Reformulate via black
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D-X-Y
@@ -2,133 +2,162 @@
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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#####################################################
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import time, torch
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from procedures import prepare_seed, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from procedures import prepare_seed, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from config_utils import dict2config
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from log_utils import AverageMeter, time_string, convert_secs2time
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from models import get_cell_based_tiny_net
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from log_utils import AverageMeter, time_string, convert_secs2time
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from models import get_cell_based_tiny_net
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__all__ = ['evaluate_for_seed', 'pure_evaluate']
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__all__ = ["evaluate_for_seed", "pure_evaluate"]
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def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
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data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
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losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
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latencies = []
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network.eval()
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with torch.no_grad():
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end = time.time()
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for i, (inputs, targets) in enumerate(xloader):
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targets = targets.cuda(non_blocking=True)
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inputs = inputs.cuda(non_blocking=True)
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data_time.update(time.time() - end)
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# forward
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features, logits = network(inputs)
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loss = criterion(logits, targets)
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batch_time.update(time.time() - end)
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if batch is None or batch == inputs.size(0):
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batch = inputs.size(0)
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latencies.append( batch_time.val - data_time.val )
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# record loss and accuracy
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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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end = time.time()
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if len(latencies) > 2: latencies = latencies[1:]
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return losses.avg, top1.avg, top5.avg, latencies
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data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
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losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
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latencies = []
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network.eval()
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with torch.no_grad():
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end = time.time()
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for i, (inputs, targets) in enumerate(xloader):
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targets = targets.cuda(non_blocking=True)
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inputs = inputs.cuda(non_blocking=True)
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data_time.update(time.time() - end)
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# forward
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features, logits = network(inputs)
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loss = criterion(logits, targets)
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batch_time.update(time.time() - end)
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if batch is None or batch == inputs.size(0):
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batch = inputs.size(0)
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latencies.append(batch_time.val - data_time.val)
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# record loss and accuracy
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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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end = time.time()
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if len(latencies) > 2:
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latencies = latencies[1:]
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return losses.avg, top1.avg, top5.avg, latencies
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def procedure(xloader, network, criterion, scheduler, optimizer, mode):
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losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
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if mode == 'train' : network.train()
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elif mode == 'valid': network.eval()
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else: raise ValueError("The mode is not right : {:}".format(mode))
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losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
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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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data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
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for i, (inputs, targets) in enumerate(xloader):
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if mode == 'train': scheduler.update(None, 1.0 * i / len(xloader))
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targets = targets.cuda(non_blocking=True)
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if mode == 'train': optimizer.zero_grad()
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# forward
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features, logits = network(inputs)
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loss = criterion(logits, targets)
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# backward
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if mode == 'train':
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loss.backward()
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optimizer.step()
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# record loss and accuracy
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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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# count time
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batch_time.update(time.time() - end)
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end = time.time()
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return losses.avg, top1.avg, top5.avg, batch_time.sum
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data_time, batch_time, end = AverageMeter(), AverageMeter(), 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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targets = targets.cuda(non_blocking=True)
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if mode == "train":
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optimizer.zero_grad()
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# forward
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features, logits = network(inputs)
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loss = criterion(logits, targets)
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# backward
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if mode == "train":
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loss.backward()
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optimizer.step()
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# record loss and accuracy
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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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# count time
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batch_time.update(time.time() - end)
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end = time.time()
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return losses.avg, top1.avg, top5.avg, batch_time.sum
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def evaluate_for_seed(arch_config, config, arch, train_loader, valid_loaders, seed, logger):
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prepare_seed(seed) # random seed
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net = get_cell_based_tiny_net(dict2config({'name': 'infer.tiny',
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'C': arch_config['channel'], 'N': arch_config['num_cells'],
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'genotype': arch, 'num_classes': config.class_num}
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, None)
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)
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#net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
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flop, param = get_model_infos(net, config.xshape)
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logger.log('Network : {:}'.format(net.get_message()), False)
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logger.log('{:} Seed-------------------------- {:} --------------------------'.format(time_string(), seed))
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logger.log('FLOP = {:} MB, Param = {:} MB'.format(flop, param))
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# train and valid
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optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), config)
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network, criterion = torch.nn.DataParallel(net).cuda(), criterion.cuda()
