add autodl

AutoDL-Projects/exps/NATS-Bench/tss-collect-patcher.py

@@ -0,0 +1,179 @@
+##############################################################################
+# NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size #
+##############################################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.08                          #
+##############################################################################
+# This file is used to re-orangize all checkpoints (created by main-tss.py)  #
+# into a single benchmark file. Besides, for each trial, we will merge the   #
+# information of all its trials into a single file.                          #
+#                                                                            #
+# Usage:                                                                     #
+# python exps/NATS-Bench/tss-collect-patcher.py                              #
+##############################################################################
+import os, re, sys, time, shutil, random, argparse, collections
+import numpy as np
+from copy import deepcopy
+import torch
+from tqdm import tqdm
+from pathlib import Path
+from collections import defaultdict, OrderedDict
+from typing import Dict, Any, Text, List
+
+from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
+from xautodl.config_utils import load_config, dict2config
+from xautodl.datasets import get_datasets
+from xautodl.models import CellStructure, get_cell_based_tiny_net, get_search_spaces
+from xautodl.procedures import (
+    bench_pure_evaluate as pure_evaluate,
+    get_nas_bench_loaders,
+)
+from xautodl.utils import get_md5_file
+
+from nats_bench import pickle_save, pickle_load, ArchResults, ResultsCount
+from nas_201_api import NASBench201API
+
+
+NATS_TSS_BASE_NAME = "NATS-tss-v1_0"  # 2020.08.28
+
+
+def simplify(save_dir, save_name, nets, total, sup_config):
+    hps, seeds = ["12", "200"], set()
+    for hp in hps:
+        sub_save_dir = save_dir / "raw-data-{:}".format(hp)
+        ckps = sorted(list(sub_save_dir.glob("arch-*-seed-*.pth")))
+        seed2names = defaultdict(list)
+        for ckp in ckps:
+            parts = re.split("-|\.", ckp.name)
+            seed2names[parts[3]].append(ckp.name)
+        print("DIR : {:}".format(sub_save_dir))
+        nums = []
+        for seed, xlist in seed2names.items():
+            seeds.add(seed)
+            nums.append(len(xlist))
+            print("  [seed={:}] there are {:} checkpoints.".format(seed, len(xlist)))
+        assert (
+            len(nets) == total == max(nums)
+        ), "there are some missed files : {:} vs {:}".format(max(nums), total)
+    print("{:} start simplify the checkpoint.".format(time_string()))
+
+    datasets = ("cifar10-valid", "cifar10", "cifar100", "ImageNet16-120")
+
+    # Create the directory to save the processed data
+    # full_save_dir contains all benchmark files with trained weights.
+    # simplify_save_dir contains all benchmark files without trained weights.
+    full_save_dir = save_dir / (save_name + "-FULL")
+    simple_save_dir = save_dir / (save_name + "-SIMPLIFY")
+    full_save_dir.mkdir(parents=True, exist_ok=True)
+    simple_save_dir.mkdir(parents=True, exist_ok=True)
+    # all data in memory
+    arch2infos, evaluated_indexes = dict(), set()
+    end_time, arch_time = time.time(), AverageMeter()
+    # save the meta information
+    for index in tqdm(range(total)):
+        arch_str = nets[index]
+        hp2info = OrderedDict()
+
+        simple_save_path = simple_save_dir / "{:06d}.pickle".format(index)
+
+        arch2infos[index] = pickle_load(simple_save_path)
+        evaluated_indexes.add(index)
+
+        # measure elapsed time
+        arch_time.update(time.time() - end_time)
+        end_time = time.time()
+        need_time = "{:}".format(
+            convert_secs2time(arch_time.avg * (total - index - 1), True)
+        )
+        # print('{:} {:06d}/{:06d} : still need {:}'.format(time_string(), index, total, need_time))
+    print("{:} {:} done.".format(time_string(), save_name))
+    final_infos = {
+        "meta_archs": nets,
+        "total_archs": total,
+        "arch2infos": arch2infos,
+        "evaluated_indexes": evaluated_indexes,
+    }
+    save_file_name = save_dir / "{:}.pickle".format(save_name)
+    pickle_save(final_infos, str(save_file_name))
+    # move the benchmark file to a new path
+    hd5sum = get_md5_file(str(save_file_name) + ".pbz2")
+    hd5_file_name = save_dir / "{:}-{:}.pickle.pbz2".format(NATS_TSS_BASE_NAME, hd5sum)
+    shutil.move(str(save_file_name) + ".pbz2", hd5_file_name)
+    print(
+        "Save {:} / {:} architecture results into {:} -> {:}.".format(
+            len(evaluated_indexes), total, save_file_name, hd5_file_name
+        )
+    )
+    # move the directory to a new path
+    hd5_full_save_dir = save_dir / "{:}-{:}-full".format(NATS_TSS_BASE_NAME, hd5sum)
+    hd5_simple_save_dir = save_dir / "{:}-{:}-simple".format(NATS_TSS_BASE_NAME, hd5sum)
+    shutil.move(full_save_dir, hd5_full_save_dir)
+    shutil.move(simple_save_dir, hd5_simple_save_dir)
+
+
+def traverse_net(max_node):
+    aa_nas_bench_ss = get_search_spaces("cell", "nats-bench")
+    archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
+    print(
+        "There are {:} archs vs {:}.".format(
+            len(archs), len(aa_nas_bench_ss) ** ((max_node - 1) * max_node / 2)
+        )
+    )
+
+    random.seed(88)  # please do not change this line for reproducibility
+    random.shuffle(archs)
+    assert (
+        archs[0].tostr()
+        == "|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|"
+    ), "please check the 0-th architecture : {:}".format(archs[0])
+    assert (
+        archs[9].tostr()
+        == "|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|"
+    ), "please check the 9-th architecture : {:}".format(archs[9])
+    assert (
+        archs[123].tostr()
+        == "|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|"
+    ), "please check the 123-th architecture : {:}".format(archs[123])
+    return [x.tostr() for x in archs]
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(
+        description="NATS-Bench (topology search space)",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--base_save_dir",
+        type=str,
+        default="./output/NATS-Bench-topology",
+        help="The base-name of folder to save checkpoints and log.",
+    )
+    parser.add_argument(
+        "--max_node", type=int, default=4, help="The maximum node in a cell."
+    )
+    parser.add_argument(
+        "--channel", type=int, default=16, help="The number of channels."
+    )
+    parser.add_argument(
+        "--num_cells", type=int, default=5, help="The number of cells in one stage."
+    )
+    parser.add_argument("--check_N", type=int, default=15625, help="For safety.")
+    parser.add_argument(
+        "--save_name", type=str, default="process", help="The save directory."
+    )
+    args = parser.parse_args()
+
+    nets = traverse_net(args.max_node)
+    if len(nets) != args.check_N:
+        raise ValueError(
+            "Pre-num-check failed : {:} vs {:}".format(len(nets), args.check_N)
+        )
+
+    save_dir = Path(args.base_save_dir)
+    simplify(
+        save_dir,
+        args.save_name,
+        nets,
+        args.check_N,
+        {"name": "infer.tiny", "channel": args.channel, "num_cells": args.num_cells},
+    )