
image_bgr = image.copy()
|
||||
image_bgr[:, :, 0] = image[:, :, 2]
|
||||
image_bgr[:, :, 1] = image[:, :, 1]
|
||||
image_bgr[:, :, 2] = image[:, :, 0]
|
||||
image_shape = image_bgr.shape[:2]
|
||||
instance_count = box.shape[0]
|
||||
gt_box = box[:, :4]
|
||||
gt_label = box[:, 4]
|
||||
gt_iscrowd = box[:, 5]
|
||||
gt_mask = mask.copy()
|
||||
n, h, w = mask_shape
|
||||
gt_mask = gt_mask.reshape(n, h, w)
|
||||
assert n == box.shape[0]
|
||||
|
||||
if not is_training:
|
||||
return _infer_data(image_bgr, image_shape, gt_box, gt_label, gt_iscrowd, gt_mask, instance_count)
|
||||
|
||||
flip = (np.random.rand() < config.flip_ratio)
|
||||
expand = (np.random.rand() < config.expand_ratio)
|
||||
|
||||
input_data = image_bgr, image_shape, gt_box, gt_label, gt_iscrowd, gt_mask
|
||||
|
||||
if expand:
|
||||
input_data = expand_column(*input_data)
|
||||
if config.keep_ratio:
|
||||
input_data = rescale_column(*input_data)
|
||||
else:
|
||||
input_data = resize_column(*input_data)
|
||||
|
||||
input_data = imnormalize_column(*input_data)
|
||||
if flip:
|
||||
input_data = flip_column(*input_data)
|
||||
|
||||
input_data = pad_to_max(*input_data, instance_count)
|
||||
output_data = transpose_column(*input_data)
|
||||
return output_data
|
||||
|
||||
return _data_aug(image, box, mask, mask_shape, is_training)
|
||||
|
||||
def annToMask(ann, height, width):
|
||||
"""Convert annotation to RLE and then to binary mask."""
|
||||
from pycocotools import mask as maskHelper
|
||||
segm = ann['segmentation']
|
||||
if isinstance(segm, list):
|
||||
rles = maskHelper.frPyObjects(segm, height, width)
|
||||
rle = maskHelper.merge(rles)
|
||||
elif isinstance(segm['counts'], list):
|
||||
rle = maskHelper.frPyObjects(segm, height, width)
|
||||
else:
|
||||
rle = ann['segmentation']
|
||||
m = maskHelper.decode(rle)
|
||||
return m
|
||||
|
||||
def create_coco_label(is_training):
|
||||
"""Get image path and annotation from COCO."""
|
||||
from pycocotools.coco import COCO
|
||||
|
||||
coco_root = config.coco_root
|
||||
data_type = config.val_data_type
|
||||
if is_training:
|
||||
data_type = config.train_data_type
|
||||
|
||||
#Classes need to train or test.
|
||||
train_cls = config.coco_classes
|
||||
train_cls_dict = {}
|
||||
for i, cls in enumerate(train_cls):
|
||||
train_cls_dict[cls] = i
|
||||
|
||||
anno_json = os.path.join(coco_root, config.instance_set.format(data_type))
|
||||
|
||||
coco = COCO(anno_json)
|
||||
classs_dict = {}
|
||||
cat_ids = coco.loadCats(coco.getCatIds())
|
||||
for cat in cat_ids:
|
||||
classs_dict[cat["id"]] = cat["name"]
|
||||
|
||||
image_ids = coco.getImgIds()
|
||||
image_files = []
|
||||
image_anno_dict = {}
|
||||
masks = {}
|
||||
masks_shape = {}
|
||||
images_num = len(image_ids)
|
||||
for ind, img_id in enumerate(image_ids):
|
||||
image_info = coco.loadImgs(img_id)
|
||||
file_name = image_info[0]["file_name"]
|
||||
image_path = os.path.join(coco_root, data_type, file_name)
|
||||
if not os.path.isfile(image_path):
|
||||
print("{}/{}: {} is in annotations but not exist".format(ind + 1, images_num, image_path))
|
||||
continue
|
||||
anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)
|
||||
anno = coco.loadAnns(anno_ids)
|
||||
image_path = os.path.join(coco_root, data_type, file_name)
|
||||
annos = []
|
||||
instance_masks = []
|
||||
image_height = coco.imgs[img_id]["height"]
|
||||
image_width = coco.imgs[img_id]["width"]
|
||||
if (ind + 1) % 10 == 0:
|
||||
print("{}/{}: parsing annotation for image={}".format(ind + 1, images_num, file_name))
|
||||
if not is_training:
|
||||
image_files.append(image_path)
|
||||
image_anno_dict[image_path] = np.array([0, 0, 0, 0, 0, 1])
|
||||
masks[image_path] = np.zeros([1, 1, 1], dtype=np.bool).tobytes()
|
||||
masks_shape[image_path] = np.array([1, 1, 1], dtype=np.int32)
|
||||
else:
|
||||
for label in anno:
|
||||
bbox = label["bbox"]
|
||||
class_name = classs_dict[label["category_id"]]
|
||||
if class_name in train_cls:
|
||||
# get coco mask
|
||||
m = annToMask(label, image_height, image_width)
|
||||
if m.max() < 1:
|
||||
print("all black mask!!!!")
|
||||
continue
|
||||
# Resize mask for the crowd
|
||||
if label['iscrowd'] and (m.shape[0] != image_height or m.shape[1] != image_width):
|
||||
m = np.ones([image_height, image_width], dtype=np.bool)
|
||||
instance_masks.append(m)
|
||||
|
||||
# get coco bbox
|
||||
x1, x2 = bbox[0], bbox[0] + bbox[2]
|
||||
y1, y2 = bbox[1], bbox[1] + bbox[3]
|
||||
annos.append([x1, y1, x2, y2] + [train_cls_dict[class_name]] + [int(label["iscrowd"])])
|
||||
else:
|
||||
print("not in classes: ", class_name)
|
||||
|
||||
image_files.append(image_path)
|
||||
if annos:
|
||||
image_anno_dict[image_path] = np.array(annos)
|
||||
instance_masks = np.stack(instance_masks, axis=0).astype(np.bool)
|
||||
masks[image_path] = np.array(instance_masks).tobytes()
|
||||
masks_shape[image_path] = np.array(instance_masks.shape, dtype=np.int32)
|
||||
else:
|
||||
print("no annotations for image ", file_name)
|
||||
image_anno_dict[image_path] = np.array([0, 0, 0, 0, 0, 1])
|
||||
masks[image_path] = np.zeros([1, image_height, image_width], dtype=np.bool).tobytes()
|
||||
masks_shape[image_path] = np.array([1, image_height, image_width], dtype=np.int32)
|
||||
|
||||
return image_files, image_anno_dict, masks, masks_shape
|
||||
|
||||
def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="maskrcnn.mindrecord", file_num=8):
|
||||
"""Create MindRecord file."""
|
||||
mindrecord_dir = config.mindrecord_dir
|
||||
mindrecord_path = os.path.join(mindrecord_dir, prefix)
|
||||
|
||||
writer = FileWriter(mindrecord_path, file_num)
|
||||
if dataset == "coco":
|
||||
image_files, image_anno_dict, masks, masks_shape = create_coco_label(is_training)
|
||||
else:
|
||||
print("Error unsupported other dataset")
|
||||
return
|
||||
|
||||
maskrcnn_json = {
|
||||
"image": {"type": "bytes"},
|
||||
"annotation": {"type": "int32", "shape": [-1, 6]},
|
||||
"mask": {"type": "bytes"},
|
||||
"mask_shape": {"type": "int32", "shape": [-1]},
|
||||
}
|
||||
writer.add_schema(maskrcnn_json, "maskrcnn_json")
|
||||
|
||||
image_files_num = len(image_files)
|
||||
for ind, image_name in enumerate(image_files):
|
||||
with open(image_name, 'rb') as f:
|
||||
img = f.read()
|
||||
annos = np.array(image_anno_dict[image_name], dtype=np.int32)
|
||||
mask = masks[image_name]
|
||||
mask_shape = masks_shape[image_name]
|
||||
row = {"image": img, "annotation": annos, "mask": mask, "mask_shape": mask_shape}
|
||||
if (ind + 1) % 10 == 0:
|
||||
print("writing {}/{} into mindrecord".format(ind + 1, image_files_num))
|
||||
writer.write_raw_data([row])
|
||||
writer.commit()
|
||||
|
||||
def create_maskrcnn_dataset(mindrecord_file, batch_size=2, device_num=1, rank_id=0,
|
||||
is_training=True, num_parallel_workers=8):
|
||||
"""Create MaskRcnn dataset with MindDataset."""
|
||||
cv2.setNumThreads(0)
|
||||
de.config.set_prefetch_size(8)
|
||||
ds = de.MindDataset(mindrecord_file, columns_list=["image", "annotation", "mask", "mask_shape"],
|
||||
num_shards=device_num, shard_id=rank_id,
|
||||
num_parallel_workers=4, shuffle=is_training)
|
||||
|
||||
decode = C.Decode()
|
||||
ds = ds.map(operations=decode, input_columns=["image"])
|
||||
compose_map_func = (lambda image, annotation, mask, mask_shape:
|
||||
preprocess_fn(image, annotation, mask, mask_shape, is_training))
|
||||
|
||||
if is_training:
|
||||
ds = ds.map(operations=compose_map_func,
|
||||
input_columns=["image", "annotation", "mask", "mask_shape"],
|
||||
output_columns=["image", "image_shape", "box", "label", "valid_num", "mask"],
|
||||
column_order=["image", "image_shape", "box", "label", "valid_num", "mask"],
|
||||
python_multiprocessing=False,
|
||||
num_parallel_workers=num_parallel_workers)
|
||||
ds = ds.batch(batch_size, drop_remainder=True, pad_info={"mask": ([config.max_instance_count, None, None], 0)})
|
||||
|
||||
else:
|
||||
ds = ds.map(operations=compose_map_func,
|
||||
input_columns=["image", "annotation", "mask", "mask_shape"],
|
||||
output_columns=["image", "image_shape", "box", "label", "valid_num", "mask"],
|
||||
column_order=["image", "image_shape", "box", "label", "valid_num", "mask"],
|
||||
num_parallel_workers=num_parallel_workers)
|
||||
ds = ds.batch(batch_size, drop_remainder=True)
|
||||
|
||||
return ds
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""lr generator for maskrcnn_mobilenetv1"""
|
||||
import math
|
||||
|
||||
def linear_warmup_learning_rate(current_step, warmup_steps, base_lr, init_lr):
|
||||
lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
|
||||
learning_rate = float(init_lr) + lr_inc * current_step
|
||||
return learning_rate
|
||||
|
||||
def a_cosine_learning_rate(current_step, base_lr, warmup_steps, decay_steps):
|
||||
base = float(current_step - warmup_steps) / float(decay_steps)
|
||||
learning_rate = (1 + math.cos(base * math.pi)) / 2 * base_lr
|
||||
return learning_rate
|
||||
|
||||
def dynamic_lr(config, rank_size=1, start_steps=0):
|
||||
"""dynamic learning rate generator"""
|
||||
base_lr = config.base_lr
|
||||
|
||||
base_step = (config.base_step // rank_size) + rank_size
|
||||
total_steps = int(base_step * config.total_epoch)
|
||||
warmup_steps = int(config.warmup_step)
|
||||
lr = []
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr.append(linear_warmup_learning_rate(i, warmup_steps, base_lr, base_lr * config.warmup_ratio))
|
||||
else:
|
||||
lr.append(a_cosine_learning_rate(i, base_lr, warmup_steps, total_steps))
|
||||
learning_rate = lr[start_steps:]
|
||||
return learning_rate
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn Init."""
|
||||
|
||||
from .mobilenetv1 import MobileNetV1, MobileNetV1_FeatureSelector
|
||||
from .bbox_assign_sample import BboxAssignSample
|
||||
from .bbox_assign_sample_stage2 import BboxAssignSampleForRcnn
|
||||
from .fpn_neck import FeatPyramidNeck
|
||||
from .proposal_generator import Proposal
|
||||
from .rcnn_cls import RcnnCls
|
||||
from .rcnn_mask import RcnnMask
|
||||
from .rpn import RPN
|
||||
from .roi_align import SingleRoIExtractor
|
||||
from .anchor_generator import AnchorGenerator
|
||||
|
||||
__all__ = [
|
||||
"MobileNetV1", "MobileNetV1_FeatureSelector", "BboxAssignSample", "BboxAssignSampleForRcnn",
|
||||
"FeatPyramidNeck", "Proposal", "RcnnCls", "RcnnMask",
|
||||
"RPN", "SingleRoIExtractor", "AnchorGenerator"
|
||||
]
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn anchor generator."""
|
||||
|
||||
import numpy as np
|
||||
|
||||
class AnchorGenerator():
|
||||
"""Anchor generator for MasKRcnn."""
|
||||
def __init__(self, base_size, scales, ratios, scale_major=True, ctr=None):
|
||||
"""Anchor generator init method."""
|
||||
self.base_size = base_size
|
||||
self.scales = np.array(scales)
|
||||
self.ratios = np.array(ratios)
|
||||
self.scale_major = scale_major
|
||||
self.ctr = ctr
|
||||
self.base_anchors = self.gen_base_anchors()
|
||||
|
||||
def gen_base_anchors(self):
|
||||
"""Generate a single anchor."""
|
||||
w = self.base_size
|
||||
h = self.base_size
|
||||
if self.ctr is None:
|
||||
x_ctr = 0.5 * (w - 1)
|
||||
y_ctr = 0.5 * (h - 1)
|
||||
else:
|
||||
x_ctr, y_ctr = self.ctr
|
||||
|
||||
h_ratios = np.sqrt(self.ratios)
|
||||
w_ratios = 1 / h_ratios
|
||||
if self.scale_major:
|
||||
ws = (w * w_ratios[:, None] * self.scales[None, :]).reshape(-1)
|
||||
hs = (h * h_ratios[:, None] * self.scales[None, :]).reshape(-1)
|
||||
else:
|
||||
ws = (w * self.scales[:, None] * w_ratios[None, :]).reshape(-1)
|
||||
hs = (h * self.scales[:, None] * h_ratios[None, :]).reshape(-1)
|
||||
|
||||
base_anchors = np.stack(
|
||||
[
|
||||
x_ctr - 0.5 * (ws - 1), y_ctr - 0.5 * (hs - 1),
|
||||
x_ctr + 0.5 * (ws - 1), y_ctr + 0.5 * (hs - 1)
|
||||
],
|
||||
axis=-1).round()
|
||||
|
||||
return base_anchors
|
||||
|
||||
def _meshgrid(self, x, y, row_major=True):
|
||||
"""Generate grid."""
|
||||
xx = np.repeat(x.reshape(1, len(x)), len(y), axis=0).reshape(-1)
|
||||
yy = np.repeat(y, len(x))
|
||||
if row_major:
|
||||
return xx, yy
|
||||
|
||||
return yy, xx
|
||||
|
||||
def grid_anchors(self, featmap_size, stride=16):
|
||||
"""Generate anchor list."""
|
||||
base_anchors = self.base_anchors
|
||||
|
||||
feat_h, feat_w = featmap_size
|
||||
shift_x = np.arange(0, feat_w) * stride
|
||||
shift_y = np.arange(0, feat_h) * stride
|
||||
shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
|
||||
shifts = np.stack([shift_xx, shift_yy, shift_xx, shift_yy], axis=-1)
|
||||
shifts = shifts.astype(base_anchors.dtype)
|
||||
# first feat_w elements correspond to the first row of shifts
|
||||
# add A anchors (1, A, 4) to K shifts (K, 1, 4) to get
|
||||
# shifted anchors (K, A, 4), reshape to (K*A, 4)
|
||||
|
||||
all_anchors = base_anchors[None, :, :] + shifts[:, None, :]
|
||||
all_anchors = all_anchors.reshape(-1, 4)
|
||||
|
||||
return all_anchors
|
||||
|
|
@ -0,0 +1,164 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn positive and negative sample screening for RPN."""
|
||||
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.common.tensor import Tensor
|
||||
import mindspore.common.dtype as mstype
|
||||
|
||||
|
||||
class BboxAssignSample(nn.Cell):
|
||||
"""
|
||||
Bbox assigner and sampler defination.
|
||||
|
||||
Args:
|
||||
config (dict): Config.
|
||||
batch_size (int): Batchsize.
|
||||
num_bboxes (int): The anchor nums.
