import os, sys import numpy as np import imageio import json import random import time import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.tensorboard import SummaryWriter from tqdm import tqdm, trange import matplotlib.pyplot as plt from run_nerf_helpers import * from load_llff import load_llff_data from load_deepvoxels import load_dv_data from load_blender import load_blender_data device = torch.device("cuda" if torch.cuda.is_available() else "cpu") np.random.seed(0) DEBUG = False def batchify(fn, chunk): """Constructs a version of 'fn' that applies to smaller batches. """ if chunk is None: return fn def ret(inputs): return torch.cat([fn(inputs[i:i+chunk]) for i in range(0, inputs.shape[0], chunk)], 0) return ret def run_network(inputs, viewdirs, fn, embed_fn, embeddirs_fn, netchunk=1024*64): """Prepares inputs and applies network 'fn'. """ inputs_flat = torch.reshape(inputs, [-1, inputs.shape[-1]]) embedded = embed_fn(inputs_flat) if viewdirs is not None: input_dirs = viewdirs[:,None].expand(inputs.shape) input_dirs_flat = torch.reshape(input_dirs, [-1, input_dirs.shape[-1]]) embedded_dirs = embeddirs_fn(input_dirs_flat) embedded = torch.cat([embedded, embedded_dirs], -1) outputs_flat = batchify(fn, netchunk)(embedded) outputs = torch.reshape(outputs_flat, list(inputs.shape[:-1]) + [outputs_flat.shape[-1]]) return outputs def batchify_rays(rays_flat, chunk=1024*32, **kwargs): """Render rays in smaller minibatches to avoid OOM. """ all_ret = {} for i in range(0, rays_flat.shape[0], chunk): ret = render_rays(rays_flat[i:i+chunk], **kwargs) for k in ret: if k not in all_ret: all_ret[k] = [] all_ret[k].append(ret[k]) all_ret = {k : torch.cat(all_ret[k], 0) for k in all_ret} return all_ret def render(H, W, focal, chunk=1024*32, rays=None, c2w=None, ndc=True, near=0., far=1., use_viewdirs=False, c2w_staticcam=None, **kwargs): """Render rays Args: H: int. Height of image in pixels. W: int. Width of image in pixels. focal: float. Focal length of pinhole camera. chunk: int. Maximum number of rays to process simultaneously. Used to control maximum memory usage. Does not affect final results. rays: array of shape [2, batch_size, 3]. Ray origin and direction for each example in batch. c2w: array of shape [3, 4]. Camera-to-world transformation matrix. ndc: bool. If True, represent ray origin, direction in NDC coordinates. near: float or array of shape [batch_size]. Nearest distance for a ray. far: float or array of shape [batch_size]. Farthest distance for a ray. use_viewdirs: bool. If True, use viewing direction of a point in space in model. c2w_staticcam: array of shape [3, 4]. If not None, use this transformation matrix for camera while using other c2w argument for viewing directions. Returns: rgb_map: [batch_size, 3]. Predicted RGB values for rays. disp_map: [batch_size]. Disparity map. Inverse of depth. acc_map: [batch_size]. Accumulated opacity (alpha) along a ray. extras: dict with everything returned by render_rays(). """ if c2w is not None: # special case to render full image rays_o, rays_d = get_rays(H, W, focal, c2w) else: # use provided ray batch rays_o, rays_d = rays if use_viewdirs: # provide ray directions as input viewdirs = rays_d if c2w_staticcam is not None: # special case to visualize effect of viewdirs rays_o, rays_d = get_rays(H, W, focal, c2w_staticcam) viewdirs = viewdirs / torch.norm(viewdirs, dim=-1, keepdim=True) viewdirs = torch.reshape(viewdirs, [-1,3]).float() sh = rays_d.shape # [..., 3] if ndc: # for forward facing scenes rays_o, rays_d = ndc_rays(H, W, focal, 1., rays_o, rays_d) # Create ray batch rays_o = torch.reshape(rays_o, [-1,3]).float() rays_d = torch.reshape(rays_d, [-1,3]).float() near, far = near * torch.ones_like(rays_d[...,:1]), far * torch.ones_like(rays_d[...,:1]) rays = torch.cat([rays_o, rays_d, near, far], -1) if use_viewdirs: rays = torch.cat([rays, viewdirs], -1) # Render and reshape all_ret = batchify_rays(rays, chunk, **kwargs) for k in all_ret: k_sh = list(sh[:-1]) + list(all_ret[k].shape[1:]) all_ret[k] = torch.reshape(all_ret[k], k_sh) k_extract = ['rgb_map', 'disp_map', 'acc_map'] ret_list = [all_ret[k] for k in k_extract] ret_dict = {k : all_ret[k] for k in all_ret if k not in k_extract} return ret_list + [ret_dict] def render_path(render_poses, hwf, chunk, render_kwargs, gt_imgs=None, savedir=None, render_factor=0): H, W, focal = hwf if render_factor!=0: # Render downsampled for speed H = H//render_factor W = W//render_factor focal = focal/render_factor rgbs = [] disps = [] t = time.time() for i, c2w in enumerate(tqdm(render_poses)): print(i, time.time() - t) t = time.time() rgb, disp, acc, _ = render(H, W, focal, chunk=chunk, c2w=c2w[:3,:4], **render_kwargs) rgbs.append(rgb.cpu().numpy()) disps.append(disp.cpu().numpy()) if i==0: print(rgb.shape, disp.shape) """ if gt_imgs is not None and render_factor==0: p = -10. * np.log10(np.mean(np.square(rgb.cpu().numpy() - gt_imgs[i]))) print(p) """ if savedir is not None: rgb8 = to8b(rgbs[-1]) filename = os.path.join(savedir, '{:03d}.png'.format(i)) imageio.imwrite(filename, rgb8) rgbs = np.stack(rgbs, 0) disps = np.stack(disps, 0) return rgbs, disps def create_nerf(args): """Instantiate NeRF's MLP model. """ embed_fn, input_ch = get_embedder(args.multires, args.i_embed) input_ch_views = 0 embeddirs_fn = None if args.use_viewdirs: embeddirs_fn, input_ch_views = get_embedder(args.multires_views, args.i_embed) output_ch = 5 if args.N_importance > 0 else 4 skips = [4] model = NeRF(D=args.netdepth, W=args.netwidth, input_ch=input_ch, output_ch=output_ch, skips=skips, input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs) model = nn.DataParallel(model).to(device) grad_vars = list(model.parameters()) model_fine = None if args.N_importance > 0: model_fine = NeRF(D=args.netdepth_fine, W=args.netwidth_fine, input_ch=input_ch, output_ch=output_ch, skips=skips, input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs) model_fine = nn.DataParallel(model_fine).to(device) grad_vars += list(model_fine.parameters()) network_query_fn = lambda inputs, viewdirs, network_fn : run_network(inputs, viewdirs, network_fn, embed_fn=embed_fn, embeddirs_fn=embeddirs_fn, netchunk=args.netchunk_per_gpu*args.n_gpus) # Create optimizer optimizer = torch.optim.Adam(params=grad_vars, lr=args.lrate, betas=(0.9, 0.999)) start = 0 basedir = args.basedir expname = args.expname ########################## # Load checkpoints if args.ft_path is not None and args.ft_path!='None': ckpts = [args.ft_path] else: ckpts = [os.path.join(basedir, expname, f) for f in sorted(os.listdir(os.path.join(basedir, expname))) if 'tar' in f] print('Found ckpts', ckpts) if len(ckpts) > 0 and not args.no_reload: ckpt_path = ckpts[-1] print('Reloading from', ckpt_path) ckpt = torch.load(ckpt_path) start = ckpt['global_step'] optimizer.load_state_dict(ckpt['optimizer_state_dict']) # Load model model.load_state_dict(ckpt['network_fn_state_dict']) if model_fine is not None: model_fine.load_state_dict(ckpt['network_fine_state_dict']) ########################## render_kwargs_train = { 'network_query_fn' : network_query_fn, 'perturb' : args.perturb, 'N_importance' : args.N_importance, 'network_fine' : model_fine, 