vsr/models/upscaling.py

import torch
import torch.nn as nn
import numpy as np
from functools import partial
from inspect import isfunction

def exists(x):
    return x is not None

def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d

def extract_into_tensor(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))

def make_beta_schedule(n_timestep, linear_start=1e-4, linear_end=2e-2):
    betas = (
            torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
    )
    return betas.numpy()

class AbstractLowScaleModel(nn.Module):
    # for concatenating a downsampled image to the latent representation
    def __init__(self, noise_schedule_config=None):
        super(AbstractLowScaleModel, self).__init__()
        if noise_schedule_config is not None:
            self.register_schedule(**noise_schedule_config)

    def register_schedule(self, timesteps=1000, linear_start=1e-4, linear_end=2e-2):
        betas = make_beta_schedule(timesteps, linear_start=linear_start, linear_end=linear_end)
        alphas = 1. - betas
        alphas_cumprod = np.cumprod(alphas, axis=0)
        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])

        timesteps, = betas.shape
        self.num_timesteps = int(timesteps)
        self.linear_start = linear_start
        self.linear_end = linear_end
        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'

        to_torch = partial(torch.tensor, dtype=torch.float32)

        self.register_buffer('betas', to_torch(betas))
        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))

    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)

    def forward(self, x):
        return x, None

    def decode(self, x):
        return x


class SimpleImageConcat(AbstractLowScaleModel):
    # no noise level conditioning
    def __init__(self):
        super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
        self.max_noise_level = 0

    def forward(self, x):
        # fix to constant noise level
        return x, torch.zeros(x.shape[0], device=x.device).long()


class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
    def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
        super().__init__(noise_schedule_config=noise_schedule_config)
        self.max_noise_level = max_noise_level

    def forward(self, x, noise_level=None):
        if noise_level is None:
            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
        else:
            assert isinstance(noise_level, torch.Tensor)
        z = self.q_sample(x, noise_level)
        return z, noise_level