updates
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@ -35,15 +35,15 @@ def create_modules(module_defs, img_size, arc):
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bias=not bn))
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bias=not bn))
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if bn:
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if bn:
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modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.1))
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modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.1))
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else:
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routs.append(i) # detection output (goes into yolo layer)
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if mdef['activation'] == 'leaky': # activation study https://github.com/ultralytics/yolov3/issues/441
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if mdef['activation'] == 'leaky': # activation study https://github.com/ultralytics/yolov3/issues/441
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modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))
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modules.add_module('activation', nn.LeakyReLU(0.1, inplace=True))
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# modules.add_module('activation', nn.PReLU(num_parameters=1, init=0.10))
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# modules.add_module('activation', nn.PReLU(num_parameters=1, init=0.10))
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elif mdef['activation'] == 'swish':
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elif mdef['activation'] == 'swish':
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modules.add_module('activation', Swish())
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modules.add_module('activation', Swish())
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if not bn: # detection output layer
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routs.append(i)
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elif mdef['type'] == 'maxpool':
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elif mdef['type'] == 'maxpool':
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size = mdef['size']
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size = mdef['size']
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stride = mdef['stride']
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stride = mdef['stride']
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