RЦi VSrSSKJrJr SSKrSSKJr "SS\R5rg)zBase training task abstraction. This module provides the base TrainingTask class that encapsulates a complete forward pass including loss computation. Tasks return a dictionary with loss components and outputs for logging. )DictOptionalNc^\rSrSrSrSS\\RS\\RS\ 4U4Sjjjr U4Sjr SS\\ S S4S jjr S \RS \RS \\\R44S jrSrU=r$) TrainingTask aBase class for training tasks. A training task encapsulates a complete forward pass including loss computation. Tasks return a dictionary containing the training loss and other components for logging. The returned dictionary must contain: - 'loss': The training loss for backward pass (required) - 'output': Model output/logits for metric computation (recommended) - Other task-specific loss components for logging (optional) Args: device: Device for task tensors/buffers (defaults to cpu) dtype: Dtype for task tensors/buffers (defaults to torch default) verbose: Enable info logging Example: >>> task = SomeTask(model, criterion, device=torch.device('cuda')) >>> >>> # Prepare for distributed training (if needed) >>> if distributed: >>> task.prepare_distributed(device_ids=[local_rank]) >>> >>> # Training loop >>> result = task(input, target) >>> result['loss'].backward() devicedtypeverbosec>[TU]5 UbUO[R"S5UlUbUO[R"5UlX0lg)Ncpu)super__init__torchrget_default_dtyper r )selfrr r __class__s M/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/timm/task/task.pyrTrainingTask.__init__)sC  & 2f U8K #/UU5L5L5N  c>[R"S5R"U0UD6nURUlURUl[ TU]"U0UD6$)zFMove task to device/dtype, keeping self.device and self.dtype in sync.r)remptytorr r )rargskwargsdummyrs rrTrainingTask.to4sJ A!!4262ll [[ wz4*6**r device_idsreturnc U$)aPrepare task for distributed training. This method wraps trainable components in DistributedDataParallel (DDP) while leaving non-trainable components (like frozen teacher models) unwrapped. Should be called after task initialization but before training loop. Args: device_ids: List of device IDs for DDP (e.g., [local_rank]) **ddp_kwargs: Additional arguments passed to DistributedDataParallel Returns: self (for method chaining) Example: >>> task = LogitDistillationTask(student, teacher, criterion) >>> task.prepare_distributed(device_ids=[args.local_rank]) >>> task = torch.compile(task) # Compile after DDP )rr ddp_kwargss rprepare_distributed TrainingTask.prepare_distributed;s 2 rinputtargetc[e)zPerform forward pass and compute loss. Args: input: Input tensor [B, C, H, W] target: Target labels [B] Returns: Dictionary with at least 'loss' key containing the training loss )NotImplementedError)rr$r%s rforwardTrainingTask.forwardVs "!r)rr r )NNT)N)__name__ __module__ __qualname____firstlineno____doc__rrrr boolrrlistr"Tensorrstrr(__static_attributes__ __classcell__)rs@rrr s:.2+/ U\\* EKK(   +*.   6"<<"LL" c5<< ""rr) r.typingrrrtorch.nnnnModulerr rrr9s' " W"299W"r