Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-05-03 18:14:16 +02:00
parent 3c55d63a9d
commit 6316171f33
4 changed files with 4 additions and 3 deletions
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2
test.py
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@ -191,7 +191,7 @@ if __name__ == '__main__':
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
opt = parser.parse_args() opt = parser.parse_args()
print(opt, end='\n\n') print(opt)
with torch.no_grad(): with torch.no_grad():
mAP = test( mAP = test(

2
train.py
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@ -294,7 +294,7 @@ if __name__ == '__main__':
parser.add_argument('--evolve', action='store_true', help='run hyperparameter evolution') parser.add_argument('--evolve', action='store_true', help='run hyperparameter evolution')
parser.add_argument('--var', default=0, type=int, help='debug variable') parser.add_argument('--var', default=0, type=int, help='debug variable')
opt = parser.parse_args() opt = parser.parse_args()
print(opt, end='\n\n') print(opt)
if opt.evolve: if opt.evolve:
opt.notest = True # save time by only testing final epoch opt.notest = True # save time by only testing final epoch

1
utils/torch_utils.py
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@ -25,6 +25,7 @@ def select_device(force_cpu=False):
print(" device%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" % print(" device%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
(i, x[i].name, x[i].total_memory / c)) (i, x[i].name, x[i].total_memory / c))
print('') # skip a line
return device return device

2
utils/utils.py
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@ -41,7 +41,7 @@ def model_info(model):
# Plots a line-by-line description of a PyTorch model # Plots a line-by-line description of a PyTorch model
n_p = sum(x.numel() for x in model.parameters()) # number parameters n_p = sum(x.numel() for x in model.parameters()) # number parameters
n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
print('\n%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
for i, (name, p) in enumerate(model.named_parameters()): for i, (name, p) in enumerate(model.named_parameters()):
name = name.replace('module_list.', '') name = name.replace('module_list.', '')
print('%5g %40s %9s %12g %20s %10.3g %10.3g' % ( print('%5g %40s %9s %12g %20s %10.3g %10.3g' % (