ref: f94bd54302d4b67f7035e11598f07dc8caa0c26b
dir: /dnn/torch/rdovae/rdovae/dataset.py/
"""
/* Copyright (c) 2022 Amazon
Written by Jan Buethe */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
"""
import torch
import numpy as np
class RDOVAEDataset(torch.utils.data.Dataset):
def __init__(self,
feature_file,
sequence_length,
num_used_features=20,
num_features=36,
lambda_min=0.0002,
lambda_max=0.0135,
quant_levels=16,
enc_stride=2):
self.sequence_length = sequence_length
self.lambda_min = lambda_min
self.lambda_max = lambda_max
self.enc_stride = enc_stride
self.quant_levels = quant_levels
self.denominator = (quant_levels - 1) / np.log(lambda_max / lambda_min)
if sequence_length % enc_stride:
raise ValueError(f"RDOVAEDataset.__init__: enc_stride {enc_stride} does not divide sequence length {sequence_length}")
self.features = np.reshape(np.fromfile(feature_file, dtype=np.float32), (-1, num_features))
self.features = self.features[:, :num_used_features]
self.num_sequences = self.features.shape[0] // sequence_length
def __len__(self):
return self.num_sequences
def __getitem__(self, index):
features = self.features[index * self.sequence_length: (index + 1) * self.sequence_length, :]
q_ids = np.random.randint(0, self.quant_levels, (1)).astype(np.int64)
q_ids = np.repeat(q_ids, self.sequence_length // self.enc_stride, axis=0)
rate_lambda = self.lambda_min * np.exp(q_ids.astype(np.float32) / self.denominator).astype(np.float32)
return features, rate_lambda, q_ids