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606 lines
28 KiB
Python
606 lines
28 KiB
Python
4 years ago
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import torch
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4 years ago
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import torch.nn as nn
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4 years ago
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import torch.optim
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import torch.distributed
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from torch.nn.parallel import DistributedDataParallel as DDP
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import torch.multiprocessing
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import os
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from collections import OrderedDict
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4 years ago
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from ddp_model import NerfNetWithAutoExpo
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4 years ago
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import time
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from data_loader_split import load_data_split
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import numpy as np
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from tensorboardX import SummaryWriter
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from utils import img2mse, mse2psnr, img_HWC2CHW, colorize, TINY_NUMBER
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import logging
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4 years ago
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import json
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4 years ago
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4 years ago
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logger = logging.getLogger(__package__)
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def setup_logger():
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# create logger
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logger = logging.getLogger(__package__)
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# logger.setLevel(logging.DEBUG)
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logger.setLevel(logging.INFO)
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# create console handler and set level to debug
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ch = logging.StreamHandler()
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ch.setLevel(logging.DEBUG)
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# create formatter
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formatter = logging.Formatter('%(asctime)s [%(levelname)s] %(name)s: %(message)s')
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# add formatter to ch
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ch.setFormatter(formatter)
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# add ch to logger
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logger.addHandler(ch)
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def intersect_sphere(ray_o, ray_d):
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'''
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ray_o, ray_d: [..., 3]
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compute the depth of the intersection point between this ray and unit sphere
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'''
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# note: d1 becomes negative if this mid point is behind camera
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d1 = -torch.sum(ray_d * ray_o, dim=-1) / torch.sum(ray_d * ray_d, dim=-1)
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p = ray_o + d1.unsqueeze(-1) * ray_d
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# consider the case where the ray does not intersect the sphere
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ray_d_cos = 1. / torch.norm(ray_d, dim=-1)
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d2 = torch.sqrt(1. - torch.sum(p * p, dim=-1)) * ray_d_cos
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return d1 + d2
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def perturb_samples(z_vals):
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# get intervals between samples
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mids = .5 * (z_vals[..., 1:] + z_vals[..., :-1])
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upper = torch.cat([mids, z_vals[..., -1:]], dim=-1)
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lower = torch.cat([z_vals[..., 0:1], mids], dim=-1)
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# uniform samples in those intervals
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t_rand = torch.rand_like(z_vals)
