diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index f7f0b28..0000000 --- a/.gitmodules +++ /dev/null @@ -1,3 +0,0 @@ -[submodule "third_party/SuperGluePretrainedNetwork"] - path = third_party/SuperGluePretrainedNetwork - url = https://github.com/magicleap/SuperGluePretrainedNetwork.git diff --git a/assets/loftr-github-demo.gif b/assets/loftr-github-demo.gif deleted file mode 100644 index f1c7276..0000000 Binary files a/assets/loftr-github-demo.gif and /dev/null differ diff --git a/assets/megadepth_test_1500_scene_info/0015_0.1_0.3.npz b/assets/megadepth_test_1500_scene_info/0015_0.1_0.3.npz deleted file mode 100644 index 0e4820c..0000000 Binary files a/assets/megadepth_test_1500_scene_info/0015_0.1_0.3.npz and /dev/null differ diff --git a/assets/megadepth_test_1500_scene_info/0015_0.3_0.5.npz b/assets/megadepth_test_1500_scene_info/0015_0.3_0.5.npz deleted file mode 100644 index 44bb564..0000000 Binary files a/assets/megadepth_test_1500_scene_info/0015_0.3_0.5.npz and /dev/null differ diff --git a/assets/megadepth_test_1500_scene_info/0022_0.1_0.3.npz b/assets/megadepth_test_1500_scene_info/0022_0.1_0.3.npz deleted file mode 100644 index af1e44c..0000000 Binary files a/assets/megadepth_test_1500_scene_info/0022_0.1_0.3.npz and /dev/null differ diff --git a/assets/megadepth_test_1500_scene_info/0022_0.3_0.5.npz b/assets/megadepth_test_1500_scene_info/0022_0.3_0.5.npz deleted file mode 100644 index 42d26c6..0000000 Binary files a/assets/megadepth_test_1500_scene_info/0022_0.3_0.5.npz and /dev/null differ diff --git a/assets/megadepth_test_1500_scene_info/0022_0.5_0.7.npz b/assets/megadepth_test_1500_scene_info/0022_0.5_0.7.npz deleted file mode 100644 index bc6bf77..0000000 Binary files a/assets/megadepth_test_1500_scene_info/0022_0.5_0.7.npz and /dev/null differ diff --git a/assets/megadepth_test_1500_scene_info/megadepth_test_1500.txt b/assets/megadepth_test_1500_scene_info/megadepth_test_1500.txt deleted file mode 100644 index 85a2e16..0000000 --- a/assets/megadepth_test_1500_scene_info/megadepth_test_1500.txt +++ /dev/null @@ -1,5 +0,0 @@ -0022_0.1_0.3 -0015_0.1_0.3 -0015_0.3_0.5 -0022_0.3_0.5 -0022_0.5_0.7 \ No newline at end of file diff --git a/assets/phototourism_sample_images/london_bridge_19481797_2295892421.jpg b/assets/phototourism_sample_images/london_bridge_19481797_2295892421.jpg deleted file mode 100644 index 83e835f..0000000 Binary files a/assets/phototourism_sample_images/london_bridge_19481797_2295892421.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/london_bridge_49190386_5209386933.jpg b/assets/phototourism_sample_images/london_bridge_49190386_5209386933.jpg deleted file mode 100644 index 8134ddc..0000000 Binary files a/assets/phototourism_sample_images/london_bridge_49190386_5209386933.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/london_bridge_78916675_4568141288.jpg b/assets/phototourism_sample_images/london_bridge_78916675_4568141288.jpg deleted file mode 100644 index 6401ec8..0000000 Binary files a/assets/phototourism_sample_images/london_bridge_78916675_4568141288.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/london_bridge_94185272_3874562886.jpg b/assets/phototourism_sample_images/london_bridge_94185272_3874562886.jpg deleted file mode 100644 index e46d69d..0000000 Binary files a/assets/phototourism_sample_images/london_bridge_94185272_3874562886.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/piazza_san_marco_06795901_3725050516.jpg b/assets/phototourism_sample_images/piazza_san_marco_06795901_3725050516.jpg deleted file mode 100644 index cc42580..0000000 Binary files a/assets/phototourism_sample_images/piazza_san_marco_06795901_3725050516.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/piazza_san_marco_15148634_5228701572.jpg b/assets/phototourism_sample_images/piazza_san_marco_15148634_5228701572.jpg deleted file mode 100644 index d37c9bc..0000000 Binary files a/assets/phototourism_sample_images/piazza_san_marco_15148634_5228701572.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/piazza_san_marco_18627786_5929294590.jpg b/assets/phototourism_sample_images/piazza_san_marco_18627786_5929294590.jpg deleted file mode 100644 index 96e09ab..0000000 Binary files a/assets/phototourism_sample_images/piazza_san_marco_18627786_5929294590.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/piazza_san_marco_43351518_2659980686.jpg b/assets/phototourism_sample_images/piazza_san_marco_43351518_2659980686.jpg deleted file mode 100644 index 5034cfe..0000000 Binary files a/assets/phototourism_sample_images/piazza_san_marco_43351518_2659980686.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/piazza_san_marco_58751010_4849458397.jpg b/assets/phototourism_sample_images/piazza_san_marco_58751010_4849458397.jpg deleted file mode 100644 index 6574355..0000000 Binary files a/assets/phototourism_sample_images/piazza_san_marco_58751010_4849458397.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/st_pauls_cathedral_30776973_2635313996.jpg b/assets/phototourism_sample_images/st_pauls_cathedral_30776973_2635313996.jpg deleted file mode 100644 index a7eb12c..0000000 Binary files a/assets/phototourism_sample_images/st_pauls_cathedral_30776973_2635313996.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/st_pauls_cathedral_37347628_10902811376.jpg b/assets/phototourism_sample_images/st_pauls_cathedral_37347628_10902811376.jpg deleted file mode 100644 index badec2c..0000000 Binary files a/assets/phototourism_sample_images/st_pauls_cathedral_37347628_10902811376.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg b/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg deleted file mode 100644 index 3e94268..0000000 Binary files a/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg and /dev/null differ diff --git a/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg b/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg deleted file mode 100644 index c7aae1d..0000000 Binary files a/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0711_00_frame-001680.jpg b/assets/scannet_sample_images/scene0711_00_frame-001680.jpg deleted file mode 100644 index 1f5d4a0..0000000 Binary files a/assets/scannet_sample_images/scene0711_00_frame-001680.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0711_00_frame-001995.jpg b/assets/scannet_sample_images/scene0711_00_frame-001995.jpg deleted file mode 100644 index b439907..0000000 Binary files a/assets/scannet_sample_images/scene0711_00_frame-001995.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0713_00_frame-001320.jpg b/assets/scannet_sample_images/scene0713_00_frame-001320.jpg deleted file mode 100644 index fe5db63..0000000 Binary files a/assets/scannet_sample_images/scene0713_00_frame-001320.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0713_00_frame-002025.jpg b/assets/scannet_sample_images/scene0713_00_frame-002025.jpg deleted file mode 100644 index 15dc881..0000000 Binary files a/assets/scannet_sample_images/scene0713_00_frame-002025.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0721_00_frame-000375.jpg b/assets/scannet_sample_images/scene0721_00_frame-000375.jpg deleted file mode 100644 index b6a0d12..0000000 Binary files a/assets/scannet_sample_images/scene0721_00_frame-000375.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0721_00_frame-002745.jpg b/assets/scannet_sample_images/scene0721_00_frame-002745.jpg deleted file mode 100644 index 1925d56..0000000 Binary files a/assets/scannet_sample_images/scene0721_00_frame-002745.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0722_00_frame-000045.jpg b/assets/scannet_sample_images/scene0722_00_frame-000045.jpg deleted file mode 100644 index e661870..0000000 Binary files a/assets/scannet_sample_images/scene0722_00_frame-000045.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0722_00_frame-000735.jpg b/assets/scannet_sample_images/scene0722_00_frame-000735.jpg deleted file mode 100644 index 1e288b8..0000000 Binary files a/assets/scannet_sample_images/scene0722_00_frame-000735.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0726_00_frame-000135.jpg b/assets/scannet_sample_images/scene0726_00_frame-000135.jpg deleted file mode 100644 index 27c580b..0000000 Binary files a/assets/scannet_sample_images/scene0726_00_frame-000135.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0726_00_frame-000210.jpg b/assets/scannet_sample_images/scene0726_00_frame-000210.jpg deleted file mode 100644 index 8b617d8..0000000 Binary files a/assets/scannet_sample_images/scene0726_00_frame-000210.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0737_00_frame-000930.jpg b/assets/scannet_sample_images/scene0737_00_frame-000930.jpg deleted file mode 100644 index 9fd8133..0000000 Binary files a/assets/scannet_sample_images/scene0737_00_frame-000930.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0737_00_frame-001095.jpg b/assets/scannet_sample_images/scene0737_00_frame-001095.jpg deleted file mode 100644 index 5733499..0000000 Binary files a/assets/scannet_sample_images/scene0737_00_frame-001095.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0738_00_frame-000885.jpg b/assets/scannet_sample_images/scene0738_00_frame-000885.jpg deleted file mode 100644 index a8c916b..0000000 Binary files a/assets/scannet_sample_images/scene0738_00_frame-000885.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0738_00_frame-001065.jpg b/assets/scannet_sample_images/scene0738_00_frame-001065.jpg deleted file mode 100644 index 9a72e9e..0000000 Binary files a/assets/scannet_sample_images/scene0738_00_frame-001065.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0743_00_frame-000000.jpg b/assets/scannet_sample_images/scene0743_00_frame-000000.jpg deleted file mode 100644 index 36acdfe..0000000 Binary files a/assets/scannet_sample_images/scene0743_00_frame-000000.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0743_00_frame-001275.jpg b/assets/scannet_sample_images/scene0743_00_frame-001275.jpg deleted file mode 100644 index 1749022..0000000 Binary files a/assets/scannet_sample_images/scene0743_00_frame-001275.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0744_00_frame-000585.jpg b/assets/scannet_sample_images/scene0744_00_frame-000585.jpg deleted file mode 100644 index f763f0f..0000000 Binary files a/assets/scannet_sample_images/scene0744_00_frame-000585.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0744_00_frame-002310.jpg b/assets/scannet_sample_images/scene0744_00_frame-002310.jpg deleted file mode 100644 index 18f1795..0000000 Binary files a/assets/scannet_sample_images/scene0744_00_frame-002310.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0747_00_frame-000000.jpg b/assets/scannet_sample_images/scene0747_00_frame-000000.jpg deleted file mode 100644 index e290f4f..0000000 Binary files a/assets/scannet_sample_images/scene0747_00_frame-000000.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0747_00_frame-001530.jpg b/assets/scannet_sample_images/scene0747_00_frame-001530.jpg deleted file mode 100644 index ddee6c5..0000000 Binary files a/assets/scannet_sample_images/scene0747_00_frame-001530.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0752_00_frame-000075.jpg b/assets/scannet_sample_images/scene0752_00_frame-000075.jpg deleted file mode 100644 index 91f696c..0000000 Binary files a/assets/scannet_sample_images/scene0752_00_frame-000075.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0752_00_frame-001440.jpg b/assets/scannet_sample_images/scene0752_00_frame-001440.jpg deleted file mode 100644 index b2f65a3..0000000 Binary files a/assets/scannet_sample_images/scene0752_00_frame-001440.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0755_00_frame-000120.jpg b/assets/scannet_sample_images/scene0755_00_frame-000120.jpg deleted file mode 100644 index c939888..0000000 Binary files a/assets/scannet_sample_images/scene0755_00_frame-000120.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0755_00_frame-002055.jpg b/assets/scannet_sample_images/scene0755_00_frame-002055.jpg deleted file mode 100644 index 6b73343..0000000 Binary files a/assets/scannet_sample_images/scene0755_00_frame-002055.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0758_00_frame-000165.jpg b/assets/scannet_sample_images/scene0758_00_frame-000165.jpg deleted file mode 100644 index bc0a81c..0000000 Binary files a/assets/scannet_sample_images/scene0758_00_frame-000165.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0758_00_frame-000510.jpg b/assets/scannet_sample_images/scene0758_00_frame-000510.jpg deleted file mode 100644 index b569954..0000000 Binary files a/assets/scannet_sample_images/scene0758_00_frame-000510.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0768_00_frame-001095.jpg b/assets/scannet_sample_images/scene0768_00_frame-001095.jpg deleted file mode 100644 index aeb1e10..0000000 Binary files a/assets/scannet_sample_images/scene0768_00_frame-001095.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0768_00_frame-003435.jpg b/assets/scannet_sample_images/scene0768_00_frame-003435.jpg deleted file mode 100644 index 1422cf5..0000000 Binary files a/assets/scannet_sample_images/scene0768_00_frame-003435.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0806_00_frame-000225.jpg b/assets/scannet_sample_images/scene0806_00_frame-000225.jpg deleted file mode 100644 index 221215d..0000000 Binary files a/assets/scannet_sample_images/scene0806_00_frame-000225.jpg and /dev/null differ diff --git a/assets/scannet_sample_images/scene0806_00_frame-001095.jpg b/assets/scannet_sample_images/scene0806_00_frame-001095.jpg deleted file mode 100644 index cc68972..0000000 Binary files a/assets/scannet_sample_images/scene0806_00_frame-001095.jpg and /dev/null differ diff --git a/assets/scannet_test_1500/intrinsics.npz b/assets/scannet_test_1500/intrinsics.npz deleted file mode 100644 index b653fad..0000000 Binary files a/assets/scannet_test_1500/intrinsics.npz and /dev/null differ diff --git a/assets/scannet_test_1500/scannet_test.txt b/assets/scannet_test_1500/scannet_test.txt deleted file mode 100644 index 45cc7ff..0000000 --- a/assets/scannet_test_1500/scannet_test.txt +++ /dev/null @@ -1 +0,0 @@ -test.npz \ No newline at end of file diff --git a/assets/scannet_test_1500/statistics.json b/assets/scannet_test_1500/statistics.json deleted file mode 100644 index 0e3ff58..0000000 --- a/assets/scannet_test_1500/statistics.