vishalghor
commented
1 year ago was able to fix it by replacing the gridencoder folder from the latest stable-dreamfusion
Open vishalghor opened 1 year ago
vishalghor
commented
1 year ago was able to fix it by replacing the gridencoder folder from the latest stable-dreamfusion
I tried to run the command for snadcastle as per the readme which was: │
│ │
│ 14 │ │ trainer.train() │
│ 15 │
│ 16 if name == 'main': │
│ ❱ 17 │ main() │
│ │
│ /home/vghorpad/stable-diff/venv_sdfusion20/lib/python3.8/site-packages/pyrallis/argparsing.py:15 │
│ 8 in wrapper_inner │
│ │
│ 155 │ │ │ argspec = inspect.getfullargspec(fn) │
│ 156 │ │ │ argtype = argspec.annotations[argspec.args[0]] │
│ 157 │ │ │ cfg = parse(config_class=argtype, config_path=config_path) │
│ ❱ 158 │ │ │ response = fn(cfg, args, kwargs) │
│ 159 │ │ │ return response │
│ 160 │ │ │
│ 161 │ │ return wrapper_inner │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/scripts/train_latent_nerf.py:14 in main │
│ │
│ 11 │ if cfg.log.eval_only: │
│ 12 │ │ trainer.full_eval() │
│ 13 │ else: │
│ ❱ 14 │ │ trainer.train() │
│ 15 │
│ 16 if name == 'main': │
│ 17 │ main() │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/src/latentnerf/training/trainer.py:124 in train │
│ │
│ 121 │ def train(self): │
│ 122 │ │ logger.info('Starting training ^^') │
│ 123 │ │ # Evaluate the initialization │
│ ❱ 124 │ │ self.evaluate(self.dataloaders['val'], self.eval_renders_path) │
│ 125 │ │ self.nerf.train() │
│ 126 │ │ │
│ 127 │ │ pbar = tqdm(total=self.cfg.optim.iters, initial=self.train_step, │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/src/latent_nerf/training/trainer.py:173 in evaluate │
│ │
│ 170 │ │ │
│ 171 │ │ for i, data in enumerate(dataloader): │
│ 172 │ │ │ with torch.cuda.amp.autocast(enabled=self.cfg.optim.fp16): │
│ ❱ 173 │ │ │ │ preds, preds_depth, preds_normals = self.eval_render(data) │
│ 174 │ │ │ │
│ 175 │ │ │ pred, pred_depth, prednormals = tensor2numpy(preds[0]), tensor2numpy(preds │
│ 176 │ │ │ │ preds_normals[0]) │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/src/latent_nerf/training/trainer.py:261 in eval_render │
│ │
│ 258 │ │ ambient_ratio = data['ambient_ratio'] if 'ambient_ratio' in data else 1.0 │
│ 259 │ │ light_d = data['light_d'] if 'light_d' in data else None │
│ 260 │ │ │
│ ❱ 261 │ │ outputs = self.nerf.render(rays_o, rays_d, staged=True, perturb=perturb, light_d │
│ 262 │ │ │ │ │ │ │ │ ambient_ratio=ambient_ratio, shading=shading, force_a │
│ 263 │ │ │
│ 264 │ │ pred_depth = outputs['depth'].reshape(B, H, W) │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/src/latent_nerf/models/renderer.py:410 in render │
│ │
│ 407 │ │ │ results['weights_sum'] = weights_sum │
│ 408 │ │ │
│ 409 │ │ else: │
│ ❱ 410 │ │ │ results = _run(rays_o, rays_d, kwargs) │
│ 411 │ │ │
│ 412 │ │ return results │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/src/latent_nerf/models/renderer.py:282 in run_cuda │
│ │
│ 279 │ │ │ │ │
│ 280 │ │ │ │ xyzs, dirs, deltas = self.raymarching.march_rays(n_alive, n_step, rays_a │
│ 281 │ │ │ │ │
│ ❱ 282 │ │ │ │ sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, s │
│ 283 │ │ │ │ self.raymarching.composite_rays(n_alive, n_step, rays_alive, rays_t, sig │
│ 284 │ │ │ │ │
│ 285 │ │ │ │ rays_alive = rays_alive[rays_alive >= 0] │
│ │
│ /home/vghorpad/stable-diff/venv_sdfusion20/lib/python3.8/site-packages/torch/nn/modules/module.p │
│ y:1190 in _call_impl │
│ │
│ 1187 │ │ # this function, and just call forward. │
│ 1188 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │
│ 1189 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1190 │ │ │ return forward_call(input, kwargs) │
│ 1191 │ │ # Do not call functions when jit is used │
│ 1192 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │
│ 1193 │ │ if self._backward_hooks or _global_backward_hooks: │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/src/latent_nerf/models/network_grid.py:108 in forward │
│ │
│ 105 │ │ │
│ 106 │ │ if shading == 'albedo': │
│ 107 │ │ │ # no need to query normal │
│ ❱ 108 │ │ │ sigma, color = self.common_forward(x) │