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z_vals = lower + (upper - lower) * t_rand # [N_rays, N_samples]
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return z_vals
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def sample_pdf(bins, weights, N_samples, det=False):
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'''
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:param bins: tensor of shape [..., M+1], M is the number of bins
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:param weights: tensor of shape [..., M]
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:param N_samples: number of samples along each ray
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:param det: if True, will perform deterministic sampling
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:return: [..., N_samples]
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'''
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# Get pdf
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weights = weights + TINY_NUMBER # prevent nans
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pdf = weights / torch.sum(weights, dim=-1, keepdim=True) # [..., M]
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cdf = torch.cumsum(pdf, dim=-1) # [..., M]
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cdf = torch.cat([torch.zeros_like(cdf[..., 0:1]), cdf], dim=-1) # [..., M+1]
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# Take uniform samples
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dots_sh = list(weights.shape[:-1])
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M = weights.shape[-1]
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min_cdf = 0.00
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max_cdf = 1.00 # prevent outlier samples
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if det:
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u = torch.linspace(min_cdf, max_cdf, N_samples, device=bins.device)
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u = u.view([1]*len(dots_sh) + [N_samples]).expand(dots_sh + [N_samples,]) # [..., N_samples]
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else:
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sh = dots_sh + [N_samples]
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u = torch.rand(*sh, device=bins.device) * (max_cdf - min_cdf) + min_cdf # [..., N_samples]
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# Invert CDF
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# [..., N_samples, 1] >= [..., 1, M] ----> [..., N_samples, M] ----> [..., N_samples,]
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above_inds = torch.sum(u.unsqueeze(-1) >= cdf[..., :M].unsqueeze(-2), dim=-1).long()
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# random sample inside each bin
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below_inds = torch.clamp(above_inds-1, min=0)
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inds_g = torch.stack((below_inds, above_inds), dim=-1) # [..., N_samples, 2]
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cdf = cdf.unsqueeze(-2).expand(dots_sh + [N_samples, M+1]) # [..., N_samples, M+1]
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cdf_g = torch.gather(input=cdf, dim=-1, index=inds_g) # [..., N_samples, 2]
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bins = bins.unsqueeze(-2).expand(dots_sh + [N_samples, M+1]) # [..., N_samples, M+1]
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bins_g = torch.gather(input=bins, dim=-1, index=inds_g) # [..., N_samples, 2]
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# fix numeric issue
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denom = cdf_g[..., 1] - cdf_g[..., 0] # [..., N_samples]
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denom = torch.where(denom<TINY_NUMBER, torch.ones_like(denom), denom)
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t = (u - cdf_g[..., 0]) / denom
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samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0] + TINY_NUMBER)
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return samples
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def render_single_image(rank, world_size, models, ray_sampler, chunk_size):
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##### parallel rendering of a single image
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ray_batch = ray_sampler.get_all()
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# split into ranks; make sure different processes don't overlap
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rank_split_sizes = [ray_batch['ray_d'].shape[0] // world_size, ] * world_size
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rank_split_sizes[-1] = ray_batch['ray_d'].shape[0] - sum(rank_split_sizes[:-1])
