File size: 3,540 Bytes
6ac44e1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
# Lint as: python3
# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Online data normalization."""
# import sonnet as snt
# import tensorflow.compat.v1 as tf
import torch
import torch.nn as nn
device = torch.device('cuda')
# class Normalizer(snt.AbstractModule):
class Normalizer(nn.Module):
"""Feature normalizer that accumulates statistics online."""
def __init__(self, size, name, max_accumulations=10 ** 6, std_epsilon=1e-8, ):
super(Normalizer, self).__init__()
self._name = name
self._max_accumulations = max_accumulations
self._std_epsilon = torch.tensor([std_epsilon], requires_grad=False).to(device)
self._acc_count = torch.zeros(1, dtype=torch.float32, requires_grad=False).to(device)
self._num_accumulations = torch.zeros(1, dtype=torch.float32, requires_grad=False).to(device)
self._acc_sum = torch.zeros(size, dtype=torch.float32, requires_grad=False).to(device)
self._acc_sum_squared = torch.zeros(size, dtype=torch.float32, requires_grad=False).to(device)
def forward(self, batched_data, node_num=None, accumulate=True):
"""Normalizes input data and accumulates statistics."""
if accumulate and self._num_accumulations < self._max_accumulations:
# stop accumulating after a million updates, to prevent accuracy issues
self._accumulate(batched_data)
return (batched_data - self._mean()) / self._std_with_epsilon()
def inverse(self, normalized_batch_data):
"""Inverse transformation of the normalizer."""
return normalized_batch_data * self._std_with_epsilon() + self._mean()
def _accumulate(self, batched_data, node_num=None):
"""Function to perform the accumulation of the batch_data statistics."""
count = torch.tensor(batched_data.shape[0], dtype=torch.float32, device=device)
data_sum = torch.sum(batched_data, dim=0)
squared_data_sum = torch.sum(batched_data ** 2, dim=0)
self._acc_sum = self._acc_sum.add(data_sum)
self._acc_sum_squared = self._acc_sum_squared.add(squared_data_sum)
self._acc_count = self._acc_count.add(count)
self._num_accumulations = self._num_accumulations.add(1.)
def _mean(self):
safe_count = torch.maximum(self._acc_count, torch.tensor([1.], device=device))
return self._acc_sum / safe_count
def _std_with_epsilon(self):
safe_count = torch.maximum(self._acc_count, torch.tensor([1.], device=device))
diff = self._acc_sum_squared / safe_count - self._mean() ** 2
if (diff < 0).any():
diff = self._std_epsilon**2
std = torch.sqrt(diff)
return torch.maximum(std, self._std_epsilon)
def get_acc_sum(self):
return self._acc_sum
|