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "scene0707_00": 15, - "scene0708_00": 15, - "scene0709_00": 15, - "scene0710_00": 15, - "scene0711_00": 15, - "scene0712_00": 15, - "scene0713_00": 15, - "scene0714_00": 15, - "scene0715_00": 15, - "scene0716_00": 15, - "scene0717_00": 15, - "scene0718_00": 15, - "scene0719_00": 15, - "scene0720_00": 15, - "scene0721_00": 15, - "scene0722_00": 15, - "scene0723_00": 15, - "scene0724_00": 15, - "scene0725_00": 15, - "scene0726_00": 15, - "scene0727_00": 15, - "scene0728_00": 15, - "scene0729_00": 15, - "scene0730_00": 15, - "scene0731_00": 15, - "scene0732_00": 15, - "scene0733_00": 15, - "scene0734_00": 15, - "scene0735_00": 15, - "scene0736_00": 15, - "scene0737_00": 15, - "scene0738_00": 15, - "scene0739_00": 15, - "scene0740_00": 15, - "scene0741_00": 15, - "scene0742_00": 15, - "scene0743_00": 15, - "scene0744_00": 15, - "scene0745_00": 15, - "scene0746_00": 15, - "scene0747_00": 15, - "scene0748_00": 15, - "scene0749_00": 15, - "scene0750_00": 15, - "scene0751_00": 15, - "scene0752_00": 15, - "scene0753_00": 15, - "scene0754_00": 15, - "scene0755_00": 15, - "scene0756_00": 15, - "scene0757_00": 15, - "scene0758_00": 15, - "scene0759_00": 15, - "scene0760_00": 15, - "scene0761_00": 15, - "scene0762_00": 15, - "scene0763_00": 15, - "scene0764_00": 15, - "scene0765_00": 15, - "scene0766_00": 15, - "scene0767_00": 15, - "scene0768_00": 15, - "scene0769_00": 15, - "scene0770_00": 15, - "scene0771_00": 15, - "scene0772_00": 15, - "scene0773_00": 15, - "scene0774_00": 15, - "scene0775_00": 15, - "scene0776_00": 15, - "scene0777_00": 15, - "scene0778_00": 15, - "scene0779_00": 15, - "scene0780_00": 15, - "scene0781_00": 15, - "scene0782_00": 15, - "scene0783_00": 15, - "scene0784_00": 15, - "scene0785_00": 15, - "scene0786_00": 15, - "scene0787_00": 15, - "scene0788_00": 15, - "scene0789_00": 15, - "scene0790_00": 15, - "scene0791_00": 15, - "scene0792_00": 15, - "scene0793_00": 15, - "scene0794_00": 15, - "scene0795_00": 15, - "scene0796_00": 15, - "scene0797_00": 15, - "scene0798_00": 15, - "scene0799_00": 15, - "scene0800_00": 15, - "scene0801_00": 15, - "scene0802_00": 15, - "scene0803_00": 15, - "scene0804_00": 15, - "scene0805_00": 15, - "scene0806_00": 15 -} \ No newline at end of file diff --git a/assets/scannet_test_1500/test.npz b/assets/scannet_test_1500/test.npz deleted file mode 100644 index a1e07f7..0000000 Binary files a/assets/scannet_test_1500/test.npz and /dev/null differ diff --git a/configs/data/__init__.py b/configs/data/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/configs/data/base.py b/configs/data/base.py deleted file mode 100644 index 03aab16..0000000 --- a/configs/data/base.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -The data config will be the last one merged into the main config. -Setups in data configs will override all existed setups! -""" - -from yacs.config import CfgNode as CN -_CN = CN() -_CN.DATASET = CN() -_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_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 deleted file mode 100644 index 94548af..0000000 --- a/configs/data/debug/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -* -*/ -!.gitignore diff --git a/configs/data/megadepth_test_1500.py b/configs/data/megadepth_test_1500.py deleted file mode 100644 index 5cc98a0..0000000 --- a/configs/data/megadepth_test_1500.py +++ /dev/null @@ -1,11 +0,0 @@ -from configs.data.base import cfg - -TEST_BASE_PATH = "assets/megadepth_test_1500_scene_info" - -cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth" -cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test" -cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}" -cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/megadepth_test_1500.txt" - -cfg.DATASET.MGDPT_IMG_RESIZE = 840 -cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 diff --git a/configs/data/megadepth_trainval_640.py b/configs/data/megadepth_trainval_640.py deleted file mode 100644 index b86791d..0000000 --- a/configs/data/megadepth_trainval_640.py +++ /dev/null @@ -1,22 +0,0 @@ -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 deleted file mode 100644 index 130212c..0000000 --- a/configs/data/megadepth_trainval_840.py +++ /dev/null @@ -1,22 +0,0 @@ -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_test_1500.py b/configs/data/scannet_test_1500.py deleted file mode 100644 index 60e560f..0000000 --- a/configs/data/scannet_test_1500.py +++ /dev/null @@ -1,11 +0,0 @@ -from configs.data.base import cfg - -TEST_BASE_PATH = "assets/scannet_test_1500" - -cfg.DATASET.TEST_DATA_SOURCE = "ScanNet" -cfg.DATASET.TEST_DATA_ROOT = "data/scannet/test" -cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}" -cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/scannet_test.txt" -cfg.DATASET.TEST_INTRINSIC_PATH = f"{TEST_BASE_PATH}/intrinsics.npz" - -cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 diff --git a/configs/data/scannet_trainval.py b/configs/data/scannet_trainval.py deleted file mode 100644 index c38d644..0000000 --- a/configs/data/scannet_trainval.py +++ /dev/null @@ -1,17 +0,0 @@ -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/buggy_pos_enc/loftr_ds.py b/configs/loftr/indoor/buggy_pos_enc/loftr_ds.py deleted file mode 100644 index b84a922..0000000 --- a/configs/loftr/indoor/buggy_pos_enc/loftr_ds.py +++ /dev/null @@ -1,6 +0,0 @@ -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = False -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/buggy_pos_enc/loftr_ds_dense.py b/configs/loftr/indoor/buggy_pos_enc/loftr_ds_dense.py deleted file mode 100644 index 20192d2..0000000 --- a/configs/loftr/indoor/buggy_pos_enc/loftr_ds_dense.py +++ /dev/null @@ -1,8 +0,0 @@ -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = False -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/indoor/buggy_pos_enc/loftr_ot.py b/configs/loftr/indoor/buggy_pos_enc/loftr_ot.py deleted file mode 100644 index 7231c8d..0000000 --- a/configs/loftr/indoor/buggy_pos_enc/loftr_ot.py +++ /dev/null @@ -1,6 +0,0 @@ -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = False -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/buggy_pos_enc/loftr_ot_dense.py b/configs/loftr/indoor/buggy_pos_enc/loftr_ot_dense.py deleted file mode 100644 index 3b42c4f..0000000 --- a/configs/loftr/indoor/buggy_pos_enc/loftr_ot_dense.py +++ /dev/null @@ -1,8 +0,0 @@ -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = False -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/indoor/debug/.gitignore b/configs/loftr/indoor/debug/.gitignore deleted file mode 100644 index 94548af..0000000 --- a/configs/loftr/indoor/debug/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -* -*/ -!.gitignore diff --git a/configs/loftr/indoor/loftr_ds.py b/configs/loftr/indoor/loftr_ds.py deleted file mode 100644 index c78018b..0000000 --- a/configs/loftr/indoor/loftr_ds.py +++ /dev/null @@ -1,5 +0,0 @@ -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 deleted file mode 100644 index b923b8c..0000000 --- a/configs/loftr/indoor/loftr_ds_dense.py +++ /dev/null @@ -1,7 +0,0 @@ -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/indoor/loftr_ot.py b/configs/loftr/indoor/loftr_ot.py deleted file mode 100644 index 33b8d7e..0000000 --- a/configs/loftr/indoor/loftr_ot.py +++ /dev/null @@ -1,5 +0,0 @@ -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 deleted file mode 100644 index 6a897d1..0000000 --- a/configs/loftr/indoor/loftr_ot_dense.py +++ /dev/null @@ -1,7 +0,0 @@ -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/indoor/scannet/loftr_ds_eval.py b/configs/loftr/indoor/scannet/loftr_ds_eval.py deleted file mode 100644 index 115fd9d..0000000 --- a/configs/loftr/indoor/scannet/loftr_ds_eval.py +++ /dev/null @@ -1,16 +0,0 @@ -""" A config only for reproducing the ScanNet evaluation results. - -We remove border matches by default, but the originally implemented -`remove_border()` has a bug, leading to only two sides of -all borders are actually removed. However, the [bug fix](https://github.com/zju3dv/LoFTR/commit/e9146c8144dea5f3cbdd98b225f3e147a171c216) -makes the scannet evaluation results worse (auc@10=40.8 => 39.5), which should be -caused by tiny result fluctuation of few image pairs. This config set `BORDER_RM` to 0 -to be consistent with the results in our paper. -""" - -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = False -cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' - -cfg.LOFTR.MATCH_COARSE.BORDER_RM = 0 diff --git a/configs/loftr/indoor/scannet/loftr_ds_eval_new.py b/configs/loftr/indoor/scannet/loftr_ds_eval_new.py deleted file mode 100644 index 4a98cbb..0000000 --- a/configs/loftr/indoor/scannet/loftr_ds_eval_new.py +++ /dev/null @@ -1,18 +0,0 @@ -""" A config only for reproducing the ScanNet evaluation results. - -We remove border matches by default, but the originally implemented -`remove_border()` has a bug, leading to only two sides of -all borders are actually removed. However, the [bug fix](https://github.com/zju3dv/LoFTR/commit/e9146c8144dea5f3cbdd98b225f3e147a171c216) -makes the scannet evaluation results worse (auc@10=40.8 => 39.5), which should be -caused by tiny result fluctuation of few image pairs. This config set `BORDER_RM` to 0 -to be consistent with the results in our paper. - -Update: This config is for testing the re-trained model with the pos-enc bug fixed. -""" - -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = True -cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' - -cfg.LOFTR.MATCH_COARSE.BORDER_RM = 0 diff --git a/configs/loftr/outdoor/buggy_pos_enc/loftr_ds.py b/configs/loftr/outdoor/buggy_pos_enc/loftr_ds.py deleted file mode 100644 index 49d7978..0000000 --- a/configs/loftr/outdoor/buggy_pos_enc/loftr_ds.py +++ /dev/null @@ -1,16 +0,0 @@ -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = False -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/buggy_pos_enc/loftr_ds_dense.py b/configs/loftr/outdoor/buggy_pos_enc/loftr_ds_dense.py deleted file mode 100644 index e36b319..0000000 --- a/configs/loftr/outdoor/buggy_pos_enc/loftr_ds_dense.py +++ /dev/null @@ -1,17 +0,0 @@ -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = False -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/buggy_pos_enc/loftr_ot.py b/configs/loftr/outdoor/buggy_pos_enc/loftr_ot.py deleted file mode 100644 index 0f91d02..0000000 --- a/configs/loftr/outdoor/buggy_pos_enc/loftr_ot.py +++ /dev/null @@ -1,16 +0,0 @@ -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = False -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/buggy_pos_enc/loftr_ot_dense.py b/configs/loftr/outdoor/buggy_pos_enc/loftr_ot_dense.py deleted file mode 100644 index d23e6bb..0000000 --- a/configs/loftr/outdoor/buggy_pos_enc/loftr_ot_dense.py +++ /dev/null @@ -1,17 +0,0 @@ -from src.config.default import _CN as cfg - -cfg.LOFTR.COARSE.TEMP_BUG_FIX = False -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/configs/loftr/outdoor/debug/.gitignore b/configs/loftr/outdoor/debug/.gitignore deleted file mode 100644 index 94548af..0000000 --- a/configs/loftr/outdoor/debug/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -* -*/ -!.gitignore diff --git a/configs/loftr/outdoor/loftr_ds.py b/configs/loftr/outdoor/loftr_ds.py deleted file mode 100644 index 2406e70..0000000 --- a/configs/loftr/outdoor/loftr_ds.py +++ /dev/null @@ -1,15 +0,0 @@ -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 deleted file mode 100644 index 2b1be4c..0000000 --- a/configs/loftr/outdoor/loftr_ds_dense.py +++ /dev/null @@ -1,16 +0,0 @@ -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 deleted file mode 100644 index f0e3a79..0000000 --- a/configs/loftr/outdoor/loftr_ot.py +++ /dev/null @@ -1,15 +0,0 @@ -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 deleted file mode 100644 index 5b30e04..0000000 --- a/configs/loftr/outdoor/loftr_ot_dense.py +++ /dev/null @@ -1,16 +0,0 @@ -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 deleted file mode 100644 index 5e7d273..0000000 --- a/data/megadepth/index/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -# Ignore everything in this directory -* -# Except this file -!.gitignore diff --git a/data/megadepth/test/.gitignore b/data/megadepth/test/.gitignore deleted file mode 100644 index 5e7d273..0000000 --- a/data/megadepth/test/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -# Ignore everything in this directory -* -# Except this file -!.gitignore diff --git a/data/megadepth/train/.gitignore b/data/megadepth/train/.gitignore deleted file mode 100644 index 5e7d273..0000000 --- a/data/megadepth/train/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -# Ignore everything in this directory -* -# Except this file -!.gitignore diff --git a/data/scannet/index/.gitignore b/data/scannet/index/.gitignore deleted file mode 100644 index 94548af..0000000 --- a/data/scannet/index/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -* -*/ -!.gitignore diff --git a/data/scannet/intrinsics.npz b/data/scannet/intrinsics.npz deleted file mode 100644 index 8235880..0000000 Binary files a/data/scannet/intrinsics.npz and /dev/null differ diff --git a/data/scannet/test/.gitignore b/data/scannet/test/.gitignore