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for key in ray_batch:
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if torch.is_tensor(ray_batch[key]):
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ray_batch[key] = torch.split(ray_batch[key], rank_split_sizes)[rank].to(rank)
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# split into chunks and render inside each process
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ray_batch_split = OrderedDict()
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for key in ray_batch:
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if torch.is_tensor(ray_batch[key]):
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ray_batch_split[key] = torch.split(ray_batch[key], chunk_size)
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# forward and backward
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ret_merge_chunk = [OrderedDict() for _ in range(models['cascade_level'])]
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for s in range(len(ray_batch_split['ray_d'])):
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ray_o = ray_batch_split['ray_o'][s]
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ray_d = ray_batch_split['ray_d'][s]
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min_depth = ray_batch_split['min_depth'][s]
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dots_sh = list(ray_d.shape[:-1])
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for m in range(models['cascade_level']):
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net = models['net_{}'.format(m)]
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# sample depths
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N_samples = models['cascade_samples'][m]
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if m == 0:
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# foreground depth
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fg_far_depth = intersect_sphere(ray_o, ray_d) # [...,]
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# fg_near_depth = 0.18 * torch.ones_like(fg_far_depth)
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fg_near_depth = min_depth # [..., 3]
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step = (fg_far_depth - fg_near_depth) / (N_samples - 1)
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fg_depth = torch.stack([fg_near_depth + i * step for i in range(N_samples)], dim=-1) # [..., N_samples]
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# background depth
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bg_depth = torch.linspace(0., 1., N_samples).view(
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[1, ] * len(dots_sh) + [N_samples,]).expand(dots_sh + [N_samples,]).to(rank)
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# delete unused memory
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del fg_near_depth
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del step
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torch.cuda.empty_cache()
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else:
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# sample pdf and concat with earlier samples
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fg_weights = ret['fg_weights'].clone().detach()
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fg_depth_mid = .5 * (fg_depth[..., 1:] + fg_depth[..., :-1]) # [..., N_samples-1]
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fg_weights = fg_weights[..., 1:-1] # [..., N_samples-2]
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fg_depth_samples = sample_pdf(bins=fg_depth_mid, weights=fg_weights,
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N_samples=N_samples, det=True) # [..., N_samples]
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fg_depth, _ = torch.sort(torch.cat((fg_depth, fg_depth_samples), dim=-1))
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# sample pdf and concat with earlier samples
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bg_weights = ret['bg_weights'].clone().detach()
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bg_depth_mid = .5 * (bg_depth[..., 1:] + bg_depth[..., :-1])
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bg_weights = bg_weights[..., 1:-1] # [..., N_samples-2]
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bg_depth_samples = sample_pdf(bins=bg_depth_mid, weights=bg_weights,
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N_samples=N_samples, det=True) # [..., N_samples]
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bg_depth, _ = torch.sort(torch.cat((bg_depth, bg_depth_samples), dim=-1))
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# delete unused memory
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del fg_weights
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del fg_depth_mid
