Reformulate via black
This commit is contained in:
D-X-Y
@@ -18,246 +18,335 @@ 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 load_config, dict2config
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from datasets import get_datasets
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from models import CellStructure, get_cell_based_tiny_net, get_search_spaces
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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 nas_201_api import NASBench201API
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from datasets import get_datasets
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from models import CellStructure, get_cell_based_tiny_net, get_search_spaces
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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 nas_201_api import NASBench201API
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api = NASBench201API('{:}/.torch/NAS-Bench-201-v1_0-e61699.pth'.format(os.environ['HOME']))
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api = NASBench201API("{:}/.torch/NAS-Bench-201-v1_0-e61699.pth".format(os.environ["HOME"]))
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NATS_TSS_BASE_NAME = 'NATS-tss-v1_0' # 2020.08.28
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NATS_TSS_BASE_NAME = "NATS-tss-v1_0" # 2020.08.28
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def create_result_count(used_seed: int, dataset: Text, arch_config: Dict[Text, Any],
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results: Dict[Text, Any], dataloader_dict: Dict[Text, Any]) -> ResultsCount:
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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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net_config = dict2config({'name': 'infer.tiny', 'C': arch_config['channel'], 'N': arch_config['num_cells'], 'genotype': CellStructure.str2structure(arch_config['arch_str']), 'num_classes': arch_config['class_num']}, None)
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if 'train_times' in results: # new version
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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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else:
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network = get_cell_based_tiny_net(net_config)
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network.load_state_dict(xresult.get_net_param())
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if dataset == 'cifar10-valid':
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xresult.update_OLD_eval('x-valid' , results['valid_acc1es'], results['valid_losses'])
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format('cifar10', 'test')], network.cuda())
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xresult.update_OLD_eval('ori-test', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
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xresult.update_latency(latencies)
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elif dataset == 'cifar10':
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xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
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xresult.update_latency(latencies)
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elif dataset == 'cifar100' or dataset == 'ImageNet16-120':
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xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'valid')], network.cuda())
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xresult.update_OLD_eval('x-valid', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
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xresult.update_OLD_eval('x-test' , {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
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xresult.update_latency(latencies)
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def create_result_count(
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used_seed: int,
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dataset: Text,
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arch_config: Dict[Text, Any],
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results: Dict[Text, Any],
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dataloader_dict: Dict[Text, Any],
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) -> ResultsCount:
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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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net_config = dict2config(
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{
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"name": "infer.tiny",
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"C": arch_config["channel"],
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"N": arch_config["num_cells"],
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"genotype": CellStructure.str2structure(arch_config["arch_str"]),
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"num_classes": arch_config["class_num"],
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},
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None,
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)
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if "train_times" in results: # new version
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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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else:
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raise ValueError('invalid dataset name : {:}'.format(dataset))
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return xresult
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network = get_cell_based_tiny_net(net_config)
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network.load_state_dict(xresult.get_net_param())
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if dataset == "cifar10-valid":
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xresult.update_OLD_eval("x-valid", results["valid_acc1es"], results["valid_losses"])
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loss, top1, top5, latencies = pure_evaluate(
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dataloader_dict["{:}@{:}".format("cifar10", "test")], network.cuda()
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)
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xresult.update_OLD_eval("ori-test", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss})
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xresult.update_latency(latencies)
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elif dataset == "cifar10":
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xresult.update_OLD_eval("ori-test", results["valid_acc1es"], results["valid_losses"])
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loss, top1, top5, latencies = pure_evaluate(
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dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
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)
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xresult.update_latency(latencies)
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elif dataset == "cifar100" or dataset == "ImageNet16-120":
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xresult.update_OLD_eval("ori-test", results["valid_acc1es"], results["valid_losses"])
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loss, top1, top5, latencies = pure_evaluate(
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dataloader_dict["{:}@{:}".format(dataset, "valid")], network.cuda()
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)
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xresult.update_OLD_eval("x-valid", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss})
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loss, top1, top5, latencies = pure_evaluate(
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dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
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)