'N_samples' : args.N_samples, 'network_fn' : model, 'use_viewdirs' : args.use_viewdirs, 'white_bkgd' : args.white_bkgd, 'raw_noise_std' : args.raw_noise_std, } # NDC only good for LLFF-style forward facing data if args.dataset_type != 'llff' or args.no_ndc: print('Not ndc!') render_kwargs_train['ndc'] = False render_kwargs_train['lindisp'] = args.lindisp render_kwargs_test = {k : render_kwargs_train[k] for k in render_kwargs_train} render_kwargs_test['perturb'] = False render_kwargs_test['raw_noise_std'] = 0. return render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer def raw2outputs(raw, z_vals, rays_d, raw_noise_std=0, white_bkgd=False, pytest=False): """Transforms model's predictions to semantically meaningful values. Args: raw: [num_rays, num_samples along ray, 4]. Prediction from model. z_vals: [num_rays, num_samples along ray]. Integration time. rays_d: [num_rays, 3]. Direction of each ray. Returns: rgb_map: [num_rays, 3]. Estimated RGB color of a ray. disp_map: [num_rays]. Disparity map. Inverse of depth map. acc_map: [num_rays]. Sum of weights along each ray. weights: [num_rays, num_samples]. Weights assigned to each sampled color. depth_map: [num_rays]. Estimated distance to object. """ raw2alpha = lambda raw, dists, act_fn=F.relu: 1.-torch.exp(-act_fn(raw)*dists) dists = z_vals[...,1:] - z_vals[...,:-1] dists = torch.cat([dists, torch.Tensor([1e10]).expand(dists[...,:1].shape)], -1) # [N_rays, N_samples] dists = dists * torch.norm(rays_d[...,None,:], dim=-1) rgb = torch.sigmoid(raw[...,:3]) # [N_rays, N_samples, 3] noise = 0. if raw_noise_std > 0.: noise = torch.randn(raw[...,3].shape) * raw_noise_std # Overwrite randomly sampled data if pytest if pytest: np.random.seed(0) noise = np.random.rand(*list(raw[...,3].shape)) * raw_noise_std noise = torch.Tensor(noise) alpha = raw2alpha(raw[...,3] + noise, dists) # [N_rays, N_samples] # weights = alpha * tf.math.cumprod(1.-alpha + 1e-10, -1, exclusive=True) weights = alpha * torch.cumprod(torch.cat([torch.ones((alpha.shape[0], 1)), 1.-alpha + 1e-10], -1), -1)[:, :-1] rgb_map = torch.sum(weights[...,None] * rgb, -2) # [N_rays, 3] depth_map = torch.sum(weights * z_vals, -1) disp_map = 1./torch.max(1e-10 * torch.ones_like(depth_map), depth_map / torch.sum(weights, -1)) acc_map = torch.sum(weights, -1) if white_bkgd: rgb_map = rgb_map + (1.-acc_map[...,None]) return rgb_map, disp_map, acc_map, weights, depth_map def render_rays(ray_batch, network_fn, network_query_fn, N_samples, retraw=False, lindisp=False, perturb=0., N_importance=0, network_fine=None, white_bkgd=False, raw_noise_std=0., verbose=False, pytest=False): """Volumetric rendering. Args: ray_batch: array of shape [batch_size, ...]. All information necessary for sampling along a ray, including: ray origin, ray direction, min dist, max dist, and unit-magnitude viewing direction. network_fn: function. Model for predicting RGB and density at each point in space. network_query_fn: function used for passing queries to network_fn. N_samples: int. Number of different times to sample along each ray. retraw: bool. If True, include model's raw, unprocessed predictions. lindisp: bool. If True, sample linearly in inverse depth rather than in depth. perturb: float, 0 or 1. If non-zero, each ray is sampled at stratified random points in time. N_importance: int. Number of additional times to sample along each ray. These samples are only passed to network_fine. network_fine: "fine" network with same spec as network_fn. white_bkgd: bool. If True, assume a white background. raw_noise_std: ... verbose: bool. If True, print more debugging info. Returns: rgb_map: [num_rays, 3]. Estimated RGB color of a ray. Comes from fine model. disp_map: [num_rays]. Disparity map. 1 / depth. acc_map: [num_rays]. Accumulated opacity along each ray. Comes from fine model. raw: [num_rays, num_samples, 4]. Raw predictions from model. rgb0: See rgb_map. Output for coarse model. disp0: See disp_map. Output for coarse model. acc0: See acc_map. Output for coarse model. z_std: [num_rays]. Standard deviation of distances along ray for each sample. """ N_rays = ray_batch.shape[0] rays_o, rays_d = ray_batch[:,0:3], ray_batch[:,3:6] # [N_rays, 3] each viewdirs = ray_batch[:,-3:] if ray_batch.shape[-1] > 8 else None bounds = torch.reshape(ray_batch[...,6:8], [-1,1,2]) near, far = bounds[...,0], bounds[...,1] # [-1,1] t_vals = torch.linspace(0., 1., steps=N_samples) if not lindisp: z_vals = near * (1.-t_vals) + far * (t_vals) else: z_vals = 1./(1./near * (1.-t_vals) + 1./far * (t_vals)) z_vals = z_vals.expand([N_rays, N_samples]) if perturb > 0.: # get intervals between samples mids = .5 * (z_vals[...,1:] + z_vals[...,:-1]) upper = torch.cat([mids, z_vals[...,-1:]], -1) lower = torch.cat([z_vals[...,:1], mids], -1) # stratified samples in those intervals t_rand = torch.rand(z_vals.shape) # Pytest, overwrite u with numpy's fixed random numbers if pytest: np.random.seed(0) t_rand = np.random.rand(*list(z_vals.shape)) t_rand = torch.Tensor(t_rand) z_vals = lower + (upper - lower) * t_rand pts = rays_o[...,None,:] + rays_d[...,None,:] * z_vals[...,:,None] # [N_rays, N_samples, 3] # raw = run_network(pts) raw = network_query_fn(pts, viewdirs, network_fn) rgb_map, disp_map, acc_map, weights, depth_map = raw2outputs(raw, z_vals, rays_d, raw_noise_std, white_bkgd, pytest=pytest) if N_importance > 0: rgb_map_0, disp_map_0, acc_map_0 = rgb_map, disp_map, acc_map z_vals_mid = .5 * (z_vals[...,1:] + z_vals[...,:-1]) z_samples = sample_pdf(z_vals_mid, weights[...,1:-1], N_importance, det=(perturb==0.), pytest=pytest) z_samples = z_samples.detach() z_vals, _ = torch.sort(torch.cat([z_vals, z_samples], -1), -1) pts = rays_o[...,None,:] + rays_d[...,None,:] * z_vals[...,:,None] # [N_rays, N_samples + N_importance, 3] run_fn = network_fn if network_fine is None else network_fine # raw = run_network(pts, fn=run_fn) raw = network_query_fn(pts, viewdirs, run_fn) rgb_map, disp_map, acc_map, weights, depth_map = raw2outputs(raw, z_vals, rays_d, raw_noise_std, white_bkgd, pytest=pytest) ret = {'rgb_map' : rgb_map, 'disp_map' : disp_map, 'acc_map' : acc_map} if retraw: ret['raw'] = raw if N_importance > 0: ret['rgb0'] = rgb_map_0 ret['disp0'] = disp_map_0 ret['acc0'] = acc_map_0 ret['z_std'] = torch.std(z_samples, dim=-1, unbiased=False) # [N_rays] for k in ret: if (torch.isnan(ret[k]).any() or torch.isinf(ret[k]).any()) and DEBUG: print(f"! [Numerical Error] {k} contains nan or inf.") return ret def config_parser(): import configargparse parser = configargparse.ArgumentParser() parser.add_argument('--config', is_config_file=True, help='config file path') parser.add_argument("--expname", type=str, help='experiment name') parser.add_argument("--basedir", type=str, default='./logs/', help='where to store ckpts and logs') parser.add_argument("--datadir", type=str, default='./data/llff/fern', help='input data directory') # training options parser.add_argument("--netdepth", type=int, default=8, help='layers in network') parser.add_argument("--netwidth", type=int, default=256, help='channels per layer') parser.add_argument("--netdepth_fine", type=int, default=8, help='layers in fine network') parser.add_argument("--netwidth_fine", type=int, default=256, help='channels