From 4feac496c1eacebc49ce53793039a8162930935e Mon Sep 17 00:00:00 2001 From: YuAng Date: Wed, 16 Jun 2021 10:00:27 +0800 Subject: [PATCH] Add LoFTR training. --- .gitignore | 12 +- .gitmodules | 3 + README.md | 6 +- configs/data/base.py | 6 +- configs/data/debug/.gitignore | 3 + configs/data/megadepth_trainval_640.py | 22 +++ configs/data/megadepth_trainval_840.py | 22 +++ configs/data/scannet_trainval.py | 17 ++ configs/loftr/indoor/debug/.gitignore | 3 + configs/loftr/{ => indoor}/loftr_ds.py | 2 + configs/loftr/indoor/loftr_ds_dense.py | 7 + configs/loftr/{ => indoor}/loftr_ot.py | 2 + configs/loftr/indoor/loftr_ot_dense.py | 7 + configs/loftr/outdoor/debug/.gitignore | 3 + configs/loftr/outdoor/loftr_ds.py | 15 ++ configs/loftr/outdoor/loftr_ds_dense.py | 16 ++ configs/loftr/outdoor/loftr_ot.py | 15 ++ configs/loftr/outdoor/loftr_ot_dense.py | 16 ++ data/megadepth/index/.gitignore | 4 + data/megadepth/test/.gitignore | 4 + data/megadepth/train/.gitignore | 4 + data/scannet/index/.gitignore | 3 + data/scannet/intrinsics.npz | Bin 0 -> 343135 bytes data/scannet/test | 1 + data/scannet/train | 1 + docs/TRAINING.md | 73 ++++++++ environment.yaml | 3 +- notebooks/demo_single_pair.ipynb | 197 +++++++++++++++++++- requirements.txt | 4 +- scripts/reproduce_train/debug/.gitignore | 3 + scripts/reproduce_train/indoor_ds.sh | 33 ++++ scripts/reproduce_train/indoor_ot.sh | 33 ++++ scripts/reproduce_train/outdoor_ds.sh | 35 ++++ scripts/reproduce_train/outdoor_ot.sh | 35 ++++ src/config/default.py | 55 +++++- src/datasets/megadepth.py | 13 +- src/datasets/sampler.py | 77 ++++++++ src/datasets/scannet.py | 46 +++-- src/lightning/data.py | 223 +++++++++++++++++++++-- src/lightning/lightning_loftr.py | 178 ++++++++++++++++-- src/loftr/utils/coarse_matching.py | 129 ++++++++++--- src/loftr/utils/fine_matching.py | 3 + src/loftr/utils/geometry.py | 54 ++++++ src/loftr/utils/supervision.py | 151 +++++++++++++++ src/losses/loftr_loss.py | 192 +++++++++++++++++++ src/optimizers/__init__.py | 42 +++++ src/utils/augment.py | 2 + src/utils/dataloader.py | 9 +- src/utils/dataset.py | 68 ++++++- src/utils/misc.py | 88 +++++++-- src/utils/plotting.py | 136 ++++++++++++-- src/utils/profiler.py | 1 - third_party/SuperGluePretrainedNetwork | 1 + train.py | 120 ++++++++++++ 54 files changed, 2076 insertions(+), 122 deletions(-) create mode 100644 .gitmodules create mode 100644 configs/data/debug/.gitignore create mode 100644 configs/data/megadepth_trainval_640.py create mode 100644 configs/data/megadepth_trainval_840.py create mode 100644 configs/data/scannet_trainval.py create mode 100644 configs/loftr/indoor/debug/.gitignore rename configs/loftr/{ => indoor}/loftr_ds.py (59%) create mode 100644 configs/loftr/indoor/loftr_ds_dense.py rename configs/loftr/{ => indoor}/loftr_ot.py (58%) create mode 100644 configs/loftr/indoor/loftr_ot_dense.py create mode 100644 configs/loftr/outdoor/debug/.gitignore create mode 100644 configs/loftr/outdoor/loftr_ds.py create mode 100644 configs/loftr/outdoor/loftr_ds_dense.py create mode 100644 configs/loftr/outdoor/loftr_ot.py create mode 100644 configs/loftr/outdoor/loftr_ot_dense.py create mode 100644 data/megadepth/index/.gitignore create mode 100644 data/megadepth/test/.gitignore create mode 100644 data/megadepth/train/.gitignore create mode 100644 data/scannet/index/.gitignore create mode 100644 data/scannet/intrinsics.npz create mode 120000 data/scannet/test create mode 120000 data/scannet/train create mode 100644 docs/TRAINING.md create mode 100644 scripts/reproduce_train/debug/.gitignore create mode 100755 scripts/reproduce_train/indoor_ds.sh create mode 100644 scripts/reproduce_train/indoor_ot.sh create mode 100644 scripts/reproduce_train/outdoor_ds.sh create mode 100644 scripts/reproduce_train/outdoor_ot.sh create mode 100644 src/datasets/sampler.py create mode 100644 src/loftr/utils/geometry.py create mode 100644 src/loftr/utils/supervision.py create mode 100644 src/losses/loftr_loss.py create mode 100644 src/optimizers/__init__.py create mode 160000 third_party/SuperGluePretrainedNetwork create mode 100644 train.py diff --git a/.gitignore b/.gitignore index 821dfc1..ceffb3d 100644 --- a/.gitignore +++ b/.gitignore @@ -13,4 +13,14 @@ dump/ demo/*.mp4 demo/demo_images/ src/loftr/utils/superglue.py -demo/utils.py \ No newline at end of file +demo/utils.py + +notebooks/QccDayNight.ipynb +notebooks/westlake.ipynb +assets/westlake +assets/qcc_pairs.txt +configs/.petrel* +tools/draw_QccDayNights.py + +scripts/slurm/ +scripts/sbatch_submit.sh diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..60d845f --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "third_party/SuperGluePretrainedNetwork"] + path = third_party/SuperGluePretrainedNetwork + url = git@github.com:magicleap/SuperGluePretrainedNetwork.git diff --git a/README.md b/README.md index 66a3fe6..4e45495 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ - [x] Inference code and pretrained models (DS and OT) (2021-4-7) - [x] Code for reproducing the test-set results (2021-4-7) - [x] Webcam demo to reproduce the result shown in the GIF above (2021-4-13) -- [ ] Training code and training data preparation (expected 2021-6-10) +- [x] Training code and training data preparation (expected 2021-6-10) The entire codebase for data pre-processing, training and validation is under major refactoring and will be released around June. Please subscribe to [this discussion thread](https://github.com/zju3dv/LoFTR/discussions/2) if you wish to be notified of the code release. @@ -177,6 +177,10 @@ Out[19]: 1684276 `data['score']` is the overlapping score defined in [SuperGlue](https://arxiv.org/pdf/1911.11763) (Page 12). + +## Training +See [Training LoFTR](./docs/TRAINING.md) for more details. + ## Citation If you find this code useful for your research, please use the following BibTeX entry. diff --git a/configs/data/base.py b/configs/data/base.py index 1d70c81..03aab16 100644 --- a/configs/data/base.py +++ b/configs/data/base.py @@ -10,22 +10,26 @@ _CN.TRAINER = CN() # training data config _CN.DATASET.TRAIN_DATA_ROOT = None +_CN.DATASET.TRAIN_POSE_ROOT = None _CN.DATASET.TRAIN_NPZ_ROOT = None _CN.DATASET.TRAIN_LIST_PATH = None _CN.DATASET.TRAIN_INTRINSIC_PATH = None # validation set config _CN.DATASET.VAL_DATA_ROOT = None +_CN.DATASET.VAL_POSE_ROOT = None _CN.DATASET.VAL_NPZ_ROOT = None _CN.DATASET.VAL_LIST_PATH = None _CN.DATASET.VAL_INTRINSIC_PATH = None # testing data config _CN.DATASET.TEST_DATA_ROOT = None +_CN.DATASET.TEST_POSE_ROOT = None _CN.DATASET.TEST_NPZ_ROOT = None _CN.DATASET.TEST_LIST_PATH = None _CN.DATASET.TEST_INTRINSIC_PATH = None # dataset config -_CN.DATASET.MIN_OVERLAP_SCORE = 0.4 +_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.4 +_CN.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val cfg = _CN diff --git a/configs/data/debug/.gitignore b/configs/data/debug/.gitignore new file mode 100644 index 0000000..94548af --- /dev/null +++ b/configs/data/debug/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/configs/data/megadepth_trainval_640.py b/configs/data/megadepth_trainval_640.py new file mode 100644 index 0000000..b86791d --- /dev/null +++ b/configs/data/megadepth_trainval_640.py @@ -0,0 +1,22 @@ +from configs.data.base import cfg + + +TRAIN_BASE_PATH = "data/megadepth/index" +cfg.DATASET.TRAINVAL_DATA_SOURCE = "MegaDepth" +cfg.DATASET.TRAIN_DATA_ROOT = "data/megadepth/train" +cfg.DATASET.TRAIN_NPZ_ROOT = f"{TRAIN_BASE_PATH}/scene_info_0.1_0.7" +cfg.DATASET.TRAIN_LIST_PATH = f"{TRAIN_BASE_PATH}/trainvaltest_list/train_list.txt" +cfg.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.0 + +TEST_BASE_PATH = "data/megadepth/index" +cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth" +cfg.DATASET.VAL_DATA_ROOT = cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test" +cfg.DATASET.VAL_NPZ_ROOT = cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}/scene_info_val_1500" +cfg.DATASET.VAL_LIST_PATH = cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/trainvaltest_list/val_list.txt" +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val + +# 368 scenes in total for MegaDepth +# (with difficulty balanced (further split each scene to 3 sub-scenes)) +cfg.TRAINER.N_SAMPLES_PER_SUBSET = 100 + +cfg.DATASET.MGDPT_IMG_RESIZE = 640 # for training on 11GB mem GPUs diff --git a/configs/data/megadepth_trainval_840.py b/configs/data/megadepth_trainval_840.py new file mode 100644 index 0000000..130212c --- /dev/null +++ b/configs/data/megadepth_trainval_840.py @@ -0,0 +1,22 @@ +from configs.data.base import cfg + + +TRAIN_BASE_PATH = "data/megadepth/index" +cfg.DATASET.TRAINVAL_DATA_SOURCE = "MegaDepth" +cfg.DATASET.TRAIN_DATA_ROOT = "data/megadepth/train" +cfg.DATASET.TRAIN_NPZ_ROOT = f"{TRAIN_BASE_PATH}/scene_info_0.1_0.7" +cfg.DATASET.TRAIN_LIST_PATH = f"{TRAIN_BASE_PATH}/trainvaltest_list/train_list.txt" +cfg.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.0 + +TEST_BASE_PATH = "data/megadepth/index" +cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth" +cfg.DATASET.VAL_DATA_ROOT = cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test" +cfg.DATASET.VAL_NPZ_ROOT = cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}/scene_info_val_1500" +cfg.DATASET.VAL_LIST_PATH = cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/trainvaltest_list/val_list.txt" +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val + +# 368 scenes in total for MegaDepth +# (with difficulty balanced (further split each scene to 3 sub-scenes)) +cfg.TRAINER.N_SAMPLES_PER_SUBSET = 100 + +cfg.DATASET.MGDPT_IMG_RESIZE = 840 # for training on 32GB meme GPUs diff --git a/configs/data/scannet_trainval.py b/configs/data/scannet_trainval.py new file mode 100644 index 0000000..c38d644 --- /dev/null +++ b/configs/data/scannet_trainval.py @@ -0,0 +1,17 @@ +from configs.data.base import cfg + + +TRAIN_BASE_PATH = "data/scannet/index" +cfg.DATASET.TRAINVAL_DATA_SOURCE = "ScanNet" +cfg.DATASET.TRAIN_DATA_ROOT = "data/scannet/train" +cfg.DATASET.TRAIN_NPZ_ROOT = f"{TRAIN_BASE_PATH}/scene_data/train" +cfg.DATASET.TRAIN_LIST_PATH = f"{TRAIN_BASE_PATH}/scene_data/train_list/scannet_all.txt" +cfg.DATASET.TRAIN_INTRINSIC_PATH = f"{TRAIN_BASE_PATH}/intrinsics.npz" + +TEST_BASE_PATH = "assets/scannet_test_1500" +cfg.DATASET.TEST_DATA_SOURCE = "ScanNet" +cfg.DATASET.VAL_DATA_ROOT = cfg.DATASET.TEST_DATA_ROOT = "data/scannet/test" +cfg.DATASET.VAL_NPZ_ROOT = cfg.DATASET.TEST_NPZ_ROOT = TEST_BASE_PATH +cfg.DATASET.VAL_LIST_PATH = cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/scannet_test.txt" +cfg.DATASET.VAL_INTRINSIC_PATH = cfg.DATASET.TEST_INTRINSIC_PATH = f"{TEST_BASE_PATH}/intrinsics.npz" +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val diff --git a/configs/loftr/indoor/debug/.gitignore b/configs/loftr/indoor/debug/.gitignore new file mode 100644 index 0000000..94548af --- /dev/null +++ b/configs/loftr/indoor/debug/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/configs/loftr/loftr_ds.py b/configs/loftr/indoor/loftr_ds.py similarity index 59% rename from configs/loftr/loftr_ds.py rename to configs/loftr/indoor/loftr_ds.py index 75616a7..c78018b 100644 --- a/configs/loftr/loftr_ds.py +++ b/configs/loftr/indoor/loftr_ds.py @@ -1,3 +1,5 @@ from src.config.default import _CN as cfg cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' + +cfg.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12, 17, 20, 23, 26, 29] diff --git a/configs/loftr/indoor/loftr_ds_dense.py b/configs/loftr/indoor/loftr_ds_dense.py new file mode 100644 index 0000000..b923b8c --- /dev/null +++ b/configs/loftr/indoor/loftr_ds_dense.py @@ -0,0 +1,7 @@ +from src.config.default import _CN as cfg + +cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' + +cfg.LOFTR.MATCH_COARSE.SPARSE_SPVS = False + +cfg.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12, 17, 20, 23, 26, 29] diff --git a/configs/loftr/loftr_ot.py b/configs/loftr/indoor/loftr_ot.py similarity index 58% rename from configs/loftr/loftr_ot.py rename to configs/loftr/indoor/loftr_ot.py index 1874650..33b8d7e 100644 --- a/configs/loftr/loftr_ot.py +++ b/configs/loftr/indoor/loftr_ot.py @@ -1,3 +1,5 @@ from src.config.default import _CN as cfg cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'sinkhorn' + +cfg.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12, 17, 20, 23, 26, 29] diff --git a/configs/loftr/indoor/loftr_ot_dense.py b/configs/loftr/indoor/loftr_ot_dense.py new file mode 100644 index 0000000..6a897d1 --- /dev/null +++ b/configs/loftr/indoor/loftr_ot_dense.py @@ -0,0 +1,7 @@ +from src.config.default import _CN as cfg + +cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'sinkhorn' + +cfg.LOFTR.MATCH_COARSE.SPARSE_SPVS = False + +cfg.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12, 17, 20, 23, 26, 29] diff --git a/configs/loftr/outdoor/debug/.gitignore b/configs/loftr/outdoor/debug/.gitignore new file mode 100644 index 0000000..94548af --- /dev/null +++ b/configs/loftr/outdoor/debug/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/configs/loftr/outdoor/loftr_ds.py b/configs/loftr/outdoor/loftr_ds.py new file mode 100644 index 0000000..2406e70 --- /dev/null +++ b/configs/loftr/outdoor/loftr_ds.py @@ -0,0 +1,15 @@ +from src.config.default import _CN as cfg + +cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' + +cfg.TRAINER.CANONICAL_LR = 8e-3 +cfg.TRAINER.WARMUP_STEP = 1875 # 3 epochs +cfg.TRAINER.WARMUP_RATIO = 0.1 +cfg.TRAINER.MSLR_MILESTONES = [8, 12, 16, 20, 24] + +# pose estimation +cfg.TRAINER.RANSAC_PIXEL_THR = 0.5 + +cfg.TRAINER.OPTIMIZER = "adamw" +cfg.TRAINER.ADAMW_DECAY = 0.1 +cfg.LOFTR.