| import logging |
| import os |
| import time |
| from typing import List |
|
|
| import torch |
|
|
| from eval import verification |
| from utils.utils_logging import AverageMeter |
|
|
|
|
| class CallBackVerification(object): |
| def __init__(self, frequent, rank, val_targets, rec_prefix, image_size=(112, 112)): |
| self.frequent: int = frequent |
| self.rank: int = rank |
| self.highest_acc: float = 0.0 |
| self.highest_acc_list: List[float] = [0.0] * len(val_targets) |
| self.ver_list: List[object] = [] |
| self.ver_name_list: List[str] = [] |
| if self.rank is 0: |
| self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size) |
|
|
| def ver_test(self, backbone: torch.nn.Module, global_step: int): |
| results = [] |
| for i in range(len(self.ver_list)): |
| acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test( |
| self.ver_list[i], backbone, 10, 10) |
| logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm)) |
| logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2)) |
| if acc2 > self.highest_acc_list[i]: |
| self.highest_acc_list[i] = acc2 |
| logging.info( |
| '[%s][%d]Accuracy-Highest: %1.5f' % (self.ver_name_list[i], global_step, self.highest_acc_list[i])) |
| results.append(acc2) |
|
|
| def init_dataset(self, val_targets, data_dir, image_size): |
| for name in val_targets: |
| path = os.path.join(data_dir, name + ".bin") |
| if os.path.exists(path): |
| data_set = verification.load_bin(path, image_size) |
| self.ver_list.append(data_set) |
| self.ver_name_list.append(name) |
|
|
| def __call__(self, num_update, backbone: torch.nn.Module): |
| if self.rank is 0 and num_update > 0 and num_update % self.frequent == 0: |
| backbone.eval() |
| self.ver_test(backbone, num_update) |
| backbone.train() |
|
|
|
|
| class CallBackLogging(object): |
| def __init__(self, frequent, rank, total_step, batch_size, world_size, writer=None): |
| self.frequent: int = frequent |
| self.rank: int = rank |
| self.time_start = time.time() |
| self.total_step: int = total_step |
| self.batch_size: int = batch_size |
| self.world_size: int = world_size |
| self.writer = writer |
|
|
| self.init = False |
| self.tic = 0 |
|
|
| def __call__(self, |
| global_step: int, |
| loss: AverageMeter, |
| epoch: int, |
| fp16: bool, |
| learning_rate: float, |
| grad_scaler: torch.cuda.amp.GradScaler): |
| if self.rank == 0 and global_step > 0 and global_step % self.frequent == 0: |
| if self.init: |
| try: |
| speed: float = self.frequent * self.batch_size / (time.time() - self.tic) |
| speed_total = speed * self.world_size |
| except ZeroDivisionError: |
| speed_total = float('inf') |
|
|
| time_now = (time.time() - self.time_start) / 3600 |
| time_total = time_now / ((global_step + 1) / self.total_step) |
| time_for_end = time_total - time_now |
| if self.writer is not None: |
| self.writer.add_scalar('time_for_end', time_for_end, global_step) |
| self.writer.add_scalar('learning_rate', learning_rate, global_step) |
| self.writer.add_scalar('loss', loss.avg, global_step) |
| if fp16: |
| msg = "Speed %.2f samples/sec Loss %.4f LearningRate %.4f Epoch: %d Global Step: %d " \ |
| "Fp16 Grad Scale: %2.f Required: %1.f hours" % ( |
| speed_total, loss.avg, learning_rate, epoch, global_step, |
| grad_scaler.get_scale(), time_for_end |
| ) |
| else: |
| msg = "Speed %.2f samples/sec Loss %.4f LearningRate %.4f Epoch: %d Global Step: %d " \ |
| "Required: %1.f hours" % ( |
| speed_total, loss.avg, learning_rate, epoch, global_step, time_for_end |
| ) |
| logging.info(msg) |
| loss.reset() |
| self.tic = time.time() |
| else: |
| self.init = True |
| self.tic = time.time() |
|
|
|
|
| class CallBackModelCheckpoint(object): |
| def __init__(self, rank, output="./"): |
| self.rank: int = rank |
| self.output: str = output |
|
|
| def __call__(self, global_step, backbone, partial_fc, ): |
| if global_step > 100 and self.rank == 0: |
| path_module = os.path.join(self.output, "backbone.pth") |
| torch.save(backbone.module.state_dict(), path_module) |
| logging.info("Pytorch Model Saved in '{}'".format(path_module)) |
|
|
| if global_step > 100 and partial_fc is not None: |
| partial_fc.save_params() |
|
|
