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# 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
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