Add int search space

exps/NAS-Bench-201/functions.py

@@ -76,7 +76,9 @@ def procedure(xloader, network, criterion, scheduler, optimizer, mode):
     return losses.avg, top1.avg, top5.avg, batch_time.sum
 
 
-def evaluate_for_seed(arch_config, config, arch, train_loader, valid_loaders, seed, logger):
+def evaluate_for_seed(
+    arch_config, config, arch, train_loader, valid_loaders, seed, logger
+):
 
     prepare_seed(seed)  # random seed
     net = get_cell_based_tiny_net(
@@ -94,14 +96,29 @@ def evaluate_for_seed(arch_config, config, arch, train_loader, valid_loaders, se
     # net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
     flop, param = get_model_infos(net, 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(), config)
     network, criterion = torch.nn.DataParallel(net).cuda(), criterion.cuda()
     # start training
-    start_time, epoch_time, total_epoch = time.time(), AverageMeter(), config.epochs + config.warmup
-    train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {}
+    start_time, epoch_time, total_epoch = (
+        time.time(),
+        AverageMeter(),
+        config.epochs + config.warmup,
+    )
+    (
+        train_losses,
+        train_acc1es,
+        train_acc5es,
+        valid_losses,
+        valid_acc1es,
+        valid_acc5es,
+    ) = ({}, {}, {}, {}, {}, {})
     train_times, valid_times = {}, {}
     for epoch in range(total_epoch):
         scheduler.update(epoch, 0.0)
@@ -126,7 +143,9 @@ def evaluate_for_seed(arch_config, config, arch, train_loader, valid_loaders, se
         # 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}%]".format(
                 time_string(),