Reformulate via black
This commit is contained in:
D-X-Y
@@ -16,263 +16,304 @@ from tqdm import tqdm
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from pathlib import Path
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from collections import defaultdict, OrderedDict
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from typing import Dict, Any, Text, List
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from log_utils import AverageMeter, time_string, convert_secs2time
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from log_utils import AverageMeter, time_string, convert_secs2time
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from config_utils import dict2config
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from models import CellStructure, get_cell_based_tiny_net
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from nats_bench import pickle_save, pickle_load, ArchResults, ResultsCount
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from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
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from utils import get_md5_file
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from models import CellStructure, get_cell_based_tiny_net
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from nats_bench import pickle_save, pickle_load, ArchResults, ResultsCount
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from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
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from utils import get_md5_file
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NATS_SSS_BASE_NAME = 'NATS-sss-v1_0' # 2020.08.28
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NATS_SSS_BASE_NAME = "NATS-sss-v1_0" # 2020.08.28
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def account_one_arch(arch_index: int, arch_str: Text, checkpoints: List[Text], datasets: List[Text]) -> ArchResults:
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information = ArchResults(arch_index, arch_str)
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information = ArchResults(arch_index, arch_str)
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for checkpoint_path in checkpoints:
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try:
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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except:
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raise ValueError('This checkpoint failed to be loaded : {:}'.format(checkpoint_path))
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used_seed = checkpoint_path.name.split('-')[-1].split('.')[0]
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ok_dataset = 0
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for dataset in datasets:
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if dataset not in checkpoint:
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print('Can not find {:} in arch-{:} from {:}'.format(dataset, arch_index, checkpoint_path))
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continue
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else:
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ok_dataset += 1
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results = checkpoint[dataset]
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assert results['finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(arch_index, used_seed, dataset, checkpoint_path)
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arch_config = {'name': 'infer.shape.tiny', 'channels': arch_str, 'arch_str': arch_str,
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'genotype': results['arch_config']['genotype'],
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'class_num': results['arch_config']['num_classes']}
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xresult = ResultsCount(dataset, results['net_state_dict'], results['train_acc1es'], results['train_losses'],
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results['param'], results['flop'], arch_config, used_seed, results['total_epoch'], None)
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xresult.update_train_info(results['train_acc1es'], results['train_acc5es'], results['train_losses'], results['train_times'])
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xresult.update_eval(results['valid_acc1es'], results['valid_losses'], results['valid_times'])
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information.update(dataset, int(used_seed), xresult)
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if ok_dataset < len(datasets): raise ValueError('{:} does find enought data : {:} vs {:}'.format(checkpoint_path, ok_dataset, len(datasets)))
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return information
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for checkpoint_path in checkpoints:
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try:
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checkpoint = torch.load(checkpoint_path, map_location="cpu")
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except:
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raise ValueError("This checkpoint failed to be loaded : {:}".format(checkpoint_path))
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used_seed = checkpoint_path.name.split("-")[-1].split(".")[0]
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ok_dataset = 0
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for dataset in datasets:
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if dataset not in checkpoint:
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print("Can not find {:} in arch-{:} from {:}".format(dataset, arch_index, checkpoint_path))
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continue
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else:
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ok_dataset += 1
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results = checkpoint[dataset]
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assert results["finish-train"], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format(
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arch_index, used_seed, dataset, checkpoint_path
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)
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arch_config = {
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"name": "infer.shape.tiny",
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"channels": arch_str,
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"arch_str": arch_str,
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"genotype": results["arch_config"]["genotype"],
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"class_num": results["arch_config"]["num_classes"],
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}
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xresult = ResultsCount(
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dataset,
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results["net_state_dict"],
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results["train_acc1es"],
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results["train_losses"],
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results["param"],
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results["flop"],
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arch_config,
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used_seed,
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results["total_epoch"],
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None,
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)
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xresult.update_train_info(
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results["train_acc1es"], results["train_acc5es"], results["train_losses"], results["train_times"]
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)
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xresult.update_eval(results["valid_acc1es"], results["valid_losses"], results["valid_times"])
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information.update(dataset, int(used_seed), xresult)
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if ok_dataset < len(datasets):
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raise ValueError(
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"{:} does find enought data : {:} vs {:}".format(checkpoint_path, ok_dataset, len(datasets))
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)
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return information
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def correct_time_related_info(hp2info: Dict[Text, ArchResults]):
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# calibrate the latency based on the number of epochs = 01, since they are trained on the same machine.