|
||||
add_gt_as_proposals (bool): add gt bboxes as proposals flag.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
bbox_targets: bbox location, (batch_size, num_bboxes, 4)
|
||||
bbox_weights: bbox weights, (batch_size, num_bboxes, 1)
|
||||
labels: label for every bboxes, (batch_size, num_bboxes, 1)
|
||||
label_weights: label weight for every bboxes, (batch_size, num_bboxes, 1)
|
||||
|
||||
Examples:
|
||||
BboxAssignSample(config, 2, 1024, True)
|
||||
"""
|
||||
|
||||
def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
|
||||
super(BboxAssignSample, self).__init__()
|
||||
cfg = config
|
||||
self.batch_size = batch_size
|
||||
|
||||
self.neg_iou_thr = Tensor(cfg.neg_iou_thr, mstype.float16)
|
||||
self.pos_iou_thr = Tensor(cfg.pos_iou_thr, mstype.float16)
|
||||
self.min_pos_iou = Tensor(cfg.min_pos_iou, mstype.float16)
|
||||
self.zero_thr = Tensor(0.0, mstype.float16)
|
||||
|
||||
self.num_bboxes = num_bboxes
|
||||
self.num_gts = cfg.num_gts
|
||||
self.num_expected_pos = cfg.num_expected_pos
|
||||
self.num_expected_neg = cfg.num_expected_neg
|
||||
self.add_gt_as_proposals = add_gt_as_proposals
|
||||
|
||||
if self.add_gt_as_proposals:
|
||||
self.label_inds = Tensor(np.arange(1, self.num_gts + 1))
|
||||
|
||||
self.concat = P.Concat(axis=0)
|
||||
self.max_gt = P.ArgMaxWithValue(axis=0)
|
||||
self.max_anchor = P.ArgMaxWithValue(axis=1)
|
||||
self.sum_inds = P.ReduceSum()
|
||||
self.iou = P.IOU()
|
||||
self.greaterequal = P.GreaterEqual()
|
||||
self.greater = P.Greater()
|
||||
self.select = P.Select()
|
||||
self.gatherND = P.GatherNd()
|
||||
self.squeeze = P.Squeeze()
|
||||
self.cast = P.Cast()
|
||||
self.logicaland = P.LogicalAnd()
|
||||
self.less = P.Less()
|
||||
self.random_choice_with_mask_pos = P.RandomChoiceWithMask(self.num_expected_pos)
|
||||
self.random_choice_with_mask_neg = P.RandomChoiceWithMask(self.num_expected_neg)
|
||||
self.reshape = P.Reshape()
|
||||
self.equal = P.Equal()
|
||||
self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
|
||||
self.scatterNdUpdate = P.ScatterNdUpdate()
|
||||
self.scatterNd = P.ScatterNd()
|
||||
self.logicalnot = P.LogicalNot()
|
||||
self.tile = P.Tile()
|
||||
self.zeros_like = P.ZerosLike()
|
||||
|
||||
self.assigned_gt_inds = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
|
||||
self.assigned_gt_zeros = Tensor(np.array(np.zeros(num_bboxes), dtype=np.int32))
|
||||
self.assigned_gt_ones = Tensor(np.array(np.ones(num_bboxes), dtype=np.int32))
|
||||
self.assigned_gt_ignores = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
|
||||
self.assigned_pos_ones = Tensor(np.array(np.ones(self.num_expected_pos), dtype=np.int32))
|
||||
|
||||
self.check_neg_mask = Tensor(np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool))
|
||||
self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(np.float16))
|
||||
self.check_gt_one = Tensor(np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16))
|
||||
self.check_anchor_two = Tensor(np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16))
|
||||
|
||||
|
||||
def construct(self, gt_bboxes_i, gt_labels_i, valid_mask, bboxes, gt_valids):
|
||||
gt_bboxes_i = self.select(self.cast(self.tile(self.reshape(self.cast(gt_valids, mstype.int32), \
|
||||
(self.num_gts, 1)), (1, 4)), mstype.bool_), gt_bboxes_i, self.check_gt_one)
|
||||
bboxes = self.select(self.cast(self.tile(self.reshape(self.cast(valid_mask, mstype.int32), \
|
||||
(self.num_bboxes, 1)), (1, 4)), mstype.bool_), bboxes, self.check_anchor_two)
|
||||
|
||||
overlaps = self.iou(bboxes, gt_bboxes_i)
|
||||
|
||||
max_overlaps_w_gt_index, max_overlaps_w_gt = self.max_gt(overlaps)
|
||||
_, max_overlaps_w_ac = self.max_anchor(overlaps)
|
||||
|
||||
neg_sample_iou_mask = self.logicaland(self.greaterequal(max_overlaps_w_gt, self.zero_thr), \
|
||||
self.less(max_overlaps_w_gt, self.neg_iou_thr))
|
||||
assigned_gt_inds2 = self.select(neg_sample_iou_mask, self.assigned_gt_zeros, self.assigned_gt_inds)
|
||||
|
||||
pos_sample_iou_mask = self.greaterequal(max_overlaps_w_gt, self.pos_iou_thr)
|
||||
assigned_gt_inds3 = self.select(pos_sample_iou_mask, \
|
||||
max_overlaps_w_gt_index + self.assigned_gt_ones, assigned_gt_inds2)
|
||||
assigned_gt_inds4 = assigned_gt_inds3
|
||||
for j in range(self.num_gts):
|
||||
max_overlaps_w_ac_j = max_overlaps_w_ac[j:j+1:1]
|
||||
overlaps_w_gt_j = self.squeeze(overlaps[j:j+1:1, ::])
|
||||
|
||||
pos_mask_j = self.logicaland(self.greaterequal(max_overlaps_w_ac_j, self.min_pos_iou), \
|
||||
self.equal(overlaps_w_gt_j, max_overlaps_w_ac_j))
|
||||
|
||||
assigned_gt_inds4 = self.select(pos_mask_j, self.assigned_gt_ones + j, assigned_gt_inds4)
|
||||
|
||||
assigned_gt_inds5 = self.select(valid_mask, assigned_gt_inds4, self.assigned_gt_ignores)
|
||||
|
||||
pos_index, valid_pos_index = self.random_choice_with_mask_pos(self.greater(assigned_gt_inds5, 0))
|
||||
|
||||
pos_check_valid = self.cast(self.greater(assigned_gt_inds5, 0), mstype.float16)
|
||||
pos_check_valid = self.sum_inds(pos_check_valid, -1)
|
||||
valid_pos_index = self.less(self.range_pos_size, pos_check_valid)
|
||||
pos_index = pos_index * self.reshape(self.cast(valid_pos_index, mstype.int32), (self.num_expected_pos, 1))
|
||||
|
||||
pos_assigned_gt_index = self.gatherND(assigned_gt_inds5, pos_index) - self.assigned_pos_ones
|
||||
pos_assigned_gt_index = pos_assigned_gt_index * self.cast(valid_pos_index, mstype.int32)
|
||||
pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, (self.num_expected_pos, 1))
|
||||
|
||||
neg_index, valid_neg_index = self.random_choice_with_mask_neg(self.equal(assigned_gt_inds5, 0))
|
||||
|
||||
num_pos = self.cast(self.logicalnot(valid_pos_index), mstype.float16)
|
||||
num_pos = self.sum_inds(num_pos, -1)
|
||||
unvalid_pos_index = self.less(self.range_pos_size, num_pos)
|
||||
valid_neg_index = self.logicaland(self.concat((self.check_neg_mask, unvalid_pos_index)), valid_neg_index)
|
||||
|
||||
pos_bboxes_ = self.gatherND(bboxes, pos_index)
|
||||
pos_gt_bboxes_ = self.gatherND(gt_bboxes_i, pos_assigned_gt_index)
|
||||
pos_gt_labels = self.gatherND(gt_labels_i, pos_assigned_gt_index)
|
||||
|
||||
pos_bbox_targets_ = self.bounding_box_encode(pos_bboxes_, pos_gt_bboxes_)
|
||||
|
||||
valid_pos_index = self.cast(valid_pos_index, mstype.int32)
|
||||
valid_neg_index = self.cast(valid_neg_index, mstype.int32)
|
||||
bbox_targets_total = self.scatterNd(pos_index, pos_bbox_targets_, (self.num_bboxes, 4))
|
||||
bbox_weights_total = self.scatterNd(pos_index, valid_pos_index, (self.num_bboxes,))
|
||||
labels_total = self.scatterNd(pos_index, pos_gt_labels, (self.num_bboxes,))
|
||||
total_index = self.concat((pos_index, neg_index))
|
||||
total_valid_index = self.concat((valid_pos_index, valid_neg_index))
|
||||
label_weights_total = self.scatterNd(total_index, total_valid_index, (self.num_bboxes,))
|
||||
|
||||
return bbox_targets_total, self.cast(bbox_weights_total, mstype.bool_), \
|
||||
labels_total, self.cast(label_weights_total, mstype.bool_)
|
||||
|
|
@ -0,0 +1,221 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn tpositive and negative sample screening for Rcnn."""
|
||||
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.common.tensor import Tensor
|
||||
|
||||
class BboxAssignSampleForRcnn(nn.Cell):
|
||||
"""
|
||||
Bbox assigner and sampler defination.
|
||||
|
||||
Args:
|
||||
config (dict): Config.
|
||||
batch_size (int): Batchsize.
|
||||
num_bboxes (int): The anchor nums.
|
||||
add_gt_as_proposals (bool): add gt bboxes as proposals flag.
|
||||
|
||||
Returns:
|
||||
Tensor, multiple output tensors.
|
||||
|
||||
Examples:
|
||||
BboxAssignSampleForRcnn(config, 2, 1024, True)
|
||||
"""
|
||||
|
||||
def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
|
||||
super(BboxAssignSampleForRcnn, self).__init__()
|
||||
cfg = config
|
||||
self.batch_size = batch_size
|
||||
self.neg_iou_thr = cfg.neg_iou_thr_stage2
|
||||
self.pos_iou_thr = cfg.pos_iou_thr_stage2
|
||||
self.min_pos_iou = cfg.min_pos_iou_stage2
|
||||
self.num_gts = cfg.num_gts
|
||||
self.num_bboxes = num_bboxes
|
||||
self.num_expected_pos = cfg.num_expected_pos_stage2
|
||||
self.num_expected_neg = cfg.num_expected_neg_stage2
|
||||
self.num_expected_total = cfg.num_expected_total_stage2
|
||||
|
||||
self.add_gt_as_proposals = add_gt_as_proposals
|
||||
self.label_inds = Tensor(np.arange(1, self.num_gts + 1).astype(np.int32))
|
||||
self.add_gt_as_proposals_valid = Tensor(np.array(self.add_gt_as_proposals * np.ones(self.num_gts),
|
||||
dtype=np.int32))
|
||||
|
||||
self.concat = P.Concat(axis=0)
|
||||
self.max_gt = P.ArgMaxWithValue(axis=0)
|
||||
self.max_anchor = P.ArgMaxWithValue(axis=1)
|
||||
self.sum_inds = P.ReduceSum()
|
||||
self.iou = P.IOU()
|
||||
self.greaterequal = P.GreaterEqual()
|
||||
self.greater = P.Greater()
|
||||
self.select = P.Select()
|
||||
self.gatherND = P.GatherNd()
|
||||
self.squeeze = P.Squeeze()
|
||||
self.cast = P.Cast()
|
||||
self.logicaland = P.LogicalAnd()
|
||||
self.less = P.Less()
|
||||
self.random_choice_with_mask_pos = P.RandomChoiceWithMask(self.num_expected_pos)
|
||||
self.random_choice_with_mask_neg = P.RandomChoiceWithMask(self.num_expected_neg)
|
||||
self.reshape = P.Reshape()
|
||||
self.equal = P.Equal()
|
||||
self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(0.1, 0.1, 0.2, 0.2))
|
||||
self.concat_axis1 = P.Concat(axis=1)
|
||||
self.logicalnot = P.LogicalNot()
|
||||
self.tile = P.Tile()
|
||||
|
||||
# Check
|
||||
self.check_gt_one = Tensor(np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16))
|
||||
self.check_anchor_two = Tensor(np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16))
|
||||
|
||||
# Init tensor
|
||||
self.assigned_gt_inds = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
|
||||
self.assigned_gt_zeros = Tensor(np.array(np.zeros(num_bboxes), dtype=np.int32))
|
||||
self.assigned_gt_ones = Tensor(np.array(np.ones(num_bboxes), dtype=np.int32))
|
||||
self.assigned_gt_ignores = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
|
||||
self.assigned_pos_ones = Tensor(np.array(np.ones(self.num_expected_pos), dtype=np.int32))
|
||||
|
||||
self.gt_ignores = Tensor(np.array(-1 * np.ones(self.num_gts), dtype=np.int32))
|
||||
self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(np.float16))
|
||||
self.check_neg_mask = Tensor(np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool))
|
||||
self.bboxs_neg_mask = Tensor(np.zeros((self.num_expected_neg, 4), dtype=np.float16))
|
||||
self.labels_neg_mask = Tensor(np.array(np.zeros(self.num_expected_neg), dtype=np.uint8))
|
||||
|
||||
self.reshape_shape_pos = (self.num_expected_pos, 1)
|
||||
self.reshape_shape_neg = (self.num_expected_neg, 1)
|
||||
|
||||
self.scalar_zero = Tensor(0.0, dtype=mstype.float16)
|
||||
self.scalar_neg_iou_thr = Tensor(self.neg_iou_thr, dtype=mstype.float16)
|
||||
self.scalar_pos_iou_thr = Tensor(self.pos_iou_thr, dtype=mstype.float16)
|
||||
self.scalar_min_pos_iou = Tensor(self.min_pos_iou, dtype=mstype.float16)
|
||||
|
||||
self.expand_dims = P.ExpandDims()
|
||||
self.split = P.Split(axis=1, output_num=4)
|
||||
self.concat_last_axis = P.Concat(axis=-1)
|
||||
self.round = P.Round()
|
||||
self.image_h_w = Tensor([cfg.img_height, cfg.img_width, cfg.img_height, cfg.img_width], dtype=mstype.float16)
|
||||
self.range = nn.Range(start=0, limit=cfg.num_expected_pos_stage2)
|
||||
self.crop_and_resize = P.CropAndResize(method="bilinear_v2")
|
||||
self.mask_shape = (cfg.mask_shape[0], cfg.mask_shape[1])
|
||||
self.squeeze_mask_last = P.Squeeze(axis=-1)
|
||||
def construct(self, gt_bboxes_i, gt_labels_i, valid_mask, bboxes, gt_valids, gt_masks_i):
|
||||
gt_bboxes_i = self.select(self.cast(self.tile(self.reshape(self.cast(gt_valids, mstype.int32), \
|
||||
(self.num_gts, 1)), (1, 4)), mstype.bool_), \
|
||||
gt_bboxes_i, self.check_gt_one)
|
||||
bboxes = self.select(self.cast(self.tile(self.reshape(self.cast(valid_mask, mstype.int32), \
|
||||
(self.num_bboxes, 1)), (1, 4)), mstype.bool_), \
|
||||
bboxes, self.check_anchor_two)
|
||||
|
||||
overlaps = self.iou(bboxes, gt_bboxes_i)
|
||||
|
||||
max_overlaps_w_gt_index, max_overlaps_w_gt = self.max_gt(overlaps)
|
||||
_, max_overlaps_w_ac = self.max_anchor(overlaps)
|
||||
|
||||
neg_sample_iou_mask = self.logicaland(self.greaterequal(max_overlaps_w_gt,
|
||||
self.scalar_zero),
|
||||
self.less(max_overlaps_w_gt,
|
||||
self.scalar_neg_iou_thr))
|
||||
|
||||
assigned_gt_inds2 = self.select(neg_sample_iou_mask, self.assigned_gt_zeros, self.assigned_gt_inds)
|
||||
|
||||
pos_sample_iou_mask = self.greaterequal(max_overlaps_w_gt, self.scalar_pos_iou_thr)
|
||||
assigned_gt_inds3 = self.select(pos_sample_iou_mask, \
|
||||
max_overlaps_w_gt_index + self.assigned_gt_ones, assigned_gt_inds2)
|
||||
|
||||
for j in range(self.num_gts):
|
||||
max_overlaps_w_ac_j = max_overlaps_w_ac[j:j+1:1]
|
||||
overlaps_w_ac_j = overlaps[j:j+1:1, ::]
|
||||
temp1 = self.greaterequal(max_overlaps_w_ac_j, self.scalar_min_pos_iou)
|
||||
temp2 = self.squeeze(self.equal(overlaps_w_ac_j, max_overlaps_w_ac_j))
|
||||
pos_mask_j = self.logicaland(temp1, temp2)
|
||||
assigned_gt_inds3 = self.select(pos_mask_j, (j+1)*self.assigned_gt_ones, assigned_gt_inds3)
|
||||
|
||||
assigned_gt_inds5 = self.select(valid_mask, assigned_gt_inds3, self.assigned_gt_ignores)
|
||||
|
||||
bboxes = self.concat((gt_bboxes_i, bboxes))
|
||||
label_inds_valid = self.select(gt_valids, self.label_inds, self.gt_ignores)
|
||||
label_inds_valid = label_inds_valid * self.add_gt_as_proposals_valid
|
||||
assigned_gt_inds5 = self.concat((label_inds_valid, assigned_gt_inds5))
|
||||
|
||||
# Get pos index
|
||||
pos_index, valid_pos_index = self.random_choice_with_mask_pos(self.greater(assigned_gt_inds5, 0))
|
||||
|
||||
pos_check_valid = self.cast(self.greater(assigned_gt_inds5, 0), mstype.float16)
|
||||
pos_check_valid = self.sum_inds(pos_check_valid, -1)
|
||||
valid_pos_index = self.less(self.range_pos_size, pos_check_valid)
|
||||
pos_index = pos_index * self.reshape(self.cast(valid_pos_index, mstype.int32), (self.num_expected_pos, 1))
|
||||
|
||||
num_pos = self.sum_inds(self.cast(self.logicalnot(valid_pos_index), mstype.float16), -1)
|
||||
valid_pos_index = self.cast(valid_pos_index, mstype.int32)
|
||||
pos_index = self.reshape(pos_index, self.reshape_shape_pos)
|
||||
valid_pos_index = self.reshape(valid_pos_index, self.reshape_shape_pos)
|
||||
pos_index = pos_index * valid_pos_index
|
||||
|
||||
pos_assigned_gt_index = self.gatherND(assigned_gt_inds5, pos_index) - self.assigned_pos_ones
|
||||
pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, self.reshape_shape_pos)
|
||||
pos_assigned_gt_index = pos_assigned_gt_index * valid_pos_index
|
||||
|
||||
pos_gt_labels = self.gatherND(gt_labels_i, pos_assigned_gt_index)
|
||||
|
||||
# Get neg index
|
||||
neg_index, valid_neg_index = self.random_choice_with_mask_neg(self.equal(assigned_gt_inds5, 0))
|
||||
|
||||
unvalid_pos_index = self.less(self.range_pos_size, num_pos)
|
||||
valid_neg_index = self.logicaland(self.concat((self.check_neg_mask, unvalid_pos_index)), valid_neg_index)
|
||||
neg_index = self.reshape(neg_index, self.reshape_shape_neg)
|
||||
|
||||
valid_neg_index = self.cast(valid_neg_index, mstype.int32)
|
||||
valid_neg_index = self.reshape(valid_neg_index, self.reshape_shape_neg)
|
||||
neg_index = neg_index * valid_neg_index
|
||||
|
||||
pos_bboxes_ = self.gatherND(bboxes, pos_index)
|
||||
|
||||
neg_bboxes_ = self.gatherND(bboxes, neg_index)
|
||||
pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, self.reshape_shape_pos)
|
||||
pos_gt_bboxes_ = self.gatherND(gt_bboxes_i, pos_assigned_gt_index)
|
||||
pos_bbox_targets_ = self.bounding_box_encode(pos_bboxes_, pos_gt_bboxes_)
|
||||
|
||||
# assign positive ROIs to gt masks
|
||||
# Pick the right front and background mask for each ROI
|
||||
roi_pos_masks_fb = self.gatherND(gt_masks_i, pos_assigned_gt_index)
|
||||
pos_masks_fb = self.cast(roi_pos_masks_fb, mstype.float32)
|
||||
# compute mask targets
|
||||
x1, y1, x2, y2 = self.split(pos_bboxes_)
|
||||
boxes = self.concat_last_axis((y1, x1, y2, x2))
|
||||
# normalized box coordinate
|
||||
boxes = boxes / self.image_h_w
|
||||
box_ids = self.range()
|
||||
pos_masks_fb = self.expand_dims(pos_masks_fb, -1)
|
||||
boxes = self.cast(boxes, mstype.float32)
|
||||
pos_masks_fb = self.crop_and_resize(pos_masks_fb, boxes, box_ids, self.mask_shape)
|
||||
|
||||
# Remove the extra dimension from masks.
|
||||
pos_masks_fb = self.squeeze_mask_last(pos_masks_fb)
|
||||
|
||||
# convert gt masks targets be 0 or 1 to use with binary cross entropy loss.
|
||||
pos_masks_fb = self.round(pos_masks_fb)
|
||||
|
||||
pos_masks_fb = self.cast(pos_masks_fb, mstype.float16)
|
||||
total_bboxes = self.concat((pos_bboxes_, neg_bboxes_))
|
||||
total_deltas = self.concat((pos_bbox_targets_, self.bboxs_neg_mask))
|
||||
total_labels = self.concat((pos_gt_labels, self.labels_neg_mask))
|
||||
|
||||
valid_pos_index = self.reshape(valid_pos_index, self.reshape_shape_pos)
|
||||
valid_neg_index = self.reshape(valid_neg_index, self.reshape_shape_neg)
|
||||
total_mask = self.concat((valid_pos_index, valid_neg_index))
|
||||
|
||||
return total_bboxes, total_deltas, total_labels, total_mask, pos_bboxes_, pos_masks_fb, \
|
||||
pos_gt_labels, valid_pos_index
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn feature pyramid network."""
