def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
for m in self.model.modules():
if type(m) is Conv and hasattr(m, 'bn'):
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
delattr(m, 'bn') # remove batchnorm
m.forward = m.fuseforward # update forward
self.info()
return self
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
LOGGER.info('Fusing layers... ')
for m in self.model.modules():
if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
delattr(m, 'bn') # remove batchnorm
m.forward = m.forward_fuse # update forward
self.info()
return self
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
for m in self.model.modules():
if type(m) is Conv:
m._non_persistent_buffers_set = set(
) # pytorch 1.6.0 compatability
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
m.bn = None # remove batchnorm
m.forward = m.fuseforward # update forward
# self.info()
return self