PVD/test_completion.py

import argparse
from pprint import pprint

import torch.nn as nn
import torch.utils.data
from torch.distributions import Normal

from datasets.shapenet_data_pc import ShapeNet15kPointClouds
from datasets.shapenet_data_sv import *
from metrics.evaluation_metrics import EMD_CD, compute_all_metrics
from model.pvcnn_completion import PVCNN2Base
from utils.file_utils import *

"""
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_mvr_dataset(pc_dataroot, views_root, npoints, category):
    tr_dataset = ShapeNet15kPointClouds(
        root_dir=pc_dataroot,
        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,
    )
    te_dataset = ShapeNet_Multiview_Points(
        root_pc=pc_dataroot,
        root_views=views_root,
        cache=os.path.join(pc_dataroot, "../cache"),
        split="val",
        categories=[category],
        npoints=npoints,
        sv_samples=200,
        all_points_mean=tr_dataset.all_points_mean,
        all_points_std=tr_dataset.all_points_std,
    )
    return te_dataset


def evaluate_recon_mvr(opt, model, save_dir, logger):
    test_dataset = get_mvr_dataset(opt.dataroot_pc, opt.dataroot_sv, opt.npoints, opt.category)

    test_dataloader = torch.utils.data.DataLoader(
        test_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=int(opt.workers), drop_last=False
    )
    ref = []
    samples = []
    masked = []
    for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc="Reconstructing Samples"):
        gt_all = data["test_points"]
        x_all = data["sv_points"]

        B, V, N, C = x_all.shape
        gt_all = gt_all[:, None, :, :].expand(-1, V, -1, -1)

        x = x_all.reshape(B * V, N, C).transpose(1, 2).contiguous()

        m, s = data["mean"].float(), data["std"].float()

        recon = (
            model.gen_samples(
                x[:, :, : opt.svpoints].cuda(), x[:, :, opt.svpoints :].shape, "cuda", clip_denoised=False
            )
            .detach()
            .cpu()
        )

        recon = recon.transpose(1, 2).contiguous()
        x = x.transpose(1, 2).contiguous()

        x_adj = x.reshape(B, V, N, C) * s + m
        recon_adj = recon.reshape(B, V, N, C) * s + m

        ref.append(gt_all * s + m)
        masked.append(x_adj[:, :, : test_dataloader.dataset.sv_samples, :])
        samples.append(recon_adj)

    ref_pcs = torch.cat(ref, dim=0)
    sample_pcs = torch.cat(samples, dim=0)
    masked = torch.cat(masked, dim=0)

    B, V, N, C = ref_pcs.shape

    torch.save(ref_pcs.reshape(B, V, N, C), os.path.join(save_dir, "recon_gt.pth"))

    torch.save(masked.reshape(B, V, *masked.shape[2:]), os.path.join(save_dir, "recon_masked.pth"))
    # Compute metrics
    results = EMD_CD(sample_pcs.reshape(B * V, N, C), ref_pcs.reshape(B * V, N, C), opt.batch_size, reduced=False)

    results = {
        ky: val.reshape(B, V)
        if val.shape
        == torch.Size(
            [
                B * V,
            ]
        )
        else val
        for ky, val in results.items()
    }

    pprint({key: val.mean().item() for key, val in results.items()})
    logger.info({key: val.mean().item() for key, val in results.items()})

    results["pc"] = sample_pcs
    torch.save(results, os.path.join(save_dir, "ours_results.pth"))

    del ref_pcs, masked, results


def evaluate_saved(opt, saved_dir):
    # ours_base = '/viscam/u/alexzhou907/research/diffusion/shape_completion/output/test_chair/2020-11-04-02-10-38/syn'

    gt_pth = saved_dir + "/recon_gt.pth"
    ours_pth = saved_dir + "/ours_results.pth"
    gt = torch.load(gt_pth).permute(1, 0, 2, 3)
    ours = torch.load(ours_pth)["pc"].permute(1, 0, 2, 3)

    all_res = {}
    for i, (gt_, ours_) in enumerate(zip(gt, ours)):
        results = compute_all_metrics(gt_, ours_, opt.batch_size)

        for key, val in results.items():
            if i == 0:
                all_res[key] = val
            else:
                all_res[key] += val
        pprint(results)
    for key, val in all_res.items():
        all_res[key] = val / gt.shape[0]

    pprint({key: val.mean().item() for key, val in all_res.items()})


def main(opt):
    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)
    logger = setup_logging(output_dir)

    (outf_syn,) = setup_output_subdirs(output_dir, "syn")

    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.cuda:
        model.cuda()

    def _transform_(m):
        return nn.parallel.DataParallel(m)

    model = model.cuda()
    model.multi_gpu_wrapper(_transform_)

    model.eval()

    with torch.no_grad():
        logger.info("Resume Path:%s" % opt.model)

        resumed_param = torch.load(opt.model)
        model.load_state_dict(resumed_param["model_state"])

        if opt.eval_recon_mvr:
            # Evaluate generation
            evaluate_recon_mvr(opt, model, outf_syn, logger)

        if opt.eval_saved:
            evaluate_saved(opt, outf_syn)


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("--batch_size", type=int, default=50, 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("--eval_recon_mvr", default=True)
    parser.add_argument("--eval_saved", default=True)

    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("--model", default="", required=True, help="path to model (to continue training)")

    """eval"""

    parser.add_argument("--eval_path", default="")

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

    parser.add_argument("--gpu", type=int, default=0, metavar="S", help="gpu id (default: 0)")

    opt = parser.parse_args()

    if torch.cuda.is_available():
        opt.cuda = True
    else:
        opt.cuda = False

    return opt


if __name__ == "__main__":
    opt = parse_args()

    main(opt)