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.3 diff --git a/configs/loftr/outdoor/loftr_ds_dense.py b/configs/loftr/outdoor/loftr_ds_dense.py new file mode 100644 index 0000000..2b1be4c --- /dev/null +++ b/configs/loftr/outdoor/loftr_ds_dense.py @@ -0,0 +1,16 @@ +from src.config.default import _CN as cfg + +cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' +cfg.LOFTR.MATCH_COARSE.SPARSE_SPVS = False + +cfg.TRAINER.CANONICAL_LR = 8e-3 +cfg.TRAINER.WARMUP_STEP = 1875 # 3 epochs +cfg.TRAINER.WARMUP_RATIO = 0.1 +cfg.TRAINER.MSLR_MILESTONES = [8, 12, 16, 20, 24] + +# pose estimation +cfg.TRAINER.RANSAC_PIXEL_THR = 0.5 + +cfg.TRAINER.OPTIMIZER = "adamw" +cfg.TRAINER.ADAMW_DECAY = 0.1 +cfg.LOFTR.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.3 diff --git a/configs/loftr/outdoor/loftr_ot.py b/configs/loftr/outdoor/loftr_ot.py new file mode 100644 index 0000000..f0e3a79 --- /dev/null +++ b/configs/loftr/outdoor/loftr_ot.py @@ -0,0 +1,15 @@ +from src.config.default import _CN as cfg + +cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'sinkhorn' + +cfg.TRAINER.CANONICAL_LR = 8e-3 +cfg.TRAINER.WARMUP_STEP = 1875 # 3 epochs +cfg.TRAINER.WARMUP_RATIO = 0.1 +cfg.TRAINER.MSLR_MILESTONES = [8, 12, 16, 20, 24] + +# pose estimation +cfg.TRAINER.RANSAC_PIXEL_THR = 0.5 + +cfg.TRAINER.OPTIMIZER = "adamw" +cfg.TRAINER.ADAMW_DECAY = 0.1 +cfg.LOFTR.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.3 diff --git a/configs/loftr/outdoor/loftr_ot_dense.py b/configs/loftr/outdoor/loftr_ot_dense.py new file mode 100644 index 0000000..5b30e04 --- /dev/null +++ b/configs/loftr/outdoor/loftr_ot_dense.py @@ -0,0 +1,16 @@ +from src.config.default import _CN as cfg + +cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'sinkhorn' +cfg.LOFTR.MATCH_COARSE.SPARSE_SPVS = False + +cfg.TRAINER.CANONICAL_LR = 8e-3 +cfg.TRAINER.WARMUP_STEP = 1875 # 3 epochs +cfg.TRAINER.WARMUP_RATIO = 0.1 +cfg.TRAINER.MSLR_MILESTONES = [8, 12, 16, 20, 24] + +# pose estimation +cfg.TRAINER.RANSAC_PIXEL_THR = 0.5 + +cfg.TRAINER.OPTIMIZER = "adamw" +cfg.TRAINER.ADAMW_DECAY = 0.1 +cfg.LOFTR.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.3 diff --git a/data/megadepth/index/.gitignore b/data/megadepth/index/.gitignore new file mode 100644 index 0000000..5e7d273 --- /dev/null +++ b/data/megadepth/index/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/data/megadepth/test/.gitignore b/data/megadepth/test/.gitignore new file mode 100644 index 0000000..5e7d273 --- /dev/null +++ b/data/megadepth/test/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/data/megadepth/train/.gitignore b/data/megadepth/train/.gitignore new file mode 100644 index 0000000..5e7d273 --- /dev/null +++ b/data/megadepth/train/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git 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one](https://github.com/ScanNet/ScanNet/tree/master/SensReader/c%2B%2B). + +```shell +# scannet +# -- # train and test dataset +ln -s /path/to/scannet_train/* /path/to/LoFTR/data/scannet/train +ln -s /path/to/scannet_test/* /path/to/LoFTR/data/scannet/test +# -- # dataset indices +ln -s /path/to/scannet_indices/* /path/to/LoFTR/data/scannet/index + +# megadepth +# -- # train and test dataset (train and test share the same dataset) +ln -s /path/to/megadepth/Undistorted_SfM/* /path/to/LoFTR/data/megadepth/train +ln -s /path/to/megadepth/Undistorted_SfM/* /path/to/LoFTR/data/megadepth/test +# -- # dataset indices +ln -s /path/to/megadepth_indices/* /path/to/LoFTR/data/megadepth/index +``` + + +## Training +We provide training scripts of ScanNet and MegaDepth. The results in the LoFTR paper can be reproduced with 32/64 GPUs with at least 11GB of RAM for ScanNet, and 8/16 GPUs with at least 24GB of RAM for MegaDepth. For a different setup (e.g., training with 4 gpus on ScanNet), we scale the learning rate and its warm-up linearly, but the final evaluation results might vary due to the different batch size & learning rate used. Thus the reproduction of results in our paper is not guaranteed. + +Training scripts of the optimal-transport matcher end with "_ot" and ones of the dual-softmax matcher end with "_ds". + +The released training scripts use smaller setups comparing to ones used for training the released models. You could manually scale the setup (e.g., using 32 gpus instead of 4) to reproduce our results. + + +### Training on ScanNet +``` shell +scripts/reproduce_train/indoor_ds.sh +``` +> NOTE: It uses 4 gpus only. Reproduction of paper results is not guaranteed under this setup. + + +### Training on MegaDepth +``` shell +scripts/reproduce_train/outdoor_ds.sh +``` +> NOTE: It uses 4 gpus only, with smaller image sizes of 640x640. Reproduction of paper results is not guaranteed under this setup. + + +## Updated Training Strategy +In the released training code, we use a slightly modified version of the coarse-level training supervision comparing to the one described in our paper. +For example, as described in our paper, we only supervise the ground-truth positive matches when training the dual-softmax model. However, the entire confidence matrix produced by the dual-softmax matcher is supervised by default in the released code, regardless of the use of softmax operators. This implementation is counter-intuitive and unusual but leads to better evaluation results on estimating relative camera poses. The same phenomenon applies to the optimal-transport matcher version as well. Note that we don't supervise the dustbin rows and columns under the dense supervision setup. + +> NOTE: To use the sparse supervision described in our paper, set `_CN.LOFTR.MATCH_COARSE.SPARSE_SPVS = False`. diff --git a/environment.yaml b/environment.yaml index 4933c73..f8ec3d0 100644 --- a/environment.yaml +++ b/environment.yaml @@ -7,8 +7,7 @@ channels: dependencies: - python=3.8 - cudatoolkit=10.2 - - pytorch=1.8.0 - - pytorch-lightning<=1.1.8 # https://github.com/PyTorchLightning/pytorch-lightning/issues/6318 + - pytorch=1.8.1 - pip - pip: - -r file:requirements.txt diff --git a/notebooks/demo_single_pair.ipynb b/notebooks/demo_single_pair.ipynb index 908c008..f386a6d 100644 --- a/notebooks/demo_single_pair.ipynb +++ b/notebooks/demo_single_pair.ipynb @@ -10,12 +10,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.8.10" }, "orig_nbformat": 2, "kernelspec": { - "name": "python378jvsc74a57bd065d19ceb1c5e26d1d1e4e93c9824aa3d4633a56cbb655ff57f645571fa154c9a", - "display_name": "Python 3.7.8 64-bit ('loftr': conda)" + "name": "python3810jvsc74a57bd065d19ceb1c5e26d1d1e4e93c9824aa3d4633a56cbb655ff57f645571fa154c9a", + "display_name": "Python 3.8.10 64-bit ('loftr': conda)" } }, "nbformat": 4, @@ -193,6 +193,197 @@ "fig = make_matching_figure(img0_raw, img1_raw, mkpts0, mkpts1, color, text)" ] }, + { + "source": [ + "# Westlake Day-Night" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/root/dev/LoFTR')\n", + "from pathlib import Path\n", + "\n", + "import pyheif\n", + "import pydegensac\n", + "from PIL import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def read_image(pth):\n", + " if isinstance(pth, Path):\n", + " suffix = pth.suffix\n", + " else:\n", + " suffix = Path(pth).suffix\n", + " \n", + " if suffix.lower() == '.heic':\n", + " heif_file = pyheif.read(pth)\n", + " image = Image.frombytes(\n", + " heif_file.mode, \n", + " heif_file.size, \n", + " heif_file.data,\n", + " \"raw\",\n", + " heif_file.mode,\n", + " heif_file.stride)\n", + " image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2GRAY)\n", + " else:\n", + " image = cv2.imread(pth, cv2.IMREAD_GRAYSCALE)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def geometric_verification(kpts0, kpts1,\n", + " px_thr=1.0, conf=0.99999, max_iters=10000,\n", + " min_candidates=10):\n", + " if len(kpts0) < min_candidates:\n", + " return None\n", + " \n", + " F, mask = pydegensac.findFundamentalMatrix(kpts0,\n", + " kpts1,\n", + " px_th=px_thr,\n", + " conf=conf,\n", + " max_iters=max_iters)\n", + " mask = mask.astype(bool)\n", + " return mask\n", + "\n", + "def extract_inliers(kpts0, kpts1, mconfs, mask):\n", + " return tuple(map(lambda x: x[mask], [kpts0, kpts1, mconfs]))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "WEST_LAKE_ROOT = Path('assets/westlake')\n", + "pairs = [('IMG_8402.HEIC', 'IMG_8688.HEIC')]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from src.loftr import LoFTR, default_cfg\n", + "\n", + "# The default config uses dual-softmax.\n", + "# The outdoor and indoor models share the same config.\n", + "# You can change the default values like thr and coarse_match_type.\n", + "matcher = LoFTR(config=default_cfg)\n", + "matcher.load_state_dict(torch.load(\"weights/outdoor_ds.ckpt\")['state_dict'])\n", + "matcher = matcher.eval().cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "del batch\n", + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Load example images\n", + "INLIER_ONLY = True\n", + "\n", + "p_id = 0\n", + "img0_pth = str(WEST_LAKE_ROOT / pairs[p_id][0])\n", + "img1_pth = str(WEST_LAKE_ROOT / pairs[p_id][1])\n", + "\n", + "img0_raw = read_image(img0_pth)\n", + "img1_raw = read_image(img1_pth)\n", + "img0_raw = cv2.resize(img0_raw, (img0_raw.shape[1]//32*8, img0_raw.shape[0]//32*8)) # input size shuold be divisible by 8\n", + "img1_raw = cv2.resize(img1_raw, (img1_raw.shape[1]//32*8, img1_raw.shape[0]//32*8))\n", + "\n", + "img0 = torch.from_numpy(img0_raw)[None][None].cuda() / 255.\n", + "img1 = torch.from_numpy(img1_raw)[None][None].cuda() / 255.\n", + "batch = {'image0': img0, 'image1': img1}\n", + "\n", + "# Inference with LoFTR and get prediction\n", + "with torch.no_grad():\n", + " matcher(batch)\n", + " mkpts0 = batch['mkpts0_f'].cpu().numpy()\n", + " mkpts1 = batch['mkpts1_f'].cpu().numpy()\n", + " mconf = batch['mconf'].cpu().numpy()\n", + "\n", + "if INLIER_ONLY:\n", + " mask = geometric_verification(mkpts0, mkpts1)\n", + " mkpts0, mkpts1, mconf = extract_inliers(mkpts0, mkpts1, mconf, mask)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((934, 2), (934, 2), (934,))" + ] + }, + "metadata": {}, + "execution_count": 31 + } + ], + "source": [ + "mkpts0.shape, mkpts1.shape, mconf.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Draw\n", + "alpha=0.2\n", + "color = cm.jet(mconf)\n", + "color[:, -1] *= alpha\n", + "text = [\n", + " 'LoFTR',\n", + " 'Matches: {}'.format(len(mkpts0)),\n", + "]\n", + "fig = make_matching_figure(img0_raw, img1_raw, mkpts0, mkpts1, color, text)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/requirements.txt b/requirements.txt index caaf249..50e621a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,4 +11,6 @@ pylint ipython jupyterlab matplotlib -h5py==3.1.0 \ No newline at end of file +h5py==3.1.0 +pytorch-lightning==1.3.5 +joblib>=1.0.1 diff --git a/scripts/reproduce_train/debug/.gitignore b/scripts/reproduce_train/debug/.gitignore new file mode 100644 index 0000000..94548af --- /dev/null +++ b/scripts/reproduce_train/debug/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/scripts/reproduce_train/indoor_ds.sh b/scripts/reproduce_train/indoor_ds.sh new file mode 100755 index 0000000..c565391 --- /dev/null +++ b/scripts/reproduce_train/indoor_ds.sh @@ -0,0 +1,33 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +data_cfg_path="configs/data/scannet_trainval.py" +main_cfg_path="configs/loftr/indoor/loftr_ds_dense.py" + +n_nodes=1 +n_gpus_per_node=4 +torch_num_workers=4 +batch_size=1 +pin_memory=true +exp_name="indoor-ds-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))" + +python -u ./train.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --exp_name=${exp_name} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \ + --check_val_every_n_epoch=1 \ + --log_every_n_steps=100 \ + --flush_logs_every_n_steps=100 \ + --limit_val_batches=1. \ + --num_sanity_val_steps=10 \ + --benchmark=True \ + --max_epochs=30 \ + --parallel_load_data diff --git a/scripts/reproduce_train/indoor_ot.sh b/scripts/reproduce_train/indoor_ot.sh new file mode 100644 index 0000000..192859d --- /dev/null +++ b/scripts/reproduce_train/indoor_ot.sh @@ -0,0 +1,33 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +data_cfg_path="configs/data/scannet_trainval.py" +main_cfg_path="configs/loftr/indoor/loftr_ot_dense.py" + +n_nodes=1 +n_gpus_per_node=4 +torch_num_workers=4 +batch_size=1 +pin_memory=true +exp_name="indoor-ot-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))" + +python -u ./train.