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'--inverse',
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dest='inverse',
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default=False,
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action='store_true',
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help='Map from xentropy to original, instead of original to xentropy. '
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'Defaults to %(default)s',
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)
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FLAGS, unparsed = parser.parse_known_args()
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# print summary of what we will do
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print("*" * 80)
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print("INTERFACE:")
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print("Data: ", FLAGS.dataset)
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print("Predictions: ", FLAGS.predictions)
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print("Split: ", FLAGS.split)
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print("Config: ", FLAGS.datacfg)
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print("Inverse: ", FLAGS.inverse)
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print("*" * 80)
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# only predictions or dataset can be handled at once and one MUST be given (xor)
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assert((FLAGS.dataset is not None) != (FLAGS.predictions is not None))
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# check name
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root_directory = ""
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label_directory = ""
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if(FLAGS.dataset is not None):
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root_directory = FLAGS.dataset
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label_directory = "labels"
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elif(FLAGS.predictions is not None):
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root_directory = FLAGS.predictions
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label_directory = "predictions"
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else:
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print("I don't even know how I got here")
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quit()
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# assert split
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assert(FLAGS.split in splits)
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print("Opening data config file %s" % FLAGS.datacfg)
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DATA = yaml.safe_load(open(FLAGS.datacfg, 'r'))
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# get number of interest classes, and the label mappings
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if FLAGS.inverse:
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print("Mapping xentropy to original labels")
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remapdict = DATA["learning_map_inv"]
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else:
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remapdict = DATA["learning_map"]
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nr_classes = len(remapdict)
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# make lookup table for mapping
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maxkey = max(remapdict.keys())
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# +100 hack making lut bigger just in case there are unknown labels
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remap_lut = np.zeros((maxkey + 100), dtype=np.int32)
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remap_lut[list(remapdict.keys())] = list(remapdict.values())
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# print(remap_lut)
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# get wanted set
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sequences = []
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sequences.extend(DATA["split"][FLAGS.split])
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# get label paths
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label_names = []
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for sequence in sequences:
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sequence = '{0:02d}'.format(int(sequence))
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label_paths = os.path.join(root_directory, "sequences",
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sequence, label_directory)
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# populate the label names
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seq_label_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
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os.path.expanduser(label_paths)) for f in fn if ".label" in f]
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seq_label_names.sort()
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label_names.extend(seq_label_names)
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# print(label_names)
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# open each file, get the tensor, and remap only the lower half (semantics)
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for label_file in label_names:
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# open label
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print(label_file)
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label = np.fromfile(label_file, dtype=np.uint32)
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label = label.reshape((-1))
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upper_half = label >> 16 # get upper half for instances
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lower_half = label & 0xFFFF # get lower half for semantics
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lower_half = remap_lut[lower_half] # do the remapping of semantics
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label = (upper_half << 16) + lower_half # reconstruct full label
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label = label.astype(np.uint32)
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label.tofile(label_file)
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# <FILESEP>
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#!/usr/bin/env python3
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#
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# A script to clean up copy-pastes from slack using GPT-4
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#
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import pasteboard
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import openai
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import sys
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import os
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openai.api_key = os.getenv("OPENAI_API_KEY")
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pb = pasteboard.Pasteboard()
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