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)