113 lines
3.4 KiB
Python
Executable File
113 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2019 Mycroft AI Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import namedtuple
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from prettyparse import create_parser
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from precise.network_runner import Listener
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from precise.params import inject_params
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from precise.train_data import TrainData
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usage = '''
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Test a model against a dataset
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:model str
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Either Keras (.net) or TensorFlow (.pb) model to test
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:-t --use-train
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Evaluate training data instead of test data
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:-nf --no-filenames
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Don't print out the names of files that failed
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...
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'''
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Stats = namedtuple('Stats', 'false_pos false_neg true_pos true_neg')
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def stats_to_dict(stats: Stats) -> dict:
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return {
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'true_pos': len(stats.true_pos),
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'true_neg': len(stats.true_neg),
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'false_pos': len(stats.false_pos),
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'false_neg': len(stats.false_neg),
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}
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def show_stats(stats: Stats, show_filenames):
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false_pos, false_neg, true_pos, true_neg = stats
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num_correct = len(true_pos) + len(true_neg)
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total = num_correct + len(false_pos) + len(false_neg)
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def prc(a: int, b: int): # Rounded percent
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return round(100.0 * (b and a / b), 2)
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if show_filenames:
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print('=== False Positives ===')
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for i in false_pos:
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print(i)
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print()
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print('=== False Negatives ===')
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for i in false_neg:
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print(i)
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print()
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print('=== Counts ===')
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print('False Positives:', len(false_pos))
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print('True Negatives:', len(true_neg))
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print('False Negatives:', len(false_neg))
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print('True Positives:', len(true_pos))
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print()
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print('=== Summary ===')
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print(num_correct, "out of", total)
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print(prc(num_correct, total), "%")
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print()
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print(prc(len(false_pos), len(false_pos) + len(true_neg)), "% false positives")
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print(prc(len(false_neg), len(false_neg) + len(true_pos)), "% false negatives")
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def calc_stats(filenames, targets, predictions) -> Stats:
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stats = Stats([], [], [], [])
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for name, target, prediction in zip(filenames, targets, predictions):
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{
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(True, False): stats.false_pos,
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(True, True): stats.true_pos,
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(False, True): stats.false_neg,
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(False, False): stats.true_neg
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}[prediction[0] > 0.5, target[0] > 0.5].append(name)
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return stats
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def main():
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args = TrainData.parse_args(create_parser(usage))
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inject_params(args.model)
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data = TrainData.from_both(args.tags_file, args.tags_folder, args.folder)
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train, test = data.load(args.use_train, not args.use_train, shuffle=False)
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inputs, targets = train if args.use_train else test
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filenames = sum(data.train_files if args.use_train else data.test_files, [])
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predictions = Listener.find_runner(args.model)(args.model).predict(inputs)
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stats = calc_stats(filenames, targets, predictions)
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print('Data:', data)
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show_stats(stats, not args.no_filenames)
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if __name__ == '__main__':
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main()
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