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View Code? Open in Web Editor NEWOpenTAD is an open-source temporal action detection (TAD) toolbox based on PyTorch.
License: Apache License 2.0
opentad's Issues
Proposal Generation Only
Hello, I am interested in performing only the task of Temporal Action Localization and do not want to classify each proposal. Could you please guide me on how to modify the code accordingly?
AdaTAD License question
Does the Apache License apply to all models in the repo, and specifically does it apply to the Adatad model?
Thanks!!
Training loss drops sharply to Nan on my own dataset
May I ask why my loss suddenly decreased to Nan during training using my own dataset? Looking forward to your answer,thanks。
feature_stride vs sample_stride
Should I use feature_stride or sample_stride?
Or both?
epic-kitchens100 eval is nan
#===========================================================================================================
annotation_path = "train_code/OpenTAD/data/epic_kitchens-100/annotations/epic_kitchens_verb.json"
class_map = "train_code/OpenTAD/data/epic_kitchens-100/annotations/category_idx_verb.txt"
data_path = "data/video_dataset/EPIC-Kitchens100/epic_kitchens_100_30fps_512x288/"
block_list = None
window_size = 768*8
scale_factor = 1
chunk_num = window_size * scale_factor // 16
# 768/16=48 chunks, since videomae takes 16 frames as input
# ==================================================
#Video Preprocessing Sliding Window
#Frame Stride 2
#Frame Number 768×8
# ==================================================
dataset = dict(
train=dict(
type="EpicKitchensSlidingDataset",
ann_file=annotation_path,
subset_name="training",
block_list=block_list,
class_map=class_map,
data_path=data_path,
filter_gt=False,
# ==================================================
#Video Preprocessing Sliding Window
#Frame Stride 2
#Frame Number 768×8
# ==================================================
# ==================================================
feature_stride=2,
sample_stride=1,
fps=30,
offset_frames=8,
window_size=window_size,
window_overlap_ratio=0.5,
# ==================================================
pipeline=[
dict(type="PrepareVideoInfo", format="mp4"),
dict(type="mmaction.DecordInit", num_threads=4),
dict(type="LoadFrames",
num_clips=1,
method="sliding_window",
scale_factor=scale_factor),
dict(type="mmaction.DecordDecode"),
#================================================================
#Frame Resolution 160×160
#RandomResizedCrop + Flip + ImgAug + ColorJitter
#================================================================
dict(type="mmaction.Resize", scale=(-1, 182)),
dict(type="mmaction.RandomResizedCrop"),
dict(type="mmaction.Resize", scale=(160, 160), keep_ratio=False),
dict(type="mmaction.Flip", flip_ratio=0.5),
dict(type="mmaction.ImgAug", transforms="default"),
dict(type="mmaction.ColorJitter"),
dict(type="mmaction.FormatShape", input_format="NCTHW"),
dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
],
),
val=dict(
type="EpicKitchensSlidingDataset",
ann_file=annotation_path,
subset_name="val",
block_list=block_list,
class_map=class_map,
data_path=data_path,
filter_gt=False,
# ==================================================
feature_stride=2,
sample_stride=1,
fps=30,
offset_frames=8,
window_size=window_size,
window_overlap_ratio=0.5,
# ==================================================
pipeline=[
dict(type="PrepareVideoInfo", format="mp4"),
dict(type="mmaction.DecordInit", num_threads=4),
dict(type="LoadFrames",
num_clips=1,
method="sliding_window",
scale_factor=scale_factor),
dict(type="mmaction.DecordDecode"),
dict(type="mmaction.Resize", scale=(-1, 160)),
dict(type="mmaction.CenterCrop", crop_size=160),
dict(type="mmaction.FormatShape", input_format="NCTHW"),
dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
],
),
test=dict(
type="EpicKitchensSlidingDataset",
ann_file=annotation_path,
subset_name="val",
block_list=block_list,
class_map=class_map,
data_path=data_path,
filter_gt=False,
# ==================================================
test_mode=True,
feature_stride=2,
