Update SuperMLP

lib/procedures/basic_main.py

@@ -7,22 +7,63 @@ from log_utils import time_string
 from utils import obtain_accuracy
 
 
-def basic_train(xloader, network, criterion, scheduler, optimizer, optim_config, extra_info, print_freq, logger):
+def basic_train(
+    xloader,
+    network,
+    criterion,
+    scheduler,
+    optimizer,
+    optim_config,
+    extra_info,
+    print_freq,
+    logger,
+):
     loss, acc1, acc5 = procedure(
-        xloader, network, criterion, scheduler, optimizer, "train", optim_config, extra_info, print_freq, logger
+        xloader,
+        network,
+        criterion,
+        scheduler,
+        optimizer,
+        "train",
+        optim_config,
+        extra_info,
+        print_freq,
+        logger,
     )
     return loss, acc1, acc5
 
 
-def basic_valid(xloader, network, criterion, optim_config, extra_info, print_freq, logger):
+def basic_valid(
+    xloader, network, criterion, optim_config, extra_info, print_freq, logger
+):
     with torch.no_grad():
         loss, acc1, acc5 = procedure(
-            xloader, network, criterion, None, None, "valid", None, extra_info, print_freq, logger
+            xloader,
+            network,
+            criterion,
+            None,
+            None,
+            "valid",
+            None,
+            extra_info,
+            print_freq,
+            logger,
         )
     return loss, acc1, acc5
 
 
-def procedure(xloader, network, criterion, scheduler, optimizer, mode, config, extra_info, print_freq, logger):
+def procedure(
+    xloader,
+    network,
+    criterion,
+    scheduler,
+    optimizer,
+    mode,
+    config,
+    extra_info,
+    print_freq,
+    logger,
+):
     data_time, batch_time, losses, top1, top5 = (
         AverageMeter(),
         AverageMeter(),
@@ -39,7 +80,9 @@ def procedure(xloader, network, criterion, scheduler, optimizer, mode, config, e
 
     # logger.log('[{:5s}] config ::  auxiliary={:}, message={:}'.format(mode, config.auxiliary if hasattr(config, 'auxiliary') else -1, network.module.get_message()))
     logger.log(
-        "[{:5s}] config ::  auxiliary={:}".format(mode, config.auxiliary if hasattr(config, "auxiliary") else -1)
+        "[{:5s}] config ::  auxiliary={:}".format(
+            mode, config.auxiliary if hasattr(config, "auxiliary") else -1
+        )
     )
     end = time.time()
     for i, (inputs, targets) in enumerate(xloader):
@@ -55,7 +98,9 @@ def procedure(xloader, network, criterion, scheduler, optimizer, mode, config, e
 
         features, logits = network(inputs)
         if isinstance(logits, list):
-            assert len(logits) == 2, "logits must has {:} items instead of {:}".format(2, len(logits))
+            assert len(logits) == 2, "logits must has {:} items instead of {:}".format(
+                2, len(logits)
+            )
             logits, logits_aux = logits
         else:
             logits, logits_aux = logits, None
@@ -97,7 +142,12 @@ def procedure(xloader, network, criterion, scheduler, optimizer, mode, config, e
 
     logger.log(
         " **{mode:5s}** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}".format(
-            mode=mode.upper(), top1=top1, top5=top5, error1=100 - top1.avg, error5=100 - top5.avg, loss=losses.avg
+            mode=mode.upper(),
+            top1=top1,
+            top5=top5,
+            error1=100 - top1.avg,
+            error5=100 - top5.avg,
+            loss=losses.avg,
         )
     )
     return losses.avg, top1.avg, top5.avg

lib/procedures/funcs_nasbench.py

@@ -78,23 +78,42 @@ def procedure(xloader, network, criterion, scheduler, optimizer, mode: str):
     return losses.avg, top1.avg, top5.avg, batch_time.sum
 
 
-def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed: int, logger):
+def evaluate_for_seed(
+    arch_config, opt_config, train_loader, valid_loaders, seed: int, logger
+):
 
     prepare_seed(seed)  # random seed
     net = get_cell_based_tiny_net(arch_config)
     # net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
     flop, param = get_model_infos(net, opt_config.xshape)
     logger.log("Network : {:}".format(net.get_message()), False)
-    logger.log("{:} Seed-------------------------- {:} --------------------------".format(time_string(), seed))
+    logger.log(
+        "{:} Seed-------------------------- {:} --------------------------".format(
+            time_string(), seed
+        )
+    )
     logger.log("FLOP = {:} MB, Param = {:} MB".format(flop, param))
     # train and valid
     optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), opt_config)
     default_device = torch.cuda.current_device()
-    network = torch.nn.DataParallel(net, device_ids=[default_device]).cuda(device=default_device)
+    network = torch.nn.DataParallel(net, device_ids=[default_device]).cuda(
+        device=default_device
+    )
     criterion = criterion.cuda(device=default_device)
     # start training
-    start_time, epoch_time, total_epoch = time.time(), AverageMeter(), opt_config.epochs + opt_config.warmup
-    train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {}
+    start_time, epoch_time, total_epoch = (
+        time.time(),
+        AverageMeter(),
+        opt_config.epochs + opt_config.warmup,
+    )
+    (
+        train_losses,
+        train_acc1es,
+        train_acc5es,
+        valid_losses,
+        valid_acc1es,
+        valid_acc5es,
+    ) = ({}, {}, {}, {}, {}, {})
     train_times, valid_times, lrs = {}, {}, {}
     for epoch in range(total_epoch):
         scheduler.update(epoch, 0.0)
@@ -120,7 +139,9 @@ def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed
         # measure elapsed time
         epoch_time.update(time.time() - start_time)
         start_time = time.time()
-        need_time = "Time Left: {:}".format(convert_secs2time(epoch_time.avg * (total_epoch - epoch - 1), True))
+        need_time = "Time Left: {:}".format(
+            convert_secs2time(epoch_time.avg * (total_epoch - epoch - 1), True)
+        )
         logger.log(
             "{:} {:} epoch={:03d}/{:03d} :: Train [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%] Valid [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%], lr={:}".format(
                 time_string(),
@@ -171,14 +192,29 @@ def get_nas_bench_loaders(workers):
     break_line = "-" * 150
     print("{:} Create data-loader for all datasets".format(time_string()))
     print(break_line)
-    TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets("cifar10", str(torch_dir / "cifar.python"), -1)
+    TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets(
+        "cifar10", str(torch_dir / "cifar.python"), -1
+    )
     print(
         "original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
             len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num
         )
     )
-    cifar10_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None)
-    assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [
+    cifar10_splits = load_config(
+        root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None
+    )
+    assert cifar10_splits.train[:10] == [
+        0,
+        5,
+        7,
+        11,
+        13,
+        15,
+        16,
+        17,
+        20,
+        24,
+    ] and cifar10_splits.valid[:10] == [
         1,
         2,
         3,
@@ -194,7 +230,11 @@ def get_nas_bench_loaders(workers):
     temp_dataset.transform = VALID_CIFAR10.transform
     # data loader
     trainval_cifar10_loader = torch.utils.data.DataLoader(
-        TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
+        TRAIN_CIFAR10,
+        batch_size=cifar_config.batch_size,
+        shuffle=True,
+        num_workers=workers,
+        pin_memory=True,
     )
     train_cifar10_loader = torch.utils.data.DataLoader(
         TRAIN_CIFAR10,
@@ -211,7 +251,11 @@ def get_nas_bench_loaders(workers):
         pin_memory=True,
     )
     test__cifar10_loader = torch.utils.data.DataLoader(
-        VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True
+        VALID_CIFAR10,
+        batch_size=cifar_config.batch_size,
+        shuffle=False,
+        num_workers=workers,
+        pin_memory=True,
     )
     print(
         "CIFAR-10  : trval-loader has {:3d} batch with {:} per batch".format(
@@ -235,14 +279,29 @@ def get_nas_bench_loaders(workers):
     )
     print(break_line)
     # CIFAR-100
-    TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets("cifar100", str(torch_dir / "cifar.python"), -1)
+    TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets(
+        "cifar100", str(torch_dir / "cifar.python"), -1
+    )
     print(
         "original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
             len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num
         )
     )
-    cifar100_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None)
-    assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [
+    cifar100_splits = load_config(
+        root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None
+    )
+    assert cifar100_splits.xvalid[:10] == [
+        1,
+        3,
+        4,
+        5,
+        8,
+        10,
+        13,
+        14,
+        15,
+        16,
+    ] and cifar100_splits.xtest[:10] == [
         0,
         2,
         6,
@@ -255,7 +314,11 @@ def get_nas_bench_loaders(workers):
         24,
     ]
     train_cifar100_loader = torch.utils.data.DataLoader(
-        TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
+        TRAIN_CIFAR100,
+        batch_size=cifar_config.batch_size,
+        shuffle=True,
+        num_workers=workers,
+        pin_memory=True,
     )
     valid_cifar100_loader = torch.utils.data.DataLoader(
         VALID_CIFAR100,
@@ -271,9 +334,15 @@ def get_nas_bench_loaders(workers):
         num_workers=workers,
         pin_memory=True,
     )
-    print("CIFAR-100  : train-loader has {:3d} batch".format(len(train_cifar100_loader)))
-    print("CIFAR-100  : valid-loader has {:3d} batch".format(len(valid_cifar100_loader)))
-    print("CIFAR-100  : test--loader has {:3d} batch".format(len(test__cifar100_loader)))
+    print(
+        "CIFAR-100  : train-loader has {:3d} batch".format(len(train_cifar100_loader))
+    )
+    print(
+        "CIFAR-100  : valid-loader has {:3d} batch".format(len(valid_cifar100_loader))
+    )
+    print(
+        "CIFAR-100  : test--loader has {:3d} batch".format(len(test__cifar100_loader))
+    )
     print(break_line)
 