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --exp_name=${exp_name} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \ + --check_val_every_n_epoch=1 \ + --log_every_n_steps=100 \ + --flush_logs_every_n_steps=100 \ + --limit_val_batches=1. \ + --num_sanity_val_steps=10 \ + --benchmark=True \ + --max_epochs=30 \ + --parallel_load_data diff --git a/scripts/reproduce_train/outdoor_ds.sh b/scripts/reproduce_train/outdoor_ds.sh new file mode 100644 index 0000000..0f49303 --- /dev/null +++ b/scripts/reproduce_train/outdoor_ds.sh @@ -0,0 +1,35 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +TRAIN_IMG_SIZE=640 +# to reproduced the results in our paper, please use: +# TRAIN_IMG_SIZE=840 +data_cfg_path="configs/data/megadepth_trainval_${TRAIN_IMG_SIZE}.py" +main_cfg_path="configs/loftr/outdoor/loftr_ds_dense.py" + +n_nodes=1 +n_gpus_per_node=4 +torch_num_workers=4 +batch_size=1 +pin_memory=true +exp_name="outdoor-ds-${TRAIN_IMG_SIZE}-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))" + +python -u ./train.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --exp_name=${exp_name} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \ + --check_val_every_n_epoch=1 \ + --log_every_n_steps=1 \ + --flush_logs_every_n_steps=1 \ + --limit_val_batches=1. \ + --num_sanity_val_steps=10 \ + --benchmark=True \ + --max_epochs=30 diff --git a/scripts/reproduce_train/outdoor_ot.sh b/scripts/reproduce_train/outdoor_ot.sh new file mode 100644 index 0000000..7a57996 --- /dev/null +++ b/scripts/reproduce_train/outdoor_ot.sh @@ -0,0 +1,35 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +TRAIN_IMG_SIZE=640 +# to reproduced the results in our paper, please use: +# TRAIN_IMG_SIZE=840 +data_cfg_path="configs/data/megadepth_trainval_${TRAIN_IMG_SIZE}.py" +main_cfg_path="configs/loftr/outdoor/loftr_ot_dense.py" + +n_nodes=1 +n_gpus_per_node=4 +torch_num_workers=4 +batch_size=1 +pin_memory=true +exp_name="outdoor-ot-${TRAIN_IMG_SIZE}-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))" + +python -u ./train.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --exp_name=${exp_name} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \ + --check_val_every_n_epoch=1 \ + --log_every_n_steps=1 \ + --flush_logs_every_n_steps=1 \ + --limit_val_batches=1. \ + --num_sanity_val_steps=10 \ + --benchmark=True \ + --max_epochs=30 diff --git a/src/config/default.py b/src/config/default.py index 1797a1f..da20420 100644 --- a/src/config/default.py +++ b/src/config/default.py @@ -30,8 +30,9 @@ _CN.LOFTR.MATCH_COARSE.DSMAX_TEMPERATURE = 0.1 _CN.LOFTR.MATCH_COARSE.SKH_ITERS = 3 _CN.LOFTR.MATCH_COARSE.SKH_INIT_BIN_SCORE = 1.0 _CN.LOFTR.MATCH_COARSE.SKH_PREFILTER = False -_CN.LOFTR.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.4 # training tricks: save GPU memory +_CN.LOFTR.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.2 # training tricks: save GPU memory _CN.LOFTR.MATCH_COARSE.TRAIN_PAD_NUM_GT_MIN = 200 # training tricks: avoid DDP deadlock +_CN.LOFTR.MATCH_COARSE.SPARSE_SPVS = True # 4. LoFTR-fine module config _CN.LOFTR.FINE = CN() @@ -41,6 +42,25 @@ _CN.LOFTR.FINE.NHEAD = 8 _CN.LOFTR.FINE.LAYER_NAMES = ['self', 'cross'] * 1 _CN.LOFTR.FINE.ATTENTION = 'linear' +# 5. LoFTR Losses +# -- # coarse-level +_CN.LOFTR.LOSS = CN() +_CN.LOFTR.LOSS.COARSE_TYPE = 'focal' # ['focal', 'cross_entropy'] +_CN.LOFTR.LOSS.COARSE_WEIGHT = 1.0 +# _CN.LOFTR.LOSS.SPARSE_SPVS = False +# -- - -- # focal loss (coarse) +_CN.LOFTR.LOSS.FOCAL_ALPHA = 0.25 +_CN.LOFTR.LOSS.FOCAL_GAMMA = 2.0 +_CN.LOFTR.LOSS.POS_WEIGHT = 1.0 +_CN.LOFTR.LOSS.NEG_WEIGHT = 1.0 +# _CN.LOFTR.LOSS.DUAL_SOFTMAX = False # whether coarse-level use dual-softmax or not. +# use `_CN.LOFTR.MATCH_COARSE.MATCH_TYPE` + +# -- # fine-level +_CN.LOFTR.LOSS.FINE_TYPE = 'l2_with_std' # ['l2_with_std', 'l2'] +_CN.LOFTR.LOSS.FINE_WEIGHT = 1.0 +_CN.LOFTR.LOSS.FINE_CORRECT_THR = 1.0 # for filtering valid fine-level gts (some gt matches might fall out of the fine-level window) + ############## Dataset ############## _CN.DATASET = CN() @@ -48,23 +68,27 @@ _CN.DATASET = CN() # training and validating _CN.DATASET.TRAINVAL_DATA_SOURCE = None # options: ['ScanNet', 'MegaDepth'] _CN.DATASET.TRAIN_DATA_ROOT = None +_CN.DATASET.TRAIN_POSE_ROOT = None # (optional directory for poses) _CN.DATASET.TRAIN_NPZ_ROOT = None _CN.DATASET.TRAIN_LIST_PATH = None _CN.DATASET.TRAIN_INTRINSIC_PATH = None _CN.DATASET.VAL_DATA_ROOT = None +_CN.DATASET.VAL_POSE_ROOT = None # (optional directory for poses) _CN.DATASET.VAL_NPZ_ROOT = None _CN.DATASET.VAL_LIST_PATH = None # None if val data from all scenes are bundled into a single npz file _CN.DATASET.VAL_INTRINSIC_PATH = None # testing _CN.DATASET.TEST_DATA_SOURCE = None _CN.DATASET.TEST_DATA_ROOT = None +_CN.DATASET.TEST_POSE_ROOT = None # (optional directory for poses) _CN.DATASET.TEST_NPZ_ROOT = None _CN.DATASET.TEST_LIST_PATH = None # None if test data from all scenes are bundled into a single npz file _CN.DATASET.TEST_INTRINSIC_PATH = None # 2. dataset config # general options -_CN.DATASET.MIN_OVERLAP_SCORE = 0.4 # discard data with overlap_score < min_overlap_score +_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.4 # discard data with overlap_score < min_overlap_score +_CN.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 _CN.DATASET.AUGMENTATION_TYPE = None # options: [None, 'dark', 'mobile'] # MegaDepth options @@ -75,10 +99,35 @@ _CN.DATASET.MGDPT_DF = 8 ############## Trainer ############## _CN.TRAINER = CN() +_CN.TRAINER.CANONICAL_BS = 64 +_CN.TRAINER.CANONICAL_LR = 6e-3 +_CN.TRAINER.SCALING = None # this will be calculated automatically +_CN.TRAINER.FIND_LR = False # use learning rate finder from pytorch-lightning + +# optimizer +_CN.TRAINER.OPTIMIZER = "adamw" # [adam, adamw] +_CN.TRAINER.TRUE_LR = None # this will be calculated automatically at runtime +_CN.TRAINER.ADAM_DECAY = 0. # ADAM: for adam +_CN.TRAINER.ADAMW_DECAY = 0.1 + +# step-based warm-up +_CN.TRAINER.WARMUP_TYPE = 'linear' # [linear, constant] +_CN.TRAINER.WARMUP_RATIO = 0. +_CN.TRAINER.WARMUP_STEP = 4800 + +# learning rate scheduler +_CN.TRAINER.SCHEDULER = 'MultiStepLR' # [MultiStepLR, CosineAnnealing, ExponentialLR] +_CN.TRAINER.SCHEDULER_INTERVAL = 'epoch' # [epoch, step] +_CN.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12] # MSLR: MultiStepLR +_CN.TRAINER.MSLR_GAMMA = 0.5 +_CN.TRAINER.COSA_TMAX = 30 # COSA: CosineAnnealing +_CN.TRAINER.ELR_GAMMA = 0.999992 # ELR: ExponentialLR, this value for 'step' interval # plotting related _CN.TRAINER.ENABLE_PLOTTING = True _CN.TRAINER.N_VAL_PAIRS_TO_PLOT = 32 # number of val/test paris for plotting +_CN.TRAINER.PLOT_MODE = 'evaluation' # ['evaluation', 'confidence'] +_CN.TRAINER.PLOT_MATCHES_ALPHA = 'dynamic' # geometric metrics and pose solver _CN.TRAINER.EPI_ERR_THR = 5e-4 # recommendation: 5e-4 for ScanNet, 1e-4 for MegaDepth (from SuperGlue) @@ -108,7 +157,7 @@ _CN.TRAINER.GRADIENT_CLIPPING = 0.5 # to be the same. When resume training from a checkpoint, it's better to use a different # seed, otherwise the sampled data will be exactly the same as before resuming, which will # cause less unique data items sampled during the entire training. -# Use of different seed value might affect the final training result, since not all data items +# Use of different seed values might affect the final training result, since not all data items # are used during training on ScanNet. (60M pairs of images sampled during traing from 230M pairs in total.) _CN.TRAINER.SEED = 66 diff --git a/src/datasets/megadepth.py b/src/datasets/megadepth.py index 1c0b524..a70ac71 100644 --- a/src/datasets/megadepth.py +++ b/src/datasets/megadepth.py @@ -21,7 +21,8 @@ class MegaDepthDataset(Dataset): augment_fn=None, **kwargs): """ - Manage one scene(npz_path) of MegaDepth dataset. + Manage one scene(npz_path) of MegaDepth dataset. + Args: root_dir (str): megadepth root directory that has `phoenix`. npz_path (str): {scene_id}.npz path. This contains image pair information of a scene. @@ -69,12 +70,14 @@ class MegaDepthDataset(Dataset): # read grayscale image and mask. (1, h, w) and (h, w) img_name0 = osp.join(self.root_dir, self.scene_info['image_paths'][idx0]) img_name1 = osp.join(self.root_dir, self.scene_info['image_paths'][idx1]) + + # TODO: Support augmentation & handle seeds for each worker correctly. image0, mask0, scale0 = read_megadepth_gray( - img_name0, self.img_resize, self.df, self.img_padding, - np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + img_name0, self.img_resize, self.df, self.img_padding, None) + # np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) image1, mask1, scale1 = read_megadepth_gray( - img_name1, self.img_resize, self.df, self.img_padding, - np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + img_name1, self.img_resize, self.df, self.img_padding, None) + # np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) # read depth. shape: (h, w) if self.mode in ['train', 'val']: diff --git a/src/datasets/sampler.py b/src/datasets/sampler.py new file mode 100644 index 0000000..81b6f43 --- /dev/null +++ b/src/datasets/sampler.py @@ -0,0 +1,77 @@ +import torch +from torch.utils.data import Sampler, ConcatDataset + + +class RandomConcatSampler(Sampler): + """ Random sampler for ConcatDataset. At each epoch, `n_samples_per_subset` samples will be draw from each subset + in the ConcatDataset. If `subset_replacement` is ``True``, sampling within each subset will be done with replacement. + However, it is impossible to sample data without replacement between epochs, unless bulding a stateful sampler lived along the entire training phase. + + For current implementation, the randomness of sampling is ensured no matter the sampler is recreated across epochs or not and call `torch.manual_seed()` or not. + Args: + shuffle (bool): shuffle the random sampled indices across all sub-datsets. + repeat (int): repeatedly use the sampled indices multiple times for training. + [arXiv:1902.05509, arXiv:1901.09335] + NOTE: Don't re-initialize the sampler between epochs (will lead to repeated samples) + NOTE: This sampler behaves differently with DistributedSampler. + It assume the dataset is splitted across ranks instead of replicated. + TODO: Add a `set_epoch()` method to fullfill sampling without replacement across epochs. + ref: https://github.com/PyTorchLightning/pytorch-lightning/blob/e9846dd758cfb1500eb9dba2d86f6912eb487587/pytorch_lightning/trainer/training_loop.py#L373 + """ + def __init__(self, + data_source: ConcatDataset, + n_samples_per_subset: int, + subset_replacement: bool=True, + shuffle: bool=True, + repeat: int=1, + seed: int=None): + if not isinstance(data_source, ConcatDataset): + raise TypeError("data_source should be torch.utils.data.ConcatDataset") + + self.data_source = data_source + self.n_subset = len(self.data_source.datasets) + self.n_samples_per_subset = n_samples_per_subset + self.n_samples = self.n_subset * self.n_samples_per_subset * repeat + self.subset_replacement = subset_replacement + self.repeat = repeat + self.shuffle = shuffle + self.generator = torch.manual_seed(seed) + assert self.repeat >= 1 + + def __len__(self): + return self.n_samples + + def __iter__(self): + indices = [] + # sample from each sub-dataset + for d_idx in range(self.n_subset): + low = 0 if d_idx==0 else self.data_source.cumulative_sizes[d_idx-1] + high = self.data_source.cumulative_sizes[d_idx] + if self.subset_replacement: + rand_tensor = torch.randint(low, high, (self.n_samples_per_subset, ), + generator=self.generator, dtype=torch.int64) + else: # sample without replacement + len_subset = len(self.data_source.datasets[d_idx]) + rand_tensor = torch.randperm(len_subset, generator=self.generator) + low + if len_subset >= self.n_samples_per_subset: + rand_tensor = rand_tensor[:self.n_samples_per_subset] + else: # padding with replacement + rand_tensor_replacement = torch.randint(low, high, (self.n_samples_per_subset - len_subset, ), + generator=self.generator, dtype=torch.int64) + rand_tensor = torch.cat([rand_tensor, rand_tensor_replacement]) + indices.append(rand_tensor) + indices = torch.cat(indices) + if self.shuffle: # shuffle the sampled dataset (from multiple subsets) + rand_tensor = torch.randperm(len(indices), generator=self.generator) + indices = indices[rand_tensor] + + # repeat the sampled indices (can be used for RepeatAugmentation or pure RepeatSampling) + if self.repeat > 1: + repeat_indices = [indices.clone() for _ in range(self.repeat - 1)] + if self.shuffle: + _choice = lambda x: x[torch.randperm(len(x), generator=self.generator)] + repeat_indices = map(_choice, repeat_indices) + indices = torch.cat([indices, *repeat_indices], 0) + + assert indices.shape[0] == self.n_samples + return iter(indices.tolist()) diff --git a/src/datasets/scannet.py b/src/datasets/scannet.py index 0e38b96..a8cfa8d 100644 --- a/src/datasets/scannet.py +++ b/src/datasets/scannet.py @@ -1,8 +1,17 @@ from os import path as osp +from typing import Dict +from unicodedata import name + import numpy as np import torch import torch.utils as utils -from src.utils.dataset import read_scannet_gray, read_scannet_depth +from numpy.linalg import inv +from src.utils.dataset import ( + read_scannet_gray, + read_scannet_depth, + read_scannet_pose, + read_scannet_intrinsic +) class ScanNetDataset(utils.data.Dataset): @@ -13,6 +22,7 @@ class ScanNetDataset(utils.data.Dataset): mode='train', min_overlap_score=0.4, augment_fn=None, + pose_dir=None, **kwargs): """Manage one scene of ScanNet Dataset. Args: @@ -21,20 +31,20 @@ class ScanNetDataset(utils.data.Dataset): intrinsic_path (str): path to depth-camera intrinsic file. mode (str): options are ['train', 'val', 'test']. augment_fn (callable, optional): augments images with pre-defined visual effects. + pose_dir (str): ScanNet root directory that contains all poses. + (we use a separate (optional) pose_dir since we store images and poses separately.) """ super().