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Zero
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# Copyright (c) 2023, Zexin He
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
class BasicTransformerBlock(nn.Module):
"""
Transformer block that takes in a cross-attention condition and another modulation vector applied to sub-blocks.
"""
# use attention from torch.nn.MultiHeadAttention
# Block contains a cross-attention layer, a self-attention layer, and a MLP
def __init__(
self,
inner_dim: int,
cond_dim: int,
num_heads: int,
eps: float,
attn_drop: float = 0.,
attn_bias: bool = False,
mlp_ratio: float = 4.,
mlp_drop: float = 0.,
):
super().__init__()
self.norm1 = nn.LayerNorm(inner_dim)
self.cross_attn = nn.MultiheadAttention(
embed_dim=inner_dim, num_heads=num_heads, kdim=cond_dim, vdim=cond_dim,
dropout=attn_drop, bias=attn_bias, batch_first=True)
self.norm2 = nn.LayerNorm(inner_dim)
self.self_attn = nn.MultiheadAttention(
embed_dim=inner_dim, num_heads=num_heads,
dropout=attn_drop, bias=attn_bias, batch_first=True)
self.norm3 = nn.LayerNorm(inner_dim)
self.mlp = nn.Sequential(
nn.Linear(inner_dim, int(inner_dim * mlp_ratio)),
nn.GELU(),
nn.Dropout(mlp_drop),
nn.Linear(int(inner_dim * mlp_ratio), inner_dim),
nn.Dropout(mlp_drop),
)
def forward(self, x, cond):
# x: [N, L, D]
# cond: [N, L_cond, D_cond]
x = x + self.cross_attn(self.norm1(x), cond, cond)[0]
before_sa = self.norm2(x)
x = x + self.self_attn(before_sa, before_sa, before_sa)[0]
x = x + self.mlp(self.norm3(x))
return x
class TriplaneTransformer(nn.Module):
"""
Transformer with condition that generates a triplane representation.
Reference:
Timm: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L486
"""
def __init__(
self,
inner_dim: int,
image_feat_dim: int,
triplane_low_res: int,
triplane_high_res: int,
triplane_dim: int,
num_layers: int,
num_heads: int,
eps: float = 1e-6,
):
super().__init__()
# attributes
self.triplane_low_res = triplane_low_res
self.triplane_high_res = triplane_high_res
self.triplane_dim = triplane_dim
# modules
# initialize pos_embed with 1/sqrt(dim) * N(0, 1)
self.pos_embed = nn.Parameter(torch.randn(1, 3*triplane_low_res**2, inner_dim) * (1. / inner_dim) ** 0.5)
self.layers = nn.ModuleList([
BasicTransformerBlock(
inner_dim=inner_dim, cond_dim=image_feat_dim, num_heads=num_heads, eps=eps)
for _ in range(num_layers)
])
self.norm = nn.LayerNorm(inner_dim, eps=eps)
self.deconv = nn.ConvTranspose2d(inner_dim, triplane_dim, kernel_size=2, stride=2, padding=0)
def forward(self, image_feats):
# image_feats: [N, L_cond, D_cond]
N = image_feats.shape[0]
H = W = self.triplane_low_res
L = 3 * H * W
x = self.pos_embed.repeat(N, 1, 1) # [N, L, D]
for layer in self.layers:
x = layer(x, image_feats)
x = self.norm(x)
# separate each plane and apply deconv
x = x.view(N, 3, H, W, -1)
x = torch.einsum('nihwd->indhw', x) # [3, N, D, H, W]
x = x.contiguous().view(3*N, -1, H, W) # [3*N, D, H, W]
x = self.deconv(x) # [3*N, D', H', W']
x = x.view(3, N, *x.shape[-3:]) # [3, N, D', H', W']
x = torch.einsum('indhw->nidhw', x) # [N, 3, D', H', W']
x = x.contiguous()
return x
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