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update expt name comment and folder parsing for training (#978) 

* comment

* fix parsing

* fix evolve

* folder

* tqdm

* Update train.py

* Update train.py

* reinstate anchors into meta dict

anchor evolution is working correctly now

* reinstate logger

prefer the single line readout for concise logging, which helps simplify notebook and tutorials etc.

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Jirka Borovec 2020-10-13 14:10:21 +02:00 committed by GitHub
parent 0ada058f63
commit 00917a6225
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2 changed files with 14 additions and 9 deletions
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13
train.py
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@ -207,7 +207,8 @@ def train(hyp, opt, device, tb_writer=None):
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda) scaler = amp.GradScaler(enabled=cuda)
logger.info('Image sizes %g train, %g test\nUsing %g dataloader workers\nLogging results to %s\n' logger.info('Image sizes %g train, %g test\n'
'Using %g dataloader workers\nLogging results to %s\n'
'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, log_dir, epochs)) 'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, log_dir, epochs))
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train() model.train()
@ -393,7 +394,7 @@ if __name__ == '__main__':
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied') parser.add_argument('--name', default='', help='renames experiment folder exp{N} to exp{N}_{name} if supplied')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
@ -448,7 +449,7 @@ if __name__ == '__main__':
if not opt.evolve: if not opt.evolve:
tb_writer = None tb_writer = None
if opt.global_rank in [-1, 0]: if opt.global_rank in [-1, 0]:
logger.info('Start Tensorboard with "tensorboard --logdir %s", view at http://localhost:6006/' % opt.logdir) logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.logdir}", view at http://localhost:6006/')
tb_writer = SummaryWriter(log_dir=log_dir) # runs/exp0 tb_writer = SummaryWriter(log_dir=log_dir) # runs/exp0
train(hyp, opt, device, tb_writer) train(hyp, opt, device, tb_writer)
@ -488,7 +489,7 @@ if __name__ == '__main__':
assert opt.local_rank == -1, 'DDP mode not implemented for --evolve' assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
opt.notest, opt.nosave = True, True # only test/save final epoch opt.notest, opt.nosave = True, True # only test/save final epoch
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
yaml_file = Path('runs/evolve/hyp_evolved.yaml') # save best result here yaml_file = Path(opt.logdir) / 'evolve' / 'hyp_evolved.yaml' # save best result here
if opt.bucket: if opt.bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
@ -532,5 +533,5 @@ if __name__ == '__main__':
# Plot results # Plot results
plot_evolution(yaml_file) plot_evolution(yaml_file)
print('Hyperparameter evolution complete. Best results saved as: %s\nCommand to train a new model with these ' print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
'hyperparameters: $ python train.py --hyp %s' % (yaml_file, yaml_file)) f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')

10
utils/general.py
View File

@ -7,6 +7,7 @@ import random
import shutil import shutil
import subprocess import subprocess
import time import time
import re
from contextlib import contextmanager from contextlib import contextmanager
from copy import copy from copy import copy
from pathlib import Path from pathlib import Path
@ -952,9 +953,12 @@ def increment_dir(dir, comment=''):
# Increments a directory runs/exp1 --> runs/exp2_comment # Increments a directory runs/exp1 --> runs/exp2_comment
n = 0 # number n = 0 # number
dir = str(Path(dir)) # os-agnostic dir = str(Path(dir)) # os-agnostic
d = sorted(glob.glob(dir + '*')) # directories dirs = sorted(glob.glob(dir + '*')) # directories
if len(d): if dirs:
n = max([int(x[len(dir):x.rfind('_') if '_' in Path(x).name else None]) for x in d]) + 1 # increment matches = [re.search(r"exp(\d+)", d) for d in dirs]
idxs = [int(m.groups()[0]) for m in matches if m]
if idxs:
n = max(idxs) + 1 # increment
return dir + str(n) + ('_' + comment if comment else '') return dir + str(n) + ('_' + comment if comment else '')