__init__() self.root_dir = root_dir + self.pose_dir = pose_dir if pose_dir is not None else root_dir self.mode = mode # prepare data_names, intrinsics and extrinsics(T) with np.load(npz_path) as data: self.data_names = data['name'] - self.T_1to2s = data['rel_pose'] - # min_overlap_score criterion if 'score' in data.keys() and mode not in ['val' or 'test']: kept_mask = data['score'] > min_overlap_score self.data_names = self.data_names[kept_mask] - self.T_1to2s = self.T_1to2s[kept_mask] self.intrinsics = dict(np.load(intrinsic_path)) # for training LoFTR @@ -43,6 +53,18 @@ class ScanNetDataset(utils.data.Dataset): def __len__(self): return len(self.data_names) + def _read_abs_pose(self, scene_name, name): + pth = osp.join(self.pose_dir, + scene_name, + 'pose', f'{name}.txt') + return read_scannet_pose(pth) + + def _compute_rel_pose(self, scene_name, name0, name1): + pose0 = self._read_abs_pose(scene_name, name0) + pose1 = self._read_abs_pose(scene_name, name1) + + return np.matmul(pose1, inv(pose0)) # (4, 4) + def __getitem__(self, idx): data_name = self.data_names[idx] scene_name, scene_sub_name, stem_name_0, stem_name_1 = data_name @@ -51,10 +73,12 @@ class ScanNetDataset(utils.data.Dataset): # read the grayscale image which will be resized to (1, 480, 640) img_name0 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_0}.jpg') img_name1 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_1}.jpg') - image0 = read_scannet_gray(img_name0, resize=(640, 480), - augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) - image1 = read_scannet_gray(img_name1, resize=(640, 480), - augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + + # TODO: Support augmentation & handle seeds for each worker correctly. + image0 = read_scannet_gray(img_name0, resize=(640, 480), augment_fn=None) + # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + image1 = read_scannet_gray(img_name1, resize=(640, 480), augment_fn=None) + # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) # read the depthmap which is stored as (480, 640) if self.mode in ['train', 'val']: @@ -67,8 +91,8 @@ class ScanNetDataset(utils.data.Dataset): K_0 = K_1 = torch.tensor(self.intrinsics[scene_name].copy(), dtype=torch.float).reshape(3, 3) # read and compute relative poses - T_0to1 = torch.tensor(self.T_1to2s[idx].copy(), dtype=torch.float).reshape(3, 4) - T_0to1 = torch.cat([T_0to1, torch.tensor([[0., 0., 0., 1.]])], dim=0).reshape(4, 4) + T_0to1 = torch.tensor(self._compute_rel_pose(scene_name, stem_name_0, stem_name_1), + dtype=torch.float32) T_1to0 = T_0to1.inverse() data = { @@ -80,7 +104,7 @@ class ScanNetDataset(utils.data.Dataset): 'T_1to0': T_1to0, 'K0': K_0, # (3, 3) 'K1': K_1, - 'dataset_name': 'scannet', + 'dataset_name': 'ScanNet', 'scene_id': scene_name, 'pair_id': idx, 'pair_names': (osp.join(scene_name, 'color', f'{stem_name_0}.jpg'), diff --git a/src/lightning/data.py b/src/lightning/data.py index 609d2fa..4af645e 100644 --- a/src/lightning/data.py +++ b/src/lightning/data.py @@ -1,23 +1,38 @@ +import os +import math +from collections import abc from loguru import logger +from torch.utils.data.dataset import Dataset from tqdm import tqdm from os import path as osp +from pathlib import Path +from joblib import Parallel, delayed import pytorch_lightning as pl from torch import distributed as dist -from torch.utils.data import DataLoader, ConcatDataset, DistributedSampler +from torch.utils.data import ( + Dataset, + DataLoader, + ConcatDataset, + DistributedSampler, + RandomSampler, + dataloader +) from src.utils.augment import build_augmentor from src.utils.dataloader import get_local_split +from src.utils.misc import tqdm_joblib +from src.utils import comm from src.datasets.megadepth import MegaDepthDataset from src.datasets.scannet import ScanNetDataset +from src.datasets.sampler import RandomConcatSampler class MultiSceneDataModule(pl.LightningDataModule): """ - For distributed training, each training process is assgined + For distributed training, each training process is assgined only a part of the training scenes to reduce memory overhead. """ - def __init__(self, args, config): super().__init__() @@ -27,22 +42,26 @@ class MultiSceneDataModule(pl.LightningDataModule): self.test_data_source = config.DATASET.TEST_DATA_SOURCE # training and validating self.train_data_root = config.DATASET.TRAIN_DATA_ROOT + self.train_pose_root = config.DATASET.TRAIN_POSE_ROOT # (optional) self.train_npz_root = config.DATASET.TRAIN_NPZ_ROOT self.train_list_path = config.DATASET.TRAIN_LIST_PATH self.train_intrinsic_path = config.DATASET.TRAIN_INTRINSIC_PATH self.val_data_root = config.DATASET.VAL_DATA_ROOT + self.val_pose_root = config.DATASET.VAL_POSE_ROOT # (optional) self.val_npz_root = config.DATASET.VAL_NPZ_ROOT self.val_list_path = config.DATASET.VAL_LIST_PATH self.val_intrinsic_path = config.DATASET.VAL_INTRINSIC_PATH # testing self.test_data_root = config.DATASET.TEST_DATA_ROOT + self.test_pose_root = config.DATASET.TEST_POSE_ROOT # (optional) self.test_npz_root = config.DATASET.TEST_NPZ_ROOT self.test_list_path = config.DATASET.TEST_LIST_PATH self.test_intrinsic_path = config.DATASET.TEST_INTRINSIC_PATH # 2. dataset config # general options - self.min_overlap_score = config.DATASET.MIN_OVERLAP_SCORE # 0.4, omit data with overlap_score < min_overlap_score + self.min_overlap_score_test = config.DATASET.MIN_OVERLAP_SCORE_TEST # 0.4, omit data with overlap_score < min_overlap_score + self.min_overlap_score_train = config.DATASET.MIN_OVERLAP_SCORE_TRAIN self.augment_fn = build_augmentor(config.DATASET.AUGMENTATION_TYPE) # None, options: [None, 'dark', 'mobile'] # MegaDepth options @@ -53,23 +72,45 @@ class MultiSceneDataModule(pl.LightningDataModule): self.coarse_scale = 1 / config.LOFTR.RESOLUTION[0] # 0.125. for training loftr. # 3.loader parameters + self.train_loader_params = { + 'batch_size': args.batch_size, + 'num_workers': args.num_workers, + 'pin_memory': args.pin_memory, + } + self.val_loader_params = { + 'batch_size': 1, + 'shuffle': False, + 'num_workers': args.num_workers, + 'pin_memory': args.pin_memory, + } self.test_loader_params = { 'batch_size': 1, 'shuffle': False, 'num_workers': args.num_workers, 'pin_memory': True } + + # 4. sampler + self.data_sampler = config.TRAINER.DATA_SAMPLER + self.n_samples_per_subset = config.TRAINER.N_SAMPLES_PER_SUBSET + self.subset_replacement = config.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT + self.shuffle = config.TRAINER.SB_SUBSET_SHUFFLE + self.repeat = config.TRAINER.SB_REPEAT + + # (optional) RandomSampler for debugging + # misc configurations + self.parallel_load_data = getattr(args, 'parallel_load_data', False) self.seed = config.TRAINER.SEED # 66 def setup(self, stage=None): - """ + """ Setup train / val / test dataset. This method will be called by PL automatically. Args: stage (str): 'fit' in training phase, and 'test' in testing phase. """ - assert stage == 'test', "only support testing yet" + assert stage in ['fit', 'test'], "stage must be either fit or test" try: self.world_size = dist.get_world_size() @@ -80,14 +121,58 @@ class MultiSceneDataModule(pl.LightningDataModule): self.rank = 0 logger.warning(str(ae) + " (set wolrd_size=1 and rank=0)") - self.test_dataset = self._setup_dataset(self.test_data_root, - self.test_npz_root, - self.test_list_path, - self.test_intrinsic_path, - mode='test') - logger.info(f'[rank:{self.rank}]: Test Dataset loaded!') + if stage == 'fit': + self.train_dataset = self._setup_dataset( + self.train_data_root, + self.train_npz_root, + self.train_list_path, + self.train_intrinsic_path, + mode='train', + min_overlap_score=self.min_overlap_score_train, + pose_dir=self.train_pose_root) + # setup multiple (optional) validation subsets + if isinstance(self.val_list_path, (list, tuple)): + self.val_dataset = [] + if not isinstance(self.val_npz_root, (list, tuple)): + self.val_npz_root = [self.val_npz_root for _ in range(len(self.val_list_path))] + for npz_list, npz_root in zip(self.val_list_path, self.val_npz_root): + self.val_dataset.append(self._setup_dataset( + self.val_data_root, + npz_root, + npz_list, + self.val_intrinsic_path, + mode='val', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.val_pose_root)) + else: + self.val_dataset = self._setup_dataset( + self.val_data_root, + self.val_npz_root, + self.val_list_path, + self.val_intrinsic_path, + mode='val', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.val_pose_root) + logger.info(f'[rank:{self.rank}] Train & Val Dataset loaded!') + else: # stage == 'test + self.test_dataset = self._setup_dataset( + self.test_data_root, + self.test_npz_root, + self.test_list_path, + self.test_intrinsic_path, + mode='test', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.test_pose_root) + logger.info(f'[rank:{self.rank}]: Test Dataset loaded!') - def _setup_dataset(self, data_root, split_npz_root, scene_list_path, intri_path, mode='train'): + def _setup_dataset(self, + data_root, + split_npz_root, + scene_list_path, + intri_path, + mode='train', + min_overlap_score=0., + pose_dir=None): """ Setup train / val / test set""" with open(scene_list_path, 'r') as f: npz_names = [name.split()[0] for name in f.readlines()] @@ -97,14 +182,31 @@ class MultiSceneDataModule(pl.LightningDataModule): else: local_npz_names = npz_names logger.info(f'[rank {self.rank}]: {len(local_npz_names)} scene(s) assigned.') + + dataset_builder = self._build_concat_dataset_parallel \ + if self.parallel_load_data \ + else self._build_concat_dataset + return dataset_builder(data_root, local_npz_names, split_npz_root, intri_path, + mode=mode, min_overlap_score=min_overlap_score, pose_dir=pose_dir) - return self._build_concat_dataset(data_root, local_npz_names, split_npz_root, intri_path, mode=mode) - - def _build_concat_dataset(self, data_root, npz_names, npz_dir, intrinsic_path, mode): + def _build_concat_dataset( + self, + data_root, + npz_names, + npz_dir, + intrinsic_path, + mode, + min_overlap_score=0., + pose_dir=None + ): datasets = [] augment_fn = self.augment_fn if mode == 'train' else None data_source = self.trainval_data_source if mode in ['train', 'val'] else self.test_data_source - for npz_name in tqdm(npz_names, desc=f'[rank:{self.rank}], loading {mode} datasets', disable=int(self.rank) != 0): + if str(data_source).lower() == 'megadepth': + npz_names = [f'{n}.npz' for n in npz_names] + for npz_name in tqdm(npz_names, + desc=f'[rank:{self.rank}] loading {mode} datasets', + disable=int(self.rank) != 0): # `ScanNetDataset`/`MegaDepthDataset` load all data from npz_path when initialized, which might take time. npz_path = osp.join(npz_dir, npz_name) if data_source == 'ScanNet': @@ -113,14 +215,15 @@ class MultiSceneDataModule(pl.LightningDataModule): npz_path, intrinsic_path, mode=mode, - min_overlap_score=self.min_overlap_score, - augment_fn=augment_fn)) + min_overlap_score=min_overlap_score, + augment_fn=augment_fn, + pose_dir=pose_dir)) elif data_source == 'MegaDepth': datasets.append( MegaDepthDataset(data_root, npz_path, mode=mode, - min_overlap_score=self.min_overlap_score, + min_overlap_score=min_overlap_score, img_resize=self.mgdpt_img_resize, df=self.mgdpt_df, img_padding=self.mgdpt_img_pad, @@ -130,8 +233,88 @@ class MultiSceneDataModule(pl.LightningDataModule): else: raise NotImplementedError() return ConcatDataset(datasets) + + def _build_concat_dataset_parallel( + self, + data_root, + npz_names, + npz_dir, + intrinsic_path, + mode, + min_overlap_score=0., + pose_dir=None, + ): + augment_fn = self.augment_fn if mode == 'train' else None + data_source = self.trainval_data_source if mode in ['train', 'val'] else self.test_data_source + if str(data_source).lower() == 'megadepth': + npz_names = [f'{n}.npz' for n in npz_names] + with tqdm_joblib(tqdm(desc=f'[rank:{self.rank}] loading {mode} datasets', + total=len(npz_names), disable=int(self.rank) != 0)): + if data_source == 'ScanNet': + datasets = Parallel(n_jobs=math.floor(len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()))( + delayed(lambda x: _build_dataset( + ScanNetDataset, + data_root, + osp.join(npz_dir, x), + intrinsic_path, + mode=mode, + min_overlap_score=min_overlap_score, + augment_fn=augment_fn, + pose_dir=pose_dir))(name) + for name in npz_names) + elif data_source == 'MegaDepth': + # TODO: _pickle.PicklingError: Could not pickle the task to send it to the workers. + raise NotImplementedError() + datasets = Parallel(n_jobs=math.floor(len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()))( + delayed(lambda x: _build_dataset( + MegaDepthDataset, + data_root, + osp.join(npz_dir, x), + mode=mode, + min_overlap_score=min_overlap_score, + img_resize=self.mgdpt_img_resize, + df=self.mgdpt_df, + img_padding=self.mgdpt_img_pad, + depth_padding=self.mgdpt_depth_pad, + augment_fn=augment_fn, + coarse_scale=self.coarse_scale))(name) + for name in npz_names) + else: + raise ValueError(f'Unknown dataset: {data_source}') + return ConcatDataset(datasets) + + def train_dataloader(self): + """ Build training dataloader for ScanNet / MegaDepth. """ + assert self.data_sampler in ['scene_balance'] + logger.info(f'[rank:{self.rank}/{self.world_size}]: Train Sampler and DataLoader re-init (should not re-init between epochs!).') + if self.data_sampler == 'scene_balance': + sampler = RandomConcatSampler(self.train_dataset, + self.n_samples_per_subset, + self.subset_replacement, + self.shuffle, self.repeat, self.seed) + else: + sampler = None + dataloader = DataLoader(self.train_dataset, sampler=sampler, **self.train_loader_params) + return dataloader + + def val_dataloader(self): + """ Build validation dataloader for ScanNet / MegaDepth. """ + logger.info(f'[rank:{self.rank}/{self.world_size}]: Val Sampler and DataLoader re-init.') + if not isinstance(self.val_dataset, abc.Sequence): + sampler = DistributedSampler(self.val_dataset, shuffle=False) + return DataLoader(self.val_dataset, sampler=sampler, **self.val_loader_params) + else: + dataloaders = [] + for dataset in self.val_dataset: + sampler = DistributedSampler(dataset, shuffle=False) + dataloaders.append(DataLoader(dataset, sampler=sampler, **self.val_loader_params)) + return dataloaders def test_dataloader(self, *args, **kwargs): logger.info(f'[rank:{self.rank}/{self.world_size}]: Test Sampler and DataLoader re-init.') sampler = DistributedSampler(self.test_dataset, shuffle=False) return DataLoader(self.test_dataset, sampler=sampler, **self.test_loader_params) + + +def _build_dataset(dataset: Dataset, *args, **kwargs): + return dataset(*args, **kwargs) diff --git a/src/lightning/lightning_loftr.py b/src/lightning/lightning_loftr.py index aa25539..b4545a4 100644 --- a/src/lightning/lightning_loftr.py +++ b/src/lightning/lightning_loftr.py @@ -1,41 +1,97 @@ + +from collections import defaultdict import pprint from loguru import logger from pathlib import Path -import numpy as np import torch +import numpy as np import pytorch_lightning as pl +from matplotlib import pyplot as plt from src.loftr import LoFTR -from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors, aggregate_metrics - -from src.utils.comm import gather +from src.loftr.utils.supervision import compute_supervision_coarse, compute_supervision_fine +from src.losses.loftr_loss import LoFTRLoss +from src.optimizers import build_optimizer, build_scheduler +from src.utils.metrics import ( + compute_symmetrical_epipolar_errors, + compute_pose_errors, + aggregate_metrics +) +from src.utils.plotting import make_matching_figures +from src.utils.comm import gather, all_gather from src.utils.misc import lower_config, flattenList from src.utils.profiler import PassThroughProfiler class PL_LoFTR(pl.LightningModule): def __init__(self, config, pretrained_ckpt=None, profiler=None, dump_dir=None): - + """ + TODO: + - use the new version of PL logging API. + """ super().__init__() # Misc self.config = config # full config - self.loftr_cfg = lower_config(self.config.LOFTR) + _config = lower_config(self.config) + self.loftr_cfg = lower_config(_config['loftr']) self.profiler = profiler or PassThroughProfiler() - self.dump_dir = dump_dir + self.n_vals_plot = max(config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1) # Matcher: LoFTR - self.matcher = LoFTR(config=self.loftr_cfg) + self.matcher = LoFTR(config=_config['loftr']) + self.loss = LoFTRLoss(_config) # Pretrained weights if pretrained_ckpt: self.matcher.load_state_dict(torch.load(pretrained_ckpt, map_location='cpu')['state_dict']) logger.info(f"Load \'{pretrained_ckpt}\' as pretrained checkpoint") - - def test_step(self, batch, batch_idx): + + # Testing + self.dump_dir = dump_dir + + def configure_optimizers(self): + # FIXME: The scheduler did not work properly when `--resume_from_checkpoint` + optimizer = build_optimizer(self, self.config) + scheduler = build_scheduler(self.config, optimizer) + return [optimizer], [scheduler] + + def optimizer_step( + self, epoch, batch_idx, optimizer, optimizer_idx, + optimizer_closure, on_tpu, using_native_amp, using_lbfgs): + # learning rate warm up + warmup_step = self.config.TRAINER.WARMUP_STEP + if self.trainer.global_step < warmup_step: + if self.config.TRAINER.WARMUP_TYPE == 'linear': + base_lr = self.config.TRAINER.WARMUP_RATIO * self.config.TRAINER.TRUE_LR + lr = base_lr + \ + (self.trainer.global_step / self.config.TRAINER.WARMUP_STEP) * \ + abs(self.config.TRAINER.TRUE_LR - base_lr) + for pg in optimizer.param_groups: + pg['lr'] = lr + elif self.config.TRAINER.WARMUP_TYPE == 'constant': + pass + else: + raise ValueError(f'Unknown lr warm-up strategy: {self.config.TRAINER.WARMUP_TYPE}') + + # update params + optimizer.step(closure=optimizer_closure) + optimizer.zero_grad() + + def _trainval_inference(self, batch): + with self.profiler.profile("Compute coarse supervision"): + compute_supervision_coarse(batch, self.config) + with self.profiler.profile("LoFTR"): self.matcher(batch) - + + with self.profiler.profile("Compute fine supervision"): + compute_supervision_fine(batch, self.config) + + with self.profiler.profile("Compute losses"): + self.loss(batch) + + def _compute_metrics(self, batch): with self.profiler.profile("Copmute metrics"): compute_symmetrical_epipolar_errors(batch) # compute epi_errs for each match compute_pose_errors(batch, self.config) # compute R_errs, t_errs, pose_errs for each pair @@ -50,6 +106,106 @@ class PL_LoFTR(pl.LightningModule): 't_errs': batch['t_errs'], 'inliers': batch['inliers']} ret_dict = {'metrics': metrics} + return ret_dict, rel_pair_names + + def training_step(self, batch, batch_idx): + self._trainval_inference(batch) + + # logging + if self.trainer.global_rank == 0 and self.global_step % self.trainer.log_every_n_steps == 0: + # scalars + for k, v in batch['loss_scalars'].items(): + self.logger.experiment.add_scalar(f'train/{k}', v, self.global_step) + + # net-params + if self.config.LOFTR.MATCH_COARSE.MATCH_TYPE == 'sinkhorn': + self.logger.experiment.add_scalar( + f'skh_bin_score', self.matcher.coarse_matching.bin_score.clone().detach().cpu().data, self.global_step) + + # figures + if self.config.TRAINER.ENABLE_PLOTTING: + compute_symmetrical_epipolar_errors(batch) # compute epi_errs for each match + figures = make_matching_figures(batch, self.config, self.config.TRAINER.PLOT_MODE) + for k, v in figures.items(): + self.logger.experiment.add_figure(f'train_match/{k}', v, self.global_step) + + return {'loss': batch['loss']} + + def training_epoch_end(self, outputs): + avg_loss = torch.stack([x['loss'] for x in outputs]).mean() + if self.trainer.global_rank == 0: + self.logger.experiment.add_scalar( + 'train/avg_loss_on_epoch', avg_loss, + global_step=self.current_epoch) + + def validation_step(self, batch, batch_idx): + self._trainval_inference(batch) + + ret_dict, _ = self._compute_metrics(batch) + + val_plot_interval = max(self.trainer.num_val_batches[0] // self.n_vals_plot, 1) + figures = {self.config.TRAINER.PLOT_MODE: []} + if batch_idx % val_plot_interval == 0: + figures = make_matching_figures(batch, self.config, mode=self.config.TRAINER.PLOT_MODE) + + return { + **ret_dict, + 'loss_scalars': batch['loss_scalars'], + 'figures': figures, + } + + def validation_epoch_end(self, outputs): + # handle multiple validation sets + multi_outputs = [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs + multi_val_metrics = defaultdict(list) + + for valset_idx, outputs in enumerate(multi_outputs): + # since pl performs sanity_check at the very begining of the training + cur_epoch = self.trainer.current_epoch + if not self.trainer.resume_from_checkpoint and self.trainer.running_sanity_check: + cur_epoch = -1 + + # 1. loss_scalars: dict of list, on cpu + _loss_scalars = [o['loss_scalars'] for o in outputs] + loss_scalars = {k: flattenList(all_gather([_ls[k] for _ls in _loss_scalars])) for k in _loss_scalars[0]} + + # 2. val metrics: dict of list, numpy + _metrics = [o['metrics'] for o in outputs] + metrics = {k: flattenList(all_gather(flattenList([_me[k] for _me in _metrics]))) for k in _metrics[0]} + # NOTE: all ranks need to `aggregate_merics`, but only log at rank-0 + val_metrics_4tb = aggregate_metrics(metrics, self.config.TRAINER.EPI_ERR_THR) + for thr in [5, 10, 20]: + multi_val_metrics[f'auc@{thr}'].append(val_metrics_4tb[f'auc@{thr}']) + + # 3. figures + _figures = [o['figures'] for o in outputs] + figures = {k: flattenList(gather(flattenList([_me[k] for _me in _figures]))) for k in _figures[0]} + + # tensorboard records only on rank 0 + if self.trainer.global_rank == 0: + for k, v in loss_scalars.items(): + mean_v = torch.stack(v).mean() + self.logger.experiment.add_scalar(f'val_{valset_idx}/avg_{k}', mean_v, global_step=cur_epoch) + + for k, v in val_metrics_4tb.items(): + self.logger.experiment.add_scalar(f"metrics_{valset_idx}/{k}", v, global_step=cur_epoch) + + for k, v in figures.items(): + if self.trainer.global_rank == 0: + for plot_idx, fig in enumerate(v): + self.logger.experiment.add_figure( + f'val_match_{valset_idx}/{k}/pair-{plot_idx}', fig, cur_epoch, close=True) + plt.close('all') + + for thr in [5, 10, 20]: + # log on all ranks for ModelCheckpoint callback to work properly + self.log(f'auc@{thr}', torch.tensor(np.mean(multi_val_metrics[f'auc@{thr}']))) # ckpt monitors on this + + def test_step(self, batch, batch_idx): + with self.profiler.profile("LoFTR"): + self.matcher(batch) + + ret_dict, rel_pair_names = self._compute_metrics(batch) with self.profiler.profile("dump_results"): if self.dump_dir is not None: diff --git a/src/loftr/utils/coarse_matching.py b/src/loftr/utils/coarse_matching.py index ffa8bfa..fc0fc4a 100644 --- a/src/loftr/utils/coarse_matching.py +++ b/src/loftr/utils/coarse_matching.py @@ -3,6 +3,7 @@ import torch.nn as nn import torch.nn.functional as F from einops.einops import rearrange +INF = 1e9 def mask_border(m, b: int, v): """ Mask borders with value @@ -36,10 +37,23 @@ def mask_border_with_padding(m, bd, v, p_m0, p_m1): h0s, w0s = p_m0.sum(1).max(-1)[0].int(), p_m0.sum(-1).max(-1)[0].int() h1s, w1s = p_m1.sum(1).max(-1)[0].int(), p_m1.sum(-1).max(-1)[0].int() for b_idx, (h0, w0, h1, w1) in enumerate(zip(h0s, w0s, h1s, w1s)): - m[b_idx, h0-bd:] = v - m[b_idx, :, w0-bd:] = v - m[b_idx, :, :, h1-bd:] = v - m[b_idx, :, :, :, w1-bd:] = v + m[b_idx, h0 - bd:] = v + m[b_idx, :, w0 - bd:] = v + m[b_idx, :, :, h1 - bd:] = v + m[b_idx, :, :, :, w1 - bd:] = v + + +def compute_max_candidates(p_m0, p_m1): + """Compute the max candidates of all pairs within a batch + + Args: + p_m0, p_m1 (torch.Tensor): padded masks + """ + h0s, w0s = p_m0.sum(1).max(-1)[0], p_m0.sum(-1).max(-1)[0] + h1s, w1s = p_m1.sum(1).max(-1)[0], p_m1.sum(-1).max(-1)[0] + max_cand = torch.sum( + torch.min(torch.stack([h0s * w0s, h1s * w1s], -1), -1)[0]) + return max_cand class CoarseMatching(nn.Module): @@ -49,6 +63,9 @@ class CoarseMatching(nn.Module): # general config self.thr = config['thr'] self.border_rm = config['border_rm'] + # -- # for trainig fine-level LoFTR + self.train_coarse_percent = config['train_coarse_percent'] + self.train_pad_num_gt_min = config['train_pad_num_gt_min'] # we provide 2 options for differentiable matching self.match_type = config['match_type'] @@ -60,7 +77,8 @@ class CoarseMatching(nn.Module): except ImportError: raise ImportError("download superglue.py first!") self.log_optimal_transport = log_optimal_transport - self.bin_score = nn.Parameter(torch.tensor(config['skh_init_bin_score'], requires_grad=True)) + self.bin_score = nn.Parameter( + torch.tensor(config['skh_init_bin_score'], requires_grad=True)) self.skh_iters = config['skh_iters'] self.skh_prefilter = config['skh_prefilter'] else: @@ -88,26 +106,29 @@ class CoarseMatching(nn.Module): N, L, S, C = feat_c0.size(0), feat_c0.size(1), feat_c1.size(1), feat_c0.size(2) # normalize - feat_c0, feat_c1 = map(lambda feat: feat / feat.shape[-1]**.5, [feat_c0, feat_c1]) + feat_c0, feat_c1 = map(lambda feat: feat / feat.shape[-1]**.5, + [feat_c0, feat_c1]) if self.match_type == 'dual_softmax': - sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, feat_c1) / self.temperature + sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, + feat_c1) / self.temperature if mask_c0 is not None: - valid_sim_mask = mask_c0[..., None] * mask_c1[:, None] - _inf = torch.zeros_like(sim_matrix) - _inf[~valid_sim_mask.bool()] = -1e9 - del valid_sim_mask - sim_matrix += _inf + sim_matrix.masked_fill_( + ~(mask_c0[..., None] * mask_c1[:, None]).bool(), + -INF) conf_matrix = F.softmax(sim_matrix, 1) * F.softmax(sim_matrix, 2) elif self.match_type == 'sinkhorn': # sinkhorn, dustbin included sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, feat_c1) if mask_c0 is not None: - sim_matrix[:, :L, :S].masked_fill_(~(mask_c0[..., None] * mask_c1[:, None]).bool(), float('-inf')) + sim_matrix[:, :L, :S].masked_fill_( + ~(mask_c0[..., None] * mask_c1[:, None]).bool(), + -INF) # build uniform prior & use sinkhorn - log_assign_matrix = self.log_optimal_transport(sim_matrix, self.bin_score, self.skh_iters) + log_assign_matrix = self.log_optimal_transport( + sim_matrix, self.bin_score, self.skh_iters) assign_matrix = log_assign_matrix.exp() conf_matrix = assign_matrix[:, :-1, :-1] @@ -118,6 +139,9 @@ class CoarseMatching(nn.Module): conf_matrix[filter0[..., None].repeat(1, 1, S)] = 0 conf_matrix[filter1[:, None].repeat(1, L, 1)] = 0 + if self.config['sparse_spvs']: + data.update({'conf_matrix_with_bin': assign_matrix.clone()}) + data.update({'conf_matrix': conf_matrix}) # predict