def __init__(self, loss_config: AttrDict):
"""
Intializer for the sum cross-entropy loss. For a single
tensor, this is equivalent to the cross-entropy loss. For a
list of tensors, this computes the sum of the cross-entropy
losses for each tensor in the list against the target.
Config params:
reduction: specifies reduction to apply to the output, optional
normalize_output: Whether to L2 normalize the outputs
world_size: total number of gpus in training. automatically inferred by vissl
"""
super(BCELogitsMultipleOutputSingleTargetLoss, self).__init__()
self.loss_config = loss_config
self._losses = torch.nn.modules.ModuleList([])
self._reduction = loss_config.get("reduction", "none")
self._normalize_output = loss_config.get("normalize_output", False)
self._world_size = loss_config["world_size"]
def __init__(self, cfg: AttrDict, path: str, split: str, dataset_name="fastmri_dataset", data_source="fastmri"):
super(FastMRIDataSet, self).__init__()
assert PathManager.isdir(path), f"Directory {path} does not exist"
self.dataset_name = "singlecoil"
self.data_source = "fastmri"
self.path = path
data = cfg.get("DATA", AttrDict({}))
self.key = data.get("KEY", "reconstruction_esc")
self.index = data.get("INDEX", 12)
self.split = split.lower()
self.dataset = self._load_data()
def __init__(self, meters_config: AttrDict):
self.num_classes = meters_config.get("num_classes")
self._total_sample_count = None
self._curr_sample_count = None
self.reset()