| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| from typing import Dict, List, Optional, Sequence, Tuple, Union |
|
|
| import torch |
|
|
|
|
| class CommonKeys: |
| """ |
| A set of common keys for dictionary based supervised training process. |
| `IMAGE` is the input image data. |
| `LABEL` is the training or evaluation label of segmentation or classification task. |
| `PRED` is the prediction data of model output. |
| `LOSS` is the loss value of current iteration. |
| `INFO` is some useful information during training or evaluation, like loss value, etc. |
| |
| """ |
|
|
| IMAGE = "image" |
| LABEL = "label" |
| PRED = "pred" |
| LOSS = "loss" |
|
|
|
|
| class GanKeys: |
| """ |
| A set of common keys for generative adversarial networks. |
| """ |
|
|
| REALS = "reals" |
| FAKES = "fakes" |
| LATENTS = "latents" |
| GLOSS = "g_loss" |
| DLOSS = "d_loss" |
|
|
|
|
| def get_devices_spec(devices: Optional[Sequence[torch.device]] = None) -> List[torch.device]: |
| """ |
| Get a valid specification for one or more devices. If `devices` is None get devices for all CUDA devices available. |
| If `devices` is and zero-length structure a single CPU compute device is returned. In any other cases `devices` is |
| returned unchanged. |
| |
| Args: |
| devices: list of devices to request, None for all GPU devices, [] for CPU. |
| |
| Raises: |
| RuntimeError: When all GPUs are selected (``devices=None``) but no GPUs are available. |
| |
| Returns: |
| list of torch.device: list of devices. |
| |
| """ |
| if devices is None: |
| devices = [torch.device(f"cuda:{d:d}") for d in range(torch.cuda.device_count())] |
|
|
| if len(devices) == 0: |
| raise RuntimeError("No GPU devices available.") |
|
|
| elif len(devices) == 0: |
| devices = [torch.device("cpu")] |
|
|
| else: |
| devices = list(devices) |
|
|
| return devices |
|
|
|
|
| def default_prepare_batch( |
| batchdata: Dict[str, torch.Tensor] |
| ) -> Union[Tuple[torch.Tensor, Optional[torch.Tensor]], torch.Tensor]: |
| assert isinstance(batchdata, dict), "default prepare_batch expects dictionary input data." |
| if CommonKeys.LABEL in batchdata: |
| return (batchdata[CommonKeys.IMAGE], batchdata[CommonKeys.LABEL]) |
| elif GanKeys.REALS in batchdata: |
| return batchdata[GanKeys.REALS] |
| else: |
| return (batchdata[CommonKeys.IMAGE], None) |
|
|
|
|
| def default_make_latent(num_latents: int, latent_size: int, real_data: Optional[torch.Tensor] = None) -> torch.Tensor: |
| return torch.randn(num_latents, latent_size) |
|
|
