def print_mutation(results, hyp, save_dir, bucket,
prefix=colorstr('evolve: ')):
evolve_csv = save_dir / 'evolve.csv'
evolve_yaml = save_dir / 'hyp_evolve.yaml'
keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5',
'metrics/mAP_0.5:0.95', 'val/box_loss', 'val/obj_loss',
'val/cls_loss') + tuple(hyp.keys()) # [results + hyps]
keys = tuple(x.strip() for x in keys)
vals = results + tuple(hyp.values())
n = len(keys)
# Download (optional)
if bucket:
url = f'gs://{bucket}/evolve.csv'
if gsutil_getsize(url) > (evolve_csv.stat().st_size
if evolve_csv.exists() else 0):
os.system(f'gsutil cp {url} {save_dir}'
) # download evolve.csv if larger than local
# Log to evolve.csv
s = '' if evolve_csv.exists() else (
('%20s,' * n % keys).rstrip(',') + '\n') # add header
with open(evolve_csv, 'a') as f:
f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
# Save yaml
with open(evolve_yaml, 'w') as f:
data = pd.read_csv(evolve_csv)
data = data.rename(columns=lambda x: x.strip()) # strip keys
i = np.argmax(fitness(data.values[:, :4])) #
generations = len(data)
f.write('# YOLOv5 Hyperparameter Evolution Results\n' +
f'# Best generation: {i}\n' +
f'# Last generation: {generations - 1}\n' + '# ' +
', '.join(f'{x.strip():>20s}' for x in keys[:7]) + '\n' +
'# ' + ', '.join(f'{x:>20.5g}'
for x in data.values[i, :7]) + '\n\n')
yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)
# Print to screen
LOGGER.info(prefix +
f'{generations} generations finished, current result:\n' +
prefix + ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' +
prefix + ', '.join(f'{x:20.5g}' for x in vals) + '\n\n')
if bucket:
os.system(
f'gsutil cp {evolve_csv} {evolve_yaml} gs://{bucket}') # upload
def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
# Print mutation results to evolve.txt (for use with train.py --evolve)
a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
c = '%10.4g' * len(
results
) % results # results (P, R, [email protected], [email protected]:0.95, val_losses x 3)
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
if bucket:
url = 'gs://%s/evolve.txt' % bucket
if gsutil_getsize(url) > (os.path.getsize('evolve.txt')
if os.path.exists('evolve.txt') else 0):
os.system('gsutil cp %s .' %
url) # download evolve.txt if larger than local
with open('evolve.txt', 'a') as f: # append result
f.write(c + b + '\n')
x = np.unique(np.loadtxt('evolve.txt', ndmin=2),
axis=0) # load unique rows
x = x[np.argsort(-fitness(x))] # sort
np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness
# Save yaml
for i, k in enumerate(hyp.keys()):
hyp[k] = float(x[0, i + 7])
with open(yaml_file, 'w') as f:
results = tuple(x[0, :7])
c = '%10.4g' * len(
results
) % results # results (P, R, [email protected], [email protected]:0.95, val_losses x 3)
f.write(
'# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: '
% len(x) + c + '\n\n')
yaml.safe_dump(hyp, f, sort_keys=False)
if bucket:
os.system('gsutil cp evolve.txt %s gs://%s' %
(yaml_file, bucket)) # upload