|
||||
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.common.initializer import initializer
|
||||
|
||||
|
||||
def bias_init_zeros(shape):
|
||||
"""Bias init method."""
|
||||
return Tensor(np.array(np.zeros(shape).astype(np.float32)).astype(np.float16))
|
||||
|
||||
def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
|
||||
"""Conv2D wrapper."""
|
||||
shape = (out_channels, in_channels, kernel_size, kernel_size)
|
||||
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
|
||||
shape_bias = (out_channels,)
|
||||
biass = bias_init_zeros(shape_bias)
|
||||
return nn.Conv2d(in_channels, out_channels,
|
||||
kernel_size=kernel_size, stride=stride, padding=padding,
|
||||
pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=biass)
|
||||
|
||||
class FeatPyramidNeck(nn.Cell):
|
||||
"""
|
||||
Feature pyramid network cell, usually uses as network neck.
|
||||
|
||||
Applies the convolution on multiple, input feature maps
|
||||
and output feature map with same channel size. if required num of
|
||||
output larger then num of inputs, add extra maxpooling for further
|
||||
downsampling;
|
||||
|
||||
Args:
|
||||
in_channels (tuple) - Channel size of input feature maps.
|
||||
out_channels (int) - Channel size output.
|
||||
num_outs (int) - Num of output features.
|
||||
|
||||
Returns:
|
||||
Tuple, with tensors of same channel size.
|
||||
|
||||
Examples:
|
||||
neck = FeatPyramidNeck([100,200,300], 50, 4)
|
||||
input_data = (normal(0,0.1,(1,c,1280//(4*2**i), 768//(4*2**i)),
|
||||
dtype=np.float32) \
|
||||
for i, c in enumerate(config.fpn_in_channels))
|
||||
x = neck(input_data)
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
num_outs):
|
||||
super(FeatPyramidNeck, self).__init__()
|
||||
self.num_outs = num_outs
|
||||
self.in_channels = in_channels
|
||||
self.fpn_layer = len(self.in_channels)
|
||||
|
||||
assert not self.num_outs < len(in_channels)
|
||||
|
||||
self.lateral_convs_list_ = []
|
||||
self.fpn_convs_ = []
|
||||
|
||||
for _, channel in enumerate(in_channels):
|
||||
l_conv = _conv(channel, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='valid')
|
||||
fpn_conv = _conv(out_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='same')
|
||||
self.lateral_convs_list_.append(l_conv)
|
||||
self.fpn_convs_.append(fpn_conv)
|
||||
self.lateral_convs_list = nn.layer.CellList(self.lateral_convs_list_)
|
||||
self.fpn_convs_list = nn.layer.CellList(self.fpn_convs_)
|
||||
self.interpolate1 = P.ResizeBilinear((48, 80))
|
||||
self.interpolate2 = P.ResizeBilinear((96, 160))
|
||||
self.interpolate3 = P.ResizeBilinear((192, 320))
|
||||
self.cast = P.Cast()
|
||||
self.maxpool = P.MaxPool(ksize=1, strides=2, padding="same")
|
||||
|
||||
def construct(self, inputs):
|
||||
x = ()
|
||||
for i in range(self.fpn_layer):
|
||||
x += (self.lateral_convs_list[i](inputs[i]),)
|
||||
|
||||
y = (x[3],)
|
||||
y = y + (x[2] + self.cast(self.interpolate1(y[self.fpn_layer - 4]), mstype.float16),)
|
||||
y = y + (x[1] + self.cast(self.interpolate2(y[self.fpn_layer - 3]), mstype.float16),)
|
||||
y = y + (x[0] + self.cast(self.interpolate3(y[self.fpn_layer - 2]), mstype.float16),)
|
||||
|
||||
z = ()
|
||||
for i in range(self.fpn_layer - 1, -1, -1):
|
||||
z = z + (y[i],)
|
||||
|
||||
outs = ()
|
||||
for i in range(self.fpn_layer):
|
||||
outs = outs + (self.fpn_convs_list[i](z[i]),)
|
||||
|
||||
for i in range(self.num_outs - self.fpn_layer):
|
||||
outs = outs + (self.maxpool(outs[3]),)
|
||||
return outs
|
||||
|
|
@ -0,0 +1,564 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn based on mobilenetv1."""
|
||||
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.common.tensor import Tensor
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore.ops import functional as F
|
||||
from .mobilenetv1 import MobileNetV1_FeatureSelector
|
||||
from .bbox_assign_sample_stage2 import BboxAssignSampleForRcnn
|
||||
from .fpn_neck import FeatPyramidNeck
|
||||
from .proposal_generator import Proposal
|
||||
from .rcnn_cls import RcnnCls
|
||||
from .rcnn_mask import RcnnMask
|
||||
from .rpn import RPN
|
||||
from .roi_align import SingleRoIExtractor
|
||||
from .anchor_generator import AnchorGenerator
|
||||
|
||||
class Mask_Rcnn_Mobilenetv1(nn.Cell):
|
||||
"""
|
||||
MaskRcnn Network.
|
||||
|
||||
Note:
|
||||
backbone = mobilenetv1
|
||||
|
||||
Returns:
|
||||
Tuple, tuple of output tensor.
|
||||
rpn_loss: Scalar, Total loss of RPN subnet.
|
||||
rcnn_loss: Scalar, Total loss of RCNN subnet.
|
||||
rpn_cls_loss: Scalar, Classification loss of RPN subnet.
|
||||
rpn_reg_loss: Scalar, Regression loss of RPN subnet.
|
||||
rcnn_cls_loss: Scalar, Classification loss of RCNNcls subnet.
|
||||
rcnn_reg_loss: Scalar, Regression loss of RCNNcls subnet.
|
||||
rcnn_mask_loss: Scalar, mask loss of RCNNmask subnet.
|
||||
|
||||
Examples:
|
||||
net = Mask_Rcnn_Mobilenetv1()
|
||||
"""
|
||||
def __init__(self, config):
|
||||
super(Mask_Rcnn_Mobilenetv1, self).__init__()
|
||||
self.train_batch_size = config.batch_size
|
||||
self.num_classes = config.num_classes
|
||||
self.anchor_scales = config.anchor_scales
|
||||
self.anchor_ratios = config.anchor_ratios
|
||||
self.anchor_strides = config.anchor_strides
|
||||
self.target_means = tuple(config.rcnn_target_means)
|
||||
self.target_stds = tuple(config.rcnn_target_stds)
|
||||
|
||||
# Anchor generator
|
||||
anchor_base_sizes = None
|
||||
self.anchor_base_sizes = list(
|
||||
self.anchor_strides) if anchor_base_sizes is None else anchor_base_sizes
|
||||
|
||||
self.anchor_generators = []
|
||||
for anchor_base in self.anchor_base_sizes:
|
||||
self.anchor_generators.append(
|
||||
AnchorGenerator(anchor_base, self.anchor_scales, self.anchor_ratios))
|
||||
|
||||
self.num_anchors = len(self.anchor_ratios) * len(self.anchor_scales)
|
||||
|
||||
featmap_sizes = config.feature_shapes
|
||||
assert len(featmap_sizes) == len(self.anchor_generators)
|
||||
|
||||
self.anchor_list = self.get_anchors(featmap_sizes)
|
||||
|
||||
# Backbone mobilenetv1
|
||||
self.backbone = MobileNetV1_FeatureSelector(1001, features_only=True).to_float(mstype.float16)
|
||||
# Fpn
|
||||
self.fpn_ncek = FeatPyramidNeck(config.fpn_in_channels,
|
||||
config.fpn_out_channels,
|
||||
config.fpn_num_outs)
|
||||
|
||||
# Rpn and rpn loss
|
||||
self.gt_labels_stage1 = Tensor(np.ones((self.train_batch_size, config.num_gts)).astype(np.uint8))
|
||||
self.rpn_with_loss = RPN(config,
|
||||
self.train_batch_size,
|
||||
config.rpn_in_channels,
|
||||
config.rpn_feat_channels,
|
||||
config.num_anchors,
|
||||
config.rpn_cls_out_channels)
|
||||
|
||||
# Proposal
|
||||
self.proposal_generator = Proposal(config,
|
||||
self.train_batch_size,
|
||||
config.activate_num_classes,
|
||||
config.use_sigmoid_cls)
|
||||
self.proposal_generator.set_train_local(config, True)
|
||||
self.proposal_generator_test = Proposal(config,
|
||||
config.test_batch_size,
|
||||
config.activate_num_classes,
|
||||
config.use_sigmoid_cls)
|
||||
self.proposal_generator_test.set_train_local(config, False)
|
||||
|
||||
# Assign and sampler stage two
|
||||
self.bbox_assigner_sampler_for_rcnn = BboxAssignSampleForRcnn(config, self.train_batch_size,
|
||||
config.num_bboxes_stage2, True)
|
||||
self.decode = P.BoundingBoxDecode(max_shape=(768, 1280), means=self.target_means, \
|
||||
stds=self.target_stds)
|
||||
|
||||
# Roi
|
||||
self.roi_align = SingleRoIExtractor(config,
|
||||
config.roi_layer,
|
||||
config.roi_align_out_channels,
|
||||
config.roi_align_featmap_strides,
|
||||
self.train_batch_size,
|
||||
config.roi_align_finest_scale,
|
||||
mask=False)
|
||||
self.roi_align.set_train_local(config, True)
|
||||
|
||||
self.roi_align_mask = SingleRoIExtractor(config,
|
||||
config.roi_layer,
|
||||
config.roi_align_out_channels,
|
||||
config.roi_align_featmap_strides,
|
||||
self.train_batch_size,
|
||||
config.roi_align_finest_scale,
|
||||
mask=True)
|
||||
self.roi_align_mask.set_train_local(config, True)
|
||||
|
||||
self.roi_align_test = SingleRoIExtractor(config,
|
||||
config.roi_layer,
|
||||
config.roi_align_out_channels,
|
||||
config.roi_align_featmap_strides,
|
||||
1,
|
||||
config.roi_align_finest_scale,
|
||||
mask=False)
|
||||
self.roi_align_test.set_train_local(config, False)
|
||||
|
||||
self.roi_align_mask_test = SingleRoIExtractor(config,
|
||||
config.roi_layer,
|
||||
config.roi_align_out_channels,
|
||||
config.roi_align_featmap_strides,
|
||||
1,
|
||||
config.roi_align_finest_scale,
|
||||
mask=True)
|
||||
self.roi_align_mask_test.set_train_local(config, False)
|
||||
|
||||
# Rcnn
|
||||
self.rcnn_cls = RcnnCls(config, self.train_batch_size, self.num_classes)
|
||||
self.rcnn_mask = RcnnMask(config, self.train_batch_size, self.num_classes)
|
||||
|
||||
# Op declare
|
||||
self.squeeze = P.Squeeze()
|
||||
self.cast = P.Cast()
|
||||
|
||||
self.concat = P.Concat(axis=0)
|
||||
self.concat_1 = P.Concat(axis=1)
|
||||
self.concat_2 = P.Concat(axis=2)
|
||||
self.reshape = P.Reshape()
|
||||
self.select = P.Select()
|
||||
self.greater = P.Greater()
|
||||
self.transpose = P.Transpose()
|
||||
|
||||
# Test mode
|
||||
self.test_batch_size = config.test_batch_size
|
||||
self.split = P.Split(axis=0, output_num=self.test_batch_size)
|
||||
self.split_shape = P.Split(axis=0, output_num=4)
|
||||
self.split_scores = P.Split(axis=1, output_num=self.num_classes)
|
||||
self.split_fb_mask = P.Split(axis=1, output_num=self.num_classes)
|
||||
self.split_cls = P.Split(axis=0, output_num=self.num_classes-1)
|
||||
self.tile = P.Tile()
|
||||
self.gather = P.GatherNd()
|
||||
|
||||
self.rpn_max_num = config.rpn_max_num
|
||||
|
||||
self.zeros_for_nms = Tensor(np.zeros((self.rpn_max_num, 3)).astype(np.float16))
|
||||
self.ones_mask = np.ones((self.rpn_max_num, 1)).astype(np.bool)
|
||||
self.zeros_mask = np.zeros((self.rpn_max_num, 1)).astype(np.bool)
|
||||
self.bbox_mask = Tensor(np.concatenate((self.ones_mask, self.zeros_mask,
|
||||
self.ones_mask, self.zeros_mask), axis=1))
|
||||
self.nms_pad_mask = Tensor(np.concatenate((self.ones_mask, self.ones_mask,
|
||||
self.ones_mask, self.ones_mask, self.zeros_mask), axis=1))
|
||||
|
||||
self.test_score_thresh = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * config.test_score_thr)
|
||||
self.test_score_zeros = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * 0)
|
||||
self.test_box_zeros = Tensor(np.ones((self.rpn_max_num, 4)).astype(np.float16) * -1)
|
||||
self.test_iou_thr = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * config.test_iou_thr)
|
||||
self.test_max_per_img = config.test_max_per_img
|
||||
self.nms_test = P.NMSWithMask(config.test_iou_thr)
|
||||
self.softmax = P.Softmax(axis=1)
|
||||
self.logicand = P.LogicalAnd()
|
||||
self.oneslike = P.OnesLike()
|
||||
self.test_topk = P.TopK(sorted=True)
|
||||
self.test_num_proposal = self.test_batch_size * self.rpn_max_num
|
||||
|
||||
# Improve speed
|
||||
self.concat_start = min(self.num_classes - 2, 55)
|
||||
self.concat_end = (self.num_classes - 1)
|
||||
|
||||
# Init tensor
|
||||
roi_align_index = [np.array(np.ones((config.num_expected_pos_stage2 + config.num_expected_neg_stage2, 1)) * i,
|
||||
dtype=np.float16) for i in range(self.train_batch_size)]
|
||||
|
||||
roi_align_index_test = [np.array(np.ones((config.rpn_max_num, 1)) * i, dtype=np.float16) \
|
||||
for i in range(self.test_batch_size)]
|
||||
|
||||
self.roi_align_index_tensor = Tensor(np.concatenate(roi_align_index))
|
||||
self.roi_align_index_test_tensor = Tensor(np.concatenate(roi_align_index_test))
|
||||
|
||||
roi_align_index_pos = [np.array(np.ones((config.num_expected_pos_stage2, 1)) * i,
|
||||
dtype=np.float16) for i in range(self.train_batch_size)]
|
||||
self.roi_align_index_tensor_pos = Tensor(np.concatenate(roi_align_index_pos))
|
||||
|
||||
self.rcnn_loss_cls_weight = Tensor(np.array(config.rcnn_loss_cls_weight).astype(np.float16))
|
||||
self.rcnn_loss_reg_weight = Tensor(np.array(config.rcnn_loss_reg_weight).astype(np.float16))
|
||||
self.rcnn_loss_mask_fb_weight = Tensor(np.array(config.rcnn_loss_mask_fb_weight).astype(np.float16))
|
||||
|
||||
self.argmax_with_value = P.ArgMaxWithValue(axis=1)
|
||||
self.on_value = Tensor(1.0, mstype.float32)
|
||||
self.off_value = Tensor(0.0, mstype.float32)
|
||||
self.onehot = P.OneHot()
|
||||
self.reducesum = P.ReduceSum()
|
||||
self.sigmoid = P.Sigmoid()
|
||||
self.expand_dims = P.ExpandDims()
|
||||
self.test_mask_fb_zeros = Tensor(np.zeros((self.rpn_max_num, 28, 28)).astype(np.float16))
|
||||
self.value = Tensor(1.0, mstype.float16)
|
||||
def construct(self, img_data, img_metas, gt_bboxes, gt_labels, gt_valids, gt_masks):
|
||||
x = self.backbone(img_data)
|
||||
x = self.fpn_ncek(x)
|
||||
|
||||
rpn_loss, cls_score, bbox_pred, rpn_cls_loss, rpn_reg_loss, _ = self.rpn_with_loss(x,
|
||||
img_metas,
|
||||
self.anchor_list,
|
||||
gt_bboxes,
|
||||
self.gt_labels_stage1,
|
||||
gt_valids)
|
||||
|
||||
if self.training:
|
||||
proposal, proposal_mask = self.proposal_generator(cls_score, bbox_pred, self.anchor_list)
|
||||
else:
|
||||
proposal, proposal_mask = self.proposal_generator_test(cls_score, bbox_pred, self.anchor_list)
|
||||
|
||||
gt_labels = self.cast(gt_labels, mstype.int32)
|
||||
gt_valids = self.cast(gt_valids, mstype.int32)
|
||||
bboxes_tuple = ()
|
||||
deltas_tuple = ()
|
||||
labels_tuple = ()
|
||||
mask_tuple = ()
|
||||
|
||||
pos_bboxes_tuple = ()
|
||||
pos_mask_fb_tuple = ()
|
||||
pos_labels_tuple = ()
|
||||
pos_mask_tuple = ()
|
||||
|
||||
if self.training:
|
||||
for i in range(self.train_batch_size):
|
||||
gt_bboxes_i = self.squeeze(gt_bboxes[i:i + 1:1, ::])
|
||||
|
||||
gt_labels_i = self.squeeze(gt_labels[i:i + 1:1, ::])
|
||||
gt_labels_i = self.cast(gt_labels_i, mstype.uint8)
|
||||
|
||||
gt_valids_i = self.squeeze(gt_valids[i:i + 1:1, ::])
|
||||
gt_valids_i = self.cast(gt_valids_i, mstype.bool_)
|
||||
|
||||
gt_masks_i = self.squeeze(gt_masks[i:i + 1:1, ::])
|
||||
gt_masks_i = self.cast(gt_masks_i, mstype.bool_)
|
||||
|
||||
bboxes, deltas, labels, mask, pos_bboxes, pos_mask_fb, pos_labels, pos_mask = \
|
||||
self.bbox_assigner_sampler_for_rcnn(gt_bboxes_i,
|
||||
gt_labels_i,
|
||||
proposal_mask[i],
|
||||
proposal[i][::, 0:4:1],
|
||||
gt_valids_i,
|
||||
gt_masks_i)
|
||||
bboxes_tuple += (bboxes,)
|
||||
deltas_tuple += (deltas,)
|
||||
labels_tuple += (labels,)
|
||||
mask_tuple += (mask,)
|
||||
|
||||
pos_bboxes_tuple += (pos_bboxes,)
|
||||
pos_mask_fb_tuple += (pos_mask_fb,)
|
||||
pos_labels_tuple += (pos_labels,)
|
||||
pos_mask_tuple += (pos_mask,)
|
||||
|
||||
bbox_targets = self.concat(deltas_tuple)
|
||||
rcnn_labels = self.concat(labels_tuple)
|
||||
bbox_targets = F.stop_gradient(bbox_targets)
|
||||
rcnn_labels = F.stop_gradient(rcnn_labels)
|
||||
rcnn_labels = self.cast(rcnn_labels, mstype.int32)
|
||||
|
||||
rcnn_pos_masks_fb = self.concat(pos_mask_fb_tuple)
|
||||
rcnn_pos_masks_fb = F.stop_gradient(rcnn_pos_masks_fb)
|
||||
rcnn_pos_labels = self.concat(pos_labels_tuple)
|
||||
rcnn_pos_labels = F.stop_gradient(rcnn_pos_labels)
|
||||
rcnn_pos_labels = self.cast(rcnn_pos_labels, mstype.int32)
|
||||
else:
|
||||
mask_tuple += proposal_mask
|
||||
bbox_targets = proposal_mask
|
||||
rcnn_labels = proposal_mask
|
||||
|
||||
rcnn_pos_masks_fb = proposal_mask
|
||||
rcnn_pos_labels = proposal_mask
|
||||
for p_i in proposal:
|
||||
bboxes_tuple += (p_i[::, 0:4:1],)
|
||||
|
||||
pos_rois = None
|
||||
if self.training:
|
||||
if self.train_batch_size > 1:
|
||||
bboxes_all = self.concat(bboxes_tuple)
|
||||
pos_bboxes_all = self.concat(pos_bboxes_tuple)
|
||||
else:
|
||||
bboxes_all = bboxes_tuple[0]
|
||||
pos_bboxes_all = pos_bboxes_tuple[0]
|
||||
rois = self.concat_1((self.roi_align_index_tensor, bboxes_all))
|
||||
pos_rois = self.concat_1((self.roi_align_index_tensor_pos, pos_bboxes_all))
|
||||
pos_rois = self.cast(pos_rois, mstype.float32)
|
||||
pos_rois = F.stop_gradient(pos_rois)
|
||||
else:
|
||||
if self.test_batch_size > 1:
|
||||
bboxes_all = self.concat(bboxes_tuple)
|
||||
else:
|
||||
bboxes_all = bboxes_tuple[0]
|
||||
rois = self.concat_1((self.roi_align_index_test_tensor, bboxes_all))
|
||||
|
||||
rois = self.cast(rois, mstype.float32)
|
||||
rois = F.stop_gradient(rois)
|
||||
|
||||
if self.training:
|
||||
roi_feats = self.roi_align(rois,
|
||||
self.cast(x[0], mstype.float32),
|
||||
self.cast(x[1], mstype.float32),
|
||||
self.cast(x[2], mstype.float32),
|
||||
self.cast(x[3], mstype.float32))
|
||||
else:
|
||||
roi_feats = self.roi_align_test(rois,
|
||||
self.cast(x[0], mstype.float32),
|
||||
self.cast(x[1], mstype.float32),
|
||||
self.cast(x[2], mstype.float32),
|
||||
self.cast(x[3], mstype.float32))
|
||||
|
||||
|
||||
roi_feats = self.cast(roi_feats, mstype.float16)
|
||||
rcnn_masks = self.concat(mask_tuple)
|
||||
rcnn_masks = F.stop_gradient(rcnn_masks)
|
||||
rcnn_mask_squeeze = self.squeeze(self.cast(rcnn_masks, mstype.bool_))
|
||||
|
||||
rcnn_pos_masks = self.concat(pos_mask_tuple)
|
||||
rcnn_pos_masks = F.stop_gradient(rcnn_pos_masks)
|
||||
rcnn_pos_mask_squeeze = self.squeeze(self.cast(rcnn_pos_masks, mstype.bool_))
|
||||
|
||||
rcnn_cls_loss, rcnn_reg_loss = self.rcnn_cls(roi_feats,
|
||||
bbox_targets,
|
||||
rcnn_labels,
|
||||
rcnn_mask_squeeze)
|
||||
|
||||
output = ()
|
||||
if self.training:
|
||||
roi_feats_mask = self.roi_align_mask(pos_rois,
|
||||
self.cast(x[0], mstype.float32),
|
||||
self.cast(x[1], mstype.float32),
|
||||
self.cast(x[2], mstype.float32),
|
||||
self.cast(x[3], mstype.float32))
|
||||
roi_feats_mask = self.cast(roi_feats_mask, mstype.float16)
|
||||
rcnn_mask_fb_loss = self.rcnn_mask(roi_feats_mask,
|
||||
rcnn_pos_labels,
|
||||
rcnn_pos_mask_squeeze,
|
||||
rcnn_pos_masks_fb)
|
||||
|
||||
rcnn_loss = self.rcnn_loss_cls_weight * rcnn_cls_loss + self.rcnn_loss_reg_weight * rcnn_reg_loss + \
|
||||
self.rcnn_loss_mask_fb_weight * rcnn_mask_fb_loss
|
||||
output += (rpn_loss, rcnn_loss, rpn_cls_loss, rpn_reg_loss, rcnn_cls_loss, rcnn_reg_loss, rcnn_mask_fb_loss)
|
||||
else:
|
||||
mask_fb_pred_all = self.rcnn_mask_test(x, bboxes_all, rcnn_cls_loss, rcnn_reg_loss)
|
||||
output = self.get_det_bboxes(rcnn_cls_loss, rcnn_reg_loss, rcnn_masks, bboxes_all,
|
||||
img_metas, mask_fb_pred_all)
|
||||
|
||||
return output
|
||||
|
||||
def get_det_bboxes(self, cls_logits, reg_logits, mask_logits, rois, img_metas, mask_fb_pred_all):
|
||||
"""Get the actual detection box."""
