853 lines
34 KiB
Python
853 lines
34 KiB
Python
import torch.multiprocessing as mp
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import torch.nn as nn
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import torch.optim as optim
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import torch.utils.data
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import argparse
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from torch.distributions import Normal
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from utils.file_utils import *
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from utils.visualize import *
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from model.pvcnn_generation import PVCNN2Base
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import torch.distributed as dist
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from datasets.shapenet_data_pc import ShapeNet15kPointClouds
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'''
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some utils
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'''
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def rotation_matrix(axis, theta):
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"""
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Return the rotation matrix associated with counterclockwise rotation about
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the given axis by theta radians.
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"""
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axis = np.asarray(axis)
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axis = axis / np.sqrt(np.dot(axis, axis))
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a = np.cos(theta / 2.0)
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b, c, d = -axis * np.sin(theta / 2.0)
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aa, bb, cc, dd = a * a, b * b, c * c, d * d
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bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
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return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
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[2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
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[2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
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def rotate(vertices, faces):
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'''
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vertices: [numpoints, 3]
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'''
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M = rotation_matrix([0, 1, 0], np.pi / 2).transpose()
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N = rotation_matrix([1, 0, 0], -np.pi / 4).transpose()
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K = rotation_matrix([0, 0, 1], np.pi).transpose()
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v, f = vertices[:,[1,2,0]].dot(M).dot(N).dot(K), faces[:,[1,2,0]]
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return v, f
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def norm(v, f):
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v = (v - v.min())/(v.max() - v.min()) - 0.5
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return v, f
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def getGradNorm(net):
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pNorm = torch.sqrt(sum(torch.sum(p ** 2) for p in net.parameters()))
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gradNorm = torch.sqrt(sum(torch.sum(p.grad ** 2) for p in net.parameters()))
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return pNorm, gradNorm
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def weights_init(m):
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"""
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xavier initialization
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"""
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classname = m.__class__.__name__
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if classname.find('Conv') != -1 and m.weight is not None:
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torch.nn.init.xavier_normal_(m.weight)
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elif classname.find('BatchNorm') != -1:
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m.weight.data.normal_()
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m.bias.data.fill_(0)
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'''
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models
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'''
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def normal_kl(mean1, logvar1, mean2, logvar2):
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"""
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KL divergence between normal distributions parameterized by mean and log-variance.
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"""
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return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2)
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+ (mean1 - mean2)**2 * torch.exp(-logvar2))
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def discretized_gaussian_log_likelihood(x, *, means, log_scales):
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# Assumes data is integers [0, 1]
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assert x.shape == means.shape == log_scales.shape
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px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
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centered_x = x - means
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inv_stdv = torch.exp(-log_scales)
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plus_in = inv_stdv * (centered_x + 0.5)
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cdf_plus = px0.cdf(plus_in)
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min_in = inv_stdv * (centered_x - .5)
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cdf_min = px0.cdf(min_in)
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log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus)*1e-12))
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log_one_minus_cdf_min = torch.log(torch.max(1. - cdf_min, torch.ones_like(cdf_min)*1e-12))
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cdf_delta = cdf_plus - cdf_min
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log_probs = torch.where(
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x < 0.001, log_cdf_plus,
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torch.where(x > 0.999, log_one_minus_cdf_min,
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torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta)*1e-12))))
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assert log_probs.shape == x.shape
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return log_probs
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class GaussianDiffusion:
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def __init__(self,betas, loss_type, model_mean_type, model_var_type):
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self.loss_type = loss_type
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self.model_mean_type = model_mean_type
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self.model_var_type = model_var_type
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assert isinstance(betas, np.ndarray)
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self.np_betas = betas = betas.astype(np.float64) # computations here in float64 for accuracy
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assert (betas > 0).all() and (betas <= 1).all()
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timesteps, = betas.shape
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self.num_timesteps = int(timesteps)
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# initialize twice the actual length so we can keep running for eval
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# betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
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alphas = 1. - betas
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alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
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alphas_cumprod_prev = torch.from_numpy(np.append(1., alphas_cumprod[:-1])).float()
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self.betas = torch.from_numpy(betas).float()
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self.alphas_cumprod = alphas_cumprod.float()