per layer in fine network') parser.add_argument("--N_rand", type=int, default=32*32*4, help='batch size (number of random rays per gradient step)') parser.add_argument("--lrate", type=float, default=5e-4, help='learning rate') parser.add_argument("--lrate_decay", type=int, default=250, help='exponential learning rate decay (in 1000 steps)') parser.add_argument("--chunk", type=int, default=1024*32, help='number of rays processed in parallel, decrease if running out of memory') parser.add_argument("--netchunk_per_gpu", type=int, default=1024*64*4, help='number of pts sent through network in parallel, decrease if running out of memory') parser.add_argument("--no_batching", action='store_true', help='only take random rays from 1 image at a time') parser.add_argument("--no_reload", action='store_true', help='do not reload weights from saved ckpt') parser.add_argument("--ft_path", type=str, default=None, help='specific weights npy file to reload for coarse network') # rendering options parser.add_argument("--N_samples", type=int, default=64, help='number of coarse samples per ray') parser.add_argument("--N_importance", type=int, default=0, help='number of additional fine samples per ray') parser.add_argument("--perturb", type=float, default=1., help='set to 0. for no jitter, 1. for jitter') parser.add_argument("--use_viewdirs", action='store_true', help='use full 5D input instead of 3D') parser.add_argument("--i_embed", type=int, default=0, help='set 0 for default positional encoding, -1 for none') parser.add_argument("--multires", type=int, default=10, help='log2 of max freq for positional encoding (3D location)') parser.add_argument("--multires_views", type=int, default=4, help='log2 of max freq for positional encoding (2D direction)') parser.add_argument("--raw_noise_std", type=float, default=0., help='std dev of noise added to regularize sigma_a output, 1e0 recommended') parser.add_argument("--render_only", action='store_true', help='do not optimize, reload weights and render out render_poses path') parser.add_argument("--render_test", action='store_true', help='render the test set instead of render_poses path') parser.add_argument("--render_factor", type=int, default=0, help='downsampling factor to speed up rendering, set 4 or 8 for fast preview') # training options parser.add_argument("--precrop_iters", type=int, default=0, help='number of steps to train on central crops') parser.add_argument("--precrop_frac", type=float, default=.5, help='fraction of img taken for central crops') # dataset options parser.add_argument("--dataset_type", type=str, default='llff', help='options: llff / blender / deepvoxels') parser.add_argument("--testskip", type=int, default=8, help='will load 1/N images from test/val sets, useful for large datasets like deepvoxels') ## deepvoxels flags parser.add_argument("--shape", type=str, default='greek', help='options : armchair / cube / greek / vase') ## blender flags parser.add_argument("--white_bkgd", action='store_true', help='set to render synthetic data on a white bkgd (always use for dvoxels)') parser.add_argument("--half_res", action='store_true', help='load blender synthetic data at 400x400 instead of 800x800') ## llff flags parser.add_argument("--factor", type=int, default=8, help='downsample factor for LLFF images') parser.add_argument("--no_ndc", action='store_true', help='do not use normalized device coordinates (set for non-forward facing scenes)') parser.add_argument("--lindisp", action='store_true', help='sampling linearly in disparity rather than