A class that keeps all conversation history.

Subclass torch's LayerNorm to handle fp16.

The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added betwe

Processes an answer similar to Eval AI copied from https://github.com/facebookresearch/mmf/blob/c46b3b3391275b4181567db80943473a8

Residual convolution module.

Residual convolution module.

Track a series of values and provide access to smoothed values over a window or the global series average.

Track a series of values and provide access to smoothed values over a window or the global series average.

Fake file-like stream object that redirects writes to a logger instance.

Vision Transformer A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.or

A predictor that runs the model asynchronously, possibly on >1 GPUs. Because rendering the visualization takes considerably amount of time,

Construct the embeddings from word and position embeddings.

Collate examples for supervised fine-tuning.

Feature fusion block.

Interpolation module.

This module is used in RetinaNet to generate extra layers, P6 and P7 from C5 feature.

This module is used in RetinaNet to generate extra layers, P6 and P7 from C5 feature.

Dataset for supervised fine-tuning.

r""" Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while keeping a bit of rand

MLP as used in Vision Transformer, MLP-Mixer and related networks

A ResNet class that is similar to torchvision's but contains the following changes: - There are now 3 "stem" convolutions as opposed to 1,

A dataset to prepare the instance and class images with the prompts for fine-tuning the model. It pre-processes the images and the tokenizes

A dataset to prepare the instance and class images with the prompts for fine-tuning the model. It pre-processes the images and the tokenizes

Split-Attention Conv2d

Split-Attention Conv2d with dcn

A dataset to prepare the instance and class images with the prompts for fine-tuning the model. It pre-processes the images and the tokenize