import math
import torch
import torch.nn as nn
from einops import rearrange
from typing import List, Tuple
# from cameractrl.models.motion_module import TemporalTransformerBlock
from models_diffusers.camera.motion_module import TemporalTransformerBlock
def get_parameter_dtype(parameter: torch.nn.Module):
try:
params = tuple(parameter.parameters())
if len(params) > 0:
return params[0].dtype
buffers = tuple(parameter.buffers())
if len(buffers) > 0:
return buffers[0].dtype
except StopIteration:
# For torch.nn.DataParallel compatibility in PyTorch 1.5
def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, torch.Tensor]]:
tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
return tuples
gen = parameter._named_members(get_members_fn=find_tensor_attributes)
first_tuple = next(gen)
return first_tuple[1].dtype
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
class PoseAdaptor(nn.Module):
def __init__(self, unet, pose_encoder):
super().__init__()
self.unet = unet
self.pose_encoder = pose_encoder
def forward(self, inp_noisy_latents, timesteps, encoder_hidden_states, added_time_ids, pose_embedding):
assert pose_embedding.ndim == 5
pose_embedding_features = self.pose_encoder(pose_embedding) # b c f h w
noise_pred = self.unet(
inp_noisy_latents,
timesteps,
encoder_hidden_states,
added_time_ids=added_time_ids,
pose_features=pose_embedding_features,
).sample
return noise_pred
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding)
else:
assert self.channels == self.out_channels
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class ResnetBlock(nn.Module):
def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
super().__init__()
ps = ksize // 2
if in_c != out_c or sk == False:
self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
else:
self.in_conv = None
self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
self.act = nn.ReLU()
self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
if sk == False:
self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
else:
self.skep = None
self.down = down
if self.down == True:
self.down_opt = Downsample(in_c, use_conv=use_conv)
def forward(self, x):
if self.down == True:
x = self.down_opt(x)
if self.in_conv is not None: # edit
x = self.in_conv(x)
h = self.block1(x)
h = self.act(h)
h = self.block2(h)
if self.skep is not None:
return h + self.skep(x)
else:
return h + x
class PositionalEncoding(nn.Module):
def __init__(
self,
d_model,
dropout=0.,
max_len=32,
):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(1, max_len, d_model)
pe[0, :, 0::2, ...] = torch.sin(position * div_term)
pe[0, :, 1::2, ...] = torch.cos(position * div_term)
pe.unsqueeze_(-1).unsqueeze_(-1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:, :x.size(1), ...]
return self.dropout(x)
class CameraPoseEncoder(nn.Module):
def __init__(self,
downscale_factor,
channels=[320, 640, 1280, 1280],
nums_rb=3,
cin=64,
ksize=3,
sk=False,
use_conv=True,
compression_factor=1,
temporal_attention_nhead=8,
attention_block_types=("Temporal_Self", ),
temporal_position_encoding=False,
temporal_position_encoding_max_len=16,
rescale_output_factor=1.0):
super(CameraPoseEncoder, self).__init__()
self.unshuffle = nn.PixelUnshuffle(downscale_factor)
self.channels = channels
self.nums_rb = nums_rb
self.encoder_down_conv_blocks = nn.ModuleList()
self.encoder_down_attention_blocks = nn.ModuleList()
for i in range(len(channels)):
conv_layers = nn.ModuleList()
temporal_attention_layers = nn.ModuleList()
for j in range(nums_rb):
if j == 0 and i != 0:
in_dim = channels[i - 1]
out_dim = int(channels[i] / compression_factor)
conv_layer = ResnetBlock(in_dim, out_dim, down=True, ksize=ksize, sk=sk, use_conv=use_conv)
elif j == 0:
in_dim = channels[0]
out_dim = int(channels[i] / compression_factor)
conv_layer = ResnetBlock(in_dim, out_dim, down=False, ksize=ksize, sk=sk, use_conv=use_conv)
elif j == nums_rb - 1:
in_dim = channels[i] / compression_factor
out_dim = channels[i]
conv_layer = ResnetBlock(in_dim, out_dim, down=False, ksize=ksize, sk=sk, use_conv=use_conv)
else:
in_dim = int(channels[i] / compression_factor)
out_dim = int(channels[i] / compression_factor)
conv_layer = ResnetBlock(in_dim, out_dim, down=False, ksize=ksize, sk=sk, use_conv=use_conv)
temporal_attention_layer = TemporalTransformerBlock(dim=out_dim,
num_attention_heads=temporal_attention_nhead,
attention_head_dim=int(out_dim / temporal_attention_nhead),
attention_block_types=attention_block_types,
dropout=0.0,
cross_attention_dim=None,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
rescale_output_factor=rescale_output_factor)
conv_layers.append(conv_layer)
temporal_attention_layers.append(temporal_attention_layer)
self.encoder_down_conv_blocks.append(conv_layers)
self.encoder_down_attention_blocks.append(temporal_attention_layers)
self.encoder_conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
@property
def dtype(self) -> torch.dtype:
"""
`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
"""
return get_parameter_dtype(self)
def forward(self, x):
# unshuffle
bs = x.shape[0]
x = rearrange(x, "b f c h w -> (b f) c h w")
x = self.unshuffle(x)
# extract features
features = []
x = self.encoder_conv_in(x)
for res_block, attention_block in zip(self.encoder_down_conv_blocks, self.encoder_down_attention_blocks):
for res_layer, attention_layer in zip(res_block, attention_block):
x = res_layer(x)
h, w = x.shape[-2:]
x = rearrange(x, '(b f) c h w -> (b h w) f c', b=bs)
x = attention_layer(x)
x = rearrange(x, '(b h w) f c -> (b f) c h w', h=h, w=w)
features.append(rearrange(x, '(b f) c h w -> b c f h w', b=bs))
return features