def __init__(self, name, in_channels, out_channels, stride, dilation):
super(BasicBlock, self).__init__()
self.g_name = name
self.in_channels = in_channels
self.stride = stride
channels = out_channels//2
if stride == 1:
assert in_channels == out_channels
self.conv = nn.Sequential(
slim.conv_bn_relu(name + '/conv1', channels, channels, 1),
slim.conv_bn(name + '/conv2',
channels, channels, 3, stride=stride,
dilation=dilation, padding=dilation, groups=channels),
slim.conv_bn_relu(name + '/conv3', channels, channels, 1),
)
else:
self.conv = nn.Sequential(
slim.conv_bn_relu(name + '/conv1', in_channels, channels, 1),
slim.conv_bn(name + '/conv2',
channels, channels, 3, stride=stride,
dilation=dilation, padding=dilation, groups=channels),
slim.conv_bn_relu(name + '/conv3', channels, channels, 1),
)
self.conv0 = nn.Sequential(
slim.conv_bn(name + '/conv4',
in_channels, in_channels, 3, stride=stride,
dilation=dilation, padding=dilation, groups=in_channels),
slim.conv_bn_relu(name + '/conv5', in_channels, channels, 1),
)
self.shuffle = slim.channel_shuffle(name + '/shuffle', 2)
def __init__(self, in_c, out_c, group, stride=2, dilation=1):
super(BasicUnitA, self).__init__()
self.stride = stride
out_c -= in_c
bottleneck = out_c // 4
assert bottleneck % group == 0
assert out_c % group == 0
assert stride == 2
self.g_conv1 = slim.conv_bn_relu(in_c,
bottleneck,
1,
stride=1,
pad=0,
group=group)
self.shuffle2 = slim.get_shuffle(group)
self.dw_conv3 = slim.conv_bn(bottleneck,
bottleneck,
3,
stride=2,
pad=1,
group=bottleneck)
self.g_conv4 = slim.conv_bn(bottleneck,
out_c,
1,
stride=1,
pad=0,
group=group)
self.avg_pool1 = nn.AvgPool2d(3, 2, 1)
def __init__(self, name, in_channels, out_channels, stride, dilation):
super(BasicBlock, self).__init__()
self.g_name = name
self.in_channels = in_channels
self.stride = stride
channels = out_channels//2
if stride == 1:
assert in_channels == out_channels
self.conv = nn.Sequential(
slim.conv_bn_relu(name + '/conv1', channels, channels, 1),
slim.conv_bn(name + '/conv2',
channels, channels, 3, stride=stride,
dilation=dilation, padding=dilation, groups=channels),
slim.conv_bn_relu(name + '/conv3', channels, channels, 1),
)
else:
self.conv = nn.Sequential(
slim.conv_bn_relu(name + '/conv1', in_channels, channels, 1),
slim.conv_bn(name + '/conv2',
channels, channels, 3, stride=stride,
dilation=dilation, padding=dilation, groups=channels),
slim.conv_bn_relu(name + '/conv3', channels, channels, 1),
)
self.conv0 = nn.Sequential(
slim.conv_bn(name + '/conv4',
in_channels, in_channels, 3, stride=stride,
dilation=dilation, padding=dilation, groups=in_channels),
slim.conv_bn_relu(name + '/conv5', in_channels, channels, 1),
)
self.shuffle = slim.channel_shuffle(name + '/shuffle', 2)
def __init__(self, in_c, out_c, group, stride=1, dilation=1):
super(BasicUnitB, self).__init__()
bottleneck = out_c // 4
assert stride == 1
#assert in_c == out_c
assert bottleneck % group == 0
self.g_conv1 = slim.conv_bn_relu(in_c,
bottleneck,
1,
stride=stride,
pad=0,
group=group)
self.shuffle2 = slim.get_shuffle(group)
self.dw_conv3 = slim.conv_bn(bottleneck,
bottleneck,
3,
stride=1,
pad=1,
group=bottleneck)
self.g_conv4 = slim.conv_bn(bottleneck,
out_c,
1,
stride=1,
pad=0,
group=group)