Spaces:
Sleeping
Sleeping
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from torch.autograd import Function | |
| from torch.autograd.function import once_differentiable | |
| from torch.cuda.amp import custom_bwd, custom_fwd | |
| try: | |
| import _gridencoder as _backend | |
| except ImportError: | |
| from .backend import _backend | |
| _gridtype_to_id = { | |
| 'hash': 0, | |
| 'tiled': 1, | |
| } | |
| class _grid_encode(Function): | |
| def forward(ctx, inputs, embeddings, offsets, per_level_scale, base_resolution, calc_grad_inputs=False, gridtype=0, align_corners=False): | |
| # inputs: [B, D], float in [0, 1] | |
| # embeddings: [sO, C], float | |
| # offsets: [L + 1], int | |
| # RETURN: [B, F], float | |
| inputs = inputs.float().contiguous() | |
| B, D = inputs.shape # batch size, coord dim | |
| L = offsets.shape[0] - 1 # level | |
| C = embeddings.shape[1] # embedding dim for each level | |
| S = np.log2(per_level_scale) # resolution multiplier at each level, apply log2 for later CUDA exp2f | |
| H = base_resolution # base resolution | |
| # manually handle autocast (only use half precision embeddings, inputs must be float for enough precision) | |
| # if C % 2 != 0, force float, since half for atomicAdd is very slow. | |
| if torch.is_autocast_enabled() and C % 2 == 0: | |
| embeddings = embeddings.to(torch.half) | |
| # L first, optimize cache for cuda kernel, but needs an extra permute later | |
| outputs = torch.empty(L, B, C, device=inputs.device, dtype=embeddings.dtype) | |
| if calc_grad_inputs: | |
| dy_dx = torch.empty(B, L * D * C, device=inputs.device, dtype=embeddings.dtype) | |
| else: | |
| dy_dx = None | |
| _backend.grid_encode_forward(inputs, embeddings, offsets, outputs, B, D, C, L, S, H, dy_dx, gridtype, align_corners) | |
| # permute back to [B, L * C] | |
| outputs = outputs.permute(1, 0, 2).reshape(B, L * C) | |
| ctx.save_for_backward(inputs, embeddings, offsets, dy_dx) | |
| ctx.dims = [B, D, C, L, S, H, gridtype] | |
| ctx.align_corners = align_corners | |
| return outputs | |
| #@once_differentiable | |
| def backward(ctx, grad): | |
| inputs, embeddings, offsets, dy_dx = ctx.saved_tensors | |
| B, D, C, L, S, H, gridtype = ctx.dims | |
| align_corners = ctx.align_corners | |
| # grad: [B, L * C] --> [L, B, C] | |
| grad = grad.view(B, L, C).permute(1, 0, 2).contiguous() | |
| grad_embeddings = torch.zeros_like(embeddings) | |
| if dy_dx is not None: | |
| grad_inputs = torch.zeros_like(inputs, dtype=embeddings.dtype) | |
| else: | |
| grad_inputs = None | |
| _backend.grid_encode_backward(grad, inputs, embeddings, offsets, grad_embeddings, B, D, C, L, S, H, dy_dx, grad_inputs, gridtype, align_corners) | |
| if dy_dx is not None: | |
| grad_inputs = grad_inputs.to(inputs.dtype) | |
| return grad_inputs, grad_embeddings, None, None, None, None, None, None | |
| grid_encode = _grid_encode.apply | |
| class GridEncoder(nn.Module): | |
| def __init__(self, input_dim=3, num_levels=16, level_dim=2, per_level_scale=2, base_resolution=16, log2_hashmap_size=19, desired_resolution=None, gridtype='hash', align_corners=False): | |
| super().__init__() | |
| # the finest resolution desired at the last level, if provided, overridee per_level_scale | |
| if desired_resolution is not None: | |
| per_level_scale = np.exp2(np.log2(desired_resolution / base_resolution) / (num_levels - 1)) | |
| self.input_dim = input_dim # coord dims, 2 or 3 | |
| self.num_levels = num_levels # num levels, each level multiply resolution by 2 | |
| self.level_dim = level_dim # encode channels per level | |
| self.per_level_scale = per_level_scale # multiply resolution by this scale at each level. | |
| self.log2_hashmap_size = log2_hashmap_size | |
| self.base_resolution = base_resolution | |
| self.output_dim = num_levels * level_dim | |
| self.gridtype = gridtype | |
| self.gridtype_id = _gridtype_to_id[gridtype] # "tiled" or "hash" | |
| self.align_corners = align_corners | |
| # allocate parameters | |
| offsets = [] | |
| offset = 0 | |
| self.max_params = 2 ** log2_hashmap_size | |
| for i in range(num_levels): | |
| resolution = int(np.ceil(base_resolution * per_level_scale ** i)) | |
| params_in_level = min(self.max_params, (resolution if align_corners else resolution + 1) ** input_dim) # limit max number | |
| params_in_level = int(np.ceil(params_in_level / 8) * 8) # make divisible | |
| offsets.append(offset) | |
| offset += params_in_level | |
| # print(resolution, params_in_level) | |
| offsets.append(offset) | |
| offsets = torch.from_numpy(np.array(offsets, dtype=np.int32)) | |
| self.register_buffer('offsets', offsets) | |
| self.n_params = offsets[-1] * level_dim | |
| # parameters | |
| self.embeddings = nn.Parameter(torch.empty(offset, level_dim)) | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| std = 1e-4 | |
| self.embeddings.data.uniform_(-std, std) | |
| def __repr__(self): | |
| return f"GridEncoder: input_dim={self.input_dim} num_levels={self.num_levels} level_dim={self.level_dim} resolution={self.base_resolution} -> {int(round(self.base_resolution * self.per_level_scale ** (self.num_levels - 1)))} per_level_scale={self.per_level_scale:.4f} params={tuple(self.embeddings.shape)} gridtype={self.gridtype} align_corners={self.align_corners}" | |
| def forward(self, inputs, bound=1): | |
| # inputs: [..., input_dim], normalized real world positions in [-bound, bound] | |
| # return: [..., num_levels * level_dim] | |
| inputs = (inputs + bound) / (2 * bound) # map to [0, 1] | |
| #print('inputs', inputs.shape, inputs.dtype, inputs.min().item(), inputs.max().item()) | |
| prefix_shape = list(inputs.shape[:-1]) | |
| inputs = inputs.view(-1, self.input_dim) | |
| outputs = grid_encode(inputs, self.embeddings, self.offsets, self.per_level_scale, self.base_resolution, inputs.requires_grad, self.gridtype_id, self.align_corners) | |
| outputs = outputs.view(prefix_shape + [self.output_dim]) | |
| #print('outputs', outputs.shape, outputs.dtype, outputs.min().item(), outputs.max().item()) | |
| return outputs |