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self.alphas_cumprod_prev = alphas_cumprod_prev.float()
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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
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self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - alphas_cumprod).float()
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self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
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self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
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self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / alphas_cumprod - 1).float()
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betas = torch.from_numpy(betas).float()
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alphas = torch.from_numpy(alphas).float()
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# calculations for posterior q(x_{t-1} | x_t, x_0)
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posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
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# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
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self.posterior_variance = 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.posterior_log_variance_clipped = torch.log(torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance)))
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self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
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self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod)
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@staticmethod
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def _extract(a, t, x_shape):
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"""
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Extract some coefficients at specified timesteps,
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then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
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"""
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bs, = t.shape
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assert x_shape[0] == bs
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out = torch.gather(a, 0, t)
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assert out.shape == torch.Size([bs])
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return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
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def q_mean_variance(self, x_start, t):
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mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
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variance = self._extract(1. - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
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log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
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return mean, variance, log_variance
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def q_sample(self, x_start, t, noise=None):
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"""
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Diffuse the data (t == 0 means diffused for 1 step)
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"""
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if noise is None:
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noise = torch.randn(x_start.shape, device=x_start.device)
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assert noise.shape == x_start.shape
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return (
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self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
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self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
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)
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def q_posterior_mean_variance(self, x_start, x_t, t):
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"""
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Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
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"""
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assert x_start.shape == x_t.shape
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posterior_mean = (
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self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start +
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self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
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)
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posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
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posterior_log_variance_clipped = self._extract(self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape)
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assert (posterior_mean.shape[0] == posterior_variance.shape[0] == posterior_log_variance_clipped.shape[0] ==
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x_start.shape[0])
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return posterior_mean, posterior_variance, posterior_log_variance_clipped
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def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
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model_output = denoise_fn(data, t)
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if self.model_var_type in ['fixedsmall', 'fixedlarge']:
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# below: only log_variance is used in the KL computations
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model_variance, model_log_variance = {
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# for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
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'fixedlarge': (self.betas.to(data.device),
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torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device)),
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'fixedsmall': (self.posterior_variance.to(data.device), self.posterior_log_variance_clipped.to(data.device)),
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}[self.model_var_type]
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model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(data)
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model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(data)
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else:
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raise NotImplementedError(self.model_var_type)
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if self.model_mean_type == 'eps':
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x_recon = self._predict_xstart_from_eps(data, t=t, eps=model_output)
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if clip_denoised:
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x_recon = torch.clamp(x_recon, -.5, .5)
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model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data, t=t)
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else:
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raise NotImplementedError(self.loss_type)
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assert model_mean.shape == x_recon.shape == data.shape
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assert model_variance.shape == model_log_variance.shape == data.shape
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if return_pred_xstart:
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return model_mean, model_variance, model_log_variance, x_recon
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else:
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return model_mean, model_variance, model_log_variance
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def _predict_xstart_from_eps(self, x_t, t, eps):
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assert x_t.shape == eps.shape
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return (
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self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t -
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self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
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)
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''' samples '''
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def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
