First attempt at using CTSegNet

CTSegNet_SavedModel_Output

(tomo)  ✘ mwolf@mal  ~/src/CTSegNet   master ●  python bin/run_segmenter_hdf5.py -c cfg_files/setup_seg_hdf5.cfg

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	Welcome to CTSegNet: AI-based 3D Segmentation

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Dataset shape: (899, 940, 2124)
Dataset size: 7.18 GB
Chunk shape: None
Slice size along 0: 7.99 MB
Slice size along 1: 7.64 MB
Slice size along 2: 3.38 MB

Starting segmentation mode ...
Traceback (most recent call last):
  File "bin/run_segmenter_hdf5.py", line 229, in <module>
    main(args)
  File "bin/run_segmenter_hdf5.py", line 84, in main
    segmenter = Segmenter(model_filename = model_filename)
  File "/home/mwolf/src/CTSegNet/ct_segnet/seg_utils.py", line 53, in __init__
    self.model = load_model(model_filename, custom_objects = custom_objects_dict)
  File "/home/mwolf/miniconda3/envs/tomo/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/save.py", line 149, in load_model
    loader_impl.parse_saved_model(filepath)
  File "/home/mwolf/miniconda3/envs/tomo/lib/python3.7/site-packages/tensorflow_core/python/saved_model/loader_impl.py", line 83, in parse_saved_model
    constants.SAVED_MODEL_FILENAME_PB))
OSError: SavedModel file does not exist at: /home/mwolf/src/CTSegNet/model_repo/M_c02_222_256.hdf5/{saved_model.pbtxt|saved_model.pb}

setup_seg_hdf5.cfg

[FILE PATHS]
ct_fpath = /run/media/mwolf/WOLFMAN_KAKUNA/tomo-2020-02-11-aps7bmb/092_Wolfman_Minicell_inSitu_No8_C4_wExp_092_rec.h5
ct_data_tag = volume
seg_path = /run/media/mwolf/WOLFMAN_KAKUNA/tomo-2020-02-11-aps7bmb/segmentation_masks/
model_path = /home/mwolf/src/CTSegNet/model_repo
model_name = M_c02_222_256
vote_maskname = TEST_VOTE

[DEFAULTS]
stats_only = False
remove_masks = True
run_ensemble = True
run_seg = True
mem_thres = 5.0 # amount of data to be read from ct data at a time.
overwrite_OK = True # if mask name already exists, overwrite.
rw_verbosity = 0 # 0 - silent, 1 - important stuff, 2 - everything
tiff_output = True # if True, final mask is output as tiff sequence
mpl_agg = Agg

[ADVANCED]
nprocs = 1 # for seg_chunk() - use these many processors on each subset of chunk
arr_split = 1 # for seg_chunk() - break down read chunk into these many subsets to process


[SEG PARAMETERS]
mask_name = [mask01, mask02, mask03]
slice_axis = [0, 1, 2]
n_patches = [(2x2), (1x2), (1x2)]
overlap = [20, 20, 20]

# mask_name = mask01
# slice_axis = 1
# n_patches = (1x2)
# overlap = 20