def __init__(self,
inplanes,
planes,
bn_norm,
num_splits,
with_ibn=False,
with_se=False,
stride=1,
downsample=None,
reduction=16):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
if with_ibn:
self.bn1 = IBN(planes, bn_norm, num_splits)
else:
self.bn1 = get_norm(bn_norm, planes, num_splits)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = get_norm(bn_norm, planes, num_splits)
self.conv3 = nn.Conv2d(planes,
planes * self.expansion,
kernel_size=1,
bias=False)
self.bn3 = get_norm(bn_norm, planes * self.expansion, num_splits)
self.relu = nn.ReLU(inplace=True)
if with_se:
self.se = SELayer(planes * self.expansion, reduction)
else:
self.se = nn.Identity()
self.downsample = downsample
self.stride = stride
def __init__(self, inplanes, planes, bn_norm, norm_opt, num_splits, with_ibn=False, with_se=False,
stride=1, downsample=None, reduction=16):
super(BasicBlock, self).__init__()
self.conv1 = meta_conv2d(inplanes, planes, kernel_size = 3, stride=stride, padding=1, bias=False)
self.bn1 = meta_norm(bn_norm, planes, norm_opt=norm_opt)
self.conv2 = meta_conv2d(planes, planes, kernel_size = 3, stride=1, padding=1, bias=False)
self.bn2 = meta_norm(bn_norm, planes, norm_opt=norm_opt)
self.relu = nn.ReLU(inplace=True)
if with_se:
self.se = SELayer(planes, reduction)
else:
self.se = nn.Identity()
self.downsample = downsample
self.stride = stride
def __init__(self, in_channel, depth, bn_norm, stride, with_se=False):
super(bottleneck_IR, self).__init__()
if in_channel == depth:
self.shortcut_layer = nn.MaxPool2d(1, stride)
else:
self.shortcut_layer = nn.Sequential(
nn.Conv2d(in_channel, depth, (1, 1), stride, bias=False),
get_norm(bn_norm, depth))
self.res_layer = nn.Sequential(
get_norm(bn_norm, in_channel),
nn.Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
nn.PReLU(depth),
nn.Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
get_norm(bn_norm, depth),
SELayer(depth, 16) if with_se else nn.Identity()
)