sample_stride=1,
fps=30,
offset_frames=8,
window_size=window_size,
window_overlap_ratio=0.5,
# ==================================================
pipeline=[
dict(type="PrepareVideoInfo", format="mp4"),
dict(type="mmaction.DecordInit", num_threads=4),
dict(type="LoadFrames",
num_clips=1,
method="sliding_window",
scale_factor=scale_factor),
dict(type="mmaction.DecordDecode"),
dict(type="mmaction.Resize", scale=(-1, 160)),
dict(type="mmaction.CenterCrop", crop_size=160),
dict(type="mmaction.FormatShape", input_format="NCTHW"),
dict(type="ConvertToTensor", keys=["imgs"]),
dict(type="Collect", inputs="imgs", keys=["masks"]),
],
),
)
evaluation = dict(
type="mAP",
subset="validation",
tiou_thresholds=[0.3, 0.4, 0.5, 0.6, 0.7],
ground_truth_filename=annotation_path,
)
#===========================================================================================================
_base_ = [
"/mnt2/ninghuayang/train_code/OpenTAD/configs/_base_/models/actionformer.py",
]
model = dict(
backbone=dict(
type="mmaction.Recognizer3D",
backbone=dict(
type="VisionTransformerAdapter",
img_size=224,
patch_size=16,
embed_dims=1024,
depth=24,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
num_frames=16,
drop_path_rate=0.1,
norm_cfg=dict(type="LN", eps=1e-6),
return_feat_map=True,
with_cp=True, # enable activation checkpointing
total_frames=window_size * scale_factor,
adapter_index=list(range(24)),
),
data_preprocessor=dict(
type="mmaction.ActionDataPreprocessor",
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
format_shape="NCTHW",
),
custom=dict(
pretrain="pretrained/vit-large-p16_videomae-k400-pre_16x4x1_kinetics-400_20221013-229dbb03.pth",
pre_processing_pipeline=[
dict(type="Rearrange",
keys=["frames"],
ops="b n c (t1 t) h w -> (b t1) n c t h w",
t1=chunk_num),
],
post_processing_pipeline=[
#=========================
#Spatial Average Pooling + Resize
#Feature Resize Length 768
#=========================
dict(type="Reduce",
keys=["feats"],
ops="b n c t h w -> b c t",
reduction="mean"),
dict(type="Rearrange",
keys=["feats"],
ops="(b t1) c t -> b c (t1 t)",
t1=chunk_num),
#=========================
#Spatial Average Pooling + Resize
#Feature Resize Length 768
#=========================
dict(type="Interpolate",
keys=["feats"],
size=768),
],
norm_eval=False, # also update the norm layers
freeze_backbone=False, # unfreeze the backbone
),
),
projection=dict(
in_channels=1024,
max_seq_len=768,
attn_cfg=dict(n_mha_win_size=9),
),
rpn_head=dict(
num_classes=97,
prior_generator=dict(
strides=[1, 2, 4, 8, 16, 32],
regression_range=[(0, 4), (2, 8), (4, 16), (8, 32), (16, 64), (32, 10000)],
),
loss_normalizer=250,
),
)
#=============
#Batch Size 2
#=============
solver = dict(
train=dict(batch_size=2, num_workers=4),
val=dict(batch_size=2, num_workers=2),
test=dict(batch_size=2, num_workers=2),
clip_grad_norm=1,
amp=True,
fp16_compress=True,
static_graph=True,
ema=True,
)
optimizer = dict(
type="AdamW",
lr=1e-4,
weight_decay=0.05,
paramwise=True,
backbone=dict(
lr=0,
weight_decay=0,
custom=[dict(name="adapter", lr=1e-4, weight_decay=0.05)],
exclude=["backbone"],
),
)
scheduler = dict(type="LinearWarmupCosineAnnealingLR",
#=============
#Warmup Epoch 5
#=============
warmup_epoch=5,
max_epoch=20)
inference = dict(load_from_raw_predictions=False, save_raw_prediction=False)
post_processing = dict(
pre_nms_topk=5000,
nms=dict(
use_soft_nms=True,
sigma=0.4,
max_seg_num=2000,
iou_threshold=0, # does not matter when use soft nms
min_score=0.001,
multiclass=True,
voting_thresh=0.75, # set 0 to disable
),
save_dict=False,
)
#=============
#Total Epoch 35
#=============
workflow = dict(
logging_interval=10,
checkpoint_interval=2,
val_loss_interval=-1,
val_eval_interval=2,
val_start_epoch=2,
end_epoch=60,
)
work_dir = "exps"
Cannot use gdown to download Anet raw video data
When I use this command to download _Anet_videos_15fps_short256.zip from google drive.
gdown [download link]
I got this error:
Failed to retrieve file url:
Cannot retrieve the public link of the file. You may need to change
the permission to 'Anyone with the link', or have had many accesses.