depth') parser.add_argument("--spherify", action='store_true', help='set for spherical 360 scenes') parser.add_argument("--llffhold", type=int, default=8, help='will take every 1/N images as LLFF test set, paper uses 8') # logging/saving options parser.add_argument("--i_print", type=int, default=100, help='frequency of console printout and metric loggin') parser.add_argument("--i_img", type=int, default=500, help='frequency of tensorboard image logging') parser.add_argument("--i_weights", type=int, default=10000, help='frequency of weight ckpt saving') parser.add_argument("--i_testset", type=int, default=50000, help='frequency of testset saving') parser.add_argument("--i_video", type=int, default=50000, help='frequency of render_poses video saving') return parser def train(): parser = config_parser() args = parser.parse_args() # Multi-GPU args.n_gpus = torch.cuda.device_count() print(f"Using {args.n_gpus} GPU(s).") # Load data if args.dataset_type == 'llff': images, poses, bds, render_poses, i_test = load_llff_data(args.datadir, args.factor, recenter=True, bd_factor=.75, spherify=args.spherify) hwf = poses[0,:3,-1] poses = poses[:,:3,:4] print('Loaded llff', images.shape, render_poses.shape, hwf, args.datadir) if not isinstance(i_test, list): i_test = [i_test] if args.llffhold > 0: print('Auto LLFF holdout,', args.llffhold) i_test = np.arange(images.shape[0])[::args.llffhold] i_val = i_test i_train = np.array([i for i in np.arange(int(images.shape[0])) if (i not in i_test and i not in i_val)]) print('DEFINING BOUNDS') if args.no_ndc: near = np.ndarray.min(bds) * .9 far = np.ndarray.max(bds) * 1. else: near = 0. far = 1. print('NEAR FAR', near, far) elif args.dataset_type == 'blender': images, poses, render_poses, hwf, i_split = load_blender_data(args.datadir, args.half_res, args.testskip) print('Loaded blender', images.shape, render_poses.shape, hwf, args.datadir) i_train, i_val, i_test = i_split near = 2. far = 6. if args.white_bkgd: images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:]) else: images = images[...,:3] elif args.dataset_type == 'deepvoxels': images, poses, render_poses, hwf, i_split = load_dv_data(scene=args.shape, basedir=args.datadir, testskip=args.testskip) print('Loaded deepvoxels', images.shape, render_poses.shape, hwf, args.datadir) i_train, i_val, i_test = i_split hemi_R = np.mean(np.linalg.norm(poses[:,:3,-1], axis=-1)) near = hemi_R-1. far = hemi_R+1. else: print('Unknown dataset type', args.dataset_type, 'exiting') return # Cast intrinsics to right types H, W, focal = hwf H, W = int(H), int(W) hwf = [H, W, focal] if args.render_test: render_poses = np.array(poses[i_test]) # Create log dir and copy the config file basedir = args.basedir expname = args.expname os.makedirs(os.path.join(basedir, expname), exist_ok=True) f = os.path.join(basedir, expname, 'args.txt') with open(f, 'w') as file: for arg in sorted(vars(args)): attr = getattr(args, arg) file.write('{} = {}\n'.format(arg, attr)) if args.config is not None: f = os.path.join(basedir, expname, 'config.txt') with open(f, 'w') as file: file.write(open(args.config, 'r').read()) # Create nerf model render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer = create_nerf(args) global_step = start bds_dict = { 'near' : near, 'far' : far, } render_kwargs_train.update(bds_dict) render_kwargs_test.update(bds_dict) # Move testing data to GPU render_poses = torch.Tensor(render_poses).to(device) # Short circuit if only rendering out from trained model if args.render_only: print('RENDER ONLY') with torch.no_grad(): if args.render_test: # render_test switches to test poses