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evonneng
5433f831ac
* add licensing headers * add licensing to run actions * rerun headers file * remove trailing white spaces
60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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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 pyrad_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 = pyrad_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 = pyrad_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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