clean code
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Kai-46
parent
2af283ff67
commit
6243abf840
2 changed files with 2 additions and 112 deletions
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@ -224,7 +224,6 @@ def render_single_image(rank, world_size, models, ray_sampler, chunk_size):
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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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@ -488,36 +487,30 @@ def config_parser():
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parser.add_argument('--config', is_config_file=True, help='config file path')
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parser.add_argument("--expname", type=str, help='experiment name')
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parser.add_argument("--basedir", type=str, default='./logs/', help='where to store ckpts and logs')
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# dataset options
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parser.add_argument("--datadir", type=str, default=None, help='input data directory')
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parser.add_argument("--scene", type=str, default=None, help='scene name')
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parser.add_argument("--testskip", type=int, default=8,
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help='will load 1/N images from test/val sets, useful for large datasets like deepvoxels')
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# model size
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parser.add_argument("--netdepth", type=int, default=8, help='layers in coarse network')
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parser.add_argument("--netwidth", type=int, default=256, help='channels per layer in coarse network')
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parser.add_argument("--use_viewdirs", action='store_true', help='use full 5D input instead of 3D')
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# checkpoints
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parser.add_argument("--no_reload", action='store_true', help='do not reload weights from saved ckpt')
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parser.add_argument("--ckpt_path", type=str, default=None,
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help='specific weights npy file to reload for coarse network')
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# batch size
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parser.add_argument("--N_rand", type=int, default=32 * 32 * 2,
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help='batch size (number of random rays per gradient step)')
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parser.add_argument("--chunk_size", type=int, default=1024 * 8,
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help='number of rays processed in parallel, decrease if running out of memory')
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# iterations
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parser.add_argument("--N_iters", type=int, default=250001,
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help='number of iterations')
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# render only
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parser.add_argument("--render_splits", type=str, default='test',
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help='splits to render')
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# cascade training
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parser.add_argument("--cascade_level", type=int, default=2,
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help='number of cascade levels')
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@ -527,26 +520,15 @@ def config_parser():
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help='cuda device for each level')
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parser.add_argument("--bg_devices", type=str, default='0,2',
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help='cuda device for the background of each level')
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# multiprocess learning
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parser.add_argument("--world_size", type=int, default='-1',
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help='number of processes')
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# mixed precison training
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parser.add_argument("--opt_level", type=str, default='O1',
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help='mixed precison training')
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parser.add_argument("--near_depth", type=float, default=0.1,
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help='near depth plane')
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parser.add_argument("--far_depth", type=float, default=50.,
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help='far depth plane')
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# learning rate options
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parser.add_argument("--lrate", type=float, default=5e-4, help='learning rate')
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parser.add_argument("--lrate_decay_factor", type=float, default=0.1,
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help='decay learning rate by a factor every specified number of steps')
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parser.add_argument("--lrate_decay_steps", type=int, default=5000,
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help='decay learning rate by a factor every specified number of steps')
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# rendering options
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parser.add_argument("--inv_uniform", action='store_true',
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help='if True, will uniformly sample inverse depths')
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@ -555,36 +537,11 @@ def config_parser():
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help='log2 of max freq for positional encoding (3D location)')
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parser.add_argument("--max_freq_log2_viewdirs", type=int, default=4,
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help='log2 of max freq for positional encoding (2D direction)')
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parser.add_argument("--N_iters_perturb", type=int, default=1000,
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help='perturb and center-crop at first 1000 iterations to prevent training from getting stuck')
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parser.add_argument("--raw_noise_std", type=float, default=1.,
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help='std dev of noise added to regularize sigma output, 1e0 recommended')
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parser.add_argument("--white_bkgd", action='store_true',
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help='apply the trick to avoid fitting to white background')
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# use implicit
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parser.add_argument("--load_min_depth", action='store_true', help='whether to load min depth')
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# no training; render only
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parser.add_argument("--render_only", action='store_true',
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help='do not optimize, reload weights and render out render_poses path')
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parser.add_argument("--render_train", action='store_true', help='render the training set')
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parser.add_argument("--render_test", action='store_true', help='render the test set instead of render_poses path')
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# no training; extract mesh only
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parser.add_argument("--mesh_only", action='store_true',
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help='do not optimize, extract mesh from pretrained model')
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parser.add_argument("--N_pts", type=int, default=256,
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help='voxel resolution; N_pts * N_pts * N_pts')
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parser.add_argument("--mesh_thres", type=str, default='10,20,30,40,50',
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help='threshold(s) for mesh extraction; can use multiple thresholds')
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# logging/saving options
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parser.add_argument("--i_print", type=int, default=100, help='frequency of console printout and metric loggin')
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parser.add_argument("--i_img", type=int, default=500, help='frequency of tensorboard image logging')
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parser.add_argument("--i_weights", type=int, default=10000, help='frequency of weight ckpt saving')
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parser.add_argument("--i_testset", type=int, default=50000, help='frequency of testset saving')
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parser.add_argument("--i_video", type=int, default=50000, help='frequency of render_poses video saving')
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return parser
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@ -180,70 +180,3 @@ class RaySamplerSingleImage(object):
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ret[k] = torch.from_numpy(ret[k])
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return ret
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# def random_sample_patches(self, N_patch, r_patch=16, center_crop=False):
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# '''
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# :param N_patch: number of patches to be sampled
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# :param r_patch: patch size will be (2*r_patch+1)*(2*r_patch+1)
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# :return:
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# '''
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# # even size patch
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# # offsets to center pixels
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# u, v = np.meshgrid(np.arange(-r_patch, r_patch),
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# np.arange(-r_patch, r_patch))
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# u = u.reshape(-1)
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# v = v.reshape(-1)
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# offsets = v * self.W + u
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# # center pixel coordinates
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# u_min = r_patch
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# u_max = self.W - r_patch
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# v_min = r_patch
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# v_max = self.H - r_patch
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# if center_crop:
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# u_min = self.W // 4 + r_patch
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# u_max = self.W - self.W // 4 - r_patch
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# v_min = self.H // 4 + r_patch
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# v_max = self.H - self.H // 4 - r_patch
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# u, v = np.meshgrid(np.arange(u_min, u_max, r_patch),
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# np.arange(v_min, v_max, r_patch))
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# u = u.reshape(-1)
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# v = v.reshape(-1)
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# select_inds = np.random.choice(u.shape[0], size=(N_patch,), replace=False)
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# # Convert back to original image
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# select_inds = v[select_inds] * self.W + u[select_inds]
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# # pick patches
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# select_inds = np.stack([select_inds + shift for shift in offsets], axis=1)
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# select_inds = select_inds.reshape(-1)
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# rays_o = self.rays_o[select_inds, :] # [N_rand, 3]
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# rays_d = self.rays_d[select_inds, :] # [N_rand, 3]
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# depth = self.depth[select_inds] # [N_rand, ]
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# if self.img is not None:
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# rgb = self.img[select_inds, :] # [N_rand, 3]
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# # ### debug
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# # import imageio
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# # imgs = rgb.reshape((N_patch, r_patch*2, r_patch*2, -1))
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# # for kk in range(imgs.shape[0]):
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# # imageio.imwrite('./debug_{}.png'.format(kk), imgs[kk])
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# # ###
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# else:
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# rgb = None
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# ret = OrderedDict([
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# ('ray_o', rays_o),
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# ('ray_d', rays_d),
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# ('depth', depth),
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# ('rgb', rgb)
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# ])
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# # return torch tensors
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# for k in ret:
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# ret[k] = torch.from_numpy(ret[k])
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# return ret
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