From 81ab1ff48bdcef0df39846b9a6e64ad65f7a6888 Mon Sep 17 00:00:00 2001 From: maysonma Date: Thu, 1 Feb 2024 15:13:19 -0500 Subject: [PATCH] hotfix(page) Fixes error in GitHub page --- CITATION.cff | 10 +- docs/CHANGELOG.md | 209 -- docs/CODE_OF_CONDUCT.md | 76 - docs/CONTRIBUTING.md | 35 - docs/GETTING_STARTED.md | 510 ----- docs/INSTALL.md | 161 -- docs/MODEL_ZOO.md | 532 ----- docs/Makefile | 20 - docs/ROBUSTNESS_BENCHMARKING.md | 109 -- docs/TECHNICAL_DETAILS.md | 226 --- docs/conf.py | 70 - docs/index.html | 666 +++++++ docs/index.rst | 19 - docs/make.bat | 35 - docs/requirements.txt | 4 - docs/static/css/bulma-carousel.min.css | 1 + docs/static/css/bulma-slider.min.css | 1 + docs/static/css/bulma.css.map.txt | 1 + docs/static/css/bulma.min.css | 1 + docs/static/css/fontawesome.all.min.css | 5 + docs/static/css/index.css | 135 ++ docs/static/images/icon.jpeg | Bin 0 -> 4701 bytes docs/static/js/bulma-carousel.js | 2371 +++++++++++++++++++++++ docs/static/js/bulma-carousel.min.js | 1 + docs/static/js/bulma-slider.js | 461 +++++ docs/static/js/bulma-slider.min.js | 1 + docs/static/js/fontawesome.all.min.js | 5 + docs/static/js/index.js | 21 + 28 files changed, 3675 insertions(+), 2011 deletions(-) delete mode 100644 docs/CHANGELOG.md delete mode 100644 docs/CODE_OF_CONDUCT.md delete mode 100644 docs/CONTRIBUTING.md delete mode 100644 docs/GETTING_STARTED.md delete mode 100644 docs/INSTALL.md delete mode 100644 docs/MODEL_ZOO.md delete mode 100644 docs/Makefile delete mode 100644 docs/ROBUSTNESS_BENCHMARKING.md delete mode 100644 docs/TECHNICAL_DETAILS.md delete mode 100644 docs/conf.py create mode 100644 docs/index.html delete mode 100644 docs/index.rst delete mode 100644 docs/make.bat delete mode 100644 docs/requirements.txt create mode 100644 docs/static/css/bulma-carousel.min.css create mode 100644 docs/static/css/bulma-slider.min.css create mode 100644 docs/static/css/bulma.css.map.txt create mode 100644 docs/static/css/bulma.min.css create mode 100644 docs/static/css/fontawesome.all.min.css create mode 100644 docs/static/css/index.css create mode 100644 docs/static/images/icon.jpeg create mode 100644 docs/static/js/bulma-carousel.js create mode 100644 docs/static/js/bulma-carousel.min.js create mode 100644 docs/static/js/bulma-slider.js create mode 100644 docs/static/js/bulma-slider.min.js create mode 100644 docs/static/js/fontawesome.all.min.js create mode 100644 docs/static/js/index.js diff --git a/CITATION.cff b/CITATION.cff index 958f6f3..33ddf15 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -1,8 +1,8 @@ cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - - name: "MMDetection3D Contributors" -title: "OpenMMLab's Next-generation Platform for General 3D Object Detection" -date-released: 2020-07-23 -url: "https://github.com/open-mmlab/mmdetection3d" -license: Apache-2.0 + - name: "Ma, Yunsheng and Lu, Juanwu and Cui, Can and Zhao, Sicheng and Cao, Xu and Ye, Wenqian and Wang, Ziran" +title: "MACP: Efficient Model Adaptation for Cooperative Perception" +date-released: 2024-02-01 +url: "https://github.com/PurdueDigitalTwin/MACP" +license: MIT diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md deleted file mode 100644 index 632b97d..0000000 --- a/docs/CHANGELOG.md +++ /dev/null @@ -1,209 +0,0 @@ -## Changelog - -### v1.1.0 (24/2/2020) - -**Highlights** -- Dataset evaluation is rewritten with a unified api, which is used by both evaluation hooks and test scripts. -- Support new methods: [CARAFE](https://arxiv.org/abs/1905.02188). - -**Breaking Changes** -- The new MMDDP inherits from the official DDP, thus the `__init__` api is changed to be the same as official DDP. -- The `mask_head` field in HTC config files is modified. -- The evaluation and testing script is updated. -- In all transforms, instance masks are stored as a numpy array shaped (n, h, w) instead of a list of (h, w) arrays, where n is the number of instances. - -**Bug Fixes** -- Fix IOU assigners when ignore_iof_thr > 0 and there is no pred boxes. (#2135) -- Fix mAP evaluation when there are no ignored boxes. (#2116) -- Fix the empty RoI input for Deformable RoI Pooling. (#2099) -- Fix the dataset settings for multiple workflows. (#2103) -- Fix the warning related to `torch.uint8` in PyTorch 1.4. (#2105) -- Fix the inference demo on devices other than gpu:0. (#2098) -- Fix Dockerfile. (#2097) -- Fix the bug that `pad_val` is unused in Pad transform. (#2093) -- Fix the albumentation transform when there is no ground truth bbox. (#2032) - -**Improvements** -- Use torch instead of numpy for random sampling. (#2094) -- Migrate to the new MMDDP implementation in MMCV v0.3. (#2090) -- Add meta information in logs. (#2086) -- Rewrite Soft NMS with pytorch extension and remove cython as a dependency. (#2056) -- Rewrite dataset evaluation. (#2042, #2087, #2114, #2128) -- Use numpy array for masks in transforms. (#2030) - -**New Features** -- Implement "CARAFE: Content-Aware ReAssembly of FEatures". (#1583) -- Add `worker_init_fn()` in data_loader when seed is set. (#2066, #2111) -- Add logging utils. (#2035) - -### v1.0.0 (30/1/2020) - -This release mainly improves the code quality and add more docstrings. - -**Highlights** -- Documentation is online now: https://mmdetection.readthedocs.io. -- Support new models: [ATSS](https://arxiv.org/abs/1912.02424). -- DCN is now available with the api `build_conv_layer` and `ConvModule` like the normal conv layer. -- A tool to collect environment information is available for trouble shooting. - -**Bug Fixes** -- Fix the incompatibility of the latest numpy and pycocotools. (#2024) -- Fix the case when distributed package is unavailable, e.g., on Windows. (#1985) -- Fix the dimension issue for `refine_bboxes()`. (#1962) -- Fix the typo when `seg_prefix` is a list. (#1906) -- Add segmentation map cropping to RandomCrop. (#1880) -- Fix the return value of `ga_shape_target_single()`. (#1853) -- Fix the loaded shape of empty proposals. (#1819) -- Fix the mask data type when using albumentation. (#1818) - -**Improvements** -- Enhance AssignResult and SamplingResult. (#1995) -- Add ability to overwrite existing module in Registry. (#1982) -- Reorganize requirements and make albumentations and imagecorruptions optional. (#1969) -- Check NaN in `SSDHead`. (#1935) -- Encapsulate the DCN in ResNe(X)t into a ConvModule & Conv_layers. (#1894) -- Refactoring for mAP evaluation and support multiprocessing and logging. (#1889) -- Init the root logger before constructing Runner to log more information. (#1865) -- Split `SegResizeFlipPadRescale` into different existing transforms. (#1852) -- Move `init_dist()` to MMCV. (#1851) -- Documentation and docstring improvements. (#1971, #1938, #1869, #1838) -- Fix the color of the same class for mask visualization. (#1834) -- Remove the option `keep_all_stages` in HTC and Cascade R-CNN. (#1806) - -**New Features** -- Add two test-time options `crop_mask` and `rle_mask_encode` for mask heads. (#2013) -- Support loading grayscale images as single channel. (#1975) -- Implement "Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection". (#1872) -- Add sphinx generated docs. (#1859, #1864) -- Add GN support for flops computation. (#1850) -- Collect env info for trouble shooting. (#1812) - - -### v1.0rc1 (13/12/2019) - -The RC1 release mainly focuses on improving the user experience, and fixing bugs. - -**Highlights** -- Support new models: [FoveaBox](https://arxiv.org/abs/1904.03797), [RepPoints](https://arxiv.org/abs/1904.11490) and [FreeAnchor](https://arxiv.org/abs/1909.02466). -- Add a Dockerfile. -- Add a jupyter notebook demo and a webcam demo. -- Setup the code style and CI. -- Add lots of docstrings and unit tests. -- Fix lots of bugs. - -**Breaking Changes** -- There was a bug for computing COCO-style mAP w.r.t different scales (AP_s, AP_m, AP_l), introduced by #621. (#1679) - -**Bug Fixes** -- Fix a sampling interval bug in Libra R-CNN. (#1800) -- Fix the learning rate in SSD300 WIDER FACE. (#1781) -- Fix the scaling issue when `keep_ratio=False`. (#1730) -- Fix typos. (#1721, #1492, #1242, #1108, #1107) -- Fix the shuffle argument in `build_dataloader`. (#1693) -- Clip the proposal when computing mask targets. (#1688) -- Fix the "index out of range" bug for samplers in some corner cases. (#1610, #1404) -- Fix the NMS issue on devices other than GPU:0. (#1603) -- Fix SSD Head and GHM Loss on CPU. (#1578) -- Fix the OOM error when there are too many gt bboxes. (#1575) -- Fix the wrong keyword argument `nms_cfg` in HTC. (#1573) -- Process masks and semantic segmentation in Expand and MinIoUCrop transforms. (#1550, #1361) -- Fix a scale bug in the Non Local op. (#1528) -- Fix a bug in transforms when `gt_bboxes_ignore` is None. (#1498) -- Fix a bug when `img_prefix` is None. (#1497) -- Pass the device argument to `grid_anchors` and `valid_flags`. (#1478) -- Fix the data pipeline for test_robustness. (#1476) -- Fix the argument type of deformable pooling. (#1390) -- Fix the coco_eval when there are only two classes. (#1376) -- Fix a bug in Modulated DeformableConv when deformable_group>1. (#1359) -- Fix the mask cropping in RandomCrop. (#1333) -- Fix zero outputs in DeformConv when not running on cuda:0. (#1326) -- Fix the type issue in Expand. (#1288) -- Fix the inference API. (#1255) -- Fix the inplace operation in Expand. (#1249) -- Fix the from-scratch training config. (#1196) -- Fix inplace add in RoIExtractor which cause an error in PyTorch 1.2. (#1160) -- Fix FCOS when input images has no positive sample. (#1136) -- Fix recursive imports. (#1099) - -**Improvements** -- Print the config file and mmdet version in the log. (#1721) -- Lint the code before compiling in travis CI. (#1715) -- Add a probability argument for the `Expand` transform. (#1651) -- Update the PyTorch and CUDA version in the docker file. (#1615) -- Raise a warning when specifying `--validate` in non-distributed training. (#1624, #1651) -- Beautify the mAP printing. (#1614) -- Add pre-commit hook. (#1536) -- Add the argument `in_channels` to backbones. (#1475) -- Add lots of docstrings and unit tests, thanks to [@Erotemic](https://github.com/Erotemic). (#1603, #1517, #1506, #1505, #1491, #1479, #1477, #1475, #1474) -- Add support for multi-node distributed test when there is no shared storage. (#1399) -- Optimize Dockerfile to reduce the image size. (#1306) -- Update new results of HRNet. (#1284, #1182) -- Add an argument `no_norm_on_lateral` in FPN. (#1240) -- Test the compiling in CI. (#1235) -- Move docs to a separate folder. (#1233) -- Add a jupyter notebook demo. (#1158) -- Support different type of dataset for training. (#1133) -- Use int64_t instead of long in cuda kernels. (#1131) -- Support unsquare RoIs for bbox and mask heads. (#1128) -- Manually add type promotion to make compatible to PyTorch 1.2. (#1114) -- Allowing validation dataset for computing validation loss. (#1093) -- Use `.scalar_type()` instead of `.type()` to suppress some warnings. (#1070) - -**New Features** -- Add an option `--with_ap` to compute the AP for each class. (#1549) -- Implement "FreeAnchor: Learning to Match Anchors for Visual Object Detection". (#1391) -- Support [Albumentations](https://github.com/albumentations-team/albumentations) for augmentations in the data pipeline. (#1354) -- Implement "FoveaBox: Beyond Anchor-based Object Detector". (#1339) -- Support horizontal and vertical flipping. (#1273, #1115) -- Implement "RepPoints: Point Set Representation for Object Detection". (#1265) -- Add test-time augmentation to HTC and Cascade R-CNN. (#1251) -- Add a COCO result analysis tool. (#1228) -- Add Dockerfile. (#1168) -- Add a webcam demo. (#1155, #1150) -- Add FLOPs counter. (#1127) -- Allow arbitrary layer order for ConvModule. (#1078) - - -### v1.0rc0 (27/07/2019) -- Implement lots of new methods and components (Mixed Precision Training, HTC, Libra R-CNN, Guided Anchoring, Empirical Attention, Mask Scoring R-CNN, Grid R-CNN (Plus), GHM, GCNet, FCOS, HRNet, Weight Standardization, etc.). Thank all collaborators! -- Support two additional datasets: WIDER FACE and Cityscapes. -- Refactoring for loss APIs and make it more flexible to adopt different losses and related hyper-parameters. -- Speed up multi-gpu testing. -- Integrate all compiling and installing in a single script. - -### v0.6.0 (14/04/2019) -- Up to 30% speedup compared to the model zoo. -- Support both PyTorch stable and nightly version. -- Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions. - -### v0.6rc0(06/02/2019) -- Migrate to PyTorch 1.0. - -### v0.5.7 (06/02/2019) -- Add support for Deformable ConvNet v2. (Many thanks to the authors and [@chengdazhi](https://github.com/chengdazhi)) -- This is the last release based on PyTorch 0.4.1. - -### v0.5.6 (17/01/2019) -- Add support for Group Normalization. -- Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead. - -### v0.5.5 (22/12/2018) -- Add SSD for COCO and PASCAL VOC. -- Add ResNeXt backbones and detection models. -- Refactoring for Samplers/Assigners and add OHEM. -- Add VOC dataset and evaluation scripts. - -### v0.5.4 (27/11/2018) -- Add SingleStageDetector and RetinaNet. - -### v0.5.3 (26/11/2018) -- Add Cascade R-CNN and Cascade Mask R-CNN. -- Add support for Soft-NMS in config files. - -### v0.5.2 (21/10/2018) -- Add support for custom datasets. -- Add a script to convert PASCAL VOC annotations to the expected format. - -### v0.5.1 (20/10/2018) -- Add BBoxAssigner and BBoxSampler, the `train_cfg` field in config files are restructured. -- `ConvFCRoIHead` / `SharedFCRoIHead` are renamed to `ConvFCBBoxHead` / `SharedFCBBoxHead` for consistency. diff --git a/docs/CODE_OF_CONDUCT.md b/docs/CODE_OF_CONDUCT.md deleted file mode 100644 index efd4305..0000000 --- a/docs/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,76 +0,0 @@ -# Contributor Covenant Code of Conduct - -## Our Pledge - -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to making participation in our project and -our community a harassment-free experience for everyone, regardless of age, body -size, disability, ethnicity, sex characteristics, gender identity and expression, -level of experience, education, socio-economic status, nationality, personal -appearance, race, religion, or sexual identity and orientation. - -## Our Standards - -Examples of behavior that contributes to creating a positive environment -include: - -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy towards other community members - -Examples of unacceptable behavior by participants include: - -* The use of sexualized language or imagery and unwelcome sexual attention or - advances -* Trolling, insulting/derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or electronic - address, without explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -## Our Responsibilities - -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. - -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. - -## Scope - -This Code of Conduct applies both within project spaces and in public spaces -when an individual is representing the project or its community. Examples of -representing a project or community include using an official project e-mail -address, posting via an official social media account, or acting as an appointed -representative at an online or offline event. Representation of a project may be -further defined and clarified by project maintainers. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting the project team at chenkaidev@gmail.com. All -complaints will be reviewed and investigated and will result in a response that -is deemed necessary and appropriate to the circumstances. The project team is -obligated to maintain confidentiality with regard to the reporter of an incident. -Further details of specific enforcement policies may be posted separately. - -Project maintainers who do not follow or enforce the Code of Conduct in good -faith may face temporary or permanent repercussions as determined by other -members of the project's leadership. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, -available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html - -[homepage]: https://www.contributor-covenant.org - -For answers to common questions about this code of conduct, see -https://www.contributor-covenant.org/faq diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md deleted file mode 100644 index 7a24fb5..0000000 --- a/docs/CONTRIBUTING.md +++ /dev/null @@ -1,35 +0,0 @@ -# Contributing to mmdetection - -All kinds of contributions are welcome, including but not limited to the following. - -- Fixes (typo, bugs) -- New features and components - -## Workflow - -1. fork and pull the latest mmdetection -2. checkout a new branch (do not use master branch for PRs) -3. commit your changes -4. create a PR - -Note -- If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first. -- If you are the author of some papers and would like to include your method to mmdetection, -please contact Wenwei Zhang (zwwdev[at]gmail[dot]com). We will much appreciate your contribution. - -## Code style - -### Python -We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style. - -We use the following tools for linting and formatting: -- [flake8](http://flake8.pycqa.org/en/latest/): linter -- [yapf](https://github.com/google/yapf): formatter -- [isort](https://github.com/timothycrosley/isort): sort imports - -Style configurations of yapf and isort can be found in [.style.yapf](.style.yapf) and [.isort.cfg](.isort.cfg). - ->Before you create a PR, make sure that your code lints and is formatted by yapf. - -### C++ and CUDA -We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html). diff --git a/docs/GETTING_STARTED.md b/docs/GETTING_STARTED.md deleted file mode 100644 index 077def3..0000000 --- a/docs/GETTING_STARTED.md +++ /dev/null @@ -1,510 +0,0 @@ -# Getting Started - -This page provides basic tutorials about the usage of MMDetection. -For installation instructions, please see [INSTALL.md](INSTALL.md). - -## Inference with pretrained models - -We provide testing scripts to evaluate a whole dataset (COCO, PASCAL VOC, Cityscapes, etc.), -and also some high-level apis for easier integration to other projects. - -### Test a dataset - -- [x] single GPU testing -- [x] multiple GPU testing -- [x] visualize detection results - -You can use the following commands to test a dataset. - -```shell -# single-gpu testing -python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show] - -# multi-gpu testing -./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] -``` - -Optional arguments: -- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file. -- `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., `proposal_fast`, `proposal`, `bbox`, `segm` are available for COCO, `mAP`, `recall` for PASCAL VOC. Cityscapes could be evaluated by `cityscapes` as well as all COCO metrics. -- `--show`: If specified, detection results will be plotted on the images and shown in a new window. It is only applicable to single GPU testing and used for debugging and visualization. Please make sure that GUI is available in your environment, otherwise you may encounter the error like `cannot connect to X server`. - -If you would like to evaluate the dataset, do not specify `--show` at the same time. - -Examples: - -Assume that you have already downloaded the checkpoints to the directory `checkpoints/`. - -1. Test Faster R-CNN and visualize the results. Press any key for the next image. - -```shell -python tools/test.py configs/faster_rcnn_r50_fpn_1x.py \ - checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth \ - --show -``` - -2. Test Faster R-CNN on PASCAL VOC (without saving the test results) and evaluate the mAP. - -```shell -python tools/test.py configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc.py \ - checkpoints/SOME_CHECKPOINT.pth \ - --eval mAP -``` - -3. Test Mask R-CNN with 8 GPUs, and evaluate the bbox and mask AP. - -```shell -./tools/dist_test.sh configs/mask_rcnn_r50_fpn_1x.py \ - checkpoints/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth \ - 8 --out results.pkl --eval bbox segm -``` - -4. Test Mask R-CNN on COCO test-dev with 8 GPUs, and generate the json file to be submit to the official evaluation server. - -```shell -./tools/dist_test.sh configs/mask_rcnn_r50_fpn_1x.py \ - checkpoints/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth \ - 8 --format-only --options "jsonfile_prefix=./mask_rcnn_test-dev_results" -``` - -You will get two json files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcnn_test-dev_results.segm.json`. - -5. Test Mask R-CNN on Cityscapes test with 8 GPUs, and generate the txt and png files to be submit to the official evaluation server. - -```shell -./tools/dist_test.sh configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py \ - checkpoints/mask_rcnn_r50_fpn_1x_cityscapes_20200227-afe51d5a.pth \ - 8 --format_only --options "outfile_prefix=./mask_rcnn_cityscapes_test_results" -``` - -The generated png and txt would be under `./mask_rcnn_cityscapes_test_results` directory. - -### Webcam demo - -We provide a webcam demo to illustrate the results. - -```shell -python demo/webcam_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--camera-id ${CAMERA-ID}] [--score-thr ${SCORE_THR}] -``` - -Examples: - -```shell -python demo/webcam_demo.py configs/faster_rcnn_r50_fpn_1x.py \ - checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth -``` - -### High-level APIs for testing images - -#### Synchronous interface -Here is an example of building the model and test given images. - -```python -from mmdet.apis import init_detector, inference_detector, show_result -import mmcv - -config_file = 'configs/faster_rcnn_r50_fpn_1x.py' -checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth' - -# build the model from a config file and a checkpoint file -model = init_detector(config_file, checkpoint_file, device='cuda:0') - -# test a single image and show the results -img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once -result = inference_detector(model, img) -# visualize the results in a new window -show_result(img, result, model.CLASSES) -# or save the visualization results to image files -show_result(img, result, model.CLASSES, out_file='result.jpg') - -# test a video and show the results -video = mmcv.VideoReader('video.mp4') -for frame in video: - result = inference_detector(model, frame) - show_result(frame, result, model.CLASSES, wait_time=1) -``` - -A notebook demo can be found in [demo/inference_demo.ipynb](https://github.com/open-mmlab/mmdetection/blob/master/demo/inference_demo.ipynb). - -#### Asynchronous interface - supported for Python 3.7+ - -Async interface allows not to block CPU on GPU bound inference code and enables better CPU/GPU utilization for single threaded application. Inference can be done concurrently either between different input data samples or between different models of some inference pipeline. - -See `tests/async_benchmark.py` to compare the speed of synchronous and asynchronous interfaces. - -```python -import asyncio -import torch -from mmdet.apis import init_detector, async_inference_detector, show_result -from mmdet.utils.contextmanagers import concurrent - -async def main(): - config_file = 'configs/faster_rcnn_r50_fpn_1x.py' - checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth' - device = 'cuda:0' - model = init_detector(config_file, checkpoint=checkpoint_file, device=device) - - # queue is used for concurrent inference of multiple images - streamqueue = asyncio.Queue() - # queue size defines concurrency level - streamqueue_size = 3 - - for _ in range(streamqueue_size): - streamqueue.put_nowait(torch.cuda.Stream(device=device)) - - # test a single image and show the results - img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once - - async with concurrent(streamqueue): - result = await async_inference_detector(model, img) - - # visualize the results in a new window - show_result(img, result, model.CLASSES) - # or save the visualization results to image files - show_result(img, result, model.CLASSES, out_file='result.jpg') - - -asyncio.run(main()) - -``` - - -## Train a model - -MMDetection implements distributed training and non-distributed training, -which uses `MMDistributedDataParallel` and `MMDataParallel` respectively. - -All outputs (log files and checkpoints) will be saved to the working directory, -which is specified by `work_dir` in the config file. - -By default we evaluate the model on the validation set after each epoch, you can change the evaluation interval by adding the interval argument in the training config. -```python -evaluation = dict(interval=12) # This evaluate the model per 12 epoch. -``` - -**\*Important\***: The default learning rate in config files is for 8 GPUs and 2 img/gpu (batch size = 8*2 = 16). -According to the [Linear Scaling Rule](https://arxiv.org/abs/1706.02677), you need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU, e.g., lr=0.01 for 4 GPUs * 2 img/gpu and lr=0.08 for 16 GPUs * 4 img/gpu. - -### Train with a single GPU - -```shell -python tools/train.py ${CONFIG_FILE} -``` - -If you want to specify the working directory in the command, you can add an argument `--work_dir ${YOUR_WORK_DIR}`. - -### Train with multiple GPUs - -```shell -./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments] -``` - -Optional arguments are: - -- `--validate` (**strongly recommended**): Perform evaluation at every k (default value is 1, which can be modified like [this](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn_r50_fpn_1x.py#L174)) epochs during the training. -- `--work_dir ${WORK_DIR}`: Override the working directory specified in the config file. -- `--resume_from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file. - -Difference between `resume_from` and `load_from`: -`resume_from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally. -`load_from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning. - -### Train with multiple machines - -If you run MMDetection on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.) - -```shell -./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} [${GPUS}] -``` - -Here is an example of using 16 GPUs to train Mask R-CNN on the dev partition. - -```shell -./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x.py /nfs/xxxx/mask_rcnn_r50_fpn_1x 16 -``` - -You can check [slurm_train.sh](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) for full arguments and environment variables. - -If you have just multiple machines connected with ethernet, you can refer to -pytorch [launch utility](https://pytorch.org/docs/stable/distributed_deprecated.html#launch-utility). -Usually it is slow if you do not have high speed networking like infiniband. - -### Launch multiple jobs on a single machine - -If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs, -you need to specify different ports (29500 by default) for each job to avoid communication conflict. - -If you use `dist_train.sh` to launch training jobs, you can set the port in commands. - -```shell -CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4 -CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4 -``` - -If you use launch training jobs with slurm, you need to modify the config files (usually the 6th line from the bottom in config files) to set different communication ports. - -In `config1.py`, -```python -dist_params = dict(backend='nccl', port=29500) -``` - -In `config2.py`, -```python -dist_params = dict(backend='nccl', port=29501) -``` - -Then you can launch two jobs with `config1.py` ang `config2.py`. - -```shell -CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} 4 -CUDA_VISIBLE_DEVICES=4,5,6,7 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} 4 -``` - -## Useful tools - -We provide lots of useful tools under `tools/` directory. - -### Analyze logs - -You can plot loss/mAP curves given a training log file. Run `pip install seaborn` first to install the dependency. - -![loss curve image](../demo/loss_curve.png) - -```shell -python tools/analyze_logs.py plot_curve [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}] -``` - -Examples: - -- Plot the classification loss of some run. - -```shell -python tools/analyze_logs.py plot_curve log.json --keys loss_cls --legend loss_cls -``` - -- Plot the classification and regression loss of some run, and save the figure to a pdf. - -```shell -python tools/analyze_logs.py plot_curve log.json --keys loss_cls loss_reg --out losses.pdf -``` - -- Compare the bbox mAP of two runs in the same figure. - -```shell -python tools/analyze_logs.py plot_curve log1.json log2.json --keys bbox_mAP --legend run1 run2 -``` - -You can also compute the average training speed. - -```shell -python tools/analyze_logs.py cal_train_time ${CONFIG_FILE} [--include-outliers] -``` - -The output is expected to be like the following. - -``` ------Analyze train time of work_dirs/some_exp/20190611_192040.log.json----- -slowest epoch 11, average time is 1.2024 -fastest epoch 1, average time is 1.1909 -time std over epochs is 0.0028 -average iter time: 1.1959 s/iter - -``` - -### Get the FLOPs and params (experimental) - -We provide a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model. - -```shell -python tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}] -``` - -You will get the result like this. - -``` -============================== -Input shape: (3, 1280, 800) -Flops: 239.32 GMac -Params: 37.74 M -============================== -``` - -**Note**: This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double check it before you adopt it in technical reports or papers. - -(1) FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 1280, 800). -(2) Some operators are not counted into FLOPs like GN and custom operators. -You can add support for new operators by modifying [`mmdet/utils/flops_counter.py`](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/utils/flops_counter.py). -(3) The FLOPs of two-stage detectors is dependent on the number of proposals. - -### Publish a model - -Before you upload a model to AWS, you may want to -(1) convert model weights to CPU tensors, (2) delete the optimizer states and -(3) compute the hash of the checkpoint file and append the hash id to the filename. - -```shell -python tools/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME} -``` - -E.g., - -```shell -python tools/publish_model.py work_dirs/faster_rcnn/latest.pth faster_rcnn_r50_fpn_1x_20190801.pth -``` - -The final output filename will be `faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth`. - -### Test the robustness of detectors - -Please refer to [ROBUSTNESS_BENCHMARKING.md](ROBUSTNESS_BENCHMARKING.md). - - -## How-to - -### Use my own datasets - -The simplest way is to convert your dataset to existing dataset formats (COCO or PASCAL VOC). - -Here we show an example of adding a custom dataset of 5 classes, assuming it is also in COCO format. - -In `mmdet/datasets/my_dataset.py`: - -```python -from .coco import CocoDataset -from .registry import DATASETS - - -@DATASETS.register_module -class MyDataset(CocoDataset): - - CLASSES = ('a', 'b', 'c', 'd', 'e') -``` - -In `mmdet/datasets/__init__.py`: - -```python -from .my_dataset import MyDataset -``` - -Then you can use `MyDataset` in config files, with the same API as CocoDataset. - - -It is also fine if you do not want to convert the annotation format to COCO or PASCAL format. -Actually, we define a simple annotation format and all existing datasets are -processed to be compatible with it, either online or offline. - -The annotation of a dataset is a list of dict, each dict corresponds to an image. -There are 3 field `filename` (relative path), `width`, `height` for testing, -and an additional field `ann` for training. `ann` is also a dict containing at least 2 fields: -`bboxes` and `labels`, both of which are numpy arrays. Some datasets may provide -annotations like crowd/difficult/ignored bboxes, we use `bboxes_ignore` and `labels_ignore` -to cover them. - -Here is an example. -``` -[ - { - 'filename': 'a.jpg', - 'width': 1280, - 'height': 720, - 'ann': { - 'bboxes': (n, 4), - 'labels': (n, ), - 'bboxes_ignore': (k, 4), - 'labels_ignore': (k, ) (optional field) - } - }, - ... -] -``` - -There are two ways to work with custom datasets. - -- online conversion - - You can write a new Dataset class inherited from `CustomDataset`, and overwrite two methods - `load_annotations(self, ann_file)` and `get_ann_info(self, idx)`, - like [CocoDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py) and [VOCDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/voc.py). - -- offline conversion - - You can convert the annotation format to the expected format above and save it to - a pickle or json file, like [pascal_voc.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/convert_datasets/pascal_voc.py). - Then you can simply use `CustomDataset`. - -### Customize optimizer - -An example of customized optimizer `CopyOfSGD` is defined in `mmdet/core/optimizer/copy_of_sgd.py`. -More generally, a customized optimizer could be defined as following. - -In `mmdet/core/optimizer/my_optimizer.py`: - -```python -from .registry import OPTIMIZERS -from torch.optim import Optimizer - - -@OPTIMIZERS.register_module -class MyOptimizer(Optimizer): - -``` - -In `mmdet/core/optimizer/__init__.py`: - -```python -from .my_optimizer import MyOptimizer -``` - -Then you can use `MyOptimizer` in `optimizer` field of config files. - -### Develop new components - -We basically categorize model components into 4 types. - -- backbone: usually an FCN network to extract feature maps, e.g., ResNet, MobileNet. -- neck: the component between backbones and heads, e.g., FPN, PAFPN. -- head: the component for specific tasks, e.g., bbox prediction and mask prediction. -- roi extractor: the part for extracting RoI features from feature maps, e.g., RoI Align. - -Here we show how to develop new components with an example of MobileNet. - -1. Create a new file `mmdet/models/backbones/mobilenet.py`. - -```python -import torch.nn as nn - -from ..registry import BACKBONES - - -@BACKBONES.register_module -class MobileNet(nn.Module): - - def __init__(self, arg1, arg2): - pass - - def forward(self, x): # should return a tuple - pass - - def init_weights(self, pretrained=None): - pass -``` - -2. Import the module in `mmdet/models/backbones/__init__.py`. - -```python -from .mobilenet import MobileNet -``` - -3. Use it in your config file. - -```python -model = dict( - ... - backbone=dict( - type='MobileNet', - arg1=xxx, - arg2=xxx), - ... -``` - -For more information on how it works, you can refer to [TECHNICAL_DETAILS.md](TECHNICAL_DETAILS.md) (TODO). diff --git a/docs/INSTALL.md b/docs/INSTALL.md deleted file mode 100644 index b22b970..0000000 --- a/docs/INSTALL.md +++ /dev/null @@ -1,161 +0,0 @@ -## Installation - -### Requirements - -- Linux (Windows is not officially supported) -- Python 3.5+ -- PyTorch 1.1 or higher -- CUDA 9.0 or higher -- NCCL 2 -- GCC 4.9 or higher -- [mmcv](https://github.com/open-mmlab/mmcv) - -We have tested the following versions of OS and softwares: - -- OS: Ubuntu 16.04/18.04 and CentOS 7.2 -- CUDA: 9.0/9.2/10.0/10.1 -- NCCL: 2.1.15/2.2.13/2.3.7/2.4.2 -- GCC(G++): 4.9/5.3/5.4/7.3 - -### Install mmdetection - -a. Create a conda virtual environment and activate it. - -```shell -conda create -n open-mmlab python=3.7 numba=0.45.1 -y -conda activate open-mmlab -``` - -b. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/), e.g., - -```shell -conda install pytorch torchvision -c pytorch -``` - -c. Clone the mmdetection repository. - -```shell -git clone https://github.com/open-mmlab/mmdetection.git -cd mmdetection -``` - -d. Install build requirements and then install mmdetection. -(We install pycocotools via the github repo instead of pypi because the pypi version is old and not compatible with the latest numpy.) - -```shell -pip install -r requirements/build.txt -pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI" -pip install -v -e . # or "python setup.py develop" -``` - -e. Clone the MMDetection3D repository. - -```shell -git clone https://github.com/open-mmlab/mmdetection3d.git -cd mmdetection3d -``` - -f. Install build requirements and then install MMDetection3D. - -```shell -pip install -r requirements/build.txt -pip install -v -e . # or "python setup.py develop" -``` - -Note: - -1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models. -It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory. - -2. Following the above instructions, mmdetection is installed on `dev` mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number). - -3. If you would like to use `opencv-python-headless` instead of `opencv-python`, -you can install it before installing MMCV. - -4. Some dependencies are optional. Simply running `pip install -v -e .` will only install the minimum runtime requirements. To use optional dependencies like `albumentations` and `imagecorruptions` either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`. - -### Another option: Docker Image - -We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection/blob/master/docker/Dockerfile) to build an image. - -```shell -# build an image with PyTorch 1.1, CUDA 10.0 and CUDNN 7.5 -docker build -t mmdetection docker/ -``` - -### Prepare datasets - -It is recommended to symlink the dataset root to `$MMDETECTION/data`. -If your folder structure is different, you may need to change the corresponding paths in config files. - -``` -mmdetection -├── mmdet -├── tools -├── configs -├── data -│ ├── coco -│ │ ├── annotations -│ │ ├── train2017 -│ │ ├── val2017 -│ │ ├── test2017 -│ ├── cityscapes -│ │ ├── annotations -│ │ ├── leftImg8bit -│ │ │ ├── train -│ │ │ ├── val -│ │ ├── gtFine -│ │ │ ├── train -│ │ │ ├── val -│ ├── VOCdevkit -│ │ ├── VOC2007 -│ │ ├── VOC2012 - -``` -The cityscapes annotations have to be converted into the coco format using `tools/convert_datasets/cityscapes.py`: -```shell -pip install cityscapesscripts -python tools/convert_datasets/cityscapes.py ./data/cityscapes --nproc 8 --out_dir ./data/cityscapes/annotations -``` -Current the config files in `cityscapes` use COCO pre-trained weights to initialize. -You could download the pre-trained models in advance if network is unavailable or slow, otherwise it would cause errors at the beginning of training. - -### A from-scratch setup script - -Here is a full script for setting up mmdetection with conda and link the dataset path (supposing that your COCO dataset path is $COCO_ROOT). - -```shell -conda create -n open-mmlab python=3.7 numba=0.45.1 -y -conda activate open-mmlab - -conda install -c pytorch pytorch torchvision -y -git clone https://github.com/open-mmlab/mmdetection.git -cd mmdetection -pip install -r requirements/build.txt -pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI" -pip install -v -e . - -git clone https://github.com/open-mmlab/mmdetection3d.git -cd mmdetection3d -pip install -r requirements/build.txt -pip install -v -e . - -mkdir data -ln -s $COCO_ROOT data -``` - -### Using multiple MMDetection3D versions - -If there are more than one mmdetection on your machine, and you want to use them alternatively, the recommended way is to create multiple conda environments and use different environments for different versions. - -Another way is to insert the following code to the main scripts (`train.py`, `test.py` or any other scripts you run) -```python -import os.path as osp -import sys -sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../')) -``` - -Or run the following command in the terminal of corresponding folder to temporally use the current one. -```shell -export PYTHONPATH=`pwd`:$PYTHONPATH -``` diff --git a/docs/MODEL_ZOO.md b/docs/MODEL_ZOO.md deleted file mode 100644 index c15a00b..0000000 --- a/docs/MODEL_ZOO.md +++ /dev/null @@ -1,532 +0,0 @@ -# Benchmark and Model Zoo - -## Environment - -### Hardware - -- 8 NVIDIA Tesla V100 GPUs -- Intel Xeon 4114 CPU @ 2.20GHz - -### Software environment - -- Python 3.6 / 3.7 -- PyTorch 1.1 -- CUDA 9.0.176 -- CUDNN 7.0.4 -- NCCL 2.1.15 - -## Mirror sites - -We use AWS as the main site to host our model zoo, and maintain a mirror on aliyun. -You can replace `https://s3.ap-northeast-2.amazonaws.com/open-mmlab` with `https://open-mmlab.oss-cn-beijing.aliyuncs.com` in model urls. - -## Common settings - -- All FPN baselines and RPN-C4 baselines were trained using 8 GPU with a batch size of 16 (2 images per GPU). Other C4 baselines were trained using 8 GPU with a batch size of 8 (1 image per GPU). -- All models were trained on `coco_2017_train`, and tested on the `coco_2017_val`. -- We use distributed training and BN layer stats are fixed. -- We adopt the same training schedules as Detectron. 1x indicates 12 epochs and 2x indicates 24 epochs, which corresponds to slightly less iterations than Detectron and the difference can be ignored. -- All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo. -- For fair comparison with other codebases, we report the GPU memory as the maximum value of `torch.cuda.max_memory_allocated()` for all 8 GPUs. Note that this value is usually less than what `nvidia-smi` shows. -- We report the inference time as the overall time including data loading, network forwarding and post processing. - - -## Baselines - -More models with different backbones will be added to the model zoo. - -### RPN - -| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR1000 | Download | -| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------: | -| R-50-C4 | caffe | 1x | - | - | 20.5 | 51.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_caffe_c4_1x-ea7d3428.pth) | -| R-50-C4 | caffe | 2x | 2.2 | 0.17 | 20.3 | 52.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_caffe_c4_2x-c6d5b958.pth) | -| R-50-C4 | pytorch | 1x | - | - | 20.1 | 50.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_c4_1x-eb38972b.pth) | -| R-50-C4 | pytorch | 2x | - | - | 20.0 | 51.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_c4_2x-3d4c1e14.pth) | -| R-50-FPN | caffe | 1x | 3.3 | 0.253 | 16.9 | 58.2 | - | -| R-50-FPN | pytorch | 1x | 3.5 | 0.276 | 17.7 | 57.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_1x_20181010-4a9c0712.pth) | -| R-50-FPN | pytorch | 2x | - | - | - | 57.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_2x_20181010-88a4a471.pth) | -| R-101-FPN | caffe | 1x | 5.2 | 0.379 | 13.9 | 59.4 | - | -| R-101-FPN | pytorch | 1x | 5.4 | 0.396 | 14.4 | 58.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_1x_20181129-f50da4bd.pth) | -| R-101-FPN | pytorch | 2x | - | - | - | 59.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_2x_20181129-e42c6c9a.pth) | -| X-101-32x4d-FPN | pytorch | 1x | 6.6 | 0.589 | 11.8 | 59.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_1x_20181218-7e379d26.pth) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | - | 59.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_2x_20181218-0510af40.pth) | -| X-101-64x4d-FPN | pytorch | 1x | 9.5 | 0.955 | 8.3 | 59.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_1x_20181218-c1a24f1f.pth) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | - | 60.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_2x_20181218-c22bdd70.pth) | - -### Faster R-CNN - -| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | -| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :--------------------------------------------------------------------------------------------------------------------------------: | -| R-50-C4 | caffe | 1x | - | - | 9.5 | 34.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_caffe_c4_1x-75ecfdfa.pth) | -| R-50-C4 | caffe | 2x | 4.0 | 0.39 | 9.3 | 36.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_caffe_c4_2x-71c67f27.pth) | -| R-50-C4 | pytorch | 1x | - | - | 9.3 | 33.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_c4_1x-642cf91f.pth) | -| R-50-C4 | pytorch | 2x | - | - | 9.4 | 35.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_c4_2x-6e4fdf4f.pth) | -| R-50-FPN | caffe | 1x | 3.6 | 0.333 | 13.5 | 36.6 | - | -| R-50-FPN | pytorch | 1x | 3.8 | 0.353 | 13.6 | 36.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth) | -| R-50-FPN | pytorch | 2x | - | - | - | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_2x_20181010-443129e1.pth) | -| R-101-FPN | caffe | 1x | 5.5 | 0.465 | 11.5 | 38.8 | - | -| R-101-FPN | pytorch | 1x | 5.7 | 0.474 | 11.9 | 38.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_1x_20181129-d1468807.pth) | -| R-101-FPN | pytorch | 2x | - | - | - | 39.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_2x_20181129-73e7ade7.pth) | -| X-101-32x4d-FPN | pytorch | 1x | 6.9 | 0.672 | 10.3 | 40.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_1x_20181218-ad81c133.pth) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | - | 40.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_2x_20181218-0ed58946.pth) | -| X-101-64x4d-FPN | pytorch | 1x | 9.8 | 1.040 | 7.3 | 41.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_1x_20181218-c9c69c8f.pth) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | - | 40.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_2x_20181218-fe94f9b8.pth) | -| HRNetV2p-W18 | pytorch | 1x | - | - | - | 36.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w18_1x_20190522-e368c387.pth) | -| HRNetV2p-W18 | pytorch | 2x | - | - | - | 38.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w18_2x_20190810-9c8615d5.pth) | -| HRNetV2p-W32 | pytorch | 1x | - | - | - | 39.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w32_1x_20190522-d22f1fef.pth) | -| HRNetV2p-W32 | pytorch | 2x | - | - | - | 40.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w32_2x_20190810-24e8912a.pth) | -| HRNetV2p-W48 | pytorch | 1x | - | - | - | 40.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_1x_20190820-5c6d0903.pth) | -| HRNetV2p-W48 | pytorch | 2x | - | - | - | 41.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_2x_20190820-79fb8bfc.pth) | - - -### Mask R-CNN - -| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | -| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------: | -| R-50-C4 | caffe | 1x | - | - | 8.1 | 35.9 | 31.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_caffe_c4_1x-02a4ad3b.pth) | -| R-50-C4 | caffe | 2x | 4.2 | 0.43 | 8.1 | 37.9 | 32.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_caffe_c4_2x-d150973a.pth) | -| R-50-C4 | pytorch | 1x | - | - | 7.9 | 35.1 | 31.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_c4_1x-a83bdd40.pth) | -| R-50-C4 | pytorch | 2x | - | - | 8.0 | 37.2 | 32.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_c4_2x-3cf169a9.pth) | -| R-50-FPN | caffe | 1x | 3.8 | 0.430 | 10.2 | 37.4 | 34.3 | - | -| R-50-FPN | pytorch | 1x | 3.9 | 0.453 | 10.6 | 37.3 | 34.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth) | -| R-50-FPN | pytorch | 2x | - | - | - | 38.5 | 35.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_2x_20181010-41d35c05.pth) | -| R-101-FPN | caffe | 1x | 5.7 | 0.534 | 9.4 | 39.9 | 36.1 | - | -| R-101-FPN | pytorch | 1x | 5.8 | 0.571 | 9.5 | 39.4 | 35.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_1x_20181129-34ad1961.pth) | -| R-101-FPN | pytorch | 2x | - | - | - | 40.3 | 36.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_2x_20181129-a254bdfc.pth) | -| X-101-32x4d-FPN | pytorch | 1x | 7.1 | 0.759 | 8.3 | 41.1 | 37.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_1x_20181218-44e635cc.pth) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | - | 41.4 | 37.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_2x_20181218-f023dffa.pth) | -| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 1.102 | 6.5 | 42.1 | 38.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_1x_20181218-cb159987.pth) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | - | 42.0 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_2x_20181218-ea936e44.pth) | -| HRNetV2p-W18 | pytorch | 1x | - | - | - | 37.3 | 34.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w18_1x_20190522-c8ad459f.pth) | -| HRNetV2p-W18 | pytorch | 2x | - | - | - | 39.2 | 35.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w18_2x_20190810-1e4747eb.pth) | -| HRNetV2p-W32 | pytorch | 1x | - | - | - | 40.7 | 36.8 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w32_1x_20190522-374aaa00.pth) | -| HRNetV2p-W32 | pytorch | 2x | - | - | - | 41.7 | 37.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w32_2x_20190810-773eca75.pth) | -| HRNetV2p-W48 | pytorch | 1x | - | - | - | 42.4 | 38.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_1x_20190820-0923d1ad.pth) | -| HRNetV2p-W48 | pytorch | 2x | - | - | - | 42.9 | 38.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_2x_20190820-70df51b2.pth) | - -### Fast R-CNN (with pre-computed proposals) - -| Backbone | Style | Type | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | -| :-------: | :-----: | :----: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------: | -| R-50-C4 | caffe | Faster | 1x | - | - | 6.7 | 35.0 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_caffe_c4_1x-0ef9a60b.pth) | -| R-50-C4 | caffe | Faster | 2x | 3.8 | 0.34 | 6.6 | 36.4 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_c4_2x-657a9fc6.pth) | -| R-50-C4 | pytorch | Faster | 1x | - | - | 6.3 | 34.2 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_c4_1x-2bc00ca9.pth) | -| R-50-C4 | pytorch | Faster | 2x | - | - | 6.1 | 35.8 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_caffe_c4_2x-9171d0fc.pth) | -| R-50-FPN | caffe | Faster | 1x | 3.3 | 0.242 | 18.4 | 36.6 | - | - | -| R-50-FPN | pytorch | Faster | 1x | 3.5 | 0.250 | 16.5 | 35.8 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_1x_20181010-08160859.pth) | -| R-50-C4 | caffe | Mask | 1x | - | - | 8.1 | 35.9 | 31.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_caffe_c4_1x-b43f7f3c.pth) | -| R-50-C4 | caffe | Mask | 2x | 4.2 | 0.43 | 8.1 | 37.9 | 32.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_caffe_c4_2x-e3580184.pth) | -| R-50-C4 | pytorch | Mask | 1x | - | - | 7.9 | 35.1 | 31.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_c4_1x-bc7fa8c8.pth) | -| R-50-C4 | pytorch | Mask | 2x | - | - | 8.0 | 37.2 | 32.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010-5048cb03.pth) | -| R-50-FPN | pytorch | Faster | 2x | - | - | - | 37.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_2x_20181010-d263ada5.pth) | -| R-101-FPN | caffe | Faster | 1x | 5.2 | 0.355 | 14.4 | 38.6 | - | - | -| R-101-FPN | pytorch | Faster | 1x | 5.4 | 0.388 | 13.2 | 38.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_1x_20181129-ffaa2eb0.pth) | -| R-101-FPN | pytorch | Faster | 2x | - | - | - | 38.8 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_2x_20181129-9dba92ce.pth) | -| R-50-FPN | caffe | Mask | 1x | 3.4 | 0.328 | 12.8 | 37.3 | 34.5 | - | -| R-50-FPN | pytorch | Mask | 1x | 3.5 | 0.346 | 12.7 | 36.8 | 34.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_1x_20181010-e030a38f.pth) | -| R-50-FPN | pytorch | Mask | 2x | - | - | - | 37.9 | 34.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010-5048cb03.pth) | -| R-101-FPN | caffe | Mask | 1x | 5.2 | 0.429 | 11.2 | 39.4 | 36.1 | - | -| R-101-FPN | pytorch | Mask | 1x | 5.4 | 0.462 | 10.9 | 38.9 | 35.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_1x_20181129-2273fa9b.pth) | -| R-101-FPN | pytorch | Mask | 2x | - | - | - | 39.9 | 36.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_2x_20181129-bf63ec5e.pth) | - -### RetinaNet - -| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | -| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------: | -| R-50-FPN | caffe | 1x | 3.4 | 0.285 | 12.5 | 35.8 | - | -| R-50-FPN | pytorch | 1x | 3.6 | 0.308 | 12.1 | 35.