Fix path errors in TAS due to lib->xautodl

exps/NATS-Bench/show-dataset.py

@@ -0,0 +1,58 @@
+##############################################################################
+# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
+##############################################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07                          #
+##############################################################################
+# python ./exps/NATS-Bench/show-dataset.py                                   #
+##############################################################################
+import os, sys, time, torch, random, argparse
+from typing import List, Text, Dict, Any
+from PIL import ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+from copy import deepcopy
+
+from xautodl.config_utils import dict2config, load_config
+from xautodl.datasets import get_datasets
+from nats_bench import create
+
+
+def show_imagenet_16_120(dataset_dir=None):
+    if dataset_dir is None:
+        torch_home_dir = (
+            os.environ["TORCH_HOME"]
+            if "TORCH_HOME" in os.environ
+            else os.path.join(os.environ["HOME"], ".torch")
+        )
+        dataset_dir = os.path.join(torch_home_dir, "cifar.python", "ImageNet16")
+    train_data, valid_data, xshape, class_num = get_datasets(
+        "ImageNet16-120", dataset_dir, -1
+    )
+    split_info = load_config(
+        "configs/nas-benchmark/ImageNet16-120-split.txt", None, None
+    )
+    print("=" * 10 + " ImageNet-16-120 " + "=" * 10)
+    print("Training Data: {:}".format(train_data))
+    print("Evaluation Data: {:}".format(valid_data))
+    print("Hold-out training: {:} images.".format(len(split_info.train)))
+    print("Hold-out valid   : {:} images.".format(len(split_info.valid)))
+
+
+if __name__ == "__main__":
+    # show_imagenet_16_120()
+    api_nats_tss = create(None, "tss", fast_mode=True, verbose=True)
+
+    valid_acc_12e = []
+    test_acc_12e = []
+    test_acc_200e = []
+    for index in range(10000):
+        info = api_nats_tss.get_more_info(index, "ImageNet16-120", hp="12")
+        valid_acc_12e.append(
+            info["valid-accuracy"]
+        )  # the validation accuracy after training the model by 12 epochs
+        test_acc_12e.append(
+            info["test-accuracy"]
+        )  # the test accuracy after training the model by 12 epochs
+        info = api_nats_tss.get_more_info(index, "ImageNet16-120", hp="200")
+        test_acc_200e.append(
+            info["test-accuracy"]
+        )  # the test accuracy after training the model by 200 epochs (which I reported in the paper)