     imagenet16_config_path = "configs/nas-benchmark/ImageNet-16.config"
@@ -286,8 +355,23 @@ def get_nas_bench_loaders(workers):
             len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num
         )
     )
-    imagenet_splits = load_config(root_dir / "configs" / "nas-benchmark" / "imagenet-16-120-test-split.txt", None, None)
-    assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [
+    imagenet_splits = load_config(
+        root_dir / "configs" / "nas-benchmark" / "imagenet-16-120-test-split.txt",
+        None,
+        None,
+    )
+    assert imagenet_splits.xvalid[:10] == [
+        1,
+        2,
+        3,
+        6,
+        7,
+        8,
+        9,
+        12,
+        16,
+        18,
+    ] and imagenet_splits.xtest[:10] == [
         0,
         4,
         5,

lib/procedures/optimizers.py

@@ -14,7 +14,9 @@ class _LRScheduler(object):
         self.optimizer = optimizer
         for group in optimizer.param_groups:
             group.setdefault("initial_lr", group["lr"])
-        self.base_lrs = list(map(lambda group: group["initial_lr"], optimizer.param_groups))
+        self.base_lrs = list(
+            map(lambda group: group["initial_lr"], optimizer.param_groups)
+        )
         self.max_epochs = epochs
         self.warmup_epochs = warmup_epochs
         self.current_epoch = 0
@@ -31,7 +33,9 @@ class _LRScheduler(object):
         )
 
     def state_dict(self):
-        return {key: value for key, value in self.__dict__.items() if key != "optimizer"}
+        return {
+            key: value for key, value in self.__dict__.items() if key != "optimizer"
+        }
 
     def load_state_dict(self, state_dict):
         self.__dict__.update(state_dict)
@@ -50,10 +54,14 @@ class _LRScheduler(object):
 
     def update(self, cur_epoch, cur_iter):
         if cur_epoch is not None:
-            assert isinstance(cur_epoch, int) and cur_epoch >= 0, "invalid cur-epoch : {:}".format(cur_epoch)
+            assert (
+                isinstance(cur_epoch, int) and cur_epoch >= 0
+            ), "invalid cur-epoch : {:}".format(cur_epoch)
             self.current_epoch = cur_epoch
         if cur_iter is not None:
-            assert isinstance(cur_iter, float) and cur_iter >= 0, "invalid cur-iter : {:}".format(cur_iter)
+            assert (
+                isinstance(cur_iter, float) and cur_iter >= 0
+            ), "invalid cur-iter : {:}".format(cur_iter)
             self.current_iter = cur_iter
         for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
             param_group["lr"] = lr
@@ -66,29 +74,44 @@ class CosineAnnealingLR(_LRScheduler):
         super(CosineAnnealingLR, self).__init__(optimizer, warmup_epochs, epochs)
 
