Reformulate via black
This commit is contained in:
D-X-Y
@@ -6,284 +6,504 @@ from copy import deepcopy
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import torch
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from pathlib import Path
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from collections import defaultdict
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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 datasets import get_datasets
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# NAS-Bench-201 related module or function
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from models import CellStructure, get_cell_based_tiny_net
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from nas_201_api import ArchResults, ResultsCount
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from procedures import bench_pure_evaluate as pure_evaluate
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from models import CellStructure, get_cell_based_tiny_net
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from nas_201_api import ArchResults, ResultsCount
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from procedures import bench_pure_evaluate as pure_evaluate
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def create_result_count(used_seed, dataset, arch_config, results, dataloader_dict):
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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 = 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({'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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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 '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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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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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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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 "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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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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for dataset in datasets:
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assert dataset in checkpoint, 'Can not find {:} in arch-{:} from {:}'.format(dataset, arch_index, checkpoint_path)
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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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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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for dataset in datasets:
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assert dataset in checkpoint, "Can not find {:} in arch-{:} from {:}".format(
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dataset, arch_index, checkpoint_path
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)
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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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return information
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def GET_DataLoaders(workers):
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torch.set_num_threads(workers)
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torch.set_num_threads(workers)
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root_dir = (Path(__file__).parent / '..' / '..').resolve()
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torch_dir = Path(os.environ['TORCH_HOME'])
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# cifar
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cifar_config_path = root_dir / 'configs' / 'nas-benchmark' / 'CIFAR.config'
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cifar_config = load_config(cifar_config_path, None, None)
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print ('{:} Create data-loader for all datasets'.format(time_string()))
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print ('-'*200)
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TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets('cifar10', str(torch_dir/'cifar.python'), -1)
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print ('original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num))
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cifar10_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar-split.txt', None, None)
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assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [1, 2, 3, 4, 6, 8, 9, 10, 12, 14]
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temp_dataset = deepcopy(TRAIN_CIFAR10)
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temp_dataset.transform = VALID_CIFAR10.transform
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# data loader
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trainval_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