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x1 = hp2info['01'].get_metrics('cifar10-valid', 'x-valid')['all_time'] / 98
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x2 = hp2info['01'].get_metrics('cifar10-valid', 'ori-test')['all_time'] / 40
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cifar010_latency = (x1 + x2) / 2
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency('cifar10-valid', None, cifar010_latency)
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arch_info.reset_latency('cifar10', None, cifar010_latency)
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# hp2info['01'].get_latency('cifar10')
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# calibrate the latency based on the number of epochs = 01, since they are trained on the same machine.
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x1 = hp2info["01"].get_metrics("cifar10-valid", "x-valid")["all_time"] / 98
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x2 = hp2info["01"].get_metrics("cifar10-valid", "ori-test")["all_time"] / 40
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cifar010_latency = (x1 + x2) / 2
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency("cifar10-valid", None, cifar010_latency)
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arch_info.reset_latency("cifar10", None, cifar010_latency)
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# hp2info['01'].get_latency('cifar10')
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x1 = hp2info['01'].get_metrics('cifar100', 'ori-test')['all_time'] / 40
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x2 = hp2info['01'].get_metrics('cifar100', 'x-test')['all_time'] / 20
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x3 = hp2info['01'].get_metrics('cifar100', 'x-valid')['all_time'] / 20
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cifar100_latency = (x1 + x2 + x3) / 3
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency('cifar100', None, cifar100_latency)
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x1 = hp2info["01"].get_metrics("cifar100", "ori-test")["all_time"] / 40
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x2 = hp2info["01"].get_metrics("cifar100", "x-test")["all_time"] / 20
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x3 = hp2info["01"].get_metrics("cifar100", "x-valid")["all_time"] / 20
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cifar100_latency = (x1 + x2 + x3) / 3
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency("cifar100", None, cifar100_latency)
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x1 = hp2info['01'].get_metrics('ImageNet16-120', 'ori-test')['all_time'] / 24
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x2 = hp2info['01'].get_metrics('ImageNet16-120', 'x-test')['all_time'] / 12
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x3 = hp2info['01'].get_metrics('ImageNet16-120', 'x-valid')['all_time'] / 12
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image_latency = (x1 + x2 + x3) / 3
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency('ImageNet16-120', None, image_latency)
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x1 = hp2info["01"].get_metrics("ImageNet16-120", "ori-test")["all_time"] / 24
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x2 = hp2info["01"].get_metrics("ImageNet16-120", "x-test")["all_time"] / 12
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x3 = hp2info["01"].get_metrics("ImageNet16-120", "x-valid")["all_time"] / 12
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image_latency = (x1 + x2 + x3) / 3
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency("ImageNet16-120", None, image_latency)
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# CIFAR10 VALID
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train_per_epoch_time = list(hp2info['01'].query('cifar10-valid', 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time = [], []
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for key, value in hp2info['01'].query('cifar10-valid', 777).eval_times.items():
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if key.startswith('ori-test@'):
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eval_ori_test_time.append(value)
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elif key.startswith('x-valid@'):
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eval_x_valid_time.append(value)
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else: raise ValueError('-- {:} --'.format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times('cifar10-valid', None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'x-valid', eval_x_valid_time)
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arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'ori-test', eval_ori_test_time)
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# CIFAR10 VALID
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train_per_epoch_time = list(hp2info["01"].query("cifar10-valid", 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time = [], []
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for key, value in hp2info["01"].query("cifar10-valid", 777).eval_times.items():
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if key.startswith("ori-test@"):
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eval_ori_test_time.append(value)
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elif key.startswith("x-valid@"):
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eval_x_valid_time.append(value)
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else:
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raise ValueError("-- {:} --".format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times("cifar10-valid", None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times("cifar10-valid", None, "x-valid", eval_x_valid_time)