coarse matches from conf_matrix @@ -140,16 +164,24 @@ class CoarseMatching(nn.Module): 'mkpts1_c' (torch.Tensor): [M, 2], 'mconf' (torch.Tensor): [M]} """ - axes_lengths = {'h0c': data['hw0_c'][0], 'w0c': data['hw0_c'][1], - 'h1c': data['hw1_c'][0], 'w1c': data['hw1_c'][1]} + axes_lengths = { + 'h0c': data['hw0_c'][0], + 'w0c': data['hw0_c'][1], + 'h1c': data['hw1_c'][0], + 'w1c': data['hw1_c'][1] + } + _device = conf_matrix.device # 1. confidence thresholding mask = conf_matrix > self.thr - mask = rearrange(mask, 'b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c', **axes_lengths) + mask = rearrange(mask, 'b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c', + **axes_lengths) if 'mask0' not in data: mask_border(mask, self.border_rm, False) else: - mask_border_with_padding(mask, self.border_rm, False, data['mask0'], data['mask1']) - mask = rearrange(mask, 'b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)', **axes_lengths) + mask_border_with_padding(mask, self.border_rm, False, + data['mask0'], data['mask1']) + mask = rearrange(mask, 'b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)', + **axes_lengths) # 2. mutual nearest mask = mask \ @@ -163,6 +195,45 @@ class CoarseMatching(nn.Module): j_ids = all_j_ids[b_ids, i_ids] mconf = conf_matrix[b_ids, i_ids, j_ids] + # 4. Random sampling of training samples for fine-level LoFTR + # (optional) pad samples with gt coarse-level matches + # NOTE: + # The sampling is performed across all pairs in a batch without manually balancing + # #samples for fine-level increases w.r.t. batch_size + if 'mask0' not in data: + num_candidates_max = mask.size(0) * max( + mask.size(1), mask.size(2)) + else: + num_candidates_max = compute_max_candidates( + data['mask0'], data['mask1']) + num_matches_train = int(num_candidates_max * + self.train_coarse_percent) + num_matches_pred = len(b_ids) + assert self.train_pad_num_gt_min < num_matches_train, "min-num-gt-pad should be less than num-train-matches" + + # pred_indices is to select from prediction + if num_matches_pred <= num_matches_train - self.train_pad_num_gt_min: + pred_indices = torch.arange(num_matches_pred, device=_device) + else: + pred_indices = torch.randint( + num_matches_pred, + (num_matches_train - self.train_pad_num_gt_min, ), + device=_device) + + # gt_pad_indices is to select from gt padding. e.g. max(3787-4800, 200) + gt_pad_indices = torch.randint( + len(data['spv_b_ids']), + (max(num_matches_train - num_matches_pred, + self.train_pad_num_gt_min), ), + device=_device) + mconf_gt = torch.zeros(len(data['spv_b_ids']), device=_device) # set conf of gt paddings to all zero + + b_ids, i_ids, j_ids, mconf = map( + lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]], + dim=0), + *zip([b_ids, data['spv_b_ids']], [i_ids, data['spv_i_ids']], + [j_ids, data['spv_j_ids']], [mconf, mconf_gt])) + # These matches select patches that feed into fine-level network coarse_matches = {'b_ids': b_ids, 'i_ids': i_ids, 'j_ids': j_ids} @@ -170,14 +241,20 @@ class CoarseMatching(nn.Module): scale = data['hw0_i'][0] / data['hw0_c'][0] scale0 = scale * data['scale0'][b_ids] if 'scale0' in data else scale scale1 = scale * data['scale1'][b_ids] if 'scale1' in data else scale - mkpts0_c = torch.stack([i_ids % data['hw0_c'][1], i_ids // data['hw0_c'][1]], dim=1) * scale0 - mkpts1_c = torch.stack([j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]], dim=1) * scale1 + mkpts0_c = torch.stack( + [i_ids % data['hw0_c'][1], i_ids // data['hw0_c'][1]], + dim=1) * scale0 + mkpts1_c = torch.stack( + [j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]], + dim=1) * scale1 # These matches is the current prediction (for visualization) - coarse_matches.update({'gt_mask': mconf == 0, - 'm_bids': b_ids[mconf != 0], # mconf == 0 => gt matches - 'mkpts0_c': mkpts0_c[mconf != 0], - 'mkpts1_c': mkpts1_c[mconf != 0], - 'mconf': mconf[mconf != 0]}) + coarse_matches.update({ + 'gt_mask': mconf == 0, + 'm_bids': b_ids[mconf != 0], # mconf == 0 => gt matches + 'mkpts0_c': mkpts0_c[mconf != 0], + 'mkpts1_c': mkpts1_c[mconf != 0], + 'mconf': mconf[mconf != 0] + }) return coarse_matches diff --git a/src/loftr/utils/fine_matching.py b/src/loftr/utils/fine_matching.py index 54e8695..6e77ade 100644 --- a/src/loftr/utils/fine_matching.py +++ b/src/loftr/utils/fine_matching.py @@ -52,6 +52,9 @@ class FineMatching(nn.Module): # compute std over var = torch.sum(grid_normalized**2 * heatmap.view(-1, WW, 1), dim=1) - coords_normalized**2 # [M, 2] std = torch.sum(torch.sqrt(torch.clamp(var, min=1e-10)), -1) # [M] clamp needed for numerical stability + + # for fine-level supervision + data.update({'expec_f': torch.cat([coords_normalized, std.unsqueeze(1)], -1)}) # compute absolute kpt coords self.get_fine_match(coords_normalized, data) diff --git a/src/loftr/utils/geometry.py b/src/loftr/utils/geometry.py new file mode 100644 index 0000000..f95cdb6 --- /dev/null +++ b/src/loftr/utils/geometry.py @@ -0,0 +1,54 @@ +import torch + + +@torch.no_grad() +def warp_kpts(kpts0, depth0, depth1, T_0to1, K0, K1): + """ Warp kpts0 from I0 to I1 with depth, K and Rt + Also check covisibility and depth consistency. + Depth is consistent if relative error < 0.2 (hard-coded). + + Args: + kpts0 (torch.Tensor): [N, L, 2] - , + depth0 (torch.Tensor): [N, H, W], + depth1 (torch.Tensor): [N, H, W], + T_0to1 (torch.Tensor): [N, 3, 4], + K0 (torch.Tensor): [N, 3, 3], + K1 (torch.Tensor): [N, 3, 3], + Returns: + calculable_mask (torch.Tensor): [N, L] + warped_keypoints0 (torch.Tensor): [N, L, 2] + """ + kpts0_long = kpts0.round().long() + + # Sample depth, get calculable_mask on depth != 0 + kpts0_depth = torch.stack( + [depth0[i, kpts0_long[i, :, 1], kpts0_long[i, :, 0]] for i in range(kpts0.shape[0])], dim=0 + ) # (N, L) + nonzero_mask = kpts0_depth != 0 + + # Unproject + kpts0_h = torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1) * kpts0_depth[..., None] # (N, L, 3) + kpts0_cam = K0.inverse() @ kpts0_h.transpose(2, 1) # (N, 3, L) + + # Rigid Transform + w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]] # (N, 3, L) + w_kpts0_depth_computed = w_kpts0_cam[:, 2, :] + + # Project + w_kpts0_h = (K1 @ w_kpts0_cam).transpose(2, 1) # (N, L, 3) + w_kpts0 = w_kpts0_h[:, :, :2] / (w_kpts0_h[:, :, [2]] + 1e-4) # (N, L, 2), +1e-4 to avoid zero depth + + # Covisible Check + h, w = depth1.shape[1:3] + covisible_mask = (w_kpts0[:, :, 0] > 0) * (w_kpts0[:, :, 0] < w-1) * \ + (w_kpts0[:, :, 1] > 0) * (w_kpts0[:, :, 1] < h-1) + w_kpts0_long = w_kpts0.long() + w_kpts0_long[~covisible_mask, :] = 0 + + w_kpts0_depth = torch.stack( + [depth1[i, w_kpts0_long[i, :, 1], w_kpts0_long[i, :, 0]] for i in range(w_kpts0_long.shape[0])], dim=0 + ) # (N, L) + consistent_mask = ((w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth).abs() < 0.2 + valid_mask = nonzero_mask * covisible_mask * consistent_mask + + return valid_mask, w_kpts0 diff --git a/src/loftr/utils/supervision.py b/src/loftr/utils/supervision.py new file mode 100644 index 0000000..8ce6e79 --- /dev/null +++ b/src/loftr/utils/supervision.py @@ -0,0 +1,151 @@ +from math import log +from loguru import logger + +import torch +from einops import repeat +from kornia.utils import create_meshgrid + +from .geometry import warp_kpts + +############## ↓ Coarse-Level supervision ↓ ############## + + +@torch.no_grad() +def mask_pts_at_padded_regions(grid_pt, mask): + """For megadepth dataset, zero-padding exists in images""" + mask = repeat(mask, 'n h w -> n (h w) c', c=2) + grid_pt[~mask.bool()] = 0 + return grid_pt + + +@torch.no_grad() +def spvs_coarse(data, config): + """ + Update: + data (dict): { + "conf_matrix_gt": [N, hw0, hw1], + 'spv_b_ids': [M] + 'spv_i_ids': [M] + 'spv_j_ids': [M] + 'spv_w_pt0_i': [N, hw0, 2], in original image resolution + 'spv_pt1_i': [N, hw1, 2], in original image resolution + } + + NOTE: + - for scannet dataset, there're 3 kinds of resolution {i, c, f} + - for megadepth dataset, there're 4 kinds of resolution {i, i_resize, c, f} + """ + # 1. misc + device = data['image0'].device + N, _, H0, W0 = data['image0'].shape + _, _, H1, W1 = data['image1'].shape + scale = config['LOFTR']['RESOLUTION'][0] + scale0 = scale * data['scale0'][:, None] if 'scale0' in data else scale + scale1 = scale * data['scale1'][:, None] if 'scale0' in data else scale + h0, w0, h1, w1 = map(lambda x: x // scale, [H0, W0, H1, W1]) + + # 2. warp grids + # create kpts in meshgrid and resize them to image resolution + grid_pt0_c = create_meshgrid(h0, w0, False, device).reshape(1, h0*w0, 2).repeat(N, 1, 1) # [N, hw, 2] + grid_pt0_i = scale0 * grid_pt0_c + grid_pt1_c = create_meshgrid(h1, w1, False, device).reshape(1, h1*w1, 2).repeat(N, 1, 1) + grid_pt1_i = scale1 * grid_pt1_c + + # mask padded region to (0, 0), so no need to manually mask conf_matrix_gt + if 'mask0' in data: + grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data['mask0']) + grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data['mask1']) + + # warp kpts bi-directionally and resize them to coarse-level resolution + # (no depth consistency check, since it leads to worse results experimentally) + # (unhandled edge case: points with 0-depth will be warped to the left-up corner) + _, w_pt0_i = warp_kpts(grid_pt0_i, data['depth0'], data['depth1'], data['T_0to1'], data['K0'], data['K1']) + _, w_pt1_i = warp_kpts(grid_pt1_i, data['depth1'], data['depth0'], data['T_1to0'], data['K1'], data['K0']) + w_pt0_c = w_pt0_i / scale1 + w_pt1_c = w_pt1_i / scale0 + + # 3. check if mutual nearest neighbor + w_pt0_c_round = w_pt0_c[:, :, :].round().long() + nearest_index1 = w_pt0_c_round[..., 0] + w_pt0_c_round[..., 1] * w1 + w_pt1_c_round = w_pt1_c[:, :, :].round().long() + nearest_index0 = w_pt1_c_round[..., 0] + w_pt1_c_round[..., 1] * w0 + + # corner case: out of boundary + def out_bound_mask(pt, w, h): + return (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h) + nearest_index1[out_bound_mask(w_pt0_c_round, w1, h1)] = 0 + nearest_index0[out_bound_mask(w_pt1_c_round, w0, h0)] = 0 + + loop_back = torch.stack([nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0) + correct_0to1 = loop_back == torch.arange(h0*w0, device=device)[None].repeat(N, 1) + correct_0to1[:, 0] = False # ignore the top-left corner + + # 4. construct a gt conf_matrix + conf_matrix_gt = torch.zeros(N, h0*w0, h1*w1, device=device) + b_ids, i_ids = torch.where(correct_0to1 != 0) + j_ids = nearest_index1[b_ids, i_ids] + + conf_matrix_gt[b_ids, i_ids, j_ids] = 1 + data.update({'conf_matrix_gt': conf_matrix_gt}) + + # 5. save coarse matches(gt) for training fine level + if len(b_ids) == 0: + logger.warning(f"No groundtruth coarse match found for: {data['pair_names']}") + # this won't affect fine-level loss calculation + b_ids = torch.tensor([0], device=device) + i_ids = torch.tensor([0], device=device) + j_ids = torch.tensor([0], device=device) + + data.update({ + 'spv_b_ids': b_ids, + 'spv_i_ids': i_ids, + 'spv_j_ids': j_ids + }) + + # 6. save intermediate results (for fast fine-level computation) + data.update({ + 'spv_w_pt0_i': w_pt0_i, + 'spv_pt1_i': grid_pt1_i + }) + + +def compute_supervision_coarse(data, config): + assert len(set(data['dataset_name'])) == 1, "Do not support mixed datasets training!" + data_source = data['dataset_name'][0] + if data_source.lower() in ['scannet', 'megadepth']: + spvs_coarse(data, config) + else: + raise ValueError(f'Unknown data source: {data_source}') + + +############## ↓ Fine-Level supervision ↓ ############## + +@torch.no_grad() +def spvs_fine(data, config): + """ + Update: + data (dict):{ + "expec_f_gt": [M, 2]} + """ + # 1. misc + # w_pt0_i, pt1_i = data.pop('spv_w_pt0_i'), data.pop('spv_pt1_i') + w_pt0_i, pt1_i = data['spv_w_pt0_i'], data['spv_pt1_i'] + scale = config['LOFTR']['RESOLUTION'][1] + radius = config['LOFTR']['FINE_WINDOW_SIZE'] // 2 + + # 2. get coarse prediction + b_ids, i_ids, j_ids = data['b_ids'], data['i_ids'], data['j_ids'] + + # 3. compute gt + scale = scale * data['scale1'][b_ids] if 'scale0' in data else scale + # `expec_f_gt` might exceed the window, i.e. abs(*) > 1, which would be filtered later + expec_f_gt = (w_pt0_i[b_ids, i_ids] - pt1_i[b_ids, j_ids]) / scale / radius # [M, 2] + data.update({"expec_f_gt": expec_f_gt}) + + +def compute_supervision_fine(data, config): + data_source = data['dataset_name'][0] + if data_source.lower() in ['scannet', 'megadepth']: + spvs_fine(data, config) + else: + raise NotImplementedError diff --git a/src/losses/loftr_loss.py b/src/losses/loftr_loss.py new file mode 100644 index 0000000..be6b079 --- /dev/null +++ b/src/losses/loftr_loss.py @@ -0,0 +1,192 @@ +from loguru import logger + +import torch +import torch.nn as nn + + +class LoFTRLoss(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config # config under the global namespace + self.loss_config = config['loftr']['loss'] + self.match_type = self.config['loftr']['match_coarse']['match_type'] + self.sparse_spvs = self.config['loftr']['match_coarse']['sparse_spvs'] + + # coarse-level + self.correct_thr = self.loss_config['fine_correct_thr'] + self.c_pos_w = self.loss_config['pos_weight'] + self.c_neg_w = self.loss_config['neg_weight'] + # fine-level + self.fine_type = self.loss_config['fine_type'] + + def