Add RTMPose 3D models and reorganize project resources

- Add RTMPose 3D pose estimation models (nano and medium variants with checkpoints)
- Add RTMPose3D wholebody 3D models (L and X configurations)
- Implement rtmpose3d module with pose estimator, loss functions, and utilities
- Move YouTube-ASL video IDs list to resource directory
- Reorganize How2Sign CSV to resource directory
- Update project configuration and documentation

Author: balaboom123

models/configs/rtmdet_m_640-8xb32_coco-person.py

@@ -0,0 +1,20 @@
+_base_ = 'mmdet::rtmdet/rtmdet_m_8xb32-300e_coco.py'
+
+checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth'  # noqa
+
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
+    bbox_head=dict(num_classes=1),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+
+train_dataloader = dict(dataset=dict(metainfo=dict(classes=('person', ))))
+
+val_dataloader = dict(dataset=dict(metainfo=dict(classes=('person', ))))
+test_dataloader = val_dataloader

models/configs/rtmdet_nano_320-8xb32_coco-person.py

@@ -0,0 +1,104 @@
+_base_ = 'mmdet::rtmdet/rtmdet_l_8xb32-300e_coco.py'
+
+input_shape = 320
+
+model = dict(
+    backbone=dict(
+        deepen_factor=0.33,
+        widen_factor=0.25,
+        use_depthwise=True,
+    ),
+    neck=dict(
+        in_channels=[64, 128, 256],
+        out_channels=64,
+        num_csp_blocks=1,
+        use_depthwise=True,
+    ),
+    bbox_head=dict(
+        in_channels=64,
+        feat_channels=64,
+        share_conv=False,
+        exp_on_reg=False,
+        use_depthwise=True,
+        num_classes=1),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='CachedMosaic',
+        img_scale=(input_shape, input_shape),
+        pad_val=114.0,
+        max_cached_images=20,
+        random_pop=False),
+    dict(
+        type='RandomResize',
+        scale=(input_shape * 2, input_shape * 2),
+        ratio_range=(0.5, 1.5),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=(input_shape, input_shape)),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='Pad',
+        size=(input_shape, input_shape),
+        pad_val=dict(img=(114, 114, 114))),
+    dict(type='PackDetInputs')
+]
+
+train_pipeline_stage2 = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='RandomResize',
+        scale=(input_shape, input_shape),
+        ratio_range=(0.5, 1.5),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=(input_shape, input_shape)),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='Pad',
+        size=(input_shape, input_shape),
+        pad_val=dict(img=(114, 114, 114))),
+    dict(type='PackDetInputs')
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(input_shape, input_shape), keep_ratio=True),
+    dict(
+        type='Pad',
+        size=(input_shape, input_shape),
+        pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+train_dataloader = dict(
+    dataset=dict(pipeline=train_pipeline, metainfo=dict(classes=('person', ))))
+
+val_dataloader = dict(
+    dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=('person', ))))
+test_dataloader = val_dataloader
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        priority=49),
+    dict(
+        type='PipelineSwitchHook',
+        switch_epoch=280,
+        switch_pipeline=train_pipeline_stage2)
+]