154 lines
4.5 KiB
Python
154 lines
4.5 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time, torch
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from log_utils import AverageMeter
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from log_utils import time_string
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from utils import obtain_accuracy
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def basic_train(
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xloader,
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network,
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criterion,
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scheduler,
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optimizer,
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optim_config,
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extra_info,
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print_freq,
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logger,
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):
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loss, acc1, acc5 = procedure(
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xloader,
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network,
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criterion,
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scheduler,
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optimizer,
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"train",
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optim_config,
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extra_info,
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print_freq,
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logger,
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)
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return loss, acc1, acc5
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def basic_valid(
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xloader, network, criterion, optim_config, extra_info, print_freq, logger
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):
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with torch.no_grad():
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loss, acc1, acc5 = procedure(
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xloader,
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network,
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criterion,
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None,
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None,
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"valid",
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None,
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extra_info,
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print_freq,
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logger,
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)
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return loss, acc1, acc5
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def procedure(
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xloader,
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network,
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criterion,
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scheduler,
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optimizer,
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mode,
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config,
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extra_info,
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print_freq,
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logger,
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):
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data_time, batch_time, losses, top1, top5 = (
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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)
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if mode == "train":
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network.train()
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elif mode == "valid":
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network.eval()
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else:
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raise ValueError("The mode is not right : {:}".format(mode))
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# logger.log('[{:5s}] config :: auxiliary={:}, message={:}'.format(mode, config.auxiliary if hasattr(config, 'auxiliary') else -1, network.module.get_message()))
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logger.log(
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"[{:5s}] config :: auxiliary={:}".format(
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mode, config.auxiliary if hasattr(config, "auxiliary") else -1
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)
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)
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end = time.time()
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for i, (inputs, targets) in enumerate(xloader):
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if mode == "train":
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scheduler.update(None, 1.0 * i / len(xloader))
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# measure data loading time
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data_time.update(time.time() - end)
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# calculate prediction and loss
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targets = targets.cuda(non_blocking=True)
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if mode == "train":
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optimizer.zero_grad()
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features, logits = network(inputs)
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if isinstance(logits, list):
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assert len(logits) == 2, "logits must has {:} items instead of {:}".format(
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2, len(logits)
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)
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logits, logits_aux = logits
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else:
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logits, logits_aux = logits, None
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loss = criterion(logits, targets)
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if config is not None and hasattr(config, "auxiliary") and config.auxiliary > 0:
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loss_aux = criterion(logits_aux, targets)
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loss += config.auxiliary * loss_aux
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if mode == "train":
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loss.backward()
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optimizer.step()
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# record
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prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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losses.update(loss.item(), inputs.size(0))
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top1.update(prec1.item(), inputs.size(0))
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top5.update(prec5.item(), inputs.size(0))
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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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if i % print_freq == 0 or (i + 1) == len(xloader):
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Sstr = (
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" {:5s} ".format(mode.upper())
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+ time_string()
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+ " [{:}][{:03d}/{:03d}]".format(extra_info, i, len(xloader))
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)
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if scheduler is not None:
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Sstr += " {:}".format(scheduler.get_min_info())
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Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
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batch_time=batch_time, data_time=data_time
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)
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Lstr = "Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})".format(
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loss=losses, top1=top1, top5=top5
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)
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Istr = "Size={:}".format(list(inputs.size()))
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logger.log(Sstr + " " + Tstr + " " + Lstr + " " + Istr)
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logger.log(
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" **{mode:5s}** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}".format(
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mode=mode.upper(),
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top1=top1,
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top5=top5,
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error1=100 - top1.avg,
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error5=100 - top5.avg,
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loss=losses.avg,
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)
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)
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return losses.avg, top1.avg, top5.avg
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