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hyperparameter printout update

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This commit is contained in:
Glenn Jocher 2020-07-08 17:21:00 -07:00
parent 0fef3f6678
commit bf6f41567a
3 changed files with 15 additions and 16 deletions
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1
test.py
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@ -19,7 +19,6 @@ def test(data,
dataloader=None,
save_dir='',
merge=False):
# Initialize/load model and set device
training = model is not None
if training: # called by train.py

19
train.py
View File

@ -20,9 +20,8 @@ except:
print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex')
mixed_precision = False # not installed
# Hyperparameters
hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD
hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD
'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3)
'momentum': 0.937, # SGD momentum/Adam beta1
'weight_decay': 5e-4, # optimizer weight decay
@ -44,6 +43,7 @@ hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD
def train(hyp):
print(f'Hyperparameters {hyp}')
log_dir = tb_writer.log_dir # run directory
wdir = str(Path(log_dir) / 'weights') + os.sep # weights directory
@ -90,7 +90,7 @@ def train(hyp):
pg0.append(v) # all else
if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
@ -176,7 +176,7 @@ def train(hyp):
yaml.dump(hyp, f, sort_keys=False)
with open(Path(log_dir) / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Class frequency
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
@ -365,7 +365,8 @@ if __name__ == '__main__':
parser.add_argument('--batch-size', type=int, default=16)
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const = 'get_last', default=False, help='resume from given path/to/last.pt, or most recent run if blank.')
parser.add_argument('--resume', nargs='?', const='get_last', default=False,
help='resume from given path/to/last.pt, or most recent run if blank.')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
@ -378,14 +379,14 @@ if __name__ == '__main__':
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')
opt = parser.parse_args()
last = get_latest_run() if opt.resume == 'get_last' else opt.resume # resume from most recent run
if last and not opt.weights:
print(f'Resuming training from {last}')
opt.weights = last if opt.resume and not opt.weights else opt.weights
opt.cfg = check_file(opt.cfg) # check file
opt.data = check_file(opt.data) # check file
opt.hyp = check_file(opt.hyp) if opt.hyp else '' # check file
opt.hyp = check_file(opt.hyp) if opt.hyp else '' # check file
print(opt)
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)
@ -394,14 +395,12 @@ if __name__ == '__main__':
# Train
if not opt.evolve:
print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
tb_writer = SummaryWriter(comment=opt.name)
if opt.hyp: # update hyps
with open(opt.hyp) as f:
hyp.update(yaml.load(f, Loader=yaml.FullLoader))
print(f'Beginning training with {hyp}\n\n')
print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
train(hyp)
# Evolve hyperparameters (optional)

11
utils/utils.py
View File

@ -37,10 +37,10 @@ def init_seeds(seed=0):
torch_utils.init_seeds(seed=seed)
def get_latest_run(search_dir = './runs'):
def get_latest_run(search_dir='./runs'):
# Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
return max(last_list, key = os.path.getctime)
last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
return max(last_list, key=os.path.getctime)
def check_git_status():
@ -1113,7 +1113,7 @@ def plot_study_txt(f='study.txt', x=None): # from utils.utils import *; plot_st
plt.savefig(f.replace('.txt', '.png'), dpi=200)
def plot_labels(labels, save_dir= ''):
def plot_labels(labels, save_dir=''):
# plot dataset labels
c, b = labels[:, 0], labels[:, 1:].transpose() # classees, boxes
@ -1180,7 +1180,8 @@ def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_re
fig.savefig(f.replace('.txt', '.png'), dpi=200)
def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir= ''): # from utils.utils import *; plot_results()
def plot_results(start=0, stop=0, bucket='', id=(), labels=(),
save_dir=''): # from utils.utils import *; plot_results()
# Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
fig, ax = plt.subplots(2, 5, figsize=(12, 6))
ax = ax.ravel()