Check FAQ in https://github.com/wkentaro/gdown?tab=readme-ov-file#faq.
You may still be able to access the file from the browser:
[download link]
but Gdown can't. Please check connections and permissions.
Could you please change the permission to 'Anyone with the link'?
AdaTAD Training Resume
Hi Shuming,
Will the training automatically resume if I kill the process and run it again?
Thanks.
VisionTransformerCP vs VisionTransformerLadder
Hi Shuming,
What are VisionTransformerCP and VisionTransformerLadder?
Thanks.
Does Adatad support multi-label temporal action detection?
Congrats on the great work! I was wondering if Adatad supports multi-label temporal action detection (e.g. for multi-thumos)?
Thanks!
epic_kitchens-100 opentad RuntimeError: CUDA error: device-side assert triggered
#===========================================================================================================
annotation_path = "train_code/OpenTAD/data/epic_kitchens-100/annotations/epic_kitchens_verb.json"
class_map = "train_code/OpenTAD/data/epic_kitchens-100/annotations/category_idx_verb.txt"
data_path = "data/video_dataset/EPIC-Kitchens100/epic_kitchens_100_30fps_512x288/"
block_list = None
window_size = 768*8
scale_factor = 1
chunk_num = window_size * scale_factor // 16
# 768/16=48 chunks, since videomae takes 16 frames as input
# ==================================================
#Video Preprocessing Sliding Window
#Frame Stride 2
#Frame Number 768×8
# ==================================================
dataset = dict(
train=dict(
type="EpicKitchensSlidingDataset",
ann_file=annotation_path,
subset_name="training",
block_list=block_list,
class_map=class_map,
data_path=data_path,
filter_gt=False,
# ==================================================
#Video Preprocessing Sliding Window
#Frame Stride 2
#Frame Number 768×8
# ==================================================
# ==================================================
feature_stride=2,
sample_stride=1,
fps=30,
offset_frames=8,
window_size=window_size,
window_overlap_ratio=0.5,
# ==================================================
pipeline=[
dict(type="PrepareVideoInfo", format="mp4"),
dict(type="mmaction.DecordInit", num_threads=4),
dict(type="LoadFrames",
num_clips=1,
method="sliding_window",
scale_factor=scale_factor),
dict(type="mmaction.DecordDecode"),
#================================================================
#Frame Resolution 160×160
#RandomResizedCrop + Flip + ImgAug + ColorJitter
#================================================================
dict(type="mmaction.Resize", scale=(-1, 182)),
dict(type="mmaction.RandomResizedCrop"),
dict(type="mmaction.Resize", scale=(160, 160), keep_ratio=False),
dict(type="mmaction.Flip", flip_ratio=0.5),
dict(type="mmaction.ImgAug", transforms="default"),
dict(type="mmaction.ColorJitter"),
dict(type="mmaction.FormatShape", input_format="NCTHW"),
dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
],
),
val=dict(
type="EpicKitchensSlidingDataset",
ann_file=annotation_path,
subset_name="val",
block_list=block_list,
class_map=class_map,
data_path=data_path,
filter_gt=False,
# ==================================================
test_mode=True,
feature_stride=2,
sample_stride=1,
fps=30,
offset_frames=8,
window_size=window_size,
window_overlap_ratio=0.5,
# ==================================================
pipeline=[
dict(type="PrepareVideoInfo", format="mp4"),
dict(type="mmaction.DecordInit", num_threads=4),
dict(type="LoadFrames",
num_clips=1,
method="sliding_window",
scale_factor=scale_factor),
dict(type="mmaction.DecordDecode"),
dict(type="mmaction.Resize", scale=(-1, 160)),
dict(type="mmaction.CenterCrop", crop_size=160),
dict(type="mmaction.FormatShape", input_format="NCTHW"),
dict(type="ConvertToTensor", keys=["imgs", "gt_segments", "gt_labels"]),
dict(type="Collect", inputs="imgs", keys=["masks", "gt_segments", "gt_labels"]),
],
),
test=dict(