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del fg_depth_samples
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del bg_weights
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del bg_depth_mid
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del bg_depth_samples
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torch.cuda.empty_cache()
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with torch.no_grad():
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ret = net(ray_o, ray_d, fg_far_depth, fg_depth, bg_depth)
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for key in ret:
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if key not in ['fg_weights', 'bg_weights']:
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if torch.is_tensor(ret[key]):
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if key not in ret_merge_chunk[m]:
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ret_merge_chunk[m][key] = [ret[key].cpu(), ]
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else:
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ret_merge_chunk[m][key].append(ret[key].cpu())
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ret[key] = None
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# clean unused memory
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torch.cuda.empty_cache()
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# merge results from different chunks
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for m in range(len(ret_merge_chunk)):
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for key in ret_merge_chunk[m]:
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ret_merge_chunk[m][key] = torch.cat(ret_merge_chunk[m][key], dim=0)
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# merge results from different processes
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if rank == 0:
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ret_merge_rank = [OrderedDict() for _ in range(len(ret_merge_chunk))]
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for m in range(len(ret_merge_chunk)):
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for key in ret_merge_chunk[m]:
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# generate tensors to store results from other processes
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sh = list(ret_merge_chunk[m][key].shape[1:])
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ret_merge_rank[m][key] = [torch.zeros(*[size,]+sh, dtype=torch.float32) for size in rank_split_sizes]
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torch.distributed.gather(ret_merge_chunk[m][key], ret_merge_rank[m][key])
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ret_merge_rank[m][key] = torch.cat(ret_merge_rank[m][key], dim=0).reshape(
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(ray_sampler.H, ray_sampler.W, -1)).squeeze()
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# print(m, key, ret_merge_rank[m][key].shape)
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else: # send results to main process
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for m in range(len(ret_merge_chunk)):
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for key in ret_merge_chunk[m]:
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torch.distributed.gather(ret_merge_chunk[m][key])
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# only rank 0 program returns
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if rank == 0:
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return ret_merge_rank
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else:
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return None
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def log_view_to_tb(writer, global_step, log_data, gt_img, mask, prefix=''):
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rgb_im = img_HWC2CHW(torch.from_numpy(gt_img))
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writer.add_image(prefix + 'rgb_gt', rgb_im, global_step)
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for m in range(len(log_data)):
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rgb_im = img_HWC2CHW(log_data[m]['rgb'])
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rgb_im = torch.clamp(rgb_im, min=0., max=1.) # just in case diffuse+specular>1
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writer.add_image(prefix + 'level_{}/rgb'.format(m), rgb_im, global_step)
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rgb_im = img_HWC2CHW(log_data[m]['fg_rgb'])
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rgb_im = torch.clamp(rgb_im, min=0., max=1.) # just in case diffuse+specular>1
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writer.add_image(prefix + 'level_{}/fg_rgb'.format(m), rgb_im, global_step)
