def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
groups=1,
base_width=64,
norm_layer=None,
reduction_ratio=16):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU()
self.se = SELayer(planes * self.expansion,
reduction_ratio) # Scale Layer.
self.downsample = downsample
self.stride = stride
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None,
reduction_ratio=16, dilation_value=2, pool_stride=2, use_ca=True, use_sa=True):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU()
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.att = MyLayer(num_channels=planes, reduction_ratio=reduction_ratio,
dilation_value=dilation_value, pool_stride=pool_stride, use_ca=use_ca, use_sa=use_sa)
self.downsample = downsample
self.stride = stride
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1,
norm_layer=None, reduction_ratio=16, kernel_size=7, use_ca=True, use_sa=True):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU()
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
self.use_cbam = (use_ca or use_sa)
self.cbam = CBAMLayer(planes * self.expansion, reduction_ratio, kernel_size, use_ca, use_sa)
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
groups=1,
base_width=64,
norm_layer=None,
reduction_ratio=16):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError(
'SEBasicBlock only supports groups=1 and base_width=64')
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU()
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.se = SELayer(planes, reduction_ratio) # Scale Layer.
self.downsample = downsample
self.stride = stride
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None,
reduction_ratio=16, dilation_value=2, pool_stride=2, use_ca=True, use_sa=True):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU()
self.att = MyLayer(num_channels=planes * self.expansion, reduction_ratio=reduction_ratio,
dilation_value=dilation_value, pool_stride=pool_stride, use_ca=use_ca, use_sa=use_sa)
self.downsample = downsample
self.stride = stride
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1,
norm_layer=None, reduction_ratio=16, kernel_size=7, use_ca=True, use_sa=True):
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU()
self.downsample = downsample
self.stride = stride
self.use_cbam = (use_ca or use_sa)
self.cbam = CBAMLayer(planes * self.expansion, reduction_ratio, kernel_size, use_ca, use_sa)