type="EpicKitchensSlidingDataset",
ann_file=annotation_path,
subset_name="val",
block_list=block_list,
class_map=class_map,
data_path=data_path,
filter_gt=False,
test_mode=True,
# ==================================================
feature_stride=2,
sample_stride=1,
fps=30,
offset_frames=8,
window_size=window_size,
window_overlap_ratio=0.5,
# ==================================================
pipeline=[
dict(type="PrepareVideoInfo", format="mp4"),
dict(type="mmaction.DecordInit", num_threads=4),
dict(type="LoadFrames",
num_clips=1,
method="sliding_window",
scale_factor=scale_factor),
dict(type="mmaction.DecordDecode"),
dict(type="mmaction.Resize", scale=(-1, 160)),
dict(type="mmaction.CenterCrop", crop_size=160),
dict(type="mmaction.FormatShape", input_format="NCTHW"),
dict(type="ConvertToTensor", keys=["imgs"]),
dict(type="Collect", inputs="imgs", keys=["masks"]),
],
),
)
evaluation = dict(
type="mAP",
subset="validation",
tiou_thresholds=[0.3, 0.4, 0.5, 0.6, 0.7],
ground_truth_filename=annotation_path,
)
#===========================================================================================================
_base_ = [
"/mnt2/ninghuayang/train_code/OpenTAD/configs/_base_/models/actionformer.py",
]
model = dict(
backbone=dict(
type="mmaction.Recognizer3D",
backbone=dict(
type="VisionTransformerAdapter",
img_size=224,
patch_size=16,
embed_dims=1024,
depth=24,
num_heads=16,
mlp_ratio=4,
qkv_bias=True,
num_frames=16,
drop_path_rate=0.1,
norm_cfg=dict(type="LN", eps=1e-6),
return_feat_map=True,
with_cp=True, # enable activation checkpointing
total_frames=window_size * scale_factor,
adapter_index=list(range(24)),
),
data_preprocessor=dict(
type="mmaction.ActionDataPreprocessor",
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
format_shape="NCTHW",
),
custom=dict(
pretrain="pretrained/vit-large-p16_videomae-k400-pre_16x4x1_kinetics-400_20221013-229dbb03.pth",
pre_processing_pipeline=[
dict(type="Rearrange",
keys=["frames"],
ops="b n c (t1 t) h w -> (b t1) n c t h w",
t1=chunk_num),
],
post_processing_pipeline=[
#=========================
#Spatial Average Pooling + Resize
#Feature Resize Length 768
#=========================
dict(type="Reduce",
keys=["feats"],
ops="b n c t h w -> b c t",
reduction="mean"),
dict(type="Rearrange",
keys=["feats"],
ops="(b t1) c t -> b c (t1 t)",
t1=chunk_num),
#=========================
#Spatial Average Pooling + Resize
#Feature Resize Length 768
#=========================
dict(type="Interpolate",
keys=["feats"],
size=window_size),
],
norm_eval=False, # also update the norm layers
freeze_backbone=False, # unfreeze the backbone
),
),
projection=dict(
in_channels=1024,
max_seq_len=window_size,
attn_cfg=dict(n_mha_win_size=-1),
),
)
#=============
#Batch Size 2
#=============
solver = dict(
train=dict(batch_size=2, num_workers=2),
val=dict(batch_size=2, num_workers=2),
test=dict(batch_size=2, num_workers=2),
clip_grad_norm=1,
amp=True,
fp16_compress=True,
static_graph=True,
ema=True,
)
optimizer = dict(
type="AdamW",
lr=1e-4,
weight_decay=0.05,
paramwise=True,
backbone=dict(
lr=0,
weight_decay=0,
custom=[dict(name="adapter", lr=1e-4, weight_decay=0.05)],
exclude=["backbone"],
),
)
scheduler = dict(type="LinearWarmupCosineAnnealingLR",
#=============
#Warmup Epoch 5
#=============
warmup_epoch=5,
max_epoch=100)
inference = dict(load_from_raw_predictions=False, save_raw_prediction=False)
post_processing = dict(
nms=dict(
use_soft_nms=True,
sigma=0.7,
max_seg_num=2000,
multiclass=True,
voting_thresh=0.7, # set 0 to disable
),
save_dict=False,
)
#=============
#Total Epoch 35
#=============
workflow = dict(
logging_interval=50,
checkpoint_interval=2,
val_loss_interval=-1,
val_eval_interval=2,
val_start_epoch=10,
end_epoch=60,
)
work_dir = "exps/thumos/adatad/e2e_actionformer_videomae_s_768x1_160_adapter"
Anet_videos_15fps_short256/missing_files.txt
Hi Shuming,
Did you upload Anet_videos_15fps_short256/missing_files.txt?
FileNotFoundError: [Errno 2] No such file or directory: 'data/activitynet-1.3/raw_data/Anet_videos_15fps_short256/missing_files.txt'
I downloaded [Update]_Anet_videos_15fps_short256.zip, but there was no missing_files.txt.
Where can I find it?
Thanks.
question about `scale_factor` in AdaTAD
I noticed that for anet you use scale_factor = 4 to account for the ViT backbone downsampling, but use scale_factor = 1 for thumos although it uses the same backbone. Can you please explain the logic?
How to get the classifier files of activitynet
such as cuhk_val_simp_7.json new_3ensemble_uniformerv2_large_only_global_anet_16x10x3.json
adatad on multithumos
I noticed that the performance of adatad on the multi-label dataset (multithumos) has not been reported. Can you tell me what its mAP is ？
Best_mAP in training
Hi Shuming, during training, is it possible to save checkpoints based on the best validation mAP?
No recurring indicator results
official
| Backbone | GPUs | Setting | Frames | Img Size | [email protected] | [email protected] | [email protected] | [email protected] | [email protected] | ave. mAP |
|---|---|---|---|---|---|---|---|---|---|---|
| VideoMAE-S | 2 | AdaTAD | 768 | 160 | 83.90 | 79.01 | 72.38 | 61.57 | 48.27 | 69.03 |
| VideoMAE-B | 2 | AdaTAD | 768 | 160 | 85.95 | 81.86 | 75.02 | 63.29 | 49.56 | 71.14 |
| VideoMAE-L | 2 | AdaTAD | 768 | 160 | 87.17 | 83.58 | 76.88 | 66.81 | 53.13 | 73.51 |
| VideoMAE-H | 2 | AdaTAD | 768 | 160 | 88.42 | 84.63 | 78.72 | 69.04 | 53.95 | 74.95 |
| VideoMAEV2-g | 2 | AdaTAD | 768 | 160 | 88.63 | 85.39 | 79.17 | 68.34 | 53.79 | 75.06 |
| VideoMAEV2-g | 2 | AdaTAD | 1536 | 224 | 89.93 | 86.83 | 81.24 | 69.97 | 57.36 | 77.07 |
e2e_thumos_videomaev2_g_768x1_160_adapter.py
2024-04-22 20:36:07 Train INFO: [Train]: Epoch 41 started
2024-04-22 20:39:06 Train INFO: [Train]: [041][00050/00099] Loss=0.3434 cls_loss=0.1847 reg_loss=0.1587 lr_backbone=6.8e-05 lr_det=6.8e-05 mem=30703MB
2024-04-22 20:41:54 Train INFO: [Train]: [041][00099/00099] Loss=0.3310 cls_loss=0.1753 reg_loss=0.1557 lr_backbone=6.7e-05 lr_det=6.7e-05 mem=30703MB
2024-04-22 20:50:28 Train INFO: Evaluation starts...
2024-04-22 20:50:48 Train INFO: Loaded annotations from validation subset.