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# start training
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start_time, epoch_time, total_epoch = time.time(), AverageMeter(), config.epochs + config.warmup
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train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {}
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train_times , valid_times = {}, {}
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for epoch in range(total_epoch):
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scheduler.update(epoch, 0.0)
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prepare_seed(seed) # random seed
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net = get_cell_based_tiny_net(
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dict2config(
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{
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"name": "infer.tiny",
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"C": arch_config["channel"],
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"N": arch_config["num_cells"],
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"genotype": arch,
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"num_classes": config.class_num,
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},
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None,
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)
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)
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# net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
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flop, param = get_model_infos(net, config.xshape)
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logger.log("Network : {:}".format(net.get_message()), False)
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logger.log("{:} Seed-------------------------- {:} --------------------------".format(time_string(), seed))
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logger.log("FLOP = {:} MB, Param = {:} MB".format(flop, param))
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# train and valid
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optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), config)
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network, criterion = torch.nn.DataParallel(net).cuda(), criterion.cuda()
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# start training
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start_time, epoch_time, total_epoch = time.time(), AverageMeter(), config.epochs + config.warmup
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train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {}
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train_times, valid_times = {}, {}
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for epoch in range(total_epoch):
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scheduler.update(epoch, 0.0)
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train_loss, train_acc1, train_acc5, train_tm = procedure(train_loader, network, criterion, scheduler, optimizer, 'train')
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train_losses[epoch] = train_loss
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train_acc1es[epoch] = train_acc1
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train_acc5es[epoch] = train_acc5
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train_times [epoch] = train_tm
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with torch.no_grad():
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for key, xloder in valid_loaders.items():
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valid_loss, valid_acc1, valid_acc5, valid_tm = procedure(xloder , network, criterion, None, None, 'valid')
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valid_losses['{:}@{:}'.format(key,epoch)] = valid_loss
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valid_acc1es['{:}@{:}'.format(key,epoch)] = valid_acc1
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valid_acc5es['{:}@{:}'.format(key,epoch)] = valid_acc5
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valid_times ['{:}@{:}'.format(key,epoch)] = valid_tm
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train_loss, train_acc1, train_acc5, train_tm = procedure(
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train_loader, network, criterion, scheduler, optimizer, "train"
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)
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train_losses[epoch] = train_loss
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train_acc1es[epoch] = train_acc1
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train_acc5es[epoch] = train_acc5
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train_times[epoch] = train_tm
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with torch.no_grad():
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for key, xloder in valid_loaders.items():
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valid_loss, valid_acc1, valid_acc5, valid_tm = procedure(
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xloder, network, criterion, None, None, "valid"
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)
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valid_losses["{:}@{:}".format(key, epoch)] = valid_loss
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valid_acc1es["{:}@{:}".format(key, epoch)] = valid_acc1
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valid_acc5es["{:}@{:}".format(key, epoch)] = valid_acc5
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valid_times["{:}@{:}".format(key, epoch)] = valid_tm
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.avg * (total_epoch-epoch-1), True) )
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logger.log('{:} {:} epoch={:03d}/{:03d} :: Train [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%] Valid [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%]'.format(time_string(), need_time, epoch, total_epoch, train_loss, train_acc1, train_acc5, valid_loss, valid_acc1, valid_acc5))
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info_seed = {'flop' : flop,
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'param': param,
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'channel' : arch_config['channel'],
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'num_cells' : arch_config['num_cells'],
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'config' : config._asdict(),
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'total_epoch' : total_epoch ,
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'train_losses': train_losses,
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'train_acc1es': train_acc1es,
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'train_acc5es': train_acc5es,
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'train_times' : train_times,
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'valid_losses': valid_losses,
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'valid_acc1es': valid_acc1es,
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'valid_acc5es': valid_acc5es,
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'valid_times' : valid_times,
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'net_state_dict': net.state_dict(),
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'net_string' : '{:}'.format(net),
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'finish-train': True
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}
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return info_seed
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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need_time = "Time Left: {:}".format(convert_secs2time(epoch_time.avg * (total_epoch - epoch - 1), True))
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logger.log(
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"{:} {:} epoch={:03d}/{:03d} :: Train [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%] Valid [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%]".format(
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time_string(),
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need_time,
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epoch,
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total_epoch,
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train_loss,
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train_acc1,
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train_acc5,
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valid_loss,
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valid_acc1,
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valid_acc5,
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)
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)
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info_seed = {
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"flop": flop,
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"param": param,
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"channel": arch_config["channel"],
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"num_cells": arch_config["num_cells"],
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"config": config._asdict(),
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"total_epoch": total_epoch,
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"train_losses": train_losses,
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"train_acc1es": train_acc1es,
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"train_acc5es": train_acc5es,
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"train_times": train_times,
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"valid_losses": valid_losses,
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"valid_acc1es": valid_acc1es,
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"valid_acc5es": valid_acc5es,
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"valid_times": valid_times,
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"net_state_dict": net.state_dict(),
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"net_string": "{:}".format(net),
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"finish-train": True,
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}
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return info_seed
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