│ 109 │ │ │ normal = None │
│ 110 │ │ │
│ 111 │ │ else: │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/src/latent_nerf/models/network_grid.py:62 in │
│ common_forward │
│ │
│ 59 │ │ # x: [N, 3], in [-bound, bound] │
│ 60 │ │ │
│ 61 │ │ # sigma │
│ ❱ 62 │ │ h = self.encoder(x, bound=self.bound) │
│ 63 │ │ │
│ 64 │ │ h = self.sigma_net(h) │
│ 65 │
│ │
│ /home/vghorpad/stable-diff/venv_sdfusion20/lib/python3.8/site-packages/torch/nn/modules/module.p │
│ y:1190 in _call_impl │
│ │
│ 1187 │ │ # this function, and just call forward. │
│ 1188 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │
│ 1189 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │
│ ❱ 1190 │ │ │ return forward_call(*input, *kwargs) │
│ 1191 │ │ # Do not call functions when jit is used │
│ 1192 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │
│ 1193 │ │ if self._backward_hooks or _global_backward_hooks: │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/src/latent_nerf/models/encoders/gridencoder/grid.py:149 │
│ in forward │
│ │
│ 146 │ │ prefix_shape = list(inputs.shape[:-1]) │
│ 147 │ │ inputs = inputs.view(-1, self.input_dim) │
│ 148 │ │ │
│ ❱ 149 │ │ outputs = grid_encode(inputs, self.embeddings, self.offsets, self.per_level_scal │
│ 150 │ │ outputs = outputs.view(prefix_shape + [self.output_dim]) │
│ 151 │ │ │
│ 152 │ │ #print('outputs', outputs.shape, outputs.dtype, outputs.min().item(), outputs.ma │
│ │
│ /home/vghorpad/stable-diff/venv_sdfusion20/lib/python3.8/site-packages/torch/cuda/amp/autocast_m │
│ ode.py:97 in decorate_fwd │
│ │
│ 94 │ def decorate_fwd(args, kwargs): │
│ 95 │ │ if cast_inputs is None: │
│ 96 │ │ │ args[0]._fwd_used_autocast = torch.is_autocast_enabled() │
│ ❱ 97 │ │ │ return fwd(*args, *kwargs) │
│ 98 │ │ else: │
│ 99 │ │ │ autocast_context = torch.is_autocast_enabled() │
│ 100 │ │ │ args[0]._fwd_used_autocast = False │
│ │
│ /home/vghorpad/stable-diff/latent-nerf/src/latent_nerf/models/encoders/gridencoder/grid.py:49 in │
│ forward │
│ │
│ 46 │ │ else: │
│ 47 │ │ │ dy_dx = None │
│ 48 │ │ │
│ ❱ 49 │ │ _backend.grid_encode_forward(inputs, embeddings, offsets, outputs, B, D, C, L, S │
│ 50 │ │ │
│ 51 │ │ # permute back to [B, L C] │
│ 52 │ │ outputs = outputs.permute(1, 0, 2).reshape(B, L * C) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: grid_encode_forward(): incompatible function arguments. The following argument types are supported:
python -m scripts.train_latent_nerf --config_path demo_configs/latent_nerf/sand_castle.yamlI have installed gridencoder from the stable-dreamfusion repo, not sure how to resolve the error. But it throws a TypeError: at Grid_encode_forward call. Below is the trace output `/usr/lib/python3.8/runpy.py:192 in _run_module_as_main │ │ │ │ 189 │ main_globals = sys.modules["main"].dict │ │ 190 │ if alter_argv: │ │ 191 │ │ sys.argv[0] = mod_spec.origin │ │ ❱ 192 │ return _run_code(code, main_globals, None, │ │ 193 │ │ │ │ │ "main", mod_spec) │ │ 194 │ │ 195 def run_module(mod_name, init_globals=None, │ │ │ │ /usr/lib/python3.8/runpy.py:85 in _run_code │ │ │ │ 82 │ │ │ │ │ loader = loader, │ │ 83 │ │ │ │ │ package = pkg_name, │ │ 84 │ │ │ │ │ spec = mod_spec) │ │ ❱ 85 │ exec(code, run_globals) │ │ 86 │ return run_globals │ │ 87 │ │ 88 def _run_module_code(code, init_globals=None, │ │ │ │ /home/vghorpad/stable-diff/latent-nerf/scripts/train_latent_nerf.py:17 inInvoked with: tensor([[0.5000, 0.5000, 0.5000], [0.5000, 0.5000, 0.5000], [0.5000, 0.5000, 0.5000], ..., [0.5000, 0.5000, 0.5000], [0.5000, 0.5000, 0.5000], [0.5000, 0.5000, 0.5000]], device='cuda:0'), tensor([[-7.7486e-07, 5.3644e-05], [-8.2314e-05, -7.3612e-05], [-3.8505e-05, 2.6822e-05], ..., [ 2.7418e-05, -5.0962e-05], [ 6.2227e-05, 7.5281e-05], [ 4.2677e-05, 9.2626e-05]], device='cuda:0', dtype=torch.float16), tensor([ 0, 4920, 18744, 51512, 136696, 352696, 876984, 1401272, 1925560, 2449848, 2974136, 3498424, 4022712, 4547000, 5071288, 5595576, 6119864], device='cuda:0', dtype=torch.int32), tensor([[[0., 0.], [0., 0.], [0., 0.], ..., [0., 0.], [0., 0.], [0., 0.]],