def __init__(self,
num_modules=1,
end_relu=False,
num_landmarks=98,
fname_pretrained=None):
super(FAN, self).__init__()
self.num_modules = num_modules
self.end_relu = end_relu
# Base part
self.conv1 = CoordConvTh(256,
256,
True,
False,
in_channels=3,
out_channels=64,
kernel_size=7,
stride=2,
padding=3)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = ConvBlock(64, 128)
self.conv3 = ConvBlock(128, 128)
self.conv4 = ConvBlock(128, 256)
# Stacking part
self.add_module('m0', HourGlass(1, 4, 256, first_one=True))
self.add_module('top_m_0', ConvBlock(256, 256))
self.add_module('conv_last0', nn.Conv2d(256, 256, 1, 1, 0))
self.add_module('bn_end0', nn.BatchNorm2d(256))
self.add_module('l0', nn.Conv2d(256, num_landmarks + 1, 1, 1, 0))
if fname_pretrained is not None:
self.load_pretrained_weights(fname_pretrained)
def __init__(self, in_planes, out_planes):
super(ConvBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
conv3x3 = partial(nn.Conv2d,
kernel_size=3,
stride=1,
padding=1,
bias=False,
dilation=1)
self.conv1 = conv3x3(in_planes, int(out_planes / 2))
self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
self.downsample = None
if in_planes != out_planes:
self.downsample = nn.Sequential(
nn.BatchNorm2d(in_planes), nn.ReLU(True),
nn.Conv2d(in_planes, out_planes, 1, 1, bias=False))
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)