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depth = log_data[m]['fg_depth']
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depth_im = img_HWC2CHW(colorize(depth, cmap_name='jet', append_cbar=True,
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mask=mask))
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writer.add_image(prefix + 'level_{}/fg_depth'.format(m), depth_im, global_step)
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rgb_im = img_HWC2CHW(log_data[m]['bg_rgb'])
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rgb_im = torch.clamp(rgb_im, min=0., max=1.) # just in case diffuse+specular>1
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writer.add_image(prefix + 'level_{}/bg_rgb'.format(m), rgb_im, global_step)
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depth = log_data[m]['bg_depth']
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depth_im = img_HWC2CHW(colorize(depth, cmap_name='jet', append_cbar=True,
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mask=mask))
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writer.add_image(prefix + 'level_{}/bg_depth'.format(m), depth_im, global_step)
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bg_lambda = log_data[m]['bg_lambda']
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bg_lambda_im = img_HWC2CHW(colorize(bg_lambda, cmap_name='hot', append_cbar=True,
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mask=mask))
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writer.add_image(prefix + 'level_{}/bg_lambda'.format(m), bg_lambda_im, global_step)
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def setup(rank, world_size):
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os.environ['MASTER_ADDR'] = 'localhost'
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# port = np.random.randint(12355, 12399)
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# os.environ['MASTER_PORT'] = '{}'.format(port)
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os.environ['MASTER_PORT'] = '12355'
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# initialize the process group
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torch.distributed.init_process_group("gloo", rank=rank, world_size=world_size)
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def cleanup():
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torch.distributed.destroy_process_group()
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4 years ago
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def create_nerf(rank, args):
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4 years ago
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###### create network and wrap in ddp; each process should do this
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# fix random seed just to make sure the network is initialized with same weights at different processes
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torch.manual_seed(777)
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# very important!!! otherwise it might introduce extra memory in rank=0 gpu
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torch.cuda.set_device(rank)
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models = OrderedDict()
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models['cascade_level'] = args.cascade_level
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models['cascade_samples'] = [int(x.strip()) for x in args.cascade_samples.split(',')]
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for m in range(models['cascade_level']):
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4 years ago
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img_names = None
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if args.optim_autoexpo:
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# load training image names for autoexposure
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f = os.path.join(args.basedir, args.expname, 'train_images.json')
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with open(f) as file:
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img_names = json.load(file)
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net = NerfNetWithAutoExpo(args, optim_autoexpo=args.optim_autoexpo, img_names=img_names).to(rank)
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net = DDP(net, device_ids=[rank], output_device=rank, find_unused_parameters=True)
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# net = DDP(net, device_ids=[rank], output_device=rank)
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4 years ago
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optim = torch.optim.Adam(net.parameters(), lr=args.lrate)
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models['net_{}'.format(m)] = net
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models['optim_{}'.format(m)] = optim