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train_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train), num_workers=workers, pin_memory=True)
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valid_cifar10_loader = torch.utils.data.DataLoader(temp_dataset , batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid), num_workers=workers, pin_memory=True)
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test__cifar10_loader = torch.utils.data.DataLoader(VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
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print ('CIFAR-10 : trval-loader has {:3d} batch with {:} per batch'.format(len(trainval_cifar10_loader), cifar_config.batch_size))
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print ('CIFAR-10 : train-loader has {:3d} batch with {:} per batch'.format(len(train_cifar10_loader), cifar_config.batch_size))
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print ('CIFAR-10 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_cifar10_loader), cifar_config.batch_size))
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print ('CIFAR-10 : test--loader has {:3d} batch with {:} per batch'.format(len(test__cifar10_loader), cifar_config.batch_size))
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print ('-'*200)
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# CIFAR-100
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TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets('cifar100', str(torch_dir/'cifar.python'), -1)
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print ('original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num))
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cifar100_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar100-test-split.txt', None, None)
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assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [0, 2, 6, 7, 9, 11, 12, 17, 20, 24]
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train_cifar100_loader = torch.utils.data.DataLoader(TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
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valid_cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True)
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test__cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest) , num_workers=workers, pin_memory=True)
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print ('CIFAR-100 : train-loader has {:3d} batch'.format(len(train_cifar100_loader)))
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print ('CIFAR-100 : valid-loader has {:3d} batch'.format(len(valid_cifar100_loader)))
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print ('CIFAR-100 : test--loader has {:3d} batch'.format(len(test__cifar100_loader)))
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print ('-'*200)
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root_dir = (Path(__file__).parent / ".." / "..").resolve()
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torch_dir = Path(os.environ["TORCH_HOME"])
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# cifar
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cifar_config_path = root_dir / "configs" / "nas-benchmark" / "CIFAR.config"
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cifar_config = load_config(cifar_config_path, None, None)
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print("{:} Create data-loader for all datasets".format(time_string()))
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print("-" * 200)
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TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets("cifar10", str(torch_dir / "cifar.python"), -1)
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print(
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"original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
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len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num
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)
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)
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cifar10_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None)
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assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [
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1,
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2,
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3,
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4,
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6,
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8,
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9,
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10,
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12,
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14,
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]
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temp_dataset = deepcopy(TRAIN_CIFAR10)
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temp_dataset.transform = VALID_CIFAR10.transform
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# data loader