|
||||
scores = self.softmax(cls_logits / self.value)
|
||||
mask_fb_logits = self.sigmoid(mask_fb_pred_all)
|
||||
|
||||
boxes_all = ()
|
||||
for i in range(self.num_classes):
|
||||
k = i * 4
|
||||
reg_logits_i = self.squeeze(reg_logits[::, k:k+4:1])
|
||||
out_boxes_i = self.decode(rois, reg_logits_i)
|
||||
boxes_all += (out_boxes_i,)
|
||||
|
||||
img_metas_all = self.split(img_metas)
|
||||
scores_all = self.split(scores)
|
||||
mask_all = self.split(self.cast(mask_logits, mstype.int32))
|
||||
mask_fb_all = self.split(mask_fb_logits)
|
||||
|
||||
boxes_all_with_batchsize = ()
|
||||
for i in range(self.test_batch_size):
|
||||
scale = self.split_shape(self.squeeze(img_metas_all[i]))
|
||||
scale_h = scale[2]
|
||||
scale_w = scale[3]
|
||||
boxes_tuple = ()
|
||||
for j in range(self.num_classes):
|
||||
boxes_tmp = self.split(boxes_all[j])
|
||||
out_boxes_h = boxes_tmp[i] / scale_h
|
||||
out_boxes_w = boxes_tmp[i] / scale_w
|
||||
boxes_tuple += (self.select(self.bbox_mask, out_boxes_w, out_boxes_h),)
|
||||
boxes_all_with_batchsize += (boxes_tuple,)
|
||||
|
||||
output = self.multiclass_nms(boxes_all_with_batchsize, scores_all, mask_all, mask_fb_all)
|
||||
|
||||
return output
|
||||
|
||||
def multiclass_nms(self, boxes_all, scores_all, mask_all, mask_fb_all):
|
||||
"""Multiscale postprocessing."""
|
||||
all_bboxes = ()
|
||||
all_labels = ()
|
||||
all_masks = ()
|
||||
all_masks_fb = ()
|
||||
|
||||
for i in range(self.test_batch_size):
|
||||
bboxes = boxes_all[i]
|
||||
scores = scores_all[i]
|
||||
masks = self.cast(mask_all[i], mstype.bool_)
|
||||
masks_fb = mask_fb_all[i]
|
||||
_mask_fb_all = self.split_fb_mask(masks_fb)
|
||||
|
||||
res_boxes_tuple = ()
|
||||
res_labels_tuple = ()
|
||||
res_masks_tuple = ()
|
||||
res_masks_fb_tuple = ()
|
||||
|
||||
for j in range(self.num_classes - 1):
|
||||
k = j + 1
|
||||
_cls_scores = scores[::, k:k + 1:1]
|
||||
_bboxes = self.squeeze(bboxes[k])
|
||||
_mask_o = self.reshape(masks, (self.rpn_max_num, 1))
|
||||
_masks_fb = self.squeeze(_mask_fb_all[k])
|
||||
|
||||
cls_mask = self.greater(_cls_scores, self.test_score_thresh)
|
||||
_mask = self.logicand(_mask_o, cls_mask)
|
||||
|
||||
_reg_mask = self.cast(self.tile(self.cast(_mask, mstype.int32), (1, 4)), mstype.bool_)
|
||||
|
||||
_bboxes = self.select(_reg_mask, _bboxes, self.test_box_zeros)
|
||||
_fb_mask = self.expand_dims(_mask, -1)
|
||||
_mask_fb_mask = self.cast(self.tile(self.cast(_fb_mask, mstype.int32), (1, 28, 28)), mstype.bool_)
|
||||
_masks_fb = self.select(_mask_fb_mask, _masks_fb, self.test_mask_fb_zeros)
|
||||
_cls_scores = self.select(_mask, _cls_scores, self.test_score_zeros)
|
||||
__cls_scores = self.squeeze(_cls_scores)
|
||||
scores_sorted, topk_inds = self.test_topk(__cls_scores, self.rpn_max_num)
|
||||
topk_inds = self.reshape(topk_inds, (self.rpn_max_num, 1))
|
||||
scores_sorted = self.reshape(scores_sorted, (self.rpn_max_num, 1))
|
||||
_bboxes_sorted = self.gather(_bboxes, topk_inds)
|
||||
_mask_fb_sorted = self.gather(_masks_fb, topk_inds)
|
||||
_mask_sorted = self.gather(_mask, topk_inds)
|
||||
|
||||
scores_sorted = self.tile(scores_sorted, (1, 4))
|
||||
cls_dets = self.concat_1((_bboxes_sorted, scores_sorted))
|
||||
cls_dets = P.Slice()(cls_dets, (0, 0), (self.rpn_max_num, 5))
|
||||
|
||||
cls_dets, _index, _mask_nms = self.nms_test(cls_dets)
|
||||
_index = self.reshape(_index, (self.rpn_max_num, 1))
|
||||
_mask_nms = self.reshape(_mask_nms, (self.rpn_max_num, 1))
|
||||
|
||||
_mask_n = self.gather(_mask_sorted, _index)
|
||||
_mask_n = self.logicand(_mask_n, _mask_nms)
|
||||
|
||||
_mask_fb = self.gather(_mask_fb_sorted, _index)
|
||||
|
||||
cls_labels = self.oneslike(_index) * j
|
||||
res_boxes_tuple += (cls_dets,)
|
||||
res_labels_tuple += (cls_labels,)
|
||||
res_masks_tuple += (_mask_n,)
|
||||
res_masks_fb_tuple += (_mask_fb,)
|
||||
|
||||
res_boxes_start = self.concat(res_boxes_tuple[:self.concat_start])
|
||||
res_labels_start = self.concat(res_labels_tuple[:self.concat_start])
|
||||
res_masks_start = self.concat(res_masks_tuple[:self.concat_start])
|
||||
res_masks_fb_start = self.concat(res_masks_fb_tuple[:self.concat_start])
|
||||
|
||||
res_boxes_end = self.concat(res_boxes_tuple[self.concat_start:self.concat_end])
|
||||
res_labels_end = self.concat(res_labels_tuple[self.concat_start:self.concat_end])
|
||||
res_masks_end = self.concat(res_masks_tuple[self.concat_start:self.concat_end])
|
||||
res_masks_fb_end = self.concat(res_masks_fb_tuple[self.concat_start:self.concat_end])
|
||||
|
||||
res_boxes = self.concat((res_boxes_start, res_boxes_end))
|
||||
res_labels = self.concat((res_labels_start, res_labels_end))
|
||||
res_masks = self.concat((res_masks_start, res_masks_end))
|
||||
res_masks_fb = self.concat((res_masks_fb_start, res_masks_fb_end))
|
||||
|
||||
reshape_size = (self.num_classes - 1) * self.rpn_max_num
|
||||
res_boxes = self.reshape(res_boxes, (1, reshape_size, 5))
|
||||
res_labels = self.reshape(res_labels, (1, reshape_size, 1))
|
||||
res_masks = self.reshape(res_masks, (1, reshape_size, 1))
|
||||
res_masks_fb = self.reshape(res_masks_fb, (1, reshape_size, 28, 28))
|
||||
|
||||
all_bboxes += (res_boxes,)
|
||||
all_labels += (res_labels,)
|
||||
all_masks += (res_masks,)
|
||||
all_masks_fb += (res_masks_fb,)
|
||||
|
||||
all_bboxes = self.concat(all_bboxes)
|
||||
all_labels = self.concat(all_labels)
|
||||
all_masks = self.concat(all_masks)
|
||||
all_masks_fb = self.concat(all_masks_fb)
|
||||
return all_bboxes, all_labels, all_masks, all_masks_fb
|
||||
|
||||
def get_anchors(self, featmap_sizes):
|
||||
"""Get anchors according to feature map sizes.
|
||||
|
||||
Args:
|
||||
featmap_sizes (list[tuple]): Multi-level feature map sizes.
|
||||
img_metas (list[dict]): Image meta info.
|
||||
|
||||
Returns:
|
||||
tuple: anchors of each image, valid flags of each image
|
||||
"""
|
||||
num_levels = len(featmap_sizes)
|
||||
|
||||
# since feature map sizes of all images are the same, we only compute
|
||||
# anchors for one time
|
||||
multi_level_anchors = ()
|
||||
for i in range(num_levels):
|
||||
anchors = self.anchor_generators[i].grid_anchors(
|
||||
featmap_sizes[i], self.anchor_strides[i])
|
||||
multi_level_anchors += (Tensor(anchors.astype(np.float16)),)
|
||||
|
||||
return multi_level_anchors
|
||||
|
||||
def rcnn_mask_test(self, x, rois, cls_pred, reg_pred):
|
||||
"""Prediction masks in an images by the bounding boxes
|
||||
"""
|
||||
cls_scores = self.softmax(cls_pred / self.value)
|
||||
|
||||
cls_scores_all = self.split(cls_scores)
|
||||
reg_pred = self.reshape(reg_pred, (-1, self.num_classes, 4))
|
||||
reg_pred_all = self.split(reg_pred)
|
||||
rois_all = self.split(rois)
|
||||
boxes_tuple = ()
|
||||
for i in range(self.test_batch_size):
|
||||
cls_score_max_index, _ = self.argmax_with_value(cls_scores_all[i])
|
||||
cls_score_max_index = self.cast(self.onehot(cls_score_max_index, self.num_classes,
|
||||
self.on_value, self.off_value), mstype.float16)
|
||||
cls_score_max_index = self.expand_dims(cls_score_max_index, -1)
|
||||
cls_score_max_index = self.tile(cls_score_max_index, (1, 1, 4))
|
||||
reg_pred_max = reg_pred_all[i] * cls_score_max_index
|
||||
reg_pred_max = self.reducesum(reg_pred_max, 1)
|
||||
out_boxes_i = self.decode(rois_all[i], reg_pred_max)
|
||||
boxes_tuple += (out_boxes_i,)
|
||||
|
||||
boxes_all = self.concat(boxes_tuple)
|
||||
boxes_rois = self.concat_1((self.roi_align_index_test_tensor, boxes_all))
|
||||
boxes_rois = self.cast(boxes_rois, mstype.float32)
|
||||
roi_feats_mask_test = self.roi_align_mask_test(boxes_rois,
|
||||
self.cast(x[0], mstype.float32),
|
||||
self.cast(x[1], mstype.float32),
|
||||
self.cast(x[2], mstype.float32),
|
||||
self.cast(x[3], mstype.float32))
|
||||
roi_feats_mask_test = self.cast(roi_feats_mask_test, mstype.float16)
|
||||
mask_fb_pred_all = self.rcnn_mask(roi_feats_mask_test)
|
||||
return mask_fb_pred_all
|
||||
|
|
@ -0,0 +1,117 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MobilenetV1 backbone."""
|
||||
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
|
||||
def conv_bn_relu(in_channel, out_channel, kernel_size, stride, depthwise, activation='relu6'):
|
||||
output = []
|
||||
output.append(nn.Conv2d(in_channel, out_channel, kernel_size, stride, pad_mode='same',
|
||||
group=1 if not depthwise else in_channel))
|
||||
output.append(nn.BatchNorm2d(out_channel))
|
||||
if activation:
|
||||
output.append(nn.get_activation(activation))
|
||||
return nn.SequentialCell(output)
|
||||
|
||||
class MobileNetV1(nn.Cell):
|
||||
"""
|
||||
MobileNet V1 backbone
|
||||
"""
|
||||
def __init__(self, class_num=1001, features_only=False):
|
||||
super(MobileNetV1, self).__init__()
|
||||
self.features_only = features_only
|
||||
cnn = [
|
||||
conv_bn_relu(3, 32, 3, 2, False),
|
||||
|
||||
conv_bn_relu(32, 32, 3, 1, True),
|
||||
conv_bn_relu(32, 64, 1, 1, False),
|
||||
|
||||
conv_bn_relu(64, 64, 3, 2, True),
|
||||
conv_bn_relu(64, 128, 1, 1, False),
|
||||
conv_bn_relu(128, 128, 3, 1, True),
|
||||
conv_bn_relu(128, 128, 1, 1, False),
|
||||
|
||||
conv_bn_relu(128, 128, 3, 2, True),
|
||||
conv_bn_relu(128, 256, 1, 1, False),
|
||||
conv_bn_relu(256, 256, 3, 1, True),
|
||||
conv_bn_relu(256, 256, 1, 1, False),
|
||||
|
||||
conv_bn_relu(256, 256, 3, 2, True),
|
||||
conv_bn_relu(256, 512, 1, 1, False),
|
||||
conv_bn_relu(512, 512, 3, 1, True),
|
||||
conv_bn_relu(512, 512, 1, 1, False),
|
||||
conv_bn_relu(512, 512, 3, 1, True),
|
||||
conv_bn_relu(512, 512, 1, 1, False),
|
||||
conv_bn_relu(512, 512, 3, 1, True),
|
||||
conv_bn_relu(512, 512, 1, 1, False),
|
||||
conv_bn_relu(512, 512, 3, 1, True),
|
||||
conv_bn_relu(512, 512, 1, 1, False),
|
||||
conv_bn_relu(512, 512, 3, 1, True),
|
||||
conv_bn_relu(512, 512, 1, 1, False),
|
||||
|
||||
conv_bn_relu(512, 512, 3, 2, True),
|
||||
conv_bn_relu(512, 1024, 1, 1, False),
|
||||
conv_bn_relu(1024, 1024, 3, 1, True),
|
||||
conv_bn_relu(1024, 1024, 1, 1, False),
|
||||
]
|
||||
|
||||
if self.features_only:
|
||||
self.network = nn.CellList(cnn)
|
||||
else:
|
||||
self.network = nn.SequentialCell(cnn)
|
||||
self.fc = nn.Dense(1024, class_num)
|
||||
|
||||
def construct(self, x):
|
||||
output = x
|
||||
if self.features_only:
|
||||
features = ()
|
||||
for block in self.network:
|
||||
output = block(output)
|
||||
features = features + (output,)
|
||||
return features
|
||||
|
||||
output = self.network(x)
|
||||
output = P.ReduceMean()(output, (2, 3))
|
||||
output = self.fc(output)
|
||||
return output
|
||||
|
||||
class FeatureSelector(nn.Cell):
|
||||
"""
|
||||
Select specific layers from en entire feature list
|
||||
"""
|
||||
def __init__(self, feature_idxes):
|
||||
super(FeatureSelector, self).__init__()
|
||||
self.feature_idxes = feature_idxes
|
||||
|
||||
def construct(self, feature_list):
|
||||
selected = ()
|
||||
for i in self.feature_idxes:
|
||||
selected = selected + (feature_list[i],)
|
||||
return selected
|
||||
|
||||
class MobileNetV1_FeatureSelector(nn.Cell):
|
||||
"""
|
||||
Mobilenet v1 feature selector
|
||||
"""
|
||||
def __init__(self, class_num=1001, features_only=False):
|
||||
super(MobileNetV1_FeatureSelector, self).__init__()
|
||||
self.mobilenet_v1 = MobileNetV1(class_num=class_num, features_only=features_only)
|
||||
self.selector = FeatureSelector([6, 10, 22, 26])
|
||||
|
||||
def construct(self, x):
|
||||
features = self.mobilenet_v1(x)
|
||||
features = self.selector(features)
|
||||
return features
|
||||
|
|
@ -0,0 +1,195 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn proposal generator."""
