costar_plan
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figure out pixel-wise training problem
pixel-wise training isn't making any progress, we need to figure out why.
Options to try fixing / possible problem sources:
- [x] try pretraining on single prediction `delta_depth
- DONE and got super crazy/surprising results! delta depth outperformed the levine model
- [ ] convert the single prediction model to pixel-wise and visualize performance
- [ ] train the pixel wise and visualize performance
- [ ] build a "pipeline validation model" that takes the answer as an additional input and make sure it is always right! https://github.com/cpaxton/costar_plan/issues/403
- [ ] 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()))
progress has been made in https://github.com/cpaxton/costar_plan/pull/410