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trainval_cifar10_loader = torch.utils.data.DataLoader(
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TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
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)
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train_cifar10_loader = torch.utils.data.DataLoader(
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TRAIN_CIFAR10,
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batch_size=cifar_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train),
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num_workers=workers,
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pin_memory=True,
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)
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valid_cifar10_loader = torch.utils.data.DataLoader(
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temp_dataset,
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batch_size=cifar_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid),
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num_workers=workers,
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pin_memory=True,
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)
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test__cifar10_loader = torch.utils.data.DataLoader(
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VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True
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)
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print(
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"CIFAR-10 : trval-loader has {:3d} batch with {:} per batch".format(
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len(trainval_cifar10_loader), cifar_config.batch_size
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)
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)
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print(
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"CIFAR-10 : train-loader has {:3d} batch with {:} per batch".format(
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len(train_cifar10_loader), cifar_config.batch_size
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)
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)
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print(
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"CIFAR-10 : valid-loader has {:3d} batch with {:} per batch".format(
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len(valid_cifar10_loader), cifar_config.batch_size
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)
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)
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print(
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"CIFAR-10 : test--loader has {:3d} batch with {:} per batch".format(
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len(test__cifar10_loader), cifar_config.batch_size
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)
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)
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print("-" * 200)
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# CIFAR-100
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TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets("cifar100", str(torch_dir / "cifar.python"), -1)
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print(
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"original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
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len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num
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)
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)
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cifar100_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None)
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assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [
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0,
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2,
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6,
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7,
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9,
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11,
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12,
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17,
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20,
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24,
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]
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train_cifar100_loader = torch.utils.data.DataLoader(
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TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
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)
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valid_cifar100_loader = torch.utils.data.DataLoader(
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VALID_CIFAR100,