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start = -1
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###### load pretrained weights; each process should do this
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if (args.ckpt_path is not None) and (os.path.isfile(args.ckpt_path)):
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ckpts = [args.ckpt_path]
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else:
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ckpts = [os.path.join(args.basedir, args.expname, f)
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for f in sorted(os.listdir(os.path.join(args.basedir, args.expname))) if f.endswith('.pth')]
|
||
|
|
def path2iter(path):
|
||
|
|
tmp = os.path.basename(path)[:-4]
|
||
|
|
idx = tmp.rfind('_')
|
||
|
|
return int(tmp[idx + 1:])
|
||
|
|
ckpts = sorted(ckpts, key=path2iter)
|
||
|
|
logger.info('Found ckpts: {}'.format(ckpts))
|
||
|
|
if len(ckpts) > 0 and not args.no_reload:
|
||
|
|
fpath = ckpts[-1]
|
||
|
|
logger.info('Reloading from: {}'.format(fpath))
|
||
|
|
start = path2iter(fpath)
|
||
|
|
# configure map_location properly for different processes
|
||
|
|
map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
|
||
|
|
to_load = torch.load(fpath, map_location=map_location)
|
||
|
|
for m in range(models['cascade_level']):
|
||
|
|
for name in ['net_{}'.format(m), 'optim_{}'.format(m)]:
|
||
|
|
models[name].load_state_dict(to_load[name])
|
||
|
|
models[name].load_state_dict(to_load[name])
|
||
|
|
|
||
|
4 years ago
|
return start, models
|
||
|
|
|
||
|
|
|
||
|
|
def ddp_train_nerf(rank, args):
|
||
|
|
###### set up multi-processing
|
||
|
|
setup(rank, args.world_size)
|
||
|
|
###### set up logger
|
||
|
|
logger = logging.getLogger(__package__)
|
||
|
|
setup_logger()
|
||
|
|
|
||
|
|
###### decide chunk size according to gpu memory
|
||
|
|
logger.info('gpu_mem: {}'.format(torch.cuda.get_device_properties(rank).total_memory))
|
||
|
|
if torch.cuda.get_device_properties(rank).total_memory / 1e9 > 14:
|
||
|
|
logger.info('setting batch size according to 24G gpu')
|
||
|
|
args.N_rand = 1024
|
||
|
|
args.chunk_size = 8192
|
||
|
|
else:
|
||
|
|
logger.info('setting batch size according to 12G gpu')
|
||
|
|
args.N_rand = 512
|
||
|
|
args.chunk_size = 4096
|
||
|
|
|
||
|
|
###### Create log dir and copy the config file
|
||
|
|
if rank == 0:
|
||
|
|
os.makedirs(os.path.join(args.basedir, args.expname), exist_ok=True)
|
||
|
|
f = os.path.join(args.basedir, args.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(args.basedir, args.expname, 'config.txt')
|
||
|
|
with open(f, 'w') as file:
|
||
|
|
file.write(open(args.config, 'r').read())
|
||
|
|
torch.distributed.barrier()
|
||
|
|
|
||
|
|
ray_samplers = load_data_split(args.datadir, args.scene, split='train',
|
||
|
|
try_load_min_depth=args.load_min_depth)
|
||
|
|
val_ray_samplers = load_data_split(args.datadir, args.scene, split='validation',
|
||
|
|
try_load_min_depth=args.load_min_depth, skip=args.testskip)
|
||
|
|
|
||
|
|
# write training image names for autoexposure
|
||
|
|
if args.optim_autoexpo:
|
||
|
|
f = os.path.join(args.basedir, args.expname, 'train_images.json')
|
||
|
|
with open(f, 'w') as file:
|
||
|
|
img_names = [ray_samplers[i].img_path for i in range(len(ray_samplers))]
|
||
|
|
json.dump(img_names, file, indent=2)
|
||
|
|
|
||
|
|
###### create network and wrap in ddp; each process should do this
|
||
|
|
start, models = create_nerf(rank, args)
|
||
|
|
|
||
|
4 years ago
|
##### important!!!
|
||
|
|
# make sure different processes sample different rays
|
||
|
|
np.random.seed((rank + 1) * 777)
|
||
|
|
# make sure different processes have different perturbations in depth samples
|
||
|
|
torch.manual_seed((rank + 1) * 777)
|
||
|
|
|
||
|
|
##### only main process should do the logging
|
||
|
|
if rank == 0:
|
||
|
|
writer = SummaryWriter(os.path.join(args.basedir, 'summaries', args.expname))
|
||
|
|
|
||
|
|
# start training
|
||
|
|
what_val_to_log = 0 # helper variable for parallel rendering of a image
|
||
|
|
what_train_to_log = 0
|
||
|
|
for global_step in range(start+1, start+1+args.N_iters):
|
||
|
|
time0 = time.time()
|
||
|
|
scalars_to_log = OrderedDict()
|
||
|
|
### Start of core optimization loop
|
||
|
|
scalars_to_log['resolution'] = ray_samplers[0].resolution_level
|
||
|
|
# randomly sample rays and move to device
|
||
|
|
i = np.random.randint(low=0, high=len(ray_samplers))
|
||
|
|
ray_batch = ray_samplers[i].random_sample(args.N_rand, center_crop=False)
|
||
|
|
for key in ray_batch:
|
||
|
|
if torch.is_tensor(ray_batch[key]):
|
||
|
|
ray_batch[key] = ray_batch[key].to(rank)
|
||
|
|
|
||
|
|
# forward and backward
|
||
|
|
dots_sh = list(ray_batch['ray_d'].shape[:-1]) # number of rays
|
||
|
|
all_rets = [] # results on different cascade levels
|
||
|
|
for m in range(models['cascade_level']):
