mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-06-27 19:32:05 +08:00
Kashif Rasul
270 lines
9.3 KiB
Python
270 lines
9.3 KiB
Python
from functools import partial
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from inspect import isfunction
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import numpy as np
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import torch
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from torch import nn, einsum
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import torch.nn.functional as F
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def default(val, d):
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if val is not None:
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return val
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return d() if isfunction(d) else d
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def extract(a, t, x_shape):
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b, *_ = t.shape
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out = a.gather(-1, t)
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return out.reshape(b, *((1,) * (len(x_shape) - 1)))
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def noise_like(shape, device, repeat=False):
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repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
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shape[0], *((1,) * (len(shape) - 1))
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)
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noise = lambda: torch.randn(shape, device=device)
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return repeat_noise() if repeat else noise()
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def cosine_beta_schedule(timesteps, s=0.008):
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"""
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cosine schedule
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as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
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"""
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steps = timesteps + 1
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x = np.linspace(0, timesteps, steps)
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alphas_cumprod = np.cos(((x / timesteps) + s) / (1 + s) * np.pi * 0.5) ** 2
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alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
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betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
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return np.clip(betas, 0, 0.999)
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class GaussianDiffusion(nn.Module):
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def __init__(
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self,
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denoise_fn,
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input_size,
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beta_end=0.1,
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diff_steps=100,
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loss_type="l2",
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betas=None,
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beta_schedule="linear",
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):
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super().__init__()
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self.denoise_fn = denoise_fn
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self.input_size = input_size
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self.__scale = None
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if betas is not None:
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betas = (
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betas.detach().cpu().numpy()
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if isinstance(betas, torch.Tensor)
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else betas
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)
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else:
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if beta_schedule == "linear":
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betas = np.linspace(1e-4, beta_end, diff_steps)
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elif beta_schedule == "quad":
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betas = np.linspace(1e-4 ** 0.5, beta_end ** 0.5, diff_steps) ** 2
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elif beta_schedule == "const":
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betas = beta_end * np.ones(diff_steps)
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elif beta_schedule == "jsd": # 1/T, 1/(T-1), 1/(T-2), ..., 1
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betas = 1.0 / np.linspace(diff_steps, 1, diff_steps)
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elif beta_schedule == "sigmoid":
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betas = np.linspace(-6, 6, diff_steps)
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betas = (beta_end - 1e-4) / (np.exp(-betas) + 1) + 1e-4
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elif beta_schedule == "cosine":
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betas = cosine_beta_schedule(diff_steps)
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else:
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raise NotImplementedError(beta_schedule)
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alphas = 1.0 - betas
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alphas_cumprod = np.cumprod(alphas, axis=0)
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alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
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(timesteps,) = betas.shape
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self.num_timesteps = int(timesteps)
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self.loss_type = loss_type
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to_torch = partial(torch.tensor, dtype=torch.float32)
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self.register_buffer("betas", to_torch(betas))
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self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
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self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
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self.register_buffer(
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"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
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)
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self.register_buffer(
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"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
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)
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self.register_buffer(
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"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
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)
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self.register_buffer(
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"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
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)
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# calculations for posterior q(x_{t-1} | x_t, x_0)
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posterior_variance = (
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betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
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)
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# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
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self.register_buffer("posterior_variance", to_torch(posterior_variance))
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# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
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self.register_buffer(
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"posterior_log_variance_clipped",
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to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
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)
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self.register_buffer(
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"posterior_mean_coef1",
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to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
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)
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self.register_buffer(
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"posterior_mean_coef2",
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to_torch(
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(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
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),
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)
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@property
