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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 torch |
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from eval import verification |
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from torch import distributed |
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from torch.utils.tensorboard import SummaryWriter |
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from utils.utils_logging import AverageMeter |
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class CallBackVerification(object): |
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def __init__(self, val_targets, rec_prefix, summary_writer=None, image_size=(112, 112), wandb_logger=None): |
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self.rank: int = distributed.get_rank() |
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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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if self.rank is 0: |
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self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size) |
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self.summary_writer = summary_writer |
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self.wandb_logger = wandb_logger |
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def ver_test(self, backbone: torch.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(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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self.summary_writer: SummaryWriter |
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self.summary_writer.add_scalar( |
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tag=self.ver_name_list[i], |
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scalar_value=acc2, |
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global_step=global_step, |
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) |
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if self.wandb_logger: |
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import wandb |
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self.wandb_logger.log( |
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{ |
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f"Acc/val-Acc1 {self.ver_name_list[i]}": acc1, |
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f"Acc/val-Acc2 {self.ver_name_list[i]}": acc2, |
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} |
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) |
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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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) |
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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(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: torch.nn.Module): |
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if self.rank is 0 and num_update > 0: |
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backbone.eval() |
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self.ver_test(backbone, num_update) |
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backbone.train() |
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class CallBackLogging(object): |
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def __init__(self, frequent, total_step, batch_size, start_step=0, writer=None): |
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self.frequent: int = frequent |
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self.rank: int = distributed.get_rank() |
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self.world_size: int = distributed.get_world_size() |
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self.time_start = time.time() |
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self.total_step: int = total_step |
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self.start_step: int = start_step |
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self.batch_size: int = batch_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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def __call__( |
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self, |
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global_step: int, |
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loss: AverageMeter, |
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epoch: int, |
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fp16: bool, |
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learning_rate: float, |
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grad_scaler: torch.cuda.amp.GradScaler, |
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): |
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if self.rank == 0 and global_step > 0 and 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() |
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time_sec = int(time_now - self.time_start) |
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time_sec_avg = time_sec / (global_step - self.start_step + 1) |
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eta_sec = time_sec_avg * (self.total_step - global_step - 1) |
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time_for_end = eta_sec / 3600 |
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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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if fp16: |
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msg = ( |
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"Speed %.2f samples/sec Loss %.4f LearningRate %.6f Epoch: %d Global Step: %d " |
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"Fp16 Grad Scale: %2.f Required: %1.f hours" |
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% ( |
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speed_total, |
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loss.avg, |
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learning_rate, |
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epoch, |
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global_step, |
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grad_scaler.get_scale(), |
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time_for_end, |
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) |
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) |
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else: |
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msg = ( |
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"Speed %.2f samples/sec Loss %.4f LearningRate %.6f Epoch: %d Global Step: %d " |
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"Required: %1.f hours" |
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% (speed_total, loss.avg, learning_rate, epoch, global_step, time_for_end) |
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) |
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logging.info(msg) |
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loss.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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