| # Copyright (c) MONAI Consortium | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch.nn as nn | |
| from monai.networks.blocks import Warp | |
| from monai.networks.nets import resnet18 | |
| from monai.networks.nets.regunet import AffineHead | |
| class RegResNet(nn.Module): | |
| def __init__( | |
| self, | |
| image_size=(64, 64), | |
| spatial_dims=2, | |
| mod=None, | |
| mode="bilinear", | |
| padding_mode="border", | |
| features=400, # feature dimension of `mod` | |
| ): | |
| super().__init__() | |
| self.features = resnet18(n_input_channels=2, spatial_dims=spatial_dims) if mod is None else mod | |
| self.affine_head = AffineHead( | |
| spatial_dims=spatial_dims, image_size=image_size, decode_size=[1] * spatial_dims, in_channels=features | |
| ) | |
| self.warp = Warp(mode=mode, padding_mode=padding_mode) | |
| self.image_size = image_size | |
| def forward(self, x): | |
| self.features.to(device=x.device) | |
| self.affine_head.to(device=x.device) | |
| out = self.features(x) | |
| ddf = self.affine_head([out], self.image_size) | |
| f = self.warp(x[:, :1], ddf) # warp the first channel | |
| return f | |