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import torch
from torch import nn
import torch.nn.functional as F
from diffusers.models.normalization import FP32LayerNorm
from diffusers.models.attention import FeedForward
from transformers.generation.logits_process import LogitsProcessor
from typing import List, Literal, Optional
from modules.bbox_gen.modules.norm import GroupNorm32, ChannelLayerNorm32
class GroupEmbedding(nn.Module):
def __init__(self, max_group_size, hidden_size=64):
super().__init__()
self.group_embedding = nn.Embedding(max_group_size + 1, hidden_size) # +1 for background
self.group_embedding.weight.data.normal_(mean=0.0, std=0.02)
def forward(self, masks):
batch_size, height, width = masks.shape
masks_flat = masks.reshape(batch_size, -1)
embeddings = self.group_embedding(masks_flat)
embeddings = embeddings.reshape(batch_size, height, width, -1)
embeddings = embeddings.permute(0, 3, 1, 2)
return embeddings
class MultiModalProjector(torch.nn.Module):
def __init__(self, in_features: int, out_features: int, pos_embed_seq_len=None):
super().__init__()
self.norm1 = FP32LayerNorm(in_features)
self.ff = FeedForward(in_features, out_features, mult=1, activation_fn="gelu")
self.norm2 = FP32LayerNorm(out_features)
if pos_embed_seq_len is not None:
self.pos_embed = nn.Parameter(torch.zeros(1, pos_embed_seq_len, in_features))
else:
self.pos_embed = None
def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
if self.pos_embed is not None:
batch_size, seq_len, embed_dim = encoder_hidden_states_image.shape
encoder_hidden_states_image = encoder_hidden_states_image.view(-1, 2 * seq_len, embed_dim)
encoder_hidden_states_image = encoder_hidden_states_image + self.pos_embed
hidden_states = self.norm1(encoder_hidden_states_image)
hidden_states = self.ff(hidden_states)
hidden_states = self.norm2(hidden_states)
return hidden_states
class MeshDecodeLogitsProcessor(LogitsProcessor):
def __init__(self, bins, BOS_id, EOS_id, PAD_id, vertices_num=8):
super().__init__()
self.bins = bins
self.BOS_id = BOS_id
self.EOS_id = EOS_id
self.PAD_id = PAD_id
self.filter_value = -float('inf')
self.vertices_num = vertices_num
def force_token(self, scores, token_id):
mask = torch.ones_like(scores, dtype=torch.bool)
mask[:, token_id] = False
scores[mask] = self.filter_value
def __call__(self, input_ids, scores):
# # all rules:
# # 1. first token: BOS
current_len = input_ids.shape[-1]
if current_len == 0:
# force bos
self.force_token(scores, self.BOS_id)
elif current_len <= self.vertices_num * 3 + 1:
scores[:, self.bins:] = self.filter_value
else:
scores[:, self.BOS_id] = self.filter_value
scores[:, self.PAD_id] = self.filter_value
effective_tokens = current_len - 1
complete_boxes = effective_tokens % (self.vertices_num * 3) == 0
# print(effective_tokens, complete_boxes)
if not complete_boxes:
scores[:, self.EOS_id] = self.filter_value
return scores
def norm_layer(norm_type: str, *args, **kwargs) -> nn.Module:
"""
Return a normalization layer.
"""
if norm_type == "group":
return GroupNorm32(32, *args, **kwargs)
elif norm_type == "layer":
return ChannelLayerNorm32(*args, **kwargs)
else:
raise ValueError(f"Invalid norm type {norm_type}")
class ResBlock3d(nn.Module):
def __init__(
self,
channels: int,
out_channels: Optional[int] = None,
norm_type: Literal["group", "layer"] = "layer",
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.norm1 = norm_layer(norm_type, channels)
self.norm2 = norm_layer(norm_type, self.out_channels)
self.conv1 = nn.Conv3d(channels, self.out_channels, 3, padding=1)
self.conv2 = zero_module(nn.Conv3d(self.out_channels, self.out_channels, 3, padding=1))
self.skip_connection = nn.Conv3d(channels, self.out_channels, 1) if channels != self.out_channels else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.norm1(x)
h = F.silu(h)
h = self.conv1(h)
h = self.norm2(h)
h = F.silu(h)
h = self.conv2(h)
h = h + self.skip_connection(x)
return h
class DownsampleBlock3d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
mode: Literal["conv", "avgpool"] = "conv",
):
assert mode in ["conv", "avgpool"], f"Invalid mode {mode}"
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
if mode == "conv":
self.conv = nn.Conv3d(in_channels, out_channels, 2, stride=2)
elif mode == "avgpool":
assert in_channels == out_channels, "Pooling mode requires in_channels to be equal to out_channels"
def forward(self, x: torch.Tensor) -> torch.Tensor:
if hasattr(self, "conv"):
return self.conv(x)
else:
return F.avg_pool3d(x, 2)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
class SparseStructureEncoder(nn.Module):
def __init__(
self,
in_channels: int,
latent_channels: int,
num_res_blocks: int,
channels: List[int],
num_res_blocks_middle: int = 2,
norm_type: Literal["group", "layer"] = "layer",
):
super().__init__()
self.in_channels = in_channels
self.latent_channels = latent_channels
self.num_res_blocks = num_res_blocks
self.channels = channels
self.num_res_blocks_middle = num_res_blocks_middle
self.norm_type = norm_type
self.dtype = torch.float16
self.input_layer = nn.Conv3d(in_channels, channels[0], 3, padding=1)
self.blocks = nn.ModuleList([])
for i, ch in enumerate(channels):
self.blocks.extend([
ResBlock3d(ch, ch)
for _ in range(num_res_blocks)
])
if i < len(channels) - 1:
self.blocks.append(
DownsampleBlock3d(ch, channels[i+1])
)
self.middle_block = nn.Sequential(*[
ResBlock3d(channels[-1], channels[-1])
for _ in range(num_res_blocks_middle)
])
@property
def device(self) -> torch.device:
"""
Return the device of the model.
"""
return next(self.parameters()).device
def forward(self, x: torch.Tensor):
h = self.input_layer(x)
h = h.type(self.dtype)
for block in self.blocks:
h = block(h)
h = self.middle_block(h)
h = h.type(x.dtype)
return h |