peihaochen / rspnet Goto Github PK

Where can I download the pre training model of s3d-g model based on Imagenet and dynamics-400 dataset? Or can you upload it to this repository?
请问哪里可以下载到基于ImageNet和Kinetics-400数据集的S3D-G模型的预训练模型？或者请问作者可以上传一下公开吗？

Hi, I finetuned the given pre-trained r(2+1)d model on ucf-101 using the given finetuning code. It only achieves (76 -77%) accuracy.
Can you confirm if the given model is the correct one. I use the same setup as mentioned in the readme.

Hi,
Thanks for your wonderful paper and code. I want to know the computational resources of your experiments.
1. What and how many GPUs you use?
2. The training time of pretraining on K400 for 200 epochs.
3. The training time of finetuning on UCF101, HMDB51, Something-V2, respectively.
Looking forward to your reply. Thanks.

hello, thank you for your great work. it's so smart idea!

can you explain about framework image?
i understand about RSP task, A-VID task is learned in 1 iteration.
i think that it means 'anchor is same'.
and i saw the algorithm, just sampling K clips in video V\v+,
however, in paper fig 2.
two clips in video, 1x clip and 2x clip 's features(green color) are going to g_a header and do contrastive learning.
i think about you want to show us randomly selected speed.... is right?
in real experiment, just c_i, c_j, {c_n}(K) clips in there? not 2K?

thank you

Thank you for your awesome work. Could you share your visualization code for the result?

I downloaded your S3D-G pre-trained model for my action recognition task on UCF101 but I keep getting this error:

argument type: <class 'str'>
Setting ulimit -n 8192
world_size=1 Using dist_url=tcp://127.0.0.1:36879
Local Rank: 0
2021-12-30 07:31:39,148|INFO |Args =
Args(parser=None, config='config/finetune/ucf101_s3dg.jsonnet', ext_config=[], debug=False, experiment_dir=PosixPath('exps/ucf101-s3dg'), _run_dir=PosixPath('exps/ucf101-s3dg/run_2_20211230_073138'), load_checkpoint=None, load_model=None, validate=False, moco_checkpoint='exps/pretext-s3dg/model_best_s3dg_200epoch.pth.tar', seed=None, world_size=1, _continue=False, no_scale_lr=False)
2021-12-30 07:31:39,149|INFO |cudnn.benchmark = True
2021-12-30 07:31:39,278|INFO |Config =
batch_size = 4
dataset {
annotation_path = "data/UCF101/ucfTrainTestlist"
fold = 1
mean = [
0.485
0.456
0.406
]
name = "ucf101"
num_classes = 101
root = "data/UCF101/UCF-101"
std = [
0.229
0.224
0.225
]
}
final_validate {
batch_size = 4
}
log_interval = 10
method = "from-scratch"
model {
arch = "s3dg"
}
model_type = "multitask"
num_epochs = 50
num_workers = 8
optimizer {
dampening = 0
lr = 0.005
milestones = [
50
100
150
]
momentum = 0.9
nesterov = false
patience = 10
schedule = "cosine"
weight_decay = 0.0001
}
spatial_transforms {
color_jitter {
brightness = 0
contrast = 0
hue = 0
saturation = 0
}
crop_area {
max = 1
min = 0.25
}
gray_scale = 0
size = 224
}
temporal_transforms {
frame_rate = 25
size = 64
strides = [
{
stride = 1
weight = 1
}
]
validate {
final_n_crop = 10
n_crop = 1
stride = 1
}
}
validate {
batch_size = 4
}
2021-12-30 07:31:39,282|INFO |Using global get_model_class({'arch': 's3dg'})
2021-12-30 07:31:39,283|INFO |Using MultiTask Wrapper
2021-12-30 07:31:39,283|WARNING |<class 'moco.split_wrapper.MultiTaskWrapper'> using groups: 1
2021-12-30 07:31:39,383|INFO |Found fc: fc with in_features: 1024
2021-12-30 07:31:42,488|INFO |Building Dataset: VID: False, Split=train
2021-12-30 07:31:42,488|INFO |Temporal transform type: clip
Traceback (most recent call last):
File "finetune.py", line 502, in
main()
File "finetune.py", line 498, in main
mp.spawn(main_worker, args=(args, dist_url,), nprocs=args.world_size)
File "/home/ubuntu/anaconda3/envs/ucf101/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 200, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/home/ubuntu/anaconda3/envs/ucf101/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 158, in start_processes
while not context.join():
File "/home/ubuntu/anaconda3/envs/ucf101/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 119, in join
raise Exception(msg)
Exception:

-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/ucf101/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
fn(i, *args)
File "/home/ubuntu/RSPNet/finetune.py", line 452, in main_worker
engine = Engine(args, cfg, local_rank=local_rank)
File "/home/ubuntu/RSPNet/finetune.py", line 171, in init
self.train_loader = self.data_loader_factory.build(
File "/home/ubuntu/RSPNet/datasets/classification/init.py", line 81, in build
temporal_transform = self.get_temporal_transform(split)
File "/home/ubuntu/RSPNet/datasets/classification/init.py", line 276, in get_temporal_transform
if tt_cfg.get_bool("force_n_crop"):
File "/home/ubuntu/anaconda3/envs/ucf101/lib/python3.8/site-packages/pyhocon/config_tree.py", line 310, in get_bool
string_value = self.get_string(key, default)
File "/home/ubuntu/anaconda3/envs/ucf101/lib/python3.8/site-packages/pyhocon/config_tree.py", line 221, in get_string
value = self.get(key, default)
File "/home/ubuntu/anaconda3/envs/ucf101/lib/python3.8/site-packages/pyhocon/config_tree.py", line 209, in get
return self._get(ConfigTree.parse_key(key), 0, default)
File "/home/ubuntu/anaconda3/envs/ucf101/lib/python3.8/site-packages/pyhocon/config_tree.py", line 151, in _get
raise ConfigMissingException(u"No configuration setting found for key {key}".format(key='.'.join(key_path[:key_index + 1])))
pyhocon.exceptions.ConfigMissingException: 'No configuration setting found for key force_n_crop'