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car-detection-bayes
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model forward() zip() removal

This commit is contained in:
Glenn Jocher 2020-04-05 15:22:32 -07:00
parent a657345b45
commit bb59ffe68f
1 changed files with 6 additions and 6 deletions
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12
models.py
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@ -230,25 +230,25 @@ class Darknet(nn.Module):
img_size = x.shape[-2:] img_size = x.shape[-2:]
yolo_out, out = [], [] yolo_out, out = [], []
if verbose: if verbose:
str = ''
print('0', x.shape) print('0', x.shape)
str = ''
for i, (mdef, module) in enumerate(zip(self.module_defs, self.module_list)): for i, module in enumerate(self.module_list):
mtype = mdef['type'] name = module.__class__.__name__
if mtype in ['shortcut', 'route']: # sum, concat if name in ['WeightedFeatureFusion', 'FeatureConcat']: # sum, concat
if verbose: if verbose:
l = [i - 1] + module.layers # layers l = [i - 1] + module.layers # layers
s = [list(x.shape)] + [list(out[i].shape) for i in module.layers] # shapes s = [list(x.shape)] + [list(out[i].shape) for i in module.layers] # shapes
str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)]) str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)])
x = module(x, out) # WeightedFeatureFusion(), FeatureConcat() x = module(x, out) # WeightedFeatureFusion(), FeatureConcat()
elif mtype == 'yolo': elif name == 'YOLOLayer':
yolo_out.append(module(x, img_size, out)) yolo_out.append(module(x, img_size, out))
else: # run module directly, i.e. mtype = 'convolutional', 'upsample', 'maxpool', 'batchnorm2d' etc. else: # run module directly, i.e. mtype = 'convolutional', 'upsample', 'maxpool', 'batchnorm2d' etc.
x = module(x) x = module(x)
out.append(x if self.routs[i] else []) out.append(x if self.routs[i] else [])
if verbose: if verbose:
print('%g/%g %s -' % (i, len(self.module_list), mtype), list(x.shape), str) print('%g/%g %s -' % (i, len(self.module_list), name), list(x.shape), str)
str = '' str = ''
if self.training: # train if self.training: # train