438 lines
15 KiB
Python
438 lines
15 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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#####################################################
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import os, time, copy, torch, pathlib
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from xautodl import datasets
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from xautodl.config_utils import load_config
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from xautodl.procedures import prepare_seed, get_optim_scheduler
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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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from xautodl.models import get_cell_based_tiny_net
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from xautodl.utils import get_model_infos
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from xautodl.procedures.eval_funcs import obtain_accuracy
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__all__ = ["evaluate_for_seed", "pure_evaluate", "get_nas_bench_loaders"]
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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, device = [], torch.cuda.current_device()
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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(device=device, non_blocking=True)
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inputs = inputs.cuda(device=device, 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: str):
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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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device = torch.cuda.current_device()
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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(device=device, 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(
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arch_config, opt_config, train_loader, valid_loaders, seed: int, logger
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):
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"""A modular function to train and evaluate a single network, using the given random seed and optimization config with the provided loaders."""
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prepare_seed(seed) # random seed
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net = get_cell_based_tiny_net(arch_config)
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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, opt_config.xshape)
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logger.log("Network : {:}".format(net.get_message()), False)
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logger.log(
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"{:} Seed-------------------------- {:} --------------------------".format(
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time_string(), seed
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)
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)
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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(), opt_config)
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default_device = torch.cuda.current_device()
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network = torch.nn.DataParallel(net, device_ids=[default_device]).cuda(
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device=default_device
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)
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criterion = criterion.cuda(device=default_device)
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# start training
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start_time, epoch_time, total_epoch = (
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time.time(),
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AverageMeter(),
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opt_config.epochs + opt_config.warmup,
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)
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(
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train_losses,
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train_acc1es,
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train_acc5es,
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valid_losses,
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valid_acc1es,
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valid_acc5es,
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) = ({}, {}, {}, {}, {}, {})
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train_times, valid_times, lrs = {}, {}, {}
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for epoch in range(total_epoch):
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scheduler.update(epoch, 0.0)
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lr = min(scheduler.get_lr())
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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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lrs[epoch] = lr
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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(
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convert_secs2time(epoch_time.avg * (total_epoch - epoch - 1), True)
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)
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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}%], lr={:}".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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lr,
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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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"arch_config": arch_config._asdict(),
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"opt_config": opt_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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"learning_rates": lrs,
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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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def get_nas_bench_loaders(workers):
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torch.set_num_threads(workers)
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root_dir = (pathlib.Path(__file__).parent / ".." / "..").resolve()
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torch_dir = pathlib.Path(os.environ["TORCH_HOME"])
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# cifar
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cifar_config_path = root_dir / "configs" / "nas-benchmark" / "CIFAR.config"
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cifar_config = load_config(cifar_config_path, None, None)
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get_datasets = datasets.get_datasets # a function to return the dataset
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break_line = "-" * 150
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print("{:} Create data-loader for all datasets".format(time_string()))
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print(break_line)
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TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets(
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"cifar10", str(torch_dir / "cifar.python"), -1
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)
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print(
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"original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
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len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num
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)
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)
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cifar10_splits = load_config(
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root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None
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)
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assert cifar10_splits.train[:10] == [
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0,
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5,
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7,
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11,
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13,
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15,
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16,
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17,
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20,
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24,
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] and cifar10_splits.valid[:10] == [
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1,
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2,
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3,
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4,
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6,
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8,
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9,
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10,
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12,
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14,
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]
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temp_dataset = copy.deepcopy(TRAIN_CIFAR10)
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temp_dataset.transform = VALID_CIFAR10.transform
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# data loader
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trainval_cifar10_loader = torch.utils.data.DataLoader(
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TRAIN_CIFAR10,
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batch_size=cifar_config.batch_size,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
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)
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train_cifar10_loader = torch.utils.data.DataLoader(
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TRAIN_CIFAR10,
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batch_size=cifar_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train),
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num_workers=workers,
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pin_memory=True,
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)
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valid_cifar10_loader = torch.utils.data.DataLoader(
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temp_dataset,
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batch_size=cifar_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid),
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num_workers=workers,
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pin_memory=True,
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)
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test__cifar10_loader = torch.utils.data.DataLoader(
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VALID_CIFAR10,
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batch_size=cifar_config.batch_size,
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shuffle=False,
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num_workers=workers,
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pin_memory=True,
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)
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print(
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"CIFAR-10 : trval-loader has {:3d} batch with {:} per batch".format(
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len(trainval_cifar10_loader), cifar_config.batch_size
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)
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)
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print(