     def extra_repr(self):
-        return "type={:}, T-max={:}, eta-min={:}".format("cosine", self.T_max, self.eta_min)
+        return "type={:}, T-max={:}, eta-min={:}".format(
+            "cosine", self.T_max, self.eta_min
+        )
 
     def get_lr(self):
         lrs = []
         for base_lr in self.base_lrs:
-            if self.current_epoch >= self.warmup_epochs and self.current_epoch < self.max_epochs:
+            if (
+                self.current_epoch >= self.warmup_epochs
+                and self.current_epoch < self.max_epochs
+            ):
                 last_epoch = self.current_epoch - self.warmup_epochs
                 # if last_epoch < self.T_max:
                 # if last_epoch < self.max_epochs:
-                lr = self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * last_epoch / self.T_max)) / 2
+                lr = (
+                    self.eta_min
+                    + (base_lr - self.eta_min)
+                    * (1 + math.cos(math.pi * last_epoch / self.T_max))
+                    / 2
+                )
                 # else:
                 #  lr = self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * (self.T_max-1.0) / self.T_max)) / 2
             elif self.current_epoch >= self.max_epochs:
                 lr = self.eta_min
             else:
-                lr = (self.current_epoch / self.warmup_epochs + self.current_iter / self.warmup_epochs) * base_lr
+                lr = (
+                    self.current_epoch / self.warmup_epochs
+                    + self.current_iter / self.warmup_epochs
+                ) * base_lr
             lrs.append(lr)
         return lrs
 
 
 class MultiStepLR(_LRScheduler):
     def __init__(self, optimizer, warmup_epochs, epochs, milestones, gammas):
-        assert len(milestones) == len(gammas), "invalid {:} vs {:}".format(len(milestones), len(gammas))
+        assert len(milestones) == len(gammas), "invalid {:} vs {:}".format(
+            len(milestones), len(gammas)
+        )
         self.milestones = milestones
         self.gammas = gammas
         super(MultiStepLR, self).__init__(optimizer, warmup_epochs, epochs)
@@ -108,7 +131,10 @@ class MultiStepLR(_LRScheduler):
                 for x in self.gammas[:idx]:
                     lr *= x
             else:
-                lr = (self.current_epoch / self.warmup_epochs + self.current_iter / self.warmup_epochs) * base_lr
+                lr = (
+                    self.current_epoch / self.warmup_epochs
+                    + self.current_iter / self.warmup_epochs
+                ) * base_lr
             lrs.append(lr)
         return lrs
 
@@ -119,7 +145,9 @@ class ExponentialLR(_LRScheduler):
         super(ExponentialLR, self).__init__(optimizer, warmup_epochs, epochs)
 
     def extra_repr(self):
-        return "type={:}, gamma={:}, base-lrs={:}".format("exponential", self.gamma, self.base_lrs)
+        return "type={:}, gamma={:}, base-lrs={:}".format(
+            "exponential", self.gamma, self.base_lrs
+        )
 
     def get_lr(self):
         lrs = []
@@ -129,7 +157,10 @@ class ExponentialLR(_LRScheduler):
                 assert last_epoch >= 0, "invalid last_epoch : {:}".format(last_epoch)
                 lr = base_lr * (self.gamma ** last_epoch)
             else:
-                lr = (self.current_epoch / self.warmup_epochs + self.current_iter / self.warmup_epochs) * base_lr
+                lr = (
+                    self.current_epoch / self.warmup_epochs
+                    + self.current_iter / self.warmup_epochs
+                ) * base_lr
             lrs.append(lr)
         return lrs
 