|
||||
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore import Tensor
|
||||
|
||||
|
||||
class Proposal(nn.Cell):
|
||||
"""
|
||||
Proposal subnet.
|
||||
|
||||
Args:
|
||||
config (dict): Config.
|
||||
batch_size (int): Batchsize.
|
||||
num_classes (int) - Class number.
|
||||
use_sigmoid_cls (bool) - Select sigmoid or softmax function.
|
||||
target_means (tuple) - Means for encode function. Default: (.0, .0, .0, .0).
|
||||
target_stds (tuple) - Stds for encode function. Default: (1.0, 1.0, 1.0, 1.0).
|
||||
|
||||
Returns:
|
||||
Tuple, tuple of output tensor,(proposal, mask).
|
||||
|
||||
Examples:
|
||||
Proposal(config = config, batch_size = 1, num_classes = 81, use_sigmoid_cls = True, \
|
||||
target_means=(.0, .0, .0, .0), target_stds=(1.0, 1.0, 1.0, 1.0))
|
||||
"""
|
||||
def __init__(self,
|
||||
config,
|
||||
batch_size,
|
||||
num_classes,
|
||||
use_sigmoid_cls,
|
||||
target_means=(.0, .0, .0, .0),
|
||||
target_stds=(1.0, 1.0, 1.0, 1.0)
|
||||
):
|
||||
super(Proposal, self).__init__()
|
||||
cfg = config
|
||||
self.batch_size = batch_size
|
||||
self.num_classes = num_classes
|
||||
self.target_means = target_means
|
||||
self.target_stds = target_stds
|
||||
self.use_sigmoid_cls = use_sigmoid_cls
|
||||
|
||||
if self.use_sigmoid_cls:
|
||||
self.cls_out_channels = num_classes - 1
|
||||
self.activation = P.Sigmoid()
|
||||
self.reshape_shape = (-1, 1)
|
||||
else:
|
||||
self.cls_out_channels = num_classes
|
||||
self.activation = P.Softmax(axis=1)
|
||||
self.reshape_shape = (-1, 2)
|
||||
|
||||
if self.cls_out_channels <= 0:
|
||||
raise ValueError('num_classes={} is too small'.format(num_classes))
|
||||
|
||||
self.num_pre = cfg.rpn_proposal_nms_pre
|
||||
self.min_box_size = cfg.rpn_proposal_min_bbox_size
|
||||
self.nms_thr = cfg.rpn_proposal_nms_thr
|
||||
self.nms_post = cfg.rpn_proposal_nms_post
|
||||
self.nms_across_levels = cfg.rpn_proposal_nms_across_levels
|
||||
self.max_num = cfg.rpn_proposal_max_num
|
||||
self.num_levels = cfg.fpn_num_outs
|
||||
|
||||
# Op Define
|
||||
self.squeeze = P.Squeeze()
|
||||
self.reshape = P.Reshape()
|
||||
self.cast = P.Cast()
|
||||
|
||||
self.feature_shapes = cfg.feature_shapes
|
||||
|
||||
self.transpose_shape = (1, 2, 0)
|
||||
|
||||
self.decode = P.BoundingBoxDecode(max_shape=(cfg.img_height, cfg.img_width), \
|
||||
means=self.target_means, \
|
||||
stds=self.target_stds)
|
||||
|
||||
self.nms = P.NMSWithMask(self.nms_thr)
|
||||
self.concat_axis0 = P.Concat(axis=0)
|
||||
self.concat_axis1 = P.Concat(axis=1)
|
||||
self.split = P.Split(axis=1, output_num=5)
|
||||
self.min = P.Minimum()
|
||||
self.gatherND = P.GatherNd()
|
||||
self.slice = P.Slice()
|
||||
self.select = P.Select()
|
||||
self.greater = P.Greater()
|
||||
self.transpose = P.Transpose()
|
||||
self.tile = P.Tile()
|
||||
self.set_train_local(config, training=True)
|
||||
|
||||
self.multi_10 = Tensor(10.0, mstype.float16)
|
||||
|
||||
def set_train_local(self, config, training=True):
|
||||
"""Set training flag."""
|
||||
self.training_local = training
|
||||
|
||||
cfg = config
|
||||
self.topK_stage1 = ()
|
||||
self.topK_shape = ()
|
||||
total_max_topk_input = 0
|
||||
if not self.training_local:
|
||||
self.num_pre = cfg.rpn_nms_pre
|
||||
self.min_box_size = cfg.rpn_min_bbox_min_size
|
||||
self.nms_thr = cfg.rpn_nms_thr
|
||||
self.nms_post = cfg.rpn_nms_post
|
||||
self.nms_across_levels = cfg.rpn_nms_across_levels
|
||||
self.max_num = cfg.rpn_max_num
|
||||
|
||||
for shp in self.feature_shapes:
|
||||
k_num = min(self.num_pre, (shp[0] * shp[1] * 3))
|
||||
total_max_topk_input += k_num
|
||||
self.topK_stage1 += (k_num,)
|
||||
self.topK_shape += ((k_num, 1),)
|
||||
|
||||
self.topKv2 = P.TopK(sorted=True)
|
||||
self.topK_shape_stage2 = (self.max_num, 1)
|
||||
self.min_float_num = -65536.0
|
||||
self.topK_mask = Tensor(self.min_float_num * np.ones(total_max_topk_input, np.float16))
|
||||
|
||||
def construct(self, rpn_cls_score_total, rpn_bbox_pred_total, anchor_list):
|
||||
proposals_tuple = ()
|
||||
masks_tuple = ()
|
||||
for img_id in range(self.batch_size):
|
||||
cls_score_list = ()
|
||||
bbox_pred_list = ()
|
||||
for i in range(self.num_levels):
|
||||
rpn_cls_score_i = self.squeeze(rpn_cls_score_total[i][img_id:img_id+1:1, ::, ::, ::])
|
||||
rpn_bbox_pred_i = self.squeeze(rpn_bbox_pred_total[i][img_id:img_id+1:1, ::, ::, ::])
|
||||
|
||||
cls_score_list = cls_score_list + (rpn_cls_score_i,)
|
||||
bbox_pred_list = bbox_pred_list + (rpn_bbox_pred_i,)
|
||||
|
||||
proposals, masks = self.get_bboxes_single(cls_score_list, bbox_pred_list, anchor_list)
|
||||
proposals_tuple += (proposals,)
|
||||
masks_tuple += (masks,)
|
||||
return proposals_tuple, masks_tuple
|
||||
|
||||
def get_bboxes_single(self, cls_scores, bbox_preds, mlvl_anchors):
|
||||
"""Get proposal boundingbox."""
|
||||
mlvl_proposals = ()
|
||||
mlvl_mask = ()
|
||||
for idx in range(self.num_levels):
|
||||
rpn_cls_score = self.transpose(cls_scores[idx], self.transpose_shape)
|
||||
rpn_bbox_pred = self.transpose(bbox_preds[idx], self.transpose_shape)
|
||||
anchors = mlvl_anchors[idx]
|
||||
|
||||
rpn_cls_score = self.reshape(rpn_cls_score, self.reshape_shape)
|
||||
rpn_cls_score = self.activation(rpn_cls_score)
|
||||
rpn_cls_score_process = self.cast(self.squeeze(rpn_cls_score[::, 0::]), mstype.float16)
|
||||
|
||||
rpn_bbox_pred_process = self.cast(self.reshape(rpn_bbox_pred, (-1, 4)), mstype.float16)
|
||||
|
||||
scores_sorted, topk_inds = self.topKv2(rpn_cls_score_process, self.topK_stage1[idx])
|
||||
|
||||
topk_inds = self.reshape(topk_inds, self.topK_shape[idx])
|
||||
|
||||
bboxes_sorted = self.gatherND(rpn_bbox_pred_process, topk_inds)
|
||||
anchors_sorted = self.cast(self.gatherND(anchors, topk_inds), mstype.float16)
|
||||
|
||||
proposals_decode = self.decode(anchors_sorted, bboxes_sorted)
|
||||
|
||||
proposals_decode = self.concat_axis1((proposals_decode, self.reshape(scores_sorted, self.topK_shape[idx])))
|
||||
proposals, _, mask_valid = self.nms(proposals_decode)
|
||||
|
||||
mlvl_proposals = mlvl_proposals + (proposals,)
|
||||
mlvl_mask = mlvl_mask + (mask_valid,)
|
||||
|
||||
proposals = self.concat_axis0(mlvl_proposals)
|
||||
masks = self.concat_axis0(mlvl_mask)
|
||||
|
||||
_, _, _, _, scores = self.split(proposals)
|
||||
scores = self.squeeze(scores)
|
||||
topk_mask = self.cast(self.topK_mask, mstype.float16)
|
||||
scores_using = self.select(masks, scores, topk_mask)
|
||||
|
||||
_, topk_inds = self.topKv2(scores_using, self.max_num)
|
||||
|
||||
topk_inds = self.reshape(topk_inds, self.topK_shape_stage2)
|
||||
proposals = self.gatherND(proposals, topk_inds)
|
||||
masks = self.gatherND(masks, topk_inds)
|
||||
return proposals, masks
|
||||
|
|
@ -0,0 +1,178 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn Rcnn classification and box regression network."""
|
||||
|
||||
import numpy as np
|
||||
import mindspore.common.dtype as mstype
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore.common.initializer import initializer
|
||||
from mindspore.common.parameter import Parameter
|
||||
|
||||
class DenseNoTranpose(nn.Cell):
|
||||
"""Dense method"""
|
||||
def __init__(self, input_channels, output_channels, weight_init):
|
||||
super(DenseNoTranpose, self).__init__()
|
||||
self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float16),
|
||||
name="weight")
|
||||
self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16).to_tensor(), name="bias")
|
||||
self.matmul = P.MatMul(transpose_b=False)
|
||||
self.bias_add = P.BiasAdd()
|
||||
|
||||
def construct(self, x):
|
||||
output = self.bias_add(self.matmul(x, self.weight), self.bias)
|
||||
return output
|
||||
|
||||
class FpnCls(nn.Cell):
|
||||
"""dense layer of classification and box head"""
|
||||
def __init__(self, input_channels, output_channels, num_classes, pool_size):
|
||||
super(FpnCls, self).__init__()
|
||||
representation_size = input_channels * pool_size * pool_size
|
||||
shape_0 = (output_channels, representation_size)
|
||||
weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16).to_tensor()
|
||||
shape_1 = (output_channels, output_channels)
|
||||
weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16).to_tensor()
|
||||
self.shared_fc_0 = DenseNoTranpose(representation_size, output_channels, weights_0)
|
||||
self.shared_fc_1 = DenseNoTranpose(output_channels, output_channels, weights_1)
|
||||
|
||||
cls_weight = initializer('Normal', shape=[num_classes, output_channels][::-1],
|
||||
dtype=mstype.float16).to_tensor()
|
||||
reg_weight = initializer('Normal', shape=[num_classes * 4, output_channels][::-1],
|
||||
dtype=mstype.float16).to_tensor()
|
||||
self.cls_scores = DenseNoTranpose(output_channels, num_classes, cls_weight)
|
||||
self.reg_scores = DenseNoTranpose(output_channels, num_classes * 4, reg_weight)
|
||||
|
||||
self.relu = P.ReLU()
|
||||
self.flatten = P.Flatten()
|
||||
|
||||
def construct(self, x):
|
||||
# two share fc layer
|
||||
x = self.flatten(x)
|
||||
|
||||
x = self.relu(self.shared_fc_0(x))
|
||||
x = self.relu(self.shared_fc_1(x))
|
||||
|
||||
# classifier head
|
||||
cls_scores = self.cls_scores(x)
|
||||
# bbox head
|
||||
reg_scores = self.reg_scores(x)
|
||||
|
||||
return cls_scores, reg_scores
|
||||
|
||||
class RcnnCls(nn.Cell):
|
||||
"""
|
||||
Rcnn for classification and box regression subnet.
|
||||
|
||||
Args:
|
||||
config (dict) - Config.
|
||||
batch_size (int) - Batchsize.
|
||||
num_classes (int) - Class number.
|
||||
target_means (list) - Means for encode function. Default: (.0, .0, .0, .0]).
|
||||
target_stds (list) - Stds for encode function. Default: (0.1, 0.1, 0.2, 0.2).
|
||||
|
||||
Returns:
|
||||
Tuple, tuple of output tensor.
|
||||
|
||||
Examples:
|
||||
RcnnCls(config=config, representation_size = 1024, batch_size=2, num_classes = 81, \
|
||||
target_means=(0., 0., 0., 0.), target_stds=(0.1, 0.1, 0.2, 0.2))
|
||||
"""
|
||||
def __init__(self,
|
||||
config,
|
||||
batch_size,
|
||||
num_classes,
|
||||
target_means=(0., 0., 0., 0.),
|
||||
target_stds=(0.1, 0.1, 0.2, 0.2)
|
||||
):
|
||||
super(RcnnCls, self).__init__()
|
||||
cfg = config
|
||||
self.rcnn_loss_cls_weight = Tensor(np.array(cfg.rcnn_loss_cls_weight).astype(np.float16))
|
||||
self.rcnn_loss_reg_weight = Tensor(np.array(cfg.rcnn_loss_reg_weight).astype(np.float16))
|
||||
self.rcnn_fc_out_channels = cfg.rcnn_fc_out_channels
|
||||
self.target_means = target_means
|
||||
self.target_stds = target_stds
|
||||
self.num_classes = num_classes
|
||||
self.in_channels = cfg.rcnn_in_channels
|
||||
self.train_batch_size = batch_size
|
||||
self.test_batch_size = cfg.test_batch_size
|
||||
|
||||
self.fpn_cls = FpnCls(self.in_channels, self.rcnn_fc_out_channels, self.num_classes, cfg.roi_layer["out_size"])
|
||||
self.relu = P.ReLU()
|
||||
self.logicaland = P.LogicalAnd()
|
||||
self.loss_cls = P.SoftmaxCrossEntropyWithLogits()
|
||||
self.loss_bbox = P.SmoothL1Loss(beta=1.0)
|
||||
self.loss_mask = P.SigmoidCrossEntropyWithLogits()
|
||||
self.reshape = P.Reshape()
|
||||
self.onehot = P.OneHot()
|
||||
self.greater = P.Greater()
|
||||
self.cast = P.Cast()
|
||||
self.sum_loss = P.ReduceSum()
|
||||
self.tile = P.Tile()
|
||||
self.expandims = P.ExpandDims()
|
||||
|
||||
self.gather = P.GatherNd()
|
||||
self.argmax = P.ArgMaxWithValue(axis=1)
|
||||
|
||||
self.on_value = Tensor(1.0, mstype.float32)
|
||||
self.off_value = Tensor(0.0, mstype.float32)
|
||||
self.value = Tensor(1.0, mstype.float16)
|
||||
|
||||
self.num_bboxes = (cfg.num_expected_pos_stage2 + cfg.num_expected_neg_stage2) * batch_size
|
||||
|
||||
rmv_first = np.ones((self.num_bboxes, self.num_classes))
|
||||
rmv_first[:, 0] = np.zeros((self.num_bboxes,))
|
||||
self.rmv_first_tensor = Tensor(rmv_first.astype(np.float16))
|
||||
|
||||
self.num_bboxes_test = cfg.rpn_max_num * cfg.test_batch_size
|
||||
|
||||
def construct(self, featuremap, bbox_targets, labels, mask):
|
||||
x_cls, x_reg = self.fpn_cls(featuremap)
|
||||
|
||||
if self.training:
|
||||
bbox_weights = self.cast(self.logicaland(self.greater(labels, 0), mask), mstype.int32) * labels
|
||||
labels = self.cast(self.onehot(labels, self.num_classes, self.on_value, self.off_value), mstype.float16)
|
||||
bbox_targets = self.tile(self.expandims(bbox_targets, 1), (1, self.num_classes, 1))
|
||||
|
||||
loss_cls, loss_reg = self.loss(x_cls, x_reg,
|
||||
bbox_targets, bbox_weights,
|
||||
labels,
|
||||
mask)
|
||||
out = (loss_cls, loss_reg)
|
||||
else:
|
||||
out = (x_cls, x_reg)
|
||||
|
||||
return out
|
||||
|
||||
def loss(self, cls_score, bbox_pred, bbox_targets, bbox_weights, labels, weights):
|
||||
"""Loss method."""
|
||||
# loss_cls
|
||||
loss_cls, _ = self.loss_cls(cls_score, labels)
|
||||
weights = self.cast(weights, mstype.float16)
|
||||
loss_cls = loss_cls * weights
|
||||
loss_cls = self.sum_loss(loss_cls, (0,)) / self.sum_loss(weights, (0,))
|
||||
|
||||
# loss_reg
|
||||
bbox_weights = self.cast(self.onehot(bbox_weights, self.num_classes, self.on_value, self.off_value),
|
||||
mstype.float16)
|
||||
bbox_weights = bbox_weights * self.rmv_first_tensor # * self.rmv_first_tensor exclude background
|
||||
pos_bbox_pred = self.reshape(bbox_pred, (self.num_bboxes, -1, 4))
|
||||
loss_reg = self.loss_bbox(pos_bbox_pred, bbox_targets)
|
||||
loss_reg = self.sum_loss(loss_reg, (2,))
|
||||
loss_reg = loss_reg * bbox_weights
|
||||
loss_reg = loss_reg / self.sum_loss(weights, (0,))
|
||||
loss_reg = self.sum_loss(loss_reg, (0, 1))
|
||||
|
||||
return loss_cls, loss_reg
|
||||
|
|
@ -0,0 +1,168 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn Rcnn for mask network."""
