File size: 8,311 Bytes
587665f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 |
import torch
import torch.nn as nn
from torch.nn.functional import interpolate
import math
from tqdm import tqdm
from modules.feature_extactor import Extractor
from modules.half_warper import HalfWarper
from modules.cupy_module.nedt import NEDT
from modules.flow_models.flow_models import (
RAFTFineFlow,
PWCFineFlow
)
from modules.synthesizer import Synthesis
class FeatureWarper(nn.Module):
def __init__(
self,
in_channels: int = 3,
channels: list[int] = [32, 64, 128, 256],
):
super().__init__()
channels = [in_channels + 1] + channels
self.half_warper = HalfWarper()
self.feature_extractor = Extractor(channels)
self.nedt = NEDT()
def forward(
self,
I0: torch.Tensor,
I1: torch.Tensor,
flow0to1: torch.Tensor,
flow1to0: torch.Tensor,
tau: torch.Tensor = None
) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
assert tau.shape == (I0.shape[0], 2), "tau shape must be (batch, 2)"
flow0tot = tau[:, 0][:, None, None, None] * flow0to1
flow1tot = tau[:, 1][:, None, None, None] * flow1to0
I0 = torch.cat([I0, self.nedt(I0)], dim=1)
I1 = torch.cat([I1, self.nedt(I1)], dim=1)
z0to1, z1to0 = HalfWarper.z_metric(I0, I1, flow0to1, flow1to0)
base0, base1 = self.half_warper(I0, I1, flow0tot, flow1tot, z0to1, z1to0)
warped0, warped1 = [base0], [base1]
features0 = self.feature_extractor(I0)
features1 = self.feature_extractor(I1)
for feat0, feat1 in zip(features0, features1):
f0 = interpolate(flow0tot, size=feat0.shape[2:], mode='bilinear', align_corners=False)
f1 = interpolate(flow1tot, size=feat0.shape[2:], mode='bilinear', align_corners=False)
z0 = interpolate(z0to1, size=feat0.shape[2:], mode='bilinear', align_corners=False)
z1 = interpolate(z1to0, size=feat0.shape[2:], mode='bilinear', align_corners=False)
w0, w1 = self.half_warper(feat0, feat1, f0, f1, z0, z1)
warped0.append(w0)
warped1.append(w1)
return warped0, warped1
class MultiInputResShift(nn.Module):
def __init__(
self,
kappa: float=2.0,
p: float =0.3,
min_noise_level: float=0.04,
etas_end: float=0.99,
timesteps: int=15,
flow_model: str = 'raft',
flow_kwargs: dict = {},
warping_kwargs: dict = {},
synthesis_kwargs: dict = {}
):
super().__init__()
self.timesteps = timesteps
self.kappa = kappa
self.eta_partition = None
sqrt_eta_1 = min(min_noise_level / kappa, min_noise_level, math.sqrt(0.001))
b0 = math.exp(1/float(timesteps - 1) * math.log(etas_end/sqrt_eta_1))
base = torch.ones(timesteps)*b0
beta = ((torch.linspace(0,1,timesteps))**p)*(timesteps-1)
sqrt_eta = torch.pow(base, beta) * sqrt_eta_1
self.register_buffer("sqrt_sum_eta", sqrt_eta)
self.register_buffer("sum_eta", sqrt_eta**2)
sum_prev_eta = torch.roll(self.sum_eta, 1)
sum_prev_eta[0] = 0
self.register_buffer("sum_prev_eta", sum_prev_eta)
self.register_buffer("sum_alpha", self.sum_eta - self.sum_prev_eta)
self.register_buffer("backward_mean_c1", self.sum_prev_eta / self.sum_eta)
self.register_buffer("backward_mean_c2", self.sum_alpha / self.sum_eta)
self.register_buffer("backward_std", self.kappa*torch.sqrt(self.sum_prev_eta*self.sum_alpha/self.sum_eta))
if flow_model == 'raft':
self.flow_model = RAFTFineFlow(**flow_kwargs)
elif flow_model == 'pwc':
self.flow_model = PWCFineFlow(**flow_kwargs)
else:
raise ValueError(f"Flow model {flow_model} not supported")
self.feature_warper = FeatureWarper(**warping_kwargs)
self.synthesis = Synthesis(**synthesis_kwargs)
