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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-02-19 18:26:45 -08:00
parent 7f1b2bfe08
commit 1043832493
1 changed files with 9 additions and 5 deletions
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14
models.py
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@ -67,7 +67,7 @@ def create_modules(module_defs, img_size, arc):
elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer
layers = mdef['from'] layers = mdef['from']
filters = output_filters[layers[0]] filters = output_filters[-1]
routs.extend([i + l if l < 0 else l for l in layers]) routs.extend([i + l if l < 0 else l for l in layers])
modules = weightedFeatureFusion(layers=layers, weight='weights_type' in mdef) modules = weightedFeatureFusion(layers=layers, weight='weights_type' in mdef)
@ -266,6 +266,7 @@ class Darknet(nn.Module):
yolo_out, out = [], [] yolo_out, out = [], []
verbose = False verbose = False
if verbose: if verbose:
str = ''
print('0', x.shape) print('0', x.shape)
for i, (mdef, module) in enumerate(zip(self.module_defs, self.module_list)): for i, (mdef, module) in enumerate(zip(self.module_defs, self.module_list)):
@ -274,14 +275,16 @@ class Darknet(nn.Module):
x = module(x) x = module(x)
elif mtype == 'shortcut': # sum elif mtype == 'shortcut': # sum
if verbose: if verbose:
l = [i] + 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
print('shortcut/add: ' + ' + '.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() x = module(x, out) # weightedFeatureFusion()
elif mtype == 'route': # concat elif mtype == 'route': # concat
layers = mdef['layers'] layers = mdef['layers']
if verbose: if verbose:
print('route/concatenate %s + %s' % (list(x.shape), [list(out[i].shape) for i in layers])) l = [i - 1] + layers # layers
s = [list(x.shape)] + [list(out[i].shape) for i in layers] # shapes
str = ' >> ' + ' + '.join(['layer %g %s' % x for x in zip(l, s)])
if len(layers) == 1: if len(layers) == 1:
x = out[layers[0]] x = out[layers[0]]
else: else:
@ -295,7 +298,8 @@ class Darknet(nn.Module):
yolo_out.append(module(x, img_size)) yolo_out.append(module(x, img_size))
out.append(x if i in self.routs else []) out.append(x if i in self.routs else [])
if verbose: if verbose:
print('%g/%g -' % (i, len(self.module_list)), list(x.shape)) print('%g/%g %s -' % (i, len(self.module_list), mtype), list(x.shape), str)
str = ''
if self.training: # train if self.training: # train
return yolo_out return yolo_out