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"""
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Sample from the model
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"""
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model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(denoise_fn, data=data, t=t, clip_denoised=clip_denoised,
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return_pred_xstart=True)
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noise = noise_fn(size=data.shape, dtype=data.dtype, device=data.device)
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assert noise.shape == data.shape
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# no noise when t == 0
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nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(data.shape) - 1))
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sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
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assert sample.shape == pred_xstart.shape
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return (sample, pred_xstart) if return_pred_xstart else sample
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def p_sample_loop(self, denoise_fn, shape, device,
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noise_fn=torch.randn, clip_denoised=True, keep_running=False):
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"""
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Generate samples
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keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
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"""
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assert isinstance(shape, (tuple, list))
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img_t = noise_fn(size=shape, dtype=torch.float, device=device)
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for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
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t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
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img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t,t=t_, noise_fn=noise_fn,
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clip_denoised=clip_denoised, return_pred_xstart=False)
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assert img_t.shape == shape
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return img_t
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def p_sample_loop_trajectory(self, denoise_fn, shape, device, freq,
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noise_fn=torch.randn,clip_denoised=True, keep_running=False):
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"""
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Generate samples, returning intermediate images
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Useful for visualizing how denoised images evolve over time
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Args:
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repeat_noise_steps (int): Number of denoising timesteps in which the same noise
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is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
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"""
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assert isinstance(shape, (tuple, list))
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total_steps = self.num_timesteps if not keep_running else len(self.betas)
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img_t = noise_fn(size=shape, dtype=torch.float, device=device)
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imgs = [img_t]
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for t in reversed(range(0,total_steps)):
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t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
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img_t = self.p_sample(denoise_fn=denoise_fn, data=img_t, t=t_, noise_fn=noise_fn,
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clip_denoised=clip_denoised,
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return_pred_xstart=False)
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if t % freq == 0 or t == total_steps-1:
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imgs.append(img_t)
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assert imgs[-1].shape == shape
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return imgs
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'''losses'''
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def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
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true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start=data_start, x_t=data_t, t=t)
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model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
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denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True)
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kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
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kl = kl.mean(dim=list(range(1, len(data_start.shape)))) / np.log(2.)
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return (kl, pred_xstart) if return_pred_xstart else kl
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def p_losses(self, denoise_fn, data_start, t, noise=None):
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"""
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Training loss calculation
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"""
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B, D, N = data_start.shape
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assert t.shape == torch.Size([B])
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if noise is None:
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noise = torch.randn(data_start.shape, dtype=data_start.dtype, device=data_start.device)
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assert noise.shape == data_start.shape and noise.dtype == data_start.dtype
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data_t = self.q_sample(x_start=data_start, t=t, noise=noise)
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if self.loss_type == 'mse':
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# predict the noise instead of x_start. seems to be weighted naturally like SNR
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eps_recon = denoise_fn(data_t, t)
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assert data_t.shape == data_start.shape
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assert eps_recon.shape == torch.Size([B, D, N])
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assert eps_recon.shape == data_start.shape
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losses = ((noise - eps_recon)**2).mean(dim=list(range(1, len(data_start.shape))))
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elif self.loss_type == 'kl':
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losses = self._vb_terms_bpd(
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denoise_fn=denoise_fn, data_start=data_start, data_t=data_t, t=t, clip_denoised=False,
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return_pred_xstart=False)
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else:
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raise NotImplementedError(self.loss_type)
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assert losses.shape == torch.Size([B])
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return losses
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'''debug'''
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def _prior_bpd(self, x_start):
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with torch.no_grad():
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B, T = x_start.shape[0], self.num_timesteps
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t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T-1)
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qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
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kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance,
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mean2=torch.tensor([0.]).to(qt_mean), logvar2=torch.tensor([0.]).to(qt_log_variance))
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assert kl_prior.shape == x_start.shape
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return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.)