@@ -151,10 +182,18 @@ class LinearLR(_LRScheduler):
             if self.current_epoch >= self.warmup_epochs:
                 last_epoch = self.current_epoch - self.warmup_epochs
                 assert last_epoch >= 0, "invalid last_epoch : {:}".format(last_epoch)
-                ratio = (self.max_LR - self.min_LR) * last_epoch / self.max_epochs / self.max_LR
+                ratio = (
+                    (self.max_LR - self.min_LR)
+                    * last_epoch
+                    / self.max_epochs
+                    / self.max_LR
+                )
                 lr = base_lr * (1 - ratio)
             else:
-                lr = (self.current_epoch / self.warmup_epochs + self.current_iter / self.warmup_epochs) * base_lr
+                lr = (
+                    self.current_epoch / self.warmup_epochs
+                    + self.current_iter / self.warmup_epochs
+                ) * base_lr
             lrs.append(lr)
         return lrs
 
@@ -176,26 +215,42 @@ class CrossEntropyLabelSmooth(nn.Module):
 
 def get_optim_scheduler(parameters, config):
     assert (
-        hasattr(config, "optim") and hasattr(config, "scheduler") and hasattr(config, "criterion")
-    ), "config must have optim / scheduler / criterion keys instead of {:}".format(config)
+        hasattr(config, "optim")
+        and hasattr(config, "scheduler")
+        and hasattr(config, "criterion")
+    ), "config must have optim / scheduler / criterion keys instead of {:}".format(
+        config
+    )
     if config.optim == "SGD":
         optim = torch.optim.SGD(
-            parameters, config.LR, momentum=config.momentum, weight_decay=config.decay, nesterov=config.nesterov
+            parameters,
+            config.LR,
+            momentum=config.momentum,
+            weight_decay=config.decay,
+            nesterov=config.nesterov,
         )
     elif config.optim == "RMSprop":
-        optim = torch.optim.RMSprop(parameters, config.LR, momentum=config.momentum, weight_decay=config.decay)
+        optim = torch.optim.RMSprop(
+            parameters, config.LR, momentum=config.momentum, weight_decay=config.decay
+        )
     else:
         raise ValueError("invalid optim : {:}".format(config.optim))
 
     if config.scheduler == "cos":
         T_max = getattr(config, "T_max", config.epochs)
-        scheduler = CosineAnnealingLR(optim, config.warmup, config.epochs, T_max, config.eta_min)
+        scheduler = CosineAnnealingLR(
+            optim, config.warmup, config.epochs, T_max, config.eta_min
+        )
     elif config.scheduler == "multistep":
-        scheduler = MultiStepLR(optim, config.warmup, config.epochs, config.milestones, config.gammas)
+        scheduler = MultiStepLR(
+            optim, config.warmup, config.epochs, config.milestones, config.gammas
+        )
     elif config.scheduler == "exponential":
         scheduler = ExponentialLR(optim, config.warmup, config.epochs, config.gamma)
     elif config.scheduler == "linear":
-        scheduler = LinearLR(optim, config.warmup, config.epochs, config.LR, config.LR_min)
+        scheduler = LinearLR(
+            optim, config.warmup, config.epochs, config.LR, config.LR_min
+        )
     else:
         raise ValueError("invalid scheduler : {:}".format(config.scheduler))
 

lib/procedures/q_exps.py

@@ -41,7 +41,10 @@ def update_gpu(config, gpu):
     if "task" in config and "model" in config["task"]:
         if "GPU" in config["task"]["model"]:
             config["task"]["model"]["GPU"] = gpu
-        elif "kwargs" in config["task"]["model"] and "GPU" in config["task"]["model"]["kwargs"]:
+        elif (
+            "kwargs" in config["task"]["model"]
+            and "GPU" in config["task"]["model"]["kwargs"]
+        ):
             config["task"]["model"]["kwargs"]["GPU"] = gpu
     elif "model" in config:
         if "GPU" in config["model"]:
@@ -68,7 +71,12 @@ def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
     model_fit_kwargs = dict(dataset=dataset)
 