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arch_info.reset_pseudo_eval_times("cifar10-valid", None, "ori-test", eval_ori_test_time)
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# CIFAR10
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train_per_epoch_time = list(hp2info['01'].query('cifar10', 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time = []
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for key, value in hp2info['01'].query('cifar10', 777).eval_times.items():
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if key.startswith('ori-test@'):
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eval_ori_test_time.append(value)
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else: raise ValueError('-- {:} --'.format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times('cifar10', None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times('cifar10', None, 'ori-test', eval_ori_test_time)
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# CIFAR10
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train_per_epoch_time = list(hp2info["01"].query("cifar10", 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time = []
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for key, value in hp2info["01"].query("cifar10", 777).eval_times.items():
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if key.startswith("ori-test@"):
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eval_ori_test_time.append(value)
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else:
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raise ValueError("-- {:} --".format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times("cifar10", None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times("cifar10", None, "ori-test", eval_ori_test_time)
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# CIFAR100
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train_per_epoch_time = list(hp2info['01'].query('cifar100', 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
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for key, value in hp2info['01'].query('cifar100', 777).eval_times.items():
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if key.startswith('ori-test@'):
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eval_ori_test_time.append(value)
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elif key.startswith('x-valid@'):
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eval_x_valid_time.append(value)
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elif key.startswith('x-test@'):
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eval_x_test_time.append(value)
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else: raise ValueError('-- {:} --'.format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times('cifar100', None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times('cifar100', None, 'x-valid', eval_x_valid_time)
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arch_info.reset_pseudo_eval_times('cifar100', None, 'x-test', eval_x_test_time)
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arch_info.reset_pseudo_eval_times('cifar100', None, 'ori-test', eval_ori_test_time)
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# CIFAR100
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train_per_epoch_time = list(hp2info["01"].query("cifar100", 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
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for key, value in hp2info["01"].query("cifar100", 777).eval_times.items():
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if key.startswith("ori-test@"):
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eval_ori_test_time.append(value)
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elif key.startswith("x-valid@"):
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eval_x_valid_time.append(value)
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elif key.startswith("x-test@"):
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eval_x_test_time.append(value)
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else:
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raise ValueError("-- {:} --".format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times("cifar100", None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times("cifar100", None, "x-valid", eval_x_valid_time)
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arch_info.reset_pseudo_eval_times("cifar100", None, "x-test", eval_x_test_time)
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arch_info.reset_pseudo_eval_times("cifar100", None, "ori-test", eval_ori_test_time)
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# ImageNet16-120
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train_per_epoch_time = list(hp2info['01'].query('ImageNet16-120', 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
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for key, value in hp2info['01'].query('ImageNet16-120', 777).eval_times.items():
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if key.startswith('ori-test@'):
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eval_ori_test_time.append(value)
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elif key.startswith('x-valid@'):
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eval_x_valid_time.append(value)
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elif key.startswith('x-test@'):
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eval_x_test_time.append(value)
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else: raise ValueError('-- {:} --'.format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times('ImageNet16-120', None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-valid', eval_x_valid_time)
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-test', eval_x_test_time)
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'ori-test', eval_ori_test_time)