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batch_size=cifar_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid),
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num_workers=workers,
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pin_memory=True,
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)
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test__cifar100_loader = torch.utils.data.DataLoader(
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VALID_CIFAR100,
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batch_size=cifar_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest),
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num_workers=workers,
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pin_memory=True,
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)
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print("CIFAR-100 : train-loader has {:3d} batch".format(len(train_cifar100_loader)))
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print("CIFAR-100 : valid-loader has {:3d} batch".format(len(valid_cifar100_loader)))
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print("CIFAR-100 : test--loader has {:3d} batch".format(len(test__cifar100_loader)))
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print("-" * 200)
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imagenet16_config_path = 'configs/nas-benchmark/ImageNet-16.config'
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imagenet16_config = load_config(imagenet16_config_path, None, None)
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TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets('ImageNet16-120', str(torch_dir/'cifar.python'/'ImageNet16'), -1)
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print ('original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num))
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imagenet_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'imagenet-16-120-test-split.txt', None, None)
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assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [0, 4, 5, 10, 11, 13, 14, 15, 17, 20]
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train_imagenet_loader = torch.utils.data.DataLoader(TRAIN_ImageNet16_120, batch_size=imagenet16_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
|
||||
valid_imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid), num_workers=workers, pin_memory=True)
|
||||
test__imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest) , num_workers=workers, pin_memory=True)
|
||||
print ('ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch'.format(len(train_imagenet_loader), imagenet16_config.batch_size))
|
||||
print ('ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_imagenet_loader), imagenet16_config.batch_size))
|
||||
print ('ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch'.format(len(test__imagenet_loader), imagenet16_config.batch_size))
|
||||
imagenet16_config_path = "configs/nas-benchmark/ImageNet-16.config"
|
||||
imagenet16_config = load_config(imagenet16_config_path, None, None)
|
||||
TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets(
|
||||
"ImageNet16-120", str(torch_dir / "cifar.python" / "ImageNet16"), -1
|
||||
)
|
||||
print(
|
||||
"original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
|
||||
len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num
|
||||
)
|
||||
)
|
||||
imagenet_splits = load_config(root_dir / "configs" / "nas-benchmark" / "imagenet-16-120-test-split.txt", None, None)
|
||||
assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [
|
||||
0,
|
||||
4,
|
||||
5,
|
||||
10,
|
||||
11,
|
||||
13,
|
||||
14,
|
||||
15,
|
||||
17,
|
||||
20,
|
||||
]
|
||||
train_imagenet_loader = torch.utils.data.DataLoader(
|
||||
TRAIN_ImageNet16_120,
|
||||
batch_size=imagenet16_config.batch_size,
|
||||
shuffle=True,
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
valid_imagenet_loader = torch.utils.data.DataLoader(
|
||||
VALID_ImageNet16_120,
|
||||
batch_size=imagenet16_config.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid),
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
test__imagenet_loader = torch.utils.data.DataLoader(
|
||||
VALID_ImageNet16_120,
|
||||
batch_size=imagenet16_config.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest),
|
||||
num_workers=workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
print(
|
||||
"ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch".format(
|
||||
len(train_imagenet_loader), imagenet16_config.batch_size
|
||||
)
|
||||
)
|
||||
print(
|
||||
"ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch".format(
|
||||
len(valid_imagenet_loader), imagenet16_config.batch_size
|
||||
)
|
||||
)
|
||||
print(
|
||||
"ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch".format(
|
||||
len(test__imagenet_loader), imagenet16_config.batch_size
|
||||
)
|
||||
)
|
||||
|
||||
# 'cifar10', 'cifar100', 'ImageNet16-120'
|
||||
loaders = {'cifar10@trainval': trainval_cifar10_loader,
|
||||
'cifar10@train' : train_cifar10_loader,
|
||||
'cifar10@valid' : valid_cifar10_loader,
|
||||
'cifar10@test' : test__cifar10_loader,
|
||||
