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Running
on
Zero
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import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from streamvggt.layers import Mlp
from streamvggt.layers.block import Block
from streamvggt.heads.head_act import activate_pose
class CameraHead(nn.Module):
def __init__(
self,
dim_in: int = 2048,
trunk_depth: int = 4,
pose_encoding_type: str = "absT_quaR_FoV",
num_heads: int = 16,
mlp_ratio: int = 4,
init_values: float = 0.01,
trans_act: str = "linear",
quat_act: str = "linear",
fl_act: str = "relu", # Field of view activations: ensures FOV values are positive.
):
super().__init__()
if pose_encoding_type == "absT_quaR_FoV":
self.target_dim = 9
else:
raise ValueError(f"Unsupported camera encoding type: {pose_encoding_type}")
self.trans_act = trans_act
self.quat_act = quat_act
self.fl_act = fl_act
self.trunk_depth = trunk_depth
# Build the trunk using a sequence of transformer blocks.
self.trunk = nn.Sequential(
*[
Block(
dim=dim_in,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
init_values=init_values,
)
for _ in range(trunk_depth)
]
)
# Normalizations for camera token and trunk output.
self.token_norm = nn.LayerNorm(dim_in)
self.trunk_norm = nn.LayerNorm(dim_in)
# Learnable empty camera pose token.
self.empty_pose_tokens = nn.Parameter(torch.zeros(1, 1, self.target_dim))
self.embed_pose = nn.Linear(self.target_dim, dim_in)
# Module for producing modulation parameters: shift, scale, and a gate.
self.poseLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(dim_in, 3 * dim_in, bias=True))
# Adaptive layer normalization without affine parameters.
self.adaln_norm = nn.LayerNorm(dim_in, elementwise_affine=False, eps=1e-6)
self.pose_branch = Mlp(
in_features=dim_in,
hidden_features=dim_in // 2,
out_features=self.target_dim,
drop=0,
)
def forward(self, aggregated_tokens_list: list, num_iterations: int = 4, past_key_values_camera = None, use_cache: bool = False) -> list:
"""
Forward pass to predict camera parameters.
Args:
aggregated_tokens_list (list): List of token tensors from the network;
the last tensor is used for prediction.
num_iterations (int, optional): Number of iterative refinement steps. Defaults to 4.
Returns:
list: A list of predicted camera encodings (post-activation) from each iteration.
"""
# Use tokens from the last block for camera prediction.
tokens = aggregated_tokens_list[-1]
# Extract the camera tokens
pose_tokens = tokens[:, :, 0]
pose_tokens = self.token_norm(pose_tokens)
if use_cache:
pred_pose_enc_list, past_key_values_camera = self.trunk_fn(pose_tokens, num_iterations, past_key_values_camera, use_cache)
return pred_pose_enc_list, past_key_values_camera
else:
pred_pose_enc_list = self.trunk_fn(pose_tokens, num_iterations, past_key_values_camera=None, use_cache=use_cache)
return pred_pose_enc_list
def trunk_fn(self, pose_tokens: torch.Tensor, num_iterations: int, past_key_values_camera, use_cache: bool) -> list:
"""
Iteratively refine camera pose predictions.
Args:
pose_tokens (torch.Tensor): Normalized camera tokens with shape [B, 1, C].
num_iterations (int): Number of refinement iterations.
Returns:
list: List of activated camera encodings from each iteration.
"""
B, S, C = pose_tokens.shape # S is expected to be 1.
pred_pose_enc = None
pred_pose_enc_list = []
for _ in range(num_iterations):
# Use a learned empty pose for the first iteration.
if pred_pose_enc is None:
module_input = self.embed_pose(self.empty_pose_tokens.expand(B, S, -1))
else:
# Detach the previous prediction to avoid backprop through time.
pred_pose_enc = pred_pose_enc.detach()
module_input = self.embed_pose(pred_pose_enc)
# Generate modulation parameters and split them into shift, scale, and gate components.
shift_msa, scale_msa, gate_msa = self.poseLN_modulation(module_input).chunk(3, dim=-1)
# Adaptive layer normalization and modulation.
pose_tokens_modulated = gate_msa * modulate(self.adaln_norm(pose_tokens), shift_msa, scale_msa)
pose_tokens_modulated = pose_tokens_modulated + pose_tokens
if not use_cache:
L = S * 1
frame_ids = torch.arange(L, device=pose_tokens_modulated.device) // 1 # [0,0,...,1,1,...,S-1]
future_frame = frame_ids.unsqueeze(1) < frame_ids.unsqueeze(0)
attn_mask = future_frame.to(pose_tokens_modulated.dtype) * torch.finfo(pose_tokens_modulated.dtype).min
else:
attn_mask = None
if use_cache:
for idx in range(self.trunk_depth):
pose_tokens_modulated, block_kv = self.trunk[idx](
pose_tokens_modulated,
attn_mask=attn_mask,
past_key_values=past_key_values_camera[idx] if past_key_values_camera[idx] is not None else None,
use_cache=True
)
past_key_values_camera[idx] = block_kv
else:
for idx in range(self.trunk_depth):
pose_tokens_modulated = self.trunk[idx](pose_tokens_modulated, attn_mask=attn_mask)
# Compute the delta update for the pose encoding.
pred_pose_enc_delta = self.pose_branch(self.trunk_norm(pose_tokens_modulated))
if pred_pose_enc is None:
pred_pose_enc = pred_pose_enc_delta
else:
pred_pose_enc = pred_pose_enc + pred_pose_enc_delta
# Apply final activation functions for translation, quaternion, and field-of-view.
activated_pose = activate_pose(
pred_pose_enc,
trans_act=self.trans_act,
quat_act=self.quat_act,
fl_act=self.fl_act,
)
pred_pose_enc_list.append(activated_pose)
if use_cache:
return pred_pose_enc_list, past_key_values_camera
return pred_pose_enc_list
def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
"""
Modulate the input tensor using scaling and shifting parameters.
"""
return x * (1 + scale) + shift
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