File size: 2,992 Bytes
e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | # Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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)
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