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def scale(self):
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return self.__scale
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@scale.setter
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def scale(self, scale):
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self.__scale = scale
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def q_mean_variance(self, x_start, t):
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mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
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variance = extract(1.0 - self.alphas_cumprod, t, x_start.shape)
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log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
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return mean, variance, log_variance
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def predict_start_from_noise(self, x_t, t, noise):
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return (
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extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
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- extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
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)
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def q_posterior(self, x_start, x_t, t):
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posterior_mean = (
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extract(self.posterior_mean_coef1, t, x_t.shape) * x_start
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+ extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
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)
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posterior_variance = extract(self.posterior_variance, t, x_t.shape)
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posterior_log_variance_clipped = extract(
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self.posterior_log_variance_clipped, t, x_t.shape
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)
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return posterior_mean, posterior_variance, posterior_log_variance_clipped
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def p_mean_variance(self, x, cond, t, clip_denoised: bool):
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x_recon = self.predict_start_from_noise(
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x, t=t, noise=self.denoise_fn(x, t, cond=cond)
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)
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if clip_denoised:
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x_recon.clamp_(-1.0, 1.0)
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model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
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x_start=x_recon, x_t=x, t=t
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)
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return model_mean, posterior_variance, posterior_log_variance
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@torch.no_grad()
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def p_sample(self, x, cond, t, clip_denoised=False, repeat_noise=False):
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b, *_, device = *x.shape, x.device
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model_mean, _, model_log_variance = self.p_mean_variance(
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x=x, cond=cond, t=t, clip_denoised=clip_denoised
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)
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noise = noise_like(x.shape, device, repeat_noise)
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# no noise when t == 0
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nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
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return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
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@torch.no_grad()
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def p_sample_loop(self, shape, cond):
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device = self.betas.device
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b = shape[0]
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img = torch.randn(shape, device=device)
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for i in reversed(range(0, self.num_timesteps)):
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img = self.p_sample(
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img, cond, torch.full((b,), i, device=device, dtype=torch.long)
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)
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return img
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@torch.no_grad()
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def sample(self, sample_shape=torch.Size(), cond=None):
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if cond is not None:
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shape = cond.shape[:-1] + (self.input_size,)
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# TODO reshape cond to (B*T, 1, -1)
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else:
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shape = sample_shape
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x_hat = self.p_sample_loop(shape, cond) # TODO reshape x_hat to (B,T,-1)
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if self.scale is not None:
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x_hat *= self.scale
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return x_hat
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@torch.no_grad()
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def interpolate(self, x1, x2, t=None, lam=0.5):
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b, *_, device = *x1.shape, x1.device
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t = default(t, self.num_timesteps - 1)
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assert x1.shape == x2.shape
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t_batched = torch.stack([torch.tensor(t, device=device)] * b)
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xt1, xt2 = map(lambda x: self.q_sample(x, t=t_batched), (x1, x2))
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img = (1 - lam) * xt1 + lam * xt2
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for i in reversed(range(0, t)):
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img = self.p_sample(
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img, torch.full((b,), i, device=device, dtype=torch.long)
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)
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return img
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def q_sample(self, x_start, t, noise=None):
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noise = default(noise, lambda: torch.randn_like(x_start))
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return (
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extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
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+ extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
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)
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def p_losses(self, x_start, cond, t, noise=None):
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noise = default(noise, lambda: torch.randn_like(x_start))
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x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
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x_recon = self.denoise_fn(x_noisy, t, cond=cond)
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if self.loss_type == "l1":
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loss = F.l1_loss(x_recon, noise)
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elif self.loss_type == "l2":
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loss = F.mse_loss(x_recon, noise)
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elif self.loss_type == "huber":
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loss = F.smooth_l1_loss(x_recon, noise)
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else:
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raise NotImplementedError()
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return loss
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def log_prob(self, x, cond, *args, **kwargs):
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if self.scale is not None:
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x /= self.scale
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B, T, _ = x.shape
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time = torch.randint(0, self.num_timesteps, (B * T,), device=x.device).long()
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loss = self.p_losses(
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x.reshape(B * T, 1, -1), cond.reshape(B * T, 1, -1), time, *args, **kwargs
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)
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return loss
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