From a0d17a14384f9ded812a069580587747703e61c4 Mon Sep 17 00:00:00 2001 From: BobRandomNumber Date: Wed, 13 Aug 2025 00:59:03 -0400 Subject: [PATCH] fix torch UserWarning --- .../base/modules/diffusionmodules/model.py | 30 +++++++++---------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/TLBVFI/model/BrownianBridge/base/modules/diffusionmodules/model.py b/TLBVFI/model/BrownianBridge/base/modules/diffusionmodules/model.py index 6ad1ab1..411fd3c 100644 --- a/TLBVFI/model/BrownianBridge/base/modules/diffusionmodules/model.py +++ b/TLBVFI/model/BrownianBridge/base/modules/diffusionmodules/model.py @@ -977,20 +977,21 @@ class WaveletTransform3D(torch.nn.Module): # Flatten the tensor along the specified dimension for Conv1d if dim == 0: # frames b, c, f, h, w = x.shape - x = x.permute(0, 1, 3, 4, 2).reshape(b * c * h * w, f) # Flatten (b, c, h, w) -> (B, frames) - - x = F.conv1d(x.unsqueeze(1), filter, padding=0).squeeze(1) # Apply Conv1d - x = x.view(b, c, h, w, -1).permute(0, 1, 4, 2, 3) # Unflatten back to original shape + x_flat = x.permute(0, 1, 3, 4, 2).reshape(b * c * h * w, f) # Flatten (b, c, h, w) -> (B, frames) + x_conv = F.conv1d(x_flat.unsqueeze(1), filter, padding=0, stride=1).squeeze(1) # Apply Conv1d + x = x_conv.view(b, c, h, w, -1).permute(0, 1, 4, 2, 3) # Unflatten back to original shape elif dim == 1: # height b, c, f, h, w = x.shape - x = x.permute(0, 1, 2, 4, 3).reshape(b * c * f * w, h) # Flatten (b, c, f, w) -> (B, height) - x = F.conv1d(x.unsqueeze(1), filter, padding="same",stride = 1).squeeze(1) # Apply Conv1d - x = x.view(b, c, f, w, -1).permute(0, 1, 2, 4, 3) # Unflatten back to original shape + x_flat = x.permute(0, 1, 2, 4, 3).reshape(b * c * f * w, h) # Flatten (b, c, f, w) -> (B, height) + x_padded = F.pad(x_flat.unsqueeze(1), (1, 0), mode='replicate') + x_conv = F.conv1d(x_padded, filter, padding=0, stride=1).squeeze(1) + x = x_conv.view(b, c, f, w, -1).permute(0, 1, 2, 4, 3) # Unflatten back to original shape elif dim == 2: # width b, c, f, h, w = x.shape - x = x.permute(0, 1, 2, 3, 4).reshape(b * c * f * h, w) # Flatten (b, c, f, h) -> (B, width) - x = F.conv1d(x.unsqueeze(1), filter, padding="same",stride = 1).squeeze(1) # Apply Conv1d - x = x.view(b, c, f, h, -1) # Unflatten back to original shape + x_flat = x.permute(0, 1, 2, 3, 4).reshape(b * c * f * h, w) # Flatten (b, c, f, h) -> (B, width) + x_padded = F.pad(x_flat.unsqueeze(1), (1, 0), mode='replicate') + x_conv = F.conv1d(x_padded, filter, padding=0, stride=1).squeeze(1) + x = x_conv.view(b, c, f, h, -1) # Unflatten back to original shape return x def forward(self, x): @@ -1307,7 +1308,7 @@ class FlowDecoderWithResidual(nn.Module): curr_res = resolution // 2**(self.num_resolutions-1) # 64 self.z_shape = (1,z_channels,curr_res,curr_res) # (1,3,64,64) #print("Working with z of shape {} = {} dimensions.".format( - self.z_shape, np.prod(self.z_shape))) + # self.z_shape, np.prod(self.z_shape))) # z to block_in self.conv_in = torch.nn.Conv2d(z_channels, @@ -1514,8 +1515,8 @@ class FlowDecoderWithResidual(nn.Module): block_in = int(ch*ch_mult[self.num_resolutions-1]) # 512 curr_res = resolution // 2**(self.num_resolutions-1) # 64 self.z_shape = (1,z_channels,curr_res,curr_res) # (1,3,64,64) - print("Working with z of shape {} = {} dimensions.".format( - self.z_shape, np.prod(self.z_shape))) + #print("Working with z of shape {} = {} dimensions.".format( + # self.z_shape, np.prod(self.z_shape))) # z to block_in self.conv_in = torch.nn.Conv3d(z_channels, @@ -1744,5 +1745,4 @@ class FlowDecoderWithResidual(nn.Module): imgs = torch.cat((img0, img1), 1) flow, flow_list = self.flownet(imgs) flow, c0, c1 = self.refinenet(feats,flow) - return flow - + return flow \ No newline at end of file