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import dataclasses
from typing import Dict, List, Tuple, Type
import torch
from torch import nn
from torch.nn import functional as F
from .common import compute_interp_weights, interpolate_features, repeat_interleave
@dataclasses.dataclass
class AuxInputs:
coords: torch.Tensor
features: torch.Tensor
centers: torch.Tensor
interp_index: torch.Tensor = None
interp_weight: torch.Tensor = None
class MaskDecoder(nn.Module):
def __init__(
self,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
) -> None:
super().__init__()
self.transformer_dim = transformer_dim
self.transformer = transformer
self.num_multimask_outputs = num_multimask_outputs
self.iou_token = nn.Embedding(1, transformer_dim)
self.num_mask_tokens = num_multimask_outputs + 1
self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
self.output_hypernetworks_mlps = nn.ModuleList(
[
MLP(transformer_dim, transformer_dim, transformer_dim, 3)
for i in range(self.num_mask_tokens)
]
)
# self.output_upscaling = nn.Sequential(
# nn.Linear(transformer_dim, transformer_dim),
# nn.LayerNorm(transformer_dim),
# nn.GELU(),
# nn.Linear(transformer_dim, transformer_dim),
# nn.GELU(),
# )
self.output_upscaling = nn.Sequential(
nn.Linear(transformer_dim, transformer_dim),
nn.LayerNorm(transformer_dim),
nn.GELU(),
nn.Linear(transformer_dim, transformer_dim),
nn.GELU(),
)
self.iou_prediction_head = MLP(
transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
)
def forward(
self,
pc_embeddings: torch.Tensor,
pc_pe: torch.Tensor,
sparse_prompt_embeddings: torch.Tensor,
dense_prompt_embeddings: torch.Tensor,
aux_inputs: AuxInputs,
multimask_output: bool,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Predict masks given pointcloud and prompt embeddings.
Arguments:
pc_embeddings (torch.Tensor): the embeddings from the point cloud encoder
pc_pe (torch.Tensor): positional encoding with the shape of pc_embeddings
sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
[B, N_prompts, D]
dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
[B, N_patches, D]
multimask_output (bool): Whether to return multiple masks or a single
mask.
Returns:
torch.Tensor: batched predicted masks
torch.Tensor: batched predictions of mask quality
"""
# Select the correct mask or masks for output
if multimask_output:
mask_slice = slice(1, None)
else:
mask_slice = slice(0, 1)
masks, iou_pred = self.predict_masks(
pc_embeddings=pc_embeddings,
pc_pe=pc_pe,
sparse_prompt_embeddings=sparse_prompt_embeddings,
dense_prompt_embeddings=dense_prompt_embeddings,
aux_inputs=aux_inputs,
mask_slice=mask_slice,
)
# # Select the correct mask or masks for output
# if multimask_output:
# mask_slice = slice(1, None)
# else:
# mask_slice = slice(0, 1)
# masks = masks[:, mask_slice, :]
# iou_pred = iou_pred[:, mask_slice]
return masks, iou_pred
def predict_masks(
self,
pc_embeddings: torch.Tensor,
pc_pe: torch.Tensor,
sparse_prompt_embeddings: torch.Tensor,
dense_prompt_embeddings: torch.Tensor,
aux_inputs: AuxInputs,
mask_slice: slice = None,
) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, torch.Tensor]]:
# Concatenate output tokens
output_tokens = torch.cat(
[self.iou_token.weight, self.mask_tokens.weight], dim=0
)
output_tokens = output_tokens.unsqueeze(0).expand(
sparse_prompt_embeddings.size(0), -1, -1
)
# [B*M, N_tokens, D]
tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
# Expand per-image data in batch direction to be per-mask
repeats = tokens.shape[0] // pc_embeddings.shape[0]
src = repeat_interleave(pc_embeddings, repeats, dim=0)
pos_src = repeat_interleave(pc_pe, repeats, dim=0)
src = src + dense_prompt_embeddings
# Run the transformer
hs, src = self.transformer(src, pos_src, tokens)
iou_token_out = hs[:, 0, :]
mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
# Upscale mask embeddings
coords = aux_inputs.coords # [B, N, 3]
centers = aux_inputs.centers # [B, L, 3]
interp_index = aux_inputs.interp_index # [B, N, 3]
interp_weight = aux_inputs.interp_weight # [B, N, 3]
if interp_index is None or interp_weight is None:
with torch.no_grad():
interp_index, interp_weight = compute_interp_weights(coords, centers)
# Update auxilary inputs for the next iteration
aux_inputs.interp_index = interp_index
aux_inputs.interp_weight = interp_weight
_repeats = tokens.shape[0] // interp_index.shape[0]
interp_index = repeat_interleave(interp_index, _repeats, dim=0)
interp_weight = repeat_interleave(interp_weight, _repeats, dim=0)
# [B*M, N, D]
interp_embedding = interpolate_features(src, interp_index, interp_weight)
upscaled_embedding = self.output_upscaling(interp_embedding)
# Predict masks using the mask tokens
hyper_in_list: List[torch.Tensor] = []
mask_indices = list(range(self.num_mask_tokens))
if mask_slice is not None:
mask_indices = mask_indices[mask_slice]
for i in mask_indices:
hyper_in_list.append(
self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])
)
hyper_in = torch.stack(hyper_in_list, dim=1) # [B*M, num_mask_tokens, D]
masks = hyper_in @ upscaled_embedding.transpose(-1, -2)
# masks = upscaled_embedding.transpose(-1, -2)
# Generate mask quality predictions
iou_pred = self.iou_prediction_head(iou_token_out)
if mask_slice is not None:
iou_pred = iou_pred[:, mask_slice]
return masks, iou_pred
# Adapted from https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
# Used in MaskDecoder for SAM
class MLP(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
output_dim: int,
num_layers: int,
sigmoid_output: bool = False,
) -> None:
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(
nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
)
self.sigmoid_output = sigmoid_output
def forward(self, x):
for i, layer in enumerate(self.layers):
x = F.relu(layer(x), inplace=True) if i < self.num_layers - 1 else layer(x)
if self.sigmoid_output:
x = F.sigmoid(x)
return x
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