def __init__(self, num_feat=64, deformable_groups=8):
super(PCDAlignment, self).__init__()
# Pyramid has three levels:
# L3: level 3, 1/4 spatial size
# L2: level 2, 1/2 spatial size
# L1: level 1, original spatial size
self.offset_conv1 = nn.ModuleDict()
self.offset_conv2 = nn.ModuleDict()
self.offset_conv3 = nn.ModuleDict()
self.dcn_pack = nn.ModuleDict()
self.feat_conv = nn.ModuleDict()
# Pyramids
for i in range(3, 0, -1):
level = f'l{i}'
self.offset_conv1[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1,
1)
if i == 3:
self.offset_conv2[level] = nn.Conv2d(num_feat, num_feat, 3, 1,
1)
else:
self.offset_conv2[level] = nn.Conv2d(num_feat * 2, num_feat, 3,
1, 1)
self.offset_conv3[level] = nn.Conv2d(num_feat, num_feat, 3, 1,
1)
self.dcn_pack[level] = DCNv2Pack(
num_feat,
num_feat,
3,
padding=1,
deformable_groups=deformable_groups)
if i < 3:
self.feat_conv[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1,
1)
# Cascading dcn
self.cas_offset_conv1 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
self.cas_offset_conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.cas_dcnpack = DCNv2Pack(num_feat,
num_feat,
3,
padding=1,
deformable_groups=deformable_groups)
self.upsample = nn.Upsample(scale_factor=2,
mode='bilinear',
align_corners=False)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
def __init__(self, num_feat=64, deformable_groups=8):
super(PreDcn, self).__init__()
self.offset_conv1 = nn.ModuleDict()
self.dcn_pack = nn.ModuleDict()
self.feat_conv = nn.ModuleDict()
# Cascading dcn
self.cas_offset_conv1 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1)
self.cas_offset_conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.cas_dcnpack = DCNv2Pack(num_feat,
num_feat,
3,
padding=1,
deformable_groups=deformable_groups)
self.upsample = nn.Upsample(scale_factor=2,
mode='bilinear',
align_corners=False)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)