def bn2d(depth, ipabn=None):
if ipabn:
if ipabn == 'sync':
return InPlaceABNSync(depth, activation='none')
else:
return InPlaceABN(depth, activation='none')
else:
return nn.BatchNorm2d(depth)
def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
super(ConvBNReLU, self).__init__()
self.conv = nn.Conv2d(in_chan,
out_chan,
kernel_size = ks,
stride = stride,
padding = padding,
bias = False)
self.bn = InPlaceABNSync(out_chan)
self.init_weight()
def __init__(self, in_chan, out_chan, *args, **kwargs):
super(AttentionRefinementModule, self).__init__()
self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
self.conv_atten = nn.Conv2d(out_chan,
out_chan,
kernel_size=1,
bias=False)
self.bn_atten = InPlaceABNSync(out_chan, activation='none')
self.sigmoid_atten = nn.Sigmoid()
self.init_weight()
def bn_act(depth, with_act, ipabn=None):
if ipabn:
if with_act:
if ipabn == 'sync':
res = InPlaceABNSync(depth, activation='relu')
else:
res = InPlaceABN(depth, activation='relu')
else:
res = InPlaceABN(depth, activation='none')
else:
res = nn.BatchNorm2d(depth)
return res
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes,
planes // self.expansion,
kernel_size=1,
bias=False)
self.bn1 = InPlaceABNSync(planes // self.expansion)
self.conv2 = nn.Conv2d(planes // self.expansion,
planes // self.expansion,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = InPlaceABNSync(planes // self.expansion)
self.conv3 = nn.Conv2d(planes // self.expansion,
planes,
kernel_size=1,
bias=False)
self.bn3 = BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.drop = nn.Dropout2d(p=drop)
def __init__(self, inplanes, planes, stride=1, rate=1, downsample=None):
super(BasicBlock, self).__init__()
if inplanes != planes:
self.conv0 = conv3x3(inplanes, planes, rate)
self.inplanes = inplanes
self.planes = planes
self.conv1 = conv3x3(planes, planes, stride)
self.bn1 = InPlaceABNSync(planes)
#self.relu = nn.ReLU(inplace=True)
#self.conv2 = conv3x3(planes, planes)
self.bn2 = BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.drop = nn.Dropout2d(p=drop)