| import torch |
| from . import initialization as init |
|
|
|
|
| class SegmentationModel(torch.nn.Module): |
| def initialize(self): |
| init.initialize_decoder(self.decoder) |
| init.initialize_head(self.segmentation_head) |
| if self.classification_head is not None: |
| init.initialize_head(self.classification_head) |
|
|
| def check_input_shape(self, x): |
|
|
| h, w = x.shape[-2:] |
| output_stride = self.encoder.output_stride |
| if h % output_stride != 0 or w % output_stride != 0: |
| new_h = (h // output_stride + 1) * output_stride if h % output_stride != 0 else h |
| new_w = (w // output_stride + 1) * output_stride if w % output_stride != 0 else w |
| raise RuntimeError( |
| f"Wrong input shape height={h}, width={w}. Expected image height and width " |
| f"divisible by {output_stride}. Consider pad your images to shape ({new_h}, {new_w})." |
| ) |
|
|
| def forward(self, x): |
| """Sequentially pass `x` trough model`s encoder, decoder and heads""" |
|
|
| self.check_input_shape(x) |
|
|
| features = self.encoder(x) |
| decoder_output = self.decoder(*features) |
|
|
| masks = self.segmentation_head(decoder_output) |
|
|
| if self.classification_head is not None: |
| labels = self.classification_head(features[-1]) |
| return masks, labels |
|
|
| return masks |
|
|
| @torch.no_grad() |
| def predict(self, x): |
| """Inference method. Switch model to `eval` mode, call `.forward(x)` with `torch.no_grad()` |
| |
| Args: |
| x: 4D torch tensor with shape (batch_size, channels, height, width) |
| |
| Return: |
| prediction: 4D torch tensor with shape (batch_size, classes, height, width) |
| |
| """ |
| if self.training: |
| self.eval() |
|
|
| x = self.forward(x) |
|
|
| return x |
|
|