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-7b0c2548.pth) | -| R-50-FPN | pytorch | 2x | - | - | - | 36.4 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/retinanet_r50_fpn_2x_20190616-75574209.pth) | -| R-101-FPN | caffe | 1x | 5.3 | 0.410 | 10.4 | 37.8 | - | -| R-101-FPN | pytorch | 1x | 5.5 | 0.429 | 10.9 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_1x_20181129-f016f384.pth) | -| R-101-FPN | pytorch | 2x | - | - | - | 38.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_2x_20181129-72c14526.pth) | -| X-101-32x4d-FPN | pytorch | 1x | 6.7 | 0.632 | 9.3 | 39.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_1x_20190501-967812ba.pth) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | - | 39.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_2x_20181218-8596452d.pth) | -| X-101-64x4d-FPN | pytorch | 1x | 9.6 | 0.993 | 7.0 | 40.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_1x_20181218-a0a22662.pth) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | - | 39.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_2x_20181218-5e88d045.pth) | - -### Cascade R-CNN - -| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | -| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------: | -| R-50-C4 | caffe | 1x | 8.7 | 0.92 | 5.0 | 38.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_caffe_c4_1x-7c85c62b.pth) | -| R-50-FPN | caffe | 1x | 3.9 | 0.464 | 10.9 | 40.5 | - | -| R-50-FPN | pytorch | 1x | 4.1 | 0.455 | 11.9 | 40.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_1x_20190501-3b6211ab.pth) | -| R-50-FPN | pytorch | 20e | - | - | - | 41.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_20e_20181123-db483a09.pth) | -| R-101-FPN | caffe | 1x | 5.8 | 0.569 | 9.6 | 42.4 | - | -| R-101-FPN | pytorch | 1x | 6.0 | 0.584 | 10.3 | 42.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_1x_20181129-d64ebac7.pth) | -| R-101-FPN | pytorch | 20e | - | - | - | 42.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_20e_20181129-b46dcede.pth) | -| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 0.770 | 8.9 | 43.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_1x_20190501-af628be5.pth) | -| X-101-32x4d-FPN | pytorch | 20e | - | - | - | 44.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_2x_20181218-28f73c4c.pth) | -| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 1.133 | 6.7 | 44.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_1x_20181218-e2dc376a.pth) | -| X-101-64x4d-FPN | pytorch | 20e | - | - | - | 44.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_2x_20181218-5add321e.pth) | -| HRNetV2p-W18 | pytorch | 20e | - | - | - | 41.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w18_20e_20190810-132012d0.pth) | -| HRNetV2p-W32 | pytorch | 20e | - | - | - | 43.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w32_20e_20190522-55bec4ee.pth)| -| HRNetV2p-W48 | pytorch | 20e | - | - | - | 44.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w48_20e_20190810-f40ed8e1.pth) | - -### Cascade Mask R-CNN - -| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | -| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------------------: | -| R-50-C4 | caffe | 1x | 9.1 | 0.99 | 4.5 | 39.3 | 32.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_caffe_c4_1x-f72cc254.pth) | -| R-50-FPN | caffe | 1x | 5.1 | 0.692 | 7.6 | 40.9 | 35.5 | - | -| R-50-FPN | pytorch | 1x | 5.3 | 0.683 | 7.4 | 41.2 | 35.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth) | -| R-50-FPN | pytorch | 20e | - | - | - | 42.3 | 36.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_20e_20181123-6e0c9713.pth) | -| R-101-FPN | caffe | 1x | 7.0 | 0.803 | 7.2 | 43.1 | 37.2 | - | -| R-101-FPN | pytorch | 1x | 7.2 | 0.807 | 6.8 | 42.6 | 37.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_1x_20181129-64f00602.pth) | -| R-101-FPN | pytorch | 20e | - | - | - | 43.3 | 37.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_20e_20181129-cb85151d.pth) | -| X-101-32x4d-FPN | pytorch | 1x | 8.4 | 0.976 | 6.6 | 44.4 | 38.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_1x_20181218-1d944c89.pth) | -| X-101-32x4d-FPN | pytorch | 20e | - | - | - | 44.7 | 38.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_20e_20181218-761a3473.pth) | -| X-101-64x4d-FPN | pytorch | 1x | 11.4 | 1.33 | 5.3 | 45.4 | 39.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_1x_20190501-827e0a70.pth) | -| X-101-64x4d-FPN | pytorch | 20e | - | - | - | 45.7 | 39.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_20e_20181218-630773a7.pth) | -| HRNetV2p-W18 | pytorch | 20e | - | - | - | 41.9 | 36.4 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_20190810-054fb7bf.pth) | -| HRNetV2p-W32 | pytorch | 20e | - | - | - | 44.5 | 38.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_20190810-76f61cd0.pth) | -| HRNetV2p-W48 | pytorch | 20e | - | - | - | 46.0 | 39.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w48_20e_20190810-d04a1415.pth) | - -**Notes:** - -- The `20e` schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs. - -### Hybrid Task Cascade (HTC) - -| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | -| :-------------: | :-----: | :-----: | :------: | :-----------------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------: | -| R-50-FPN | pytorch | 1x | 7.4 | 0.936 | 4.1 | 42.1 | 37.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r50_fpn_1x_20190408-878c1712.pth) | -| R-50-FPN | pytorch | 20e | - | - | - | 43.2 | 38.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r50_fpn_20e_20190408-c03b7015.pth) | -| R-101-FPN | pytorch | 20e | 9.3 | 1.051 | 4.0 | 44.9 | 39.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r101_fpn_20e_20190408-a2e586db.pth) | -| X-101-32x4d-FPN | pytorch | 20e | 5.8 | 0.769 | 3.8 | 46.1 | 40.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_x101_32x4d_fpn_20e_20190408-9eae4d0b.pth) | -| X-101-64x4d-FPN | pytorch | 20e | 7.5 | 1.120 | 3.5 | 46.9 | 40.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_x101_64x4d_fpn_20e_20190408-497f2561.pth) | -| HRNetV2p-W18 | pytorch | 20e | - | - | - | 43.1 | 37.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w18_20e_20190810-d70072af.pth) | -| HRNetV2p-W32 | pytorch | 20e | - | - | - | 45.3 | 39.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w32_20e_20190810-82f9ef5a.pth) | -| HRNetV2p-W48 | pytorch | 20e | - | - | - | 46.8 | 40.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_20e_20190810-f6d2c3fd.pth) | -| HRNetV2p-W48 | pytorch | 28e | - | - | - | 47.0 | 41.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_28e_20190810-a4274b38.pth) | - -**Notes:** - -- Please refer to [Hybrid Task Cascade](https://github.com/open-mmlab/mmdetection/blob/master/configs/htc) for details and more a powerful model (50.7/43.9). - -### SSD - -| Backbone | Size | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | -| :------: | :---: | :---: | :-----: | :------: | :-----------------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------: | -| VGG16 | 300 | caffe | 120e | 3.5 | 0.256 | 25.9 / 34.6 | 25.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_coco_vgg16_caffe_120e_20181221-84d7110b.pth) | -| VGG16 | 512 | caffe | 120e | 7.6 | 0.412 | 20.7 / 25.4 | 29.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd512_coco_vgg16_caffe_120e_20181221-d48b0be8.pth) | - -**Notes:** - -- `cudnn.benchmark` is set as `True` for SSD training and testing. -- Inference time is reported for batch size = 1 and batch size = 8. -- The speed on COCO and VOC are different due to model parameters and nms. - -### Group Normalization (GN) - -Please refer to [Group Normalization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn) for details. - -### Weight Standardization - -Please refer to [Weight Standardization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn+ws) for details. - -### Deformable Convolution v2 - -Please refer to [Deformable Convolutional Networks](https://github.com/open-mmlab/mmdetection/blob/master/configs/dcn) for details. - -### CARAFE: Content-Aware ReAssembly of FEatures -Please refer to [CARAFE](https://github.com/open-mmlab/mmdetection/blob/master/configs/carafe) for details. - -### Instaboost - -Please refer to [Instaboost](https://github.com/open-mmlab/mmdetection/blob/master/configs/instaboost) for details. - -### Libra R-CNN - -Please refer to [Libra R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/libra_rcnn) for details. - -### Guided Anchoring - -Please refer to [Guided Anchoring](https://github.com/open-mmlab/mmdetection/blob/master/configs/guided_anchoring) for details. - -### FCOS - -Please refer to [FCOS](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos) for details. - -### FoveaBox - -Please refer to [FoveaBox](https://github.com/open-mmlab/mmdetection/blob/master/configs/foveabox) for details. - -### RepPoints - -Please refer to [RepPoints](https://github.com/open-mmlab/mmdetection/blob/master/configs/reppoints) for details. - -### FreeAnchor - -Please refer to [FreeAnchor](https://github.com/open-mmlab/mmdetection/blob/master/configs/free_anchor) for details. - -### Grid R-CNN (plus) - -Please refer to [Grid R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/grid_rcnn) for details. - -### GHM - -Please refer to [GHM](https://github.com/open-mmlab/mmdetection/blob/master/configs/ghm) for details. - -### GCNet - -Please refer to [GCNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/gcnet) for details. - -### HRNet -Please refer to [HRNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet) for details. - -### Mask Scoring R-CNN - -Please refer to [Mask Scoring R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/ms_rcnn) for details. - -### Train from Scratch - -Please refer to [Rethinking ImageNet Pre-training](https://github.com/open-mmlab/mmdetection/blob/master/configs/scratch) for details. - -### NAS-FPN -Please refer to [NAS-FPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/nas_fpn) for details. - -### ATSS -Please refer to [ATSS](https://github.com/open-mmlab/mmdetection/blob/master/configs/atss) for details. - -### Other datasets - -We also benchmark some methods on [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc), [Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes) and [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face). - - -## Comparison with Detectron and maskrcnn-benchmark - -We compare mmdetection with [Detectron](https://github.com/facebookresearch/Detectron) -and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark). The backbone used is R-50-FPN. - -In general, mmdetection has 3 advantages over Detectron. - -- **Higher performance** (especially in terms of mask AP) -- **Faster training speed** -- **Memory efficient** - -### Performance - -Detectron and maskrcnn-benchmark use caffe-style ResNet as the backbone. -We report results using both caffe-style (weights converted from -[here](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models)) -and pytorch-style (weights from the official model zoo) ResNet backbone, -indicated as *pytorch-style results* / *caffe-style results*. - -We find that pytorch-style ResNet usually converges slower than caffe-style ResNet, -thus leading to slightly lower results in 1x schedule, but the final results -of 2x schedule is higher. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TypeLr schdDetectronmaskrcnn-benchmarkmmdetection
RPN1x57.2-57.1 / 58.2
2x--57.6 / -
Faster R-CNN1x36.736.836.4 / 36.6
2x37.9-37.7 / -
Mask R-CNN1x37.7 & 33.937.8 & 34.237.3 & 34.2 / 37.4 & 34.3
2x38.6 & 34.5-38.5 & 35.1 / -
Fast R-CNN1x36.4-35.8 / 36.6
2x36.8-37.1 / -
Fast R-CNN (w/mask)1x37.3 & 33.7-36.8 & 34.1 / 37.3 & 34.5
2x37.7 & 34.0-37.9 & 34.8 / -
- -### Training Speed - -The training speed is measure with s/iter. The lower, the better. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TypeDetectron (P1001)maskrcnn-benchmark (V100)mmdetection (V1002)
RPN0.416-0.253
Faster R-CNN0.5440.3530.333
Mask R-CNN0.8890.4540.430
Fast R-CNN0.285-0.242
Fast R-CNN (w/mask)0.377-0.328
- -\*1. Facebook's Big Basin servers (P100/V100) is slightly faster than the servers we use. mmdetection can also run slightly faster on FB's servers. - -\*2. For fair comparison, we list the caffe-style results here. - - -### Inference Speed - -The inference speed is measured with fps (img/s) on a single GPU. The higher, the better. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TypeDetectron (P100)maskrcnn-benchmark (V100)mmdetection (V100)
RPN12.5-16.9
Faster R-CNN10.37.913.5
Mask R-CNN8.57.710.2
Fast R-CNN12.5-18.4
Fast R-CNN (w/mask)9.9-12.8
- -### Training memory - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TypeDetectronmaskrcnn-benchmarkmmdetection
RPN6.4-3.3
Faster R-CNN7.24.43.6
Mask R-CNN8.65.23.8
Fast R-CNN6.0-3.3
Fast R-CNN (w/mask)7.9-3.4
- -There is no doubt that maskrcnn-benchmark and mmdetection is more memory efficient than Detectron, -and the main advantage is PyTorch itself. We also perform some memory optimizations to push it forward. - -Note that Caffe2 and PyTorch have different apis to obtain memory usage with different implementations. -For all codebases, `nvidia-smi` shows a larger memory usage than the reported number in the above table. diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index d4bb2cb..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/ROBUSTNESS_BENCHMARKING.md b/docs/ROBUSTNESS_BENCHMARKING.md deleted file mode 100644 index 1ed441a..0000000 --- a/docs/ROBUSTNESS_BENCHMARKING.md +++ /dev/null @@ -1,109 +0,0 @@ -# Corruption Benchmarking - -## Introduction - -We provide tools to test object detection and instance segmentation models on the image corruption benchmark defined in [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484). -This page provides basic tutorials how to use the benchmark. - -``` -@article{michaelis2019winter, - title={Benchmarking Robustness in Object Detection: - Autonomous Driving when Winter is Coming}, - author={Michaelis, Claudio and Mitzkus, Benjamin and - Geirhos, Robert and Rusak, Evgenia and - Bringmann, Oliver and Ecker, Alexander S. and - Bethge, Matthias and Brendel, Wieland}, - journal={arXiv:1907.07484}, - year={2019} -} -``` - -![image corruption example](../demo/corruptions_sev_3.png) - -## About the benchmark - -To submit results to the benchmark please visit the [benchmark homepage](https://github.com/bethgelab/robust-detection-benchmark) - -The benchmark is modelled after the [imagenet-c benchmark](https://github.com/hendrycks/robustness) which was originally -published in [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261) (ICLR 2019) by Dan Hendrycks and Thomas Dietterich. - -The image corruption functions are included in this library but can be installed separately using: - -```shell -pip install imagecorruptions -``` - -Compared to imagenet-c a few changes had to be made to handle images of arbitrary size and greyscale images. -We also modfied the 'motion blur' and 'snow' corruptions to remove dependency from a linux specific library, -which would have to be installed separately otherwise. For details please refer to the [imagecorruptions repository](https://github.com/bethgelab/imagecorruptions). - -## Inference with pretrained models - -We provide a testing script to evaluate a models performance on any combination of the corruptions provided in the benchmark. - -### Test a dataset - -- [x] single GPU testing -- [ ] multiple GPU testing -- [ ] visualize detection results - -You can use the following commands to test a models performance under the 15 corruptions used in the benchmark. - -```shell -# single-gpu testing -python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] -``` - -Alternatively different group of corruptions can be selected. - -```shell -# noise -python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions noise - -# blur -python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions blur - -# wetaher -python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions weather - -# digital -python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions digital -``` - -Or a costom set of corruptions e.g.: -```shell -# gaussian noise, zoom blur and snow -python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions gaussian_noise zoom_blur snow -``` - -Finally the corruption severities to evaluate can be chosen. -Severity 0 corresponds to clean data and the effect increases from 1 to 5. - -```shell -# severity 1 -python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 1 - -# severities 0,2,4 -python tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 0 2 4 -``` - -## Results for modelzoo models - -The results on COCO 2017val are shown in the below table. - -Model | Backbone | Style | Lr schd | box AP clean | box AP corr. | box % | mask AP clean | mask AP corr. | mask % | -:-----:|:---------:|:-------:|:-------:|:------------:|:------------:|:-----:|:-------------:|:-------------:|:------:| -Faster R-CNN | R-50-FPN | pytorch | 1x | 36.3 | 18.2 | 50.2 | - | - | - | -Faster R-CNN | R-101-FPN | pytorch | 1x | 38.5 | 20.9 | 54.2 | - | - | - | -Faster R-CNN | X-101-32x4d-FPN | pytorch |1x | 40.1 | 22.3 | 55.5 | - | - | - | -Faster R-CNN | X-101-64x4d-FPN | pytorch |1x | 41.3 | 23.4 | 56.6 | - | - | - | -Faster R-CNN | R-50-FPN-DCN | pytorch | 1x | 40.0 | 22.4 | 56.1 | - | - | - | -Faster R-CNN | X-101-32x4d-FPN-DCN | pytorch | 1x | 43.4 | 26.7 | 61.6 | - | - | - | -Mask R-CNN | R-50-FPN | pytorch | 1x | 37.3 | 18.7 | 50.1 | 34.2 | 16.8 | 49.1 | -Mask R-CNN | R-50-FPN-DCN | pytorch | 1x | 41.1 | 23.3 | 56.7 | 37.2 | 20.7 | 55.7 | -Cascade R-CNN | R-50-FPN | pytorch | 1x | 40.4 | 20.1 | 49.7 | - | - | - | -Cascade Mask R-CNN | R-50-FPN | pytorch | 1x| 41.2 | 20.7 | 50.2 | 35.7 | 17.6 | 49.3 | -RetinaNet | R-50-FPN | pytorch | 1x | 35.6 | 17.8 | 50.1 | - | - | - | -Hybrid Task Cascade | X-101-64x4d-FPN-DCN | pytorch | 1x | 50.6 | 32.7 | 64.7 | 43.8 | 28.1 | 64.0 | - -Results may vary slightly due to the stochastic application of the corruptions. diff --git a/docs/TECHNICAL_DETAILS.md b/docs/TECHNICAL_DETAILS.md deleted file mode 100644 index 91b0cfb..0000000 --- a/docs/TECHNICAL_DETAILS.md +++ /dev/null @@ -1,226 +0,0 @@ -# Technical Details - -In this section, we will introduce the main units of training a detector: -data pipeline, model and iteration pipeline. - -## Data pipeline - -Following typical conventions, we use `Dataset` and `DataLoader` for data loading -with multiple workers. `Dataset` returns a dict of data items corresponding -the arguments of models' forward method. -Since the data in object detection may not be the same size (image size, gt bbox size, etc.), -we introduce a new `DataContainer` type in MMCV to help collect and distribute -data of different size. -See [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py) for more details. - -The data preparation pipeline and the dataset is decomposed. Usually a dataset -defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict. -A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform. - -We present a classical pipeline in the following figure. The blue blocks are pipeline operations. With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange). -![pipeline figure](../demo/data_pipeline.png) - -The operations are categorized into data loading, pre-processing, formatting and test-time augmentation. - -Here is an pipeline example for Faster R-CNN. -```python -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(1333, 800), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']), - ]) -] -``` - -For each operation, we list the related dict fields that are added/updated/removed. - -### Data loading - -`LoadImageFromFile` -- add: img, img_shape, ori_shape - -`LoadAnnotations` -- add: gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg, bbox_fields, mask_fields - -`LoadProposals` -- add: proposals - -### Pre-processing - -`Resize` -- add: scale, scale_idx, pad_shape, scale_factor, keep_ratio -- update: img, img_shape, *bbox_fields, *mask_fields, *seg_fields - -`RandomFlip` -- add: flip -- update: img, *bbox_fields, *mask_fields, *seg_fields - -`Pad` -- add: pad_fixed_size, pad_size_divisor -- update: img, pad_shape, *mask_fields, *seg_fields - -`RandomCrop` -- update: img, pad_shape, gt_bboxes, gt_labels, gt_masks, *bbox_fields - -`Normalize` -- add: img_norm_cfg -- update: img - -`SegRescale` -- update: gt_semantic_seg - -`PhotoMetricDistortion` -- update: img - -`Expand` -- update: img, gt_bboxes - -`MinIoURandomCrop` -- update: img, gt_bboxes, gt_labels - -`Corrupt` -- update: img - -### Formatting - -`ToTensor` -- update: specified by `keys`. - -`ImageToTensor` -- update: specified by `keys`. - -`Transpose` -- update: specified by `keys`. - -`ToDataContainer` -- update: specified by `fields`. - -`DefaultFormatBundle` -- update: img, proposals, gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg - -`Collect` -- add: img_meta (the keys of img_meta is specified by `meta_keys`) -- remove: all other keys except for those specified by `keys` - -### Test time augmentation - -`MultiScaleFlipAug` - -## Model - -In MMDetection, model components are basically categorized as 4 types. - -- backbone: usually a FCN network to extract feature maps, e.g., ResNet. -- neck: the part between backbones and heads, e.g., FPN, ASPP. -- head: the part for specific tasks, e.g., bbox prediction and mask prediction. -- roi extractor: the part for extracting features from feature maps, e.g., RoI Align. - -We also write implement some general detection pipelines with the above components, -such as `SingleStageDetector` and `TwoStageDetector`. - -### Build a model with basic components - -Following some basic pipelines (e.g., two-stage detectors), the model structure -can be customized through config files with no pains. - -If we want to implement some new components, e.g, the path aggregation -FPN structure in [Path Aggregation Network for Instance Segmentation](https://arxiv.org/abs/1803.01534), there are two things to do. - -1. create a new file in `mmdet/models/necks/pafpn.py`. - - ```python - from ..registry import NECKS - - @NECKS.register - class PAFPN(nn.Module): - - def __init__(self, - in_channels, - out_channels, - num_outs, - start_level=0, - end_level=-1, - add_extra_convs=False): - pass - - def forward(self, inputs): - # implementation is ignored - pass - ``` - -2. Import the module in `mmdet/models/necks/__init__.py`. - - ```python - from .pafpn import PAFPN - ``` - -2. modify the config file from - - ```python - neck=dict( - type='FPN', - in_channels=[256, 512, 1024, 2048], - out_channels=256, - num_outs=5) - ``` - - to - - ```python - neck=dict( - type='PAFPN', - in_channels=[256, 512, 1024, 2048], - out_channels=256, - num_outs=5) - ``` - -We will release more components (backbones, necks, heads) for research purpose. - -### Write a new model - -To write a new detection pipeline, you need to inherit from `BaseDetector`, -which defines the following abstract methods. - -- `extract_feat()`: given an image batch of shape (n, c, h, w), extract the feature map(s). -- `forward_train()`: forward method of the training mode -- `simple_test()`: single scale testing without augmentation -- `aug_test()`: testing with augmentation (multi-scale, flip, etc.) - -[TwoStageDetector](https://github.com/hellock/mmdetection/blob/master/mmdet/models/detectors/two_stage.py) -is a good example which shows how to do that. - -## Iteration pipeline - -We adopt distributed training for both single machine and multiple machines. -Supposing that the server has 8 GPUs, 8 processes will be started and each process runs on a single GPU. - -Each process keeps an isolated model, data loader, and optimizer. -Model parameters are only synchronized once at the beginning. -After a forward and backward pass, gradients will be allreduced among all GPUs, -and the optimizer will update model parameters. -Since the gradients are allreduced, the model parameter stays the same for all processes after the iteration. - -## Other information - -For more information, please refer to our [technical report](https://arxiv.org/abs/1906.07155). diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index aad51b6..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,70 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -# import os -# import sys -# sys.path.insert(0, os.path.abspath('.')) - -# -- Project information ----------------------------------------------------- - -project = 'MMDetection' -copyright = '2018-2020, OpenMMLab' -author = 'OpenMMLab' - -# The full version, including alpha/beta/rc tags -release = '1.0.0' - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.napoleon', - 'sphinx.ext.viewcode', - 'recommonmark', - 'sphinx_markdown_tables', -] - -autodoc_mock_imports = ['torch', 'torchvision', 'mmcv'] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -source_suffix = { - '.rst': 'restructuredtext', - '.md': 'markdown', -} - -# The master toctree document. -master_doc = 'index' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'sphinx_rtd_theme' - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..4809e0b --- /dev/null +++ b/docs/index.html @@ -0,0 +1,666 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + MACP: Efficient Model Adaptation for Cooperative Perception + + + + + + + + + + + + + + + + + + + + + + + + + +
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MACP: Efficient Model + Adaptation for Cooperative Perception

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+ + + Yunsheng + Ma1*, + + + Juanwu Lu1*, + + + Can + Cui1, +
+ + Sicheng Zhao2, + + + Xu + Cao3, + + + Wenqian + Ye4, + + + Ziran + Wang1, + +
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+ + 1 Purdue University, + 2 Tsinghua University,
+ 3 University of Illinois Urbana-Champaign, + 4 University of Virginia + +
+ IEEE/CVF Winter Conference on Applications of Computer + Vision (WACV) 2024 +
*Indicates Equal + Contribution +
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Abstract

+ v2v_conada +
Overview of the MACP Framework

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+ Vehicle-to-vehicle (V2V) communications have greatly enhanced the + perception capabilities of connected and automated vehicles + (CAVs) by enabling information sharing to "see through the + occlusions", resulting in significant performance improvements. + However, developing and training complex multi-agent perception + models from scratch can be expensive and unnecessary when + existing single-agent models show remarkable generalization + capabilities. In this paper, we propose a new framework termed + MACP, which equips a single-agent pre-trained model with + cooperation capabilities. We approach this objective by + identifying the key challenges of shifting from single-agent to + cooperative settings, adapting the model by freezing most of its + parameters, and adding a few lightweight modules. We demonstrate + in our experiments that the proposed framework can effectively + utilize cooperative observations and outperform other + state-of-the-art approaches in both simulated and real-world + cooperative perception benchmarks, while requiring substantially + fewer tunable parameters with reduced communication costs. +

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Performance Results

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Effective Cooperation

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z5!kng9CR_;oVnjH+D&ADn-*b0tm~!RO#onUb||kgThf(*Vs+ffd46 1 && arguments[1] !== undefined ? arguments[1] : false; + + var height = element.offsetHeight; + if (withMargin) { + var style = window.getComputedStyle(element); + height += parseInt(style.marginTop) + parseInt(style.marginBottom); + } + return height; +}; + +var outerWidth = function outerWidth(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var width = element.offsetWidth; + if (withMargin) { + var style = window.getComputedStyle(element); + width += parseInt(style.marginLeft) + parseInt(style.marginRight); + } + return width; +}; + +var position = function position(element) { + return { + left: element.offsetLeft, + top: element.offsetTop + }; +}; + +var css = function css(element, obj) { + if (!obj) { + return window.getComputedStyle(element); + } + if (Object(__WEBPACK_IMPORTED_MODULE_0__type__["b" /* isObject */])(obj)) { + var style = ''; + Object.keys(obj).forEach(function (key) { + style += key + ': ' + obj[key] + ';'; + }); + + element.style.cssText += style; + } +}; + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (immutable) */ __webpack_exports__["a"] = detectSupportsPassive; +function detectSupportsPassive() { + var supportsPassive = false; + + try { + var opts = Object.defineProperty({}, 'passive', { + get: function get() { + supportsPassive = true; + } + }); + + window.addEventListener('testPassive', null, opts); + window.removeEventListener('testPassive', null, opts); + } catch (e) {} + + return supportsPassive; +} + +/***/ }), +/* 2 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isFunction; }); +/* unused harmony export isNumber */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return isString; }); +/* unused harmony export isDate */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return isObject; }); +/* unused harmony export isEmptyObject */ +/* unused harmony export isNode */ +/* unused harmony export isVideo */ +/* unused harmony export isHTML5 */ +/* unused harmony export isIFrame */ +/* unused harmony export isYoutube */ +/* unused harmony export isVimeo */ +var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; + +var isFunction = function isFunction(unknown) { + return typeof unknown === 'function'; +}; +var isNumber = function isNumber(unknown) { + return typeof unknown === "number"; +}; +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; +var isDate = function isDate(unknown) { + return (Object.prototype.toString.call(unknown) === '[object Date]' || unknown instanceof Date) && !isNaN(unknown.valueOf()); +}; +var isObject = function isObject(unknown) { + return (typeof unknown === 'function' || (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && !!unknown) && !Array.isArray(unknown); +}; +var isEmptyObject = function isEmptyObject(unknown) { + for (var name in unknown) { + if (unknown.hasOwnProperty(name)) { + return false; + } + } + return true; +}; + +var isNode = function isNode(unknown) { + return !!(unknown && unknown.nodeType === HTMLElement | SVGElement); +}; +var isVideo = function isVideo(unknown) { + return isYoutube(unknown) || isVimeo(unknown) || isHTML5(unknown); +}; +var isHTML5 = function isHTML5(unknown) { + return isNode(unknown) && unknown.tagName === 'VIDEO'; +}; +var isIFrame = function isIFrame(unknown) { + return isNode(unknown) && unknown.tagName === 'IFRAME'; +}; +var isYoutube = function isYoutube(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/\/\/.*?youtube(-nocookie)?\.[a-z]+\/(watch\?v=[^&\s]+|embed)|youtu\.be\/.*/); +}; +var isVimeo = function isVimeo(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/vimeo\.com\/video\/.*/); +}; + +/***/ }), +/* 3 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var events = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this.events = new Map(events); + } + + _createClass(EventEmitter, [{ + key: "on", + value: function on(name, cb) { + var _this = this; + + this.events.set(name, [].concat(_toConsumableArray(this.events.has(name) ? this.events.get(name) : []), [cb])); + + return function () { + return _this.events.set(name, _this.events.get(name).filter(function (fn) { + return fn !== cb; + })); + }; + } + }, { + key: "emit", + value: function emit(name) { + for (var _len = arguments.length, args = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) { + args[_key - 1] = arguments[_key]; + } + + return this.events.has(name) && this.events.get(name).map(function (fn) { + return fn.apply(undefined, args); + }); + } + }]); + + return EventEmitter; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (EventEmitter); + +/***/ }), +/* 4 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Coordinate = function () { + function Coordinate() { + var x = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + var y = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0; + + _classCallCheck(this, Coordinate); + + this._x = x; + this._y = y; + } + + _createClass(Coordinate, [{ + key: 'add', + value: function add(coord) { + return new Coordinate(this._x + coord._x, this._y + coord._y); + } + }, { + key: 'sub', + value: function sub(coord) { + return new Coordinate(this._x - coord._x, this._y - coord._y); + } + }, { + key: 'distance', + value: function distance(coord) { + var deltaX = this._x - coord._x; + var deltaY = this._y - coord._y; + + return Math.sqrt(Math.pow(deltaX, 2) + Math.pow(deltaY, 2)); + } + }, { + key: 'max', + value: function max(coord) { + var x = Math.max(this._x, coord._x); + var y = Math.max(this._y, coord._y); + + return new Coordinate(x, y); + } + }, { + key: 'equals', + value: function equals(coord) { + if (this == coord) { + return true; + } + if (!coord || coord == null) { + return false; + } + return this._x == coord._x && this._y == coord._y; + } + }, { + key: 'inside', + value: function inside(northwest, southeast) { + if (this._x >= northwest._x && this._x <= southeast._x && this._y >= northwest._y && this._y <= southeast._y) { + + return true; + } + return false; + } + }, { + key: 'constrain', + value: function constrain(min, max) { + if (min._x > max._x || min._y > max._y) { + return this; + } + + var x = this._x, + y = this._y; + + if (min._x !== null) { + x = Math.max(x, min._x); + } + if (max._x !== null) { + x = Math.min(x, max._x); + } + if (min._y !== null) { + y = Math.max(y, min._y); + } + if (max._y !