'cifar100@train' : train_cifar100_loader,
|
||||
'cifar100@valid' : valid_cifar100_loader,
|
||||
'cifar100@test' : test__cifar100_loader,
|
||||
'ImageNet16-120@train': train_imagenet_loader,
|
||||
'ImageNet16-120@valid': valid_imagenet_loader,
|
||||
'ImageNet16-120@test' : test__imagenet_loader}
|
||||
return loaders
|
||||
# 'cifar10', 'cifar100', 'ImageNet16-120'
|
||||
loaders = {
|
||||
"cifar10@trainval": trainval_cifar10_loader,
|
||||
"cifar10@train": train_cifar10_loader,
|
||||
"cifar10@valid": valid_cifar10_loader,
|
||||
"cifar10@test": test__cifar10_loader,
|
||||
"cifar100@train": train_cifar100_loader,
|
||||
"cifar100@valid": valid_cifar100_loader,
|
||||
"cifar100@test": test__cifar100_loader,
|
||||
"ImageNet16-120@train": train_imagenet_loader,
|
||||
"ImageNet16-120@valid": valid_imagenet_loader,
|
||||
"ImageNet16-120@test": test__imagenet_loader,
|
||||
}
|
||||
return loaders
|
||||
|
||||
|
||||
def simplify(save_dir, meta_file, basestr, target_dir):
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs'] # a list of architecture strings
|
||||
meta_num_archs = meta_infos['total']
|
||||
meta_max_node = meta_infos['max_node']
|
||||
assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
|
||||
meta_infos = torch.load(meta_file, map_location="cpu")
|
||||
meta_archs = meta_infos["archs"] # a list of architecture strings
|
||||
meta_num_archs = meta_infos["total"]
|
||||
meta_max_node = meta_infos["max_node"]
|
||||
assert meta_num_archs == len(meta_archs), "invalid number of archs : {:} vs {:}".format(
|
||||
meta_num_archs, len(meta_archs)
|
||||
)
|
||||
|
||||
sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
|
||||
print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
|
||||
|
||||
subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
|
||||
num_seeds = defaultdict(lambda: 0)
|
||||
for index, sub_dir in enumerate(sub_model_dirs):
|
||||
xcheckpoints = list(sub_dir.glob('arch-*-seed-*.pth'))
|
||||
arch_indexes = set()
|
||||
for checkpoint in xcheckpoints:
|
||||
temp_names = checkpoint.name.split('-')
|
||||
assert len(temp_names) == 4 and temp_names[0] == 'arch' and temp_names[2] == 'seed', 'invalid checkpoint name : {:}'.format(checkpoint.name)
|
||||
arch_indexes.add( temp_names[1] )
|
||||
subdir2archs[sub_dir] = sorted(list(arch_indexes))
|
||||
num_evaluated_arch += len(arch_indexes)
|
||||
# count number of seeds for each architecture
|
||||
for arch_index in arch_indexes:
|
||||
num_seeds[ len(list(sub_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))) ] += 1
|
||||
print('{:} There are {:5d} architectures that have been evaluated ({:} in total).'.format(time_string(), num_evaluated_arch, meta_num_archs))
|
||||
for key in sorted( list( num_seeds.keys() ) ): print ('{:} There are {:5d} architectures that are evaluated {:} times.'.format(time_string(), num_seeds[key], key))
|
||||
sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
|
||||
print("{:} find {:} directories used to save checkpoints".format(time_string(), len(sub_model_dirs)))
|
||||
|
||||
dataloader_dict = GET_DataLoaders( 6 )
|
||||
subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
|
||||
num_seeds = defaultdict(lambda: 0)
|
||||
for index, sub_dir in enumerate(sub_model_dirs):
|
||||
xcheckpoints = list(sub_dir.glob("arch-*-seed-*.pth"))
|
||||
arch_indexes = set()
|
||||
for checkpoint in xcheckpoints:
|
||||
temp_names = checkpoint.name.split("-")
|
||||
assert (
|
||||
len(temp_names) == 4 and temp_names[0] == "arch" and temp_names[2] == "seed"
|
||||
), "invalid checkpoint name : {:}".format(checkpoint.name)
|
||||
arch_indexes.add(temp_names[1])
|
||||
subdir2archs[sub_dir] = sorted(list(arch_indexes))
|
||||
num_evaluated_arch += len(arch_indexes)
|
||||
# count number of seeds for each architecture
|
||||
for arch_index in arch_indexes:
|
||||
num_seeds[len(list(sub_dir.glob("arch-{:}-seed-*.pth".format(arch_index))))] += 1
|
||||
print(
|
||||
"{:} There are {:5d} architectures that have been evaluated ({:} in total).".format(
|
||||
time_string(), num_evaluated_arch, meta_num_archs
|
||||
)
|
||||
)
|
||||
for key in sorted(list(num_seeds.keys())):
|
||||
print(
|
||||
"{:} There are {:5d} architectures that are evaluated {:} times.".format(time_string(), num_seeds[key], key)
|
||||
)
|
||||
|
||||
to_save_simply = save_dir / 'simplifies'
|
||||
to_save_allarc = save_dir / 'simplifies' / 'architectures'
|
||||
if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
|
||||
if not to_save_allarc.exists(): to_save_allarc.mkdir(parents=True, exist_ok=True)
|
||||
dataloader_dict = GET_DataLoaders(6)
|
||||
|
||||
assert (save_dir / target_dir) in subdir2archs, 'can not find {:}'.format(target_dir)
|
||||
arch2infos, datasets = {}, ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
|
||||
evaluated_indexes = set()
|
||||
target_directory = save_dir / target_dir
|
||||
target_less_dir = save_dir / '{:}-LESS'.format(target_dir)
|
||||
arch_indexes = subdir2archs[ target_directory ]
|
||||
num_seeds = defaultdict(lambda: 0)
|
||||
end_time = time.time()
|
||||
arch_time = AverageMeter()
|
||||
for idx, arch_index in enumerate(arch_indexes):
|
||||
