An abstract class representing a :class:
All datasets that represent a map from keys to data samples should subclass
it. All subclasses should overwrite :meth:
__getitem__, supporting fetching a
data sample for a given key. Subclasses could also optionally overwrite
__len__, which is expected to return the size of the dataset by many
~torch.utils.data.Sampler implementations and the default options
~torch.utils.data.DataLoader by default constructs a index
sampler that yields integral indices. To make it work with a map-style
dataset with non-integral indices/keys, a custom sampler must be provided.
Normalized, temperature-scaled cross-entropy criterion, as suggested in the SimCLR paper.
Parameters: temperature (float, optional): temperature to scale the confidences. Defaults to 0.5.
SimCLR training network for a generic torchvision model (restricted to