|
||
|
|
optim = models['optim_{}'.format(m)]
|
||
|
|
net = models['net_{}'.format(m)]
|
||
|
|
|
||
|
|
# sample depths
|
||
|
|
N_samples = models['cascade_samples'][m]
|
||
|
|
if m == 0:
|
||
|
|
# foreground depth
|
||
|
|
fg_far_depth = intersect_sphere(ray_batch['ray_o'], ray_batch['ray_d']) # [...,]
|
||
|
|
# fg_near_depth = 0.18 * torch.ones_like(fg_far_depth)
|
||
|
|
fg_near_depth = ray_batch['min_depth'] # [..., 3]
|
||
|
|
step = (fg_far_depth - fg_near_depth) / (N_samples - 1)
|
||
|
|
fg_depth = torch.stack([fg_near_depth + i * step for i in range(N_samples)], dim=-1) # [..., N_samples]
|
||
|
|
fg_depth = perturb_samples(fg_depth) # random perturbation during training
|
||
|
|
|
||
|
|
# background depth
|
||
|
|
bg_depth = torch.linspace(0., 1., N_samples).view(
|
||
|
|
[1, ] * len(dots_sh) + [N_samples,]).expand(dots_sh + [N_samples,]).to(rank)
|
||
|
|
bg_depth = perturb_samples(bg_depth) # random perturbation during training
|
||
|
|
else:
|
||
|
|
# sample pdf and concat with earlier samples
|
||
|
|
fg_weights = ret['fg_weights'].clone().detach()
|
||
|
|
fg_depth_mid = .5 * (fg_depth[..., 1:] + fg_depth[..., :-1]) # [..., N_samples-1]
|
||
|
|
fg_weights = fg_weights[..., 1:-1] # [..., N_samples-2]
|
||
|
|
fg_depth_samples = sample_pdf(bins=fg_depth_mid, weights=fg_weights,
|
||
|
|
N_samples=N_samples, det=False) # [..., N_samples]
|
||
|
|
fg_depth, _ = torch.sort(torch.cat((fg_depth, fg_depth_samples), dim=-1))
|
||
|
|
|
||
|
|
# sample pdf and concat with earlier samples
|
||
|
|
bg_weights = ret['bg_weights'].clone().detach()
|
||
|
|
bg_depth_mid = .5 * (bg_depth[..., 1:] + bg_depth[..., :-1])
|
||
|
|
bg_weights = bg_weights[..., 1:-1] # [..., N_samples-2]
|
||
|
|
bg_depth_samples = sample_pdf(bins=bg_depth_mid, weights=bg_weights,
|
||
|
|
N_samples=N_samples, det=False) # [..., N_samples]
|
||
|
|
bg_depth, _ = torch.sort(torch.cat((bg_depth, bg_depth_samples), dim=-1))
|
||
|
|
|
||
|
|
optim.zero_grad()
|
||
|
4 years ago
|
ret = net(ray_batch['ray_o'], ray_batch['ray_d'], fg_far_depth, fg_depth, bg_depth, img_name=ray_batch['img_name'])
|
||
|
4 years ago
|
all_rets.append(ret)
|
||
|
|
|
||
|
|
rgb_gt = ray_batch['rgb'].to(rank)
|
||
|
4 years ago
|
if 'autoexpo' in ret:
|
||
|
|
scale, shift = ret['autoexpo']
|
||
|
|
scalars_to_log['level_{}/autoexpo_scale'.format(m)] = scale.item()
|
||
|
|
scalars_to_log['level_{}/autoexpo_shift'.format(m)] = shift.item()
|
||
|
|
# rgb_gt = scale * rgb_gt + shift
|
||
|
|
rgb_pred = (ret['rgb'] - shift) / scale
|
||
|
|
rgb_loss = img2mse(rgb_pred, rgb_gt)
|
||
|
|
loss = rgb_loss + args.lambda_autoexpo * (torch.abs(scale-1.)+torch.abs(shift))
|
||
|
|
else:
|
||
|
|
rgb_loss = img2mse(ret['rgb'], rgb_gt)
|
||
|
|
loss = rgb_loss
|
||
|
|
scalars_to_log['level_{}/loss'.format(m)] = rgb_loss.item()
|
||
|
|
scalars_to_log['level_{}/pnsr'.format(m)] = mse2psnr(rgb_loss.item())
|
||
|
4 years ago
|
loss.backward()
|
||
|
|
optim.step()
|
||
|
|
|
||
|
|
# # clean unused memory
|
||
|
|
# torch.cuda.empty_cache()
|
||
|
|
|
||
|
|
### end of core optimization loop
|
||
|
|
dt = time.time() - time0
|
||
|
|
scalars_to_log['iter_time'] = dt
|
||
|
|
|
||
|
|
### only main process should do the logging
|
||
|
|
if rank == 0 and (global_step % args.i_print == 0 or global_step < 10):
|
||
|
|
logstr = '{} step: {} '.format(args.expname, global_step)
|
||
|
|
for k in scalars_to_log:
|
||
|
|
logstr += ' {}: {:.6f}'.format(k, scalars_to_log[k])
|
||
|
|
writer.add_scalar(k, scalars_to_log[k], global_step)
|
||
|
|
logger.info(logstr)
|
||
|
|
|
||
|
|
### each process should do this; but only main process merges the results
|
||
|
|
if global_step % args.i_img == 0 or global_step == start+1:
|
||
|
|
#### critical: make sure each process is working on the same random image
|
||
|
|
time0 = time.time()
|
||
|
|
idx = what_val_to_log % len(val_ray_samplers)
|
||
|
|
log_data = render_single_image(rank, args.world_size, models, val_ray_samplers[idx], args.chunk_size)
|
||
|
|
what_val_to_log += 1
|
||
|
|
dt = time.time() - time0
|
||
|
|
if rank == 0: # only main process should do this
|
||
|
|
logger.info('Logged a random validation view in {} seconds'.format(dt))
|
||
|
|
log_view_to_tb(writer, global_step, log_data, gt_img=val_ray_samplers[idx].get_img(), mask=None, prefix='val/')
|
||
|
|
|
||
|
|
time0 = time.time()
|
||
|
|
idx = what_train_to_log % len(ray_samplers)
|
||
|
|
log_data = render_single_image(rank, args.world_size, models, ray_samplers[idx], args.chunk_size)
|
||
|
|
what_train_to_log += 1
|
||
|
|
dt = time.time() - time0
|
||
|
|
if rank == 0: # only main process should do this
|
||
|
|
logger.info('Logged a random training view in {} seconds'.format(dt))
|
||
|
|
log_view_to_tb(writer, global_step, log_data, gt_img=ray_samplers[idx].get_img(), mask=None, prefix='train/')
|
||
|
|
|
||
|
4 years ago
|
del log_data
|
||
