| import os |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| import backbones |
| import decoders |
|
|
|
|
| class BasicModel(nn.Module): |
| def __init__(self, args): |
| nn.Module.__init__(self) |
|
|
| self.backbone = getattr(backbones, args['backbone'])(**args.get('backbone_args', {})) |
| self.decoder = getattr(decoders, args['decoder'])(**args.get('decoder_args', {})) |
|
|
| def forward(self, data, *args, **kwargs): |
| return self.decoder(self.backbone(data), *args, **kwargs) |
|
|
|
|
| def parallelize(model, distributed, local_rank): |
| if distributed: |
| return nn.parallel.DistributedDataParallel( |
| model, |
| device_ids=[local_rank], |
| output_device=[local_rank], |
| find_unused_parameters=True) |
| else: |
| return nn.DataParallel(model) |
|
|
| class SegDetectorModel(nn.Module): |
| def __init__(self, args, device, distributed: bool = False, local_rank: int = 0): |
| super(SegDetectorModel, self).__init__() |
| from decoders.seg_detector_loss import SegDetectorLossBuilder |
|
|
| self.model = BasicModel(args) |
| |
| self.model = parallelize(self.model, distributed, local_rank) |
| self.criterion = SegDetectorLossBuilder( |
| args['loss_class'], *args.get('loss_args', []), **args.get('loss_kwargs', {})).build() |
| self.criterion = parallelize(self.criterion, distributed, local_rank) |
| self.device = device |
| self.to(self.device) |
|
|
| @staticmethod |
| def model_name(args): |
| return os.path.join('seg_detector', args['backbone'], args['loss_class']) |
|
|
| def forward(self, batch, training=True): |
| if isinstance(batch, dict): |
| data = batch['image'].to(self.device) |
| else: |
| data = batch.to(self.device) |
| data = data.float() |
| pred = self.model(data, training=self.training) |
|
|
| if self.training: |
| for key, value in batch.items(): |
| if value is not None: |
| if hasattr(value, 'to'): |
| batch[key] = value.to(self.device) |
| loss_with_metrics = self.criterion(pred, batch) |
| loss, metrics = loss_with_metrics |
| return loss, pred, metrics |
| return pred |