|
||||
|
||||
import numpy as np
|
||||
import mindspore.common.dtype as mstype
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore.common.initializer import initializer
|
||||
|
||||
def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
|
||||
"""Conv2D wrapper."""
|
||||
shape = (out_channels, in_channels, kernel_size, kernel_size)
|
||||
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
|
||||
shape_bias = (out_channels,)
|
||||
bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
|
||||
return nn.Conv2d(in_channels, out_channels,
|
||||
kernel_size=kernel_size, stride=stride, padding=padding,
|
||||
pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)
|
||||
|
||||
def _convTanspose(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
|
||||
"""ConvTranspose wrapper."""
|
||||
shape = (out_channels, in_channels, kernel_size, kernel_size)
|
||||
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
|
||||
shape_bias = (out_channels,)
|
||||
bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
|
||||
return nn.Conv2dTranspose(in_channels, out_channels,
|
||||
kernel_size=kernel_size, stride=stride, padding=padding,
|
||||
pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)
|
||||
|
||||
class FpnMask(nn.Cell):
|
||||
"""conv layers of mask head"""
|
||||
def __init__(self, input_channels, output_channels, num_classes):
|
||||
super(FpnMask, self).__init__()
|
||||
self.mask_conv1 = _conv(input_channels, output_channels, kernel_size=3, pad_mode="same")
|
||||
self.mask_relu1 = P.ReLU()
|
||||
|
||||
self.mask_conv2 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
|
||||
self.mask_relu2 = P.ReLU()
|
||||
|
||||
self.mask_conv3 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
|
||||
self.mask_relu3 = P.ReLU()
|
||||
|
||||
self.mask_conv4 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
|
||||
self.mask_relu4 = P.ReLU()
|
||||
|
||||
self.mask_deconv5 = _convTanspose(output_channels, output_channels, kernel_size=2, stride=2, pad_mode="valid")
|
||||
self.mask_relu5 = P.ReLU()
|
||||
self.mask_conv6 = _conv(output_channels, num_classes, kernel_size=1, stride=1, pad_mode="valid")
|
||||
|
||||
def construct(self, x):
|
||||
x = self.mask_conv1(x)
|
||||
x = self.mask_relu1(x)
|
||||
|
||||
x = self.mask_conv2(x)
|
||||
x = self.mask_relu2(x)
|
||||
|
||||
x = self.mask_conv3(x)
|
||||
x = self.mask_relu3(x)
|
||||
|
||||
x = self.mask_conv4(x)
|
||||
x = self.mask_relu4(x)
|
||||
|
||||
x = self.mask_deconv5(x)
|
||||
x = self.mask_relu5(x)
|
||||
|
||||
x = self.mask_conv6(x)
|
||||
|
||||
return x
|
||||
|
||||
class RcnnMask(nn.Cell):
|
||||
"""
|
||||
Rcnn for mask subnet.
|
||||
|
||||
Args:
|
||||
config (dict) - Config.
|
||||
batch_size (int) - Batchsize.
|
||||
num_classes (int) - Class number.
|
||||
target_means (list) - Means for encode function. Default: (.0, .0, .0, .0]).
|
||||
target_stds (list) - Stds for encode function. Default: (0.1, 0.1, 0.2, 0.2).
|
||||
|
||||
Returns:
|
||||
Tuple, tuple of output tensor.
|
||||
|
||||
Examples:
|
||||
RcnnMask(config=config, representation_size = 1024, batch_size=2, num_classes = 81, \
|
||||
target_means=(0., 0., 0., 0.), target_stds=(0.1, 0.1, 0.2, 0.2))
|
||||
"""
|
||||
def __init__(self,
|
||||
config,
|
||||
batch_size,
|
||||
num_classes,
|
||||
target_means=(0., 0., 0., 0.),
|
||||
target_stds=(0.1, 0.1, 0.2, 0.2)
|
||||
):
|
||||
super(RcnnMask, self).__init__()
|
||||
cfg = config
|
||||
self.rcnn_loss_mask_fb_weight = Tensor(np.array(cfg.rcnn_loss_mask_fb_weight).astype(np.float16))
|
||||
self.rcnn_mask_out_channels = cfg.rcnn_mask_out_channels
|
||||
self.target_means = target_means
|
||||
self.target_stds = target_stds
|
||||
self.num_classes = num_classes
|
||||
self.in_channels = cfg.rcnn_in_channels
|
||||
|
||||
self.fpn_mask = FpnMask(self.in_channels, self.rcnn_mask_out_channels, self.num_classes)
|
||||
|
||||
self.logicaland = P.LogicalAnd()
|
||||
self.loss_mask = P.SigmoidCrossEntropyWithLogits()
|
||||
self.onehot = P.OneHot()
|
||||
self.greater = P.Greater()
|
||||
self.cast = P.Cast()
|
||||
self.sum_loss = P.ReduceSum()
|
||||
self.tile = P.Tile()
|
||||
self.expandims = P.ExpandDims()
|
||||
|
||||
self.on_value = Tensor(1.0, mstype.float32)
|
||||
self.off_value = Tensor(0.0, mstype.float32)
|
||||
|
||||
self.num_bboxes = cfg.num_expected_pos_stage2 * batch_size
|
||||
rmv_first = np.ones((self.num_bboxes, self.num_classes))
|
||||
rmv_first[:, 0] = np.zeros((self.num_bboxes,))
|
||||
self.rmv_first_tensor = Tensor(rmv_first.astype(np.float16))
|
||||
self.mean_loss = P.ReduceMean()
|
||||
|
||||
def construct(self, mask_featuremap, labels=None, mask=None, mask_fb_targets=None):
|
||||
x_mask_fb = self.fpn_mask(mask_featuremap)
|
||||
|
||||
if self.training:
|
||||
bbox_weights = self.cast(self.logicaland(self.greater(labels, 0), mask), mstype.int32) * labels
|
||||
mask_fb_targets = self.tile(self.expandims(mask_fb_targets, 1), (1, self.num_classes, 1, 1))
|
||||
|
||||
loss_mask_fb = self.loss(x_mask_fb, bbox_weights, mask, mask_fb_targets)
|
||||
out = loss_mask_fb
|
||||
else:
|
||||
out = x_mask_fb
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def loss(self, masks_fb_pred, bbox_weights, weights, masks_fb_targets):
|
||||
"""Loss method."""
|
||||
weights = self.cast(weights, mstype.float16)
|
||||
bbox_weights = self.cast(self.onehot(bbox_weights, self.num_classes, self.on_value, self.off_value),
|
||||
mstype.float16)
|
||||
bbox_weights = bbox_weights * self.rmv_first_tensor # * self.rmv_first_tensor exclude background
|
||||
|
||||
# loss_mask_fb
|
||||
masks_fb_targets = self.cast(masks_fb_targets, mstype.float16)
|
||||
loss_mask_fb = self.loss_mask(masks_fb_pred, masks_fb_targets)
|
||||
loss_mask_fb = self.mean_loss(loss_mask_fb, (2, 3))
|
||||
loss_mask_fb = loss_mask_fb * bbox_weights
|
||||
loss_mask_fb = loss_mask_fb / self.sum_loss(weights, (0,))
|
||||
loss_mask_fb = self.sum_loss(loss_mask_fb, (0, 1))
|
||||
|
||||
return loss_mask_fb
|
||||
|
|
@ -0,0 +1,186 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn ROIAlign module."""
|
||||
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops import composite as C
|
||||
from mindspore.nn import layer as L
|
||||
from mindspore.common.tensor import Tensor
|
||||
|
||||
class ROIAlign(nn.Cell):
|
||||
"""
|
||||
Extract RoI features from mulitple feature map.
|
||||
|
||||
Args:
|
||||
out_size_h (int) - RoI height.
|
||||
out_size_w (int) - RoI width.
|
||||
spatial_scale (int) - RoI spatial scale.
|
||||
sample_num (int) - RoI sample number.
|
||||
roi_align_mode (int)- RoI align mode
|
||||
"""
|
||||
def __init__(self,
|
||||
out_size_h,
|
||||
out_size_w,
|
||||
spatial_scale,
|
||||
sample_num=0,
|
||||
roi_align_mode=1):
|
||||
super(ROIAlign, self).__init__()
|
||||
|
||||
self.out_size = (out_size_h, out_size_w)
|
||||
self.spatial_scale = float(spatial_scale)
|
||||
self.sample_num = int(sample_num)
|
||||
self.align_op = P.ROIAlign(self.out_size[0], self.out_size[1],
|
||||
self.spatial_scale, self.sample_num, roi_align_mode)
|
||||
|
||||
def construct(self, features, rois):
|
||||
return self.align_op(features, rois)
|
||||
|
||||
def __repr__(self):
|
||||
format_str = self.__class__.__name__
|
||||
format_str += '(out_size={}, spatial_scale={}, sample_num={}'.format(
|
||||
self.out_size, self.spatial_scale, self.sample_num)
|
||||
return format_str
|
||||
|
||||
|
||||
class SingleRoIExtractor(nn.Cell):
|
||||
"""
|
||||
Extract RoI features from a single level feature map.
|
||||
|
||||
If there are mulitple input feature levels, each RoI is mapped to a level
|
||||
according to its scale.
|
||||
|
||||
Args:
|
||||
config (dict): Config
|
||||
roi_layer (dict): Specify RoI layer type and arguments.
|
||||
out_channels (int): Output channels of RoI layers.
|
||||
featmap_strides (int): Strides of input feature maps.
|
||||
batch_size (int)： Batchsize.
|
||||
finest_scale (int): Scale threshold of mapping to level 0.
|
||||
mask (bool): Specify ROIAlign for cls or mask branch
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
config,
|
||||
roi_layer,
|
||||
out_channels,
|
||||
featmap_strides,
|
||||
batch_size=1,
|
||||
finest_scale=56,
|
||||
mask=False):
|
||||
super(SingleRoIExtractor, self).__init__()
|
||||
cfg = config
|
||||
self.train_batch_size = batch_size
|
||||
self.out_channels = out_channels
|
||||
self.featmap_strides = featmap_strides
|
||||
self.num_levels = len(self.featmap_strides)
|
||||
self.out_size = roi_layer['mask_out_size'] if mask else roi_layer['out_size']
|
||||
self.mask = mask
|
||||
self.sample_num = roi_layer['sample_num']
|
||||
self.roi_layers = self.build_roi_layers(self.featmap_strides)
|
||||
self.roi_layers = L.CellList(self.roi_layers)
|
||||
|
||||
self.sqrt = P.Sqrt()
|
||||
self.log = P.Log()
|
||||
self.finest_scale_ = finest_scale
|
||||
self.clamp = C.clip_by_value
|
||||
|
||||
self.cast = P.Cast()
|
||||
self.equal = P.Equal()
|
||||
self.select = P.Select()
|
||||
|
||||
_mode_16 = False
|
||||
self.dtype = np.float16 if _mode_16 else np.float32
|
||||
self.ms_dtype = mstype.float16 if _mode_16 else mstype.float32
|
||||
self.set_train_local(cfg, training=True)
|
||||
|
||||
def set_train_local(self, config, training=True):
|
||||
"""Set training flag."""
|
||||
self.training_local = training
|
||||
|
||||
cfg = config
|
||||
# Init tensor
|
||||
roi_sample_num = cfg.num_expected_pos_stage2 if self.mask else cfg.roi_sample_num
|
||||
self.batch_size = roi_sample_num if self.training_local else cfg.rpn_max_num
|
||||
self.batch_size = self.train_batch_size*self.batch_size \
|
||||
if self.training_local else cfg.test_batch_size*self.batch_size
|
||||
self.ones = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype))
|
||||
finest_scale = np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * self.finest_scale_
|
||||
self.finest_scale = Tensor(finest_scale)
|
||||
self.epslion = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype)*self.dtype(1e-6))
|
||||
self.zeros = Tensor(np.array(np.zeros((self.batch_size, 1)), dtype=np.int32))
|
||||
self.max_levels = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=np.int32)*(self.num_levels-1))
|
||||
self.twos = Tensor(np.array(np.ones((self.batch_size, 1)), dtype=self.dtype) * 2)
|
||||
self.res_ = Tensor(np.array(np.zeros((self.batch_size, self.out_channels,
|
||||
self.out_size, self.out_size)), dtype=self.dtype))
|
||||
def num_inputs(self):
|
||||
return len(self.featmap_strides)
|
||||
|
||||
def init_weights(self):
|
||||
pass
|
||||
|
||||
def log2(self, value):
|
||||
return self.log(value) / self.log(self.twos)
|
||||
|
||||
def build_roi_layers(self, featmap_strides):
|
||||
roi_layers = []
|
||||
for s in featmap_strides:
|
||||
layer_cls = ROIAlign(self.out_size, self.out_size,
|
||||
spatial_scale=1 / s,
|
||||
sample_num=self.sample_num,
|
||||
roi_align_mode=0)
|
||||
roi_layers.append(layer_cls)
|
||||
return roi_layers
|
||||
|
||||
def _c_map_roi_levels(self, rois):
|
||||
"""Map rois to corresponding feature levels by scales.
|
||||
|
||||
- scale < finest_scale * 2: level 0
|
||||
- finest_scale * 2 <= scale < finest_scale * 4: level 1
|
||||
- finest_scale * 4 <= scale < finest_scale * 8: level 2
|
||||
- scale >= finest_scale * 8: level 3
|
||||
|
||||
Args:
|
||||
rois (Tensor): Input RoIs, shape (k, 5).
|
||||
num_levels (int): Total level number.
|
||||
|
||||
Returns:
|
||||
Tensor: Level index (0-based) of each RoI, shape (k, )
|
||||
"""
|
||||
scale = self.sqrt(rois[::, 3:4:1] - rois[::, 1:2:1] + self.ones) * \
|
||||
self.sqrt(rois[::, 4:5:1] - rois[::, 2:3:1] + self.ones)
|
||||
|
||||
target_lvls = self.log2(scale / self.finest_scale + self.epslion)
|
||||
target_lvls = P.Floor()(target_lvls)
|
||||
target_lvls = self.cast(target_lvls, mstype.int32)
|
||||
target_lvls = self.clamp(target_lvls, self.zeros, self.max_levels)
|
||||
|
||||
return target_lvls
|
||||
|
||||
def construct(self, rois, feat1, feat2, feat3, feat4):
|
||||
feats = (feat1, feat2, feat3, feat4)
|
||||
res = self.res_
|
||||
target_lvls = self._c_map_roi_levels(rois)
|
||||
for i in range(self.num_levels):
|
||||
mask = self.equal(target_lvls, P.ScalarToArray()(i))
|
||||
mask = P.Reshape()(mask, (-1, 1, 1, 1))
|
||||
roi_feats_t = self.roi_layers[i](feats[i], rois)
|
||||
mask = self.cast(P.Tile()(self.cast(mask, mstype.int32), (1, 256, self.out_size, self.out_size)),
|
||||
mstype.bool_)
|
||||
res = self.select(mask, roi_feats_t, res)
|
||||
|
||||
return res
|
||||
|
|
@ -0,0 +1,311 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""RPN for MaskRCNN"""
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore import Tensor
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore.common.initializer import initializer
|
||||
from .bbox_assign_sample import BboxAssignSample
|
||||
|
||||
|
||||
class RpnRegClsBlock(nn.Cell):
|
||||
"""
|
||||
Rpn reg cls block for rpn layer
|
||||
|
||||
Args:
|
||||
in_channels (int) - Input channels of shared convolution.
|
||||
feat_channels (int) - Output channels of shared convolution.
|
||||
num_anchors (int) - The anchor number.
|
||||
cls_out_channels (int) - Output channels of classification convolution.
|
||||
weight_conv (Tensor) - weight init for rpn conv.
|
||||
bias_conv (Tensor) - bias init for rpn conv.
|
||||
weight_cls (Tensor) - weight init for rpn cls conv.
|
||||
bias_cls (Tensor) - bias init for rpn cls conv.
|
||||
weight_reg (Tensor) - weight init for rpn reg conv.
|
||||
bias_reg (Tensor) - bias init for rpn reg conv.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
"""
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
feat_channels,
|
||||
num_anchors,
|
||||
cls_out_channels,
|
||||
weight_conv,
|
||||
bias_conv,
|
||||
weight_cls,
|
||||
bias_cls,
|
||||
weight_reg,
|
||||
bias_reg):
|
||||
super(RpnRegClsBlock, self).__init__()
|
||||
self.rpn_conv = nn.Conv2d(in_channels, feat_channels, kernel_size=3, stride=1, pad_mode='same',
|
||||
has_bias=True, weight_init=weight_conv, bias_init=bias_conv)
|
||||
self.relu = nn.ReLU()
|
||||
|
||||
self.rpn_cls = nn.Conv2d(feat_channels, num_anchors * cls_out_channels, kernel_size=1, pad_mode='valid',
|
||||
has_bias=True, weight_init=weight_cls, bias_init=bias_cls)
|
||||
self.rpn_reg = nn.Conv2d(feat_channels, num_anchors * 4, kernel_size=1, pad_mode='valid',
|
||||
has_bias=True, weight_init=weight_reg, bias_init=bias_reg)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.relu(self.rpn_conv(x))
|
||||
|
||||
x1 = self.rpn_cls(x)
|
||||
x2 = self.rpn_reg(x)
|
||||
|
||||
return x1, x2
|
||||
|
||||
|
||||
class RPN(nn.Cell):
|
||||
"""
|
||||
ROI proposal network..
|
||||
|
||||
Args:
|
||||
config (dict) - Config.
|
||||
batch_size (int) - Batchsize.
|
||||
in_channels (int) - Input channels of shared convolution.
|
||||
feat_channels (int) - Output channels of shared convolution.
|
||||
num_anchors (int) - The anchor number.
|
||||
cls_out_channels (int) - Output channels of classification convolution.
|
||||
|
||||
Returns:
|
||||
Tuple, tuple of output tensor.
|
||||
|
||||
Examples:
|
||||
RPN(config=config, batch_size=2, in_channels=256, feat_channels=1024,
|
||||
num_anchors=3, cls_out_channels=512)
|
||||
"""
|
||||
def __init__(self,
|
||||
config,
|
||||
batch_size,
|
||||
in_channels,
|
||||
feat_channels,
|
||||
num_anchors,
|
||||
cls_out_channels):
|
||||
super(RPN, self).__init__()
|
||||
cfg_rpn = config
|
||||
self.num_bboxes = cfg_rpn.num_bboxes
|
||||
self.slice_index = ()
|
||||
self.feature_anchor_shape = ()
|
||||
self.slice_index += (0,)
|
||||
index = 0
|
||||
for shape in cfg_rpn.feature_shapes:
|
||||
self.slice_index += (self.slice_index[index] + shape[0] * shape[1] * num_anchors,)
|
||||
self.feature_anchor_shape += (shape[0] * shape[1] * num_anchors * batch_size,)
|
||||
index += 1
|
||||
|
||||
self.num_anchors = num_anchors
|
||||
self.batch_size = batch_size
|
||||
self.test_batch_size = cfg_rpn.test_batch_size
|
||||
self.num_layers = 5
|
||||
self.real_ratio = Tensor(np.ones((1, 1)).astype(np.float16))
|
||||
|
||||
self.rpn_convs_list = nn.layer.CellList(self._make_rpn_layer(self.num_layers, in_channels, feat_channels,
|
||||
num_anchors, cls_out_channels))
|
||||
|
||||
self.transpose = P.Transpose()
|
||||
self.reshape = P.Reshape()
|
||||
self.concat = P.Concat(axis=0)
|
||||
self.fill = P.Fill()
|
||||
self.placeh1 = Tensor(np.ones((1,)).astype(np.float16))
|
||||
|
||||
self.trans_shape = (0, 2, 3, 1)
|
||||
|
||||
self.reshape_shape_reg = (-1, 4)
|
||||
self.reshape_shape_cls = (-1,)
|
||||
self.rpn_loss_reg_weight = Tensor(np.array(cfg_rpn.rpn_loss_reg_weight).astype(np.float16))
|
||||
self.rpn_loss_cls_weight = Tensor(np.array(cfg_rpn.rpn_loss_cls_weight).astype(np.float16))
|
||||
self.num_expected_total = Tensor(np.array(cfg_rpn.num_expected_neg * self.batch_size).astype(np.float16))
|
||||
self.num_bboxes = cfg_rpn.num_bboxes
|
||||
self.get_targets = BboxAssignSample(cfg_rpn, self.batch_size, self.num_bboxes, False)
|
||||
self.CheckValid = P.CheckValid()
|
||||
self.sum_loss = P.ReduceSum()
|
||||
self.loss_cls = P.SigmoidCrossEntropyWithLogits()
|
||||
self.loss_bbox = P.SmoothL1Loss(beta=1.0/9.0)
|
||||
self.squeeze = P.Squeeze()
|
||||
self.cast = P.Cast()
|
||||
self.tile = P.Tile()
|
||||
self.zeros_like = P.ZerosLike()
|
||||
self.loss = Tensor(np.zeros((1,)).astype(np.float16))
|
||||
self.clsloss = Tensor(np.zeros((1,)).astype(np.float16))
|
||||
self.regloss = Tensor(np.zeros((1,)).astype(np.float16))
|
||||
|
||||
def _make_rpn_layer(self, num_layers, in_channels, feat_channels, num_anchors, cls_out_channels):
|
||||
"""
|
||||
make rpn layer for rpn proposal network
|
||||
|
||||
Args:
|
||||
num_layers (int) - layer num.
|
||||
in_channels (int) - Input channels of shared convolution.
|
||||
feat_channels (int) - Output channels of shared convolution.
|
||||
num_anchors (int) - The anchor number.
|
||||
cls_out_channels (int) - Output channels of classification convolution.
|
||||
|
||||
Returns:
|
||||
List, list of RpnRegClsBlock cells.
|
||||
"""
|
||||
rpn_layer = []
|
||||
|
||||
shp_weight_conv = (feat_channels, in_channels, 3, 3)
|
||||
shp_bias_conv = (feat_channels,)
|
||||
weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16).to_tensor()
|
||||
bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16).to_tensor()
|
||||
|
||||
shp_weight_cls = (num_anchors * cls_out_channels, feat_channels, 1, 1)
|
||||
shp_bias_cls = (num_anchors * cls_out_channels,)
|
||||
weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16).to_tensor()
|
||||
bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16).to_tensor()
|
||||
|
||||
shp_weight_reg = (num_anchors * 4, feat_channels, 1, 1)
|
||||
shp_bias_reg = (num_anchors * 4,)
|
||||
weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16).to_tensor()
|
||||
bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16).to_tensor()
|
||||
|
||||
for i in range(num_layers):
|
||||
rpn_layer.append(RpnRegClsBlock(in_channels, feat_channels, num_anchors, cls_out_channels, \
|
||||
weight_conv, bias_conv, weight_cls, \
|
||||
bias_cls, weight_reg, bias_reg))
|
||||
|
||||
for i in range(1, num_layers):
|
||||
rpn_layer[i].rpn_conv.weight = rpn_layer[0].rpn_conv.weight
|
||||
rpn_layer[i].rpn_cls.weight = rpn_layer[0].rpn_cls.weight
|
||||
rpn_layer[i].rpn_reg.weight = rpn_layer[0].rpn_reg.weight
|
||||
|
||||
rpn_layer[i].rpn_conv.bias = rpn_layer[0].rpn_conv.bias
|
||||
rpn_layer[i].rpn_cls.bias = rpn_layer[0].rpn_cls.bias
|
||||
rpn_layer[i].rpn_reg.bias = rpn_layer[0].rpn_reg.bias
|
||||
|
||||
return rpn_layer
|
||||
|
||||
def construct(self, inputs, img_metas, anchor_list, gt_bboxes, gt_labels, gt_valids):
|
||||
loss_print = ()
|
||||
rpn_cls_score = ()
|
||||
rpn_bbox_pred = ()
|
||||
rpn_cls_score_total = ()
|
||||
rpn_bbox_pred_total = ()
|
||||
|
||||
for i in range(self.num_layers):
|
||||
x1, x2 = self.rpn_convs_list[i](inputs[i])
|
||||
|
||||
rpn_cls_score_total = rpn_cls_score_total + (x1,)
|
||||
rpn_bbox_pred_total = rpn_bbox_pred_total + (x2,)
|
||||
|
||||
x1 = self.transpose(x1, self.trans_shape)
|
||||
x1 = self.reshape(x1, self.reshape_shape_cls)
|
||||
|
||||
x2 = self.transpose(x2, self.trans_shape)
|
||||
x2 = self.reshape(x2, self.reshape_shape_reg)
|
||||
|
||||
rpn_cls_score = rpn_cls_score + (x1,)
|
||||
rpn_bbox_pred = rpn_bbox_pred + (x2,)
|
||||
|
||||
loss = self.loss
|
||||
clsloss = self.clsloss
|
||||
regloss = self.regloss
|
||||
bbox_targets = ()
|
||||
bbox_weights = ()
|
||||
labels = ()
|
||||
label_weights = ()
|
||||
|
||||
output = ()
|
||||
if self.training:
|
||||
for i in range(self.batch_size):
|
||||
multi_level_flags = ()
|
||||
anchor_list_tuple = ()
|
||||
|
||||
for j in range(self.num_layers):
|
||||
res = self.cast(self.CheckValid(anchor_list[j], self.squeeze(img_metas[i:i + 1:1, ::])),
|
||||
mstype.int32)
|
||||
multi_level_flags = multi_level_flags + (res,)
|
||||
anchor_list_tuple = anchor_list_tuple + (anchor_list[j],)
|
||||
|
||||
valid_flag_list = self.concat(multi_level_flags)
|
||||
anchor_using_list = self.concat(anchor_list_tuple)
|
||||
|
||||
gt_bboxes_i = self.squeeze(gt_bboxes[i:i + 1:1, ::])
|
||||
gt_labels_i = self.squeeze(gt_labels[i:i + 1:1, ::])
|
||||
gt_valids_i = self.squeeze(gt_valids[i:i + 1:1, ::])
|
||||
|
||||
bbox_target, bbox_weight, label, label_weight = self.get_targets(gt_bboxes_i,
|
||||
gt_labels_i,
|
||||
self.cast(valid_flag_list,
|
||||
mstype.bool_),
|
||||
anchor_using_list, gt_valids_i)
|
||||
|
||||
bbox_weight = self.cast(bbox_weight, mstype.float16)
|
||||
label = self.cast(label, mstype.float16)
|
||||
label_weight = self.cast(label_weight, mstype.float16)
|
||||
|
||||
for j in range(self.num_layers):
|
||||
begin = self.slice_index[j]
|
||||
end = self.slice_index[j + 1]
|
||||
stride = 1
|
||||
bbox_targets += (bbox_target[begin:end:stride, ::],)
|
||||
bbox_weights += (bbox_weight[begin:end:stride],)
|
||||
labels += (label[begin:end:stride],)
|
||||
label_weights += (label_weight[begin:end:stride],)
|
||||
|
||||
for i in range(self.num_layers):
|
||||
bbox_target_using = ()
|
||||
bbox_weight_using = ()
|
||||
label_using = ()
|
||||
label_weight_using = ()
|
||||
|
||||
for j in range(self.batch_size):
|
||||
bbox_target_using += (bbox_targets[i + (self.num_layers * j)],)
|
||||
bbox_weight_using += (bbox_weights[i + (self.num_layers * j)],)
|
||||
label_using += (labels[i + (self.num_layers * j)],)
|
||||
label_weight_using += (label_weights[i + (self.num_layers * j)],)
|
||||
|
||||
bbox_target_with_batchsize = self.concat(bbox_target_using)
|
||||
bbox_weight_with_batchsize = self.concat(bbox_weight_using)
|
||||
label_with_batchsize = self.concat(label_using)
|
||||
label_weight_with_batchsize = self.concat(label_weight_using)
|
||||
|
||||
# stop
|
||||
bbox_target_ = F.stop_gradient(bbox_target_with_batchsize)
|
||||
bbox_weight_ = F.stop_gradient(bbox_weight_with_batchsize)
|
||||
label_ = F.stop_gradient(label_with_batchsize)
|
||||
label_weight_ = F.stop_gradient(label_weight_with_batchsize)
|
||||
|
||||
cls_score_i = rpn_cls_score[i]
|
||||
reg_score_i = rpn_bbox_pred[i]
|
||||
|
||||
loss_cls = self.loss_cls(cls_score_i, label_)
|
||||
loss_cls_item = loss_cls * label_weight_
|
||||
loss_cls_item = self.sum_loss(loss_cls_item, (0,)) / self.num_expected_total
|
||||
|
||||
loss_reg = self.loss_bbox(reg_score_i, bbox_target_)
|
||||
bbox_weight_ = self.tile(self.reshape(bbox_weight_, (self.feature_anchor_shape[i], 1)), (1, 4))
|
||||
loss_reg = loss_reg * bbox_weight_
|
||||
loss_reg_item = self.sum_loss(loss_reg, (1,))
|
||||
loss_reg_item = self.sum_loss(loss_reg_item, (0,)) / self.num_expected_total
|
||||
|
||||
loss_total = self.rpn_loss_cls_weight * loss_cls_item + self.rpn_loss_reg_weight * loss_reg_item
|
||||
|
||||
loss += loss_total
|
||||
loss_print += (loss_total, loss_cls_item, loss_reg_item)
|
||||
clsloss += loss_cls_item
|
||||
regloss += loss_reg_item
|
||||
|
||||
output = (loss, rpn_cls_score_total, rpn_bbox_pred_total, clsloss, regloss, loss_print)
|
||||
else:
|
||||
output = (self.placeh1, rpn_cls_score_total, rpn_bbox_pred_total, self.placeh1, self.placeh1, self.placeh1)
|
||||
|
||||
return output
|
||||
|
|
@ -0,0 +1,221 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MaskRcnn training network wrapper."""
|
||||
|
||||
import time
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore.ops import composite as C
|
||||
from mindspore import ParameterTuple
|
||||
from mindspore.train.callback import Callback
|
||||
from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
|
||||
|
||||
time_stamp_init = False
|
||||
time_stamp_first = 0
|
||||
|
||||
GRADIENT_CLIP_TYPE = 1
|
||||
GRADIENT_CLIP_VALUE = 1.0
|
||||
|
||||
clip_grad = C.MultitypeFuncGraph("clip_grad")
|
||||
|
||||
|
||||
@clip_grad.register("Number", "Number", "Tensor")
|
||||
def _clip_grad(clip_type, clip_value, grad):
|
||||
"""
|
||||
Clip gradients.
|
||||
|
||||
Inputs:
|
||||
clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'.
|
||||
clip_value (float): Specifies how much to clip.
|
||||
grad (tuple[Tensor]): Gradients.
|
||||
|
||||
Outputs:
|
||||
tuple[Tensor], clipped gradients.
|
||||
"""
|
||||
if clip_type not in (0, 1):
|
||||
return grad
|
||||
dt = F.dtype(grad)
|
||||
if clip_type == 0:
|
||||
new_grad = C.clip_by_value(grad, F.cast(F.tuple_to_array((-clip_value,)), dt),
|
||||
F.cast(F.tuple_to_array((clip_value,)), dt))
|
||||
else:
|
||||
new_grad = nn.ClipByNorm()(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
|
||||
return F.cast(new_grad, dt)
|
||||
|
||||
class LossCallBack(Callback):
|
||||
"""
|
||||
Monitor the loss in training.
|
||||
|
||||
If the loss is NAN or INF terminating training.
|
||||
|
||||
Note:
|
||||
If per_print_times is 0 do not print loss.
|
||||
|
||||
Args:
|
||||
per_print_times (int): Print loss every times. Default: 1.
|
||||
"""
|
||||
|
||||
def __init__(self, per_print_times=1, rank_id=0):
|
||||
super(LossCallBack, self).__init__()
|
||||
if not isinstance(per_print_times, int) or per_print_times < 0:
|
||||
raise ValueError("print_step must be int and >= 0.")
|
||||
self._per_print_times = per_print_times
|
||||
self.count = 0
|
||||
self.rpn_loss_sum = 0
|
||||
self.rcnn_loss_sum = 0
|
||||
self.rpn_cls_loss_sum = 0
|
||||
self.rpn_reg_loss_sum = 0
|
||||
self.rcnn_cls_loss_sum = 0
|
||||
self.rcnn_reg_loss_sum = 0
|
||||
self.rcnn_mask_loss_sum = 0
|
||||
self.rank_id = rank_id
|
||||
|
||||
global time_stamp_init, time_stamp_first
|
||||
if not time_stamp_init:
|
||||
time_stamp_first = time.time()
|
||||
time_stamp_init = True
|
||||
|
||||
def step_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
rpn_loss = cb_params.net_outputs[0].asnumpy()
|
||||
rcnn_loss = cb_params.net_outputs[1].asnumpy()
|
||||
rpn_cls_loss = cb_params.net_outputs[2].asnumpy()
|
||||
|
||||
rpn_reg_loss = cb_params.net_outputs[3].asnumpy()
|
||||
rcnn_cls_loss = cb_params.net_outputs[4].asnumpy()
|
||||
rcnn_reg_loss = cb_params.net_outputs[5].asnumpy()
|
||||
rcnn_mask_loss = cb_params.net_outputs[6].asnumpy()
|
||||
|
||||
self.count += 1
|
||||
self.rpn_loss_sum += float(rpn_loss)
|
||||
self.rcnn_loss_sum += float(rcnn_loss)
|
||||
self.rpn_cls_loss_sum += float(rpn_cls_loss)
|
||||
self.rpn_reg_loss_sum += float(rpn_reg_loss)
|
||||
self.rcnn_cls_loss_sum += float(rcnn_cls_loss)
|
||||
self.rcnn_reg_loss_sum += float(rcnn_reg_loss)
|
||||
self.rcnn_mask_loss_sum += float(rcnn_mask_loss)
|
||||
|
||||
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
|
||||
|
||||
if self.count >= 1:
|
||||
global time_stamp_first
|
||||
time_stamp_current = time.time()
|
||||
|
||||
rpn_loss = self.rpn_loss_sum/self.count
|
||||
rcnn_loss = self.rcnn_loss_sum/self.count
|
||||
rpn_cls_loss = self.rpn_cls_loss_sum/self.count
|
||||
|
||||
rpn_reg_loss = self.rpn_reg_loss_sum/self.count
|
||||
rcnn_cls_loss = self.rcnn_cls_loss_sum/self.count
|
||||
rcnn_reg_loss = self.rcnn_reg_loss_sum/self.count
|
||||
rcnn_mask_loss = self.rcnn_mask_loss_sum/self.count
|
||||
|
||||
total_loss = rpn_loss + rcnn_loss
|
||||
|
||||
loss_file = open("./loss_{}.log".format(self.rank_id), "a+")
|
||||
loss_file.write("%lu epoch: %s step: %s ,rpn_loss: %.5f, rcnn_loss: %.5f, rpn_cls_loss: %.5f, "
|
||||
"rpn_reg_loss: %.5f, rcnn_cls_loss: %.5f, rcnn_reg_loss: %.5f, rcnn_mask_loss: %.5f, "
|
||||
"total_loss: %.5f" %
|
||||
(time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cur_step_in_epoch,
|
||||
rpn_loss, rcnn_loss, rpn_cls_loss, rpn_reg_loss,
|
||||
rcnn_cls_loss, rcnn_reg_loss, rcnn_mask_loss, total_loss))
|
||||
loss_file.write("\n")
|
||||
loss_file.close()
|
||||
|
||||
self.count = 0
|
||||
self.rpn_loss_sum = 0
|
||||
self.rcnn_loss_sum = 0
|
||||
self.rpn_cls_loss_sum = 0
|
||||
self.rpn_reg_loss_sum = 0
|
||||
self.rcnn_cls_loss_sum = 0
|
||||
self.rcnn_reg_loss_sum = 0
|
||||
self.rcnn_mask_loss_sum = 0
|
||||
|
||||
class LossNet(nn.Cell):
|
||||
"""MaskRcnn loss method"""
|
||||
def construct(self, x1, x2, x3, x4, x5, x6, x7):
|
||||
return x1 + x2
|
||||
|
||||
class WithLossCell(nn.Cell):
|
||||
"""
|
||||
Wrap the network with loss function to compute loss.
|
||||
|
||||
Args:
|
||||
backbone (Cell): The target network to wrap.
|
||||
loss_fn (Cell): The loss function used to compute loss.
|
||||
"""
|
||||
def __init__(self, backbone, loss_fn):
|
||||
super(WithLossCell, self).__init__(auto_prefix=False)
|
||||
self._backbone = backbone
|
||||
self._loss_fn = loss_fn
|
||||
|
||||
def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask):
|
||||
loss1, loss2, loss3, loss4, loss5, loss6, loss7 = self._backbone(x, img_shape, gt_bboxe, gt_label,
|
||||
gt_num, gt_mask)
|
||||
return self._loss_fn(loss1, loss2, loss3, loss4, loss5, loss6, loss7)
|
||||
|
||||
@property
|
||||
def backbone_network(self):
|
||||
"""
|
||||
Get the backbone network.
|
||||
|
||||
Returns:
|
||||
Cell, return backbone network.
|
||||
"""
|
||||
return self._backbone
|
||||
|
||||
|
||||
class TrainOneStepCell(nn.Cell):
|
||||
"""
|
||||
Network training package class.
|
||||
|
||||
Append an optimizer to the training network after that the construct function
|
||||
can be called to create the backward graph.
|
||||
|
||||
Args:
|
||||
network (Cell): The training network.
|
||||
network_backbone (Cell): The forward network.
|
||||
optimizer (Cell): Optimizer for updating the weights.
|
||||
sens (Number): The adjust parameter. Default value is 1.0.
|
||||
reduce_flag (bool): The reduce flag. Default value is False.
|
||||
mean (bool): Allreduce method. Default value is False.
|
||||
degree (int): Device number. Default value is None.