== null) { + y = Math.min(y, max._y); + } + + return new Coordinate(x, y); + } + }, { + key: 'reposition', + value: function reposition(element) { + element.style['top'] = this._y + 'px'; + element.style['left'] = this._x + 'px'; + } + }, { + key: 'toString', + value: function toString() { + return '(' + this._x + ',' + this._y + ')'; + } + }, { + key: 'x', + get: function get() { + return this._x; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._x = value; + return this; + } + }, { + key: 'y', + get: function get() { + return this._y; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._y = value; + return this; + } + }]); + + return Coordinate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Coordinate); + +/***/ }), +/* 5 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +Object.defineProperty(__webpack_exports__, "__esModule", { value: true }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_index__ = __webpack_require__(6); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_type__ = __webpack_require__(2); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__components_autoplay__ = __webpack_require__(7); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__ = __webpack_require__(9); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_6__components_infinite__ = __webpack_require__(10); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_7__components_loop__ = __webpack_require__(11); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_8__components_navigation__ = __webpack_require__(13); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_9__components_pagination__ = __webpack_require__(15); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_10__components_swipe__ = __webpack_require__(18); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_11__components_transitioner__ = __webpack_require__(19); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_12__defaultOptions__ = __webpack_require__(22); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_13__templates__ = __webpack_require__(23); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_14__templates_item__ = __webpack_require__(24); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _defineProperty(obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + + + + + + + + + + + + + + + + +var bulmaCarousel = function (_EventEmitter) { + _inherits(bulmaCarousel, _EventEmitter); + + function bulmaCarousel(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaCarousel); + + var _this = _possibleConstructorReturn(this, (bulmaCarousel.__proto__ || Object.getPrototypeOf(bulmaCarousel)).call(this)); + + _this.element = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + _this._clickEvents = ['click', 'touch']; + + // Use Element dataset values to override options + var elementConfig = _this.element.dataset ? Object.keys(_this.element.dataset).filter(function (key) { + return Object.keys(__WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */]).includes(key); + }).reduce(function (obj, key) { + return _extends({}, obj, _defineProperty({}, key, _this.element.dataset[key])); + }, {}) : {}; + // Set default options - dataset attributes are master + _this.options = _extends({}, __WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */], options, elementConfig); + + _this._id = Object(__WEBPACK_IMPORTED_MODULE_0__utils_index__["a" /* uuid */])('slider'); + + _this.onShow = _this.onShow.bind(_this); + + // Initiate plugin + _this._init(); + return _this; + } + + /** + * Initiate all DOM element containing datePicker class + * @method + * @return {Array} Array of all datePicker instances + */ + + + _createClass(bulmaCarousel, [{ + key: '_init', + + + /**************************************************** + * * + * PRIVATE FUNCTIONS * + * * + ****************************************************/ + /** + * Initiate plugin instance + * @method _init + * @return {Slider} Current plugin instance + */ + value: function _init() { + this._items = Array.from(this.element.children); + + // Load plugins + this._breakpoint = new __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__["a" /* default */](this); + this._autoplay = new __WEBPACK_IMPORTED_MODULE_4__components_autoplay__["a" /* default */](this); + this._navigation = new __WEBPACK_IMPORTED_MODULE_8__components_navigation__["a" /* default */](this); + this._pagination = new __WEBPACK_IMPORTED_MODULE_9__components_pagination__["a" /* default */](this); + this._infinite = new __WEBPACK_IMPORTED_MODULE_6__components_infinite__["a" /* default */](this); + this._loop = new __WEBPACK_IMPORTED_MODULE_7__components_loop__["a" /* default */](this); + this._swipe = new __WEBPACK_IMPORTED_MODULE_10__components_swipe__["a" /* default */](this); + + this._build(); + + if (Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["a" /* isFunction */])(this.options.onReady)) { + this.options.onReady(this); + } + + return this; + } + + /** + * Build Slider HTML component and append it to the DOM + * @method _build + */ + + }, { + key: '_build', + value: function _build() { + var _this2 = this; + + // Generate HTML Fragment of template + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_13__templates__["a" /* default */])(this.id)); + // Save pointers to template parts + this._ui = { + wrapper: this.node.firstChild, + container: this.node.querySelector('.slider-container') + + // Add slider to DOM + };this.element.appendChild(this.node); + this._ui.wrapper.classList.add('is-loading'); + this._ui.container.style.opacity = 0; + + this._transitioner = new __WEBPACK_IMPORTED_MODULE_11__components_transitioner__["a" /* default */](this); + + // Wrap all items by slide element + this._slides = this._items.map(function (item, index) { + return _this2._createSlide(item, index); + }); + + this.reset(); + + this._bindEvents(); + + this._ui.container.style.opacity = 1; + this._ui.wrapper.classList.remove('is-loading'); + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.on('show', this.onShow); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.off('show', this.onShow); + } + }, { + key: '_createSlide', + value: function _createSlide(item, index) { + var slide = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_14__templates_item__["a" /* default */])()).firstChild; + slide.dataset.sliderIndex = index; + slide.appendChild(item); + return slide; + } + + /** + * Calculate slider dimensions + */ + + }, { + key: '_setDimensions', + value: function _setDimensions() { + var _this3 = this; + + if (!this.options.vertical) { + if (this.options.centerMode) { + this._ui.wrapper.style.padding = '0px ' + this.options.centerPadding; + } + } else { + this._ui.wrapper.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this.slidesToShow; + if (this.options.centerMode) { + this._ui.wrapper.style.padding = this.options.centerPadding + ' 0px'; + } + } + + this._wrapperWidth = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this._ui.wrapper); + this._wrapperHeight = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._ui.wrapper); + + if (!this.options.vertical) { + this._slideWidth = Math.ceil(this._wrapperWidth / this.slidesToShow); + this._containerWidth = Math.ceil(this._slideWidth * this._slides.length); + this._ui.container.style.width = this._containerWidth + 'px'; + } else { + this._slideWidth = Math.ceil(this._wrapperWidth); + this._containerHeight = Math.ceil(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this._slides.length); + this._ui.container.style.height = this._containerHeight + 'px'; + } + + this._slides.forEach(function (slide) { + slide.style.width = _this3._slideWidth + 'px'; + }); + } + }, { + key: '_setHeight', + value: function _setHeight() { + if (this.options.effect !== 'translate') { + this._ui.container.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[this.state.index]) + 'px'; + } + } + + // Update slides classes + + }, { + key: '_setClasses', + value: function _setClasses() { + var _this4 = this; + + this._slides.forEach(function (slide) { + Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["d" /* removeClasses */])(slide, 'is-active is-current is-slide-previous is-slide-next'); + if (Math.abs((_this4.state.index - 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-previous'); + } + if (Math.abs(_this4.state.index % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-current'); + } + if (Math.abs((_this4.state.index + 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-next'); + } + }); + } + + /**************************************************** + * * + * GETTERS and SETTERS * + * * + ****************************************************/ + + /** + * Get id of current datePicker + */ + + }, { + key: 'onShow', + + + /**************************************************** + * * + * EVENTS FUNCTIONS * + * * + ****************************************************/ + value: function onShow(e) { + this._navigation.refresh(); + this._pagination.refresh(); + this._setClasses(); + } + + /**************************************************** + * * + * PUBLIC FUNCTIONS * + * * + ****************************************************/ + + }, { + key: 'next', + value: function next() { + if (!this.options.loop && !this.options.infinite && this.state.index + this.slidesToScroll > this.state.length - this.slidesToShow && !this.options.centerMode) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index + this.slidesToScroll; + } + this.show(); + } + }, { + key: 'previous', + value: function previous() { + if (!this.options.loop && !this.options.infinite && this.state.index === 0) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index - this.slidesToScroll; + } + this.show(); + } + }, { + key: 'start', + value: function start() { + this._autoplay.start(); + } + }, { + key: 'pause', + value: function pause() { + this._autoplay.pause(); + } + }, { + key: 'stop', + value: function stop() { + this._autoplay.stop(); + } + }, { + key: 'show', + value: function show(index) { + var force = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + // If all slides are already visible then return + if (!this.state.length || this.state.length <= this.slidesToShow) { + return; + } + + if (typeof index === 'Number') { + this.state.next = index; + } + + if (this.options.loop) { + this._loop.apply(); + } + if (this.options.infinite) { + this._infinite.apply(); + } + + // If new slide is already the current one then return + if (this.state.index === this.state.next) { + return; + } + + this.emit('before:show', this.state); + this._transitioner.apply(force, this._setHeight.bind(this)); + this.emit('after:show', this.state); + + this.emit('show', this); + } + }, { + key: 'reset', + value: function reset() { + var _this5 = this; + + this.state = { + length: this._items.length, + index: Math.abs(this.options.initialSlide), + next: Math.abs(this.options.initialSlide), + prev: undefined + }; + + // Fix options + if (this.options.loop && this.options.infinite) { + this.options.loop = false; + } + if (this.options.slidesToScroll > this.options.slidesToShow) { + this.options.slidesToScroll = this.slidesToShow; + } + this._breakpoint.init(); + + if (this.state.index >= this.state.length && this.state.index !== 0) { + this.state.index = this.state.index - this.slidesToScroll; + } + if (this.state.length <= this.slidesToShow) { + this.state.index = 0; + } + + this._ui.wrapper.appendChild(this._navigation.init().render()); + this._ui.wrapper.appendChild(this._pagination.init().render()); + + if (this.options.navigationSwipe) { + this._swipe.bindEvents(); + } else { + this._swipe._bindEvents(); + } + + this._breakpoint.apply(); + // Move all created slides into slider + this._slides.forEach(function (slide) { + return _this5._ui.container.appendChild(slide); + }); + this._transitioner.init().apply(true, this._setHeight.bind(this)); + + if (this.options.autoplay) { + this._autoplay.init().start(); + } + } + + /** + * Destroy Slider + * @method destroy + */ + + }, { + key: 'destroy', + value: function destroy() { + var _this6 = this; + + this._unbindEvents(); + this._items.forEach(function (item) { + _this6.element.appendChild(item); + }); + this.node.remove(); + } + }, { + key: 'id', + get: function get() { + return this._id; + } + }, { + key: 'index', + set: function set(index) { + this._index = index; + }, + get: function get() { + return this._index; + } + }, { + key: 'length', + set: function set(length) { + this._length = length; + }, + get: function get() { + return this._length; + } + }, { + key: 'slides', + get: function get() { + return this._slides; + }, + set: function set(slides) { + this._slides = slides; + } + }, { + key: 'slidesToScroll', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToScroll() : 1; + } + }, { + key: 'slidesToShow', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToShow() : 1; + } + }, { + key: 'direction', + get: function get() { + return this.element.dir.toLowerCase() === 'rtl' || this.element.style.direction === 'rtl' ? 'rtl' : 'ltr'; + } + }, { + key: 'wrapper', + get: function get() { + return this._ui.wrapper; + } + }, { + key: 'wrapperWidth', + get: function get() { + return this._wrapperWidth || 0; + } + }, { + key: 'container', + get: function get() { + return this._ui.container; + } + }, { + key: 'containerWidth', + get: function get() { + return this._containerWidth || 0; + } + }, { + key: 'slideWidth', + get: function get() { + return this._slideWidth || 0; + } + }, { + key: 'transitioner', + get: function get() { + return this._transitioner; + } + }], [{ + key: 'attach', + value: function attach() { + var _this7 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '.slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + [].forEach.call(elements, function (element) { + if (typeof element[_this7.constructor.name] === 'undefined') { + var instance = new bulmaCarousel(element, options); + element[_this7.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this7.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaCarousel; +}(__WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaCarousel); + +/***/ }), +/* 6 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return uuid; }); +/* unused harmony export isRtl */ +/* unused harmony export defer */ +/* unused harmony export getNodeIndex */ +/* unused harmony export camelize */ +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +var uuid = function uuid() { + var prefix = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ''; + return prefix + ([1e7] + -1e3 + -4e3 + -8e3 + -1e11).replace(/[018]/g, function (c) { + return (c ^ crypto.getRandomValues(new Uint8Array(1))[0] & 15 >> c / 4).toString(16); + }); +}; +var isRtl = function isRtl() { + return document.documentElement.getAttribute('dir') === 'rtl'; +}; + +var defer = function defer() { + this.promise = new Promise(function (resolve, reject) { + this.resolve = resolve; + this.reject = reject; + }.bind(this)); + + this.then = this.promise.then.bind(this.promise); + this.catch = this.promise.catch.bind(this.promise); +}; + +var getNodeIndex = function getNodeIndex(node) { + return [].concat(_toConsumableArray(node.parentNode.children)).indexOf(node); +}; +var camelize = function camelize(str) { + return str.replace(/-(\w)/g, toUpper); +}; + +/***/ }), +/* 7 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_device__ = __webpack_require__(8); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + +var onVisibilityChange = Symbol('onVisibilityChange'); +var onMouseEnter = Symbol('onMouseEnter'); +var onMouseLeave = Symbol('onMouseLeave'); + +var defaultOptions = { + autoplay: false, + autoplaySpeed: 3000 +}; + +var Autoplay = function (_EventEmitter) { + _inherits(Autoplay, _EventEmitter); + + function Autoplay(slider) { + _classCallCheck(this, Autoplay); + + var _this = _possibleConstructorReturn(this, (Autoplay.__proto__ || Object.getPrototypeOf(Autoplay)).call(this)); + + _this.slider = slider; + + _this.onVisibilityChange = _this.onVisibilityChange.bind(_this); + _this.onMouseEnter = _this.onMouseEnter.bind(_this); + _this.onMouseLeave = _this.onMouseLeave.bind(_this); + return _this; + } + + _createClass(Autoplay, [{ + key: 'init', + value: function init() { + this._bindEvents(); + return this; + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + document.addEventListener('visibilitychange', this.onVisibilityChange); + if (this.slider.options.pauseOnHover) { + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + document.removeEventListener('visibilitychange', this.onVisibilityChange); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + }, { + key: 'start', + value: function start() { + var _this2 = this; + + this.stop(); + if (this.slider.options.autoplay) { + this.emit('start', this); + this._interval = setInterval(function () { + if (!(_this2._hovering && _this2.slider.options.pauseOnHover)) { + if (!_this2.slider.options.centerMode && _this2.slider.state.next >= _this2.slider.state.length - _this2.slider.slidesToShow && !_this2.slider.options.loop && !_this2.slider.options.infinite) { + _this2.stop(); + } else { + _this2.slider.next(); + } + } + }, this.slider.options.autoplaySpeed); + } + } + }, { + key: 'stop', + value: function stop() { + this._interval = clearInterval(this._interval); + this.emit('stop', this); + } + }, { + key: 'pause', + value: function pause() { + var _this3 = this; + + var speed = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + if (this.paused) { + return; + } + if (this.timer) { + this.stop(); + } + this.paused = true; + if (speed === 0) { + this.paused = false; + this.start(); + } else { + this.slider.on('transition:end', function () { + if (!_this3) { + return; + } + _this3.paused = false; + if (!