vsr v1.0.0

backend/inpaint/video/model/modules/deformconv.py

@@ -0,0 +1,54 @@
+import torch
+import torch.nn as nn
+from torch.nn import init as init
+from torch.nn.modules.utils import _pair, _single
+import math
+
+class ModulatedDeformConv2d(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 deform_groups=1,
+                 bias=True):
+        super(ModulatedDeformConv2d, self).__init__()
+        
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = _pair(kernel_size)
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.groups = groups
+        self.deform_groups = deform_groups
+        self.with_bias = bias
+        # enable compatibility with nn.Conv2d
+        self.transposed = False
+        self.output_padding = _single(0)
+
+        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size))
+        if bias:
+            self.bias = nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.init_weights()
+
+    def init_weights(self):
+        n = self.in_channels
+        for k in self.kernel_size:
+            n *= k
+        stdv = 1. / math.sqrt(n)
+        self.weight.data.uniform_(-stdv, stdv)
+        if self.bias is not None:
+            self.bias.data.zero_()
+
+        if hasattr(self, 'conv_offset'):
+            self.conv_offset.weight.data.zero_()
+            self.conv_offset.bias.data.zero_()
+
+    def forward(self, x, offset, mask):
+        pass