     # Let's start the experiment.
-    with R.start(experiment_name=experiment_name, recorder_name=recorder_name, uri=uri, resume=True):
+    with R.start(
+        experiment_name=experiment_name,
+        recorder_name=recorder_name,
+        uri=uri,
+        resume=True,
+    ):
         # Setup log
         recorder_root_dir = R.get_recorder().get_local_dir()
         log_file = os.path.join(recorder_root_dir, "{:}.log".format(experiment_name))

lib/procedures/search_main.py

@@ -36,7 +36,12 @@ def search_train(
     logger,
 ):
     data_time, batch_time = AverageMeter(), AverageMeter()
-    base_losses, arch_losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
+    base_losses, arch_losses, top1, top5 = (
+        AverageMeter(),
+        AverageMeter(),
+        AverageMeter(),
+        AverageMeter(),
+    )
     arch_cls_losses, arch_flop_losses = AverageMeter(), AverageMeter()
     epoch_str, flop_need, flop_weight, flop_tolerant = (
         extra_info["epoch-str"],
@@ -46,10 +51,16 @@ def search_train(
     )
 
     network.train()
-    logger.log("[Search] : {:}, FLOP-Require={:.2f} MB, FLOP-WEIGHT={:.2f}".format(epoch_str, flop_need, flop_weight))
+    logger.log(
+        "[Search] : {:}, FLOP-Require={:.2f} MB, FLOP-WEIGHT={:.2f}".format(
+            epoch_str, flop_need, flop_weight
+        )
+    )
     end = time.time()
     network.apply(change_key("search_mode", "search"))
-    for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(search_loader):
+    for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
+        search_loader
+    ):
         scheduler.update(None, 1.0 * step / len(search_loader))
         # calculate prediction and loss
         base_targets = base_targets.cuda(non_blocking=True)
@@ -75,7 +86,9 @@ def search_train(
         arch_optimizer.zero_grad()
         logits, expected_flop = network(arch_inputs)
         flop_cur = network.module.get_flop("genotype", None, None)
-        flop_loss, flop_loss_scale = get_flop_loss(expected_flop, flop_cur, flop_need, flop_tolerant)
+        flop_loss, flop_loss_scale = get_flop_loss(
+            expected_flop, flop_cur, flop_need, flop_tolerant
+        )
         acls_loss = criterion(logits, arch_targets)
         arch_loss = acls_loss + flop_loss * flop_weight
         arch_loss.backward()
@@ -90,7 +103,11 @@ def search_train(
         batch_time.update(time.time() - end)
         end = time.time()
         if step % print_freq == 0 or (step + 1) == len(search_loader):
-            Sstr = "**TRAIN** " + time_string() + " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(search_loader))
+            Sstr = (
+                "**TRAIN** "
+                + time_string()
+                + " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(search_loader))
+            )
             Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
                 batch_time=batch_time, data_time=data_time
             )
@@ -153,7 +170,11 @@ def search_valid(xloader, network, criterion, extra_info, print_freq, logger):
             end = time.time()
 
             if i % print_freq == 0 or (i + 1) == len(xloader):
-                Sstr = "**VALID** " + time_string() + " [{:}][{:03d}/{:03d}]".format(extra_info, i, len(xloader))
+                Sstr = (
+                    "**VALID** "
+                    + time_string()
+                    + " [{:}][{:03d}/{:03d}]".format(extra_info, i, len(xloader))
+                )
                 Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
                     batch_time=batch_time, data_time=data_time
                 )
@@ -165,7 +186,11 @@ def search_valid(xloader, network, criterion, extra_info, print_freq, logger):
 
     logger.log(
         " **VALID** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}".format(
-            top1=top1, top5=top5, error1=100 - top1.avg, error5=100 - top5.avg, loss=losses.avg
+            top1=top1,
+            top5=top5,
+            error1=100 - top1.avg,
+            error5=100 - top5.avg,
+            loss=losses.avg,
         )
     )
 

lib/procedures/search_main_v2.py

@@ -36,7 +36,12 @@ def search_train_v2(
     logger,
 ):
     data_time, batch_time = AverageMeter(), AverageMeter()
-    base_losses, arch_losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
+    base_losses, arch_losses, top1, top5 = (
+        AverageMeter(),
+        AverageMeter(),
+        AverageMeter(),
+        AverageMeter(),
+    )
     arch_cls_losses, arch_flop_losses = AverageMeter(), AverageMeter()
     epoch_str, flop_need, flop_weight, flop_tolerant = (
         extra_info["epoch-str"],
@@ -46,10 +51,16 @@ def search_train_v2(
     )
 
     network.train()
-    logger.log("[Search] : {:}, FLOP-Require={:.2f} MB, FLOP-WEIGHT={:.2f}".format(epoch_str, flop_need, flop_weight))
+    logger.log(
+        "[Search] : {:}, FLOP-Require={:.2f} MB, FLOP-WEIGHT={:.2f}".format(
+            epoch_str, flop_need, flop_weight
+        )
+    )
     end = time.time()
     network.apply(change_key("search_mode", "search"))
-    for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(search_loader):
+    for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
+        search_loader
+    ):
         scheduler.update(None, 1.0 * step / len(search_loader))
         # calculate prediction and loss
         base_targets = base_targets.cuda(non_blocking=True)
@@ -73,7 +84,9 @@ def search_train_v2(
         arch_optimizer.zero_grad()
         logits, expected_flop = network(arch_inputs)
         flop_cur = network.module.get_flop("genotype", None, None)
-        flop_loss, flop_loss_scale = get_flop_loss(expected_flop, flop_cur, flop_need, flop_tolerant)
+        flop_loss, flop_loss_scale = get_flop_loss(
+            expected_flop, flop_cur, flop_need, flop_tolerant
+        )
         acls_loss = criterion(logits, arch_targets)
         arch_loss = acls_loss + flop_loss * flop_weight
         arch_loss.backward()
@@ -88,7 +101,11 @@ def search_train_v2(
         batch_time.update(time.time() - end)
         end = time.time()
         if step % print_freq == 0 or (step + 1) == len(search_loader):
-            Sstr = "**TRAIN** " + time_string() + " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(search_loader))
+            Sstr = (
+                "**TRAIN** "
+                + time_string()
+                + " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(search_loader))
+            )
             Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
                 batch_time=batch_time, data_time=data_time
             )

lib/procedures/simple_KD_main.py

@@ -10,7 +10,16 @@ from utils import obtain_accuracy
 
 
 def simple_KD_train(
-    xloader, teacher, network, criterion, scheduler, optimizer, optim_config, extra_info, print_freq, logger
+    xloader,
+    teacher,
+    network,
+    criterion,
+    scheduler,
+    optimizer,
+    optim_config,
+    extra_info,
+    print_freq,
+    logger,
 ):
     loss, acc1, acc5 = procedure(
         xloader,
@@ -28,25 +37,58 @@ def simple_KD_train(
     return loss, acc1, acc5
 
 
-def simple_KD_valid(xloader, teacher, network, criterion, optim_config, extra_info, print_freq, logger):
+def simple_KD_valid(
+    xloader, teacher, network, criterion, optim_config, extra_info, print_freq, logger
+):
     with torch.no_grad():
         loss, acc1, acc5 = procedure(
-            xloader, teacher, network, criterion, None, None, "valid", optim_config, extra_info, print_freq, logger
+            xloader,
+            teacher,
+            network,
+            criterion,
+            None,
+            None,
+            "valid",
+            optim_config,
+            extra_info,
+            print_freq,
+            logger,
         )
     return loss, acc1, acc5
 
 
 def loss_KD_fn(
-    criterion, student_logits, teacher_logits, studentFeatures, teacherFeatures, targets, alpha, temperature
+    criterion,
+    student_logits,
+    teacher_logits,
+    studentFeatures,
+    teacherFeatures,
+    targets,
+    alpha,
+    temperature,
 ):
     basic_loss = criterion(student_logits, targets) * (1.0 - alpha)
     log_student = F.log_softmax(student_logits / temperature, dim=1)
     sof_teacher = F.softmax(teacher_logits / temperature, dim=1)
-    KD_loss = F.kl_div(log_student, sof_teacher, reduction="batchmean") * (alpha * temperature * temperature)
+    KD_loss = F.kl_div(log_student, sof_teacher, reduction="batchmean") * (
+        alpha * temperature * temperature
+    )
     return basic_loss + KD_loss
 
 
-def procedure(xloader, teacher, network, criterion, scheduler, optimizer, mode, config, extra_info, print_freq, logger):
+def procedure(
+    xloader,
+    teacher,
+    network,
+    criterion,
+    scheduler,
+    optimizer,
+    mode,
+    config,
+    extra_info,
+    print_freq,
+    logger,
+):
     data_time, batch_time, losses, top1, top5 = (
         AverageMeter(),
         AverageMeter(),
@@ -65,7 +107,10 @@ def procedure(xloader, teacher, network, criterion, scheduler, optimizer, mode,
 
     logger.log(
         "[{:5s}] config :: auxiliary={:}, KD :: [alpha={:.2f}, temperature={:.2f}]".format(
-            mode, config.auxiliary if hasattr(config, "auxiliary") else -1, config.KD_alpha, config.KD_temperature
+            mode,
+            config.auxiliary if hasattr(config, "auxiliary") else -1,
+            config.KD_alpha,
+            config.KD_temperature,
         )
     )
     end = time.time()
@@ -82,7 +127,9 @@ def procedure(xloader, teacher, network, criterion, scheduler, optimizer, mode,
 
         student_f, logits = network(inputs)
         if isinstance(logits, list):
-            assert len(logits) == 2, "logits must has {:} items instead of {:}".format(2, len(logits))
+            assert len(logits) == 2, "logits must has {:} items instead of {:}".format(
+                2, len(logits)
+            )
             logits, logits_aux = logits
         else:
             logits, logits_aux = logits, None
@@ -90,7 +137,14 @@ def procedure(xloader, teacher, network, criterion, scheduler, optimizer, mode,
             teacher_f, teacher_logits = teacher(inputs)
 
         loss = loss_KD_fn(
-            criterion, logits, teacher_logits, student_f, teacher_f, targets, config.KD_alpha, config.KD_temperature
+            criterion,
+            logits,
+            teacher_logits,
+            student_f,
+            teacher_f,
+            targets,
+            config.KD_alpha,
+            config.KD_temperature,
         )
         if config is not None and hasattr(config, "auxiliary") and config.auxiliary > 0:
             loss_aux = criterion(logits_aux, targets)
@@ -139,7 +193,12 @@ def procedure(xloader, teacher, network, criterion, scheduler, optimizer, mode,
     )
     logger.log(
         " **{mode:5s}** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}".format(
-            mode=mode.upper(), top1=top1, top5=top5, error1=100 - top1.avg, error5=100 - top5.avg, loss=losses.avg
+            mode=mode.upper(),
+            top1=top1,
+            top5=top5,
+            error1=100 - top1.avg,
+            error5=100 - top5.avg,
+            loss=losses.avg,
         )
     )
     return losses.avg, top1.avg, top5.avg

lib/procedures/starts.py

@@ -31,7 +31,9 @@ def prepare_logger(xargs):
     logger.log("CUDA GPU numbers : {:}".format(torch.cuda.device_count()))
     logger.log(
         "CUDA_VISIBLE_DEVICES : {:}".format(
-            os.environ["CUDA_VISIBLE_DEVICES"] if "CUDA_VISIBLE_DEVICES" in os.environ else "None"
+            os.environ["CUDA_VISIBLE_DEVICES"]
+            if "CUDA_VISIBLE_DEVICES" in os.environ
+            else "None"
         )
     )
     return logger
@@ -54,10 +56,14 @@ def get_machine_info():
 def save_checkpoint(state, filename, logger):
     if osp.isfile(filename):
         if hasattr(logger, "log"):
-            logger.log("Find {:} exist, delete is at first before saving".format(filename))
+            logger.log(
+                "Find {:} exist, delete is at first before saving".format(filename)
+            )
         os.remove(filename)
     torch.save(state, filename)
-    assert osp.isfile(filename), "save filename : {:} failed, which is not found.".format(filename)
+    assert osp.isfile(
+        filename
+    ), "save filename : {:} failed, which is not found.".format(filename)
     if hasattr(logger, "log"):
         logger.log("save checkpoint into {:}".format(filename))
     return filename