compute_coarse_loss(self, conf, conf_gt, weight=None): + """ Point-wise CE / Focal Loss with 0 / 1 confidence as gt. + Args: + conf (torch.Tensor): (N, HW0, HW1) / (N, HW0+1, HW1+1) + conf_gt (torch.Tensor): (N, HW0, HW1) + weight (torch.Tensor): (N, HW0, HW1) + """ + pos_mask, neg_mask = conf_gt == 1, conf_gt == 0 + c_pos_w, c_neg_w = self.c_pos_w, self.c_neg_w + # corner case: no gt coarse-level match at all + if not pos_mask.any(): # assign a wrong gt + pos_mask[0, 0, 0] = True + if weight is not None: + weight[0, 0, 0] = 0. + c_pos_w = 0. + if not neg_mask.any(): + neg_mask[0, 0, 0] = True + if weight is not None: + weight[0, 0, 0] = 0. + c_neg_w = 0. + + if self.loss_config['coarse_type'] == 'cross_entropy': + assert not self.sparse_spvs, 'Sparse Supervision for cross-entropy not implemented!' + conf = torch.clamp(conf, 1e-6, 1-1e-6) + loss_pos = - torch.log(conf[pos_mask]) + loss_neg = - torch.log(1 - conf[neg_mask]) + if weight is not None: + loss_pos = loss_pos * weight[pos_mask] + loss_neg = loss_neg * weight[neg_mask] + return c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() + elif self.loss_config['coarse_type'] == 'focal': + conf = torch.clamp(conf, 1e-6, 1-1e-6) + alpha = self.loss_config['focal_alpha'] + gamma = self.loss_config['focal_gamma'] + + if self.sparse_spvs: + pos_conf = conf[:, :-1, :-1][pos_mask] \ + if self.match_type == 'sinkhorn' \ + else conf[pos_mask] + loss_pos = - alpha * torch.pow(1 - pos_conf, gamma) * pos_conf.log() + # calculate losses for negative samples + if self.match_type == 'sinkhorn': + neg0, neg1 = conf_gt.sum(-1) == 0, conf_gt.sum(1) == 0 + neg_conf = torch.cat([conf[:, :-1, -1][neg0], conf[:, -1, :-1][neg1]], 0) + loss_neg = - alpha * torch.pow(1 - neg_conf, gamma) * neg_conf.log() + else: + # These is no dustbin for dual_softmax, so we left unmatchable patches without supervision. + # we could also add 'pseudo negtive-samples' + pass + # handle loss weights + if weight is not None: + # Different from dense-spvs, the loss w.r.t. padded regions aren't directly zeroed out, + # but only through manually setting corresponding regions in sim_matrix to '-inf'. + loss_pos = loss_pos * weight[pos_mask] + if self.match_type == 'sinkhorn': + neg_w0 = (weight.sum(-1) != 0)[neg0] + neg_w1 = (weight.sum(1) != 0)[neg1] + neg_mask = torch.cat([neg_w0, neg_w1], 0) + loss_neg = loss_neg[neg_mask] + + loss = c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() \ + if self.match_type == 'sinkhorn' \ + else c_pos_w * loss_pos.mean() + return loss + # positive and negative elements occupy similar propotions. => more balanced loss weights needed + else: # dense supervision (in the case of match_type=='sinkhorn', the dustbin is not supervised.) + loss_pos = - alpha * torch.pow(1 - conf[pos_mask], gamma) * (conf[pos_mask]).log() + loss_neg = - alpha * torch.pow(conf[neg_mask], gamma) * (1 - conf[neg_mask]).log() + if weight is not None: + loss_pos = loss_pos * weight[pos_mask] + loss_neg = loss_neg * weight[neg_mask] + return c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() + # each negative element occupy a smaller propotion than positive elements. => higher negative loss weight needed + else: + raise ValueError('Unknown coarse loss: {type}'.format(type=self.loss_config['coarse_type'])) + + def compute_fine_loss(self, expec_f, expec_f_gt): + if self.fine_type == 'l2_with_std': + return self._compute_fine_loss_l2_std(expec_f, expec_f_gt) + elif self.fine_type == 'l2': + return self._compute_fine_loss_l2(expec_f, expec_f_gt) + else: + raise NotImplementedError() + + def _compute_fine_loss_l2(self, expec_f, expec_f_gt): + """ + Args: + expec_f (torch.Tensor): [M, 2] + expec_f_gt (torch.Tensor): [M, 2] + """ + correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr + if correct_mask.sum() == 0: + if self.training: # this seldomly happen when training, since we pad prediction with gt + logger.warning("assign a false supervision to avoid ddp deadlock") + correct_mask[0] = True + else: + return None + offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask]) ** 2).sum(-1) + return offset_l2.mean() + + def _compute_fine_loss_l2_std(self, expec_f, expec_f_gt): + """ + Args: + expec_f (torch.Tensor): [M, 3] + expec_f_gt (torch.Tensor): [M, 2] + """ + # correct_mask tells you which pair to compute fine-loss + correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr + + # use std as weight that measures uncertainty + std = expec_f[:, 2] + inverse_std = 1. / torch.clamp(std, min=1e-10) + weight = (inverse_std / torch.mean(inverse_std)).detach() # avoid minizing loss through increase std + + # corner case: no correct coarse match found + if not correct_mask.any(): + if self.training: # this seldomly happen during training, since we pad prediction with gt + # sometimes there is not coarse-level gt at all. + logger.warning("assign a false supervision to avoid ddp deadlock") + correct_mask[0] = True + weight[0] = 0. + else: + return None + + # l2 loss with std + offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask, :2]) ** 2).sum(-1) + loss = (offset_l2 * weight[correct_mask]).mean() + + return loss + + @torch.no_grad() + def compute_c_weight(self, data): + """ compute element-wise weights for computing coarse-level loss. """ + if 'mask0' in data: + c_weight = (data['mask0'].flatten(-2)[..., None] * data['mask1'].flatten(-2)[:, None]).float() + else: + c_weight = None + return c_weight + + def forward(self, data): + """ + Update: + data (dict): update{ + 'loss': [1] the reduced loss across a batch, + 'loss_scalars' (dict): loss scalars for tensorboard_record + } + """ + loss_scalars = {} + # 0. compute element-wise loss weight + c_weight = self.compute_c_weight(data) + + # 1. coarse-level loss + loss_c = self.compute_coarse_loss( + data['conf_matrix_with_bin'] if self.sparse_spvs and self.match_type == 'sinkhorn' \ + else data['conf_matrix'], + data['conf_matrix_gt'], + weight=c_weight) + loss = loss_c * self.loss_config['coarse_weight'] + loss_scalars.update({"loss_c": loss_c.clone().detach().cpu()}) + + # 2. fine-level loss + loss_f = self.compute_fine_loss(data['expec_f'], data['expec_f_gt']) + if loss_f is not None: + loss += loss_f * self.loss_config['fine_weight'] + loss_scalars.update({"loss_f": loss_f.clone().detach().cpu()}) + else: + assert self.training is False + loss_scalars.update({'loss_f': torch.tensor(1.)}) # 1 is the upper bound + + loss_scalars.update({'loss': loss.clone().detach().cpu()}) + data.update({"loss": loss, "loss_scalars": loss_scalars}) diff --git a/src/optimizers/__init__.py b/src/optimizers/__init__.py new file mode 100644 index 0000000..e1db228 --- /dev/null +++ b/src/optimizers/__init__.py @@ -0,0 +1,42 @@ +import torch +from torch.optim.lr_scheduler import MultiStepLR, CosineAnnealingLR, ExponentialLR + + +def build_optimizer(model, config): + name = config.TRAINER.OPTIMIZER + lr = config.TRAINER.TRUE_LR + + if name == "adam": + return torch.optim.Adam(model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAM_DECAY) + elif name == "adamw": + return torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAMW_DECAY) + else: + raise ValueError(f"TRAINER.OPTIMIZER = {name} is not a valid optimizer!") + + +def build_scheduler(config, optimizer): + """ + Returns: + scheduler (dict):{ + 'scheduler': lr_scheduler, + 'interval': 'step', # or 'epoch' + 'monitor': 'val_f1', (optional) + 'frequency': x, (optional) + } + """ + scheduler = {'interval': config.TRAINER.SCHEDULER_INTERVAL} + name = config.TRAINER.SCHEDULER + + if name == 'MultiStepLR': + scheduler.update( + {'scheduler': MultiStepLR(optimizer, config.TRAINER.MSLR_MILESTONES, gamma=config.TRAINER.MSLR_GAMMA)}) + elif name == 'CosineAnnealing': + scheduler.update( + {'scheduler': CosineAnnealingLR(optimizer, config.TRAINER.COSA_TMAX)}) + elif name == 'ExponentialLR': + scheduler.update( + {'scheduler': ExponentialLR(optimizer, config.TRAINER.ELR_GAMMA)}) + else: + raise NotImplementedError() + + return scheduler diff --git a/src/utils/augment.py b/src/utils/augment.py index 3bf228e..d7c5d3e 100644 --- a/src/utils/augment.py +++ b/src/utils/augment.py @@ -39,6 +39,8 @@ class MobileAug(object): def build_augmentor(method=None, **kwargs): + if method is not None: + raise NotImplementedError('Using of augmentation functions are not supported yet!') if method == 'dark': return DarkAug() elif method == 'mobile': diff --git a/src/utils/dataloader.py b/src/utils/dataloader.py index 45cb79c..6da37b8 100644 --- a/src/utils/dataloader.py +++ b/src/utils/dataloader.py @@ -4,15 +4,16 @@ import numpy as np # --- PL-DATAMODULE --- def get_local_split(items: list, world_size: int, rank: int, seed: int): - """ The local rank only loads a split of dataset. """ + """ The local rank only loads a split of the dataset. """ n_items = len(items) items_permute = np.random.RandomState(seed).permutation(items) if n_items % world_size == 0: padded_items = items_permute else: - padding = np.random.RandomState(seed).choice(items, - world_size - (n_items % world_size), - replace=True) + padding = np.random.RandomState(seed).choice( + items, + world_size - (n_items % world_size), + replace=True) padded_items = np.concatenate([items_permute, padding]) assert len(padded_items) % world_size == 0, \ f'len(padded_items): {len(padded_items)}; world_size: {world_size}; len(padding): {len(padding)}' diff --git a/src/utils/dataset.py b/src/utils/dataset.py index 243a6fa..247a2cd 100644 --- a/src/utils/dataset.py +++ b/src/utils/dataset.py @@ -1,15 +1,46 @@ +import io +from loguru import logger + import cv2 import numpy as np import h5py import torch +from numpy.linalg import inv + +MEGADEPTH_CLIENT = SCANNET_CLIENT = None # --- DATA IO --- -def imread_gray(path, augment_fn=None): - if augment_fn is None: - image = cv2.imread(str(path), cv2.IMREAD_GRAYSCALE) +def load_array_from_s3( + path, client, cv_type, + use_h5py=False, +): + byte_str = client.Get(path) + try: + if not use_h5py: + raw_array = np.fromstring(byte_str, np.uint8) + data = cv2.imdecode(raw_array, cv_type) + else: + f = io.BytesIO(byte_str) + data = np.array(h5py.File(f, 'r')['/depth']) + except Exception as ex: + print(f"==> Data loading failure: {path}") + raise ex + + assert data is not None + return data + + +def imread_gray(path, augment_fn=None, client=SCANNET_CLIENT): + cv_type = cv2.IMREAD_GRAYSCALE if augment_fn is None \ + else cv2.IMREAD_COLOR + if str(path).startswith('s3://'): + image = load_array_from_s3(str(path), client, cv_type) else: + image = cv2.imread(str(path), cv_type) + + if augment_fn is not None: image = cv2.imread(str(path), cv2.IMREAD_COLOR) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = augment_fn(image) @@ -68,7 +99,7 @@ def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=No scale (torch.tensor): [w/w_new, h/h_new] """ # read image - image = imread_gray(path, augment_fn) + image = imread_gray(path, augment_fn, client=MEGADEPTH_CLIENT) # resize image w, h = image.shape[1], image.shape[0] @@ -91,7 +122,10 @@ def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=No def read_megadepth_depth(path, pad_to=None): - depth = np.array(h5py.File(path, 'r')['depth']) + if str(path).startswith('s3://'): + depth = load_array_from_s3(path, MEGADEPTH_CLIENT, None, use_h5py=True) + else: + depth = np.array(h5py.File(path, 'r')['depth']) if pad_to is not None: depth, _ = pad_bottom_right(depth, pad_to, ret_mask=False) depth = torch.from_numpy(depth).float() # (h, w) @@ -120,6 +154,28 @@ def read_scannet_gray(path, resize=(640, 480), augment_fn=None): def read_scannet_depth(path): - depth = cv2.imread(str(path), cv2.IMREAD_UNCHANGED) / 1000 + if str(path).startswith('s3://'): + depth = load_array_from_s3(str(path), SCANNET_CLIENT, cv2.IMREAD_UNCHANGED) + else: + depth = cv2.imread(str(path), cv2.IMREAD_UNCHANGED) + depth = depth / 1000 depth = torch.from_numpy(depth).float() # (h, w) return depth + + +def read_scannet_pose(path): + """ Read ScanNet's Camera2World pose and transform it to World2Camera. + + Returns: + pose_w2c (np.ndarray): (4, 4) + """ + cam2world = np.loadtxt(path, delimiter=' ') + world2cam = inv(cam2world) + return world2cam + + +def read_scannet_intrinsic(path): + """ Read ScanNet's intrinsic matrix and return the 3x3 matrix. + """ + intrinsic = np.loadtxt(path, delimiter=' ') + return intrinsic[:-1, :-1] diff --git a/src/utils/misc.py b/src/utils/misc.py index 445dfe1..9c8db04 100644 --- a/src/utils/misc.py +++ b/src/utils/misc.py @@ -1,7 +1,14 @@ -from loguru import logger -from yacs.config import CfgNode as CN +import os +import contextlib +import joblib +from typing import Union +from loguru import _Logger, logger from itertools import chain +import torch +from yacs.config import CfgNode as CN +from pytorch_lightning.utilities import rank_zero_only + def lower_config(yacs_cfg): if not isinstance(yacs_cfg, CN): @@ -21,21 +28,74 @@ def log_on(condition, message, level): logger.log(level, message) +def get_rank_zero_only_logger(logger: _Logger): + if rank_zero_only.rank == 0: + return logger + else: + for _level in logger._core.levels.keys(): + level = _level.lower() + setattr(logger, level, + lambda x: None) + logger._log = lambda x: None + return logger + + +def setup_gpus(gpus: Union[str, int]) -> int: + """ A temporary fix for pytorch-lighting 1.3.x """ + gpus = str(gpus) + gpu_ids = [] + + if ',' not in gpus: + n_gpus = int(gpus) + return n_gpus if n_gpus != -1 else torch.cuda.device_count() + else: + gpu_ids = [i.strip() for i in gpus.split(',') if i != ''] + + # setup environment variables + visible_devices = os.getenv('CUDA_VISIBLE_DEVICES') + if visible_devices is None: + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(i) for i in gpu_ids) + visible_devices = os.getenv('CUDA_VISIBLE_DEVICES') + logger.warning(f'[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}') + else: + logger.warning('[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process.') + return len(gpu_ids) + + def flattenList(x): return list(chain(*x)) -if __name__ == '__main__': - _CN = CN() - _CN.A = CN() - _CN.A.AA = CN() - _CN.A.AA.AAA = CN() - _CN.A.AA.AAA.AAAA = "AAAAA" +@contextlib.contextmanager +def tqdm_joblib(tqdm_object): + """Context manager to patch joblib to report into tqdm progress bar given as argument + + Usage: + with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar: + Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10)) + + When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing) + ret_vals = Parallel(n_jobs=args.world_size)( + delayed(lambda x: _compute_cov_score(pid, *x))(param) + for param in tqdm(combinations(image_ids, 2), + desc=f'Computing cov_score of [{pid}]', + total=len(image_ids)*(len(image_ids)-1)/2)) + Src: https://stackoverflow.com/a/58936697 + """ + class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def __call__(self, *args, **kwargs): + tqdm_object.update(n=self.batch_size) + return super().