File size: 5,578 Bytes
01bd570 |
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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
# Copyright (c) MONAI Consortium
# 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.
from __future__ import annotations
import warnings
from typing import Any
import torch
import torch.distributed as dist
from monai.config import NdarrayOrTensor
class CumulativeAverage:
"""
A utility class to keep track of average values. For example during training/validation loop,
we need to accumulate the per-batch metrics and calculate the final average value for the whole dataset.
When training in multi-gpu environment, with DistributedDataParallel, it will average across the processes.
Example:
.. code-block:: python
from monai.metrics import CumulativeAverage
run_avg = CumulativeAverage()
batch_size = 8
for i in range(len(train_set)):
...
val = calc_metric(x,y) #some metric value
run_avg.append(val, count=batch_size)
val_avg = run_avg.aggregate() #average value
"""
def __init__(self) -> None:
self.reset()
def reset(self) -> None:
"""
Reset all stats
"""
self.val: torch.Tensor = None # type: ignore
self.sum = torch.tensor(0, dtype=torch.float)
self.count = torch.tensor(0, dtype=torch.float)
self.is_distributed = dist.is_available() and dist.is_initialized()
def get_current(self, to_numpy: bool = True) -> NdarrayOrTensor:
"""
returns the most recent value (averaged across processes)
Args:
to_numpy: whether to convert to numpy array. Defaults to True
"""
if self.val is None:
return 0
val = self.val.clone()
val[~torch.isfinite(val)] = 0
if self.is_distributed:
val = val / dist.get_world_size()
dist.all_reduce(val)
if to_numpy:
val = val.cpu().numpy()
return val
def aggregate(self, to_numpy: bool = True) -> NdarrayOrTensor:
"""
returns the total average value (averaged across processes)
Args:
to_numpy: whether to convert to numpy array. Defaults to True
"""
if self.val is None:
return 0
sum = self.sum
count = self.count
if self.is_distributed:
sum = sum.to(self.val, copy=True)
count = count.to(self.val, copy=True)
dist.all_reduce(sum)
dist.all_reduce(count)
val = torch.where(count > 0, sum / count, sum)
if to_numpy:
val = val.cpu().numpy()
return val
def append(self, val: Any, count: Any | None = 1) -> None:
"""
Append with a new value, and an optional count. Any data type is supported that is convertable
with torch.as_tensor() e.g. number, list, numpy array, or Tensor.
Args:
val: value (e.g. number, list, numpy array or Tensor) to keep track of
count: count (e.g. number, list, numpy array or Tensor), to update the contribution count
For example:
# a simple constant tracking
avg = CumulativeAverage()
avg.append(0.6)
avg.append(0.8)
print(avg.aggregate()) #prints 0.7
# an array tracking, e.g. metrics from 3 classes
avg= CumulativeAverage()
avg.append([0.2, 0.4, 0.4])
avg.append([0.4, 0.6, 0.4])
print(avg.aggregate()) #prints [0.3, 0.5. 0.4]
# different contributions / counts
avg= CumulativeAverage()
avg.append(1, count=4) #avg metric 1 coming from a batch of 4
avg.append(2, count=6) #avg metric 2 coming from a batch of 6
print(avg.aggregate()) #prints 1.6 == (1*4 +2*6)/(4+6)
# different contributions / counts
avg= CumulativeAverage()
avg.append([0.5, 0.5, 0], count=[1, 1, 0]) # last elements count is zero to ignore it
avg.append([0.5, 0.5, 0.5], count=[1, 1, 1]) #
print(avg.aggregate()) #prints [0.5, 0.5, 0,5] == ([0.5, 0.5, 0] + [0.5, 0.5, 0.5]) / ([1, 1, 0] + [1, 1, 1])
"""
self.val = torch.as_tensor(val, dtype=torch.float)
if self.val.requires_grad:
self.val = self.val.detach().clone()
count = torch.as_tensor(count, dtype=torch.float, device="cpu")
if count.ndim > 0 and count.shape != self.val.shape:
raise ValueError(
f"Count shape must match val shape, unless count is a single number: {count} val {self.val.cpu()}"
)
val = count * self.val.cpu()
# account for possible non-finite numbers in val and replace them with 0s
nfin = torch.isfinite(val)
if not torch.all(nfin):
warnings.warn(f"non-finite inputs received: val: {val}, count: {count}")
count = torch.where(nfin, count, torch.zeros_like(count))
val = torch.where(nfin, val, torch.zeros_like(val))
self.count = self.count + count
self.sum = self.sum + val
|