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car-detection-bayes
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cleanup

This commit is contained in:
Glenn Jocher 2020-03-19 16:23:44 -07:00
parent 83c9cfb7de
commit 1b68fe7fde
1 changed files with 2 additions and 6 deletions
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8
models.py
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@ -63,10 +63,8 @@ def create_modules(module_defs, img_size):
elif mdef['type'] == 'route': # nn.Sequential() placeholder for 'route' layer
layers = mdef['layers']
filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
routs.extend([l if l > 0 else l + i for l in layers])
# if mdef[i+1]['type'] == 'reorg3d':
# modules = nn.Upsample(scale_factor=1/float(mdef[i+1]['stride']), mode='nearest') # reorg3d
filters = sum([output_filters[l + 1 if l > 0 else l] for l in layers])
routs.extend([i + l if l < 0 else l for l in layers])
elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer
layers = mdef['from']
@ -75,8 +73,6 @@ def create_modules(module_defs, img_size):
modules = weightedFeatureFusion(layers=layers, weight='weights_type' in mdef)
elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale
# torch.Size([16, 128, 104, 104])
# torch.Size([16, 64, 208, 208]) <-- # stride 2 interpolate dimensions 2 and 3 to cat with prior layer
pass
elif mdef['type'] == 'yolo':