deleted file mode 100644 index 94548af..0000000 --- a/data/scannet/test/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -* -*/ -!.gitignore diff --git a/data/scannet/train b/data/scannet/train deleted file mode 120000 index 43d51fa..0000000 --- a/data/scannet/train +++ /dev/null @@ -1 +0,0 @@ -/mnt/lustre/share/3dv/dataset/scannet/out/output \ No newline at end of file diff --git a/demo/demo_loftr.py b/demo/demo_loftr.py deleted file mode 100644 index 60974d9..0000000 --- a/demo/demo_loftr.py +++ /dev/null @@ -1,240 +0,0 @@ -front_matter = """ ------------------------------------------------------------------------- -Online demo for [LoFTR](https://zju3dv.github.io/loftr/). - -This demo is heavily inspired by [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork/). -We thank the authors for their execellent work. ------------------------------------------------------------------------- -""" - -import os -import argparse -from pathlib import Path -import cv2 -import torch -import numpy as np -import matplotlib.cm as cm - -os.sys.path.append("../") # Add the project directory -from src.loftr import LoFTR, default_cfg -from src.config.default import get_cfg_defaults -try: - from demo.utils import (AverageTimer, VideoStreamer, - make_matching_plot_fast, make_matching_plot, frame2tensor) -except: - raise ImportError("This demo requires utils.py from SuperGlue, please use run_demo.sh to start this script.") - - -torch.set_grad_enabled(False) - -if __name__ == '__main__': - parser = argparse.ArgumentParser( - description='LoFTR online demo', - formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument('--weight', type=str, help="Path to the checkpoint.") - parser.add_argument( - '--input', type=str, default='0', - help='ID of a USB webcam, URL of an IP camera, ' - 'or path to an image directory or movie file') - parser.add_argument( - '--output_dir', type=str, default=None, - help='Directory where to write output frames (If None, no output)') - parser.add_argument( - '--image_glob', type=str, nargs='+', default=['*.png', '*.jpg', '*.jpeg'], - help='Glob if a directory of images is specified') - parser.add_argument( - '--skip', type=int, default=1, - help='Images to skip if input is a movie or directory') - parser.add_argument( - '--max_length', type=int, default=1000000, - help='Maximum length if input is a movie or directory') - parser.add_argument( - '--resize', type=int, nargs='+', default=[640, 480], - help='Resize the input image before running inference. If two numbers, ' - 'resize to the exact dimensions, if one number, resize the max ' - 'dimension, if -1, do not resize') - parser.add_argument( - '--no_display', action='store_true', - help='Do not display images to screen. Useful if running remotely') - parser.add_argument( - '--save_video', action='store_true', - help='Save output (with match visualizations) to a video.') - parser.add_argument( - '--save_input', action='store_true', - help='Save the input images to a video (for gathering repeatable input source).') - parser.add_argument( - '--skip_frames', type=int, default=1, - help="Skip frames from webcam input.") - parser.add_argument( - '--top_k', type=int, default=2000, help="The max vis_range (please refer to the code).") - parser.add_argument( - '--bottom_k', type=int, default=0, help="The min vis_range (please refer to the code).") - - opt = parser.parse_args() - print(front_matter) - parser.print_help() - - if len(opt.resize) == 2 and opt.resize[1] == -1: - opt.resize = opt.resize[0:1] - if len(opt.resize) == 2: - print('Will resize to {}x{} (WxH)'.format( - opt.resize[0], opt.resize[1])) - elif len(opt.resize) == 1 and opt.resize[0] > 0: - print('Will resize max dimension to {}'.format(opt.resize[0])) - elif len(opt.resize) == 1: - print('Will not resize images') - else: - raise ValueError('Cannot specify more than two integers for --resize') - - if torch.cuda.is_available(): - device = 'cuda' - else: - raise RuntimeError("GPU is required to run this demo.") - - # Initialize LoFTR - matcher = LoFTR(config=default_cfg) - matcher.load_state_dict(torch.load(opt.weight)['state_dict']) - matcher = matcher.eval().to(device=device) - - # Configure I/O - if opt.save_video: - print('Writing video to loftr-matches.mp4...') - writer = cv2.VideoWriter('loftr-matches.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 15, (640*2 + 10, 480)) - if opt.save_input: - print('Writing video to demo-input.mp4...') - input_writer = cv2.VideoWriter('demo-input.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 15, (640, 480)) - - vs = VideoStreamer(opt.input, opt.resize, opt.skip, - opt.image_glob, opt.max_length) - frame, ret = vs.next_frame() - assert ret, 'Error when reading the first frame (try different --input?)' - - frame_id = 0 - last_image_id = 0 - frame_tensor = frame2tensor(frame, device) - last_data = {'image0': frame_tensor} - last_frame = frame - - if opt.output_dir is not None: - print('==> Will write outputs to {}'.format(opt.output_dir)) - Path(opt.output_dir).mkdir(exist_ok=True) - - # Create a window to display the demo. - if not opt.no_display: - window_name = 'LoFTR Matches' - cv2.namedWindow(window_name, cv2.WINDOW_NORMAL) - cv2.resizeWindow(window_name, (640*2, 480)) - else: - print('Skipping visualization, will not show a GUI.') - - # Print the keyboard help menu. - print('==> Keyboard control:\n' - '\tn: select the current frame as the reference image (left)\n' - '\td/f: move the range of the matches (ranked by confidence) to visualize\n' - '\tc/v: increase/decrease the length of the visualization range (i.e., total number of matches) to show\n' - '\tq: quit') - - timer = AverageTimer() - vis_range = [opt.bottom_k, opt.top_k] - - while True: - frame_id += 1 - frame, ret = vs.next_frame() - if frame_id % opt.skip_frames != 0: - # print("Skipping frame.") - continue - if opt.save_input: - inp = np.stack([frame]*3, -1) - inp_rgb = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB) - input_writer.write(inp_rgb) - if not ret: - print('Finished demo_loftr.py') - break - timer.update('data') - stem0, stem1 = last_image_id, vs.i - 1 - - frame_tensor = frame2tensor(frame, device) - last_data = {**last_data, 'image1': frame_tensor} - matcher(last_data) - - total_n_matches = len(last_data['mkpts0_f']) - mkpts0 = last_data['mkpts0_f'].cpu().numpy()[vis_range[0]:vis_range[1]] - mkpts1 = last_data['mkpts1_f'].cpu().numpy()[vis_range[0]:vis_range[1]] - mconf = last_data['mconf'].cpu().numpy()[vis_range[0]:vis_range[1]] - - # Normalize confidence. - if len(mconf) > 0: - conf_vis_min = 0. - conf_min = mconf.min() - conf_max = mconf.max() - mconf = (mconf - conf_vis_min) / (conf_max - conf_vis_min + 1e-5) - - timer.update('forward') - alpha = 0 - color = cm.jet(mconf, alpha=alpha) - - text = [ - f'LoFTR', - '# Matches (showing/total): {}/{}'.format(len(mkpts0), total_n_matches), - ] - small_text = [ - f'Showing matches from {vis_range[0]}:{vis_range[1]}', - f'Confidence Range: {conf_min:.2f}:{conf_max:.2f}', - 'Image Pair: {:06}:{:06}'.format(stem0, stem1), - ] - out = make_matching_plot_fast( - last_frame, frame, mkpts0, mkpts1, mkpts0, mkpts1, color, text, - path=None, show_keypoints=False, small_text=small_text) - - # Save high quality png, optionally with dynamic alpha support (unreleased yet). - # save_path = 'demo_vid/{:06}'.format(frame_id) - # make_matching_plot( - # last_frame, frame, mkpts0, mkpts1, mkpts0, mkpts1, color, text, - # path=save_path, show_keypoints=opt.show_keypoints, small_text=small_text) - - if not opt.no_display: - if opt.save_video: - writer.write(out) - cv2.imshow('LoFTR Matches', out) - key = chr(cv2.waitKey(1) & 0xFF) - if key == 'q': - if opt.save_video: - writer.release() - if opt.save_input: - input_writer.release() - vs.cleanup() - print('Exiting...') - break - elif key == 'n': - last_data['image0'] = frame_tensor - last_frame = frame - last_image_id = (vs.i - 1) - frame_id_left = frame_id - elif key in ['d', 'f']: - if key == 'd': - if vis_range[0] >= 0: - vis_range[0] -= 200 - vis_range[1] -= 200 - if key =='f': - vis_range[0] += 200 - vis_range[1] += 200 - print(f'\nChanged the vis_range to {vis_range[0]}:{vis_range[1]}') - elif key in ['c', 'v']: - if key == 'c': - vis_range[1] -= 50 - if key =='v': - vis_range[1] += 50 - print(f'\nChanged the vis_range[1] to {vis_range[1]}') - elif opt.output_dir is not None: - stem = 'matches_{:06}_{:06}'.format(stem0, stem1) - out_file = str(Path(opt.output_dir, stem + '.png')) - print('\nWriting image to {}'.format(out_file)) - cv2.imwrite(out_file, out) - else: - raise ValueError("output_dir is required when no display is given.") - timer.update('viz') - timer.print() - - - cv2.destroyAllWindows() - vs.cleanup() diff --git a/demo/run_demo.sh b/demo/run_demo.sh deleted file mode 100755 index a498ace..0000000 --- a/demo/run_demo.sh +++ /dev/null @@ -1,34 +0,0 @@ -#!/bin/bash -set -e -# set -x - -if [ ! -f utils.py ]; then - echo "Downloading utils.py from the SuperGlue repo." - echo "We cannot provide this file directly due to its strict licence." - wget https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/utils.py -fi - -# Use webcam 0 as input source. -input=0 -# or use a pre-recorded video given the path. -# input=/home/sunjiaming/Downloads/scannet_test/$scene_name.mp4 - -# Toggle indoor/outdoor model here. -model_ckpt=../weights/indoor_ds.ckpt -# model_ckpt=../weights/outdoor_ds.ckpt - -# Optionally assign the GPU ID. -# export CUDA_VISIBLE_DEVICES=0 - -echo "Running LoFTR demo.." -eval "$(conda shell.bash hook)" -conda activate loftr -python demo_loftr.py --weight $model_ckpt --input $input -# To save the input video and output match visualizations. -# python demo_loftr.py --weight $model_ckpt --input $input --save_video --save_input - -# Running on remote GPU servers with no GUI. -# Save images first. -# python demo_loftr.py --weight $model_ckpt --input $input --no_display --output_dir="./demo_images/" -# Then convert them to a video. -# ffmpeg -framerate 15 -pattern_type glob -i '*.png' -c:v libx264 -r 30 -pix_fmt yuv420p out.mp4 diff --git a/docs/TRAINING.md b/docs/TRAINING.md deleted file mode 100644 index 2c2735e..0000000 --- a/docs/TRAINING.md +++ /dev/null @@ -1,85 +0,0 @@ - -# Traininig LoFTR - -## Dataset setup -Generally, two parts of data are needed for training LoFTR, the original dataset, i.e., ScanNet and MegaDepth, and the offline generated dataset indices. The dataset indices store scenes, image pairs, and other metadata within each dataset used for training/validation/testing. For the MegaDepth dataset, the relative poses between images used for training are directly cached in the indexing files. However, the relative poses of ScanNet image pairs are not stored due to the enormous resulting file size. - -### Download datasets -#### MegaDepth -We use depth maps provided in the [original MegaDepth dataset](https://www.cs.cornell.edu/projects/megadepth/) as well as undistorted images, corresponding camera intrinsics and extrinsics preprocessed by [D2-Net](https://github.com/mihaidusmanu/d2-net#downloading-and-preprocessing-the-megadepth-dataset). You can download them separately from the following links. -- [MegaDepth undistorted images and processed depths](https://www.cs.cornell.edu/projects/megadepth/dataset/Megadepth_v1/MegaDepth_v1.tar.gz) - - Note that we only use depth maps. - - Path of the download data will be referreed to as `/path/to/megadepth` -- [D2-Net preprocessed images](https://drive.google.com/drive/folders/1hxpOsqOZefdrba_BqnW490XpNX_LgXPB) - - Images are undistorted manually in D2-Net since the undistorted images from MegaDepth do not come with corresponding intrinsics. - - Path of the download data will be referreed to as `/path/to/megadepth_d2net` - -#### ScanNet -Please set up the ScanNet dataset following [the official guide](https://github.com/ScanNet/ScanNet#scannet-data) -> NOTE: We use the [python exported data](https://github.com/ScanNet/ScanNet/tree/master/SensReader/python), -instead of the [c++ exported one](https://github.com/ScanNet/ScanNet/tree/master/SensReader/c%2B%2B). - -### Download the dataset indices - -You can download the required dataset indices from the [following link](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf). -After downloading, unzip the required files. -```shell -unzip downloaded-file.zip - -# extract dataset indices -tar xf train-data/megadepth_indices.tar -tar xf train-data/scannet_indices.tar - -# extract testing data (optional) -tar xf testdata/megadepth_test_1500.tar -tar xf testdata/scannet_test_1500.tar -``` - -### Build the dataset symlinks - -We symlink the datasets to the `data` directory under the main LoFTR project directory. - -```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 -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/LoFTR/data/megadepth/train -ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/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/inference/data_management.py b/inference/data_management.py new file mode 100644 index 0000000..b98022c --- /dev/null +++ b/inference/data_management.py @@ -0,0 +1,73 @@ + +import matplotlib.pyplot as plt +import cv2 +import numpy as np + +CONF_FACTOR = 0.0 +RUN_EDGES = False +DRAW_EPIPOLAR_LINES = False +import h5py +import io + +def normalize_image(image): + """ + Normalize image to [0, 1] + """ + image = image - np.min(image) + image = image / np.max(image) + return image + +def load_image(img_path, normalize_img: bool = True, max2zero : bool = False): + """ + Load image from path + """ + img = cv2.imread(img_path, cv2.IMREAD_ANYDEPTH).astype(np.float32) + if normalize_img: + img = normalize_image(img) + + if max2zero: # convert all max values to 0 + img[np.max(img) == img] = 0 + + # convert to [0, 255] range + img = (img * 255.).astype(np.uint8) + return img + +def get_resized_wh(w, h, resize=None): + """ + Get resized width and height + """ + if resize is not None: # resize the longer edge + scale = resize / max(h, w) + w_new, h_new = int(round(w*scale)), int(round(h*scale)) + else: + w_new, h_new = w, h + return w_new, h_new +def load_array_from_s3(path, client, cv_type,use_h5py=False,): + """ + Load array from s3 path + """ + 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 read_float32_image(path): + img = cv2.imread(path, cv2.IMREAD_ANYDEPTH).astype(np.float32) + # img = img / 65535.0 + # image histogram + plt.figure() + plt.hist(img.ravel(), bins=256, range=(0.0, 1.0), fc='k', ec='k') + plt.figure() + plt.imshow(img.astype(np.uint8), cmap='gray') + plt.show() + return img \ No newline at end of file diff --git a/inference/drawing.py b/inference/drawing.py new file mode 100644 index 0000000..78d1f8c --- /dev/null +++ b/inference/drawing.py @@ -0,0 +1,100 @@ +# %% IMPORTS +import matplotlib.pyplot as plt +import numpy as np + + +def draw_images(images: list, save_path: str =None): + """ + Draw images + """ + fig, ax = plt.subplots( len(images) - len(images)//2,len(images)//2, figsize=(10, 10)) + ax = ax.flatten() + for i in range(len(images)): + ax[i].imshow(images[i], cmap='gray') + ax[i].set_xticks([]) + ax[i].set_yticks([]) + if save_path is not None: + plt.savefig(save_path) + else: + plt.show() + + +def draw_epipolar_line_on_image(img, x_shift, line, color, ax): + """ + Draw epipolar line on image + """ + # draw epipolar lines on destination image + h1, w1 = img.shape + a, b, c = line + # check if point is out of y axes range + p1_y = -c / b + if p1_y > h1 - 1: + p1 = np.array([x_shift + (-(b * (h1 - 1) + c) / a), h1 - 1]) + elif p1_y < 0: + p1 = np.array([x_shift + (-(b * 0 + c) / a), 0]) + else: + p1 = np.array([x_shift, p1_y]) + # check if point is out of x axes range + p2_y = -(a * (w1 - 1) + c) / b + if p2_y < 0: + p2 = np.array([x_shift - (c / a), 0]) + elif p2_y > h1 - 1: + p2 = np.array([x_shift - ((b * (h1 - 1) + c) / a), h1 - 1]) + else: + p2 = np.array([x_shift + w1 - 1, p2_y]) + + # ax.axline(p1, slope=-a/b, color=p[0].get_color(), linewidth=0.5) + ax.plot([p1[0], p2[0]], [p1[1], p2[1]], '--', linewidth=0.4, color=color) + +def draw_matches_on_images(img0, img1, kpts0, kpts1, conf, title:str = "LoFTR", draw_epipolar_lines: bool = False, + f_matrix: np.ndarray = None, show_confidences_hist: bool = False, conf_factor: float = 0.0, save_path: str = None): + """ + Draw matches on images + """ + + plt.rcParams['figure.autolayout'] = True + + x_shift = img0.shape[1] + if show_confidences_hist: # plot histogram of confidences + fig = plt.figure() + plt.hist(conf, bins=10) + plt.title('Confidence Histogram') + plt.xlabel('Confidence') + plt.ylabel('Count') + plt.xlim(0, 1) + # plt.ylim(0, 1000) + plt.grid(True) + plt.show() + + kpts0 = kpts0[conf > conf_factor] + kpts1 = kpts1[conf > conf_factor] + # kpts1[:, 0] += x_shift + image = np.concatenate([img0, img1], axis=1) + # fit the fig plot to screen size + + + fig, ax = plt.subplots(1, 1) + ax.imshow(image, cmap='gray') + ax.set_title(title) + for i in range(len(kpts0)): + kpts1_shift = np.array([kpts1[i, 0] + x_shift, kpts1[i, 1]]) + p = ax.plot(*np.vstack([kpts0[i], kpts1_shift]).T, 'o-', markersize=4, fillstyle='none', linewidth=0.9) + # p = ax.plot(*np.vstack([kpts0[i], kpts1_shift]).T, 'o', markersize=4, fillstyle='none', linewidth=0.9) + if draw_epipolar_lines: + # draw epipolar lines on destination image + line_1 = f_matrix @ np.hstack([kpts0[i], 1]) + draw_epipolar_line_on_image(img1, x_shift, line_1, p[0].get_color(), ax) + + # draw epipolar lines on source image + line_0 = f_matrix.T@np.hstack([kpts1[i], 1]) + draw_epipolar_line_on_image(img0, 0, line_0, p[0].get_color(), ax) + + + ax.set_xticks([]) + ax.set_yticks([]) + plt.tight_layout() + if save_path is not None: + plt.savefig(save_path) + plt.close(fig) + else: + plt.show() \ No newline at end of file diff --git a/inference/matching.py b/inference/matching.py new file mode 100644 index 0000000..4ec344b --- /dev/null +++ b/inference/matching.py @@ -0,0 +1,50 @@ +import torch +import cv2 + +from src.loftr import LoFTR, default_cfg +import time + + + +def matching_by_loftr(img0, img1, matcher): + # inputs to matcher + batch = {'image0': img0, 'image1': img1} + + # Inference with LoFTR and get prediction + with torch.no_grad(): + # measure time + start_time = time.time() + matcher(batch) + end_time = time.time() + print(f"running duration {(end_time - start_time):.2f} seconds") + mkpts0 = batch['mkpts0_f'].cpu().numpy() + mkpts1 = batch['mkpts1_f'].cpu().numpy() + mconf = batch['mconf'].cpu().numpy() + m_bids = batch['m_bids'].cpu().numpy() + return mkpts0, mkpts1, mconf, m_bids + +def matching_by_sift(img0, img1): + sift = cv2.SIFT_create() + kp0, des0 = sift.detectAndCompute(img0, None) + kp1, des1 = sift.detectAndCompute(img1, None) + + # BFMatcher with default params + bf = cv2.BFMatcher() + matches = bf.knnMatch(des0, des1, k=2) + # Apply ratio test + good = [] + for m, n in matches: + if m.distance < 0.78 * n.distance: + good.append([m]) + return kp0, kp1, good + +def init_model(weights_path): + """ + Init LoFTR model + """ + # init model + model = LoFTR(config=default_cfg) + # load the pretrained model + model.load_state_dict(torch.load(weights_path)['state_dict']) + model = model.eval().cuda() + return model \ No newline at end of file diff --git a/inference/preprocessing.py b/inference/preprocessing.py new file mode 100644 index 0000000..b273d02 --- /dev/null +++ b/inference/preprocessing.py @@ -0,0 +1,155 @@ +# %% IMPORT PACKAGES +import torch +import cv2 +import numpy as np +import matplotlib.pyplot as plt + +def resize_matching_images(img0: np.ndarray, img1: np.ndarray,img0_to_show: np.ndarray, width_len: int = 480, keep_aspect_ratio: bool = True): + """ + Resize images to a given height length, while keeping the aspect ratio. + """ + new_img0_h, new_img1_h = get_new_height_for_img(img0.shape, img1.shape, width_len,keep_aspect_ratio) + + # resize images + img0_resize = cv2.resize(img0, (new_img0_h, width_len)) + img1_resize = cv2.resize(img1, (new_img1_h, width_len)) + img0_to_show = cv2.resize(img0_to_show, (new_img1_h, width_len)) + + # padding images + pad_size = abs(new_img1_h - new_img0_h) + if new_img0_h < new_img1_h: + # pad image 0 + img0_with_padding = np.pad(img0_resize, ((0, 0), (0, pad_size)), 'constant', constant_values=(0)) + return img0_resize, img1_resize,img0_to_show, img0_with_padding, img1_resize + else: + # pad image 1 + img1_with_padding = np.pad(img1_resize, ((0, 0), (0, pad_size)), 'constant', constant_values=(0)) + return img0_resize, img1_resize,img0_to_show, img0_resize, img1_with_padding +def get_new_height_for_img(img0_shape, img1_shape, width_len: int = 480, + keep_aspect_ratio: bool = True): + """ + Calculate new height for images to a given width length, while keeping the aspect ratio. + """ + if keep_aspect_ratio: + h0, w0 = img0_shape + h1, w1 = img1_shape + new_img0_h = int(((w0 / h0) * width_len) // 8 * 8) + new_img1_h = int(((w1 / h1) * width_len) // 8 * 8) + else: + new_img0_h, new_img1_h = width_len, width_len + return new_img0_h, new_img1_h + +def normalize_image(image): + """ + Normalize image to [0, 1] + """ + image = image - np.min(image) + image = image / np.max(image) + return image.astype(np.uint8) +def equalize_hist(image): + """ + Equalize image histogram + """ + equalized_image = cv2.equalizeHist(image.astype(np.uint8)) + return equalized_image + +def get_divisible_wh(w, h, df=None): + """ + Get divisible width and height + """ + if df is not None: + w_new, h_new = map(lambda x: int(x // df * df), [w, h]) + else: + w_new, h_new = w, h + return w_new, h_new +def pad_bottom_right(inp, pad_size, ret_mask=False): + assert isinstance(pad_size, int) and pad_size >= max(inp.shape[-2:]), f"{pad_size} < {max(inp.shape[-2:])}" + mask = None + if inp.ndim == 2: + padded = np.zeros((pad_size, pad_size), dtype=inp.dtype) + padded[:inp.shape[0], :inp.shape[1]] = inp + if ret_mask: + mask = np.zeros((pad_size, pad_size), dtype=bool) + mask[:inp.shape[0], :inp.shape[1]] = True + elif inp.ndim == 3: + padded = np.zeros((inp.shape[0], pad_size, pad_size), dtype=inp.dtype) + padded[:, :inp.shape[1], :inp.shape[2]] = inp + if ret_mask: + mask = np.zeros((inp.shape[0], pad_size, pad_size), dtype=bool) + mask[:, :inp.shape[1], :inp.shape[2]] = True + else: + raise NotImplementedError() + return padded, mask + + +def pad_match_images(h0, h1, img0, img1): + # padding images + pad_size = abs(h1 - h0) + if h0 < h1: + # pad image 0 + img0 = np.pad(img0, ((0, 0), (0, pad_size)), 'constant', constant_values=(0)) + else: + # pad image 1 + img1 = np.pad(img1, ((0, 0), (0, pad_size)), 'constant', constant_values=(0)) + return img0, img1 + + +def img2net_input(img): + """ + Convert image to network input + """ + return torch.from_numpy(img)[None][None].cuda() / 255. + + +def matching_images_preprocess(img0_raw: np.ndarray, img1_raw: np.ndarray,equaliz_depth: bool = True, is_indoor: bool = False,width_len: int = 640, debug=False, plot_equalized_hist: bool = False): + """ + Preprocess images for matching inference + """ + + if equaliz_depth: + # equalize depth image + img1_raw = equalize_hist(img1_raw) + + if plot_equalized_hist: + # Plot the equalized image + plt.subplot(2, 1, 1) + plt.title('Equalized Image') + + plt.imshow(normalize_image(img1_raw.astype(np.float32)), cmap='gray') + plt.axis('off') + plt.subplot(2, 1, 2) + histogram, bins = np.histogram(img1_raw.flatten(), bins=256, range=[1, 256]) + plt.title('Equalized Image Pixel Histogram') + plt.xlabel('Pixel Value') + plt.ylabel('Frequency') + plt.bar(bins[:-1], histogram, width=1, align='center', color='b') + plt.xlim([0, 256]) + + plt.tight_layout() + plt.show() + + print(f"origin image 0 shape: {img0_raw.shape}\norigin image 1 shape: {img1_raw.shape}") + + # Preprocess images + if is_indoor: + img0_resize = cv2.resize(img0_raw, (640, 480)) + img1_resize = cv2.resize(img1_raw, (640, 480)) + + if debug: + fig, ax = plt.subplots(1, 2) + ax[0].imshow(img0_resize, cmap='gray') + ax[1].imshow(img1_resize, cmap='gray') + ax[0].set_xticks([]) + ax[0].set_yticks([]) + ax[1].set_xticks([]) + ax[1].set_yticks([]) + plt.show() + else: + new_h0, new_h1 = get_new_height_for_img(img0_raw.shape, img1_raw.shape, width_len, keep_aspect_ratio=True) + img0_resize = cv2.resize(img0_raw, (new_h0, width_len)) + img1_resize = cv2.resize(img1_raw, (new_h1, width_len)) + print(f"resize image 0 shape: {img0_resize.shape}\nresize image 1 shape: {img1_resize.shape}") + # prepare images for inference + img0_torch = img2net_input(img0_resize) + img1_torch = img2net_input(img1_resize) + return img0_torch, img1_torch, img0_resize, img1_resize \ No newline at end of file diff --git a/loftr_inference.py b/loftr_inference.py new file mode 100644 index 0000000..0fa9907 --- /dev/null +++ b/loftr_inference.py @@ -0,0 +1,55 @@ +import torch +import numpy as np + +from src.loftr import LoFTR, default_cfg +import time + +class LoFTR_Inference: + def __init__(self, weights_path: str = None): + self.weights_path = weights_path + self.matcher = self.init_model() + + def init_model(self,): + """ + Init LoFTR model + """ + # init model + model = LoFTR(config=default_cfg) + # load the pretrained model + model.load_state_dict(torch.load(self.weights_path)['state_dict']) + model = model.eval().cuda() + return model + + @staticmethod + def img2net_input(img: np.ndarray): + """ + Convert image to network input + """ + return torch.from_numpy(img).cuda() / 255. + + def run_matching(self, img0: torch.tensor, img1: torch.tensor): + # inputs to matcher + batch = {'image0': img0, 'image1': img1} + + # Inference with LoFTR and get prediction + with torch.no_grad(): + # measure time + start_time = time.time() + self.matcher(batch) + end_time = time.time() + print(f"running duration {(end_time - start_time):.2f} seconds") + mkpts0 = batch['mkpts0_f'].cpu().numpy() + mkpts1 = batch['mkpts1_f'].cpu().numpy() + mconf = batch['mconf'].cpu().numpy() + m_bids = batch['m_bids'].cpu().numpy() + return mkpts0, mkpts1, mconf, m_bids + + def predict(self, img0: np.ndarray, img1: np.ndarray): + # Preprocess images + img0_torch, img1_torch = self.img2net_input(img0), self.img2net_input(img1) + + # run matcher inference + mkpts0, mkpts1, mconf, mbids = self.run_matching(img0_torch, img1_torch) + + return mkpts0, mkpts1, mconf, mbids + diff --git a/notebooks/demo_single_pair.ipynb b/notebooks/demo_single_pair.ipynb deleted file mode 100644 index 0b67c71..0000000 --- a/notebooks/demo_single_pair.ipynb +++ /dev/null @@ -1,247 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Demo LoFTR-DS on a single pair of images\n", - "\n", - "This notebook shows how to use the loftr matcher with default config(dual-softmax) and the pretrained weights." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 20, - "source": [ - "import os\n", - "os.chdir(\"..