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return hp2info
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# ImageNet16-120
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train_per_epoch_time = list(hp2info["01"].query("ImageNet16-120", 777).train_times.values())
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time, eval_x_test_time = [], [], []
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for key, value in hp2info["01"].query("ImageNet16-120", 777).eval_times.items():
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if key.startswith("ori-test@"):
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eval_ori_test_time.append(value)
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elif key.startswith("x-valid@"):
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eval_x_valid_time.append(value)
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elif key.startswith("x-test@"):
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eval_x_test_time.append(value)
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else:
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raise ValueError("-- {:} --".format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times("ImageNet16-120", None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times("ImageNet16-120", None, "x-valid", eval_x_valid_time)
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arch_info.reset_pseudo_eval_times("ImageNet16-120", None, "x-test", eval_x_test_time)
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arch_info.reset_pseudo_eval_times("ImageNet16-120", None, "ori-test", eval_ori_test_time)
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return hp2info
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def simplify(save_dir, save_name, nets, total):
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hps, seeds = ['01', '12', '90'], set()
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for hp in hps:
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sub_save_dir = save_dir / 'raw-data-{:}'.format(hp)
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ckps = sorted(list(sub_save_dir.glob('arch-*-seed-*.pth')))
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seed2names = defaultdict(list)
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for ckp in ckps:
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parts = re.split('-|\.', ckp.name)
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seed2names[parts[3]].append(ckp.name)
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print('DIR : {:}'.format(sub_save_dir))
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nums = []
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for seed, xlist in seed2names.items():
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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()
|
||||
|
||||
for index in tqdm(range(total)):
|
||||
arch_str = nets[index]
|
||||
hp2info = OrderedDict()
|
||||
|
||||
full_save_path = full_save_dir / '{:06d}.pickle'.format(index)
|
||||
simple_save_path = simple_save_dir / '{:06d}.pickle'.format(index)
|
||||
|
||||
hps, seeds = ["01", "12", "90"], set()
|
||||
for hp in hps:
|
||||
sub_save_dir = save_dir / 'raw-data-{:}'.format(hp)
|
||||
ckps = [sub_save_dir / 'arch-{:06d}-seed-{:}.pth'.format(index, seed) for seed in seeds]
|
||||
ckps = [x for x in ckps if x.exists()]
|
||||
if len(ckps) == 0:
|
||||
raise ValueError('Invalid data : index={:}, hp={:}'.format(index, hp))
|
||||
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()))
|
||||
|
||||
arch_info = account_one_arch(index, arch_str, ckps, datasets)
|
||||
hp2info[hp] = arch_info
|
||||
|
||||
hp2info = correct_time_related_info(hp2info)
|
||||
evaluated_indexes.add(index)
|
||||
datasets = ("cifar10-valid", "cifar10", "cifar100", "ImageNet16-120")
|
||||
|
||||
hp2info['01'].clear_params() # to save some spaces...
|
||||
to_save_data = OrderedDict({'01': hp2info['01'].state_dict(),
|
||||
'12': hp2info['12'].state_dict(),
|
||||
'90': hp2info['90'].state_dict()})
|
||||
pickle_save(to_save_data, str(full_save_path))
|
||||
|
||||
for hp in hps: hp2info[hp].clear_params()
|
||||
to_save_data = OrderedDict({'01': hp2info['01'].state_dict(),
|
||||
'12': hp2info['12'].state_dict(),
|
||||
'90': hp2info['90'].state_dict()})
|
||||
pickle_save(to_save_data, str(simple_save_path))
|
||||
arch2infos[index] = to_save_data
|
||||
# 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_SSS_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_SSS_BASE_NAME, hd5sum)
|
||||
hd5_simple_save_dir = save_dir / '{:}-{:}-simple'.format(NATS_SSS_BASE_NAME, hd5sum)
|
||||
shutil.move(full_save_dir, hd5_full_save_dir)
|
||||
shutil.move(simple_save_dir, hd5_simple_save_dir)
|
||||
# save the meta information for simple and full
|
||||
final_infos['arch2infos'] = None
|
||||
final_infos['evaluated_indexes'] = set()
|
||||
pickle_save(final_infos, str(hd5_full_save_dir / 'meta.pickle'))
|
||||
pickle_save(final_infos, str(hd5_simple_save_dir / 'meta.pickle'))
|
||||
# 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()
|
||||
|
||||
for index in tqdm(range(total)):
|
||||
arch_str = nets[index]
|
||||
hp2info = OrderedDict()
|
||||
|
||||
full_save_path = full_save_dir / "{:06d}.pickle".format(index)
|
||||
simple_save_path = simple_save_dir / "{:06d}.pickle".format(index)
|
||||
|
||||
for hp in hps:
|
||||
sub_save_dir = save_dir / "raw-data-{:}".format(hp)
|
||||
ckps = [sub_save_dir / "arch-{:06d}-seed-{:}.pth".format(index, seed) for seed in seeds]
|
||||
ckps = [x for x in ckps if x.exists()]
|
||||
if len(ckps) == 0:
|
||||
raise ValueError("Invalid data : index={:}, hp={:}".format(index, hp))
|
||||
|
||||
arch_info = account_one_arch(index, arch_str, ckps, datasets)
|
||||
hp2info[hp] = arch_info
|
||||
|
||||
hp2info = correct_time_related_info(hp2info)
|
||||
evaluated_indexes.add(index)
|
||||
|
||||
hp2info["01"].clear_params() # to save some spaces...
