pytorch-image-models/timm/bits/tracker.py

import time
from typing import Optional

from .avg_scalar import AvgMinMaxScalar


class Tracker:

    def __init__(self):
        self.data_time = AvgMinMaxScalar()  # time for data loader to produce batch of samples
        self.step_time = AvgMinMaxScalar()  # time for model step
        self.iter_time = AvgMinMaxScalar()  # full iteration time incl. data, step, and book-keeping
        self.epoch_time = AvgMinMaxScalar()

        self.iter_timestamp: Optional[float] = None
        self.prev_timestamp: Optional[float] = None
        self.epoch_timestamp: Optional[float] = None

    def _measure_iter(self, ref_timestamp=None):
        timestamp = time.perf_counter()
        self.prev_timestamp = timestamp

    def mark_iter(self):
        timestamp = time.perf_counter()
        if self.iter_timestamp is not None:
            iter_time = timestamp - self.iter_timestamp
            self.iter_time.update(iter_time)
        self.iter_timestamp = self.prev_timestamp = timestamp

    def mark_iter_data_end(self):
        assert self.prev_timestamp is not None
        timestamp = time.perf_counter()
        data_time = timestamp - self.prev_timestamp
        self.data_time.update(data_time)
        self.prev_timestamp = timestamp

    def mark_iter_step_end(self):
        assert self.prev_timestamp is not None
        timestamp = time.perf_counter()
        step_time = timestamp - self.prev_timestamp
        self.step_time.update(step_time)
        self.prev_timestamp = timestamp

    def mark_epoch(self):
        timestamp = time.perf_counter()
        if self.epoch_timestamp is not None:
            epoch_time = timestamp - self.epoch_timestamp
            self.epoch_time.update(epoch_time)
        self.epoch_timestamp = timestamp

    def get_avg_iter_rate(self, num_per_iter: int):
        if num_per_iter == 0 or self.iter_time.avg == 0:
            return 0
        return num_per_iter / self.iter_time.avg

    def get_last_iter_rate(self, num_per_iter: int):
        if num_per_iter == 0 or self.iter_time.val == 0:
            return 0
        return num_per_iter / self.iter_time.val