mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-12 19:04:03 +08:00
Ethan Weber
676beebb14
* some initial cuda code as I learn. using git to save progress. * first cuda kernel for ray sampling that mostly works * cleaning up code. working on lr. structs for scene bounds aabb * added scene bounds collider code * updated setup * setup * setup * black * including some files * exporting env variable * new file * git workflows * github actions * github actions * github actions * removing env var
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
"""
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Sampler.
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"""
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import math
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import torch
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from torch import nn
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import mattport_cuda
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class LLTMFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input, weights, bias, old_h, old_cell):
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outputs = mattport_cuda.forward(input, weights, bias, old_h, old_cell)
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new_h, new_cell = outputs[:2]
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variables = outputs[1:] + [weights]
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ctx.save_for_backward(*variables)
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return new_h, new_cell
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@staticmethod
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def backward(ctx, grad_h, grad_cell):
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outputs = mattport_cuda.backward(grad_h.contiguous(), grad_cell.contiguous(), *ctx.saved_variables)
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d_old_h, d_input, d_weights, d_bias, d_old_cell, d_gates = outputs
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return d_input, d_weights, d_bias, d_old_h, d_old_cell
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class Sampler(nn.Module):
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def __init__(self, input_features, state_size):
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super().__init__()
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self.input_features = input_features
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self.state_size = state_size
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self.weights = nn.Parameter(torch.Tensor(3 * state_size, input_features + state_size))
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self.bias = nn.Parameter(torch.Tensor(1, 3 * state_size))
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self.reset_parameters()
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def reset_parameters(self):
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stdv = 1.0 / math.sqrt(self.state_size)
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for weight in self.parameters():
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weight.data.uniform_(-stdv, +stdv)
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def forward(self, input, state):
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return LLTMFunction.apply(input, self.weights, self.bias, *state)
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