\")\n", - "from copy import deepcopy\n", - "\n", - "import torch\n", - "import cv2\n", - "import numpy as np\n", - "import matplotlib.cm as cm\n", - "from src.utils.plotting import make_matching_figure" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## Indoor Example" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 9, - "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", - "_default_cfg = deepcopy(default_cfg)\n", - "_default_cfg['coarse']['temp_bug_fix'] = True # set to False when using the old ckpt\n", - "matcher = LoFTR(config=_default_cfg)\n", - "matcher.load_state_dict(torch.load(\"weights/indoor_ds_new.ckpt\")['state_dict'])\n", - "matcher = matcher.eval().cuda()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 10, - "source": [ - "# Load example images\n", - "img0_pth = \"assets/scannet_sample_images/scene0711_00_frame-001680.jpg\"\n", - "img1_pth = \"assets/scannet_sample_images/scene0711_00_frame-001995.jpg\"\n", - "img0_raw = cv2.imread(img0_pth, cv2.IMREAD_GRAYSCALE)\n", - "img1_raw = cv2.imread(img1_pth, cv2.IMREAD_GRAYSCALE)\n", - "img0_raw = cv2.resize(img0_raw, (640, 480))\n", - "img1_raw = cv2.resize(img1_raw, (640, 480))\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()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 11, - "source": [ - "# Draw\n", - "color = cm.jet(mconf)\n", - "text = [\n", - " 'LoFTR',\n", - " 'Matches: {}'.format(len(mkpts0)),\n", - "]\n", - "fig = make_matching_figure(img0_raw, img1_raw, mkpts0, mkpts1, color, text=text)" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-08-18T00:38:02.543658\n image/svg+xml\n \n \n Matplotlib v3.3.4, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "image/png": "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" - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## Outdoor Example" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 16, - "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()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 19, - "source": [ - "default_cfg['coarse']" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'d_model': 256,\n", - " 'd_ffn': 256,\n", - " 'nhead': 8,\n", - " 'layer_names': ['self',\n", - " 'cross',\n", - " 'self',\n", - " 'cross',\n", - " 'self',\n", - " 'cross',\n", - " 'self',\n", - " 'cross'],\n", - " 'attention': 'linear',\n", - " 'temp_bug_fix': True}" - ] - }, - "metadata": {}, - "execution_count": 19 - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 17, - "source": [ - "# Load example images\n", - "img0_pth = \"assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg\"\n", - "img1_pth = \"assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg\"\n", - "img0_raw = cv2.imread(img0_pth, cv2.IMREAD_GRAYSCALE)\n", - "img1_raw = cv2.imread(img1_pth, cv2.IMREAD_GRAYSCALE)\n", - "img0_raw = cv2.resize(img0_raw, (img0_raw.shape[1]//8*8, img0_raw.shape[0]//8*8)) # input size shuold be divisible by 8\n", - "img1_raw = cv2.resize(img1_raw, (img1_raw.shape[1]//8*8, img1_raw.shape[0]//8*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()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 18, - "source": [ - "# Draw\n", - "color = cm.jet(mconf)\n", - "text = [\n", - " 'LoFTR',\n", - " 'Matches: {}'.format(len(mkpts0)),\n", - "]\n", - "fig = make_matching_figure(img0_raw, img1_raw, mkpts0, mkpts1, color, text=text)" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-08-18T00:41:19.149192\n image/svg+xml\n \n \n Matplotlib v3.3.4, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "image/png": "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" - }, - "metadata": {} - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [], - "outputs": [], - "metadata": {} - } - ], - "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3.8.8 64-bit ('svcnn': conda)" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - }, - "orig_nbformat": 2, - "interpreter": { - "hash": "5b8911f875a754a9ad2a8804064d078bf6a1985972bb0389b9d67771213c8e20" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/notebooks/visualize_dump_results.ipynb b/notebooks/visualize_dump_results.ipynb deleted file mode 100644 index 1fcadea..0000000 --- a/notebooks/visualize_dump_results.ipynb +++ /dev/null @@ -1,159 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "%matplotlib inline\n", - "import os\n", - "os.chdir(\"..\")\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import cv2\n", - "from pathlib import Path\n", - "from src.utils.plotting import make_matching_figure, error_colormap" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def make_prediction_and_evaluation_plot(root_dir, pe, path=None, source='ScanNet'):\n", - " img0 = cv2.imread(str(root_dir / pe['pair_names'][0]), cv2.IMREAD_GRAYSCALE)\n", - " img1 = cv2.imread(str(root_dir / pe['pair_names'][1]), cv2.IMREAD_GRAYSCALE)\n", - " if source == 'ScanNet':\n", - " img0 = cv2.resize(img0, (640, 480))\n", - " img1 = cv2.resize(img1, (640, 480))\n", - "\n", - " thr = 5e-4\n", - " mkpts0 = pe['mkpts0_f']\n", - " mkpts1 = pe['mkpts1_f']\n", - " color = error_colormap(pe['epi_errs'], thr, alpha=0.3)\n", - "\n", - " text = [\n", - " f\"LoFTR\",\n", - " f\"#Matches: {len(mkpts0)}\",\n", - " f\"$\\\\Delta$R:{pe['R_errs']:.2f}°, $\\\\Delta$t:{pe['t_errs']:.2f}°\",\n", - " ]\n", - "\n", - " if path:\n", - " make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=text, path=path)\n", - " else:\n", - " return make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualization on ScanNet\n", - "- Prediction and Evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "root_dir = Path(\"data/scannet/test\") # Scannet\n", - "npy_path = \"dump/loftr_ds_indoor/LoFTR_pred_eval.npy\"\n", - "dumps = np.load(npy_path, allow_pickle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = make_prediction_and_evaluation_plot(root_dir, dumps[2], source='ScanNet')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualization on MegaDepth\n", - "- Prediction and Evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "root_dir = Path(\"data/megadepth/test\") # MegaDepth\n", - "npy_path = \"dump/loftr_ds_outdoor/LoFTR_pred_eval.npy\"\n", - "dumps = np.load(npy_path, allow_pickle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = make_prediction_and_evaluation_plot(root_dir, dumps[51], source='MegaDepth')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.8 64-bit ('loftr': conda)", - "name": "python388jvsc74a57bd0e2d1507a0fcefcbd70c2e8d5c2edae879585b2c8df0be6cdbe280bf251175c7f" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8-final" - }, - "orig_nbformat": 2 - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/run_demo.py b/run_demo.py new file mode 100644 index 0000000..9ec9fc0 --- /dev/null +++ b/run_demo.py @@ -0,0 +1,121 @@ +import os +# os.chdir("..") +from copy import deepcopy +from inference.matching import * +from inference.preprocessing import * +from inference.drawing import * +from inference.data_management import * +import matplotlib.pyplot as plt +import torch +import cv2 +import numpy as np +from src.loftr import LoFTR, default_cfg +import time +RUN_EDGES = False +DRAW_EPIPOLAR_LINES = False +CONF_FACTOR = 0.7 + + +os.environ['TORCH_CUDA_ARCH_LIST'] = '8.6' + +BASE_DIR ="/home/avichaih/Projects/MyLoFTR" +BASE_DATA_DIR = os.path.join(BASE_DIR, 'data') + +def remove_outliers_by_epipolar_constraint(kpts0: np.ndarray, kpts1: np.ndarray, conf: np.ndarray): + """ + Remove outliers by epipolar constraint + """ + Fm, inliers = cv2.findFundamentalMat(kpts0, kpts1, cv2.FM_RANSAC, 0.5, 0.999, 100000) + kpts0 = kpts0[inliers.flatten() == 1, :] + kpts1 = kpts1[inliers.flatten() == 1, :] + conf = conf[inliers.flatten() == 1] + return kpts0, kpts1, conf, Fm + +def run_inference(img0: np.ndarray, img1: np.ndarray, weights_path: str = None, width_len: int =640, is_indoor: bool = False, debug: bool = False,): + # initiate LoFTR model + matcher = init_model(weights_path=weights_path, is_indoor=is_indoor) + + # Preprocess images + img0_torch, img1_torch, img0_resize, img1_resize = matching_images_preprocess(img0_raw=img0, img1_raw=img1, is_indoor=is_indoor, debug=debug, width_len=width_len) + + # run matcher inference + mkpts0, mkpts1, mconf = matching_by_loftr(img0_torch, img1_torch, matcher) + + return mkpts0, mkpts1, mconf, img0_resize, img1_resize + +def run_demo(img0: np.ndarray, img1: np.ndarray, weights_path: str = None,width_len: int =640, remove_outliers: bool = False, is_indoor: bool = False, debug: bool = False,save_path: str = None): + # inference + kpts0_loftr, kpts1_loftr, conf_loftr, img0_resize, img1_resize = run_inference(img0=img0, img1=img1, weights_path=weights_path, is_indoor=is_indoor, debug=debug, width_len=width_len) + + # remove outliers by epipolar constraint + if remove_outliers: + kpts0_loftr, kpts1_loftr, conf_loftr, Fm = remove_outliers_by_epipolar_constraint(kpts0_loftr, kpts1_loftr, conf_loftr) + else: + Fm = None + + # draw matches + draw_matches_on_images(img0_resize, img1_resize, kpts0_loftr, kpts1_loftr, conf_loftr, title='LoFTR Matcher - reference (left) to query (right)', + draw_epipolar_lines=DRAW_EPIPOLAR_LINES, f_matrix=Fm, conf_factor=CONF_FACTOR, save_path=save_path) + + +def load_outdoor_demo_data(debug: bool = False): + # outdoor demo + # img0_outdoor = "/home/ubuntu/projects/lang-segment-anything/results/gaza1/0.png" + # img1_outdoor = "/home/ubuntu/projects/lang-segment-anything/results/gaza1/10.png" + img0_outdoor = "/home/ubuntu/Data/source.png" + img1_outdoor = "/home/ubuntu/Data/target.png" + weights_outdoor = "weights/outdoor_ds.ckpt" + + # load images + img0 = load_image(img0_outdoor) + img1 = load_image(img1_outdoor) + + if debug: + eq_img0 = equalize_hist(img0) + eq_img1 = equalize_hist(img1) + # plot image histogram + his1 = cv2.calcHist([img0], [0], None, [256], [0, 256]) + his2 = cv2.calcHist([eq_img0], [0], None, [256], [0, 256]) + + plt.plot(his1, color='r', label='source original') + plt.plot(his2, color='b', label='source equalized') + plt.legend() + plt.show() + # plot original images and equalized images + fig, ax = plt.subplots(2, 2, figsize=(10, 10)) + ax[0, 0].imshow(img0, 'gray') + ax[0, 0].set_title('original image source') + ax[0, 1].imshow(eq_img0, 'gray') + ax[0, 1].set_title('equalized image source') + ax[1, 0].imshow(img1, 'gray') + ax[1, 0].set_title('original image target') + ax[1, 1].imshow(eq_img1, 'gray') + ax[1, 1].set_title('equalized image target') + plt.show() + return img0, img1, weights_outdoor + + +def run_queries(base_path:str = '/home/ubuntu/Data/'): + queries_files_path = base_path + 'W7_EDlXWTBiXAEEniNoMPwAAYamdpeGl2cXZqAYsGfNuqAYsGfNtTAAAAAQ/queries/' + references_files_path = base_path +'/W7_EDlXWTBiXAEEniNoMPwAAYamdpeGl2cXZqAYsGfNuqAYsGfNtTAAAAAQ/references/' + num_imgs = len(os.listdir(queries_files_path)) + for i in tqdm(range(num_imgs)): + img0 = load_image(os.path.join(references_files_path, f'{i}.png')) + img1 = load_image(os.path.join(queries_files_path, f'{i}.png')) + save_path = 'results/' + queries_files_path.split('Data/')[-1].split('queries/')[ + 0] + f'matching_frame_idx_{i}.png' + + run_demo(img0, img1, weights_path="weights/outdoor_ds.ckpt", is_indoor=False, remove_outliers=False, + width_len=512, save_path=save_path) + + + +if __name__ == '__main__': + from tqdm import tqdm + from loftr_inference import * + img0, img1, weights = load_outdoor_demo_data(False) + loftr = LoFTR_Inference(weights) + loftr.predict(img0[None][None], img1[None][None]) + # run_demo(img0, img1, weights_path=weights,is_indoor=False, remove_outliers=False, width_len=512, save_path=None) + + diff --git a/scripts/reproduce_test/indoor_ds.sh b/scripts/reproduce_test/indoor_ds.sh deleted file mode 100755 index 86df40f..0000000 --- a/scripts/reproduce_test/indoor_ds.sh +++ /dev/null @@ -1,29 +0,0 @@ -#!/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_test_1500.py" -main_cfg_path="configs/loftr/indoor/scannet/loftr_ds_eval.py" -ckpt_path="weights/indoor_ds.ckpt" -dump_dir="dump/loftr_ds_indoor" -profiler_name="inference" -n_nodes=1 # mannually keep this the same with --nodes -n_gpus_per_node=-1 -torch_num_workers=4 -batch_size=1 # per gpu - -python -u ./test.py \ - ${data_cfg_path} \ - ${main_cfg_path} \ - --ckpt_path=${ckpt_path} \ - --dump_dir=${dump_dir} \ - --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ - --batch_size=${batch_size} --num_workers=${torch_num_workers}\ - --profiler_name=${profiler_name} \ - --benchmark - \ No newline at end of file diff --git a/scripts/reproduce_test/indoor_ds_new.sh b/scripts/reproduce_test/indoor_ds_new.sh deleted file mode 100755 index b11c1e8..0000000 --- a/scripts/reproduce_test/indoor_ds_new.sh +++ /dev/null @@ -1,30 +0,0 @@ -#!/bin/bash -l -# a indoor_ds model with the pos_enc impl bug fixed. - -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_test_1500.py" -main_cfg_path="configs/loftr/indoor/scannet/loftr_ds_eval_new.py" -ckpt_path="weights/indoor_ds_new.ckpt" -dump_dir="dump/loftr_ds_indoor_new" -profiler_name="inference" -n_nodes=1 # mannually keep this the same with --nodes -n_gpus_per_node=-1 -torch_num_workers=4 -batch_size=1 # per gpu - -python -u ./test.py \ - ${data_cfg_path} \ - ${main_cfg_path} \ - --ckpt_path=${ckpt_path} \ - --dump_dir=${dump_dir} \ - --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ - --batch_size=${batch_size} --num_workers=${torch_num_workers}\ - --profiler_name=${profiler_name} \ - --benchmark - \ No newline at end of file diff --git a/scripts/reproduce_test/indoor_ot.sh b/scripts/reproduce_test/indoor_ot.sh deleted file mode 100755 index 59ad819..0000000 --- a/scripts/reproduce_test/indoor_ot.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/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_test_1500.py" -main_cfg_path="configs/loftr/indoor/buggy_pos_enc/loftr_ot.py" -ckpt_path="weights/indoor_ot.ckpt" -dump_dir="dump/loftr_ot_indoor" -profiler_name="inference" -n_nodes=1 # mannually keep this the same with --nodes -n_gpus_per_node=-1 -torch_num_workers=4 -batch_size=1 # per gpu - -python -u ./test.py \ - ${data_cfg_path} \ - ${main_cfg_path} \ - --ckpt_path=${ckpt_path} \ - --dump_dir=${dump_dir} \ - --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ - --batch_size=${batch_size} --num_workers=${torch_num_workers}\ - --profiler_name=${profiler_name} \ - --benchmark diff --git a/scripts/reproduce_test/outdoor_ds.sh b/scripts/reproduce_test/outdoor_ds.sh deleted file mode 100755 index ad30188..0000000 --- a/scripts/reproduce_test/outdoor_ds.sh +++ /dev/null @@ -1,29 +0,0 @@ -#!