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import torch
import torch.nn as nn
import numpy as np
import math
from pdb import set_trace as st
from .dit_models import DiT, DiTBlock, DiT_models, get_2d_sincos_pos_embed
class DiT_Triplane_V1(DiT):
"""
1. merge the 3*H*W as L, and 8 as C only
2. pachify, flat into 224*(224*3) with 8 channels for pachify
3. unpachify accordingly
"""
def __init__(self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=False):
input_size = (input_size, input_size*3)
super().__init__(input_size, patch_size, in_channels//3, hidden_size, # type: ignore
depth, num_heads, mlp_ratio, class_dropout_prob,
num_classes, learn_sigma)
def initialize_weights(self):
"""all the same except the PE part
"""
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(
self.pos_embed.shape[-1], self.x_embedder.grid_size)
# st()
self.pos_embed.data.copy_(
torch.from_numpy(pos_embed).float().unsqueeze(0))
# ! untouched below
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.x_embedder.proj.bias, 0)
# Initialize label embedding table:
if self.y_embedder is not None:
nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
# Zero-out adaLN modulation layers in DiT blocks:
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def unpatchify(self, x):
# TODO
"""
x: (N, L, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0] # type: ignore
h = w = int((x.shape[1]//3)**0.5)
assert h * w * 3 == x.shape[1] # merge triplane 3 dims with hw
x = x.reshape(shape=(x.shape[0], h, w, 3, p, p, c))
x = torch.einsum('nhwzpqc->nczhpwq', x)
imgs = x.reshape(shape=(x.shape[0], c*3, h * p, h * p)) # type: ignore
return imgs # B 8*3 H W
def forward(self, x, t, y=None):
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
# ! merge tri-channel into w chanenl for 3D-aware TX
x = x.reshape(x.shape[0], -1, 3, x.shape[2], x.shape[3]) # B 8 3 H W
x = x.permute(0,1,3,4,2).reshape(x.shape[0], -1, x.shape[-2], x.shape[-1]*3) # B 8 H W83
x = self.x_embedder(
x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
t = self.t_embedder(t) # (N, D)
if self.y_embedder is not None:
assert y is not None
y = self.y_embedder(y, self.training) # (N, D)
c = t + y # (N, D)
else:
c = t
for block in self.blocks:
x = block(x, c) # (N, T, D)
x = self.final_layer(x, c) # (N, T, patch_size ** 2 * out_channels)
x = self.unpatchify(x) # (N, out_channels, H, W)
return x
class DiT_Triplane_V1_learnedPE(DiT_Triplane_V1):
"""
1. learned PE, default cos/sin wave
"""
def __init__(self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True):
super().__init__(input_size, patch_size, in_channels, hidden_size,
depth, num_heads, mlp_ratio, class_dropout_prob,
num_classes, learn_sigma)
class DiT_Triplane_V1_fixed3DPE(DiT_Triplane_V1):
"""
1. 3D aware PE, fixed
"""
def __init__(self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True):
super().__init__(input_size, patch_size, in_channels, hidden_size,
depth, num_heads, mlp_ratio, class_dropout_prob,
num_classes, learn_sigma)
class DiT_Triplane_V1_learned3DPE(DiT_Triplane_V1):
"""
1. init with 3D aware PE, learnable
"""
def __init__(self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True):
super().__init__(input_size, patch_size, in_channels, hidden_size,
depth, num_heads, mlp_ratio, class_dropout_prob,
num_classes, learn_sigma)
def V1_Triplane_DiT_S_2(**kwargs):
return DiT_Triplane_V1(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
def V1_Triplane_DiT_S_4(**kwargs):
return DiT_Triplane_V1(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
def V1_Triplane_DiT_S_8(**kwargs):
return DiT_Triplane_V1(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
def V1_Triplane_DiT_B_8(**kwargs):
return DiT_Triplane_V1(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
def V1_Triplane_DiT_B_16(**kwargs): # ours cfg
return DiT_Triplane_V1(depth=12, hidden_size=768, patch_size=16, num_heads=12, **kwargs)
DiT_models.update({
'v1-T-DiT-S/2': V1_Triplane_DiT_S_2,
'v1-T-DiT-S/4': V1_Triplane_DiT_S_4,
'v1-T-DiT-S/8': V1_Triplane_DiT_S_8,
'v1-T-DiT-B/8': V1_Triplane_DiT_B_8,
'v1-T-DiT-B/16': V1_Triplane_DiT_B_16,
}) |