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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 _freqencoder as _backend
except ImportError:
from .backend import _backend
class _freq_encoder(Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float32) # force float32 for better precision
def forward(ctx, inputs, degree, output_dim):
# inputs: [B, input_dim], float
# RETURN: [B, F], float
if not inputs.is_cuda: inputs = inputs.cuda()
inputs = inputs.contiguous()
B, input_dim = inputs.shape # batch size, coord dim
outputs = torch.empty(B, output_dim, dtype=inputs.dtype, device=inputs.device)
_backend.freq_encode_forward(inputs, B, input_dim, degree, output_dim, outputs)
ctx.save_for_backward(inputs, outputs)
ctx.dims = [B, input_dim, degree, output_dim]
return outputs
@staticmethod
#@once_differentiable
@custom_bwd
def backward(ctx, grad):
# grad: [B, C * C]
grad = grad.contiguous()
inputs, outputs = ctx.saved_tensors
B, input_dim, degree, output_dim = ctx.dims
grad_inputs = torch.zeros_like(inputs)
_backend.freq_encode_backward(grad, outputs, B, input_dim, degree, output_dim, grad_inputs)
return grad_inputs, None, None
freq_encode = _freq_encoder.apply
class FreqEncoder(nn.Module):
def __init__(self, input_dim=3, degree=4):
super().__init__()
self.input_dim = input_dim
self.degree = degree
self.output_dim = input_dim + input_dim * 2 * degree
def __repr__(self):
return f"FreqEncoder: input_dim={self.input_dim} degree={self.degree} output_dim={self.output_dim}"
def forward(self, inputs, **kwargs):
# inputs: [..., input_dim]
# return: [..., ]
prefix_shape = list(inputs.shape[:-1])
inputs = inputs.reshape(-1, self.input_dim)
outputs = freq_encode(inputs, self.degree, self.output_dim)
outputs = outputs.reshape(prefix_shape + [self.output_dim])
return outputs |