|
||||
"""
|
||||
def __init__(self, network, network_backbone, optimizer, sens=1.0, reduce_flag=False, mean=True, degree=None):
|
||||
super(TrainOneStepCell, self).__init__(auto_prefix=False)
|
||||
self.network = network
|
||||
self.network.set_grad()
|
||||
self.backbone = network_backbone
|
||||
self.weights = ParameterTuple(network.trainable_params())
|
||||
self.optimizer = optimizer
|
||||
self.grad = C.GradOperation(get_by_list=True,
|
||||
sens_param=True)
|
||||
self.sens = Tensor((np.ones((1,)) * sens).astype(np.float16))
|
||||
self.reduce_flag = reduce_flag
|
||||
self.hyper_map = C.HyperMap()
|
||||
if reduce_flag:
|
||||
self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
|
||||
|
||||
def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask):
|
||||
weights = self.weights
|
||||
loss1, loss2, loss3, loss4, loss5, loss6, loss7 = self.backbone(x, img_shape, gt_bboxe, gt_label,
|
||||
gt_num, gt_mask)
|
||||
grads = self.grad(self.network, weights)(x, img_shape, gt_bboxe, gt_label, gt_num, gt_mask, self.sens)
|
||||
if self.reduce_flag:
|
||||
grads = self.grad_reducer(grads)
|
||||
grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
|
||||
return F.depend(loss1, self.optimizer(grads)), loss2, loss3, loss4, loss5, loss6, loss7
|
||||
|
|
@ -0,0 +1,272 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""coco eval for maskrcnn_mobilenetv1"""
|
||||
import json
|
||||
import numpy as np
|
||||
from pycocotools.coco import COCO
|
||||
from pycocotools.cocoeval import COCOeval
|
||||
from pycocotools import mask as maskUtils
|
||||
import mmcv
|
||||
|
||||
from src.config import config
|
||||
|
||||
_init_value = np.array(0.0)
|
||||
summary_init = {
|
||||
'Precision/mAP': _init_value,
|
||||
'Precision/mAP@.50IOU': _init_value,
|
||||
'Precision/mAP@.75IOU': _init_value,
|
||||
'Precision/mAP (small)': _init_value,
|
||||
'Precision/mAP (medium)': _init_value,
|
||||
'Precision/mAP (large)': _init_value,
|
||||
'Recall/AR@1': _init_value,
|
||||
'Recall/AR@10': _init_value,
|
||||
'Recall/AR@100': _init_value,
|
||||
'Recall/AR@100 (small)': _init_value,
|
||||
'Recall/AR@100 (medium)': _init_value,
|
||||
'Recall/AR@100 (large)': _init_value,
|
||||
}
|
||||
|
||||
|
||||
def coco_eval(result_files, result_types, coco, max_dets=(100, 300, 1000), single_result=False):
|
||||
"""coco eval for maskrcnn_mobilenetv1"""
|
||||
anns = json.load(open(result_files['bbox']))
|
||||
if not anns:
|
||||
return summary_init
|
||||
if mmcv.is_str(coco):
|
||||
coco = COCO(coco)
|
||||
assert isinstance(coco, COCO)
|
||||
|
||||
for res_type in result_types:
|
||||
result_file = result_files[res_type]
|
||||
assert result_file.endswith('.json')
|
||||
|
||||
coco_dets = coco.loadRes(result_file)
|
||||
gt_img_ids = coco.getImgIds()
|
||||
det_img_ids = coco_dets.getImgIds()
|
||||
iou_type = 'bbox' if res_type == 'proposal' else res_type
|
||||
cocoEval = COCOeval(coco, coco_dets, iou_type)
|
||||
if res_type == 'proposal':
|
||||
cocoEval.params.useCats = 0
|
||||
cocoEval.params.maxDets = list(max_dets)
|
||||
|
||||
tgt_ids = gt_img_ids if not single_result else det_img_ids
|
||||
|
||||
if single_result:
|
||||
res_dict = dict()
|
||||
for id_i in tgt_ids:
|
||||
cocoEval = COCOeval(coco, coco_dets, iou_type)
|
||||
if res_type == 'proposal':
|
||||
cocoEval.params.useCats = 0
|
||||
cocoEval.params.maxDets = list(max_dets)
|
||||
|
||||
cocoEval.params.imgIds = [id_i]
|
||||
cocoEval.evaluate()
|
||||
cocoEval.accumulate()
|
||||
cocoEval.summarize()
|
||||
res_dict.update({coco.imgs[id_i]['file_name']: cocoEval.stats[1]})
|
||||
|
||||
cocoEval = COCOeval(coco, coco_dets, iou_type)
|
||||
if res_type == 'proposal':
|
||||
cocoEval.params.useCats = 0
|
||||
cocoEval.params.maxDets = list(max_dets)
|
||||
|
||||
cocoEval.params.imgIds = tgt_ids
|
||||
cocoEval.evaluate()
|
||||
cocoEval.accumulate()
|
||||
cocoEval.summarize()
|
||||
|
||||
summary_metrics = {
|
||||
'Precision/mAP': cocoEval.stats[0],
|
||||
'Precision/mAP@.50IOU': cocoEval.stats[1],
|
||||
'Precision/mAP@.75IOU': cocoEval.stats[2],
|
||||
'Precision/mAP (small)': cocoEval.stats[3],
|
||||
'Precision/mAP (medium)': cocoEval.stats[4],
|
||||
'Precision/mAP (large)': cocoEval.stats[5],
|
||||
'Recall/AR@1': cocoEval.stats[6],
|
||||
'Recall/AR@10': cocoEval.stats[7],
|
||||
'Recall/AR@100': cocoEval.stats[8],
|
||||
'Recall/AR@100 (small)': cocoEval.stats[9],
|
||||
'Recall/AR@100 (medium)': cocoEval.stats[10],
|
||||
'Recall/AR@100 (large)': cocoEval.stats[11],
|
||||
}
|
||||
|
||||
return summary_metrics
|
||||
|
||||
|
||||
def xyxy2xywh(bbox):
|
||||
_bbox = bbox.tolist()
|
||||
return [
|
||||
_bbox[0],
|
||||
_bbox[1],
|
||||
_bbox[2] - _bbox[0] + 1,
|
||||
_bbox[3] - _bbox[1] + 1,
|
||||
]
|
||||
|
||||
def bbox2result_1image(bboxes, labels, num_classes):
|
||||
"""Convert detection results to a list of numpy arrays.
|
||||
|
||||
Args:
|
||||
bboxes (Tensor): shape (n, 5)
|
||||
labels (Tensor): shape (n, )
|
||||
num_classes (int): class number, including background class
|
||||
|
||||
Returns:
|
||||
list(ndarray): bbox results of each class
|
||||
"""
|
||||
if bboxes.shape[0] == 0:
|
||||
result = [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes - 1)]
|
||||
else:
|
||||
result = [bboxes[labels == i, :] for i in range(num_classes - 1)]
|
||||
|
||||
return result
|
||||
|
||||
def proposal2json(dataset, results):
|
||||
"""convert proposal to json mode"""
|
||||
img_ids = dataset.getImgIds()
|
||||
json_results = []
|
||||
dataset_len = dataset.get_dataset_size()*2
|
||||
for idx in range(dataset_len):
|
||||
img_id = img_ids[idx]
|
||||
bboxes = results[idx]
|
||||
for i in range(bboxes.shape[0]):
|
||||
data = dict()
|
||||
data['image_id'] = img_id
|
||||
data['bbox'] = xyxy2xywh(bboxes[i])
|
||||
data['score'] = float(bboxes[i][4])
|
||||
data['category_id'] = 1
|
||||
json_results.append(data)
|
||||
return json_results
|
||||
|
||||
def det2json(dataset, results):
|
||||
"""convert det to json mode"""
|
||||
cat_ids = dataset.getCatIds()
|
||||
img_ids = dataset.getImgIds()
|
||||
json_results = []
|
||||
dataset_len = len(img_ids)
|
||||
for idx in range(dataset_len):
|
||||
img_id = img_ids[idx]
|
||||
if idx == len(results): break
|
||||
result = results[idx]
|
||||
for label, result_label in enumerate(result):
|
||||
bboxes = result_label
|
||||
for i in range(bboxes.shape[0]):
|
||||
data = dict()
|
||||
data['image_id'] = img_id
|
||||
data['bbox'] = xyxy2xywh(bboxes[i])
|
||||
data['score'] = float(bboxes[i][4])
|
||||
data['category_id'] = cat_ids[label]
|
||||
json_results.append(data)
|
||||
return json_results
|
||||
|
||||
def segm2json(dataset, results):
|
||||
"""convert segm to json mode"""
|
||||
cat_ids = dataset.getCatIds()
|
||||
img_ids = dataset.getImgIds()
|
||||
bbox_json_results = []
|
||||
segm_json_results = []
|
||||
|
||||
dataset_len = len(img_ids)
|
||||
assert dataset_len == len(results)
|
||||
for idx in range(dataset_len):
|
||||
img_id = img_ids[idx]
|
||||
if idx == len(results): break
|
||||
det, seg = results[idx]
|
||||
for label, det_label in enumerate(det):
|
||||
bboxes = det_label
|
||||
for i in range(bboxes.shape[0]):
|
||||
data = dict()
|
||||
data['image_id'] = img_id
|
||||
data['bbox'] = xyxy2xywh(bboxes[i])
|
||||
data['score'] = float(bboxes[i][4])
|
||||
data['category_id'] = cat_ids[label]
|
||||
bbox_json_results.append(data)
|
||||
|
||||
if len(seg) == 2:
|
||||
segms = seg[0][label]
|
||||
mask_score = seg[1][label]
|
||||
else:
|
||||
segms = seg[label]
|
||||
mask_score = [bbox[4] for bbox in bboxes]
|
||||
for i in range(bboxes.shape[0]):
|
||||
data = dict()
|
||||
data['image_id'] = img_id
|
||||
data['score'] = float(mask_score[i])
|
||||
data['category_id'] = cat_ids[label]
|
||||
segms[i]['counts'] = segms[i]['counts'].decode()
|
||||
data['segmentation'] = segms[i]
|
||||
segm_json_results.append(data)
|
||||
return bbox_json_results, segm_json_results
|
||||
|
||||
def results2json(dataset, results, out_file):
|
||||
"""convert result convert to json mode"""
|
||||
result_files = dict()
|
||||
if isinstance(results[0], list):
|
||||
json_results = det2json(dataset, results)
|
||||
result_files['bbox'] = '{}.{}.json'.format(out_file, 'bbox')
|
||||
result_files['proposal'] = '{}.{}.json'.format(out_file, 'bbox')
|
||||
mmcv.dump(json_results, result_files['bbox'])
|
||||
elif isinstance(results[0], tuple):
|
||||
json_results = segm2json(dataset, results)
|
||||
result_files['bbox'] = '{}.{}.json'.format(out_file, 'bbox')
|
||||
result_files['segm'] = '{}.{}.json'.format(out_file, 'segm')
|
||||
mmcv.dump(json_results[0], result_files['bbox'])
|
||||
mmcv.dump(json_results[1], result_files['segm'])
|
||||
elif isinstance(results[0], np.ndarray):
|
||||
json_results = proposal2json(dataset, results)
|
||||
result_files['proposal'] = '{}.{}.json'.format(out_file, 'proposal')
|
||||
mmcv.dump(json_results, result_files['proposal'])
|
||||
else:
|
||||
raise TypeError('invalid type of results')
|
||||
return result_files
|
||||
|
||||
def get_seg_masks(mask_pred, det_bboxes, det_labels, img_meta, rescale, num_classes):
|
||||
"""Get segmentation masks from mask_pred and bboxes"""
|
||||
mask_pred = mask_pred.astype(np.float32)
|
||||
|
||||
cls_segms = [[] for _ in range(num_classes - 1)]
|
||||
bboxes = det_bboxes[:, :4]
|
||||
labels = det_labels + 1
|
||||
|
||||
ori_shape = img_meta[:2].astype(np.int32)
|
||||
scale_factor = img_meta[2:].astype(np.int32)
|
||||
|
||||
if rescale:
|
||||
img_h, img_w = ori_shape[:2]
|
||||
else:
|
||||
img_h = np.round(ori_shape[0] * scale_factor[0]).astype(np.int32)
|
||||
img_w = np.round(ori_shape[1] * scale_factor[1]).astype(np.int32)
|
||||
|
||||
for i in range(bboxes.shape[0]):
|
||||
bbox = (bboxes[i, :] / 1.0).astype(np.int32)
|
||||
label = labels[i]
|
||||
w = max(bbox[2] - bbox[0] + 1, 1)
|
||||
h = max(bbox[3] - bbox[1] + 1, 1)
|
||||
w = min(w, img_w - bbox[0])
|
||||
h = min(h, img_h - bbox[1])
|
||||
if w <= 0 or h <= 0:
|
||||
print("there is invalid proposal bbox, index={} bbox={} w={} h={}".format(i, bbox, w, h))
|
||||
w = max(w, 1)
|
||||
h = max(h, 1)
|
||||
mask_pred_ = mask_pred[i, :, :]
|
||||
im_mask = np.zeros((img_h, img_w), dtype=np.uint8)
|
||||
bbox_mask = mmcv.imresize(mask_pred_, (w, h))
|
||||
bbox_mask = (bbox_mask > config.mask_thr_binary).astype(np.uint8)
|
||||
im_mask[bbox[1]:bbox[1] + h, bbox[0]:bbox[0] + w] = bbox_mask
|
||||
|
||||
rle = maskUtils.encode(
|
||||
np.array(im_mask[:, :, np.newaxis], order='F'))[0]
|
||||
cls_segms[label - 1].append(rle)
|
||||
|
||||
return cls_segms
|
||||
|
|
@ -0,0 +1,142 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# less required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
"""train MaskRcnn and get checkpoint files."""
|
||||
|
||||
import os
|
||||
import time
|
||||
import argparse
|
||||
import ast
|
||||
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore import context, Tensor
|
||||
from mindspore.communication.management import init
|
||||
from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor
|
||||
from mindspore.train import Model
|
||||
from mindspore.context import ParallelMode
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
from mindspore.nn import Momentum
|
||||
from mindspore.common import set_seed
|
||||
|
||||
from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
|
||||
from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
|
||||
from src.config import config
|
||||
from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
|
||||
from src.lr_schedule import dynamic_lr
|
||||
|
||||
set_seed(1)
|
||||
|
||||
parser = argparse.ArgumentParser(description="MaskRcnn training")
|
||||
parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False, help="If set it true, only create "
|
||||
"Mindrecord, default is false.")
|
||||
parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default is false.")
|
||||
parser.add_argument("--do_train", type=ast.literal_eval, default=True, help="Do train or not, default is true.")
|
||||
parser.add_argument("--do_eval", type=ast.literal_eval, default=False, help="Do eval or not, default is false.")
|
||||
parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
|
||||
parser.add_argument("--pre_trained", type=str, default="", help="Pretrain file path.")
|
||||
parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
|
||||
parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
|
||||
parser.add_argument("--rank_id", type=int, default=0, help="Rank id, default is 0.")
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
|
||||
|
||||
if __name__ == '__main__':
|
||||
print("Start train for maskrcnn_mobilenetv1!")
|
||||
if not args_opt.do_eval and args_opt.run_distribute:
|
||||
rank = args_opt.rank_id
|
||||
device_num = args_opt.device_num
|
||||
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||
gradients_mean=True)
|
||||
init()
|
||||
else:
|
||||
rank = 0
|
||||
device_num = 1
|
||||
|
||||
print("Start create dataset!")
|
||||
|
||||
# It will generate mindrecord file in args_opt.mindrecord_dir,
|
||||
# and the file name is MaskRcnn.mindrecord0, 1, ... file_num.
|
||||
prefix = "MaskRcnn.mindrecord"
|
||||
mindrecord_dir = config.mindrecord_dir
|
||||
mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
|
||||
if rank == 0 and not os.path.exists(mindrecord_file):
|
||||
if not os.path.isdir(mindrecord_dir):
|
||||
os.makedirs(mindrecord_dir)
|
||||
if args_opt.dataset == "coco":
|
||||
if os.path.isdir(config.coco_root):
|
||||
print("Create Mindrecord.")
|
||||
data_to_mindrecord_byte_image("coco", True, prefix)
|
||||
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
|
||||
else:
|
||||
raise Exception("coco_root not exits.")
|
||||
else:
|
||||
if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
|
||||
print("Create Mindrecord.")
|
||||
data_to_mindrecord_byte_image("other", True, prefix)
|
||||
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
|
||||
else:
|
||||
raise Exception("IMAGE_DIR or ANNO_PATH not exits.")
|
||||
while not os.path.exists(mindrecord_file+".db"):
|
||||
time.sleep(5)
|
||||
|
||||
if not args_opt.only_create_dataset:
|
||||
loss_scale = float(config.loss_scale)
|
||||
|
||||
# When create MindDataset, using the fitst mindrecord file, such as MaskRcnn.mindrecord0.
|
||||
dataset = create_maskrcnn_dataset(mindrecord_file, batch_size=config.batch_size,
|
||||
device_num=device_num, rank_id=rank)
|
||||
|
||||
dataset_size = dataset.get_dataset_size()
|
||||
print("total images num: ", dataset_size)
|
||||
print("Create dataset done!")
|
||||
|
||||
net = Mask_Rcnn_Mobilenetv1(config=config)
|
||||
net = net.set_train()
|
||||
|
||||
load_path = args_opt.pre_trained
|
||||
if load_path != "":
|
||||
param_dict = load_checkpoint(load_path)
|
||||
if config.pretrain_epoch_size == 0:
|
||||
for item in list(param_dict.keys()):
|
||||
if not (item.startswith('backbone') or item.startswith('rcnn_mask')):
|
||||
param_dict.pop(item)
|
||||
load_param_into_net(net, param_dict)
|
||||
|
||||
loss = LossNet()
|
||||
lr = Tensor(dynamic_lr(config, rank_size=device_num, start_steps=config.pretrain_epoch_size * dataset_size),
|
||||
mstype.float32)
|
||||
opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,
|
||||
weight_decay=config.weight_decay, loss_scale=config.loss_scale)
|
||||
|
||||
net_with_loss = WithLossCell(net, loss)
|
||||
if args_opt.run_distribute:
|
||||
net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale, reduce_flag=True,
|
||||
mean=True, degree=device_num)
|
||||
else:
|
||||
net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale)
|
||||
|
||||
time_cb = TimeMonitor(data_size=dataset_size)
|
||||
loss_cb = LossCallBack(rank_id=rank)
|
||||
cb = [time_cb, loss_cb]
|
||||
if config.save_checkpoint:
|
||||
ckptconfig = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * dataset_size,
|
||||
keep_checkpoint_max=config.keep_checkpoint_max)
|
||||
save_checkpoint_path = os.path.join(config.save_checkpoint_path, 'ckpt_' + str(rank) + '/')
|
||||
ckpoint_cb = ModelCheckpoint(prefix='mask_rcnn', directory=save_checkpoint_path, config=ckptconfig)
|
||||
cb += [ckpoint_cb]
|
||||
|
||||
model = Model(net)
|
||||
model.train(config.epoch_size, dataset, callbacks=cb)
|
||||
Loading…
Reference in New Issue