2024-04-22 20:50:48 Train INFO: Number of ground truth instances: 3325
2024-04-22 20:50:48 Train INFO: Number of predictions: 422000
2024-04-22 20:50:48 Train INFO: Fixed threshold for tiou score: [0.3, 0.4, 0.5, 0.6, 0.7]
2024-04-22 20:50:48 Train INFO: **Average-mAP: 74.85 (%)**
2024-04-22 20:50:48 Train INFO: mAP at tIoU 0.30 is 88.80%
2024-04-22 20:50:48 Train INFO: mAP at tIoU 0.40 is 85.10%
2024-04-22 20:50:48 Train INFO: mAP at tIoU 0.50 is 78.95%
2024-04-22 20:50:48 Train INFO: mAP at tIoU 0.60 is 68.09%
2024-04-22 20:50:48 Train INFO: mAP at tIoU 0.70 is 53.30%
e2e_thumos_videomaev2_g_768x2_224_adapter.py
2024-04-23 08:09:01 Train INFO: [Train]: Epoch 39 started
2024-04-23 08:18:31 Train INFO: [Train]: [039][00050/00099] Loss=0.2967 cls_loss=0.1572 reg_loss=0.1395 lr_backbone=1.4e-04 lr_det=7.1e-05 mem=51851MB
2024-04-23 08:27:33 Train INFO: [Train]: [039][00099/00099] Loss=0.3542 cls_loss=0.1892 reg_loss=0.1650 lr_backbone=1.4e-04 lr_det=7.0e-05 mem=51851MB
2024-04-23 09:00:06 Train INFO: Evaluation starts...
2024-04-23 09:00:26 Train INFO: Loaded annotations from validation subset.
2024-04-23 09:00:26 Train INFO: Number of ground truth instances: 3325
2024-04-23 09:00:26 Train INFO: Number of predictions: 422000
2024-04-23 09:00:26 Train INFO: Fixed threshold for tiou score: [0.3, 0.4, 0.5, 0.6, 0.7]
2024-04-23 09:00:26 Train INFO: **Average-mAP: 75.73 (%)**
2024-04-23 09:00:26 Train INFO: mAP at tIoU 0.30 is 88.47%
2024-04-23 09:00:26 Train INFO: mAP at tIoU 0.40 is 85.66%
2024-04-23 09:00:26 Train INFO: mAP at tIoU 0.50 is 79.79%
2024-04-23 09:00:26 Train INFO: mAP at tIoU 0.60 is 69.55%
2024-04-23 09:00:26 Train INFO: mAP at tIoU 0.70 is 55.19%
Train again without changing anything
e2e_thumos_videomaev2_g_768x2_224_adapter.py
2024-04-20 07:39:00 Train INFO: [Train]: Epoch 39 started
2024-04-20 07:48:33 Train INFO: [Train]: [039][00050/00099] Loss=0.3000 cls_loss=0.1612 reg_loss=0.1388 lr_backbone=1.4e-04 lr_det=7.1e-05 mem=51859MB
2024-04-20 07:57:37 Train INFO: [Train]: [039][00099/00099] Loss=0.3349 cls_loss=0.1770 reg_loss=0.1578 lr_backbone=1.4e-04 lr_det=7.0e-05 mem=51859MB
2024-04-20 08:30:23 Train INFO: Evaluation starts...
2024-04-20 08:30:42 Train INFO: Loaded annotations from validation subset.
2024-04-20 08:30:42 Train INFO: Number of ground truth instances: 3325
2024-04-20 08:30:42 Train INFO: Number of predictions: 422000
2024-04-20 08:30:42 Train INFO: Fixed threshold for tiou score: [0.3, 0.4, 0.5, 0.6, 0.7]
2024-04-20 08:30:42 Train INFO: **Average-mAP: 76.32 (%)**
2024-04-20 08:30:42 Train INFO: mAP at tIoU 0.30 is 89.55%
2024-04-20 08:30:42 Train INFO: mAP at tIoU 0.40 is 86.40%
2024-04-20 08:30:42 Train INFO: mAP at tIoU 0.50 is 79.45%
2024-04-20 08:30:42 Train INFO: mAP at tIoU 0.60 is 70.78%
2024-04-20 08:30:42 Train INFO: mAP at tIoU 0.70 is 55.43%
Roadmap and Feedback
We keep this issue open to collect feature requests and feedback from users, and thus keep improving this codebase.
If you didn't find the features you need in the Road Map, please leave a message here.
Thank you!
position of the adapter
Thank you for your outstanding work! How to train adapters in TAD models outside the backbone network
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