This module contains all the code for extracting features and comparing features from SimCLR and SIFT models.
sift_frame_sim(features_i, features_j, codebook, df, vw)
simclr_frame_sim(features_i, features_j, codebook, df, vw)
SimCLRDataset(dataset) :: Dataset
An abstract class representing a :class:Dataset.
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
:meth:__len__, which is expected to return the size of the dataset by many
:class:~torch.utils.data.Sampler implementations and the default options
of :class:~torch.utils.data.DataLoader.
.. note::
:class:~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.
imagenet_normalize_transform()
get_train_transforms(size=224, color_jitter_prob=0.8, grayscale_prob=0.2)
get_val_transforms(size=224)
NTXEntCriterion(temperature=0.5) :: Module
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.
SimCLRModel(model_name='resnet18', pretrained=True, projection_dim=64, temperature=0.5, batch_size=128, image_size=224, save_hparams=True) :: LightningModule
SimCLR training network for a generic torchvision model (restricted to allowed_models).