checkpoints = list(target_directory.glob('arch-{:}-seed-*.pth'.format(arch_index)))
|
||||
ckps_less = list(target_less_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))
|
||||
# create the arch info for each architecture
|
||||
try:
|
||||
arch_info_full = account_one_arch(arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict)
|
||||
arch_info_less = account_one_arch(arch_index, meta_archs[int(arch_index)], ckps_less, ['cifar10-valid'], dataloader_dict)
|
||||
num_seeds[ len(checkpoints) ] += 1
|
||||
except:
|
||||
print('Loading {:} failed, : {:}'.format(arch_index, checkpoints))
|
||||
continue
|
||||
assert int(arch_index) not in evaluated_indexes, 'conflict arch-index : {:}'.format(arch_index)
|
||||
assert 0 <= int(arch_index) < len(meta_archs), 'invalid arch-index {:} (not found in meta_archs)'.format(arch_index)
|
||||
arch_info = {'full': arch_info_full, 'less': arch_info_less}
|
||||
evaluated_indexes.add( int(arch_index) )
|
||||
arch2infos[int(arch_index)] = arch_info
|
||||
torch.save({'full': arch_info_full.state_dict(),
|
||||
'less': arch_info_less.state_dict()}, to_save_allarc / '{:}-FULL.pth'.format(arch_index))
|
||||
arch_info['full'].clear_params()
|
||||
arch_info['less'].clear_params()
|
||||
torch.save({'full': arch_info_full.state_dict(),
|
||||
'less': arch_info_less.state_dict()}, to_save_allarc / '{:}-SIMPLE.pth'.format(arch_index))
|
||||
# measure elapsed time
|
||||
arch_time.update(time.time() - end_time)
|
||||
end_time = time.time()
|
||||
need_time = '{:}'.format( convert_secs2time(arch_time.avg * (len(arch_indexes)-idx-1), True) )
|
||||
print('{:} {:} [{:03d}/{:03d}] : {:} still need {:}'.format(time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time))
|
||||
# measure time
|
||||
xstrs = ['{:}:{:03d}'.format(key, num_seeds[key]) for key in sorted( list( num_seeds.keys() ) ) ]
|
||||
print('{:} {:} done : {:}'.format(time_string(), target_dir, xstrs))
|
||||
final_infos = {'meta_archs' : meta_archs,
|
||||
'total_archs': meta_num_archs,
|
||||
'basestr' : basestr,
|
||||
'arch2infos' : arch2infos,
|
||||
'evaluated_indexes': evaluated_indexes}
|
||||
save_file_name = to_save_simply / '{:}.pth'.format(target_dir)
|
||||
torch.save(final_infos, save_file_name)
|
||||
print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
|
||||
to_save_simply = save_dir / "simplifies"
|
||||
to_save_allarc = save_dir / "simplifies" / "architectures"
|
||||
if not to_save_simply.exists():
|
||||
to_save_simply.mkdir(parents=True, exist_ok=True)
|
||||
if not to_save_allarc.exists():
|
||||
to_save_allarc.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
assert (save_dir / target_dir) in subdir2archs, "can not find {:}".format(target_dir)
|
||||
arch2infos, datasets = {}, ("cifar10-valid", "cifar10", "cifar100", "ImageNet16-120")
|
||||
evaluated_indexes = set()
|
||||
target_directory = save_dir / target_dir
|
||||
target_less_dir = save_dir / "{:}-LESS".format(target_dir)
|
||||
arch_indexes = subdir2archs[target_directory]
|
||||
num_seeds = defaultdict(lambda: 0)
|
||||
end_time = time.time()
|
||||
arch_time = AverageMeter()
|
||||
for idx, arch_index in enumerate(arch_indexes):
|
||||
checkpoints = list(target_directory.glob("arch-{:}-seed-*.pth".format(arch_index)))
|
||||
ckps_less = list(target_less_dir.glob("arch-{:}-seed-*.pth".format(arch_index)))
|
||||
# create the arch info for each architecture
|
||||
try:
|
||||
arch_info_full = account_one_arch(
|
||||
arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict
|
||||
)
|
||||
arch_info_less = account_one_arch(
|
||||
arch_index, meta_archs[int(arch_index)], ckps_less, ["cifar10-valid"], dataloader_dict
|
||||
)
|
||||
num_seeds[len(checkpoints)] += 1
|
||||
except:
|
||||
print("Loading {:} failed, : {:}".format(arch_index, checkpoints))
|
||||
continue
|
||||
assert int(arch_index) not in evaluated_indexes, "conflict arch-index : {:}".format(arch_index)
|
||||
assert 0 <= int(arch_index) < len(meta_archs), "invalid arch-index {:} (not found in meta_archs)".format(
|
||||
arch_index
|
||||
)
|
||||
arch_info = {"full": arch_info_full, "less": arch_info_less}
|
||||
evaluated_indexes.add(int(arch_index))
|
||||
arch2infos[int(arch_index)] = arch_info
|
||||
torch.save(
|
||||
{"full": arch_info_full.state_dict(), "less": arch_info_less.state_dict()},
|
||||
to_save_allarc / "{:}-FULL.pth".format(arch_index),
|
||||
)
|
||||
arch_info["full"].clear_params()
|
||||
arch_info["less"].clear_params()
|
||||
torch.save(
|
||||
{"full": arch_info_full.state_dict(), "less": arch_info_less.state_dict()},
|
||||
to_save_allarc / "{:}-SIMPLE.pth".format(arch_index),
|
||||
)
|
||||
# measure elapsed time
|
||||
arch_time.update(time.time() - end_time)
|
||||
end_time = time.time()
|
||||
need_time = "{:}".format(convert_secs2time(arch_time.avg * (len(arch_indexes) - idx - 1), True))
|
||||
print(
|
||||
"{:} {:} [{:03d}/{:03d}] : {:} still need {:}".format(
|
||||
time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time
|
||||
)
|
||||
)
|
||||
# measure time
|
||||
xstrs = ["{:}:{:03d}".format(key, num_seeds[key]) for key in sorted(list(num_seeds.keys()))]
|
||||