|
4 years ago
|
torch.cuda.empty_cache()
|
||
|
|
|
||
|
|
if rank == 0 and (global_step % args.i_weights == 0 and global_step > 0):
|
||
|
|
# saving checkpoints and logging
|
||
|
|
fpath = os.path.join(args.basedir, args.expname, 'model_{:06d}.pth'.format(global_step))
|
||
|
|
to_save = OrderedDict()
|
||
|
|
for m in range(models['cascade_level']):
|
||
|
|
name = 'net_{}'.format(m)
|
||
|
|
to_save[name] = models[name].state_dict()
|
||
|
|
|
||
|
|
name = 'optim_{}'.format(m)
|
||
|
|
to_save[name] = models[name].state_dict()
|
||
|
|
torch.save(to_save, fpath)
|
||
|
|
|
||
|
|
# clean up for multi-processing
|
||
|
|
cleanup()
|
||
|
|
|
||
|
|
|
||
|
|
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')
|
||
|
|
# dataset options
|
||
|
|
parser.add_argument("--datadir", type=str, default=None, help='input data directory')
|
||
|
|
parser.add_argument("--scene", type=str, default=None, help='scene name')
|
||
|
|
parser.add_argument("--testskip", type=int, default=8,
|
||
|
|
help='will load 1/N images from test/val sets, useful for large datasets like deepvoxels')
|
||
|
|
# model size
|
||
|
|
parser.add_argument("--netdepth", type=int, default=8, help='layers in coarse network')
|
||
|
|
parser.add_argument("--netwidth", type=int, default=256, help='channels per layer in coarse network')
|
||
|
|
parser.add_argument("--use_viewdirs", action='store_true', help='use full 5D input instead of 3D')
|
||
|
|
# checkpoints
|
||
|
|
parser.add_argument("--no_reload", action='store_true', help='do not reload weights from saved ckpt')
|
||
|
|
parser.add_argument("--ckpt_path", type=str, default=None,
|
||
|
|
help='specific weights npy file to reload for coarse network')
|
||
|
|
# batch size
|
||
|
|
parser.add_argument("--N_rand", type=int, default=32 * 32 * 2,
|
||
|
|
help='batch size (number of random rays per gradient step)')
|
||
|
|
parser.add_argument("--chunk_size", type=int, default=1024 * 8,
|
||
|
|
help='number of rays processed in parallel, decrease if running out of memory')
|
||
|
|
# iterations
|
||
|
|
parser.add_argument("--N_iters", type=int, default=250001,
|
||
|
|
help='number of iterations')
|
||
|
4 years ago
|
# render only
|
||
|
4 years ago
|
parser.add_argument("--render_splits", type=str, default='test',
|
||
|
|
help='splits to render')
|
||
|
|
# cascade training
|
||
|
|
parser.add_argument("--cascade_level", type=int, default=2,
|
||
|
|
help='number of cascade levels')
|
||
|
|
parser.add_argument("--cascade_samples", type=str, default='64,64',
|
||
|
|
help='samples at each level')
|
||
|
|
parser.add_argument("--devices", type=str, default='0,1',
|
||
|
|
help='cuda device for each level')
|
||
|
|
parser.add_argument("--bg_devices", type=str, default='0,2',
|
||
|
|
help='cuda device for the background of each level')
|
||
|
4 years ago
|
# multiprocess learning
|
||
|
4 years ago
|
parser.add_argument("--world_size", type=int, default='-1',
|
||
|
|
help='number of processes')
|
||
|
4 years ago
|
# optimize autoexposure
|
||
|
|
parser.add_argument("--optim_autoexpo", action='store_true',
|
||
|
|
help='optimize autoexposure parameters')
|
||
|
|
parser.add_argument("--lambda_autoexpo", type=float, default=1., help='regularization weight for autoexposure')
|
||
|
|
|
||
|
4 years ago
|
# learning rate options
|
||
|
|
parser.add_argument("--lrate", type=float, default=5e-4, help='learning rate')
|
||
|
|
parser.add_argument("--lrate_decay_factor", type=float, default=0.1,
|
||
|
|
help='decay learning rate by a factor every specified number of steps')
|
||
|
|
parser.add_argument("--lrate_decay_steps", type=int, default=5000,
|
||
|
|
help='decay learning rate by a factor every specified number of steps')
|
||
|
|
# rendering options
|
||
|
|
parser.add_argument("--det", action='store_true', help='deterministic sampling for coarse and fine samples')
|
||
|
|
parser.add_argument("--max_freq_log2", type=int, default=10,
|
||
|
|
help='log2 of max freq for positional encoding (3D location)')
|
||
|
|
parser.add_argument("--max_freq_log2_viewdirs", type=int, default=4,
|
||
|
|
help='log2 of max freq for positional encoding (2D direction)')
|
||
|
|
parser.add_argument("--load_min_depth", action='store_true', help='whether to load min depth')
|
||
|
|
# 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')
|
||
|
|
|
||
|
|
return parser
|
||
|
|
|
||
|
|
|
||
|
|
def train():
|
||
|
|
parser = config_parser()
|
||
|
|
args = parser.parse_args()
|
||
|
|
logger.info(parser.format_values())
|
||
|
|
|
||
|
|
if args.world_size == -1:
|
||
|
|
args.world_size = torch.cuda.device_count()
|
||
|
|
logger.info('Using # gpus: {}'.format(args.world_size))
|
||
|
|
torch.multiprocessing.spawn(ddp_train_nerf,
|
||
|
|
args=(args,),
|
||
|
|
nprocs=args.world_size,
|
||
|
|
join=True)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
setup_logger()
|
||
|
|
train()
|
||
|
|
|
||
|
|
|