merged

exps/NAS-Bench-201/functions.py

@@ -2,11 +2,11 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
 #####################################################
 import time, torch
-from procedures import prepare_seed, get_optim_scheduler
+from xautodl.procedures import prepare_seed, get_optim_scheduler
-from utils import get_model_infos, obtain_accuracy
+from xautodl.utils import get_model_infos, obtain_accuracy
-from config_utils import dict2config
+from xautodl.config_utils import dict2config
-from log_utils import AverageMeter, time_string, convert_secs2time
+from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
-from models import get_cell_based_tiny_net
+from xautodl.models import get_cell_based_tiny_net
 
 
 __all__ = ["evaluate_for_seed", "pure_evaluate"]

exps/NAS-Bench-201/main.py

@@ -16,8 +16,9 @@ from xautodl.procedures import get_machine_info
 from xautodl.datasets import get_datasets
 from xautodl.log_utils import Logger, AverageMeter, time_string, convert_secs2time
 from xautodl.models import CellStructure, CellArchitectures, get_search_spaces
-from xautodl.functions import evaluate_for_seed
+from functions import evaluate_for_seed
 
+from torchvision import datasets, transforms
 
 def evaluate_all_datasets(
     arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger
@@ -46,47 +47,85 @@ def evaluate_all_datasets(
             split_info = load_config(
                 "configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
             )
+        elif dataset.startswith("aircraft"):
+            if use_less:
+                config_path = "configs/nas-benchmark/LESS.config"
+            else:
+                config_path = "configs/nas-benchmark/aircraft.config"
+            split_info = load_config(
+                "configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
+            )
+
         else:
             raise ValueError("invalid dataset : {:}".format(dataset))
         config = load_config(
             config_path, {"class_num": class_num, "xshape": xshape}, logger
         )
         # check whether use splited validation set
+        # if dataset == 'aircraft':
+        #     split = True
         if bool(split):
-            assert dataset == "cifar10"
+            if dataset == "cifar10" or dataset == "cifar100":
-            ValLoaders = {
+                assert dataset == "cifar10"
-                "ori-test": torch.utils.data.DataLoader(
+                ValLoaders = {
-                    valid_data,
+                    "ori-test": torch.utils.data.DataLoader(
+                        valid_data,
+                        batch_size=config.batch_size,
+                        shuffle=False,
+                        num_workers=workers,
+                        pin_memory=True,
+                    )
+                }
+                assert len(train_data) == len(split_info.train) + len(
+                    split_info.valid
+                ), "invalid length : {:} vs {:} + {:}".format(
+                    len(train_data), len(split_info.train), len(split_info.valid)
+                )
+                train_data_v2 = deepcopy(train_data)
+                train_data_v2.transform = valid_data.transform
+                valid_data = train_data_v2
+                # data loader
+                train_loader = torch.utils.data.DataLoader(
+                    train_data,
                     batch_size=config.batch_size,
-                    shuffle=False,
+                    sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train),
                     num_workers=workers,
                     pin_memory=True,
                 )
-            }
+                valid_loader = torch.utils.data.DataLoader(
-            assert len(train_data) == len(split_info.train) + len(
+                    valid_data,
-                split_info.valid
+                    batch_size=config.batch_size,
-            ), "invalid length : {:} vs {:} + {:}".format(
+                    sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid),
-                len(train_data), len(split_info.train), len(split_info.valid)
+                    num_workers=workers,
-            )
+                    pin_memory=True,
-            train_data_v2 = deepcopy(train_data)
+                )
-            train_data_v2.transform = valid_data.transform
+                ValLoaders["x-valid"] = valid_loader
-            valid_data = train_data_v2
+            elif dataset == "aircraft":
-            # data loader
+                ValLoaders = {
-            train_loader = torch.utils.data.DataLoader(
+                    "ori-test": torch.utils.data.DataLoader(
-                train_data,
+                        valid_data,
-                batch_size=config.batch_size,
+                        batch_size=config.batch_size,
-                sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train),
+                        shuffle=False,
-                num_workers=workers,
+                        num_workers=workers,
-                pin_memory=True,
+                        pin_memory=True,
-            )
+                    )
-            valid_loader = torch.utils.data.DataLoader(
+                }
-                valid_data,
+                train_data_v2 = deepcopy(train_data)
-                batch_size=config.batch_size,
+                train_data_v2.transform = valid_data.transform
-                sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid),
+                valid_data = train_data_v2
-                num_workers=workers,
+                # 使用 DataLoader
-                pin_memory=True,
+                train_loader = torch.utils.data.DataLoader(
-            )
+                    train_data, 
-            ValLoaders["x-valid"] = valid_loader
+                    batch_size=config.batch_size, 
+                    sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train),
+                    num_workers=workers,
+                    pin_memory=True)
+                valid_loader = torch.utils.data.DataLoader(
+                    valid_data,
+                    batch_size=config.batch_size, 
+                    sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid),
+                    num_workers=workers,
+                    pin_memory=True)
         else:
             # data loader
             train_loader = torch.utils.data.DataLoader(
@@ -103,7 +142,7 @@ def evaluate_all_datasets(
                 num_workers=workers,
                 pin_memory=True,
             )
-            if dataset == "cifar10":
+            if dataset == "cifar10" or dataset == "aircraft":
                 ValLoaders = {"ori-test": valid_loader}
             elif dataset == "cifar100":
                 cifar100_splits = load_config(

scripts-search/NAS-Bench-201/train-models.sh

@@ -28,16 +28,30 @@ else
   mode=cover
 fi
 
+# OMP_NUM_THREADS=4 python ./exps/NAS-Bench-201/main.py \
+# 	--mode ${mode} --save_dir ${save_dir} --max_node 4 \
+# 	--use_less ${use_less} \
+# 	--datasets cifar10 cifar10 cifar100 ImageNet16-120 \
+# 	--splits   1       0       0        0 \
+# 	--xpaths $TORCH_HOME/cifar.python \
+# 		 $TORCH_HOME/cifar.python \
+# 		 $TORCH_HOME/cifar.python \
+# 		 $TORCH_HOME/cifar.python/ImageNet16 \
+# 	--channel 16 --num_cells 5 \
+# 	--workers 4 \
+# 	--srange ${xstart} ${xend} --arch_index ${arch_index} \
+# 	--seeds ${all_seeds}
+
 OMP_NUM_THREADS=4 python ./exps/NAS-Bench-201/main.py \
 	--mode ${mode} --save_dir ${save_dir} --max_node 4 \
 	--use_less ${use_less} \
-	--datasets cifar10 cifar10 cifar100 ImageNet16-120 \
+	--datasets aircraft \
-	--splits   1       0       0        0 \
+	--xpaths /lustre/hpe/ws11/ws11.1/ws/xmuhanma-SWAP/train_datasets/datasets/fgvc-aircraft-2013b/data/ \
-	--xpaths $TORCH_HOME/cifar.python \
+	--channel 16 \
-		 $TORCH_HOME/cifar.python \
+	--splits 1 \
-		 $TORCH_HOME/cifar.python \
+	--num_cells 5 \
-		 $TORCH_HOME/cifar.python/ImageNet16 \
-	--channel 16 --num_cells 5 \
 	--workers 4 \
 	--srange ${xstart} ${xend} --arch_index ${arch_index} \
 	--seeds ${all_seeds}
+
+

xautodl/datasets/get_dataset_with_transform.py

@@ -24,6 +24,8 @@ Dataset2Class = {
     "ImageNet16-150": 150,
     "ImageNet16-120": 120,
     "ImageNet16-200": 200,
+    "aircraft": 100,
+    "oxford": 102
 }
 
 
@@ -109,6 +111,12 @@ def get_datasets(name, root, cutout):
     elif name.startswith("ImageNet16"):
         mean = [x / 255 for x in [122.68, 116.66, 104.01]]
         std = [x / 255 for x in [63.22, 61.26, 65.09]]
+    elif name == 'aircraft':
+        mean = [0.4785, 0.5100, 0.5338]
+        std  = [0.1845, 0.1830, 0.2060]
+    elif name == 'oxford':
+        mean = [0.4811, 0.4492, 0.3957]
+        std  = [0.2260, 0.2231, 0.2249]
     else:
         raise TypeError("Unknow dataset : {:}".format(name))
 
@@ -127,6 +135,13 @@ def get_datasets(name, root, cutout):
             [transforms.ToTensor(), transforms.Normalize(mean, std)]
         )
         xshape = (1, 3, 32, 32)
+    elif name.startswith("aircraft") or name.startswith("oxford"):
+        lists = [transforms.RandomCrop(16, padding=0), transforms.ToTensor(), transforms.Normalize(mean, std)]
+        if cutout > 0:
+            lists += [CUTOUT(cutout)]
+        train_transform = transforms.Compose(lists)
+        test_transform  = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)])
+        xshape = (1, 3, 16, 16)
     elif name.startswith("ImageNet16"):
         lists = [
             transforms.RandomHorizontalFlip(),
@@ -207,6 +222,10 @@ def get_datasets(name, root, cutout):
             root, train=False, transform=test_transform, download=True
         )
         assert len(train_data) == 50000 and len(test_data) == 10000
+    elif name == "aircraft":
+        train_data = dset.ImageFolder(root='/lustre/hpe/ws11/ws11.1/ws/xmuhanma-SWAP/train_datasets/datasets/fgvc-aircraft-2013b/data/train_sorted_image', transform=train_transform)
+        test_data = dset.ImageFolder(root='/lustre/hpe/ws11/ws11.1/ws/xmuhanma-SWAP/train_datasets/datasets/fgvc-aircraft-2013b/data/train_sorted_image', transform=test_transform)
+
     elif name.startswith("imagenet-1k"):
         train_data = dset.ImageFolder(osp.join(root, "train"), train_transform)
         test_data = dset.ImageFolder(osp.join(root, "val"), test_transform)