/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/megadepth_test_1500.py" -main_cfg_path="configs/loftr/outdoor/buggy_pos_enc/loftr_ds.py" -ckpt_path="weights/outdoor_ds.ckpt" -dump_dir="dump/loftr_ds_outdoor" -profiler_name="inference" -n_nodes=1 # mannually keep this the same with --nodes -n_gpus_per_node=-1 -torch_num_workers=4 -batch_size=1 # per gpu - -python -u ./test.py \ - ${data_cfg_path} \ - ${main_cfg_path} \ - --ckpt_path=${ckpt_path} \ - --dump_dir=${dump_dir} \ - --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ - --batch_size=${batch_size} --num_workers=${torch_num_workers}\ - --profiler_name=${profiler_name} \ - --benchmark - \ No newline at end of file diff --git a/scripts/reproduce_test/outdoor_ot.sh b/scripts/reproduce_test/outdoor_ot.sh deleted file mode 100755 index 169eae0..0000000 --- a/scripts/reproduce_test/outdoor_ot.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/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/megadepth_test_1500.py" -main_cfg_path="configs/loftr/outdoor/buggy_pos_enc/loftr_ot.py" -ckpt_path="weights/outdoor_ot.ckpt" -dump_dir="dump/loftr_ot_outdoor" -profiler_name="inference" -n_nodes=1 # mannually keep this the same with --nodes -n_gpus_per_node=-1 -torch_num_workers=4 -batch_size=1 # per gpu - -python -u ./test.py \ - ${data_cfg_path} \ - ${main_cfg_path} \ - --ckpt_path=${ckpt_path} \ - --dump_dir=${dump_dir} \ - --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ - --batch_size=${batch_size} --num_workers=${torch_num_workers}\ - --profiler_name=${profiler_name} \ - --benchmark diff --git a/scripts/reproduce_train/debug/.gitignore b/scripts/reproduce_train/debug/.gitignore deleted file mode 100644 index 94548af..0000000 --- a/scripts/reproduce_train/debug/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -* -*/ -!.gitignore diff --git a/scripts/reproduce_train/indoor_ds.sh b/scripts/reproduce_train/indoor_ds.sh deleted file mode 100755 index c565391..0000000 --- a/scripts/reproduce_train/indoor_ds.sh +++ /dev/null @@ -1,33 +0,0 @@ -#!/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=${pin_memory} \ - --check_val_every_n_epoch=1 \ - --log_every_n_steps=100 \ - --flush_logs_every_n_steps=100 \ - --limit_val_batches=1. \ - --num_sanity_val_steps=10 \ - --benchmark=True \ - --max_epochs=30 \ - --parallel_load_data diff --git a/scripts/reproduce_train/indoor_ot.sh b/scripts/reproduce_train/indoor_ot.sh deleted file mode 100644 index 192859d..0000000 --- a/scripts/reproduce_train/indoor_ot.sh +++ /dev/null @@ -1,33 +0,0 @@ -#!/bin/bash -l - -SCRIPTPATH=$(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=${pin_memory} \ - --check_val_every_n_epoch=1 \ - --log_every_n_steps=100 \ - --flush_logs_every_n_steps=100 \ - --limit_val_batches=1. \ - --num_sanity_val_steps=10 \ - --benchmark=True \ - --max_epochs=30 \ - --parallel_load_data diff --git a/scripts/reproduce_train/outdoor_ds.sh b/scripts/reproduce_train/outdoor_ds.sh deleted file mode 100644 index 0f49303..0000000 --- a/scripts/reproduce_train/outdoor_ds.sh +++ /dev/null @@ -1,35 +0,0 @@ -#!/bin/bash -l - -SCRIPTPATH=$(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=${pin_memory} \ - --check_val_every_n_epoch=1 \ - --log_every_n_steps=1 \ - --flush_logs_every_n_steps=1 \ - --limit_val_batches=1. \ - --num_sanity_val_steps=10 \ - --benchmark=True \ - --max_epochs=30 diff --git a/scripts/reproduce_train/outdoor_ot.sh b/scripts/reproduce_train/outdoor_ot.sh deleted file mode 100644 index 7a57996..0000000 --- a/scripts/reproduce_train/outdoor_ot.sh +++ /dev/null @@ -1,35 +0,0 @@ -#!/bin/bash -l - -SCRIPTPATH=$(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/datasets/megadepth.py b/src/datasets/megadepth.py deleted file mode 100644 index a70ac71..0000000 --- a/src/datasets/megadepth.py +++ /dev/null @@ -1,127 +0,0 @@ -import os.path as osp -import numpy as np -import torch -import torch.nn.functional as F -from torch.utils.data import Dataset -from loguru import logger - -from src.utils.dataset import read_megadepth_gray, read_megadepth_depth - - -class MegaDepthDataset(Dataset): - def __init__(self, - root_dir, - npz_path, - mode='train', - min_overlap_score=0.4, - img_resize=None, - df=None, - img_padding=False, - depth_padding=False, - augment_fn=None, - **kwargs): - """ - 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. - mode (str): options are ['train', 'val', 'test'] - min_overlap_score (float): how much a pair should have in common. In range of [0, 1]. Set to 0 when testing. - img_resize (int, optional): the longer edge of resized images. None for no resize. 640 is recommended. - This is useful during training with batches and testing with memory intensive algorithms. - df (int, optional): image size division factor. NOTE: this will change the final image size after img_resize. - img_padding (bool): If set to 'True', zero-pad the image to squared size. This is useful during training. - depth_padding (bool): If set to 'True', zero-pad depthmap to (2000, 2000). This is useful during training. - augment_fn (callable, optional): augments images with pre-defined visual effects. - """ - super().__init__() - self.root_dir = root_dir - self.mode = mode - self.scene_id = npz_path.split('.')[0] - - # prepare scene_info and pair_info - if mode == 'test' and min_overlap_score != 0: - logger.warning("You are using `min_overlap_score`!=0 in test mode. Set to 0.") - min_overlap_score = 0 - self.scene_info = np.load(npz_path, allow_pickle=True) - self.pair_infos = self.scene_info['pair_infos'].copy() - del self.scene_info['pair_infos'] - self.pair_infos = [pair_info for pair_info in self.pair_infos if pair_info[1] > min_overlap_score] - - # parameters for image resizing, padding and depthmap padding - if mode == 'train': - assert img_resize is not None and img_padding and depth_padding - self.img_resize = img_resize - self.df = df - self.img_padding = img_padding - self.depth_max_size = 2000 if depth_padding else None # the upperbound of depthmaps size in megadepth. - - # for training LoFTR - self.augment_fn = augment_fn if mode == 'train' else None - self.coarse_scale = getattr(kwargs, 'coarse_scale', 0.125) - - def __len__(self): - return len(self.pair_infos) - - def __getitem__(self, idx): - (idx0, idx1), overlap_score, central_matches = self.pair_infos[idx] - - # 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, 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, None) - # np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) - - # read depth. shape: (h, w) - if self.mode in ['train', 'val']: - depth0 = read_megadepth_depth( - osp.join(self.root_dir, self.scene_info['depth_paths'][idx0]), pad_to=self.depth_max_size) - depth1 = read_megadepth_depth( - osp.join(self.root_dir, self.scene_info['depth_paths'][idx1]), pad_to=self.depth_max_size) - else: - depth0 = depth1 = torch.tensor([]) - - # read intrinsics of original size - K_0 = torch.tensor(self.scene_info['intrinsics'][idx0].copy(), dtype=torch.float).reshape(3, 3) - K_1 = torch.tensor(self.scene_info['intrinsics'][idx1].copy(), dtype=torch.float).reshape(3, 3) - - # read and compute relative poses - T0 = self.scene_info['poses'][idx0] - T1 = self.scene_info['poses'][idx1] - T_0to1 = torch.tensor(np.matmul(T1, np.linalg.inv(T0)), dtype=torch.float)[:4, :4] # (4, 4) - T_1to0 = T_0to1.inverse() - - data = { - 'image0': image0, # (1, h, w) - 'depth0': depth0, # (h, w) - 'image1': image1, - 'depth1': depth1, - 'T_0to1': T_0to1, # (4, 4) - 'T_1to0': T_1to0, - 'K0': K_0, # (3, 3) - 'K1': K_1, - 'scale0': scale0, # [scale_w, scale_h] - 'scale1': scale1, - 'dataset_name': 'MegaDepth', - 'scene_id': self.scene_id, - 'pair_id': idx, - 'pair_names': (self.scene_info['image_paths'][idx0], self.scene_info['image_paths'][idx1]), - } - - # for LoFTR training - if mask0 is not None: # img_padding is True - if self.coarse_scale: - [ts_mask_0, ts_mask_1] = F.interpolate(torch.stack([mask0, mask1], dim=0)[None].float(), - scale_factor=self.coarse_scale, - mode='nearest', - recompute_scale_factor=False)[0].bool() - data.update({'mask0': ts_mask_0, 'mask1': ts_mask_1}) - - return data diff --git a/src/datasets/sampler.py b/src/datasets/sampler.py deleted file mode 100644 index 81b6f43..0000000 --- a/src/datasets/sampler.py +++ /dev/null @@ -1,77 +0,0 @@ -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 deleted file mode 100644 index a8cfa8d..0000000 --- a/src/datasets/scannet.py +++ /dev/null @@ -1,114 +0,0 @@ -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 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): - def __init__(self, - root_dir, - npz_path, - intrinsic_path, - mode='train', - min_overlap_score=0.4, - augment_fn=None, - pose_dir=None, - **kwargs): - """Manage one scene of ScanNet Dataset. - Args: - root_dir (str): ScanNet root directory that contains scene folders. - npz_path (str): {scene_id}.npz path. This contains image pair information of a scene. - 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'] - 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.intrinsics = dict(np.load(intrinsic_path)) - - # for training LoFTR - self.augment_fn = augment_fn if mode == 'train' else None - - 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 - scene_name = f'scene{scene_name:04d}_{scene_sub_name:02d}' - - # 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') - - # 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']: - depth0 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_0}.png')) - depth1 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_1}.png')) - else: - depth0 = depth1 = torch.tensor([]) - - # read the intrinsic of depthmap - 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._compute_rel_pose(scene_name, stem_name_0, stem_name_1), - dtype=torch.float32) - T_1to0 = T_0to1.inverse() - - data = { - 'image0': image0, # (1, h, w) - 'depth0': depth0, # (h, w) - 'image1': image1, - 'depth1': depth1, - 'T_0to1': T_0to1, # (4, 4) - 'T_1to0': T_1to0, - 'K0': K_0, # (3, 3) - 'K1': K_1, - 'dataset_name': 'ScanNet', - 'scene_id': scene_name, - 'pair_id': idx, - 'pair_names': (osp.join(scene_name, 'color', f'{stem_name_0}.jpg'), - osp.join(scene_name, 'color', f'{stem_name_1}.jpg')) - } - - return data diff --git a/src/lightning/data.py b/src/lightning/data.py deleted file mode 100644 index 6deb105..0000000 --- a/src/lightning/data.py +++ /dev/null @@ -1,320 +0,0 @@ -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 ( - 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 - only a part of the training scenes to reduce memory overhead. - """ - def __init__(self, args, config): - super().__init__() - - # 1. data config - # Train and Val should from the same data source - self.trainval_data_source = config.DATASET.TRAINVAL_DATA_SOURCE - 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_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 - self.mgdpt_img_resize = config.DATASET.MGDPT_IMG_RESIZE # 840 - self.mgdpt_img_pad = config.DATASET.MGDPT_IMG_PAD # True - self.mgdpt_depth_pad = config.DATASET.MGDPT_DEPTH_PAD # True - self.mgdpt_df = config.DATASET.MGDPT_DF # 8 - 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': getattr(args, 'pin_memory', True) - } - self.val_loader_params = { - 'batch_size': 1, - 'shuffle': False, - 'num_workers': args.num_workers, - 'pin_memory': getattr(args, 'pin_memory', True) - } - 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 in ['fit', 'test'], "stage must be either fit or test" - - try: - self.world_size = dist.get_world_size() - self.rank = dist.get_rank() - logger.info(f"[rank:{self.rank}] world_size: {self.world_size}") - except AssertionError as ae: - self.world_size = 1 - self.rank = 0 - logger.warning(str(ae) + " (set wolrd_size=1 and rank=0)") - - 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', - 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()] - - if mode == 'train': - local_npz_names = get_local_split(npz_names, self.world_size, self.rank, self.seed) - 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) - - 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 - 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': - datasets.append( - ScanNetDataset(data_root, - npz_path, - intrinsic_path, - mode=mode, - 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=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)) - 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 deleted file mode 100644 index 87dc729..0000000 --- a/src/lightning/lightning_loftr.py +++ /dev/null @@ -1,249 +0,0 @@ - -from collections import defaultdict -import pprint -from loguru import logger -from pathlib import Path - -import torch -import numpy as np -import pytorch_lightning as pl -from matplotlib import pyplot as plt - -from src.loftr import LoFTR -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 - _config = lower_config(self.config) - self.loftr_cfg = lower_config(_config['loftr']) - self.profiler = profiler or PassThroughProfiler() - self.n_vals_plot = max(config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1) - - # Matcher: LoFTR - self.matcher = LoFTR(config=_config['loftr']) - self.loss = LoFTRLoss(_config) - - # Pretrained weights - if pretrained_ckpt: - state_dict = torch.load(pretrained_ckpt, map_location='cpu')['state_dict'] - self.matcher.load_state_dict(state_dict, strict=True) - logger.info(f"Load \'{pretrained_ckpt}\' as pretrained checkpoint") - - # 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 - - rel_pair_names = list(zip(*batch['pair_names'])) - bs = batch['image0'].size(0) - metrics = { - # to filter duplicate pairs caused by DistributedSampler - 'identifiers': ['#'.join(rel_pair_names[b]) for b in range(bs)], - 'epi_errs': [batch['epi_errs'][batch['m_bids'] == b].cpu().numpy() for b in range(bs)], - 'R_errs': batch['R_errs'], - '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: - # dump results for further analysis - keys_to_save = {'mkpts0_f', 'mkpts1_f', 'mconf', 'epi_errs'} - pair_names = list(zip(*batch['pair_names'])) - bs = batch['image0'].shape[0] - dumps = [] - for b_id in range(bs): - item = {} - mask = batch['m_bids'] == b_id - item['pair_names'] = pair_names[b_id] - item['identifier'] = '#'.join(rel_pair_names[b_id]) - for key in keys_to_save: - item[key] = batch[key][mask].cpu().numpy() - for key in ['R_errs', 't_errs', 'inliers']: - item[key] = batch[key][b_id] - dumps.append(item) - ret_dict['dumps'] = dumps - - return ret_dict - - def test_epoch_end(self, outputs): - # metrics: dict of list, numpy - _metrics = [o['metrics'] for o in outputs] - metrics = {k: flattenList(gather(flattenList([_me[k] for _me in _metrics]))) for k in _metrics[0]} - - # [{key: [{...}, *#bs]}, *#batch] - if self.dump_dir is not None: - Path(self.dump_dir).mkdir(parents=True, exist_ok=True) - _dumps = flattenList([o['dumps'] for o in outputs]) # [{...}, #bs*#batch] - dumps = flattenList(gather(_dumps)) # [{...}, #proc*#bs*#batch] - logger.info(f'Prediction and evaluation results will be saved to: {self.dump_dir}') - - if self.trainer.global_rank == 0: - print(self.profiler.summary()) - val_metrics_4tb = aggregate_metrics(metrics, self.config.TRAINER.EPI_ERR_THR) - logger.info('\n' + pprint.pformat(val_metrics_4tb)) - if self.dump_dir is not None: - np.save(Path(self.dump_dir) / 'LoFTR_pred_eval', dumps) diff --git a/src/loftr/utils/coarse_matching.py b/src/loftr/utils/coarse_matching.py index a972633..cacf195 100644 --- a/src/loftr/utils/coarse_matching.py +++ b/src/loftr/utils/coarse_matching.py @@ -243,10 +243,11 @@ def get_coarse_match(self, conf_matrix, data): 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]], + [i_ids % data['hw0_c'][1], torch.div(i_ids, data['hw0_c'][1], rounding_mode='floor')], dim=1) * scale0 + mkpts1_c = torch.stack( - [j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]], + [j_ids % data['hw1_c'][1], torch.div(j_ids, data['hw1_c'][1], rounding_mode='floor')], dim=1) * scale1 # These matches is the current prediction (for visualization) diff --git a/src/losses/loftr_loss.py b/src/losses/loftr_loss.py deleted file mode 100644 index be6b079..0000000 --- a/src/losses/loftr_loss.py +++ /dev/null @@ -1,192 +0,0 @@ -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 deleted file mode 100644 index e1db228..0000000 --- a/src/optimizers/__init__.py +++ /dev/null @@ -1,42 +0,0 @@ -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 deleted file mode 100644 index d7c5d3e..0000000 --- a/src/utils/augment.py +++ /dev/null @@ -1,55 +0,0 @@ -import albumentations as A - - -class DarkAug(object): - """ - Extreme dark augmentation aiming at Aachen Day-Night - """ - - def __init__(self) -> None: - self.augmentor = A.Compose([ - A.RandomBrightnessContrast(p=0.75, brightness_limit=(-0.6, 0.0), contrast_limit=(-0.5, 0.3)), - A.Blur(p=0.1, blur_limit=(3, 9)), - A.MotionBlur(p=0.2, blur_limit=(3, 25)), - A.RandomGamma(p=0.1, gamma_limit=(15, 65)), - A.HueSaturationValue(p=0.1, val_shift_limit=(-100, -40)) - ], p=0.75) - - def __call__(self, x): - return self.augmentor(image=x)['image'] - - -class MobileAug(object): - """ - Random augmentations aiming at images of mobile/handhold devices. - """ - - def __init__(self): - self.augmentor = A.Compose([ - A.MotionBlur(p=0.25), - A.ColorJitter(p=0.5), - A.RandomRain(p=0.1), # random occlusion - A.RandomSunFlare(p=0.1), - A.JpegCompression(p=0.25), - A.ISONoise(p=0.25) - ], p=1.0) - - def __call__(self, x): - return self.augmentor(image=x)['image'] - - -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': - return MobileAug() - elif method is None: - return None - else: - raise ValueError(f'Invalid augmentation method: {method}') - - -if __name__ == '__main__': - augmentor = build_augmentor('FDA') diff --git a/src/utils/comm.py b/src/utils/comm.py deleted file mode 100644 index 26ec951..0000000 --- a/src/utils/comm.py +++ /dev/null @@ -1,265 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -[Copied from detectron2] -This file contains primitives for multi-gpu communication. -This is useful when doing distributed training. -""" - -import functools -import logging -import numpy as np -import pickle -import torch -import torch.distributed as dist - -_LOCAL_PROCESS_GROUP = None -""" -A torch process group which only includes processes that on the same machine as the current process. -This variable is set when processes are spawned by `launch()` in "engine/launch.py". -""" - - -def get_world_size() -> int: - if not dist.is_available(): - return 1 - if not dist.is_initialized(): - return 1 - return dist.get_world_size() - - -def get_rank() -> int: - if not dist.is_available(): - return 0 - if not dist.is_initialized(): - return 0 - return dist.get_rank() - - -def get_local_rank() -> int: - """ - Returns: - The rank of the current process within the local (per-machine) process group. - """ - if not dist.is_available(): - return 0 - if not dist.is_initialized(): - return 0 - assert _LOCAL_PROCESS_GROUP is not None - return dist.get_rank(group=_LOCAL_PROCESS_GROUP) - - -def get_local_size() -> int: - """ - Returns: - The size of the per-machine process group, - i.e. the number of processes per machine. - """ - if not dist.is_available(): - return 1 - if not dist.is_initialized(): - return 1 - return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) - - -def is_main_process() -> bool: - return get_rank() == 0 - - -def synchronize(): - """ - Helper function to synchronize (barrier) among all processes when - using distributed training - """ - if not dist.is_available(): - return - if not dist.is_initialized(): - return - world_size = dist.get_world_size() - if world_size == 1: - return - dist.barrier() - - -@functools.lru_cache() -def _get_global_gloo_group(): - """ - Return a process group based on gloo backend, containing all the ranks - The result is cached. - """ - if dist.get_backend() == "nccl": - return dist.new_group(backend="gloo") - else: - return dist.group.WORLD - - -def _serialize_to_tensor(data, group): - backend = dist.get_backend(group) - assert backend in ["gloo", "nccl"] - device = torch.device("cpu" if backend == "gloo" else "cuda") - - buffer = pickle.dumps(data) - if len(buffer) > 1024 ** 3: - logger = logging.getLogger(__name__) - logger.warning( - "Rank {} trying to all-gather {:.2f} GB of data on device {}".format( - get_rank(), len(buffer) / (1024 ** 3), device - ) - ) - storage = torch.ByteStorage.from_buffer(buffer) - tensor = torch.ByteTensor(storage).to(device=device) - return tensor - - -def _pad_to_largest_tensor(tensor, group): - """ - Returns: - list[int]: size of the tensor, on each rank - Tensor: padded tensor that has the max size - """ - world_size = dist.get_world_size(group=group) - assert ( - world_size >= 1 - ), "comm.gather/all_gather must be called from ranks within the given group!" - local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device) - size_list = [ - torch.zeros([1], dtype=torch.int64, device=tensor.device) for _ in range(world_size) - ] - dist.all_gather(size_list, local_size, group=group) - - size_list = [int(size.item()) for size in size_list] - - max_size = max(size_list) - - # we pad the tensor because torch all_gather does not support - # gathering tensors of different shapes - if local_size != max_size: - padding = torch.zeros((max_size - local_size,), dtype=torch.uint8, device=tensor.device) - tensor = torch.cat((tensor, padding), dim=0) - return size_list, tensor - - -def all_gather(data, group=None): - """ - Run all_gather on arbitrary picklable data (not necessarily tensors). - - Args: - data: any picklable object - group: a torch process group. By default, will use a group which - contains all ranks on gloo backend. - - Returns: - list[data]: list of data gathered from each rank - """ - if get_world_size() == 1: - return [data] - if group is None: - group = _get_global_gloo_group() - if dist.get_world_size(group) == 1: - return [data] - - tensor = _serialize_to_tensor(data, group) - - size_list, tensor = _pad_to_largest_tensor(tensor, group) - max_size = max(size_list) - - # receiving Tensor from all ranks - tensor_list = [ - torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list - ] - dist.all_gather(tensor_list, tensor, group=group) - - data_list = [] - for size, tensor in zip(size_list, tensor_list): - buffer = tensor.cpu().numpy().tobytes()[:size] - data_list.append(pickle.loads(buffer)) - - return data_list - - -def gather(data, dst=0, group=None): - """ - Run gather on arbitrary picklable data (not necessarily tensors). - - Args: - data: any picklable object - dst (int): destination rank - group: a torch process group. By default, will use a group which - contains all ranks on gloo backend. - - Returns: - list[data]: on dst, a list of data gathered from each rank. Otherwise, - an empty list. - """ - if get_world_size() == 1: - return [data] - if group is None: - group = _get_global_gloo_group() - if dist.get_world_size(group=group) == 1: - return [data] - rank = dist.get_rank(group=group) - - tensor = _serialize_to_tensor(data, group) - size_list, tensor = _pad_to_largest_tensor(tensor, group) - - # receiving Tensor from all ranks - if rank == dst: - max_size = max(size_list) - tensor_list = [ - torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list - ] - dist.gather(tensor, tensor_list, dst=dst, group=group) - - data_list = [] - for size, tensor in zip(size_list, tensor_list): - buffer = tensor.cpu().numpy().tobytes()[:size] - data_list.append(pickle.loads(buffer)) - return data_list - else: - dist.gather(tensor, [], dst=dst, group=group) - return [] - - -def shared_random_seed(): - """ - Returns: - int: a random number that is the same across all workers. - If workers need a shared RNG, they can use this shared seed to - create one. - - All workers must call this function, otherwise it will deadlock. - """ - ints = np.random.randint(2 ** 31) - all_ints = all_gather(ints) - return all_ints[0] - - -def reduce_dict(input_dict, average=True): - """ - Reduce the values in the dictionary from all processes so that process with rank - 0 has the reduced results. - - Args: - input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. - average (bool): whether to do average or sum - - Returns: - a dict with the same keys as input_dict, after reduction. - """ - world_size = get_world_size() - if world_size < 2: - return input_dict - with torch.no_grad(): - names = [] - values = [] - # sort the keys so that they are consistent across processes - for k in sorted(input_dict.keys()): - names.append(k) - values.append(input_dict[k]) - values = torch.stack(values, dim=0) - dist.reduce(values, dst=0) - if dist.get_rank() == 0 and average: - # only main process gets accumulated, so only divide by - # world_size in this case - values /= world_size - reduced_dict = {k: v for k, v in zip(names, values)} - return reduced_dict diff --git a/src/utils/dataloader.py b/src/utils/dataloader.py deleted file mode 100644 index 6da37b8..0000000 --- a/src/utils/dataloader.py +++ /dev/null @@ -1,23 +0,0 @@ -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 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) - 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)}' - n_per_rank = len(padded_items) // world_size - local_items = padded_items[n_per_rank * rank: n_per_rank * (rank+1)] - - return local_items diff --git a/src/utils/dataset.py b/src/utils/dataset.py deleted file mode 100644 index cd33a11..0000000 --- a/src/utils/dataset.py +++ /dev/null @@ -1,185 +0,0 @@ -import io -from loguru import logger - -import cv2 -import numpy as np -import h5py -import torch -from numpy.linalg import inv - - -try: - # for internel use only - from .client import MEGADEPTH_CLIENT, SCANNET_CLIENT -except Exception: - MEGADEPTH_CLIENT = SCANNET_CLIENT = None - -# --- DATA IO --- - -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) - image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) - return image # (h, w) - - -def get_resized_wh(w, h, resize=None): - if resize is not None: # resize the longer edge - scale = resize / max(h, w) - w_new, h_new = int(round(w*scale)), int(round(h*scale)) - else: - w_new, h_new = w, h - return w_new, h_new - - -def get_divisible_wh(w, h, df=None): - if df is not None: - w_new, h_new = map(lambda x: int(x // df * df), [w, h]) - else: - w_new, h_new = w, h - return w_new, h_new - - -def pad_bottom_right(inp, pad_size, ret_mask=False): - assert isinstance(pad_size, int) and pad_size >= max(inp.shape[-2:]), f"{pad_size} < {max(inp.shape[-2:])}" - mask = None - if inp.ndim == 2: - padded = np.zeros((pad_size, pad_size), dtype=inp.dtype) - padded[:inp.shape[0], :inp.shape[1]] = inp - if ret_mask: - mask = np.zeros((pad_size, pad_size), dtype=bool) - mask[:inp.shape[0], :inp.shape[1]] = True - elif inp.ndim == 3: - padded = np.zeros((inp.shape[0], pad_size, pad_size), dtype=inp.dtype) - padded[:, :inp.shape[1], :inp.shape[2]] = inp - if ret_mask: - mask = np.zeros((inp.shape[0], pad_size, pad_size), dtype=bool) - mask[:, :inp.shape[1], :inp.shape[2]] = True - else: - raise NotImplementedError() - return padded, mask - - -# --- MEGADEPTH --- - -def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=None): - """ - Args: - resize (int, optional): the longer edge of resized images. None for no resize. - padding (bool): If set to 'True', zero-pad resized images to squared size. - augment_fn (callable, optional): augments images with pre-defined visual effects - Returns: - image (torch.tensor): (1, h, w) - mask (torch.tensor): (h, w) - scale (torch.tensor): [w/w_new, h/h_new] - """ - # read image - image = imread_gray(path, augment_fn, client=MEGADEPTH_CLIENT) - - # resize image - w, h = image.shape[1], image.shape[0] - w_new, h_new = get_resized_wh(w, h, resize) - w_new, h_new = get_divisible_wh(w_new, h_new, df) - - image = cv2.resize(image, (w_new, h_new)) - scale = torch.tensor([w/w_new, h/h_new], dtype=torch.float) - - if padding: # padding - pad_to = max(h_new, w_new) - image, mask = pad_bottom_right(image, pad_to, ret_mask=True) - else: - mask = None - - image = torch.from_numpy(image).float()[None] / 255 # (h, w) -> (1, h, w) and normalized - mask = torch.from_numpy(mask) - - return image, mask, scale - - -def read_megadepth_depth(path, pad_to=None): - 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) - return depth - - -# --- ScanNet --- - -def read_scannet_gray(path, resize=(640, 480), augment_fn=None): - """ - Args: - resize (tuple): align image to depthmap, in (w, h). - augment_fn (callable, optional): augments images with pre-defined visual effects - Returns: - image (torch.tensor): (1, h, w) - mask (torch.tensor): (h, w) - scale (torch.tensor): [w/w_new, h/h_new] - """ - # read and resize image - image = imread_gray(path, augment_fn) - image = cv2.resize(image, resize) - - # (h, w) -> (1, h, w) and normalized - image = torch.from_numpy(image).float()[None] / 255 - return image - - -def read_scannet_depth(path): - 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/metrics.py b/src/utils/metrics.py deleted file mode 100644 index 8804e20..0000000 --- a/src/utils/metrics.py +++ /dev/null @@ -1,193 +0,0 @@ -import torch -import cv2 -import numpy as np -from collections import OrderedDict -from loguru import logger -from kornia.geometry.epipolar import numeric -from kornia.geometry.conversions import convert_points_to_homogeneous - - -# --- METRICS --- - -def relative_pose_error(T_0to1, R, t, ignore_gt_t_thr=0.0): - # angle error between 2 vectors - t_gt = T_0to1[:3, 3] - n = np.linalg.norm(t) * np.linalg.norm(t_gt) - t_err = np.rad2deg(np.arccos(np.clip(np.dot(t, t_gt) / n, -1.0, 1.0))) - t_err = np.minimum(t_err, 180 - t_err) # handle E ambiguity - if np.linalg.norm(t_gt) < ignore_gt_t_thr: # pure rotation is challenging - t_err = 0 - - # angle error between 2 rotation matrices - R_gt = T_0to1[:3, :3] - cos = (np.trace(np.dot(R.T, R_gt)) - 1) / 2 - cos = np.clip(cos, -1., 1.) # handle numercial errors - R_err = np.rad2deg(np.abs(np.arccos(cos))) - - return t_err, R_err - - -def symmetric_epipolar_distance(pts0, pts1, E, K0, K1): - """Squared symmetric epipolar distance. - This can be seen as a biased estimation of the reprojection error. - Args: - pts0 (torch.Tensor): [N, 2] - E (torch.Tensor): [3, 3] - """ - pts0 = (pts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None] - pts1 = (pts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None] - pts0 = convert_points_to_homogeneous(pts0) - pts1 = convert_points_to_homogeneous(pts1) - - Ep0 = pts0 @ E.T # [N, 3] - p1Ep0 = torch.sum(pts1 * Ep0, -1) # [N,] - Etp1 = pts1 @ E # [N, 3] - - d = p1Ep0**2 * (1.0 / (Ep0[:, 0]**2 + Ep0[:, 1]**2) + 1.0 / (Etp1[:, 0]**2 + Etp1[:, 1]**2)) # N - return d - - -def compute_symmetrical_epipolar_errors(data): - """ - Update: - data (dict):{"epi_errs": [M]} - """ - Tx = numeric.cross_product_matrix(data['T_0to1'][:, :3, 3]) - E_mat = Tx @ data['T_0to1'][:, :3, :3] - - m_bids = data['m_bids'] - pts0 = data['mkpts0_f'] - pts1 = data['mkpts1_f'] - - epi_errs = [] - for bs in range(Tx.size(0)): - mask = m_bids == bs - epi_errs.append( - symmetric_epipolar_distance(pts0[mask], pts1[mask], E_mat[bs], data['K0'][bs], data['K1'][bs])) - epi_errs = torch.cat(epi_errs, dim=0) - - data.update({'epi_errs': epi_errs}) - - -def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999): - if len(kpts0) < 5: - return None - # normalize keypoints - kpts0 = (kpts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None] - kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None] - - # normalize ransac threshold - ransac_thr = thresh / np.mean([K0[0, 0], K1[1, 1], K0[0, 0], K1[1, 1]]) - - # compute pose with cv2 - E, mask = cv2.findEssentialMat( - kpts0, kpts1, np.eye(3), threshold=ransac_thr, prob=conf, method=cv2.RANSAC) - if E is None: - print("\nE is None while trying to recover pose.\n") - return None - - # recover pose from E - best_num_inliers = 0 - ret = None - for _E in np.split(E, len(E) / 3): - n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask) - if n > best_num_inliers: - ret = (R, t[:, 0], mask.ravel() > 0) - best_num_inliers = n - - return ret - - -def compute_pose_errors(data, config): - """ - Update: - data (dict):{ - "R_errs" List[float]: [N] - "t_errs" List[float]: [N] - "inliers" List[np.ndarray]: [N] - } - """ - pixel_thr = config.TRAINER.RANSAC_PIXEL_THR # 0.5 - conf = config.TRAINER.RANSAC_CONF # 0.99999 - data.update({'R_errs': [], 't_errs': [], 'inliers': []}) - - m_bids = data['m_bids'].cpu().numpy() - pts0 = data['mkpts0_f'].cpu().numpy() - pts1 = data['mkpts1_f'].cpu().numpy() - K0 = data['K0'].cpu().numpy() - K1 = data['K1'].cpu().numpy() - T_0to1 = data['T_0to1'].cpu().numpy() - - for bs in range(K0.shape[0]): - mask = m_bids == bs - ret = estimate_pose(pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf) - - if ret is None: - data['R_errs'].append(np.inf) - data['t_errs'].append(np.inf) - data['inliers'].append(np.array([]).astype(np.bool)) - else: - R, t, inliers = ret - t_err, R_err = relative_pose_error(T_0to1[bs], R, t, ignore_gt_t_thr=0.0) - data['R_errs'].append(R_err) - data['t_errs'].append(t_err) - data['inliers'].append(inliers) - - -# --- METRIC AGGREGATION --- - -def error_auc(errors, thresholds): - """ - Args: - errors (list): [N,] - thresholds (list) - """ - errors = [0] + sorted(list(errors)) - recall = list(np.linspace(0, 1, len(errors))) - - aucs = [] - thresholds = [5, 10, 20] - for thr in thresholds: - last_index = np.searchsorted(errors, thr) - y = recall[:last_index] + [recall[last_index-1]] - x = errors[:last_index] + [thr] - aucs.append(np.trapz(y, x) / thr) - - return {f'auc@{t}': auc for t, auc in zip(thresholds, aucs)} - - -def epidist_prec(errors, thresholds, ret_dict=False): - precs = [] - for thr in thresholds: - prec_ = [] - for errs in errors: - correct_mask = errs < thr - prec_.append(np.mean(correct_mask) if len(correct_mask) > 0 else 0) - precs.append(np.mean(prec_) if len(prec_) > 0 else 0) - if ret_dict: - return {f'prec@{t:.0e}': prec for t, prec in zip(thresholds, precs)} - else: - return precs - - -def aggregate_metrics(metrics, epi_err_thr=5e-4): - """ Aggregate metrics for the whole dataset: - (This method should be called once per dataset) - 1. AUC of the pose error (angular) at the threshold [5, 10, 20] - 2. Mean matching precision at the threshold 5e-4(ScanNet), 1e-4(MegaDepth) - """ - # filter duplicates - unq_ids = OrderedDict((iden, id) for id, iden in enumerate(metrics['identifiers'])) - unq_ids = list(unq_ids.values()) - logger.info(f'Aggregating metrics over {len(unq_ids)} unique items...') - - # pose auc - angular_thresholds = [5, 10, 20] - pose_errors = np.max(np.stack([metrics['R_errs'], metrics['t_errs']]), axis=0)[unq_ids] - aucs = error_auc(pose_errors, angular_thresholds) # (auc@5, auc@10, auc@20) - - # matching precision - dist_thresholds = [epi_err_thr] - precs = epidist_prec(np.array(metrics['epi_errs'], dtype=object)[unq_ids], dist_thresholds, True) # (prec@err_thr) - - return {**aucs, **precs} diff --git a/src/utils/misc.py b/src/utils/misc.py deleted file mode 100644 index 9c8db04..0000000 --- a/src/utils/misc.py +++ /dev/null @@ -1,101 +0,0 @@ -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): - return yacs_cfg - return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()} - - -def upper_config(dict_cfg): - if not isinstance(dict_cfg, dict): - return dict_cfg - return {k.upper(): upper_config(v) for k, v in dict_cfg.items()} - - -def log_on(condition, message, level): - if condition: - assert level in ['INFO', 'DEBUG', 'WARNING', 'ERROR', 'CRITICAL'] - 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)) - - -@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) - - old_batch_callback = joblib.parallel.BatchCompletionCallBack - joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback - try: - yield tqdm_object - finally: - joblib.parallel.BatchCompletionCallBack = old_batch_callback - tqdm_object.close() - diff --git a/src/utils/plotting.py b/src/utils/plotting.py deleted file mode 100644 index 3d4c5ca..0000000 --- a/src/utils/plotting.py +++ /dev/null @@ -1,154 +0,0 @@ -import bisect -import numpy as np -import matplotlib.pyplot as plt -import matplotlib - - -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 make_matching_figure( - img0, img1, mkpts0, mkpts1, color, - kpts0=None, kpts1=None, text=[], dpi=75, path=None): - # draw image pair - 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 - axes[i].get_yaxis().set_ticks([]) - axes[i].get_xaxis().set_ticks([]) - 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 - axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c='w', s=2) - axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c='w', s=2) - - # draw matches - 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' - fig.text( - 0.01, 0.99, '\n'.join(text), transform=fig.axes[0].transAxes, - fontsize=15, va='top', ha='left', color=txt_color) - - # save or return figure - if path: - plt.savefig(str(path), bbox_inches='tight', pad_inches=0) - plt.close() - else: - 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]] - """ - 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) - return np.clip( - np.stack([2-x*2, x*2, np.zeros_like(x), np.ones_like(x)*alpha], -1), 0, 1) diff --git a/src/utils/profiler.py b/src/utils/profiler.py deleted file mode 100644 index 6d21ed7..0000000 --- a/src/utils/profiler.py +++ /dev/null @@ -1,39 +0,0 @@ -import torch -from pytorch_lightning.profiler import SimpleProfiler, PassThroughProfiler -from contextlib import contextmanager -from pytorch_lightning.utilities import rank_zero_only - - -class InferenceProfiler(SimpleProfiler): - """ - This profiler records duration of actions with cuda.synchronize() - Use this in test time. - """ - - def __init__(self): - super().__init__() - self.start = rank_zero_only(self.start) - self.stop = rank_zero_only(self.stop) - self.summary = rank_zero_only(self.summary) - - @contextmanager - def profile(self, action_name: str) -> None: - try: - torch.cuda.synchronize() - self.start(action_name) - yield action_name - finally: - torch.cuda.synchronize() - self.stop(action_name) - - -def build_profiler(name): - if name == 'inference': - return InferenceProfiler() - elif name == 'pytorch': - from pytorch_lightning.profiler import PyTorchProfiler - return PyTorchProfiler(use_cuda=True, profile_memory=True, row_limit=100) - elif name is None: - return PassThroughProfiler() - else: - raise ValueError(f'Invalid profiler: {name}') diff --git a/test.py b/test.py deleted file mode 100644 index 452e0c8..0000000 --- a/test.py +++ /dev/null @@ -1,68 +0,0 @@ -import pytorch_lightning as pl -import argparse -import pprint -from loguru import logger as loguru_logger - -from src.config.default import get_cfg_defaults -from src.utils.profiler import build_profiler - -from src.lightning.data import MultiSceneDataModule -from src.lightning.lightning_loftr import PL_LoFTR - - -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( - 'data_cfg_path', type=str, help='data config path') - parser.add_argument( - 'main_cfg_path', type=str, help='main config path') - parser.add_argument( - '--ckpt_path', type=str, default="weights/indoor_ds.ckpt", help='path to the checkpoint') - parser.add_argument( - '--dump_dir', type=str, default=None, help="if set, the matching results will be dump to dump_dir") - parser.add_argument( - '--profiler_name', type=str, default=None, help='options: [inference, pytorch], or leave it unset') - parser.add_argument( - '--batch_size', type=int, default=1, help='batch_size per gpu') - parser.add_argument( - '--num_workers', type=int, default=2) - parser.add_argument( - '--thr', type=float, default=None, help='modify the coarse-level matching threshold.') - - parser = pl.Trainer.add_argparse_args(parser) - return parser.parse_args() - - -if __name__ == '__main__': - # parse arguments - args = parse_args() - 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 - - # tune when testing - if args.thr is not None: - config.LOFTR.MATCH_COARSE.THR = args.thr - - loguru_logger.info(f"Args and config initialized!") - - # lightning module - profiler = build_profiler(args.profiler_name) - model = PL_LoFTR(config, pretrained_ckpt=args.ckpt_path, profiler=profiler, dump_dir=args.dump_dir) - loguru_logger.info(f"LoFTR-lightning initialized!") - - # lightning data - data_module = MultiSceneDataModule(args, config) - loguru_logger.info(f"DataModule initialized!") - - # lightning trainer - trainer = pl.Trainer.from_argparse_args(args, replace_sampler_ddp=False, logger=False) - - loguru_logger.info(f"Start testing!") - trainer.test(model, datamodule=data_module, verbose=False) diff --git a/third_party/SuperGluePretrainedNetwork b/third_party/SuperGluePretrainedNetwork deleted file mode 160000 index c0626d5..0000000 --- a/third_party/SuperGluePretrainedNetwork +++ /dev/null @@ -1 +0,0 @@ -Subproject commit c0626d58c843ee0464b0fa1dd4de4059bfae0ab4 diff --git a/train.py b/train.py deleted file mode 100644 index fa8a0bd..0000000 --- a/train.py +++ /dev/null @@ -1,123 +0,0 @@ -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( - 'data_cfg_path', type=str, help='data config path') - parser.add_argument( - 'main_cfg_path', type=str, help='main config path') - 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, - help='pretrained checkpoint path, helpful for using a pre-trained coarse-only LoFTR') - 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()