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xresult.update_OLD_eval("x-test", {results["total_epoch"] - 1: top1}, {results["total_epoch"] - 1: loss})
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xresult.update_latency(latencies)
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else:
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raise ValueError("invalid dataset name : {:}".format(dataset))
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return xresult
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def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dict):
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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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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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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 = {'channel': results['channel'], 'num_cells': results['num_cells'], 'arch_str': arch_str, 'class_num': results['config']['class_num']}
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xresult = create_result_count(used_seed, dataset, arch_config, results, dataloader_dict)
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information.update(dataset, int(used_seed), xresult)
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if ok_dataset == 0: raise ValueError('{:} does not find any data'.format(checkpoint_path))
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return information
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for checkpoint_path in checkpoints:
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checkpoint = torch.load(checkpoint_path, map_location="cpu")
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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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"channel": results["channel"],
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"num_cells": results["num_cells"],
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"arch_str": arch_str,
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"class_num": results["config"]["class_num"],
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}
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xresult = create_result_count(used_seed, dataset, arch_config, results, dataloader_dict)
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information.update(dataset, int(used_seed), xresult)
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if ok_dataset == 0:
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raise ValueError("{:} does not find any data".format(checkpoint_path))
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return information
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def correct_time_related_info(arch_index: int, arch_infos: Dict[Text, ArchResults]):
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# calibrate the latency based on NAS-Bench-201-v1_0-e61699.pth
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cifar010_latency = (api.get_latency(arch_index, 'cifar10-valid', hp='200') + api.get_latency(arch_index, 'cifar10', hp='200')) / 2
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cifar100_latency = api.get_latency(arch_index, 'cifar100', hp='200')
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image_latency = api.get_latency(arch_index, 'ImageNet16-120', hp='200')
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for hp, arch_info in arch_infos.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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arch_info.reset_latency('cifar100', None, cifar100_latency)
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arch_info.reset_latency('ImageNet16-120', None, image_latency)
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# calibrate the latency based on NAS-Bench-201-v1_0-e61699.pth
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cifar010_latency = (
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api.get_latency(arch_index, "cifar10-valid", hp="200") + api.get_latency(arch_index, "cifar10", hp="200")
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) / 2
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cifar100_latency = api.get_latency(arch_index, "cifar100", hp="200")
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image_latency = api.get_latency(arch_index, "ImageNet16-120", hp="200")
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for hp, arch_info in arch_infos.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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arch_info.reset_latency("cifar100", None, cifar100_latency)
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arch_info.reset_latency("ImageNet16-120", None, image_latency)
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train_per_epoch_time = list(arch_infos['12'].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 arch_infos['12'].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, eval_x_valid_time = float(np.mean(eval_ori_test_time)), float(np.mean(eval_x_valid_time))
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nums = {'ImageNet16-120-train': 151700, 'ImageNet16-120-valid': 3000, 'ImageNet16-120-test': 6000,
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'cifar10-valid-train': 25000, 'cifar10-valid-valid': 25000,
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'cifar10-train': 50000, 'cifar10-test': 10000,
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'cifar100-train': 50000, 'cifar100-test': 10000, 'cifar100-valid': 5000}
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eval_per_sample = (eval_ori_test_time + eval_x_valid_time) / (nums['cifar10-valid-valid'] + nums['cifar10-test'])
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for hp, arch_info in arch_infos.items():
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arch_info.reset_pseudo_train_times('cifar10-valid', None,
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train_per_epoch_time / nums['cifar10-valid-train'] * nums['cifar10-valid-train'])
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arch_info.reset_pseudo_train_times('cifar10', None,
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train_per_epoch_time / nums['cifar10-valid-train'] * nums['cifar10-train'])
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arch_info.reset_pseudo_train_times('cifar100', None,
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train_per_epoch_time / nums['cifar10-valid-train'] * nums['cifar100-train'])
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arch_info.reset_pseudo_train_times('ImageNet16-120', None,
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train_per_epoch_time / nums['cifar10-valid-train'] * nums['ImageNet16-120-train'])
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arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'x-valid', eval_per_sample*nums['cifar10-valid-valid'])
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arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'ori-test', eval_per_sample * nums['cifar10-test'])
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arch_info.reset_pseudo_eval_times('cifar10', None, 'ori-test', eval_per_sample * nums['cifar10-test'])
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arch_info.reset_pseudo_eval_times('cifar100', None, 'x-valid', eval_per_sample * nums['cifar100-valid'])
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arch_info.reset_pseudo_eval_times('cifar100', None, 'x-test', eval_per_sample * nums['cifar100-valid'])
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arch_info.reset_pseudo_eval_times('cifar100', None, 'ori-test', eval_per_sample * nums['cifar100-test'])
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-valid', eval_per_sample * nums['ImageNet16-120-valid'])
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-test', eval_per_sample * nums['ImageNet16-120-valid'])
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arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'ori-test', eval_per_sample * nums['ImageNet16-120-test'])
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return arch_infos
