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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):
@staticmethod
@custom_fwd
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.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
@staticmethod
#@once_differentiable
@custom_bwd
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
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 |