print("{:} {:} done : {:}".format(time_string(), target_dir, xstrs))
|
||||
final_infos = {
|
||||
"meta_archs": meta_archs,
|
||||
"total_archs": meta_num_archs,
|
||||
"basestr": basestr,
|
||||
"arch2infos": arch2infos,
|
||||
"evaluated_indexes": evaluated_indexes,
|
||||
}
|
||||
save_file_name = to_save_simply / "{:}.pth".format(target_dir)
|
||||
torch.save(final_infos, save_file_name)
|
||||
print(
|
||||
"Save {:} / {:} architecture results into {:}.".format(len(evaluated_indexes), meta_num_archs, save_file_name)
|
||||
)
|
||||
|
||||
|
||||
def merge_all(save_dir, meta_file, basestr):
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs']
|
||||
meta_num_archs = meta_infos['total']
|
||||
meta_max_node = meta_infos['max_node']
|
||||
assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
|
||||
meta_infos = torch.load(meta_file, map_location="cpu")
|
||||
meta_archs = meta_infos["archs"]
|
||||
meta_num_archs = meta_infos["total"]
|
||||
meta_max_node = meta_infos["max_node"]
|
||||
assert meta_num_archs == len(meta_archs), "invalid number of archs : {:} vs {:}".format(
|
||||
meta_num_archs, len(meta_archs)
|
||||
)
|
||||
|
||||
sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
|
||||
print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
|
||||
for index, sub_dir in enumerate(sub_model_dirs):
|
||||
arch_info_files = sorted( list(sub_dir.glob('arch-*-seed-*.pth') ) )
|
||||
print ('The {:02d}/{:02d}-th directory : {:} : {:} runs.'.format(index, len(sub_model_dirs), sub_dir, len(arch_info_files)))
|
||||
|
||||
arch2infos, evaluated_indexes = dict(), set()
|
||||
for IDX, sub_dir in enumerate(sub_model_dirs):
|
||||
ckp_path = sub_dir.parent / 'simplifies' / '{:}.pth'.format(sub_dir.name)
|
||||
if ckp_path.exists():
|
||||
sub_ckps = torch.load(ckp_path, map_location='cpu')
|
||||
assert sub_ckps['total_archs'] == meta_num_archs and sub_ckps['basestr'] == basestr
|
||||
xarch2infos = sub_ckps['arch2infos']
|
||||
xevalindexs = sub_ckps['evaluated_indexes']
|
||||
for eval_index in xevalindexs:
|
||||
assert eval_index not in evaluated_indexes and eval_index not in arch2infos
|
||||
#arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
|
||||
arch2infos[eval_index] = {'full': xarch2infos[eval_index]['full'].state_dict(),
|
||||
'less': xarch2infos[eval_index]['less'].state_dict()}
|
||||
evaluated_indexes.add( eval_index )
|
||||
print ('{:} [{:03d}/{:03d}] merge data from {:} with {:} models.'.format(time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)))
|
||||
sub_model_dirs = sorted(list(save_dir.glob("*-*-{:}".format(basestr))))
|
||||
print("{:} find {:} directories used to save checkpoints".format(time_string(), len(sub_model_dirs)))
|
||||
for index, sub_dir in enumerate(sub_model_dirs):
|
||||
arch_info_files = sorted(list(sub_dir.glob("arch-*-seed-*.pth")))
|
||||
print(
|
||||
"The {:02d}/{:02d}-th directory : {:} : {:} runs.".format(
|
||||
index, len(sub_model_dirs), sub_dir, len(arch_info_files)
|
||||
)
|
||||
)
|
||||
|
||||
arch2infos, evaluated_indexes = dict(), set()
|
||||
for IDX, sub_dir in enumerate(sub_model_dirs):
|
||||
ckp_path = sub_dir.parent / "simplifies" / "{:}.pth".format(sub_dir.name)
|
||||
if ckp_path.exists():
|
||||
sub_ckps = torch.load(ckp_path, map_location="cpu")
|
||||
assert sub_ckps["total_archs"] == meta_num_archs and sub_ckps["basestr"] == basestr
|
||||
xarch2infos = sub_ckps["arch2infos"]
|
||||
xevalindexs = sub_ckps["evaluated_indexes"]
|
||||
for eval_index in xevalindexs:
|
||||
assert eval_index not in evaluated_indexes and eval_index not in arch2infos
|
||||
# arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
|
||||
arch2infos[eval_index] = {
|
||||
"full": xarch2infos[eval_index]["full"].state_dict(),
|
||||
"less": xarch2infos[eval_index]["less"].state_dict(),
|
||||
}
|
||||
evaluated_indexes.add(eval_index)
|
||||
print(
|
||||
"{:} [{:03d}/{:03d}] merge data from {:} with {:} models.".format(
|
||||
time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)
|
||||
)
|
||||
)
|
||||
else:
|
||||
raise ValueError("Can not find {:}".format(ckp_path))
|
||||
# print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
|
||||
|
||||
evaluated_indexes = sorted(list(evaluated_indexes))
|
||||
print("Finally, there are {:} architectures that have been trained and evaluated.".format(len(evaluated_indexes)))
|
||||
|
||||
to_save_simply = save_dir / "simplifies"
|
||||
if not to_save_simply.exists():
|
||||
to_save_simply.mkdir(parents=True, exist_ok=True)
|
||||
final_infos = {
|
||||
"meta_archs": meta_archs,
|
||||
"total_archs": meta_num_archs,
|
||||
"arch2infos": arch2infos,
|
||||
"evaluated_indexes": evaluated_indexes,
|
||||
}
|
||||
save_file_name = to_save_simply / "{:}-final-infos.pth".format(basestr)
|
||||
torch.save(final_infos, save_file_name)
|
||||
print(
|
||||
"Save {:} / {:} architecture results into {:}.".format(len(evaluated_indexes), meta_num_archs, save_file_name)
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="NAS-BENCH-201", formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
parser.add_argument("--mode", type=str, choices=["cal", "merge"], help="The running mode for this script.")
