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