_this3.run) { + _this3.stop(); + } else { + _this3.start(); + } + }); + } + } + }, { + key: 'onVisibilityChange', + value: function onVisibilityChange(e) { + if (document.hidden) { + this.stop(); + } else { + this.start(); + } + } + }, { + key: 'onMouseEnter', + value: function onMouseEnter(e) { + this._hovering = true; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }, { + key: 'onMouseLeave', + value: function onMouseLeave(e) { + this._hovering = false; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }]); + + return Autoplay; +}(__WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["a"] = (Autoplay); + +/***/ }), +/* 8 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export isIE */ +/* unused harmony export isIETouch */ +/* unused harmony export isAndroid */ +/* unused harmony export isiPad */ +/* unused harmony export isiPod */ +/* unused harmony export isiPhone */ +/* unused harmony export isSafari */ +/* unused harmony export isUiWebView */ +/* unused harmony export supportsTouchEvents */ +/* unused harmony export supportsPointerEvents */ +/* unused harmony export supportsTouch */ +/* unused harmony export pointerDown */ +/* unused harmony export pointerMove */ +/* unused harmony export pointerUp */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return pointerEnter; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return pointerLeave; }); +var isIE = window.navigator.pointerEnabled || window.navigator.msPointerEnabled; +var isIETouch = window.navigator.msPointerEnabled && window.navigator.msMaxTouchPoints > 1 || window.navigator.pointerEnabled && window.navigator.maxTouchPoints > 1; +var isAndroid = navigator.userAgent.match(/(Android);?[\s\/]+([\d.]+)?/); +var isiPad = navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/); +var isiPod = navigator.userAgent.match(/(iPod)(.*OS\s([\d_]+))?/); +var isiPhone = !navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/) && navigator.userAgent.match(/(iPhone\sOS)\s([\d_]+)/); +var isSafari = navigator.userAgent.toLowerCase().indexOf('safari') >= 0 && navigator.userAgent.toLowerCase().indexOf('chrome') < 0 && navigator.userAgent.toLowerCase().indexOf('android') < 0; +var isUiWebView = /(iPhone|iPod|iPad).*AppleWebKit(?!.*Safari)/i.test(navigator.userAgent); + +var supportsTouchEvents = !!('ontouchstart' in window); +var supportsPointerEvents = !!('PointerEvent' in window); +var supportsTouch = supportsTouchEvents || window.DocumentTouch && document instanceof DocumentTouch || navigator.maxTouchPoints; // IE >=11 +var pointerDown = !supportsTouch ? 'mousedown' : 'mousedown ' + (supportsTouchEvents ? 'touchstart' : 'pointerdown'); +var pointerMove = !supportsTouch ? 'mousemove' : 'mousemove ' + (supportsTouchEvents ? 'touchmove' : 'pointermove'); +var pointerUp = !supportsTouch ? 'mouseup' : 'mouseup ' + (supportsTouchEvents ? 'touchend' : 'pointerup'); +var pointerEnter = supportsTouch && supportsPointerEvents ? 'pointerenter' : 'mouseenter'; +var pointerLeave = supportsTouch && supportsPointerEvents ? 'pointerleave' : 'mouseleave'; + +/***/ }), +/* 9 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var onResize = Symbol('onResize'); + +var Breakpoints = function () { + function Breakpoints(slider) { + _classCallCheck(this, Breakpoints); + + this.slider = slider; + this.options = slider.options; + + this[onResize] = this[onResize].bind(this); + + this._bindEvents(); + } + + _createClass(Breakpoints, [{ + key: 'init', + value: function init() { + this._defaultBreakpoint = { + slidesToShow: this.options.slidesToShow, + slidesToScroll: this.options.slidesToScroll + }; + this.options.breakpoints.sort(function (a, b) { + return parseInt(a.changePoint, 10) > parseInt(b.changePoint, 10); + }); + this._currentBreakpoint = this._getActiveBreakpoint(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + window.addEventListener('resize', this[onResize]); + window.addEventListener('orientationchange', this[onResize]); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + window.removeEventListener('resize', this[onResize]); + window.removeEventListener('orientationchange', this[onResize]); + } + }, { + key: '_getActiveBreakpoint', + value: function _getActiveBreakpoint() { + //Get breakpoint for window width + var _iteratorNormalCompletion = true; + var _didIteratorError = false; + var _iteratorError = undefined; + + try { + for (var _iterator = this.options.breakpoints[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { + var point = _step.value; + + if (point.changePoint >= window.innerWidth) { + return point; + } + } + } catch (err) { + _didIteratorError = true; + _iteratorError = err; + } finally { + try { + if (!_iteratorNormalCompletion && _iterator.return) { + _iterator.return(); + } + } finally { + if (_didIteratorError) { + throw _iteratorError; + } + } + } + + return this._defaultBreakpoint; + } + }, { + key: 'getSlidesToShow', + value: function getSlidesToShow() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToShow : this._defaultBreakpoint.slidesToShow; + } + }, { + key: 'getSlidesToScroll', + value: function getSlidesToScroll() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToScroll : this._defaultBreakpoint.slidesToScroll; + } + }, { + key: 'apply', + value: function apply() { + if (this.slider.state.index >= this.slider.state.length && this.slider.state.index !== 0) { + this.slider.state.index = this.slider.state.index - this._currentBreakpoint.slidesToScroll; + } + if (this.slider.state.length <= this._currentBreakpoint.slidesToShow) { + this.slider.state.index = 0; + } + + if (this.options.loop) { + this.slider._loop.init().apply(); + } + + if (this.options.infinite) { + this.slider._infinite.init().apply(); + } + + this.slider._setDimensions(); + this.slider._transitioner.init().apply(true, this.slider._setHeight.bind(this.slider)); + this.slider._setClasses(); + + this.slider._navigation.refresh(); + this.slider._pagination.refresh(); + } + }, { + key: onResize, + value: function value(e) { + var newBreakPoint = this._getActiveBreakpoint(); + if (newBreakPoint.slidesToShow !== this._currentBreakpoint.slidesToShow) { + this._currentBreakpoint = newBreakPoint; + this.apply(); + } + } + }]); + + return Breakpoints; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Breakpoints); + +/***/ }), +/* 10 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Infinite = function () { + function Infinite(slider) { + _classCallCheck(this, Infinite); + + this.slider = slider; + } + + _createClass(Infinite, [{ + key: 'init', + value: function init() { + if (this.slider.options.infinite && this.slider.options.effect === 'translate') { + if (this.slider.options.centerMode) { + this._infiniteCount = Math.ceil(this.slider.slidesToShow + this.slider.slidesToShow / 2); + } else { + this._infiniteCount = this.slider.slidesToShow; + } + + var frontClones = []; + var slideIndex = 0; + for (var i = this.slider.state.length; i > this.slider.state.length - 1 - this._infiniteCount; i -= 1) { + slideIndex = i - 1; + frontClones.unshift(this._cloneSlide(this.slider.slides[slideIndex], slideIndex - this.slider.state.length)); + } + + var backClones = []; + for (var _i = 0; _i < this._infiniteCount + this.slider.state.length; _i += 1) { + backClones.push(this._cloneSlide(this.slider.slides[_i % this.slider.state.length], _i + this.slider.state.length)); + } + + this.slider.slides = [].concat(frontClones, _toConsumableArray(this.slider.slides), backClones); + } + return this; + } + }, { + key: 'apply', + value: function apply() {} + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.slider.options.infinite) { + if (this.slider.state.next >= this.slider.state.length) { + this.slider.state.index = this.slider.state.next = this.slider.state.next - this.slider.state.length; + this.slider.transitioner.apply(true); + } else if (this.slider.state.next < 0) { + this.slider.state.index = this.slider.state.next = this.slider.state.length + this.slider.state.next; + this.slider.transitioner.apply(true); + } + } + } + }, { + key: '_cloneSlide', + value: function _cloneSlide(slide, index) { + var newSlide = slide.cloneNode(true); + newSlide.dataset.sliderIndex = index; + newSlide.dataset.cloned = true; + var ids = newSlide.querySelectorAll('[id]') || []; + ids.forEach(function (id) { + id.setAttribute('id', ''); + }); + return newSlide; + } + }]); + + return Infinite; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Infinite); + +/***/ }), +/* 11 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_dom__ = __webpack_require__(12); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Loop = function () { + function Loop(slider) { + _classCallCheck(this, Loop); + + this.slider = slider; + } + + _createClass(Loop, [{ + key: "init", + value: function init() { + return this; + } + }, { + key: "apply", + value: function apply() { + if (this.slider.options.loop) { + if (this.slider.state.next > 0) { + if (this.slider.state.next < this.slider.state.length) { + if (this.slider.state.next > this.slider.state.length - this.slider.slidesToShow && Object(__WEBPACK_IMPORTED_MODULE_0__utils_dom__["a" /* isInViewport */])(this.slider._slides[this.slider.state.length - 1], this.slider.wrapper)) { + this.slider.state.next = 0; + } else { + this.slider.state.next = Math.min(Math.max(this.slider.state.next, 0), this.slider.state.length - this.slider.slidesToShow); + } + } else { + this.slider.state.next = 0; + } + } else { + if (this.slider.state.next <= 0 - this.slider.slidesToScroll) { + this.slider.state.next = this.slider.state.length - this.slider.slidesToShow; + } else { + this.slider.state.next = 0; + } + } + } + } + }]); + + return Loop; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Loop); + +/***/ }), +/* 12 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isInViewport; }); +var isInViewport = function isInViewport(element, html) { + var rect = element.getBoundingClientRect(); + html = html || document.documentElement; + return rect.top >= 0 && rect.left >= 0 && rect.bottom <= (window.innerHeight || html.clientHeight) && rect.right <= (window.innerWidth || html.clientWidth); +}; + +/***/ }), +/* 13 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_navigation__ = __webpack_require__(14); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Navigation = function () { + function Navigation(slider) { + _classCallCheck(this, Navigation); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPreviousClick = this.onPreviousClick.bind(this); + this.onNextClick = this.onNextClick.bind(this); + this.onKeyUp = this.onKeyUp.bind(this); + } + + _createClass(Navigation, [{ + key: 'init', + value: function init() { + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_navigation__["a" /* default */])(this.slider.options.icons)); + this._ui = { + previous: this.node.querySelector('.slider-navigation-previous'), + next: this.node.querySelector('.slider-navigation-next') + }; + + this._unbindEvents(); + this._bindEvents(); + + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + this.slider.wrapper.addEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this._ui.previous.addEventListener(clickEvent, _this.onPreviousClick); + _this._ui.next.addEventListener(clickEvent, _this.onNextClick); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + this.slider.wrapper.removeEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this2._ui.previous.removeEventListener(clickEvent, _this2.onPreviousClick); + _this2._ui.next.removeEventListener(clickEvent, _this2.onNextClick); + }); + } + }, { + key: 'onNextClick', + value: function onNextClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.next(); + } + } + }, { + key: 'onPreviousClick', + value: function onPreviousClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.previous(); + } + } + }, { + key: 'onKeyUp', + value: function onKeyUp(e) { + if (this.slider.options.keyNavigation) { + if (e.key === 'ArrowRight' || e.key === 'Right') { + this.slider.next(); + } else if (e.key === 'ArrowLeft' || e.key === 'Left') { + this.slider.previous(); + } + } + } + }, { + key: 'refresh', + value: function refresh() { + // let centerOffset = Math.floor(this.options.slidesToShow / 2); + if (!this.slider.options.loop && !this.slider.options.infinite) { + if (this.slider.options.navigation && this.slider.state.length > this.slider.slidesToShow) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + if (this.slider.state.next === 0) { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - this.slider.slidesToShow && !this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - 1 && this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } else { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Navigation; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Navigation); + +/***/ }), +/* 14 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (icons) { + return "
" + icons.previous + "
\n
" + icons.next + "
"; +}); + +/***/ }), +/* 15 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_pagination__ = __webpack_require__(16); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__ = __webpack_require__(17); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + + +var Pagination = function () { + function Pagination(slider) { + _classCallCheck(this, Pagination); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPageClick = this.onPageClick.bind(this); + this.onResize = this.onResize.bind(this); + } + + _createClass(Pagination, [{ + key: 'init', + value: function init() { + this._pages = []; + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_pagination__["a" /* default */])()); + this._ui = { + container: this.node.firstChild + }; + + this._count = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + + this._draw(); + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + window.addEventListener('resize', this.onResize); + window.addEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this._pages.forEach(function (page) { + return page.addEventListener(clickEvent, _this.onPageClick); + }); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + window.removeEventListener('resize', this.onResize); + window.removeEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this2._pages.forEach(function (page) { + return page.removeEventListener(clickEvent, _this2.onPageClick); + }); + }); + } + }, { + key: '_draw', + value: function _draw() { + this._ui.container.innerHTML = ''; + if (this.slider.options.pagination && this.slider.state.length > this.slider.slidesToShow) { + for (var i = 0; i <= this._count; i++) { + var newPageNode = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__["a" /* default */])()).firstChild; + newPageNode.dataset.index = i * this.slider.slidesToScroll; + this._pages.push(newPageNode); + this._ui.container.appendChild(newPageNode); + } + this._bindEvents(); + } + } + }, { + key: 'onPageClick', + value: function onPageClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + this.slider.state.next = e.currentTarget.dataset.index; + this.slider.show(); + } + }, { + key: 'onResize', + value: function onResize() { + this._draw(); + } + }, { + key: 'refresh', + value: function refresh() { + var _this3 = this; + + var newCount = void 0; + + if (this.slider.options.infinite) { + newCount = Math.ceil(this.slider.state.length - 1 / this.slider.slidesToScroll); + } else { + newCount = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + } + if (newCount !== this._count) { + this._count = newCount; + this._draw(); + } + + this._pages.forEach(function (page) { + page.classList.remove('is-active'); + if (parseInt(page.dataset.index, 10) === _this3.slider.state.next % _this3.slider.state.length) { + page.classList.add('is-active'); + } + }); + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Pagination; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Pagination); + +/***/ }), +/* 16 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 17 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 18 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Swipe = function () { + function Swipe(slider) { + _classCallCheck(this, Swipe); + + this.slider = slider; + + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onStartDrag = this.onStartDrag.bind(this); + this.onMoveDrag = this.onMoveDrag.bind(this); + this.onStopDrag = this.onStopDrag.bind(this); + + this._init(); + } + + _createClass(Swipe, [{ + key: '_init', + value: function _init() {} + }, { + key: 'bindEvents', + value: function bindEvents() { + var _this = this; + + this.slider.container.addEventListener('dragstart', function (e) { + if (!_this._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.addEventListener('mousedown', this.onStartDrag); + this.slider.container.addEventListener('touchstart', this.onStartDrag); + + window.addEventListener('mousemove', this.onMoveDrag); + window.addEventListener('touchmove', this.onMoveDrag); + + window.addEventListener('mouseup', this.onStopDrag); + window.addEventListener('touchend', this.onStopDrag); + window.addEventListener('touchcancel', this.onStopDrag); + } + }, { + key: 'unbindEvents', + value: function unbindEvents() { + var _this2 = this; + + this.slider.container.removeEventListener('dragstart', function (e) { + if (!