# --------------------------------------------------------
# Adapted from: https://github.com/openai/point-e
# Licensed under the MIT License
# Copyright (c) 2022 OpenAI
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# --------------------------------------------------------
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple
import torch
from torch import nn
from spar3d.models.utils import BaseModule
def init_linear(layer, stddev):
nn.init.normal_(layer.weight, std=stddev)
if layer.bias is not None:
nn.init.constant_(layer.bias, 0.0)
class MultiheadAttention(nn.Module):
def __init__(
self,
*,
width: int,
heads: int,
init_scale: float,
):
super().__init__()
self.width = width
self.heads = heads
self.c_qkv = nn.Linear(width, width * 3)
self.c_proj = nn.Linear(width, width)
init_linear(self.c_qkv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
x = self.c_qkv(x)
bs, n_ctx, width = x.shape
attn_ch = width // self.heads // 3
scale = 1 / math.sqrt(attn_ch)
x = x.view(bs, n_ctx, self.heads, -1)
q, k, v = torch.split(x, attn_ch, dim=-1)
x = (
torch.nn.functional.scaled_dot_product_attention(
q.permute(0, 2, 1, 3),
k.permute(0, 2, 1, 3),
v.permute(0, 2, 1, 3),
scale=scale,
)
.permute(0, 2, 1, 3)
.reshape(bs, n_ctx, -1)
)
x = self.c_proj(x)
return x
class MLP(nn.Module):
def __init__(self, *, width: int, init_scale: float):
super().__init__()
self.width = width
self.c_fc = nn.Linear(width, width * 4)
self.c_proj = nn.Linear(width * 4, width)
self.gelu = nn.GELU()
init_linear(self.c_fc, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
return self.c_proj(self.gelu(self.c_fc(x)))
class ResidualAttentionBlock(nn.Module):
def __init__(self, *, width: int, heads: int, init_scale: float = 1.0):
super().__init__()
self.attn = MultiheadAttention(
width=width,
heads=heads,
init_scale=init_scale,
)
self.ln_1 = nn.LayerNorm(width)
self.mlp = MLP(width=width, init_scale=init_scale)
self.ln_2 = nn.LayerNorm(width)
def forward(self, x: torch.Tensor):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class Transformer(nn.Module):
def __init__(
self,
*,
width: int,
layers: int,
heads: int,
init_scale: float = 0.25,
):
super().__init__()
self.width = width
self.layers = layers
init_scale = init_scale * math.sqrt(1.0 / width)
self.resblocks = nn.ModuleList(
[
ResidualAttentionBlock(
width=width,
heads=heads,
init_scale=init_scale,
)
for _ in range(layers)
]
)
def forward(self, x: torch.Tensor):
for block in self.resblocks:
x = block(x)
return x
class PointDiffusionTransformer(nn.Module):
def __init__(
self,
*,
input_channels: int = 3,
output_channels: int = 3,
width: int = 512,
layers: int = 12,
heads: int = 8,
init_scale: float = 0.25,
time_token_cond: bool = False,
):
super().__init__()
self.input_channels = input_channels
self.output_channels = output_channels
self.time_token_cond = time_token_cond
self.time_embed = MLP(
width=width,
init_scale=init_scale * math.sqrt(1.0 / width),
)
self.ln_pre = nn.LayerNorm(width)
self.backbone = Transformer(
width=width,
layers=layers,
heads=heads,
init_scale=init_scale,
)
self.ln_post = nn.LayerNorm(width)
self.input_proj = nn.Linear(input_channels, width)
self.output_proj = nn.Linear(width, output_channels)
with torch.no_grad():
self.output_proj.weight.zero_()
self.output_proj.bias.zero_()
def forward(self, x: torch.Tensor, t: torch.Tensor):
"""
:param x: an [N x C x T] tensor.
:param t: an [N] tensor.
:return: an [N x C' x T] tensor.
"""
t_embed = self.time_embed(timestep_embedding(t, self.backbone.width))
return self._forward_with_cond(x, [(t_embed, self.time_token_cond)])
def _forward_with_cond(
self, x: torch.Tensor, cond_as_token: List[Tuple[torch.Tensor, bool]]
) -> torch.Tensor:
h = self.input_proj(x.permute(0, 2, 1)) # NCL -> NLC
for emb, as_token in cond_as_token:
if not as_token:
h = h + emb[:, None]
extra_tokens = [
(emb[:, None] if len(emb.shape) == 2 else emb)
for emb, as_token in cond_as_token
if as_token
]
if len(extra_tokens):
h = torch.cat(extra_tokens + [h], dim=1)
h = self.ln_pre(h)
h = self.backbone(h)
h = self.ln_post(h)
if len(extra_tokens):
h = h[:, sum(h.shape[1] for h in extra_tokens) :]
h = self.output_proj(h)
return h.permute(0, 2, 1)
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period)
* torch.arange(start=0, end=half, dtype=torch.float32)
/ half
).to(device=timesteps.device)
args = timesteps[:, None].to(timesteps.dtype) * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
class PointEDenoiser(BaseModule):
@dataclass
class Config(BaseModule.Config):
num_attention_heads: int = 8
in_channels: Optional[int] = None
out_channels: Optional[int] = None
num_layers: int = 12
width: int = 512
cond_dim: Optional[int] = None
cfg: Config
def configure(self) -> None:
self.denoiser = PointDiffusionTransformer(
input_channels=self.cfg.in_channels,
output_channels=self.cfg.out_channels,
width=self.cfg.width,
layers=self.cfg.num_layers,
heads=self.cfg.num_attention_heads,
init_scale=0.25,
time_token_cond=True,
)
self.cond_embed = nn.Sequential(
nn.LayerNorm(self.cfg.cond_dim),
nn.Linear(self.cfg.cond_dim, self.cfg.width),
)
def forward(
self,
x,
t,
condition=None,
):
# renormalize with the per-sample standard deviation
x_std = torch.std(x.reshape(x.shape[0], -1), dim=1, keepdim=True)
x = x / x_std.reshape(-1, *([1] * (len(x.shape) - 1)))
t_embed = self.denoiser.time_embed(
timestep_embedding(t, self.denoiser.backbone.width)
)
condition = self.cond_embed(condition)
cond = [(t_embed, True), (condition, True)]
x_denoised = self.denoiser._forward_with_cond(x, cond)
return x_denoised