| | 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 |