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"CIFAR-10 : train-loader has {:3d} batch with {:} per batch".format(
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len(train_cifar10_loader), cifar_config.batch_size
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)
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)
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print(
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"CIFAR-10 : valid-loader has {:3d} batch with {:} per batch".format(
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len(valid_cifar10_loader), cifar_config.batch_size
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)
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)
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print(
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"CIFAR-10 : test--loader has {:3d} batch with {:} per batch".format(
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len(test__cifar10_loader), cifar_config.batch_size
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)
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)
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print(break_line)
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# CIFAR-100
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TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets(
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"cifar100", str(torch_dir / "cifar.python"), -1
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)
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print(
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"original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
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len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num
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)
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)
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cifar100_splits = load_config(
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root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None
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)
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assert cifar100_splits.xvalid[:10] == [
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1,
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3,
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4,
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5,
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8,
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10,
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13,
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14,
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15,
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16,
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] and cifar100_splits.xtest[:10] == [
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0,
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2,
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6,
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7,
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9,
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11,
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12,
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17,
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20,
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24,
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]
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train_cifar100_loader = torch.utils.data.DataLoader(
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TRAIN_CIFAR100,
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batch_size=cifar_config.batch_size,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
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)
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valid_cifar100_loader = torch.utils.data.DataLoader(
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VALID_CIFAR100,
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batch_size=cifar_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid),
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num_workers=workers,
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pin_memory=True,
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)
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test__cifar100_loader = torch.utils.data.DataLoader(
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VALID_CIFAR100,
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batch_size=cifar_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest),
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num_workers=workers,
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pin_memory=True,
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)
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print(
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"CIFAR-100 : train-loader has {:3d} batch".format(len(train_cifar100_loader))
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)
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print(
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"CIFAR-100 : valid-loader has {:3d} batch".format(len(valid_cifar100_loader))
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)
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print(
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"CIFAR-100 : test--loader has {:3d} batch".format(len(test__cifar100_loader))
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)
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print(break_line)
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imagenet16_config_path = "configs/nas-benchmark/ImageNet-16.config"
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imagenet16_config = load_config(imagenet16_config_path, None, None)
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TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets(
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"ImageNet16-120", str(torch_dir / "cifar.python" / "ImageNet16"), -1
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)
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print(
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"original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
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len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num
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)
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)
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imagenet_splits = load_config(
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root_dir / "configs" / "nas-benchmark" / "imagenet-16-120-test-split.txt",
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None,
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None,
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)
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assert imagenet_splits.xvalid[:10] == [
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1,
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2,
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3,
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6,
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7,
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8,
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9,
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12,
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16,
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18,
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] and imagenet_splits.xtest[:10] == [
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0,
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4,
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5,
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10,
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11,
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13,
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14,
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15,
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17,
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20,
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]
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train_imagenet_loader = torch.utils.data.DataLoader(
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TRAIN_ImageNet16_120,
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batch_size=imagenet16_config.batch_size,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
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)
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valid_imagenet_loader = torch.utils.data.DataLoader(
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VALID_ImageNet16_120,
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batch_size=imagenet16_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid),
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num_workers=workers,
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pin_memory=True,
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)
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test__imagenet_loader = torch.utils.data.DataLoader(
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VALID_ImageNet16_120,
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batch_size=imagenet16_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest),
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num_workers=workers,
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pin_memory=True,
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)
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print(
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"ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch".format(
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len(train_imagenet_loader), imagenet16_config.batch_size
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)
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)
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print(
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"ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch".format(
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len(valid_imagenet_loader), imagenet16_config.batch_size
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)
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)
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print(
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"ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch".format(
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len(test__imagenet_loader), imagenet16_config.batch_size
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)
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)
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# 'cifar10', 'cifar100', 'ImageNet16-120'
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loaders = {
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"cifar10@trainval": trainval_cifar10_loader,
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"cifar10@train": train_cifar10_loader,
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"cifar10@valid": valid_cifar10_loader,
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"cifar10@test": test__cifar10_loader,
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"cifar100@train": train_cifar100_loader,
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"cifar100@valid": valid_cifar100_loader,
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"cifar100@test": test__cifar100_loader,
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"ImageNet16-120@train": train_imagenet_loader,
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"ImageNet16-120@valid": valid_imagenet_loader,
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"ImageNet16-120@test": test__imagenet_loader,
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}
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return loaders
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