Testing problem about tf_deformable_net HOT 2 OPEN

Hi, I figured out that it was because of the threshold being set to 0.7 in faster_rcnn/test_net.py. Changing the threshold to 0 allows me to see lower APs. However, the APs are really low. Here is what I have, using the default training and testing code:

Results:
0.002
0.032
0.003
0.009
0.002
0.002
0.031
0.004
0.004
0.005
0.002
0.007
0.021
0.004
0.044
0.002
0.009
0.003
0.002
0.002
0.010
~~~~~~~~

Here is my train command:

python ./faster_rcnn/train_net.py \
    --gpu 0 \
    --weights ./data/pretrain_model/Resnet50.npy \
    --imdb voc_2007_trainval \
    --iters 150000 \
    --cfg  ./experiments/cfgs/faster_rcnn_end2end_resnet.yml \
    --network Resnet50_train \
    --set EXP_DIR exp_dir_resnet50

and my test command:

python ./faster_rcnn/test_net.py \
    --gpu 0 \
    --weights ./output/exp_dir_resnet50/voc_2007_trainval \
    --imdb voc_2007_test \
    --cfg ./experiments/cfgs/faster_rcnn_end2end_resnet.yml \
    --network Resnet50_test

I already ran the test code inside lib/deform_conv_layer and lib/deform_psroi_pooling_layer and compare with my installation of MxNet version. The results and gradients are similar (except some small numerical issures, i.e. some entries in the gradient are different by a factor of 1e-6). So it looks like the libraries are built correctly.

Do you have any suggestion of what could be the issues here? Thank you.

from tf_deformable_net.

Hi, I figured out that it was because of the threshold being set to 0.7 in faster_rcnn/test_net.py. Changing the threshold to 0 allows me to see lower APs. However, the APs are really low. Here is what I have, using the default training and testing code:

Results:
0.002
0.032
0.003
0.009
0.002
0.002
0.031
0.004
0.004
0.005
0.002
0.007
0.021
0.004
0.044
0.002
0.009
0.003
0.002
0.002
0.010
~~~~~~~~

Here is my train command:

python ./faster_rcnn/train_net.py \
    --gpu 0 \
    --weights ./data/pretrain_model/Resnet50.npy \
    --imdb voc_2007_trainval \
    --iters 150000 \
    --cfg  ./experiments/cfgs/faster_rcnn_end2end_resnet.yml \
    --network Resnet50_train \
    --set EXP_DIR exp_dir_resnet50

and my test command:

python ./faster_rcnn/test_net.py \
    --gpu 0 \
    --weights ./output/exp_dir_resnet50/voc_2007_trainval \
    --imdb voc_2007_test \
    --cfg ./experiments/cfgs/faster_rcnn_end2end_resnet.yml \
    --network Resnet50_test

I already ran the test code inside lib/deform_conv_layer and lib/deform_psroi_pooling_layer and compare with my installation of MxNet version. The results and gradients are similar (except some small numerical issures, i.e. some entries in the gradient are different by a factor of 1e-6). So it looks like the libraries are built correctly.

Do you have any suggestion of what could be the issues here? Thank you.

ha

Hi, I figured out that it was because of the threshold being set to 0.7 in faster_rcnn/test_net.py. Changing the threshold to 0 allows me to see lower APs. However, the APs are really low. Here is what I have, using the default training and testing code:

Results:
0.002
0.032
0.003
0.009
0.002
0.002
0.031
0.004
0.004
0.005
0.002
0.007
0.021
0.004
0.044
0.002
0.009
0.003
0.002
0.002
0.010
~~~~~~~~

Here is my train command:

python ./faster_rcnn/train_net.py \
    --gpu 0 \
    --weights ./data/pretrain_model/Resnet50.npy \
    --imdb voc_2007_trainval \
    --iters 150000 \
    --cfg  ./experiments/cfgs/faster_rcnn_end2end_resnet.yml \
    --network Resnet50_train \
    --set EXP_DIR exp_dir_resnet50

and my test command:

python ./faster_rcnn/test_net.py \
    --gpu 0 \
    --weights ./output/exp_dir_resnet50/voc_2007_trainval \
    --imdb voc_2007_test \
    --cfg ./experiments/cfgs/faster_rcnn_end2end_resnet.yml \
    --network Resnet50_test

I already ran the test code inside lib/deform_conv_layer and lib/deform_psroi_pooling_layer and compare with my installation of MxNet version. The results and gradients are similar (except some small numerical issures, i.e. some entries in the gradient are different by a factor of 1e-6). So it looks like the libraries are built correctly.

Do you have any suggestion of what could be the issues here? Thank you.

I got same problem, have you solved it?

from tf_deformable_net.