blackfeather-wang / adafocus Goto Github PK

Thanks for your great work!

I want to know which part of the codes is about AdaFouces+ since I wonder how to batch-process videos with different number of frames for some skipped ones. Or does it process per frame in a video one by one?

Thanks for your great job which will motivate a lot of works in this area! After checking your code, i found that for frames in a video it seems to assign a identical spatial sampling location. I wonder if it is true? If it is, where do the locations in fig.7 which are independent for each frame come from?

And the number of num_segments_glancer is different from the number of num_segments_focuser. 但是论文Figure 2. Overview of AdaFocus. 中 输入到fG网络 和 fL网络的帧是一一对应的。请问，输入到fG网络 和 fL网络的帧的需要是一一对应的吗？

I use the following parameters and take mobilenet and resnet-50 which trained by TSN as pre-trained. But the training results are strange！From the beginning, the training accuracy has reached 100%, while the test accuracy is basically unchanged.
CUDA_VISIBLE_DEVICES=4 python stage1.py
dataset=ucf101
data_dir=/data/ymy/data/
train_stage=1
batch_size=32
num_segments_glancer=8
num_segments_focuser=12
glance_size=224
patch_size=144
random_patch=True
epochs=50
backbone_lr=0.001
fc_lr=0.01
lr_type=step
dropout=0.5
load_pretrained_focuser_fc=False
dist_url=tcp://127.0.0.1:8816
eval_freq=1
start_eval=0
print_freq=25
workers=16
pretrained_glancer='/AdaFocus-main/new_mobile.tar'
pretrained_focuser='/AdaFocus-main/new_resnet.tar'

Epoch: [5][ 0/298] Time 43.183 (43.183) Data 42.607 (42.607) Loss 1.1841e-03 (1.1841e-03) Acc@1 100.00 (100.00) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][ 25/298] Time 0.674 ( 2.839) Data 0.107 ( 2.276) Loss 1.7993e-03 (8.2321e-03) Acc@1 100.00 ( 99.76) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][ 50/298] Time 1.080 ( 2.122) Data 0.526 ( 1.560) Loss 1.7797e-02 (1.1389e-02) Acc@1 100.00 ( 99.63) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][ 75/298] Time 0.615 ( 1.833) Data 0.048 ( 1.272) Loss 2.5565e-04 (1.1153e-02) Acc@1 100.00 ( 99.63) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][100/298] Time 0.624 ( 1.724) Data 0.056 ( 1.163) Loss 1.6186e-03 (9.6181e-03) Acc@1 100.00 ( 99.72) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][125/298] Time 0.640 ( 1.601) Data 0.082 ( 1.041) Loss 6.2654e-02 (9.9088e-03) Acc@1 96.88 ( 99.68) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][150/298] Time 0.618 ( 1.596) Data 0.061 ( 1.036) Loss 1.9718e-04 (9.0484e-03) Acc@1 100.00 ( 99.71) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][175/298] Time 0.673 ( 1.526) Data 0.107 ( 0.965) Loss 1.8096e-03 (9.6376e-03) Acc@1 100.00 ( 99.70) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][200/298] Time 0.630 ( 1.523) Data 0.061 ( 0.962) Loss 2.6468e-03 (9.3167e-03) Acc@1 100.00 ( 99.72) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][225/298] Time 11.313 ( 1.514) Data 10.754 ( 0.952) Loss 9.3352e-03 (9.5301e-03) Acc@1 100.00 ( 99.72) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][250/298] Time 0.643 ( 1.475) Data 0.086 ( 0.913) Loss 1.7089e-03 (1.0416e-02) Acc@1 100.00 ( 99.70) Acc@5 100.00 ( 99.99) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][275/298] Time 0.604 ( 1.472) Data 0.046 ( 0.910) Loss 1.3999e-03 (9.9850e-03) Acc@1 100.00 ( 99.73) Acc@5 100.00 ( 99.99) Focuser BackBone LR: 0.001 FC LR: 0
Epoch: [5][297/298] Time 0.647 ( 1.410) Data 0.094 ( 0.848) Loss 1.0134e-03 (1.0606e-02) Acc@1 100.00 ( 99.72) Acc@5 100.00 ( 99.98) Focuser BackBone LR: 0.001 FC LR: 0
Test: [ 0/119] Time 21.262 (21.262) Loss 6.7998e-01 (6.7998e-01) Acc@1 81.25 ( 81.25) Acc@5 100.00 (100.00)
Test: [ 25/119] Time 0.381 ( 1.223) Loss 2.3228e-01 (6.1051e-01) Acc@1 93.75 ( 85.10) Acc@5 100.00 ( 97.60)
Test: [ 50/119] Time 0.366 ( 0.818) Loss 4.2509e-01 (8.5970e-01) Acc@1 93.75 ( 81.07) Acc@5 96.88 ( 95.22)
Test: [ 75/119] Time 0.406 ( 0.680) Loss 2.0299e-01 (1.0306e+00) Acc@1 93.75 ( 78.12) Acc@5 100.00 ( 93.09)
Test: [100/119] Time 0.362 ( 0.609) Loss 3.9213e-01 (9.9937e-01) Acc@1 96.88 ( 78.53) Acc@5 96.88 ( 93.56)
Test: [118/119] Time 0.122 ( 0.571) Loss 1.6555e+00 (9.4728e-01) Acc@1 28.57 ( 79.33) Acc@5 100.00 ( 94.00)
Testing Results: Prec@1 79.329 Prec@5 93.999 Loss 0.94728

when will u be releasing your codes ?..thanks

I'm very interseted in your works. But when I experimented with the UCF101 dataset, the results were not encouraging ( just around 1%). Looking forward to your reply. THX！

The parameters of the experiment are as follows：

CUDA_VISIBLE_DEVICES=0,3,4,5 python stage1.py
dataset=ucf101
data_dir=/data/ymy/data/
train_stage=1
batch_size=32
num_segments_glancer=8
num_segments_focuser=12
glance_size=224
patch_size=144
random_patch=True
epochs=10
backbone_lr=0.00001
fc_lr=0.01
lr_type=cos
dropout=0.5
load_pretrained_focuser_fc=False
dist_url=tcp://127.0.0.1:8816
eval_freq=1
start_eval=0
print_freq=25
workers=16
pretrained_glancer='/data/AdaFocus-main/mobilenetv2_segment8.pth.tar'
pretrained_focuser='/data/AdaFocus-main/resnet50_segment12.pt.tar' # load the pretrained model

Hi, when I run the program. It will raise an error about the hydra module.
It seems that the "strict" parameter has already been deprecated from the hydra 1.0 version. But after I remove the "strict".
Another error about the default.yaml appears.

Also I don't see the "pretty" argument in the "default.yaml" file.
Thanks for your help in advance.

Hello. Thanks for your work.
Do you have the checkpoints and the corresponding codes for evaluating AdaFocus+, which leverages temporal redundancy in your release?

A wonderful work!

But I have a problem with evaluation. I can't flnd the code about the article SCSampler:Sampling Salient Clips from Video for Efficient Action Recognition. How can i evaluate these two models on a certain dataset?

With the same setting and same checkpoint (128s3_checkpoint.pth.tar), 75.0 mAP cannot be reproduced in my environment (achieved 74.4 ). The difference I know is that I use FPS1 frames, the data list provided seems to be 30 fps. However, as far as I know, FPS should not make such a big difference.