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def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
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with torch.no_grad():
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B, T = x_start.shape[0], self.num_timesteps
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vals_bt_, mse_bt_= torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
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for t in reversed(range(T)):
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t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
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# Calculate VLB term at the current timestep
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new_vals_b, pred_xstart = self._vb_terms_bpd(
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denoise_fn, data_start=x_start, data_t=self.q_sample(x_start=x_start, t=t_b), t=t_b,
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clip_denoised=clip_denoised, return_pred_xstart=True)
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# MSE for progressive prediction loss
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assert pred_xstart.shape == x_start.shape
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new_mse_b = ((pred_xstart-x_start)**2).mean(dim=list(range(1, len(x_start.shape))))
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assert new_vals_b.shape == new_mse_b.shape == torch.Size([B])
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# Insert the calculated term into the tensor of all terms
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mask_bt = t_b[:, None]==torch.arange(T, device=t_b.device)[None, :].float()
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vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
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mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
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assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])
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prior_bpd_b = self._prior_bpd(x_start)
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total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
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assert vals_bt_.shape == mse_bt_.shape == torch.Size([B, T]) and \
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total_bpd_b.shape == prior_bpd_b.shape == torch.Size([B])
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return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()
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class PVCNN2(PVCNN2Base):
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sa_blocks = [
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|
((32, 2, 32), (1024, 0.1, 32, (32, 64))),
|
|
((64, 3, 16), (256, 0.2, 32, (64, 128))),
|
|
((128, 3, 8), (64, 0.4, 32, (128, 256))),
|
|
(None, (16, 0.8, 32, (256, 256, 512))),
|
|
]
|
|
fp_blocks = [
|
|
((256, 256), (256, 3, 8)),
|
|
((256, 256), (256, 3, 8)),
|
|
((256, 128), (128, 2, 16)),
|
|
((128, 128, 64), (64, 2, 32)),
|
|
]
|
|
|
|
def __init__(self, num_classes, embed_dim, use_att,dropout, extra_feature_channels=3, width_multiplier=1,
|
|
voxel_resolution_multiplier=1):
|
|
super().__init__(
|
|
num_classes=num_classes, embed_dim=embed_dim, use_att=use_att,
|
|
dropout=dropout, extra_feature_channels=extra_feature_channels,
|
|
width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
|
|
)
|
|
|
|
|
|
class Model(nn.Module):
|
|
def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type:str):
|
|
super(Model, self).__init__()
|
|
self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type)
|
|
|
|
self.model = PVCNN2(num_classes=args.nc, embed_dim=args.embed_dim, use_att=args.attention,
|
|
dropout=args.dropout, extra_feature_channels=0)
|
|
|
|
def prior_kl(self, x0):
|
|
return self.diffusion._prior_bpd(x0)
|
|
|
|
def all_kl(self, x0, clip_denoised=True):
|
|
total_bpd_b, vals_bt, prior_bpd_b, mse_bt = self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
|
|
|
|
return {
|
|
'total_bpd_b': total_bpd_b,
|
|
'terms_bpd': vals_bt,
|
|
'prior_bpd_b': prior_bpd_b,
|
|
'mse_bt':mse_bt
|
|
}
|
|
|
|
|
|
def _denoise(self, data, t):
|
|
B, D,N= data.shape
|
|
assert data.dtype == torch.float
|
|
assert t.shape == torch.Size([B]) and t.dtype == torch.int64
|
|
|
|
out = self.model(data, t)
|
|
|
|
assert out.shape == torch.Size([B, D, N])
|
|
return out
|
|
|
|
def get_loss_iter(self, data, noises=None):
|
|
B, D, N = data.shape
|
|
t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
|
|
|
|
if noises is not None:
|
|
noises[t!=0] = torch.randn((t!=0).sum(), *noises.shape[1:]).to(noises)
|
|
|
|
losses = self.diffusion.p_losses(
|
|
denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
|
|
assert losses.shape == t.shape == torch.Size([B])
|
|
return losses
|
|
|
|
def gen_samples(self, shape, device, noise_fn=torch.randn,
|
|
clip_denoised=True,
|
|
keep_running=False):
|
|
return self.diffusion.p_sample_loop(self._denoise, shape=shape, device=device, noise_fn=noise_fn,
|
|
clip_denoised=clip_denoised,
|
|
keep_running=keep_running)
|
|
|
|
def gen_sample_traj(self, shape, device, freq, noise_fn=torch.randn,
|
|
clip_denoised=True,keep_running=False):
|
|
return self.diffusion.p_sample_loop_trajectory(self._denoise, shape=shape, device=device, noise_fn=noise_fn, freq=freq,
|
|
clip_denoised=clip_denoised,
|
|
keep_running=keep_running)
|
|
|
|
def train(self):
|
|
self.model.train()
|
|
|
|
def eval(self):
|
|
self.model.eval()
|
|
|
|
def multi_gpu_wrapper(self, f):
|
|
self.model = f(self.model)
|
|
|
|
|
|
def get_betas(schedule_type, b_start, b_end, time_num):
|
|
if schedule_type == 'linear':
|
|
betas = np.linspace(b_start, b_end, time_num)
|
|
elif schedule_type == 'warm0.1':
|
|
|
|
betas = b_end * np.ones(time_num, dtype=np.float64)
|
|
warmup_time = int(time_num * 0.1)
|
|
betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
|
|
elif schedule_type == 'warm0.2':
|
|
|
|
betas = b_end * np.ones(time_num, dtype=np.float64)
|
|
warmup_time = int(time_num * 0.2)
|
|
betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
|
|
elif schedule_type == 'warm0.5':
|
|
|
|
betas = b_end * np.ones(time_num, dtype=np.float64)
|
|
warmup_time = int(time_num * 0.5)
|
|
betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
|
|
else:
|
|
raise NotImplementedError(schedule_type)
|
|
return betas
|
|
|
|
|
|
def get_dataset(dataroot, npoints,category):
|
|
tr_dataset = ShapeNet15kPointClouds(root_dir=dataroot,
|
|
categories=[category], split='train',
|
|
tr_sample_size=npoints,
|
|
te_sample_size=npoints,
|
|
scale=1.,
|
|
normalize_per_shape=False,
|
|
normalize_std_per_axis=False,
|
|
random_subsample=True)
|
|
te_dataset = ShapeNet15kPointClouds(root_dir=dataroot,
|
|
categories=[category], split='val',
|
|
tr_sample_size=npoints,
|
|
te_sample_size=npoints,
|
|
scale=1.,
|
|
normalize_per_shape=False,
|
|
normalize_std_per_axis=False,
|
|
all_points_mean=tr_dataset.all_points_mean,
|
|
all_points_std=tr_dataset.all_points_std,
|
|
)
|
|
return tr_dataset, te_dataset
|
|
|
|
|
|
def get_dataloader(opt, train_dataset, test_dataset=None):
|
|
|
|
if opt.distribution_type == 'multi':
|
|
train_sampler = torch.utils.data.distributed.DistributedSampler(
|
|
train_dataset,
|
|
num_replicas=opt.world_size,
|
|
rank=opt.rank
|
|
)
|
|
if test_dataset is not None:
|
|
test_sampler = torch.utils.data.distributed.DistributedSampler(
|
|
test_dataset,
|
|
num_replicas=opt.world_size,
|
|
rank=opt.rank
|
|
)
|
|
else:
|
|
test_sampler = None
|
|
