| 197 | self.dataflow: dict = {} |
| 198 | |
| 199 | def initialize(self): |
| 200 | self._props_vals = {} |
| 201 | self._is_initialized = True |
| 202 | self.net = UNet( |
| 203 | spatial_dims=3, |
| 204 | in_channels=1, |
| 205 | out_channels=2, |
| 206 | channels=(16, 32, 64, 128), |
| 207 | strides=(2, 2, 2), |
| 208 | num_res_units=2, |
| 209 | ).to(self.device) |
| 210 | preprocessing = Compose( |
| 211 | [ |
| 212 | EnsureChannelFirstd(keys=["image"]), |
| 213 | ScaleIntensityd(keys="image"), |
| 214 | ScaleIntensityRanged(keys="image", a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True), |
| 215 | ] |
| 216 | ) |
| 217 | self.dataset = Dataset(data=[self.dataflow], transform=preprocessing) |
| 218 | self.postprocessing = Compose([Activationsd(keys="pred", softmax=True), AsDiscreted(keys="pred", argmax=True)]) |
| 219 | |
| 220 | def run(self): |
| 221 | data = self.dataset[0] |