74 lines
2.0 KiB
ReStructuredText
Executable File
74 lines
2.0 KiB
ReStructuredText
Executable File
.. _example_eval:
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Evaluation
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==========
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Data Preparation
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^^^^^^^^^^^^^^^^
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The first step of evaluation is to prepare your own results.
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You need to run your code and generate a `GraspGroup` for each image in each scene.
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Then call the `save_npy` function of `GraspGroup` to dump the results.
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To generate a `GraspGroup` and save it, you can directly input a 2D numpy array for the `GraspGroup` class:
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::
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gg=GraspGroup(np.array([[score_1, width_1, height_1, depth_1, rotation_matrix_1(9), translation_1(3), object_id_1],
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[score_2, width_2, height_2, depth_2, rotation_matrix_2(9), translation_2(3), object_id_2],
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...,
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[score_N, width_N, height_N, depth_N, rotation_matrix_N(9), translation_N(3), object_id_N]]
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))
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gg.save_npy(save_path)
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where your algorithm predicts N grasp poses for an image. For the `object_id`, you can simply input `0`. For the meaning of other entries, you should refer to the doc for Grasp Label Format-API Loaded Labels
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The file structure of dump folder should be as follows:
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::
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|-- dump_folder
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|-- scene_0100
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| |-- kinect
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| | |
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| | --- 0000.npy to 0255.npy
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| --- realsense
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| --- 0000.npy to 0255.npy
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|-- scene_0101
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...
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--- scene_0189
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You can choose to generate dump files for only one camera, there will be no error for doing that.
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Evaluation API
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^^^^^^^^^^^^^^
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Get GraspNetEval instances.
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.. literalinclude:: ../../examples/exam_eval.py
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:lines: 4-17
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Evaluate A Single Scene
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-----------------------
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.. literalinclude:: ../../examples/exam_eval.py
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:lines: 19-23
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Evaluate All Scenes
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-------------------
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.. literalinclude:: ../../examples/exam_eval.py
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:lines: 25-27
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Evaluate 'Seen' Split
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---------------------
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.. literalinclude:: ../../examples/exam_eval.py
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:lines: 29-31
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