images = images[i_test] else: # Default is smoother render_poses path images = None testsavedir = os.path.join(basedir, expname, 'renderonly_{}_{:06d}'.format('test' if args.render_test else 'path', start)) os.makedirs(testsavedir, exist_ok=True) print('test poses shape', render_poses.shape) rgbs, _ = render_path(render_poses, hwf, args.chunk, render_kwargs_test, gt_imgs=images, savedir=testsavedir, render_factor=args.render_factor) print('Done rendering', testsavedir) imageio.mimwrite(os.path.join(testsavedir, 'video.mp4'), to8b(rgbs), fps=30, quality=8) return # Prepare raybatch tensor if batching random rays N_rand = args.N_rand use_batching = not args.no_batching if use_batching: # For random ray batching print('get rays') rays = np.stack([get_rays_np(H, W, focal, p) for p in poses[:,:3,:4]], 0) # [N, ro+rd, H, W, 3] print('done, concats') rays_rgb = np.concatenate([rays, images[:,None]], 1) # [N, ro+rd+rgb, H, W, 3] rays_rgb = np.transpose(rays_rgb, [0,2,3,1,4]) # [N, H, W, ro+rd+rgb, 3] rays_rgb = np.stack([rays_rgb[i] for i in i_train], 0) # train images only rays_rgb = np.reshape(rays_rgb, [-1,3,3]) # [(N-1)*H*W, ro+rd+rgb, 3] rays_rgb = rays_rgb.astype(np.float32) print('shuffle rays') np.random.shuffle(rays_rgb) print('done') i_batch = 0 # Move training data to GPU images = torch.Tensor(images).to(device) poses = torch.Tensor(poses).to(device) if use_batching: rays_rgb = torch.Tensor(rays_rgb).to(device) N_iters = 200000 + 1 print('Begin') print('TRAIN views are', i_train) print('TEST views are', i_test) print('VAL views are', i_val) # Summary writers # writer = SummaryWriter(os.path.join(basedir, 'summaries', expname)) start = start + 1 for i in trange(start, N_iters): time0 = time.time() # Sample random ray batch if use_batching: # Random over all images batch = rays_rgb[i_batch:i_batch+N_rand] # [B, 2+1, 3*?] batch = torch.transpose(batch, 0, 1) batch_rays, target_s = batch[:2], batch[2] i_batch += N_rand if i_batch >= rays_rgb.shape[0]: print("Shuffle data after an epoch!") rand_idx = torch.randperm(rays_rgb.shape[0]) rays_rgb = rays_rgb[rand_idx] i_batch = 0 else: # Random from one image img_i = np.random.choice(i_train) target = images[img_i] pose = poses[img_i, :3,:4] if N_rand is not None: rays_o, rays_d = get_rays(H, W, focal, torch.Tensor(pose)) # (H, W, 3), (H, W, 3) if i < args.precrop_iters: dH = int(H//2 * args.precrop_frac) dW = int(W//2 * args.precrop_frac) coords = torch.stack( torch.meshgrid( torch.linspace(H//2 - dH, H//2 + dH - 1, 2*dH), torch.linspace(W//2 - dW, W//2 + dW - 1, 2*dW) ), -1) if i == start: print(f"[Config] Center cropping of size {2*dH} x {2*dW} is enabled until iter {args.precrop_iters}") else: coords = torch.stack(torch.meshgrid(torch.linspace(0, H-1, H), torch.linspace(0, W-1, W)), -1) # (H, W, 2) coords = torch.reshape(coords, [-1,2]) # (H * W, 2) select_inds = np.random.choice(coords.shape[0], size=[N_rand], replace=False) # (N_rand,) select_coords = coords[select_inds].long() # (N_rand, 2) rays_o = rays_o[select_coords[:, 0], select_coords[:, 1]] # (N_rand, 3) rays_d = rays_d[select_coords[:, 0], select_coords[:, 1]] # (N_rand, 3) batch_rays = torch.stack([rays_o, rays_d], 0) # (2, N_rand, 3) target_s = target[select_coords[:, 0], select_coords[:, 1]] # (N_rand, 3) ##### Core optimization loop ##### rgb, disp, acc, extras = render(H, W, focal, chunk=args.chunk, rays=batch_rays, verbose=i < 10, retraw=True, **render_kwargs_train) optimizer.zero_grad() img_loss = img2mse(rgb, target_s) trans = extras['raw'][...,-1] loss = img_loss psnr = mse2psnr(img_loss) if 'rgb0' in extras: img_loss0 = img2mse(extras['rgb0'], target_s) loss = loss + img_loss0 psnr0 = mse2psnr(img_loss0) loss.backward() optimizer.step() # NOTE: IMPORTANT! ### update learning rate ### decay_rate = 0.1 decay_steps = args.lrate_decay * 1000 new_lrate = args.lrate * (decay_rate ** (global_step / decay_steps)) for param_group in optimizer.param_groups: param_group['lr'] = new_lrate ################################ dt = time.time()-time0 # print(f"Step: {global_step}, Loss: {loss}, Time: {dt}") ##### end ##### # Rest is logging if i%args.i_weights==0: path = os.path.join(basedir, expname, '{:06d}.tar'.format(i)) torch.save({ 'global_step': global_step, 'network_fn_state_dict': render_kwargs_train['network_fn'].state_dict(), 'network_fine_state_dict': render_kwargs_train['network_fine'].state_dict(), 'optimizer_state_dict': optimizer.state_dict(), }, path) print('Saved checkpoints at', path) if i%args.i_video==0 and i > 0: # Turn on testing mode with torch.no_grad(): rgbs, disps = render_path(render_poses, hwf, args.chunk, render_kwargs_test) print('Done, saving', rgbs.shape, disps.shape) moviebase = os.path.join(basedir, expname, '{}_spiral_{:06d}_'.format(expname, i)) imageio.mimwrite(moviebase + 'rgb.mp4', to8b(rgbs), fps=30, quality=8) imageio.mimwrite(moviebase + 'disp.mp4', to8b(disps / np.max(disps)), fps=30, quality=8) # if args.use_viewdirs: # render_kwargs_test['c2w_staticcam'] = render_poses[0][:3,:4] # with torch.no_grad(): # rgbs_still, _ = render_path(render_poses, hwf, args.chunk, render_kwargs_test) # render_kwargs_test['c2w_staticcam'] = None # imageio.mimwrite(moviebase + 'rgb_still.mp4', to8b(rgbs_still), fps=30, quality=8) if i%args.i_testset==0 and i > 0: testsavedir = os.path.join(basedir, expname, 'testset_{:06d}'.format(i)) os.makedirs(testsavedir, exist_ok=True) print('test poses shape', poses[i_test].shape) with torch.no_grad(): render_path(torch.Tensor(poses[i_test]).to(device), hwf, args.chunk, render_kwargs_test, gt_imgs=images[i_test], savedir=testsavedir) print('Saved test set') if i%args.i_print==0: tqdm.write(f"[TRAIN] Iter: {i} Loss: {loss.item()} PSNR: {psnr.item()}") """ print(expname, i, psnr.numpy(), loss.numpy(), global_step.numpy()) print('iter time {:.05f}'.format(dt)) with tf.contrib.summary.record_summaries_every_n_global_steps(args.i_print): tf.contrib.summary.scalar('loss', loss) tf.contrib.summary.scalar('psnr', psnr) tf.contrib.summary.histogram('tran', trans) if args.N_importance > 0: tf.contrib.summary.scalar('psnr0', psnr0) if i%args.i_img==0: # Log a rendered validation view to Tensorboard img_i=np.random.choice(i_val) target = images[img_i] pose = poses[img_i, :3,:4] with torch.no_grad(): rgb, disp, acc, extras = render(H, W, focal, chunk=args.chunk, c2w=pose, **render_kwargs_test) psnr = mse2psnr(img2mse(rgb, target)) with tf.contrib.summary.record_summaries_every_n_global_steps(args.i_img): tf.contrib.summary.image('rgb', to8b(rgb)[tf.newaxis]) tf.contrib.summary.image('disp', disp[tf.newaxis,...,tf.newaxis]) tf.contrib.summary.image('acc', acc[tf.newaxis,...,tf.newaxis]) tf.contrib.summary.scalar('psnr_holdout', psnr) tf.contrib.summary.image('rgb_holdout', target[tf.newaxis]) if args.N_importance > 0: with tf.contrib.summary.record_summaries_every_n_global_steps(args.i_img): tf.contrib.summary.image('rgb0', to8b(extras['rgb0'])[tf.newaxis]) tf.contrib.summary.image('disp0', extras['disp0'][tf.newaxis,...,tf.newaxis]) tf.contrib.summary.image('z_std', extras['z_std'][tf.newaxis,...,tf.newaxis]) """ global_step += 1 if __name__=='__main__': torch.set_default_tensor_type('torch.cuda.FloatTensor') train()