_this2._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.removeEventListener('mousedown', this.onStartDrag); + this.slider.container.removeEventListener('touchstart', this.onStartDrag); + + window.removeEventListener('mousemove', this.onMoveDrag); + window.removeEventListener('touchmove', this.onMoveDrag); + + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('touchcancel', this.onStopDrag); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStartDrag', + value: function onStartDrag(e) { + if (e.touches) { + if (e.touches.length > 1) { + return; + } else { + e = e.touches[0]; + } + } + + this._origin = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](e.screenX, e.screenY); + this.width = this.slider.wrapperWidth; + this.slider.transitioner.disable(); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onMoveDrag', + value: function onMoveDrag(e) { + if (this._origin) { + var point = e.touches ? e.touches[0] : e; + this._lastTranslate = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](point.screenX - this._origin.x, point.screenY - this._origin.y); + if (e.touches) { + if (Math.abs(this._lastTranslate.x) > Math.abs(this._lastTranslate.y)) { + if (!this._supportsPassive) { + e.preventDefault(); + } + e.stopPropagation(); + } + } + } + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStopDrag', + value: function onStopDrag(e) { + if (this._origin && this._lastTranslate) { + if (Math.abs(this._lastTranslate.x) > 0.2 * this.width) { + if (this._lastTranslate.x < 0) { + this.slider.next(); + } else { + this.slider.previous(); + } + } else { + this.slider.show(true); + } + } + this._origin = null; + this._lastTranslate = null; + } + }]); + + return Swipe; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Swipe); + +/***/ }), +/* 19 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__transitions_fade__ = __webpack_require__(20); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__transitions_translate__ = __webpack_require__(21); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Transitioner = function () { + function Transitioner(slider) { + _classCallCheck(this, Transitioner); + + this.slider = slider; + this.options = slider.options; + + this._animating = false; + this._animation = undefined; + + this._translate = new __WEBPACK_IMPORTED_MODULE_1__transitions_translate__["a" /* default */](this, slider, slider.options); + this._fade = new __WEBPACK_IMPORTED_MODULE_0__transitions_fade__["a" /* default */](this, slider, slider.options); + } + + _createClass(Transitioner, [{ + key: 'init', + value: function init() { + this._fade.init(); + this._translate.init(); + return this; + } + }, { + key: 'isAnimating', + value: function isAnimating() { + return this._animating; + } + }, { + key: 'enable', + value: function enable() { + this._animation && this._animation.enable(); + } + }, { + key: 'disable', + value: function disable() { + this._animation && this._animation.disable(); + } + }, { + key: 'apply', + value: function apply(force, callback) { + // If we don't force refresh and animation in progress then return + if (this._animating && !force) { + return; + } + + switch (this.options.effect) { + case 'fade': + this._animation = this._fade; + break; + case 'translate': + default: + this._animation = this._translate; + break; + } + + this._animationCallback = callback; + + if (force) { + this._animation && this._animation.disable(); + } else { + this._animation && this._animation.enable(); + this._animating = true; + } + + this._animation && this._animation.apply(); + + if (force) { + this.end(); + } + } + }, { + key: 'end', + value: function end() { + this._animating = false; + this._animation = undefined; + this.slider.state.index = this.slider.state.next; + if (this._animationCallback) { + this._animationCallback(); + } + } + }]); + + return Transitioner; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Transitioner); + +/***/ }), +/* 20 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Fade = function () { + function Fade(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Fade); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + } + + _createClass(Fade, [{ + key: 'init', + value: function init() { + var _this = this; + + if (this.options.effect === 'fade') { + this.slider.slides.forEach(function (slide, index) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(slide, { + position: 'absolute', + left: 0, + top: 0, + bottom: 0, + 'z-index': slide.dataset.sliderIndex == _this.slider.state.index ? 0 : -2, + opacity: slide.dataset.sliderIndex == _this.slider.state.index ? 1 : 0 + }); + }); + } + return this; + } + }, { + key: 'enable', + value: function enable() { + var _this2 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + if (this._oldSlide) { + this._oldSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + } + } + }, { + key: 'disable', + value: function disable() { + var _this3 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = 'none'; + if (this._oldSlide) { + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = 'none'; + } + } + } + }, { + key: 'apply', + value: function apply(force) { + var _this4 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.next; + })[0]; + + if (this._oldSlide && this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + opacity: 0 + }); + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + opacity: 1, + 'z-index': force ? 0 : -1 + }); + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'fade') { + if (this.transitioner.isAnimating() && e.target == this._newSlide) { + if (this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + 'z-index': 0 + }); + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + if (this._oldSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + 'z-index': -2 + }); + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + } + this.transitioner.end(); + } + } + }]); + + return Fade; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Fade); + +/***/ }), +/* 21 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Translate = function () { + function Translate(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Translate); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + + this.onTransitionEnd = this.onTransitionEnd.bind(this); + } + + _createClass(Translate, [{ + key: 'init', + value: function init() { + this._position = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](this.slider.container.offsetLeft, this.slider.container.offsetTop); + this._bindEvents(); + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.slider.container.addEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.slider.container.removeEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: 'enable', + value: function enable() { + this.slider.container.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + }, { + key: 'disable', + value: function disable() { + this.slider.container.style.transition = 'none'; + } + }, { + key: 'apply', + value: function apply() { + var _this = this; + + var maxOffset = void 0; + if (this.options.effect === 'translate') { + var slide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this.slider.state.next; + })[0]; + var slideOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](slide.offsetLeft, slide.offsetTop); + if (this.options.centerMode) { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container))); + } else { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.wrapper)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.wrapper))); + } + var nextOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.min(Math.max(slideOffset.x * -1, maxOffset.x * -1), 0), Math.min(Math.max(slideOffset.y * -1, maxOffset.y * -1), 0)); + if (this.options.loop) { + if (!this.options.vertical && Math.abs(this._position.x) > maxOffset.x) { + nextOffset.x = 0; + this.slider.state.next = 0; + } else if (this.options.vertical && Math.abs(this._position.y) > maxOffset.y) { + nextOffset.y = 0; + this.slider.state.next = 0; + } + } + + this._position.x = nextOffset.x; + this._position.y = nextOffset.y; + if (this.options.centerMode) { + this._position.x = this._position.x + this.slider.wrapperWidth / 2 - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(slide) / 2; + } + + if (this.slider.direction === 'rtl') { + this._position.x = -this._position.x; + this._position.y = -this._position.y; + } + this.slider.container.style.transform = 'translate3d(' + this._position.x + 'px, ' + this._position.y + 'px, 0)'; + + /** + * update the index with the nextIndex only if + * the offset of the nextIndex is in the range of the maxOffset + */ + if (slideOffset.x > maxOffset.x) { + this.slider.transitioner.end(); + } + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'translate') { + + if (this.transitioner.isAnimating() && e.target == this.slider.container) { + if (this.options.infinite) { + this.slider._infinite.onTransitionEnd(e); + } + } + this.transitioner.end(); + } + } + }]); + + return Translate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Translate); + +/***/ }), +/* 22 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var defaultOptions = { + initialSlide: 0, + slidesToScroll: 1, + slidesToShow: 1, + + navigation: true, + navigationKeys: true, + navigationSwipe: true, + + pagination: true, + + loop: false, + infinite: false, + + effect: 'translate', + duration: 300, + timing: 'ease', + + autoplay: false, + autoplaySpeed: 3000, + pauseOnHover: true, + breakpoints: [{ + changePoint: 480, + slidesToShow: 1, + slidesToScroll: 1 + }, { + changePoint: 640, + slidesToShow: 2, + slidesToScroll: 2 + }, { + changePoint: 768, + slidesToShow: 3, + slidesToScroll: 3 + }], + + onReady: null, + icons: { + 'previous': '\n \n ', + 'next': '\n \n ' + } +}; + +/* harmony default export */ __webpack_exports__["a"] = (defaultOptions); + +/***/ }), +/* 23 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (id) { + return "
\n
\n
"; +}); + +/***/ }), +/* 24 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }) +/******/ ])["default"]; +}); \ No newline at end of file diff --git a/docs/static/js/bulma-carousel.min.js b/docs/static/js/bulma-carousel.min.js new file mode 100644 index 0000000..5fff069 --- /dev/null +++ b/docs/static/js/bulma-carousel.min.js @@ -0,0 +1 @@ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaCarousel=e():t.bulmaCarousel=e()}("undefined"!=typeof self?self:this,function(){return function(i){var n={};function s(t){if(n[t])return n[t].exports;var e=n[t]={i:t,l:!1,exports:{}};return i[t].call(e.exports,e,e.exports,s),e.l=!0,e.exports}return s.m=i,s.c=n,s.d=function(t,e,i){s.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:i})},s.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return s.d(e,"a",e),e},s.o=function(t,e){return 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"symbol" : typeof obj; }; + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; + +var bulmaSlider = function (_EventEmitter) { + _inherits(bulmaSlider, _EventEmitter); + + function bulmaSlider(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaSlider); + + var _this = _possibleConstructorReturn(this, (bulmaSlider.__proto__ || Object.getPrototypeOf(bulmaSlider)).call(this)); + + _this.element = typeof selector === 'string' ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + + _this._clickEvents = ['click']; + /// Set default options and merge with instance defined + _this.options = _extends({}, options); + + _this.onSliderInput = _this.onSliderInput.bind(_this); + + _this.init(); + return _this; + } + + /** + * Initiate all DOM element containing selector + * @method + * @return {Array} Array of all slider instances + */ + + + _createClass(bulmaSlider, [{ + key: 'init', + + + /** + * Initiate plugin + * @method init + * @return {void} + */ + value: function init() { + this._id = 'bulmaSlider' + new Date().getTime() + Math.floor(Math.random() * Math.floor(9999)); + this.output = this._findOutputForSlider(); + + this._bindEvents(); + + if (this.output) { + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + } + + this.emit('bulmaslider:ready', this.element.value); + } + }, { + key: '_findOutputForSlider', + value: function _findOutputForSlider() { + var _this2 = this; + + var result = null; + var outputs = document.getElementsByTagName('output') || []; + + Array.from(outputs).forEach(function (output) { + if (output.htmlFor == _this2.element.getAttribute('id')) { + result = output; + return true; + } + }); + return result; + } + }, { + key: '_getSliderOutputPosition', + value: function _getSliderOutputPosition() { + // Update output position + var newPlace, minValue; + + var style = window.getComputedStyle(this.element, null); + // Measure width of range input + var sliderWidth = parseInt(style.getPropertyValue('width'), 10); + + // Figure out placement percentage between left and right of input + if (!this.element.getAttribute('min')) { + minValue = 0; + } else { + minValue = this.element.getAttribute('min'); + } + var newPoint = (this.element.value - minValue) / (this.element.getAttribute('max') - minValue); + + // Prevent bubble from going beyond left or right (unsupported browsers) + if (newPoint < 0) { + newPlace = 0; + } else if (newPoint > 1) { + newPlace = sliderWidth; + } else { + newPlace = sliderWidth * newPoint; + } + + return { + 'position': newPlace + 'px' + }; + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + if (this.output) { + // Add event listener to update output when slider value change + this.element.addEventListener('input', this.onSliderInput, false); + } + } + }, { + key: 'onSliderInput', + value: function onSliderInput(e) { + e.preventDefault(); + + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + + // Check for prefix and postfix + var prefix = this.output.hasAttribute('data-prefix') ? this.output.getAttribute('data-prefix') : ''; + var postfix = this.output.hasAttribute('data-postfix') ? this.output.getAttribute('data-postfix') : ''; + + // Update output with slider value + this.output.value = prefix + this.element.value + postfix; + + this.emit('bulmaslider:ready', this.element.value); + } + }], [{ + key: 'attach', + value: function attach() { + var _this3 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'input[type="range"].slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = isString(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + elements.forEach(function (element) { + if (typeof element[_this3.constructor.name] === 'undefined') { + var instance = new bulmaSlider(element, options); + element[_this3.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this3.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaSlider; +}(__WEBPACK_IMPORTED_MODULE_0__events__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaSlider); + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var listeners = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this._listeners = new Map(listeners); + this._middlewares = new Map(); + } + + _createClass(EventEmitter, [{ + key: "listenerCount", + value: function listenerCount(eventName) { + if (!this._listeners.has(eventName)) { + return 0; + } + + var eventListeners = this._listeners.get(eventName); + return eventListeners.length; + } + }, { + key: "removeListeners", + value: function removeListeners() { + var _this = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + var middleware = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this.removeListeners(e, middleware); + }); + } else { + this._listeners.delete(eventName); + + if (middleware) { + this.removeMiddleware(eventName); + } + } + } else { + this._listeners = new Map(); + } + } + }, { + key: "middleware", + value: function middleware(eventName, fn) { + var _this2 = this; + + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this2.middleware(e, fn); + }); + } else { + if (!Array.isArray(this._middlewares.get(eventName))) { + this._middlewares.set(eventName, []); + } + + this._middlewares.get(eventName).push(fn); + } + } + }, { + key: "removeMiddleware", + value: function removeMiddleware() { + var _this3 = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this3.removeMiddleware(e); + }); + } else { + this._middlewares.delete(eventName); + } + } else { + this._middlewares = new Map(); + } + } + }, { + key: "on", + value: function on(name, callback) { + var _this4 = this; + + var once = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + if (Array.isArray(name)) { + name.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + name = name.toString(); + var split = name.split(/,|, | /); + + if (split.length > 1) { + split.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + if (!Array.isArray(this._listeners.get(name))) { + this._listeners.set(name, []); + } + + this._listeners.get(name).push({ once: once, callback: callback }); + } + } + } + }, { + key: "once", + value: function once(name, callback) { + this.on(name, callback, true); + } + }, { + key: "emit", + value: function emit(name, data) { + var _this5 = this; + + var silent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + name = name.toString(); + var listeners = this._listeners.get(name); + var middlewares = null; + var doneCount = 0; + var execute = silent; + + if (Array.isArray(listeners)) { + listeners.forEach(function (listener, index) { + // Start Middleware checks unless we're doing a silent emit + if (!silent) { + middlewares = _this5._middlewares.get(name); 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