else:
|
|
train_sampler = None
|
|
test_sampler = None
|
|
|
|
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=train_sampler,
|
|
shuffle=train_sampler is None, num_workers=int(opt.workers), drop_last=True)
|
|
|
|
if test_dataset is not None:
|
|
test_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.bs,sampler=test_sampler,
|
|
shuffle=False, num_workers=int(opt.workers), drop_last=False)
|
|
else:
|
|
test_dataloader = None
|
|
|
|
return train_dataloader, test_dataloader, train_sampler, test_sampler
|
|
|
|
|
|
def train(gpu, opt, output_dir, noises_init):
|
|
|
|
set_seed(opt)
|
|
logger = setup_logging(output_dir)
|
|
if opt.distribution_type == 'multi':
|
|
should_diag = gpu==0
|
|
else:
|
|
should_diag = True
|
|
if should_diag:
|
|
outf_syn, = setup_output_subdirs(output_dir, 'syn')
|
|
|
|
if opt.distribution_type == 'multi':
|
|
if opt.dist_url == "env://" and opt.rank == -1:
|
|
opt.rank = int(os.environ["RANK"])
|
|
|
|
base_rank = opt.rank * opt.ngpus_per_node
|
|
opt.rank = base_rank + gpu
|
|
dist.init_process_group(backend=opt.dist_backend, init_method=opt.dist_url,
|
|
world_size=opt.world_size, rank=opt.rank)
|
|
|
|
opt.bs = int(opt.bs / opt.ngpus_per_node)
|
|
opt.workers = 0
|
|
|
|
opt.saveIter = int(opt.saveIter / opt.ngpus_per_node)
|
|
opt.diagIter = int(opt.diagIter / opt.ngpus_per_node)
|
|
opt.vizIter = int(opt.vizIter / opt.ngpus_per_node)
|
|
|
|
|
|
''' data '''
|
|
train_dataset, _ = get_dataset(opt.dataroot, opt.npoints, opt.category)
|
|
dataloader, _, train_sampler, _ = get_dataloader(opt, train_dataset, None)
|
|
|
|
|
|
'''
|
|
create networks
|
|
'''
|
|
|
|
betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
|
|
model = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
|
|
|
|
if opt.distribution_type == 'multi': # Multiple processes, single GPU per process
|
|
def _transform_(m):
|
|
return nn.parallel.DistributedDataParallel(
|
|
m, device_ids=[gpu], output_device=gpu)
|
|
|
|
torch.cuda.set_device(gpu)
|
|
model.cuda(gpu)
|
|
model.multi_gpu_wrapper(_transform_)
|
|
|
|
|
|
elif opt.distribution_type == 'single':
|
|
def _transform_(m):
|
|
return nn.parallel.DataParallel(m)
|
|
model = model.cuda()
|
|
model.multi_gpu_wrapper(_transform_)
|
|
|
|
elif gpu is not None:
|
|
torch.cuda.set_device(gpu)
|
|
model = model.cuda(gpu)
|
|
else:
|
|
raise ValueError('distribution_type = multi | single | None')
|
|
|
|
if should_diag:
|
|
logger.info(opt)
|
|
|
|
optimizer= optim.Adam(model.parameters(), lr=opt.lr, weight_decay=opt.decay, betas=(opt.beta1, 0.999))
|
|
|
|
lr_scheduler = optim.lr_scheduler.ExponentialLR(optimizer, opt.lr_gamma)
|
|
|
|
if opt.model != '':
|
|
ckpt = torch.load(opt.model)
|
|
model.load_state_dict(ckpt['model_state'])
|
|
optimizer.load_state_dict(ckpt['optimizer_state'])
|
|
|
|
if opt.model != '':
|
|
start_epoch = torch.load(opt.model)['epoch'] + 1
|
|
else:
|
|
start_epoch = 0
|
|
|
|
def new_x_chain(x, num_chain):
|
|
return torch.randn(num_chain, *x.shape[1:], device=x.device)
|
|
|
|
|
|
|
|
for epoch in range(start_epoch, opt.niter):
|
|
|
|
if opt.distribution_type == 'multi':
|
|
train_sampler.set_epoch(epoch)
|
|
|
|
lr_scheduler.step(epoch)
|
|
|
|
for i, data in enumerate(dataloader):
|
|
x = data['train_points'].transpose(1,2)
|
|
noises_batch = noises_init[data['idx']].transpose(1,2)
|
|
|
|
'''
|
|
train diffusion
|
|
'''
|
|
|
|
if opt.distribution_type == 'multi' or (opt.distribution_type is None and gpu is not None):
|
|
x = x.cuda(gpu)
|
|
noises_batch = noises_batch.cuda(gpu)
|
|
elif opt.distribution_type == 'single':
|
|
x = x.cuda()
|
|
noises_batch = noises_batch.cuda()
|
|
|
|
loss = model.get_loss_iter(x, noises_batch).mean()
|
|
|
|
optimizer.zero_grad()
|
|
loss.backward()
|
|
netpNorm, netgradNorm = getGradNorm(model)
|
|
if opt.grad_clip is not None:
