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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import numpy as np
import scipy.signal
import torch
from torch_utils import persistence
from torch_utils import misc
from torch_utils.ops import upfirdn2d
from torch_utils.ops import grid_sample_gradfix
from torch_utils.ops import conv2d_gradfix
from training.diffaug import DiffAugment
from training.adaaug import AdaAugment
#----------------------------------------------------------------------------
# Helpers for doing defusion process.
def get_beta_schedule(beta_schedule, beta_start, beta_end, num_diffusion_timesteps):
def sigmoid(x):
return 1 / (np.exp(-x) + 1)
def continuous_t_beta(t, T):
b_max = 5.
b_min = 0.1
alpha = np.exp(-b_min / T - 0.5 * (b_max - b_min) * (2 * t - 1) / T ** 2)
return 1 - alpha
if beta_schedule == "continuous_t":
betas = continuous_t_beta(np.arange(1, num_diffusion_timesteps+1), num_diffusion_timesteps)
elif beta_schedule == "quad":
betas = (
np.linspace(
beta_start ** 0.5,
beta_end ** 0.5,
num_diffusion_timesteps,
dtype=np.float64,
)
** 2
)
elif beta_schedule == "linear":
betas = np.linspace(
beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
)
elif beta_schedule == "const":
betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
elif beta_schedule == "jsd": # 1/T, 1/(T-1), 1/(T-2), ..., 1
betas = 1.0 / np.linspace(
num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
)
elif beta_schedule == "sigmoid":
betas = np.linspace(-6, 6, num_diffusion_timesteps)
betas = sigmoid(betas) * (beta_end - beta_start) + beta_start
elif beta_schedule == 'cosine':
"""
cosine schedule
as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
"""
s = 0.008
steps = num_diffusion_timesteps + 1
x = np.linspace(0, steps, steps)
alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
betas_clipped = np.clip(betas, a_min=0, a_max=0.999)
return betas_clipped
else:
raise NotImplementedError(beta_schedule)
assert betas.shape == (num_diffusion_timesteps,)
return betas
def q_sample(x_0, alphas_bar_sqrt, one_minus_alphas_bar_sqrt, t, noise_type='gauss', noise_std=1.0):
if noise_type == 'gauss':
noise = torch.randn_like(x_0, device=x_0.device) * noise_std
elif noise_type == 'bernoulli':
noise = (torch.bernoulli(torch.ones_like(x_0) * 0.5) * 2 - 1.) * noise_std
else:
raise NotImplementedError(noise_type)
alphas_t_sqrt = alphas_bar_sqrt[t].view(-1, 1, 1, 1)
one_minus_alphas_bar_t_sqrt = one_minus_alphas_bar_sqrt[t].view(-1, 1, 1, 1)
x_t = alphas_t_sqrt * x_0 + one_minus_alphas_bar_t_sqrt * noise
return x_t
def q_sample_c(x_0, alphas_bar_sqrt, one_minus_alphas_bar_sqrt, t, noise_type='gauss', noise_std=1.0):
batch_size, num_channels, _, _ = x_0.shape
if noise_type == 'gauss':
noise = torch.randn_like(x_0, device=x_0.device) * noise_std
elif noise_type == 'bernoulli':
noise = (torch.bernoulli(torch.ones_like(x_0) * 0.5) * 2 - 1.) * noise_std
else:
raise NotImplementedError(noise_type)
alphas_t_sqrt = alphas_bar_sqrt[t].view(batch_size, num_channels, 1, 1)
one_minus_alphas_bar_t_sqrt = one_minus_alphas_bar_sqrt[t].view(batch_size, num_channels, 1, 1)
x_t = alphas_t_sqrt * x_0 + one_minus_alphas_bar_t_sqrt * noise
return x_t
class Identity(torch.nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
@persistence.persistent_class
class AugmentPipe(torch.nn.Module):
def __init__(self,
beta_schedule='linear', beta_start=1e-4, beta_end=2e-2, t_min=10, t_max=1000,
noise_std=0.05, aug='NO', ada_maxp=None, ts_dist='priority', update_beta=True,
):
super().__init__()
self.p = 0.0 # Overall multiplier for augmentation probability.
self.aug_type = aug
self.ada_maxp = ada_maxp
self.noise_type = self.base_noise_type = 'gauss'
self.beta_schedule = beta_schedule
self.beta_start = beta_start
self.beta_end = beta_end
self.t_min = t_min
self.t_max = t_max
self.t_add = int(t_max - t_min)
self.ts_dist = ts_dist
# Image-space corruptions.
self.noise_std = float(noise_std) # Standard deviation of additive RGB noise.
self.noise_type = "gauss"
if aug == 'ADA':
self.aug = AdaAugment(p=0.0)
elif aug == 'DIFF':
self.aug = DiffAugment()
else:
self.aug = Identity()
self.update_beta = update_beta
if not update_beta:
self.set_diffusion_process(t_max, beta_schedule)
self.update_T()
def set_diffusion_process(self, t, beta_schedule):
betas = get_beta_schedule(
beta_schedule=beta_schedule,
beta_start=self.beta_start,
beta_end=self.beta_end,
num_diffusion_timesteps=t,
)
betas = self.betas = torch.from_numpy(betas).float()
self.num_timesteps = betas.shape[0]
alphas = self.alphas = 1.0 - betas
alphas_cumprod = torch.cat([torch.tensor([1.]), alphas.cumprod(dim=0)])
self.alphas_bar_sqrt = torch.sqrt(alphas_cumprod)
self.one_minus_alphas_bar_sqrt = torch.sqrt(1 - alphas_cumprod)
def update_T(self):
if self.aug_type == 'ADA':
_p = min(self.p, self.ada_maxp) if self.ada_maxp else self.p
self.aug.p.copy_(torch.tensor(_p))
t_adjust = round(self.p * self.t_add)
t = np.clip(int(self.t_min + t_adjust), a_min=self.t_min, a_max=self.t_max)
if self.update_beta:
if self.beta_schedule == 'linear_cosine':
if t >= 500:
self.set_diffusion_process(t, 'cosine')
else:
self.set_diffusion_process(t, 'linear')
else:
self.set_diffusion_process(t, self.beta_schedule)
# sampling t
self.t_epl = np.zeros(64, dtype=np.int)
diffusion_ind = 32
t_diffusion = np.zeros((diffusion_ind,)).astype(np.int)
if self.ts_dist == 'priority':
prob_t = np.arange(t) / np.arange(t).sum()
t_diffusion = np.random.choice(np.arange(1, t + 1), size=diffusion_ind, p=prob_t)
elif self.ts_dist == 'uniform':
t_diffusion = np.random.choice(np.arange(1, t + 1), size=diffusion_ind)
self.t_epl[:diffusion_ind] = t_diffusion
def forward(self, x_0):
x_0 = self.aug(x_0)
assert isinstance(x_0, torch.Tensor) and x_0.ndim == 4
batch_size, num_channels, height, width = x_0.shape
device = x_0.device
alphas_bar_sqrt = self.alphas_bar_sqrt.to(device)
one_minus_alphas_bar_sqrt = self.one_minus_alphas_bar_sqrt.to(device)
t = torch.from_numpy(np.random.choice(self.t_epl, size=batch_size, replace=True)).to(device)
x_t = q_sample(x_0, alphas_bar_sqrt, one_minus_alphas_bar_sqrt, t,
noise_type=self.noise_type,
noise_std=self.noise_std)
# x_t = self.aug(x_t)
return x_t, t.view(-1, 1)
#----------------------------------------------------------------------------
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