mirror of
https://github.com/deepinsight/insightface.git
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108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
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import logging
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import os
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import time
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from typing import List
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import oneflow as flow
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from eval import verification
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from utils.utils_logging import AverageMeter
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class CallBackVerification(object):
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def __init__(self, frequent, val_targets, rec_prefix, image_size=(112, 112),world_size=1):
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self.frequent: int = frequent
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self.highest_acc: float = 0.0
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self.highest_acc_list: List[float] = [0.0] * len(val_targets)
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self.ver_list: List[object] = []
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self.ver_name_list: List[str] = []
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self.world_size=world_size
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self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size)
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def ver_test(self, backbone: flow.nn.Module, global_step: int):
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results = []
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for i in range(len(self.ver_list)):
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acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(
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self.ver_list[i], backbone, 10, 10)
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logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm))
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logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2))
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if acc2 > self.highest_acc_list[i]:
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self.highest_acc_list[i] = acc2
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logging.info(
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'[%s][%d]Accuracy-Highest: %1.5f' % (self.ver_name_list[i], global_step, self.highest_acc_list[i]))
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results.append(acc2)
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def init_dataset(self, val_targets, data_dir, image_size):
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for name in val_targets:
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path = os.path.join(data_dir, name + ".bin")
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if os.path.exists(path):
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data_set = verification.load_bin_cv(path, image_size)
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self.ver_list.append(data_set)
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self.ver_name_list.append(name)
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def __call__(self, num_update, backbone):
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if num_update > 0 and num_update % self.frequent == 0:
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self.ver_test(backbone, num_update)
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class CallBackLogging(object):
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def __init__(self, frequent, total_step, batch_size, world_size, writer=None):
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self.frequent: int = frequent
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self.time_start = time.time()
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self.total_step: int = total_step
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self.batch_size: int = batch_size
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self.world_size: int = world_size
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self.writer = writer
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self.init = False
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self.tic = 0
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self.losses=AverageMeter()
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def metric_cb(self,
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global_step: int,
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epoch: int,
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learning_rate: float):
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def callback(loss):
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loss=loss.mean()
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self.losses.update(loss, 1)
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if global_step % self.frequent == 0:
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if self.init:
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try:
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speed: float = self.frequent * self.batch_size / (time.time() - self.tic)
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speed_total = speed * self.world_size
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except ZeroDivisionError:
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speed_total = float('inf')
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time_now = (time.time() - self.time_start) / 3600
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time_total = time_now / ((global_step + 1) / self.total_step)
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time_for_end = time_total - time_now
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if self.writer is not None:
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self.writer.add_scalar('time_for_end', time_for_end, global_step)
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self.writer.add_scalar('learning_rate', learning_rate, global_step)
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self.writer.add_scalar('loss', loss.avg, global_step)
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else:
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msg = "Speed %.2f samples/sec Loss %.4f LearningRate %.4f Epoch: %d Global Step: %d " \
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"Required: %1.f hours" % (
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speed_total, self.losses.avg, learning_rate, epoch, global_step, time_for_end
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)
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logging.info(msg)
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self.losses.reset()
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self.tic = time.time()
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else:
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self.init = True
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self.tic = time.time()
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return callback
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