|
|
torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
|
|
|
|
optimizer.step()
|
|
|
|
|
|
if i % opt.print_freq == 0 and should_diag:
|
|
|
|
logger.info('[{:>3d}/{:>3d}][{:>3d}/{:>3d}] loss: {:>10.4f}, '
|
|
'netpNorm: {:>10.2f}, netgradNorm: {:>10.2f} '
|
|
.format(
|
|
epoch, opt.niter, i, len(dataloader),loss.item(),
|
|
netpNorm, netgradNorm,
|
|
))
|
|
|
|
|
|
if (epoch + 1) % opt.diagIter == 0 and should_diag:
|
|
|
|
logger.info('Diagnosis:')
|
|
|
|
x_range = [x.min().item(), x.max().item()]
|
|
kl_stats = model.all_kl(x)
|
|
logger.info(' [{:>3d}/{:>3d}] '
|
|
'x_range: [{:>10.4f}, {:>10.4f}], '
|
|
'total_bpd_b: {:>10.4f}, '
|
|
'terms_bpd: {:>10.4f}, '
|
|
'prior_bpd_b: {:>10.4f} '
|
|
'mse_bt: {:>10.4f} '
|
|
.format(
|
|
epoch, opt.niter,
|
|
*x_range,
|
|
kl_stats['total_bpd_b'].item(),
|
|
kl_stats['terms_bpd'].item(), kl_stats['prior_bpd_b'].item(), kl_stats['mse_bt'].item()
|
|
))
|
|
|
|
|
|
|
|
if (epoch + 1) % opt.vizIter == 0 and should_diag:
|
|
logger.info('Generation: eval')
|
|
|
|
model.eval()
|
|
with torch.no_grad():
|
|
|
|
x_gen_eval = model.gen_samples(new_x_chain(x, 25).shape, x.device, clip_denoised=False)
|
|
x_gen_list = model.gen_sample_traj(new_x_chain(x, 1).shape, x.device, freq=40, clip_denoised=False)
|
|
x_gen_all = torch.cat(x_gen_list, dim=0)
|
|
|
|
gen_stats = [x_gen_eval.mean(), x_gen_eval.std()]
|
|
gen_eval_range = [x_gen_eval.min().item(), x_gen_eval.max().item()]
|
|
|
|
logger.info(' [{:>3d}/{:>3d}] '
|
|
'eval_gen_range: [{:>10.4f}, {:>10.4f}] '
|
|
'eval_gen_stats: [mean={:>10.4f}, std={:>10.4f}] '
|
|
.format(
|
|
epoch, opt.niter,
|
|
*gen_eval_range, *gen_stats,
|
|
))
|
|
|
|
visualize_pointcloud_batch('%s/epoch_%03d_samples_eval.png' % (outf_syn, epoch),
|
|
x_gen_eval.transpose(1, 2), None, None,
|
|
None)
|
|
|
|
visualize_pointcloud_batch('%s/epoch_%03d_samples_eval_all.png' % (outf_syn, epoch),
|
|
x_gen_all.transpose(1, 2), None,
|
|
None,
|
|
None)
|
|
|
|
visualize_pointcloud_batch('%s/epoch_%03d_x.png' % (outf_syn, epoch), x.transpose(1, 2), None,
|
|
None,
|
|
None)
|
|
|
|
logger.info('Generation: train')
|
|
model.train()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if (epoch + 1) % opt.saveIter == 0:
|
|
|
|
if should_diag:
|
|
|
|
|
|
save_dict = {
|
|
'epoch': epoch,
|
|
'model_state': model.state_dict(),
|
|
'optimizer_state': optimizer.state_dict()
|
|
}
|
|
|
|
torch.save(save_dict, '%s/epoch_%d.pth' % (output_dir, epoch))
|
|
|
|
|
|
if opt.distribution_type == 'multi':
|
|
dist.barrier()
|
|
map_location = {'cuda:%d' % 0: 'cuda:%d' % gpu}
|
|
model.load_state_dict(
|
|
torch.load('%s/epoch_%d.pth' % (output_dir, epoch), map_location=map_location)['model_state'])
|
|
|
|
dist.destroy_process_group()
|
|
|
|
def main():
|
|
opt = parse_args()
|
|
if opt.category == 'airplane':
|
|
opt.beta_start = 1e-5
|
|
opt.beta_end = 0.008
|
|
opt.schedule_type = 'warm0.1'
|
|
|
|
exp_id = os.path.splitext(os.path.basename(__file__))[0]
|
|
dir_id = os.path.dirname(__file__)
|
|
output_dir = get_output_dir(dir_id, exp_id)
|
|
copy_source(__file__, output_dir)
|
|
|
|
''' workaround '''
|
|
train_dataset, _ = get_dataset(opt.dataroot, opt.npoints, opt.category)
|
|
noises_init = torch.randn(len(train_dataset), opt.npoints, opt.nc)
|
|
|
|
if opt.dist_url == "env://" and opt.world_size == -1:
|
|
opt.world_size = int(os.environ["WORLD_SIZE"])
|
|
|
|
if opt.distribution_type == 'multi':
|
|
opt.ngpus_per_node = torch.cuda.device_count()
|
|
opt.world_size = opt.ngpus_per_node * opt.world_size
|
|
mp.spawn(train, nprocs=opt.ngpus_per_node, args=(opt, output_dir, noises_init))
|
|
else:
|
|
train(opt.gpu, opt, output_dir, noises_init)
|
|
|
|
|
|
|
|
def parse_args():
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--dataroot', default='ShapeNetCore.v2.PC15k/')
|
|
parser.add_argument('--category', default='chair')
|
|
|
|
parser.add_argument('--bs', type=int, default=16, help='input batch size')
|
|
parser.add_argument('--workers', type=int, default=16, help='workers')
|
|
parser.add_argument('--niter', type=int, default=10000, help='number of epochs to train for')
|
|
|
|
parser.add_argument('--nc', default=3)
|
|
parser.add_argument('--npoints', default=2048)
|
|
'''model'''
|
|
parser.add_argument('--beta_start', default=0.0001)
|
|
parser.add_argument('--beta_end', default=0.02)
|
|
parser.add_argument('--schedule_type', default='linear')
|
|
parser.add_argument('--time_num', default=1000)
|
|
|
|
#params
|
|
parser.add_argument('--attention', default=True)
|
|
parser.add_argument('--dropout', default=0.1)
|
|
parser.add_argument('--embed_dim', type=int, default=64)
|
|
parser.add_argument('--loss_type', default='mse')
|
|
parser.add_argument('--model_mean_type', default='eps')
|
|
parser.add_argument('--model_var_type', default='fixedsmall')
|
|
|
|
parser.add_argument('--lr', type=float, default=2e-4, help='learning rate for E, default=0.0002')
|
|
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
|
|
parser.add_argument('--decay', type=float, default=0, help='weight decay for EBM')
|
|
parser.add_argument('--grad_clip', type=float, default=None, help='weight decay for EBM')
|
|
parser.add_argument('--lr_gamma', type=float, default=0.998, help='lr decay for EBM')
|
|
|
|
parser.add_argument('--model', default='', help="path to model (to continue training)")
|
|
|
|
|
|
'''distributed'''
|
|
parser.add_argument('--world_size', default=1, type=int,
|
|
help='Number of distributed nodes.')
|
|
parser.add_argument('--dist_url', default='tcp://127.0.0.1:9991', type=str,
|
|
help='url used to set up distributed training')
|
|
parser.add_argument('--dist_backend', default='nccl', type=str,
|
|
help='distributed backend')
|
|
parser.add_argument('--distribution_type', default='single', choices=['multi', 'single', None],
|
|
help='Use multi-processing distributed training to launch '
|
|
'N processes per node, which has N GPUs. This is the '
|
|
'fastest way to use PyTorch for either single node or '
|
|
'multi node data parallel training')
|
|
parser.add_argument('--rank', default=0, type=int,
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help='node rank for distributed training')
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parser.add_argument('--gpu', default=None, type=int,
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help='GPU id to use. None means using all available GPUs.')
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'''eval'''
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parser.add_argument('--saveIter', default=100, help='unit: epoch')
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parser.add_argument('--diagIter', default=50, help='unit: epoch')
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parser.add_argument('--vizIter', default=50, help='unit: epoch')
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parser.add_argument('--print_freq', default=50, help='unit: iter')
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|
|
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parser.add_argument('--manualSeed', default=42, type=int, help='random seed')
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|
|
|
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opt = parser.parse_args()
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|
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|
return opt
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|
|
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if __name__ == '__main__':
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main()
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