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train_per_epoch_time = list(arch_infos["12"].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 arch_infos["12"].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, eval_x_valid_time = float(np.mean(eval_ori_test_time)), float(np.mean(eval_x_valid_time))
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nums = {
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"ImageNet16-120-train": 151700,
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"ImageNet16-120-valid": 3000,
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"ImageNet16-120-test": 6000,
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"cifar10-valid-train": 25000,
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"cifar10-valid-valid": 25000,
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"cifar10-train": 50000,
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"cifar10-test": 10000,
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"cifar100-train": 50000,
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"cifar100-test": 10000,
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"cifar100-valid": 5000,
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}
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eval_per_sample = (eval_ori_test_time + eval_x_valid_time) / (nums["cifar10-valid-valid"] + nums["cifar10-test"])
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for hp, arch_info in arch_infos.items():
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arch_info.reset_pseudo_train_times(
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"cifar10-valid", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar10-valid-train"]
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)
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arch_info.reset_pseudo_train_times(
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"cifar10", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar10-train"]
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)
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arch_info.reset_pseudo_train_times(
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"cifar100", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar100-train"]
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)
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arch_info.reset_pseudo_train_times(
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"ImageNet16-120", None, train_per_epoch_time / nums["cifar10-valid-train"] * nums["ImageNet16-120-train"]
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)
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arch_info.reset_pseudo_eval_times(
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"cifar10-valid", None, "x-valid", eval_per_sample * nums["cifar10-valid-valid"]
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)
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arch_info.reset_pseudo_eval_times("cifar10-valid", None, "ori-test", eval_per_sample * nums["cifar10-test"])
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arch_info.reset_pseudo_eval_times("cifar10", None, "ori-test", eval_per_sample * nums["cifar10-test"])
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arch_info.reset_pseudo_eval_times("cifar100", None, "x-valid", eval_per_sample * nums["cifar100-valid"])
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arch_info.reset_pseudo_eval_times("cifar100", None, "x-test", eval_per_sample * nums["cifar100-valid"])
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arch_info.reset_pseudo_eval_times("cifar100", None, "ori-test", eval_per_sample * nums["cifar100-test"])
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arch_info.reset_pseudo_eval_times(
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"ImageNet16-120", None, "x-valid", eval_per_sample * nums["ImageNet16-120-valid"]
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)
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arch_info.reset_pseudo_eval_times(
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"ImageNet16-120", None, "x-test", eval_per_sample * nums["ImageNet16-120-valid"]
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)
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arch_info.reset_pseudo_eval_times(
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"ImageNet16-120", None, "ori-test", eval_per_sample * nums["ImageNet16-120-test"]
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)
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return arch_infos
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def simplify(save_dir, save_name, nets, total, sup_config):
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dataloader_dict = get_nas_bench_loaders(6)
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hps, seeds = ['12', '200'], 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)
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nums.append(len(xlist))
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print(' [seed={:}] there are {:} checkpoints.'.format(seed, len(xlist)))
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assert len(nets) == total == max(nums), 'there are some missed files : {:} vs {:}'.format(max(nums), total)
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print('{:} start simplify the checkpoint.'.format(time_string()))
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|
||||
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
|
||||
temp_final_infos = {'meta_archs' : nets,
|
||||
'total_archs': total,
|
||||
'arch2infos' : None,
|
||||
'evaluated_indexes': set()}
|
||||
pickle_save(temp_final_infos, str(full_save_dir / 'meta.pickle'))
|
||||
pickle_save(temp_final_infos, str(simple_save_dir / 'meta.pickle'))
|
||||
|
||||
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)
|
||||
dataloader_dict = get_nas_bench_loaders(6)
|
||||
hps, seeds = ["12", "200"], 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, dataloader_dict)
|
||||
hp2info[hp] = arch_info
|
||||
|
||||
hp2info = correct_time_related_info(index, hp2info)
|
||||
evaluated_indexes.add(index)
|
||||
|
||||
to_save_data = OrderedDict({'12': hp2info['12'].state_dict(),
|
||||
'200': hp2info['200'].state_dict()})
|
||||
pickle_save(to_save_data, str(full_save_path))
|
||||
|
||||
for hp in hps: hp2info[hp].clear_params()
|
||||
to_save_data = OrderedDict({'12': hp2info['12'].state_dict(),
|
||||
'200': hp2info['200'].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_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)
|
||||
# save the meta information for simple and full
|
||||
# final_infos['arch2infos'] = None
|
||||
# final_infos['evaluated_indexes'] = set()
|
||||
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
|
||||
temp_final_infos = {"meta_archs": nets, "total_archs": total, "arch2infos": None, "evaluated_indexes": set()}
|
||||
pickle_save(temp_final_infos, str(full_save_dir / "meta.pickle"))
|
||||
pickle_save(temp_final_infos, str(simple_save_dir / "meta.pickle"))
|
||||
|
||||
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, dataloader_dict)
|
||||
hp2info[hp] = arch_info
|
||||
|
||||
hp2info = correct_time_related_info(index, hp2info)
|
||||
evaluated_indexes.add(index)
|
||||
|
||||
to_save_data = OrderedDict({"12": hp2info["12"].state_dict(), "200": hp2info["200"].state_dict()})
|
||||
pickle_save(to_save_data, str(full_save_path))
|
||||
|
||||
for hp in hps:
|
||||
hp2info[hp].clear_params()
|
||||
to_save_data = OrderedDict({"12": hp2info["12"].state_dict(), "200": hp2info["200"].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_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)
|
||||
# save the meta information for simple and full
|
||||
# final_infos['arch2infos'] = None
|
||||
# final_infos['evaluated_indexes'] = set()
|
||||
|
||||
|
||||
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)))
|
||||
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]
|
||||
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__':
|
||||
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})
|
||||
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},
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user