__call__(*args, **kwargs) - _CN.B = CN() - _CN.B.BB = CN() - _CN.B.BB.BBB = CN() - _CN.B.BB.BBB.BBBB = "BBBBB" + old_batch_callback = joblib.parallel.BatchCompletionCallBack + joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback + try: + yield tqdm_object + finally: + joblib.parallel.BatchCompletionCallBack = old_batch_callback + tqdm_object.close() - print(lower_config(_CN)) - print(lower_config(_CN.A)) diff --git a/src/utils/plotting.py b/src/utils/plotting.py index 0cc80a9..2b69609 100644 --- a/src/utils/plotting.py +++ b/src/utils/plotting.py @@ -1,13 +1,32 @@ +import bisect import numpy as np import matplotlib.pyplot as plt import matplotlib -# --- VISUALIZATION --- +def _compute_conf_thresh(data): + dataset_name = data['dataset_name'][0].lower() + if dataset_name == 'scannet': + thr = 5e-4 + elif dataset_name == 'megadepth': + thr = 1e-4 + else: + raise ValueError(f'Unknown dataset: {dataset_name}') + return thr + + +# --- VISUALIZATION --- # +def plot_keypoints(axes, kpts0, kpts1, color='w', ps=2): + axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps) + axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps) + -def make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=[], path=None): +def make_matching_figure( + img0, img1, mkpts0, mkpts1, color, + kpts0=None, kpts1=None, text=[], dpi=75, path=None): # draw image pair - fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=75) + assert mkpts0.shape[0] == mkpts1.shape[0], f'mkpts0: {mkpts0.shape[0]} v.s. mkpts1: {mkpts1.shape[0]}' + fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi) axes[0].imshow(img0, cmap='gray') axes[1].imshow(img1, cmap='gray') for i in range(2): # clear all frames @@ -16,17 +35,25 @@ def make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=[], path=None): for spine in axes[i].spines.values(): spine.set_visible(False) plt.tight_layout(pad=1) + + if kpts0 is not None: + assert kpts1 is not None + # plot_keypoints(axes, kpts0, kpts1, color='k', ps=4) + plot_keypoints(axes, kpts0, kpts1, color='w', ps=2) # draw matches - fig.canvas.draw() - transFigure = fig.transFigure.inverted() - fkpts0 = transFigure.transform(axes[0].transData.transform(mkpts0)) - fkpts1 = transFigure.transform(axes[1].transData.transform(mkpts1)) - fig.lines = [matplotlib.lines.Line2D((fkpts0[i, 0], fkpts1[i, 0]), (fkpts0[i, 1], fkpts1[i, 1]), - transform=fig.transFigure, c=color[i], linewidth=1) for i in range(len(mkpts0))] - - axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color, s=4) - axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color, s=4) + if mkpts0.shape[0] != 0 and mkpts1.shape[0] != 0: + fig.canvas.draw() + transFigure = fig.transFigure.inverted() + fkpts0 = transFigure.transform(axes[0].transData.transform(mkpts0)) + fkpts1 = transFigure.transform(axes[1].transData.transform(mkpts1)) + fig.lines = [matplotlib.lines.Line2D((fkpts0[i, 0], fkpts1[i, 0]), + (fkpts0[i, 1], fkpts1[i, 1]), + transform=fig.transFigure, c=color[i], linewidth=1) + for i in range(len(mkpts0))] + + axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color, s=4) + axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color, s=4) # put txts txt_color = 'k' if img0[:100, :200].mean() > 200 else 'w' @@ -42,6 +69,91 @@ def make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=[], path=None): return fig +def _make_evaluation_figure(data, b_id, alpha='dynamic'): + b_mask = data['m_bids'] == b_id + conf_thr = _compute_conf_thresh(data) + + img0 = (data['image0'][b_id][0].cpu().numpy() * 255).round().astype(np.int32) + img1 = (data['image1'][b_id][0].cpu().numpy() * 255).round().astype(np.int32) + kpts0 = data['mkpts0_f'][b_mask].cpu().numpy() + kpts1 = data['mkpts1_f'][b_mask].cpu().numpy() + + # for megadepth, we visualize matches on the resized image + if 'scale0' in data: + kpts0 = kpts0 / data['scale0'][b_id].cpu().numpy()[[1, 0]] + kpts1 = kpts1 / data['scale1'][b_id].cpu().numpy()[[1, 0]] + + epi_errs = data['epi_errs'][b_mask].cpu().numpy() + correct_mask = epi_errs < conf_thr + precision = np.mean(correct_mask) if len(correct_mask) > 0 else 0 + n_correct = np.sum(correct_mask) + n_gt_matches = int(data['conf_matrix_gt'][b_id].sum().cpu()) + recall = 0 if n_gt_matches == 0 else n_correct / (n_gt_matches) + # recall might be larger than 1, since the calculation of conf_matrix_gt + # uses groundtruth depths and camera poses, but epipolar distance is used here. + + # matching info + if alpha == 'dynamic': + alpha = dynamic_alpha(len(correct_mask)) + color = error_colormap(epi_errs, conf_thr, alpha=alpha) + + text = [ + f'Matches {len(kpts0)}', + f'Precision({conf_thr:.2e}) ({100 * precision:.1f}%): {n_correct}/{len(kpts0)}', + f'Recall({conf_thr:.2e}) ({100 * recall:.1f}%): {n_correct}/{n_gt_matches}' + ] + + # make the figure + figure = make_matching_figure(img0, img1, kpts0, kpts1, + color, text=text) + return figure + +def _make_confidence_figure(data, b_id): + # TODO: Implement confidence figure + raise NotImplementedError() + + +def make_matching_figures(data, config, mode='evaluation'): + """ Make matching figures for a batch. + Args: + data (Dict): a batch updated by PL_LoFTR. + config (Dict): matcher config + Returns: + figures (Dict[str, List[plt.figure]] + TODO: + - confidence mode plotting + - parallel plotting + - evaluation mode & confidence mode at the same time + """ + assert mode in ['evaluation', 'confidence'] # 'confidence' + figures = {mode: []} + for b_id in range(data['image0'].size(0)): + if mode == 'evaluation': + fig = _make_evaluation_figure( + data, b_id, + alpha=config.TRAINER.PLOT_MATCHES_ALPHA) + elif mode == 'confidence': + fig = _make_confidence_figure(data, b_id) + else: + raise ValueError(f'Unknown plot mode: {mode}') + figures[mode].append(fig) + return figures + + +def dynamic_alpha(n_matches, + milestones=[0, 300, 1000, 2000], + alphas=[1.0, 0.8, 0.4, 0.2]): + if n_matches == 0: + return 1.0 + ranges = list(zip(alphas, alphas[1:] + [None])) + loc = bisect.bisect_right(milestones, n_matches) - 1 + _range = ranges[loc] + if _range[1] is None: + return _range[0] + return _range[1] + (milestones[loc + 1] - n_matches) / ( + milestones[loc + 1] - milestones[loc]) * (_range[0] - _range[1]) + + def error_colormap(err, thr, alpha=1.0): assert alpha <= 1.0 and alpha > 0, f"Invaid alpha value: {alpha}" x = 1 - np.clip(err / (thr * 2), 0, 1) diff --git a/src/utils/profiler.py b/src/utils/profiler.py index fcecaea..6d21ed7 100644 --- a/src/utils/profiler.py +++ b/src/utils/profiler.py @@ -32,7 +32,6 @@ def build_profiler(name): return InferenceProfiler() elif name == 'pytorch': from pytorch_lightning.profiler import PyTorchProfiler - # TODO: this profiler will be introduced after upgrading pl dependency to 1.3.0 @zehong return PyTorchProfiler(use_cuda=True, profile_memory=True, row_limit=100) elif name is None: return PassThroughProfiler() diff --git a/third_party/SuperGluePretrainedNetwork b/third_party/SuperGluePretrainedNetwork new file mode 160000 index 0000000..c0626d5 --- /dev/null +++ b/third_party/SuperGluePretrainedNetwork @@ -0,0 +1 @@ +Subproject commit c0626d58c843ee0464b0fa1dd4de4059bfae0ab4 diff --git a/train.py b/train.py new file mode 100644 index 0000000..fb7b94f --- /dev/null +++ b/train.py @@ -0,0 +1,120 @@ +import math +import argparse +import pprint +from distutils.util import strtobool +from pathlib import Path +from loguru import logger as loguru_logger + +import pytorch_lightning as pl +from pytorch_lightning.utilities import rank_zero_only +from pytorch_lightning.loggers import TensorBoardLogger +from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor +from pytorch_lightning.plugins import DDPPlugin + +from src.config.default import get_cfg_defaults +from src.utils.misc import get_rank_zero_only_logger, setup_gpus +from src.utils.profiler import build_profiler +from src.lightning.data import MultiSceneDataModule +from src.lightning.lightning_loftr import PL_LoFTR + +loguru_logger = get_rank_zero_only_logger(loguru_logger) + + +def parse_args(): + # init a costum parser which will be added into pl.Trainer parser + # check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + parser.add_argument( + parser.add_argument( + '--exp_name', type=str, default='default_exp_name') + parser.add_argument( + '--batch_size', type=int, default=4, help='batch_size per gpu') + parser.add_argument( + '--num_workers', type=int, default=4) + parser.add_argument( + '--pin_memory', type=lambda x: bool(strtobool(x)), + nargs='?', default=True, help='whether loading data to pinned memory or not') + parser.add_argument( + '--ckpt_path', type=str, default=None, + parser.add_argument( + '--disable_ckpt', action='store_true', + help='disable checkpoint saving (useful for debugging).') + parser.add_argument( + '--profiler_name', type=str, default=None, + help='options: [inference, pytorch], or leave it unset') + parser.add_argument( + '--parallel_load_data', action='store_true', + help='load datasets in with multiple processes.') + + parser = pl.Trainer.add_argparse_args(parser) + return parser.parse_args() + + +def main(): + # parse arguments + args = parse_args() + rank_zero_only(pprint.pprint)(vars(args)) + + # init default-cfg and merge it with the main- and data-cfg + config = get_cfg_defaults() + config.merge_from_file(args.main_cfg_path) + config.merge_from_file(args.data_cfg_path) + pl.seed_everything(config.TRAINER.SEED) # reproducibility + # TODO: Use different seeds for each dataloader workers + # This is needed for data augmentation + + # scale lr and warmup-step automatically + args.gpus = _n_gpus = setup_gpus(args.gpus) + config.TRAINER.WORLD_SIZE = _n_gpus * args.num_nodes + config.TRAINER.TRUE_BATCH_SIZE = config.TRAINER.WORLD_SIZE * args.batch_size + _scaling = config.TRAINER.TRUE_BATCH_SIZE / config.TRAINER.CANONICAL_BS + config.TRAINER.SCALING = _scaling + config.TRAINER.TRUE_LR = config.TRAINER.CANONICAL_LR * _scaling + config.TRAINER.WARMUP_STEP = math.floor(config.TRAINER.WARMUP_STEP / _scaling) + + # lightning module + profiler = build_profiler(args.profiler_name) + model = PL_LoFTR(config, pretrained_ckpt=args.ckpt_path, profiler=profiler) + loguru_logger.info(f"LoFTR LightningModule initialized!") + + # lightning data + data_module = MultiSceneDataModule(args, config) + loguru_logger.info(f"LoFTR DataModule initialized!") + + # TensorBoard Logger + logger = TensorBoardLogger(save_dir='logs/tb_logs', name=args.exp_name, default_hp_metric=False) + ckpt_dir = Path(logger.log_dir) / 'checkpoints' + + # Callbacks + # TODO: update ModelCheckpoint to monitor multiple metrics + ckpt_callback = ModelCheckpoint(monitor='auc@10', verbose=True, save_top_k=5, mode='max', + save_last=True, + dirpath=str(ckpt_dir), + filename='{epoch}-{auc@5:.3f}-{auc@10:.3f}-{auc@20:.3f}') + lr_monitor = LearningRateMonitor(logging_interval='step') + callbacks = [lr_monitor] + if not args.disable_ckpt: + callbacks.append(ckpt_callback) + + # Lightning Trainer + trainer = pl.Trainer.from_argparse_args( + args, + plugins=DDPPlugin(find_unused_parameters=False, + num_nodes=args.num_nodes, + sync_batchnorm=config.TRAINER.WORLD_SIZE > 0), + gradient_clip_val=config.TRAINER.GRADIENT_CLIPPING, + callbacks=callbacks, + logger=logger, + sync_batchnorm=config.TRAINER.WORLD_SIZE > 0, + replace_sampler_ddp=False, # use custom sampler + reload_dataloaders_every_epoch=False, # avoid repeated samples! + weights_summary='full', + profiler=profiler) + loguru_logger.info(f"Trainer initialized!") + loguru_logger.info(f"Start training!") + trainer.fit(model, datamodule=data_module) + + +if __name__ == '__main__': + main()