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change weights dir (wdir) to be unique to each run, under log_dir

pull/104/head
Alex Stoken 2020-06-16 15:09:51 -05:00
parent d9f446cd81
commit 4418809cf5
1 changed files with 11 additions and 8 deletions
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19
train.py
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@ -18,11 +18,6 @@ except:
print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex') print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex')
mixed_precision = False # not installed mixed_precision = False # not installed
wdir = 'weights' + os.sep # weights dir
os.makedirs(wdir, exist_ok=True)
last = wdir + 'last.pt'
best = wdir + 'best.pt'
results_file = 'results.txt'
# Hyperparameters # Hyperparameters
hyp = {'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3) hyp = {'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3)
@ -59,13 +54,21 @@ if hyp['fl_gamma']:
def train(hyp): def train(hyp):
#write all results to the tb log_dir, so all data from one run is together
log_dir = tb_writer.log_dir
#weights dir unique to each experiment
wdir = os.path.join(log_dir, 'weights') + os.sep # weights dir
os.makedirs(wdir, exist_ok=True)
last = wdir + 'last.pt'
best = wdir + 'best.pt'
results_file = 'results.txt'
epochs = opt.epochs # 300 epochs = opt.epochs # 300
batch_size = opt.batch_size # 64 batch_size = opt.batch_size # 64
weights = opt.weights # initial training weights weights = opt.weights # initial training weights
#write all results to the tb log_dir, so all data from one run is together
log_dir = tb_writer.log_dir
# Configure # Configure
init_seeds(1) init_seeds(1)
with open(opt.data) as f: with open(opt.data) as f: