PVD/train_completion.py

import argparse

import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
from torch.distributions import Normal

from dataset.shapenet_data_pc import ShapeNet15kPointClouds
from dataset.shapenet_data_sv import ShapeNet_Multiview_Points
from model.pvcnn_completion import PVCNN2Base
from utils.file_utils import *
from utils.visualize import *

"""
some utils
"""


def rotation_matrix(axis, theta):
    """
    Return the rotation matrix associated with counterclockwise rotation about
    the given axis by theta radians.
    """
    axis = np.asarray(axis)
    axis = axis / np.sqrt(np.dot(axis, axis))
    a = np.cos(theta / 2.0)
    b, c, d = -axis * np.sin(theta / 2.0)
    aa, bb, cc, dd = a * a, b * b, c * c, d * d
    bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
    return np.array(
        [
            [aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
            [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
            [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc],
        ]
    )


def rotate(vertices, faces):
    """
    vertices: [numpoints, 3]
    """
    M = rotation_matrix([0, 1, 0], np.pi / 2).transpose()
    N = rotation_matrix([1, 0, 0], -np.pi / 4).transpose()
    K = rotation_matrix([0, 0, 1], np.pi).transpose()

    v, f = vertices[:, [1, 2, 0]].dot(M).dot(N).dot(K), faces[:, [1, 2, 0]]
    return v, f


def norm(v, f):
    v = (v - v.min()) / (v.max() - v.min()) - 0.5

    return v, f


def getGradNorm(net):
    pNorm = torch.sqrt(sum(torch.sum(p**2) for p in net.parameters()))
    gradNorm = torch.sqrt(sum(torch.sum(p.grad**2) for p in net.parameters()))
    return pNorm, gradNorm


def weights_init(m):
    """
    xavier initialization
    """
    classname = m.__class__.__name__
    if classname.find("Conv") != -1 and m.weight is not None:
        torch.nn.init.xavier_normal_(m.weight)
    elif classname.find("BatchNorm") != -1:
        m.weight.data.normal_()
        m.bias.data.fill_(0)


"""
models
"""


def normal_kl(mean1, logvar1, mean2, logvar2):
    """
    KL divergence between normal distributions parameterized by mean and log-variance.
    """
    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + (mean1 - mean2) ** 2 * torch.exp(-logvar2))


def discretized_gaussian_log_likelihood(x, *, means, log_scales):
    # Assumes data is integers [0, 1]
    assert x.shape == means.shape == log_scales.shape
    px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))

    centered_x = x - means
    inv_stdv = torch.exp(-log_scales)
    plus_in = inv_stdv * (centered_x + 0.5)
    cdf_plus = px0.cdf(plus_in)
    min_in = inv_stdv * (centered_x - 0.5)
    cdf_min = px0.cdf(min_in)
    log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus) * 1e-12))
    log_one_minus_cdf_min = torch.log(torch.max(1.0 - cdf_min, torch.ones_like(cdf_min) * 1e-12))
    cdf_delta = cdf_plus - cdf_min

    log_probs = torch.where(
        x < 0.001,
        log_cdf_plus,
        torch.where(
            x > 0.999, log_one_minus_cdf_min, torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta) * 1e-12))
        ),
    )
    assert log_probs.shape == x.shape
    return log_probs


class GaussianDiffusion:
    def __init__(self, betas, loss_type, model_mean_type, model_var_type, sv_points):
        self.loss_type = loss_type
        self.model_mean_type = model_mean_type
        self.model_var_type = model_var_type
        assert isinstance(betas, np.ndarray)
        self.np_betas = betas = betas.astype(np.float64)  # computations here in float64 for accuracy
        assert (betas > 0).all() and (betas <= 1).all()
        (timesteps,) = betas.shape
        self.num_timesteps = int(timesteps)
        self.sv_points = sv_points
        # initialize twice the actual length so we can keep running for eval
        # betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])

        alphas = 1.0 - betas
        alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
        alphas_cumprod_prev = torch.from_numpy(np.append(1.0, alphas_cumprod[:-1])).float()

        self.betas = torch.from_numpy(betas).float()
        self.alphas_cumprod = alphas_cumprod.float()
        self.alphas_cumprod_prev = alphas_cumprod_prev.float()

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod).float()
        self.log_one_minus_alphas_cumprod = torch.log(1.0 - alphas_cumprod).float()
        self.sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod).float()
        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod - 1).float()

        betas = torch.from_numpy(betas).float()
        alphas = torch.from_numpy(alphas).float()
        # calculations for posterior q(x_{t-1} | x_t, x_0)
        posterior_variance = betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
        self.posterior_variance = posterior_variance
        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
        self.posterior_log_variance_clipped = torch.log(
            torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance))
        )
        self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)
        self.posterior_mean_coef2 = (1.0 - alphas_cumprod_prev) * torch.sqrt(alphas) / (1.0 - alphas_cumprod)

    @staticmethod
    def _extract(a, t, x_shape):
        """
        Extract some coefficients at specified timesteps,
        then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
        """
        (bs,) = t.shape
        assert x_shape[0] == bs
        out = torch.gather(a, 0, t)
        assert out.shape == torch.Size([bs])
        return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))

    def q_mean_variance(self, x_start, t):
        mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
        variance = self._extract(1.0 - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
        log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
        return mean, variance, log_variance

    def q_sample(self, x_start, t, noise=None):
        """
        Diffuse the data (t == 0 means diffused for 1 step)
        """
        if noise is None:
            noise = torch.randn(x_start.shape, device=x_start.device)
        assert noise.shape == x_start.shape
        return (
            self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
            + self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
        )

    def q_posterior_mean_variance(self, x_start, x_t, t):
        """
        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
        """
        assert x_start.shape == x_t.shape
        posterior_mean = (
            self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start
            + self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
        )
        posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
        posterior_log_variance_clipped = self._extract(
            self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape
        )
        assert (
            posterior_mean.shape[0]
            == posterior_variance.shape[0]
            == posterior_log_variance_clipped.shape[0]
            == x_start.shape[0]
        )
        return posterior_mean, posterior_variance, posterior_log_variance_clipped

    def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
        model_output = denoise_fn(data, t)[:, :, self.sv_points :]

        if self.model_var_type in ["fixedsmall", "fixedlarge"]:
            # below: only log_variance is used in the KL computations
            model_variance, model_log_variance = {
                # for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
                "fixedlarge": (
                    self.betas.to(data.device),
                    torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device),
                ),
                "fixedsmall": (
                    self.posterior_variance.to(data.device),
                    self.posterior_log_variance_clipped.to(data.device),
                ),
            }[self.model_var_type]
            model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(model_output)
            model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(model_output)
        else:
            raise NotImplementedError(self.model_var_type)

        if self.model_mean_type == "eps":
            x_recon = self._predict_xstart_from_eps(data[:, :, self.sv_points :], t=t, eps=model_output)

            model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data[:, :, self.sv_points :], t=t)
        else:
            raise NotImplementedError(self.loss_type)

        assert model_mean.shape == x_recon.shape
        assert model_variance.shape == model_log_variance.shape
        if return_pred_xstart:
            return model_mean, model_variance, model_log_variance, x_recon
        else:
            return model_mean, model_variance, model_log_variance

    def _predict_xstart_from_eps(self, x_t, t, eps):
        assert x_t.shape == eps.shape
        return (
            self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t
            - self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
        )

    """ samples """

    def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False):
        """
        Sample from the model
        """
        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
            denoise_fn, data=data, t=t, clip_denoised=clip_denoised, return_pred_xstart=True
        )
        noise = noise_fn(size=model_mean.shape, dtype=model_mean.dtype, device=model_mean.device)

        # no noise when t == 0
        nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(model_mean.shape) - 1))

        sample = model_mean + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
        sample = torch.cat([data[:, :, : self.sv_points], sample], dim=-1)
        return (sample, pred_xstart) if return_pred_xstart else sample

    def p_sample_loop(
        self, partial_x, denoise_fn, shape, device, noise_fn=torch.randn, clip_denoised=True, keep_running=False
    ):
        """
        Generate samples
        keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps

        """

        assert isinstance(shape, (tuple, list))
        img_t = torch.cat([partial_x, noise_fn(size=shape, dtype=torch.float, device=device)], dim=-1)
        for t in reversed(range(0, self.num_timesteps if not keep_running else len(self.betas))):
            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
            img_t = self.p_sample(
                denoise_fn=denoise_fn,
                data=img_t,
                t=t_,
                noise_fn=noise_fn,
                clip_denoised=clip_denoised,
                return_pred_xstart=False,
            )

        assert img_t[:, :, self.sv_points :].shape == shape
        return img_t

    def p_sample_loop_trajectory(
        self, denoise_fn, shape, device, freq, noise_fn=torch.randn, clip_denoised=True, keep_running=False
    ):
        """
        Generate samples, returning intermediate images
        Useful for visualizing how denoised images evolve over time
        Args:
          repeat_noise_steps (int): Number of denoising timesteps in which the same noise
            is used across the batch. If >= 0, the initial noise is the same for all batch elemements.
        """
        assert isinstance(shape, (tuple, list))

        total_steps = self.num_timesteps if not keep_running else len(self.betas)

        img_t = noise_fn(size=shape, dtype=torch.float, device=device)
        imgs = [img_t]
        for t in reversed(range(0, total_steps)):
            t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
            img_t = self.p_sample(
                denoise_fn=denoise_fn,
                data=img_t,
                t=t_,
                noise_fn=noise_fn,
                clip_denoised=clip_denoised,
                return_pred_xstart=False,
            )
            if t % freq == 0 or t == total_steps - 1:
                imgs.append(img_t)

        assert imgs[-1].shape == shape
        return imgs

    """losses"""

    def _vb_terms_bpd(self, denoise_fn, data_start, data_t, t, clip_denoised: bool, return_pred_xstart: bool):
        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
            x_start=data_start[:, :, self.sv_points :], x_t=data_t[:, :, self.sv_points :], t=t
        )
        model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
            denoise_fn, data=data_t, t=t, clip_denoised=clip_denoised, return_pred_xstart=True
        )

        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean, model_log_variance)
        kl = kl.mean(dim=list(range(1, len(model_mean.shape)))) / np.log(2.0)

        return (kl, pred_xstart) if return_pred_xstart else kl

    def p_losses(self, denoise_fn, data_start, t, noise=None):
        """
        Training loss calculation
        """
        B, D, N = data_start.shape
        assert t.shape == torch.Size([B])

        if noise is None:
            noise = torch.randn(
                data_start[:, :, self.sv_points :].shape, dtype=data_start.dtype, device=data_start.device
            )

        data_t = self.q_sample(x_start=data_start[:, :, self.sv_points :], t=t, noise=noise)

        if self.loss_type == "mse":
            # predict the noise instead of x_start. seems to be weighted naturally like SNR
            eps_recon = denoise_fn(torch.cat([data_start[:, :, : self.sv_points], data_t], dim=-1), t)[
                :, :, self.sv_points :
            ]

            losses = ((noise - eps_recon) ** 2).mean(dim=list(range(1, len(data_start.shape))))
        elif self.loss_type == "kl":
            losses = self._vb_terms_bpd(
                denoise_fn=denoise_fn,
                data_start=data_start,
                data_t=data_t,
                t=t,
                clip_denoised=False,
                return_pred_xstart=False,
            )
        else:
            raise NotImplementedError(self.loss_type)

        assert losses.shape == torch.Size([B])
        return losses

    """debug"""

    def _prior_bpd(self, x_start):
        with torch.no_grad():
            B, T = x_start.shape[0], self.num_timesteps
            t_ = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(T - 1)
            qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t=t_)
            kl_prior = normal_kl(
                mean1=qt_mean,
                logvar1=qt_log_variance,
                mean2=torch.tensor([0.0]).to(qt_mean),
                logvar2=torch.tensor([0.0]).to(qt_log_variance),
            )
            assert kl_prior.shape == x_start.shape
            return kl_prior.mean(dim=list(range(1, len(kl_prior.shape)))) / np.log(2.0)

    def calc_bpd_loop(self, denoise_fn, x_start, clip_denoised=True):
        with torch.no_grad():
            B, T = x_start.shape[0], self.num_timesteps

            vals_bt_, mse_bt_ = torch.zeros([B, T], device=x_start.device), torch.zeros([B, T], device=x_start.device)
            for t in reversed(range(T)):
                t_b = torch.empty(B, dtype=torch.int64, device=x_start.device).fill_(t)
                # Calculate VLB term at the current timestep
                data_t = torch.cat(
                    [x_start[:, :, : self.sv_points], self.q_sample(x_start=x_start[:, :, self.sv_points :], t=t_b)],
                    dim=-1,
                )
                new_vals_b, pred_xstart = self._vb_terms_bpd(
                    denoise_fn,
                    data_start=x_start,
                    data_t=data_t,
                    t=t_b,
                    clip_denoised=clip_denoised,
                    return_pred_xstart=True,
                )
                # MSE for progressive prediction loss
                assert pred_xstart.shape == x_start[:, :, self.sv_points :].shape
                new_mse_b = ((pred_xstart - x_start[:, :, self.sv_points :]) ** 2).mean(
                    dim=list(range(1, len(pred_xstart.shape)))
                )
                assert new_vals_b.shape == new_mse_b.shape == torch.Size([B])
                # Insert the calculated term into the tensor of all terms
                mask_bt = t_b[:, None] == torch.arange(T, device=t_b.device)[None, :].float()
                vals_bt_ = vals_bt_ * (~mask_bt) + new_vals_b[:, None] * mask_bt
                mse_bt_ = mse_bt_ * (~mask_bt) + new_mse_b[:, None] * mask_bt
                assert mask_bt.shape == vals_bt_.shape == vals_bt_.shape == torch.Size([B, T])