def forward_process(
self,
x: torch.Tensor | None,
Y: list[torch.Tensor],
tau: torch.Tensor | float | None,
t: torch.Tensor | int
) -> torch.Tensor:
if tau is None:
tau: torch.Tensor = torch.full((x.shape[0], len(Y)), 0.5, device=x.device, dtype=x.dtype)
elif isinstance(tau, float):
assert tau >= 0 and tau <= 1, "tau must be between 0 and 1"
tau: torch.Tensor = torch.cat([
torch.full((x.shape[0], 1), tau, device=x.device, dtype=x.dtype),
torch.full((x.shape[0], 1), 1 - tau, device=x.device, dtype=x.dtype)
], dim=1)
if not torch.is_tensor(t):
t: torch.Tensor = torch.tensor([t], device=x.device, dtype=torch.long)
if x is None:
x: torch.Tensor = torch.zeros_like(Y[0])
eta = self.sum_eta[t][:, None] * tau
eta = eta[:, :, None, None, None].transpose(0, 1)
e_i = torch.stack([y - x for y in Y])
mean = x + (eta*e_i).sum(dim=0)
sqrt_sum_eta = self.sqrt_sum_eta[t][:, None, None, None]
std = self.kappa*sqrt_sum_eta
epsilon = torch.randn_like(x)
return mean + std*epsilon
@torch.inference_mode()
def reverse_process(
self,
Y: list[torch.Tensor],
tau: torch.Tensor | float,
flows: list[torch.Tensor] | None = None,
) -> torch.Tensor:
y = Y[0]
batch, device, dtype = y.shape[0], y.device, y.dtype
if isinstance(tau, float):
assert tau >= 0 and tau <= 1, "tau must be between 0 and 1"
tau: torch.Tensor = torch.cat([
torch.full((batch, 1), tau, device=device, dtype=dtype),
torch.full((batch, 1), 1 - tau, device=device, dtype=dtype)
], dim=1)
if flows is None:
flow0to1, flow1to0 = self.flow_model(Y[0], Y[1])
else:
flow0to1, flow1to0 = flows
warp0to1, warp1to0 = self.feature_warper(Y[0], Y[1], flow0to1, flow1to0, tau)
T = torch.tensor([self.timesteps-1,] * batch, device=device, dtype=torch.long)
x = self.forward_process(torch.zeros_like(Y[0]), [warp0to1[0][:, :3], warp1to0[0][:, :3]], tau, T)
pbar = tqdm(total=self.timesteps, desc="Reversing Process")
for i in reversed(range(self.timesteps)):
t = torch.ones(batch, device = device, dtype=torch.long) * i
predicted_x0 = self.synthesis(x, warp0to1, warp1to0, t)
mean_c1 = self.backward_mean_c1[t][:, None, None, None]
mean_c2 = self.backward_mean_c2[t][:, None, None, None]
std = self.backward_std[t][:, None, None, None]
eta = self.sum_eta[t][:, None] * tau
prev_eta = self.sum_prev_eta[t][:, None] * tau
eta = eta[:, :, None, None, None].transpose(0, 1)
prev_eta = prev_eta[:, :, None, None, None].transpose(0, 1)
e_i = torch.stack([y - predicted_x0 for y in Y])
mean = (
mean_c1*(x + (eta*e_i).sum(dim=0))
+ mean_c2*predicted_x0
- (prev_eta*e_i).sum(dim=0)
)
x = mean + std*torch.randn_like(x)
pbar.update(1)
pbar.close()
return x
# Training Step Only
def forward(
self,
I0: torch.Tensor,
It: torch.Tensor,
I1: torch.Tensor,
flow1to0: torch.Tensor | None = None,
flow0to1: torch.Tensor | None = None,
tau: torch.Tensor | None = None,
t: torch.Tensor | None = None
) -> torch.Tensor:
if tau is None:
tau = torch.full((It.shape[0], 2), 0.5, device=It.device, dtype=It.dtype)
if flow0to1 is None or flow1to0 is None:
flow0to1, flow1to0 = self.flow_model(I0, I1)
if t is None:
t = torch.randint(low=1, high=self.timesteps, size=(It.shape[0],), device=It.device, dtype=torch.long)
warp0to1, warp1to0 = self.feature_warper(I0, I1, flow0to1, flow1to0, tau)
x_t = self.forward_process(It, [warp0to1[0][:, :3], warp1to0[0][:, :3]], tau, t)
predicted_It = self.synthesis(x_t, warp0to1, warp1to0, t)
return predicted_It
|