Hypertree model inference#547
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This really needs to be documented and parameterized. It is very hard to make sense of this, and a bunch of things seem to be hard coded. Can we also clean this up a bit so the "inference mode" part becomes usable for validation/test? We also have some example code of loading a model from the web at https://github.com/jhu-lcsr/costar_plan/blob/master/ctp_integration/scripts/costar_hyper_prediction.py#L48 |
| import matplotlib.ticker as ticker | ||
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| class CostarHyperTreeInference(): |
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Sorry about that. I'll add more documentation.
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| class CostarHyperTreeInference(): | ||
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| def __init__(self, filenames, hyperparams_json, load_weights, problem_name): |
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I'll add more documentation.
| self.gripper_action_goal_idx = [] | ||
| self.inference_mode_gen(self.filenames) | ||
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| def inference_mode_gen(self, file_names): |
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does everything get loaded into memory at once? What happens when we are loading 128 examples, will it just run the computer right out of memory?
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Everything does not get loaded at once. I'll add more comments in the code.
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| # print("len of X---", len(data[0])) | ||
| frame_counter += 1 % frame_len | ||
| score = model.evaluate(data[0], data[1]) |
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won't this break with other data configurations?
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It shouldn't. I'll verify it anyways.
| ax.set_color_cycle([plt.cm.cool(i) for i in np.linspace(0, 1, n_lines)]) | ||
| count = 0 | ||
| for i in indexes[1:]: | ||
| goals = self.gripper_action_goal_idx[count] |
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needs comments, this code is very confusing, can't parts of this be split to separate simple functions?
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I'll add more comments and clean up the code.
I wanted a single function for plotting because making a function every time we add a new plot did not make sense. Anyways, I'll split it into two for now.
| load_weights = "2018-09-04-20-17-25_train_v0.3_msle-vgg_semantic_rotation_regression_model--dataset_costar_block_stacking-grasp_goal_aaxyz_nsc_5-epoch-412-val_loss-0.002-val_angle_error-0.279.h5" | ||
| # filenames_updated, file_len_list = inference_mode_gen(filenames[:2]) | ||
| print(len(filenames)) | ||
| training_generator = CostarBlockStackingSequence( |
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were there any changes to the block stacking sequence class itself?
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I did make some changes but then changed this code to work without those changes.
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| if __name__ == "__main__": | ||
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| filenames = glob.glob(os.path.expanduser(r'C:\Users\Varun\JHU\LAB\Projects\costar_task_planning_stacking_dataset_v0.1/*success.h5f')) |
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all of this needs comments + command line parameter support
…tation + code cleanup
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@j-varun Hey we are in the middle of porting things to pytorch, can the keras specific parts be in a separate file? GitHub.com/ahundt/costar_dataset @RexxarCHL we will need to bring these changes over too |
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Will do. |
…t_generator.py to enable an easier pytorch port of inference code
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