            prior_bpd_b = self._prior_bpd(x_start[:, :, self.sv_points :])
            total_bpd_b = vals_bt_.sum(dim=1) + prior_bpd_b
            assert vals_bt_.shape == mse_bt_.shape == torch.Size(
                [B, T]
            ) and total_bpd_b.shape == prior_bpd_b.shape == torch.Size([B])
            return total_bpd_b.mean(), vals_bt_.mean(), prior_bpd_b.mean(), mse_bt_.mean()


class PVCNN2(PVCNN2Base):
    sa_blocks = [
        ((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,
        sv_points,
        embed_dim,
        use_att,
        dropout,
        extra_feature_channels=3,
        width_multiplier=1,
        voxel_resolution_multiplier=1,
    ):
        super().__init__(
            num_classes=num_classes,
            sv_points=sv_points,
            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, args.svpoints)

        self.model = PVCNN2(
            num_classes=args.nc,
            sv_points=args.svpoints,
            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)

        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, partial_x, shape, device, noise_fn=torch.randn, clip_denoised=True, keep_running=False):
        return self.diffusion.p_sample_loop(
            partial_x,
            self._denoise,
            shape=shape,
            device=device,
            noise_fn=noise_fn,
            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_pc, dataroot_sv, npoints, svpoints, category):
    tr_dataset = ShapeNet15kPointClouds(
        root_dir=dataroot_pc,
        categories=[category],
        split="train",
        tr_sample_size=npoints,
        te_sample_size=npoints,
        scale=1.0,
        normalize_per_shape=False,
        normalize_std_per_axis=False,
        random_subsample=True,
    )
    tr_dataset = ShapeNet_Multiview_Points(
        root_pc=dataroot_pc,
        root_views=dataroot_sv,
        cache=os.path.join(dataroot_pc, "../cache"),
        split="train",
        categories=[category],
        npoints=npoints,
        sv_samples=svpoints,
        all_points_mean=tr_dataset.all_points_mean,
        all_points_std=tr_dataset.all_points_std,
    )
    return tr_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_pc, opt.dataroot_sv, opt.npoints, opt.svpoints, 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

    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):
            randind = np.random.choice(20)  # 20 views
            x = data["train_points"].transpose(1, 2)
            sv_x = data["sv_points"][:, randind].transpose(1, 2)

            sv_x[:, :, opt.svpoints :] = x[:, :, opt.svpoints :]
            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):
                sv_x = sv_x.cuda(gpu)
                noises_batch = noises_batch.cuda(gpu)
            elif opt.distribution_type == "single":
                sv_x = sv_x.cuda()
                noises_batch = noises_batch.cuda()

            loss = model.get_loss_iter(sv_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(sv_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()
            m, s = train_dataset.all_points_mean.reshape(1, -1), train_dataset.all_points_std.reshape(1, -1)

            with torch.no_grad():
                x_gen_eval = (
                    model.gen_samples(
                        sv_x[:, :, : opt.svpoints], sv_x[:, :, opt.svpoints :].shape, sv_x.device, clip_denoised=False
                    )
                    .detach()
                    .cpu()
                )

                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,
                    )
                )

            export_to_pc_batch(
                "%s/epoch_%03d_samples_eval" % (outf_syn, epoch), (x_gen_eval.transpose(1, 2) * s + m).numpy() * 3
            )

            export_to_pc_batch(
                "%s/epoch_%03d_ground_truth" % (outf_syn, epoch),
                (sv_x.transpose(1, 2).detach().cpu() * s + m).numpy() * 3,
            )

            export_to_pc_batch(
                "%s/epoch_%03d_partial" % (outf_syn, epoch),
                (sv_x[:, :, : opt.svpoints].transpose(1, 2).detach().cpu() * s + m).numpy() * 3,
            )

            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()

    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_pc, opt.dataroot_sv, opt.npoints, opt.svpoints, opt.category)
    noises_init = torch.randn(len(train_dataset), opt.npoints - opt.svpoints, 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_pc", default="ShapeNetCore.v2.PC15k/")
    parser.add_argument("--dataroot_sv", default="GenReData/")
    parser.add_argument("--category", default="chair")

    parser.add_argument("--bs", type=int, default=48, 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)
    parser.add_argument("--svpoints", default=200)
    """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, help="node rank for distributed training")
    parser.add_argument("--gpu", default=None, type=int, help="GPU id to use. None means using all available GPUs.")

    """eval"""
    parser.add_argument("--saveIter", default=100, help="unit: epoch")
    parser.add_argument("--diagIter", default=50, help="unit: epoch")
    parser.add_argument("--vizIter", default=50, help="unit: epoch")
    parser.add_argument("--print_freq", default=50, help="unit: iter")

    parser.add_argument("--manualSeed", default=42, type=int, help="random seed")

    opt = parser.parse_args()

    return opt


if __name__ == "__main__":
    main()