|
||||
to_save_data = OrderedDict(
|
||||
{"01": hp2info["01"].state_dict(), "12": hp2info["12"].state_dict(), "90": hp2info["90"].state_dict()}
|
||||
)
|
||||
pickle_save(to_save_data, str(full_save_path))
|
||||
|
||||
for hp in hps:
|
||||
hp2info[hp].clear_params()
|
||||
to_save_data = OrderedDict(
|
||||
{"01": hp2info["01"].state_dict(), "12": hp2info["12"].state_dict(), "90": hp2info["90"].state_dict()}
|
||||
)
|
||||
pickle_save(to_save_data, str(simple_save_path))
|
||||
arch2infos[index] = to_save_data
|
||||
# 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_SSS_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_SSS_BASE_NAME, hd5sum)
|
||||
hd5_simple_save_dir = save_dir / "{:}-{:}-simple".format(NATS_SSS_BASE_NAME, hd5sum)
|
||||
shutil.move(full_save_dir, hd5_full_save_dir)
|
||||
shutil.move(simple_save_dir, hd5_simple_save_dir)
|
||||
# save the meta information for simple and full
|
||||
final_infos["arch2infos"] = None
|
||||
final_infos["evaluated_indexes"] = set()
|
||||
pickle_save(final_infos, str(hd5_full_save_dir / "meta.pickle"))
|
||||
pickle_save(final_infos, str(hd5_simple_save_dir / "meta.pickle"))
|
||||
|
||||
|
||||
def traverse_net(candidates: List[int], N: int):
|
||||
nets = ['']
|
||||
for i in range(N):
|
||||
new_nets = []
|
||||
for net in nets:
|
||||
for C in candidates:
|
||||
new_nets.append(str(C) if net == '' else "{:}:{:}".format(net,C))
|
||||
nets = new_nets
|
||||
return nets
|
||||
nets = [""]
|
||||
for i in range(N):
|
||||
new_nets = []
|
||||
for net in nets:
|
||||
for C in candidates:
|
||||
new_nets.append(str(C) if net == "" else "{:}:{:}".format(net, C))
|
||||
nets = new_nets
|
||||
return nets
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='NATS-Bench (size search space)', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--base_save_dir', type=str, default='./output/NATS-Bench-size', help='The base-name of folder to save checkpoints and log.')
|
||||
parser.add_argument('--candidateC' , type=int, nargs='+', default=[8, 16, 24, 32, 40, 48, 56, 64], help='.')
|
||||
parser.add_argument('--num_layers' , type=int, default=5, help='The number of layers in a network.')
|
||||
parser.add_argument('--check_N' , type=int, default=32768, help='For safety.')
|
||||
parser.add_argument('--save_name' , type=str, default='process', help='The save directory.')
|
||||
args = parser.parse_args()
|
||||
|
||||
nets = traverse_net(args.candidateC, args.num_layers)
|
||||
if len(nets) != args.check_N:
|
||||
raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N))
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(
|
||||
description="NATS-Bench (size search space)", formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
parser.add_argument(
|
||||
"--base_save_dir",
|
||||
type=str,
|
||||
default="./output/NATS-Bench-size",
|
||||
help="The base-name of folder to save checkpoints and log.",
|
||||
)
|
||||
parser.add_argument("--candidateC", type=int, nargs="+", default=[8, 16, 24, 32, 40, 48, 56, 64], help=".")
|
||||
parser.add_argument("--num_layers", type=int, default=5, help="The number of layers in a network.")
|
||||
parser.add_argument("--check_N", type=int, default=32768, help="For safety.")
|
||||
parser.add_argument("--save_name", type=str, default="process", help="The save directory.")
|
||||
args = parser.parse_args()
|
||||
|
||||
save_dir = Path(args.base_save_dir)
|
||||
simplify(save_dir, args.save_name, nets, args.check_N)
|
||||
nets = traverse_net(args.candidateC, args.num_layers)
|
||||
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)
|
||||
|
||||
Reference in New Issue
Block a user