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Hi, i'm very interested in your excellent work, but confused on the affinity matrix.... At the code
CSPN/cspn_pytorch/models/torch_resnet_cspn_nyu.py
Line 372 in b3e487b
guidance = self.gud_up_proj_layer6(x)
class Simple_Gudi_UpConv_Block_Last_Layer(nn.Module): def __init__(self, in_channels, out_channels, oheight=0, owidth=0): super(Simple_Gudi_UpConv_Block_Last_Layer, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.oheight = oheight self.owidth = owidth self._up_pool = Unpool(in_channels) def _up_pooling(self, x, scale): x = self._up_pool(x) if self.oheight != 0 and self.owidth != 0: x = x.narrow(2, 0, self.oheight) x = x.narrow(3, 0, self.owidth) return x def forward(self, x): x = self._up_pooling(x, 2) out = self.conv1(x) return out
in function _up_pooling, with self._up_pool(x), the shape of x should be [batch, 8, 228*2, 304*2] and
x = x.narrow(2, 0, self.oheight) x = x.narrow(3, 0, self.owidth)
the guidance feature's shape should be [batch, 8, 228, 304]
Why the guidance feature only retain the left-top area?
Looking forward to your reply.
The text was updated successfully, but these errors were encountered:
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Hi, i'm very interested in your excellent work, but confused on the affinity matrix....
At the code
CSPN/cspn_pytorch/models/torch_resnet_cspn_nyu.py
Line 372 in b3e487b
guidance = self.gud_up_proj_layer6(x)
in function _up_pooling, with self._up_pool(x), the shape of x should be [batch, 8, 228*2, 304*2]
and
the guidance feature's shape should be [batch, 8, 228, 304]
Why the guidance feature only retain the left-top area?
Looking forward to your reply.
The text was updated successfully, but these errors were encountered: