import dataclasses
import torch
from labml import tracker
from . import Metric
@dataclasses.dataclass
class AccuracyState:
samples: int = 0
correct: int = 0
def reset(self):
self.samples = 0
self.correct = 0
[docs]class Accuracy(Metric):
data: AccuracyState
def __init__(self, ignore_index: int = -1):
super().__init__()
self.ignore_index = ignore_index
def __call__(self, output: torch.Tensor, target: torch.Tensor):
output = output.view(-1, output.shape[-1])
target = target.view(-1)
pred = output.argmax(dim=-1)
mask = target == self.ignore_index
pred.masked_fill_(mask, self.ignore_index)
n_masked = mask.sum().item()
self.data.correct += pred.eq(target).sum().item() - n_masked
self.data.samples += len(target) - n_masked
def create_state(self):
return AccuracyState()
def set_state(self, data: any):
self.data = data
def on_epoch_start(self):
self.data.reset()
def on_epoch_end(self):
self.track()
def track(self):
if self.data.samples == 0:
return
tracker.add("accuracy.", self.data.correct / self.data.samples)
class AccuracyMovingAvg(Metric):
def __init__(self, ignore_index: int = -1, queue_size: int = 5):
super().__init__()
self.ignore_index = ignore_index
tracker.set_queue('accuracy.*', queue_size, is_print=True)
def __call__(self, output: torch.Tensor, target: torch.Tensor):
output = output.view(-1, output.shape[-1])
target = target.view(-1)
pred = output.argmax(dim=-1)
mask = target == self.ignore_index
pred.masked_fill_(mask, self.ignore_index)
n_masked = mask.sum().item()
if len(target) - n_masked > 0:
tracker.add('accuracy.', (pred.eq(target).sum().item() - n_masked) / (len(target) - n_masked))
def create_state(self):
return None
def set_state(self, data: any):
pass
def on_epoch_start(self):
pass
def on_epoch_end(self):
pass
[docs]class BinaryAccuracy(Accuracy):
def __call__(self, output: torch.Tensor, target: torch.Tensor):
pred = output.view(-1) > 0
target = target.view(-1)
self.data.correct += pred.eq(target).sum().item()
self.data.samples += len(target)
[docs]class AccuracyDirect(Accuracy):
data: AccuracyState
def __call__(self, output: torch.Tensor, target: torch.Tensor):
output = output.view(-1)
target = target.view(-1)
self.data.correct += output.eq(target).sum().item()
self.data.samples += len(target)