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@ -433,9 +433,10 @@ class DeformableConv2d(nn.Module):
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offset = self.offset_conv(x)
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offset = self.offset_conv(x)
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modulator = 2. * torch.sigmoid(self.modulator_conv(x))
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modulator = 2. * torch.sigmoid(self.modulator_conv(x))
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dtype = self.regular_conv.weight.dtype
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dtype = self.regular_conv.weight.dtype
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x = x.to(dtype)
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device = self.regular_conv.weight.device
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offset = offset.to(dtype)
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x = x.to(dtype).to(device)
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modulator = modulator.to(dtype)
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offset = offset.to(dtype).to(device)
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modulator = modulator.to(dtype).to(device)
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x = deform_conv2d(
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x = deform_conv2d(
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input=x,
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input=x,
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offset=offset,
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offset=offset,
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@ -516,6 +517,8 @@ class ASPPDeformable(nn.Module):
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self.relu = nn.ReLU(inplace=True)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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def forward(self, x):
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device = self.conv1.weight.device
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x = x.to(device)
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x1 = self.aspp1(x)
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x1 = self.aspp1(x)
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x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
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x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
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x5 = self.global_avg_pool(x)
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x5 = self.global_avg_pool(x)
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@ -634,7 +637,8 @@ class Decoder(nn.Module):
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return torch.cat(patches_batch, dim=0)
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return torch.cat(patches_batch, dim=0)
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def forward(self, features):
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def forward(self, features):
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x, x1, x2, x3, x4 = features
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device = next(self.ipt_blk5.parameters()).device
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x, x1, x2, x3, x4 = [t.to(device) for t in features]
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patches_batch = self.get_patches_batch(x, x4) if self.split else x
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patches_batch = self.get_patches_batch(x, x4) if self.split else x
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x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
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x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
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