Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

DPT Head for dense prediction tasks. This implementation follows the architecture described in "Vision Transformers for Dense Prediction"

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

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

Deterministic scene-level benchmark dataset. Each __getitem__ returns all fixed frames of one scene, used for model inference and evaluation.

# DPT Head for dense prediction tasks. # This module implements the DPT (Dense Prediction Transformer) head as proposed in # "Vision Tra

Convenience class to apply the same operation to a whole set of images. This class wraps a list of PIL.Image objects and provides methods to

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

nn.Module wrapper around Tensor.permute for cleaner nn.Sequential usage.

nn.Module wrapper around Tensor.permute for cleaner nn.Sequential usage.

Residual convolution module.

CameraHead predicts camera parameters from token representations using iterative refinement. It applies a series of transformer blocks (the

Args: dim_in (int): Input dimension (channels). patch_size (int, optional): Patch size. Default is 14. output_dim (int, o

DPT Head for dense prediction tasks. This implementation follows the architecture described in "Vision Transformers for Dense Prediction"

DPT Head for dense prediction tasks. This implementation follows the architecture described in "Vision Transformers for Dense Prediction" (h

DPT Head for dense prediction tasks. This implementation follows the architecture described in "Vision Transformers for Dense Prediction"

DPT Head for dense prediction tasks. This implementation follows the architecture described in "Vision Transformers for Dense Prediction"

DPT Head for dense prediction tasks. This implementation follows the architecture described in "Vision Transformers for Dense Prediction"

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Describes a fallback procedure to execute.

CPU-side ring buffer for frame images, used for bridge frame re-runs.

Interpolation module.

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

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

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

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

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

2D image to patch embedding: (B,C,H,W) -> (B,N,D) Args: img_size: Image size. patch_size: Patch token size. in_chans

ResidualBlock: construct a block of two conv layers with residual connections

Remember to set model.train() to enable gradient checkpointing to reduce memory usage. Args: img_size (int): Image size in pixels.

Streaming causal aggregator with FlashInfer paged KV cache. Features: - Temporal causal attention (each frame only attends to past frame

CameraHead predicts camera parameters from token representations using iterative refinement. It applies a series of transformer blocks (the

DPT Head for dense prediction tasks. This implementation follows the architecture described in "Vision Transformers for Dense Prediction"

DPT Head for dense prediction tasks. This implementation follows the architecture described in "Vision Transformers for Dense Prediction"

DPT Head for dense prediction tasks. This implementation follows the architecture described in "Vision Transformers for Dense Prediction"

A wrapper around ``DistributedAttention`` that handles sequence parallelism when the sequence length is not a multiple of the sequence parallel wo

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).