[ ] Try pixel wise input on single grasp prediction models
[ ] accessing the wrong coordinate when applying a loss (flipped axes for example)
[ ] need to apply the loss over a larger area (gaussian loss could help)
[ ] different input than [delta_depth, sin(theta), cos(theta)] might produce better results
[ ] are pixel-wise models set up correctly? Try pascal_voc as a baseline to check
[ ] should we be doing a fixed crop + resize in eval, perhaps a scale difference is the problem?
[ ] xyz images and thus delta depth values not correct? See warning below:
WARNING: expected 10 time steps but found 0 in feature: move_to_grasp/time_ordered/xyz_image/preprocessed in
dataset 097 File "grasp_train.py", line 641, in <module>
main()
check get_training_dictionaries(). File "grasp_train.py", line 636, in main
model_name=FLAGS.grasp_model)
check get_training_dictionaries(). File "grasp_train.py", line 459, in eval
grasp_sequence_max_time_step=grasp_sequence_max_time_step)
check get_training_dictionaries(). File "/home/ahundt/src/costar_ws/src/costar_plan/costar_google_brainrobo
tdata/grasp_dataset.py", line 1962, in get_training_tensors
random_crop_dimensions=random_crop_dimensions, random_crop_offset=random_crop_offset)
check get_training_dictionaries(). File "/home/ahundt/src/costar_ws/src/costar_plan/costar_google_brainrobo
tdata/grasp_dataset.py", line 1842, in get_training_dictionaries
' check get_training_dictionaries().'.join(traceback.format_stack()))
pixel-wise training isn't making any progress, we need to figure out why.
Options to try fixing / possible problem sources: