add oxford and aircraft

Update some links in README_CN.md and fix some typos.

README.md

@@ -61,13 +61,13 @@ At this moment, this project provides the following algorithms and scripts to ru
     <tr> <!-- (6-th row) -->
     <td align="center" valign="middle"> NATS-Bench </td>
     <td align="center" valign="middle"> <a href="https://xuanyidong.com/assets/projects/NATS-Bench"> NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size</a> </td>
-    <td align="center" valign="middle"> <a href="https://github.com/D-X-Y/NATS-Bench">NATS-Bench.md</a> </td>
+    <td align="center" valign="middle"> <a href="https://github.com/D-X-Y/NATS-Bench/blob/main/README.md">NATS-Bench.md</a> </td>
     </tr>
     <tr> <!-- (7-th row) -->
     <td align="center" valign="middle"> ... </td>
     <td align="center" valign="middle"> ENAS / REA / REINFORCE / BOHB </td>
     <td align="center" valign="middle"> Please check the original papers </td>
-    <td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a>  <a href="https://github.com/D-X-Y/NATS-Bench">NATS-Bench.md</a> </td>
+    <td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a>  <a href="https://github.com/D-X-Y/NATS-Bench/blob/main/README.md">NATS-Bench.md</a> </td>
     </tr>
     <tr> <!-- (start second block) -->
     <td rowspan="1" align="center" valign="middle" halign="middle"> HPO </td>

docs/NAS-Bench-201-PURE.md

@@ -29,7 +29,7 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s
 You can move it to anywhere you want and send its path to our API for initialization.
 - [2020.02.25] APIv1.0/FILEv1.0: [`NAS-Bench-201-v1_0-e61699.pth`](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) (2.2G), where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
 - [2020.02.25] APIv1.0/FILEv1.0: The full data of each architecture can be download from [
-NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
+NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the trained weights.
 - [2020.02.25] APIv1.0/FILEv1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
 - [2020.03.09] APIv1.2/FILEv1.0: More robust API with more functions and descriptions
 - [2020.03.16] APIv1.3/FILEv1.1: [`NAS-Bench-201-v1_1-096897.pth`](https://drive.google.com/open?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_) (4.7G), where `096897` is the last six digits for this file. It contains information of more trials compared to `NAS-Bench-201-v1_0-e61699.pth`, especially all models trained by 12 epochs on all datasets are avaliable.

docs/NAS-Bench-201.md

@@ -27,7 +27,7 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s
 You can move it to anywhere you want and send its path to our API for initialization.
 - [2020.02.25] APIv1.0/FILEv1.0: [`NAS-Bench-201-v1_0-e61699.pth`](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) (2.2G), where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
 - [2020.02.25] APIv1.0/FILEv1.0: The full data of each architecture can be download from [
-NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
+NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the trained weights.
 - [2020.02.25] APIv1.0/FILEv1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
 - [2020.03.09] APIv1.2/FILEv1.0: More robust API with more functions and descriptions
 - [2020.03.16] APIv1.3/FILEv1.1: [`NAS-Bench-201-v1_1-096897.pth`](https://drive.google.com/open?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_) (4.7G), where `096897` is the last six digits for this file. It contains information of more trials compared to `NAS-Bench-201-v1_0-e61699.pth`, especially all models trained by 12 epochs on all datasets are avaliable.

docs/README_CN.md

@@ -3,7 +3,7 @@
 </p>
 
 ---------
-[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md)
+[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](../LICENSE.md)
 
 自动深度学习库 (AutoDL-Projects) 是一个开源的，轻量级的，功能强大的项目。
 该项目实现了多种网络结构搜索(NAS)和超参数优化(HPO)算法。
@@ -142,8 +142,8 @@
 
 # 其他
 
-如果你想要给这份代码库做贡献，请看[CONTRIBUTING.md](.github/CONTRIBUTING.md)。
-此外，使用规范请参考[CODE-OF-CONDUCT.md](.github/CODE-OF-CONDUCT.md)。
+如果你想要给这份代码库做贡献，请看[CONTRIBUTING.md](../.github/CONTRIBUTING.md)。
+此外，使用规范请参考[CODE-OF-CONDUCT.md](../.github/CODE-OF-CONDUCT.md)。
 
 # 许可证
-The entire codebase is under [MIT license](LICENSE.md)
+The entire codebase is under [MIT license](../LICENSE.md)

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 utils import get_model_infos, obtain_accuracy
-from config_utils import dict2config
-from log_utils import AverageMeter, time_string, convert_secs2time
-from models import get_cell_based_tiny_net
+from xautodl.procedures import prepare_seed, get_optim_scheduler
+from xautodl.utils import get_model_infos, obtain_accuracy
+from xautodl.config_utils import dict2config
+from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
+from xautodl.models import get_cell_based_tiny_net
 
 
 __all__ = ["evaluate_for_seed", "pure_evaluate"]

exps/NAS-Bench-201/main.py

@@ -16,10 +16,96 @@ 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
+
+# NASBENCH201_CONFIG_PATH = os.path.join( os.getcwd(), 'main_exp', 'transfer_nag')
+
+NASBENCH201_CONFIG_PATH = '/lustre/hpe/ws11/ws11.1/ws/xmuhanma-nbdit/autodl-projects/configs/nas-benchmark'
 
 
-def evaluate_all_datasets(
+def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed,
+                          arch_config, workers, logger):
+    machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
+    all_infos = {'info': machine_info}
+    all_dataset_keys = []
+    # look all the datasets
+    for dataset, xpath, split in zip(datasets, xpaths, splits):
+        # train valid data
+        task = None
+        train_data, valid_data, xshape, class_num = get_datasets(
+            dataset, xpath, -1, task)
+
+        # load the configuration
+        if dataset in ['mnist', 'svhn', 'aircraft', 'oxford']:
+            if use_less:
+                # config_path = os.path.join(
+                #     NASBENCH201_CONFIG_PATH, 'nas_bench_201/configs/nas-benchmark/LESS.config')
+                config_path = os.path.join(
+                    NASBENCH201_CONFIG_PATH, 'LESS.config')
+            else:
+                # config_path = os.path.join(
+                #     NASBENCH201_CONFIG_PATH, 'nas_bench_201/configs/nas-benchmark/{}.config'.format(dataset))
+                config_path = os.path.join(
+                    NASBENCH201_CONFIG_PATH, '{}.config'.format(dataset))
+
+
+            p = os.path.join(
+                NASBENCH201_CONFIG_PATH, '{:}-split.txt'.format(dataset))
+            if not os.path.exists(p):
+                import json
+                label_list = list(range(len(train_data)))
+                random.shuffle(label_list)
+                strlist = [str(label_list[i]) for i in range(len(label_list))]
+                splited = {'train': ["int", strlist[:len(train_data) // 2]],
+                           'valid': ["int", strlist[len(train_data) // 2:]]}
+                with open(p, 'w') as f:
+                    f.write(json.dumps(splited))
+            split_info = load_config(os.path.join(
+                NASBENCH201_CONFIG_PATH, '{:}-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)
+        # data loader
+        train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size,
+                                                   shuffle=True, num_workers=workers, pin_memory=True)
+        valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size,
+                                                   shuffle=False, num_workers=workers, pin_memory=True)
+        splits = load_config(os.path.join(
+            NASBENCH201_CONFIG_PATH, '{}-test-split.txt'.format(dataset)), None, None)
+        ValLoaders = {'ori-test': valid_loader,
+                      'x-valid': torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size,
+                                                             sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                                                                 splits.xvalid),
+                                                             num_workers=workers, pin_memory=True),
+                      'x-test': torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size,
+                                                            sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                                                                splits.xtest),
+                                                            num_workers=workers, pin_memory=True)
+                      }
+        dataset_key = '{:}'.format(dataset)
+        if bool(split):
+            dataset_key = dataset_key + '-valid'
+        logger.log(
+            'Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.
+            format(dataset_key, len(train_data), len(valid_data), len(train_loader), len(valid_loader), config.batch_size))
+        logger.log('Evaluate ||||||| {:10s} ||||||| Config={:}'.format(
+            dataset_key, config))
+        for key, value in ValLoaders.items():
+            logger.log(
+                'Evaluate ---->>>> {:10s} with {:} batchs'.format(key, len(value)))
+
+        results = evaluate_for_seed(
+            arch_config, config, arch, train_loader, ValLoaders, seed, logger)
+        all_infos[dataset_key] = results
+        all_dataset_keys.append(dataset_key)
+    all_infos['all_dataset_keys'] = all_dataset_keys
+    return all_infos
+
+def evaluate_all_datasets1(
     arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger
 ):
     machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
@@ -46,47 +132,117 @@ 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
+            )
+        elif dataset.startswith("oxford"):
+            if use_less:
+                config_path = "configs/nas-benchmark/LESS.config"
+            else:
+                config_path = "configs/nas-benchmark/oxford.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"
-            ValLoaders = {
-                "ori-test": torch.utils.data.DataLoader(
-                    valid_data,
+            if dataset == "cifar10" or dataset == "cifar100":
+                assert dataset == "cifar10"
+                ValLoaders = {
+                    "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,
                 )
-            }
-            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,
-                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,
-            )
-            ValLoaders["x-valid"] = valid_loader
+                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,
+                )
+                ValLoaders["x-valid"] = valid_loader
+            elif dataset == "aircraft":
+                ValLoaders = {
+                    "ori-test": torch.utils.data.DataLoader(
+                        valid_data,
+                        batch_size=config.batch_size,
+                        shuffle=False,
+                        num_workers=workers,
+                        pin_memory=True,
+                    )
+                }
+                train_data_v2 = deepcopy(train_data)
+                train_data_v2.transform = valid_data.transform
+                valid_data = train_data_v2
+                # 使用 DataLoader
+                train_loader = torch.utils.data.DataLoader(
+                    train_data, 
+                    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)
+            elif dataset == "oxford":
+                ValLoaders = {
+                    "ori-test": torch.utils.data.DataLoader(
+                        valid_data,
+                        batch_size=config.batch_size,
+                        shuffle=False,
+                        num_workers=workers,
+                        pin_memory=True
+                    )
+                }
+                # train_data_v2 = deepcopy(train_data)
+                # train_data_v2.transform = valid_data.transform
+                train_loader = torch.utils.data.DataLoader(
+                    train_data, 
+                    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 +259,7 @@ def evaluate_all_datasets(
                 num_workers=workers,
                 pin_memory=True,
             )
-            if dataset == "cifar10":
+            if dataset == "cifar10" or dataset == "aircraft" or dataset == "oxford":
                 ValLoaders = {"ori-test": valid_loader}
             elif dataset == "cifar100":
                 cifar100_splits = load_config(

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

@@ -28,16 +28,41 @@ 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 \
-	--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 \
+	--datasets aircraft \
+	--xpaths /lustre/hpe/ws11/ws11.1/ws/xmuhanma-SWAP/train_datasets/datasets/ \
+	--channel 16 \
+	--splits 1 \
+	--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 oxford\
+# 	--xpaths /lustre/hpe/ws11/ws11.1/ws/xmuhanma-SWAP/train_datasets/datasets/ \
+# 	--channel 16 \
+# 	--splits 1 \
+# 	--num_cells 5 \
+# 	--workers 4 \
+# 	--srange ${xstart} ${xend} --arch_index ${arch_index} \
+# 	--seeds ${all_seeds}
+

test_network.py

@@ -0,0 +1,616 @@
+from nas_201_api import NASBench201API as API
+import os
+
+import os, sys, time, torch, random, argparse
+from PIL import ImageFile
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+from copy import deepcopy
+from pathlib import Path
+
+from xautodl.config_utils import load_config
+from xautodl.procedures import save_checkpoint, copy_checkpoint
+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
+
+import time, torch
+from xautodl.procedures import prepare_seed, get_optim_scheduler
+from xautodl.utils import get_model_infos, obtain_accuracy
+from xautodl.config_utils import dict2config
+from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
+from xautodl.models import get_cell_based_tiny_net
+
+cur_path = os.path.abspath(os.path.curdir)
+data_path = os.path.join(cur_path, 'NAS-Bench-201-v1_1-096897.pth')
+print(f'loading data from {data_path}')
+print(f'loading')
+api = API(data_path)
+print(f'loaded')
+
+def find_best_index(dataset):
+    len = 15625
+    accs = []
+    for i in range(1, len):
+        results = api.query_by_index(i, dataset)
+        dict_items = list(results.items())
+        train_info = dict_items[0][1].get_train()
+        acc = train_info['accuracy']
+        accs.append((i, acc))
+    return max(accs, key=lambda x: x[1])
+
+best_cifar_10_index, best_cifar_10_acc = find_best_index('cifar10')
+best_cifar_100_index, best_cifar_100_acc = find_best_index('cifar100')
+best_ImageNet16_index, best_ImageNet16_acc= find_best_index('ImageNet16-120')
+print(f'find best cifar10 index: {best_cifar_10_index}, acc: {best_cifar_10_acc}')
+print(f'find best cifar100 index: {best_cifar_100_index}, acc: {best_cifar_100_acc}')
+print(f'find best ImageNet16 index: {best_ImageNet16_index}, acc: {best_ImageNet16_acc}')
+
+from xautodl.models import get_cell_based_tiny_net
+def get_network_str_by_id(id, dataset):
+    config = api.get_net_config(id, dataset)
+    return config['arch_str']
+
+best_cifar_10_str = get_network_str_by_id(best_cifar_10_index, 'cifar10')
+best_cifar_100_str = get_network_str_by_id(best_cifar_100_index, 'cifar100')
+best_ImageNet16_str = get_network_str_by_id(best_ImageNet16_index, 'ImageNet16-120')
+
+def evaluate_all_datasets(
+    arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger
+):
+    machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
+    all_infos = {"info": machine_info}
+    all_dataset_keys = []
+    # look all the datasets
+    for dataset, xpath, split in zip(datasets, xpaths, splits):
+        # train valid data
+        train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
+        # load the configuration
+        if dataset == "cifar10" or dataset == "cifar100":
+            if use_less:
+                config_path = "configs/nas-benchmark/LESS.config"
+            else:
+                config_path = "configs/nas-benchmark/CIFAR.config"
+            split_info = load_config(
+                "configs/nas-benchmark/cifar-split.txt", None, None
+            )
+        elif dataset.startswith("ImageNet16"):
+            if use_less:
+                config_path = "configs/nas-benchmark/LESS.config"
+            else:
+                config_path = "configs/nas-benchmark/ImageNet-16.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 bool(split):
+            assert dataset == "cifar10"
+            ValLoaders = {
+                "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,
+                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,
+            )
+            ValLoaders["x-valid"] = valid_loader
+        else:
+            # data loader
+            train_loader = torch.utils.data.DataLoader(
+                train_data,
+                batch_size=config.batch_size,
+                shuffle=True,
+                num_workers=workers,
+                pin_memory=True,
+            )
+            valid_loader = torch.utils.data.DataLoader(
+                valid_data,
+                batch_size=config.batch_size,
+                shuffle=False,
+                num_workers=workers,
+                pin_memory=True,
+            )
+            if dataset == "cifar10":
+                ValLoaders = {"ori-test": valid_loader}
+            elif dataset == "cifar100":
+                cifar100_splits = load_config(
+                    "configs/nas-benchmark/cifar100-test-split.txt", None, None
+                )
+                ValLoaders = {
+                    "ori-test": valid_loader,
+                    "x-valid": torch.utils.data.DataLoader(
+                        valid_data,
+                        batch_size=config.batch_size,
+                        sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                            cifar100_splits.xvalid
+                        ),
+                        num_workers=workers,
+                        pin_memory=True,
+                    ),
+                    "x-test": torch.utils.data.DataLoader(
+                        valid_data,
+                        batch_size=config.batch_size,
+                        sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                            cifar100_splits.xtest
+                        ),
+                        num_workers=workers,
+                        pin_memory=True,
+                    ),
+                }
+            elif dataset == "ImageNet16-120":
+                imagenet16_splits = load_config(
+                    "configs/nas-benchmark/imagenet-16-120-test-split.txt", None, None
+                )
+                ValLoaders = {
+                    "ori-test": valid_loader,
+                    "x-valid": torch.utils.data.DataLoader(
+                        valid_data,
+                        batch_size=config.batch_size,
+                        sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                            imagenet16_splits.xvalid
+                        ),
+                        num_workers=workers,
+                        pin_memory=True,
+                    ),
+                    "x-test": torch.utils.data.DataLoader(
+                        valid_data,
+                        batch_size=config.batch_size,
+                        sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                            imagenet16_splits.xtest
+                        ),
+                        num_workers=workers,
+                        pin_memory=True,
+                    ),
+                }
+            else:
+                raise ValueError("invalid dataset : {:}".format(dataset))
+
+        dataset_key = "{:}".format(dataset)
+        if bool(split):
+            dataset_key = dataset_key + "-valid"
+        logger.log(
+            "Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
+                dataset_key,
+                len(train_data),
+                len(valid_data),
+                len(train_loader),
+                len(valid_loader),
+                config.batch_size,
+            )
+        )
+        logger.log(
+            "Evaluate ||||||| {:10s} ||||||| Config={:}".format(dataset_key, config)
+        )
+        for key, value in ValLoaders.items():
+            logger.log(
+                "Evaluate ---->>>> {:10s} with {:} batchs".format(key, len(value))
+            )
+        results = evaluate_for_seed(
+            arch_config, config, arch, train_loader, ValLoaders, seed, logger
+        )
+        all_infos[dataset_key] = results
+        all_dataset_keys.append(dataset_key)
+    all_infos["all_dataset_keys"] = all_dataset_keys
+    return all_infos
+
+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(
+        dict2config(
+            {
+                "name": "infer.tiny",
+                "C": arch_config["channel"],
+                "N": arch_config["num_cells"],
+                "genotype": arch,
+                "num_classes": config.class_num,
+            },
+            None,
+        )
+    )
+    # 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("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,
+    ) = ({}, {}, {}, {}, {}, {})
+    train_times, valid_times = {}, {}
+    for epoch in range(total_epoch):
+        scheduler.update(epoch, 0.0)
+
+        train_loss, train_acc1, train_acc5, train_tm = procedure(
+            train_loader, network, criterion, scheduler, optimizer, "train"
+        )
+        train_losses[epoch] = train_loss
+        train_acc1es[epoch] = train_acc1
+        train_acc5es[epoch] = train_acc5
+        train_times[epoch] = train_tm
+        with torch.no_grad():
+            for key, xloder in valid_loaders.items():
+                valid_loss, valid_acc1, valid_acc5, valid_tm = procedure(
+                    xloder, network, criterion, None, None, "valid"
+                )
+                valid_losses["{:}@{:}".format(key, epoch)] = valid_loss
+                valid_acc1es["{:}@{:}".format(key, epoch)] = valid_acc1
+                valid_acc5es["{:}@{:}".format(key, epoch)] = valid_acc5
+                valid_times["{:}@{:}".format(key, epoch)] = valid_tm
+
+        # 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)
+        )
+        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,
+            )
+        )
+    info_seed = {
+        "flop": flop,
+        "param": param,
+        "channel": arch_config["channel"],
+        "num_cells": arch_config["num_cells"],
+        "config": config._asdict(),
+        "total_epoch": total_epoch,
+        "train_losses": train_losses,
+        "train_acc1es": train_acc1es,
+        "train_acc5es": train_acc5es,
+        "train_times": train_times,
+        "valid_losses": valid_losses,
+        "valid_acc1es": valid_acc1es,
+        "valid_acc5es": valid_acc5es,
+        "valid_times": valid_times,
+        "net_state_dict": net.state_dict(),
+        "net_string": "{:}".format(net),
+        "finish-train": True,
+    }
+    return info_seed
+
+def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
+    data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
+    losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
+    latencies = []
+    network.eval()
+    with torch.no_grad():
+        end = time.time()
+        for i, (inputs, targets) in enumerate(xloader):
+            targets = targets.cuda(non_blocking=True)
+            inputs = inputs.cuda(non_blocking=True)
+            data_time.update(time.time() - end)
+            # forward
+            features, logits = network(inputs)
+            loss = criterion(logits, targets)
+            batch_time.update(time.time() - end)
+            if batch is None or batch == inputs.size(0):
+                batch = inputs.size(0)
+                latencies.append(batch_time.val - data_time.val)
+            # record loss and accuracy
+            prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
+            losses.update(loss.item(), inputs.size(0))
+            top1.update(prec1.item(), inputs.size(0))
+            top5.update(prec5.item(), inputs.size(0))
+            end = time.time()
+    if len(latencies) > 2:
+        latencies = latencies[1:]
+    return losses.avg, top1.avg, top5.avg, latencies
+
+
+def procedure(xloader, network, criterion, scheduler, optimizer, mode):
+    losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
+    if mode == "train":
+        network.train()
+    elif mode == "valid":
+        network.eval()
+    else:
+        raise ValueError("The mode is not right : {:}".format(mode))
+
+    data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
+    for i, (inputs, targets) in enumerate(xloader):
+        if mode == "train":
+            scheduler.update(None, 1.0 * i / len(xloader))
+
+        targets = targets.cuda(non_blocking=True)
+        if mode == "train":
+            optimizer.zero_grad()
+        # forward
+        features, logits = network(inputs)
+        loss = criterion(logits, targets)
+        # backward
+        if mode == "train":
+            loss.backward()
+            optimizer.step()
+        # record loss and accuracy
+        prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
+        losses.update(loss.item(), inputs.size(0))
+        top1.update(prec1.item(), inputs.size(0))
+        top5.update(prec5.item(), inputs.size(0))
+        # count time
+        batch_time.update(time.time() - end)
+        end = time.time()
+    return losses.avg, top1.avg, top5.avg, batch_time.sum
+
+def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
+    data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
+    losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
+    latencies = []
+    network.eval()
+    with torch.no_grad():
+        end = time.time()
+        for i, (inputs, targets) in enumerate(xloader):
+            targets = targets.cuda(non_blocking=True)
+            inputs = inputs.cuda(non_blocking=True)
+            data_time.update(time.time() - end)
+            # forward
+            features, logits = network(inputs)
+            loss = criterion(logits, targets)
+            batch_time.update(time.time() - end)
+            if batch is None or batch == inputs.size(0):
+                batch = inputs.size(0)
+                latencies.append(batch_time.val - data_time.val)
+            # record loss and accuracy
+            prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
+            losses.update(loss.item(), inputs.size(0))
+            top1.update(prec1.item(), inputs.size(0))
+            top5.update(prec5.item(), inputs.size(0))
+            end = time.time()
+    if len(latencies) > 2:
+        latencies = latencies[1:]
+    return losses.avg, top1.avg, top5.avg, latencies
+
+
+def procedure(xloader, network, criterion, scheduler, optimizer, mode):
+    losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
+    if mode == "train":
+        network.train()
+    elif mode == "valid":
+        network.eval()
+    else:
+        raise ValueError("The mode is not right : {:}".format(mode))
+
+    data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
+    for i, (inputs, targets) in enumerate(xloader):
+        if mode == "train":
+            scheduler.update(None, 1.0 * i / len(xloader))
+
+        targets = targets.cuda(non_blocking=True)
+        if mode == "train":
+            optimizer.zero_grad()
+        # forward
+        features, logits = network(inputs)
+        loss = criterion(logits, targets)
+        # backward
+        if mode == "train":
+            loss.backward()
+            optimizer.step()
+        # record loss and accuracy
+        prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
+        losses.update(loss.item(), inputs.size(0))
+        top1.update(prec1.item(), inputs.size(0))
+        top5.update(prec5.item(), inputs.size(0))
+        # count time
+        batch_time.update(time.time() - end)
+        end = time.time()
+    return losses.avg, top1.avg, top5.avg, batch_time.sum
+
+def train_single_model(
+    save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config
+):
+    assert torch.cuda.is_available(), "CUDA is not available."
+    torch.backends.cudnn.enabled = True
+    torch.backends.cudnn.deterministic = True
+    # torch.backends.cudnn.benchmark = True
+    torch.set_num_threads(workers)
+
+    save_dir = (
+        Path(save_dir)
+        / "specifics"
+        / "{:}-{:}-{:}-{:}".format(
+            "LESS" if use_less else "FULL",
+            model_str,
+            arch_config["channel"],
+            arch_config["num_cells"],
+        )
+    )
+    logger = Logger(str(save_dir), 0, False)
+    print(CellArchitectures)
+    if model_str in CellArchitectures:
+        arch = CellArchitectures[model_str]
+        logger.log(
+            "The model string is found in pre-defined architecture dict : {:}".format(
+                model_str
+            )
+        )
+    else:
+        try:
+            arch = CellStructure.str2structure(model_str)
+        except:
+            raise ValueError(
+                "Invalid model string : {:}. It can not be found or parsed.".format(
+                    model_str
+                )
+            )
+    assert arch.check_valid_op(
+        get_search_spaces("cell", "nas-bench-201")
+    ), "{:} has the invalid op.".format(arch)
+    logger.log("Start train-evaluate {:}".format(arch.tostr()))
+    logger.log("arch_config : {:}".format(arch_config))
+
+    start_time, seed_time = time.time(), AverageMeter()
+    for _is, seed in enumerate(seeds):
+        logger.log(
+            "\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------".format(
+                _is, len(seeds), seed
+            )
+        )
+        to_save_name = save_dir / "seed-{:04d}.pth".format(seed)
+        if to_save_name.exists():
+            logger.log(
+                "Find the existing file {:}, directly load!".format(to_save_name)
+            )
+            checkpoint = torch.load(to_save_name)
+        else:
+            logger.log(
+                "Does not find the existing file {:}, train and evaluate!".format(
+                    to_save_name
+                )
+            )
+            checkpoint = evaluate_all_datasets(
+                arch,
+                datasets,
+                xpaths,
+                splits,
+                use_less,
+                seed,
+                arch_config,
+                workers,
+                logger,
+            )
+            torch.save(checkpoint, to_save_name)
+        # log information
+        logger.log("{:}".format(checkpoint["info"]))
+        all_dataset_keys = checkpoint["all_dataset_keys"]
+        for dataset_key in all_dataset_keys:
+            logger.log(
+                "\n{:} dataset : {:} {:}".format("-" * 15, dataset_key, "-" * 15)
+            )
+            dataset_info = checkpoint[dataset_key]
+            # logger.log('Network ==>\n{:}'.format( dataset_info['net_string'] ))
+            logger.log(
+                "Flops = {:} MB, Params = {:} MB".format(
+                    dataset_info["flop"], dataset_info["param"]
+                )
+            )
+            logger.log("config : {:}".format(dataset_info["config"]))
+            logger.log(
+                "Training State (finish) = {:}".format(dataset_info["finish-train"])
+            )
+            last_epoch = dataset_info["total_epoch"] - 1
+            train_acc1es, train_acc5es = (
+                dataset_info["train_acc1es"],
+                dataset_info["train_acc5es"],
+            )
+            valid_acc1es, valid_acc5es = (
+                dataset_info["valid_acc1es"],
+                dataset_info["valid_acc5es"],
+            )
+            print(dataset_info["train_acc1es"])
+            print(dataset_info["train_acc5es"])
+            print(dataset_info["valid_acc1es"])
+            print(dataset_info["valid_acc5es"])
+            logger.log(
+                "Last Info : Train = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%, Test = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%".format(
+                    train_acc1es[last_epoch],
+                    train_acc5es[last_epoch],
+                    100 - train_acc1es[last_epoch],
+                    valid_acc1es['ori-test@'+str(last_epoch)],
+                    valid_acc5es['ori-test@'+str(last_epoch)],
+                    100 - valid_acc1es['ori-test@'+str(last_epoch)],
+                )
+            )
+        # measure elapsed time
+        seed_time.update(time.time() - start_time)
+        start_time = time.time()
+        need_time = "Time Left: {:}".format(
+            convert_secs2time(seed_time.avg * (len(seeds) - _is - 1), True)
+        )
+        logger.log(
+            "\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}".format(
+                _is, len(seeds), seed, need_time
+            )
+        )
+    logger.close()
+
+# |nor_conv_3x3~0|+|nor_conv_1x1~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|
+train_strs = [best_cifar_10_str, best_cifar_100_str, best_ImageNet16_str]
+train_single_model(
+    save_dir="./outputs",
+    workers=8,
+    datasets=["ImageNet16-120"], 
+    xpaths="./datasets/imagenet16-120",
+    splits=[0, 0, 0],
+    use_less=False,
+    seeds=[777],
+    model_str=best_ImageNet16_str,
+    arch_config={"channel": 16, "num_cells": 8},)
+train_single_model(
+    save_dir="./outputs",
+    workers=8,
+    datasets=["cifar10"], 
+    xpaths="./datasets/cifar10",
+    splits=[0, 0, 0],
+    use_less=False,
+    seeds=[777],
+    model_str=best_cifar_10_str,
+    arch_config={"channel": 16, "num_cells": 8},)
+train_single_model(
+    save_dir="./outputs",
+    workers=8,
+    datasets=["cifar100"], 
+    xpaths="./datasets/cifar100",
+    splits=[0, 0, 0],
+    use_less=False,
+    seeds=[777],
+    model_str=best_cifar_100_str,
+    arch_config={"channel": 16, "num_cells": 8},)

xautodl/datasets/get_dataset_with_transform.py

@@ -1,40 +1,39 @@
 ##################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+# Modified by Hayeon Lee, Eunyoung Hyung 2021. 03.
 ##################################################
-import os, sys, torch
+import os
+import sys
+import torch
 import os.path as osp
 import numpy as np
 import torchvision.datasets as dset
 import torchvision.transforms as transforms
 from copy import deepcopy
-from PIL import Image
-
-from xautodl.config_utils import load_config
-
-from .DownsampledImageNet import ImageNet16
 from .SearchDatasetWrap import SearchDataset
 
+# from PIL import Image
+import random
+import pdb
+from .aircraft import FGVCAircraft
+from .pets import PetDataset
+from config_utils import load_config
 
-Dataset2Class = {
-    "cifar10": 10,
-    "cifar100": 100,
-    "imagenet-1k-s": 1000,
-    "imagenet-1k": 1000,
-    "ImageNet16": 1000,
-    "ImageNet16-150": 150,
-    "ImageNet16-120": 120,
-    "ImageNet16-200": 200,
-}
+Dataset2Class = {'cifar10': 10,
+                 'cifar100': 100,
+                 'mnist': 10,
+                 'svhn': 10,
+                 'aircraft': 30,
+                 'oxford': 37}
 
 
 class CUTOUT(object):
+
     def __init__(self, length):
         self.length = length
 
     def __repr__(self):
-        return "{name}(length={length})".format(
-            name=self.__class__.__name__, **self.__dict__
-        )
+        return ('{name}(length={length})'.format(name=self.__class__.__name__, **self.__dict__))
 
     def __call__(self, img):
         h, w = img.size(1), img.size(2)
@@ -47,7 +46,7 @@ class CUTOUT(object):
         x1 = np.clip(x - self.length // 2, 0, w)
         x2 = np.clip(x + self.length // 2, 0, w)
 
-        mask[y1:y2, x1:x2] = 0.0
+        mask[y1: y2, x1: x2] = 0.
         mask = torch.from_numpy(mask)
         mask = mask.expand_as(img)
         img *= mask
@@ -55,21 +54,19 @@ class CUTOUT(object):
 
 
 imagenet_pca = {
-    "eigval": np.asarray([0.2175, 0.0188, 0.0045]),
-    "eigvec": np.asarray(
-        [
-            [-0.5675, 0.7192, 0.4009],
-            [-0.5808, -0.0045, -0.8140],
-            [-0.5836, -0.6948, 0.4203],
-        ]
-    ),
+    'eigval': np.asarray([0.2175, 0.0188, 0.0045]),
+    'eigvec': np.asarray([
+        [-0.5675, 0.7192, 0.4009],
+        [-0.5808, -0.0045, -0.8140],
+        [-0.5836, -0.6948, 0.4203],
+    ])
 }
 
 
 class Lighting(object):
-    def __init__(
-        self, alphastd, eigval=imagenet_pca["eigval"], eigvec=imagenet_pca["eigvec"]
-    ):
+    def __init__(self, alphastd,
+                 eigval=imagenet_pca['eigval'],
+                 eigvec=imagenet_pca['eigvec']):
         self.alphastd = alphastd
         assert eigval.shape == (3,)
         assert eigvec.shape == (3, 3)
@@ -77,10 +74,10 @@ class Lighting(object):
         self.eigvec = eigvec
 
     def __call__(self, img):
-        if self.alphastd == 0.0:
+        if self.alphastd == 0.:
             return img
         rnd = np.random.randn(3) * self.alphastd
-        rnd = rnd.astype("float32")
+        rnd = rnd.astype('float32')
         v = rnd
         old_dtype = np.asarray(img).dtype
         v = v * self.eigval
@@ -89,275 +86,222 @@ class Lighting(object):
         img = np.add(img, inc)
         if old_dtype == np.uint8:
             img = np.clip(img, 0, 255)
-        img = Image.fromarray(img.astype(old_dtype), "RGB")
+        img = Image.fromarray(img.astype(old_dtype), 'RGB')
         return img
 
     def __repr__(self):
-        return self.__class__.__name__ + "()"
+        return self.__class__.__name__ + '()'
 
 
-def get_datasets(name, root, cutout):
-
-    if name == "cifar10":
+def get_datasets(name, root, cutout, use_num_cls=None):
+    if name == 'cifar10':
         mean = [x / 255 for x in [125.3, 123.0, 113.9]]
         std = [x / 255 for x in [63.0, 62.1, 66.7]]
-    elif name == "cifar100":
+    elif name == 'cifar100':
         mean = [x / 255 for x in [129.3, 124.1, 112.4]]
         std = [x / 255 for x in [68.2, 65.4, 70.4]]
-    elif name.startswith("imagenet-1k"):
-        mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
-    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.startswith('mnist'):
+        mean, std = [0.1307, 0.1307, 0.1307], [0.3081, 0.3081, 0.3081]
+    elif name.startswith('svhn'):
+        mean, std = [0.4376821, 0.4437697, 0.47280442], [ 0.19803012, 0.20101562, 0.19703614]
+    elif name.startswith('aircraft'):
+        mean = [0.48933587508932375, 0.5183537408957618, 0.5387914411673883]
+		std = [0.22388883112804625, 0.21641635409388751, 0.24615605842636115]
+    elif name.startswith('oxford'):
+        mean = [0.4828895122298728, 0.4448394893850807, 0.39566558230789783]
+		std = [0.25925664613996574, 0.2532760018681693, 0.25981017205097917]
     else:
         raise TypeError("Unknow dataset : {:}".format(name))
 
     # Data Argumentation
-    if name == "cifar10" or name == "cifar100":
-        lists = [
-            transforms.RandomHorizontalFlip(),
-            transforms.RandomCrop(32, padding=4),
-            transforms.ToTensor(),
-            transforms.Normalize(mean, std),
-        ]
+    if name == 'cifar10' or name == 'cifar100':
+        lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(),
+                 transforms.Normalize(mean, std)]
         if cutout > 0:
             lists += [CUTOUT(cutout)]
         train_transform = transforms.Compose(lists)
         test_transform = transforms.Compose(
-            [transforms.ToTensor(), transforms.Normalize(mean, std)]
-        )
+            [transforms.ToTensor(), transforms.Normalize(mean, std)])
         xshape = (1, 3, 32, 32)
-    elif name.startswith("ImageNet16"):
-        lists = [
-            transforms.RandomHorizontalFlip(),
-            transforms.RandomCrop(16, padding=2),
+    elif name.startswith('cub200'):
+        train_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
             transforms.ToTensor(),
-            transforms.Normalize(mean, std),
-        ]
-        if cutout > 0:
-            lists += [CUTOUT(cutout)]
-        train_transform = transforms.Compose(lists)
-        test_transform = transforms.Compose(
-            [transforms.ToTensor(), transforms.Normalize(mean, std)]
-        )
-        xshape = (1, 3, 16, 16)
-    elif name == "tiered":
-        lists = [
-            transforms.RandomHorizontalFlip(),
-            transforms.RandomCrop(80, padding=4),
+            transforms.Normalize(mean=mean, std=std)
+        ])
+        test_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
             transforms.ToTensor(),
-            transforms.Normalize(mean, std),
-        ]
-        if cutout > 0:
-            lists += [CUTOUT(cutout)]
-        train_transform = transforms.Compose(lists)
-        test_transform = transforms.Compose(
-            [
-                transforms.CenterCrop(80),
-                transforms.ToTensor(),
-                transforms.Normalize(mean, std),
-            ]
-        )
+            transforms.Normalize(mean=mean, std=std)
+        ])
+        xshape = (1, 3, 32, 32)
+    elif name.startswith('mnist'):
+        train_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
+            transforms.ToTensor(),
+            transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
+            transforms.Normalize(mean, std),
+        ])
+        test_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
+            transforms.ToTensor(),
+            transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
+            transforms.Normalize(mean, std)
+        ])
+        xshape = (1, 3, 32, 32)
+    elif name.startswith('svhn'):
+        train_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std)
+        ])
+        test_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std)
+        ])
+        xshape = (1, 3, 32, 32)
+    elif name.startswith('aircraft'):
+        train_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std)
+        ])
+        test_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
+        ])
+        xshape = (1, 3, 32, 32)
+    elif name.startswith('oxford'):
+        train_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std)
+        ])
+        test_transform = transforms.Compose([
+            transforms.Resize((32, 32)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
+        ])
         xshape = (1, 3, 32, 32)
-    elif name.startswith("imagenet-1k"):
-        normalize = transforms.Normalize(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-        if name == "imagenet-1k":
-            xlists = [transforms.RandomResizedCrop(224)]
-            xlists.append(
-                transforms.ColorJitter(
-                    brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2
-                )
-            )
-            xlists.append(Lighting(0.1))
-        elif name == "imagenet-1k-s":
-            xlists = [transforms.RandomResizedCrop(224, scale=(0.2, 1.0))]
-        else:
-            raise ValueError("invalid name : {:}".format(name))
-        xlists.append(transforms.RandomHorizontalFlip(p=0.5))
-        xlists.append(transforms.ToTensor())
-        xlists.append(normalize)
-        train_transform = transforms.Compose(xlists)
-        test_transform = transforms.Compose(
-            [
-                transforms.Resize(256),
-                transforms.CenterCrop(224),
-                transforms.ToTensor(),
-                normalize,
-            ]
-        )
-        xshape = (1, 3, 224, 224)
     else:
         raise TypeError("Unknow dataset : {:}".format(name))
 
-    if name == "cifar10":
+    if name == 'cifar10':
         train_data = dset.CIFAR10(
-            root, train=True, transform=train_transform, download=True
-        )
+            root, train=True, transform=train_transform, download=True)
         test_data = dset.CIFAR10(
-            root, train=False, transform=test_transform, download=True
-        )
+            root, train=False, transform=test_transform, download=True)
         assert len(train_data) == 50000 and len(test_data) == 10000
-    elif name == "cifar100":
+    elif name == 'cifar100':
         train_data = dset.CIFAR100(
-            root, train=True, transform=train_transform, download=True
-        )
+            root, train=True, transform=train_transform, download=True)
         test_data = dset.CIFAR100(
-            root, train=False, transform=test_transform, download=True
-        )
+            root, train=False, transform=test_transform, download=True)
         assert len(train_data) == 50000 and len(test_data) == 10000
-    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)
-        assert (
-            len(train_data) == 1281167 and len(test_data) == 50000
-        ), "invalid number of images : {:} & {:} vs {:} & {:}".format(
-            len(train_data), len(test_data), 1281167, 50000
-        )
-    elif name == "ImageNet16":
-        train_data = ImageNet16(root, True, train_transform)
-        test_data = ImageNet16(root, False, test_transform)
-        assert len(train_data) == 1281167 and len(test_data) == 50000
-    elif name == "ImageNet16-120":
-        train_data = ImageNet16(root, True, train_transform, 120)
-        test_data = ImageNet16(root, False, test_transform, 120)
-        assert len(train_data) == 151700 and len(test_data) == 6000
-    elif name == "ImageNet16-150":
-        train_data = ImageNet16(root, True, train_transform, 150)
-        test_data = ImageNet16(root, False, test_transform, 150)
-        assert len(train_data) == 190272 and len(test_data) == 7500
-    elif name == "ImageNet16-200":
-        train_data = ImageNet16(root, True, train_transform, 200)
-        test_data = ImageNet16(root, False, test_transform, 200)
-        assert len(train_data) == 254775 and len(test_data) == 10000
+    elif name == 'mnist':
+        train_data = dset.MNIST(
+            root, train=True, transform=train_transform, download=True)
+        test_data = dset.MNIST(
+            root, train=False, transform=test_transform, download=True)
+        assert len(train_data) == 60000 and len(test_data) == 10000
+    elif name == 'svhn':
+        train_data = dset.SVHN(root, split='train',
+                               transform=train_transform, download=True)
+        test_data = dset.SVHN(root, split='test',
+                              transform=test_transform, download=True)
+        assert len(train_data) == 73257 and len(test_data) == 26032
+    elif name == 'aircraft':
+        train_data = FGVCAircraft(root, class_type='manufacturer', split='trainval',
+                                  transform=train_transform, download=False)
+        test_data = FGVCAircraft(root, class_type='manufacturer', split='test',
+                                 transform=test_transform, download=False)
+        assert len(train_data) == 6667 and len(test_data) == 3333
+    elif name == 'oxford':
+        train_data = PetDataset(root, train=True, num_cl=37,
+                                val_split=0.15, transforms=train_transform)
+        test_data = PetDataset(root, train=False, num_cl=37,
+                               val_split=0.15, transforms=test_transform)
     else:
         raise TypeError("Unknow dataset : {:}".format(name))
 
-    class_num = Dataset2Class[name]
+    class_num = Dataset2Class[name] if use_num_cls is None else len(
+        use_num_cls)
     return train_data, test_data, xshape, class_num
 
 
-def get_nas_search_loaders(
-    train_data, valid_data, dataset, config_root, batch_size, workers
-):
+def get_nas_search_loaders(train_data, valid_data, dataset, config_root, batch_size, workers, num_cls=None):
     if isinstance(batch_size, (list, tuple)):
         batch, test_batch = batch_size
     else:
         batch, test_batch = batch_size, batch_size
-    if dataset == "cifar10":
+    if dataset == 'cifar10':
         # split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
-        cifar_split = load_config("{:}/cifar-split.txt".format(config_root), None, None)
-        train_split, valid_split = (
-            cifar_split.train,
-            cifar_split.valid,
-        )  # search over the proposed training and validation set
+        cifar_split = load_config(
+            '{:}/cifar-split.txt'.format(config_root), None, None)
+        # search over the proposed training and validation set
+        train_split, valid_split = cifar_split.train, cifar_split.valid
         # logger.log('Load split file from {:}'.format(split_Fpath))      # they are two disjoint groups in the original CIFAR-10 training set
         # To split data
         xvalid_data = deepcopy(train_data)
-        if hasattr(xvalid_data, "transforms"):  # to avoid a print issue
+        if hasattr(xvalid_data, 'transforms'):  # to avoid a print issue
             xvalid_data.transforms = valid_data.transform
         xvalid_data.transform = deepcopy(valid_data.transform)
-        search_data = SearchDataset(dataset, train_data, train_split, valid_split)
+        search_data = SearchDataset(
+            dataset, train_data, train_split, valid_split)
         # data loader
-        search_loader = torch.utils.data.DataLoader(
-            search_data,
-            batch_size=batch,
-            shuffle=True,
-            num_workers=workers,
-            pin_memory=True,
-        )
-        train_loader = torch.utils.data.DataLoader(
-            train_data,
-            batch_size=batch,
-            sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
-            num_workers=workers,
-            pin_memory=True,
-        )
-        valid_loader = torch.utils.data.DataLoader(
-            xvalid_data,
-            batch_size=test_batch,
-            sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
-            num_workers=workers,
-            pin_memory=True,
-        )
-    elif dataset == "cifar100":
+        search_loader = torch.utils.data.DataLoader(search_data, batch_size=batch, shuffle=True, num_workers=workers,
+                                                    pin_memory=True)
+        train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch,
+                                                   sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                                                       train_split),
+                                                   num_workers=workers, pin_memory=True)
+        valid_loader = torch.utils.data.DataLoader(xvalid_data, batch_size=test_batch,
+                                                   sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                                                       valid_split),
+                                                   num_workers=workers, pin_memory=True)
+    elif dataset == 'cifar100':
         cifar100_test_split = load_config(
-            "{:}/cifar100-test-split.txt".format(config_root), None, None
-        )
+            '{:}/cifar100-test-split.txt'.format(config_root), None, None)
         search_train_data = train_data
         search_valid_data = deepcopy(valid_data)
         search_valid_data.transform = train_data.transform
-        search_data = SearchDataset(
-            dataset,
-            [search_train_data, search_valid_data],
-            list(range(len(search_train_data))),
-            cifar100_test_split.xvalid,
-        )
-        search_loader = torch.utils.data.DataLoader(
-            search_data,
-            batch_size=batch,
-            shuffle=True,
-            num_workers=workers,
-            pin_memory=True,
-        )
-        train_loader = torch.utils.data.DataLoader(
-            train_data,
-            batch_size=batch,
-            shuffle=True,
-            num_workers=workers,
-            pin_memory=True,
-        )
-        valid_loader = torch.utils.data.DataLoader(
-            valid_data,
-            batch_size=test_batch,
-            sampler=torch.utils.data.sampler.SubsetRandomSampler(
-                cifar100_test_split.xvalid
-            ),
-            num_workers=workers,
-            pin_memory=True,
-        )
-    elif dataset == "ImageNet16-120":
-        imagenet_test_split = load_config(
-            "{:}/imagenet-16-120-test-split.txt".format(config_root), None, None
-        )
+        search_data = SearchDataset(dataset, [search_train_data, search_valid_data],
+                                    list(range(len(search_train_data))),
+                                    cifar100_test_split.xvalid)
+        search_loader = torch.utils.data.DataLoader(search_data, batch_size=batch, shuffle=True, num_workers=workers,
+                                                    pin_memory=True)
+        train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch, shuffle=True, num_workers=workers,
+                                                   pin_memory=True)
+        valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=test_batch,
+                                                   sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                                                       cifar100_test_split.xvalid), num_workers=workers, pin_memory=True)
+    elif dataset in ['mnist', 'svhn', 'aircraft', 'oxford']:
+        if not os.path.exists('{:}/{}-test-split.txt'.format(config_root, dataset)):
+            import json
+            label_list = list(range(len(valid_data)))
+            random.shuffle(label_list)
+            strlist = [str(label_list[i]) for i in range(len(label_list))]
+            split = {'xvalid': ["int", strlist[:len(valid_data) // 2]],
+                     'xtest': ["int", strlist[len(valid_data) // 2:]]}
+            with open('{:}/{}-test-split.txt'.format(config_root, dataset), 'w') as f:
+                f.write(json.dumps(split))
+        test_split = load_config(
+            '{:}/{}-test-split.txt'.format(config_root, dataset), None, None)
+
         search_train_data = train_data
         search_valid_data = deepcopy(valid_data)
         search_valid_data.transform = train_data.transform
-        search_data = SearchDataset(
-            dataset,
-            [search_train_data, search_valid_data],
-            list(range(len(search_train_data))),
-            imagenet_test_split.xvalid,
-        )
-        search_loader = torch.utils.data.DataLoader(
-            search_data,
-            batch_size=batch,
-            shuffle=True,
-            num_workers=workers,
-            pin_memory=True,
-        )
-        train_loader = torch.utils.data.DataLoader(
-            train_data,
-            batch_size=batch,
-            shuffle=True,
-            num_workers=workers,
-            pin_memory=True,
-        )
-        valid_loader = torch.utils.data.DataLoader(
-            valid_data,
-            batch_size=test_batch,
-            sampler=torch.utils.data.sampler.SubsetRandomSampler(
-                imagenet_test_split.xvalid
-            ),
-            num_workers=workers,
-            pin_memory=True,
-        )
+        search_data = SearchDataset(dataset, [search_train_data, search_valid_data],
+                                    list(range(len(search_train_data))), test_split.xvalid)
+        search_loader = torch.utils.data.DataLoader(search_data, batch_size=batch, shuffle=True,
+                                                    num_workers=workers, pin_memory=True)
+        train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch, shuffle=True,
+                                                   num_workers=workers, pin_memory=True)
+        valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=test_batch,
+                                                   sampler=torch.utils.data.sampler.SubsetRandomSampler(
+                                                       test_split.xvalid), num_workers=workers, pin_memory=True)
     else:
-        raise ValueError("invalid dataset : {:}".format(dataset))
+        raise ValueError('invalid dataset : {:}'.format(dataset))
     return search_loader, train_loader, valid_loader
-
-
-# if __name__ == '__main__':
-#  train_data, test_data, xshape, class_num = dataset = get_datasets('cifar10', '/data02/dongxuanyi/.torch/cifar.python/', -1)
-#  import pdb; pdb.set_trace()

xautodl/models/cell_searchs/genotypes.py

@@ -213,6 +213,13 @@ AllConv3x3_CODE = Structure(
         (("nor_conv_3x3", 0), ("nor_conv_3x3", 1), ("nor_conv_3x3", 2)),
     ]  # node-3
 )
+Number_5374 = Structure(
+    [
+        (("nor_conv_3x3", 0),),  # node-1
+        (("nor_conv_1x1", 0), ("nor_conv_3x3", 1)),  # node-2
+        (("skip_connect", 0), ("none", 1), ("nor_conv_3x3", 2)),  # node-3
+    ]
+)
 
 AllFull_CODE = Structure(
     [
@@ -271,4 +278,5 @@ architectures = {
     "all_c1x1": AllConv1x1_CODE,
     "all_idnt": AllIdentity_CODE,
     "all_full": AllFull_CODE,
+    "5374": Number_5374,
 }

xautodl/utils/evaluation_utils.py

@@ -12,6 +12,7 @@ def obtain_accuracy(output, target, topk=(1,)):
 
     res = []
     for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        # correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
         res.append(correct_k.mul_(100.0 / batch_size))
     return res