|
||||
parser.add_argument(
|
||||
"--base_save_dir",
|
||||
type=str,
|
||||
default="./output/NAS-BENCH-201-4",
|
||||
help="The base-name of folder to save checkpoints and log.",
|
||||
)
|
||||
parser.add_argument("--target_dir", type=str, help="The target directory.")
|
||||
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.")
|
||||
args = parser.parse_args()
|
||||
|
||||
save_dir = Path(args.base_save_dir)
|
||||
meta_path = save_dir / "meta-node-{:}.pth".format(args.max_node)
|
||||
assert save_dir.exists(), "invalid save dir path : {:}".format(save_dir)
|
||||
assert meta_path.exists(), "invalid saved meta path : {:}".format(meta_path)
|
||||
print("start the statistics of our nas-benchmark from {:} using {:}.".format(save_dir, args.target_dir))
|
||||
basestr = "C{:}-N{:}".format(args.channel, args.num_cells)
|
||||
|
||||
if args.mode == "cal":
|
||||
simplify(save_dir, meta_path, basestr, args.target_dir)
|
||||
elif args.mode == "merge":
|
||||
merge_all(save_dir, meta_path, basestr)
|
||||
else:
|
||||
raise ValueError('Can not find {:}'.format(ckp_path))
|
||||
#print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
|
||||
|
||||
evaluated_indexes = sorted( list( evaluated_indexes ) )
|
||||
print ('Finally, there are {:} architectures that have been trained and evaluated.'.format(len(evaluated_indexes)))
|
||||
|
||||
to_save_simply = save_dir / 'simplifies'
|
||||
if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
|
||||
final_infos = {'meta_archs' : meta_archs,
|
||||
'total_archs': meta_num_archs,
|
||||
'arch2infos' : arch2infos,
|
||||
'evaluated_indexes': evaluated_indexes}
|
||||
save_file_name = to_save_simply / '{:}-final-infos.pth'.format(basestr)
|
||||
torch.save(final_infos, save_file_name)
|
||||
print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
parser = argparse.ArgumentParser(description='NAS-BENCH-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--mode' , type=str, choices=['cal', 'merge'], help='The running mode for this script.')
|
||||
parser.add_argument('--base_save_dir', type=str, default='./output/NAS-BENCH-201-4', help='The base-name of folder to save checkpoints and log.')
|
||||
parser.add_argument('--target_dir' , type=str, help='The target directory.')
|
||||
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.')
|
||||
args = parser.parse_args()
|
||||
|
||||
save_dir = Path(args.base_save_dir)
|
||||
meta_path = save_dir / 'meta-node-{:}.pth'.format(args.max_node)
|
||||
assert save_dir.exists(), 'invalid save dir path : {:}'.format(save_dir)
|
||||
assert meta_path.exists(), 'invalid saved meta path : {:}'.format(meta_path)
|
||||
print ('start the statistics of our nas-benchmark from {:} using {:}.'.format(save_dir, args.target_dir))
|
||||
basestr = 'C{:}-N{:}'.format(args.channel, args.num_cells)
|
||||
|
||||
if args.mode == 'cal':
|
||||
simplify(save_dir, meta_path, basestr, args.target_dir)
|
||||
elif args.mode == 'merge':
|
||||
merge_all(save_dir, meta_path, basestr)
|
||||
else:
|
||||
raise ValueError('invalid mode : {:}'.format(args.mode))
|
||||
raise ValueError("invalid mode : {:}".format(args.mode))
|
||||
|
||||
Reference in New Issue
Block a user