add autodl
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665
AutoDL-Projects/exps/NAS-Bench-201/statistics.py
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665
AutoDL-Projects/exps/NAS-Bench-201/statistics.py
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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#####################################################
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import os, sys, time, argparse, collections
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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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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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from xautodl.config_utils import load_config, dict2config
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from xautodl.datasets import get_datasets
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# NAS-Bench-201 related module or function
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from xautodl.models import CellStructure, get_cell_based_tiny_net
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from xautodl.procedures import bench_pure_evaluate as pure_evaluate
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from nas_201_api import ArchResults, ResultsCount
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def create_result_count(used_seed, dataset, arch_config, results, dataloader_dict):
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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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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"],
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results["train_acc5es"],
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results["train_losses"],
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results["train_times"],
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)
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xresult.update_eval(
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results["valid_acc1es"], results["valid_losses"], results["valid_times"]
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)
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else:
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if dataset == "cifar10-valid":
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xresult.update_OLD_eval(
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"x-valid", results["valid_acc1es"], results["valid_losses"]
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)
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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(
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"ori-test",
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{results["total_epoch"] - 1: top1},
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{results["total_epoch"] - 1: loss},
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)
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xresult.update_latency(latencies)
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elif dataset == "cifar10":
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xresult.update_OLD_eval(
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"ori-test", results["valid_acc1es"], results["valid_losses"]
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)
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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(
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"ori-test", results["valid_acc1es"], results["valid_losses"]
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)
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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(
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"x-valid",
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{results["total_epoch"] - 1: top1},
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{results["total_epoch"] - 1: loss},
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)
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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(
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"x-test",
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{results["total_epoch"] - 1: top1},
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{results["total_epoch"] - 1: loss},
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)
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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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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 (
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dataset in checkpoint
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), "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[
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"finish-train"
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], "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(
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used_seed, dataset, arch_config, results, dataloader_dict
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)
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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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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(
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"cifar10", str(torch_dir / "cifar.python"), -1
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)
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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(
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root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None
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)
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assert cifar10_splits.train[:10] == [
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0,
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5,
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7,
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11,
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13,
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15,
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16,
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17,
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20,
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24,
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] 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,
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batch_size=cifar_config.batch_size,
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shuffle=True,
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num_workers=workers,
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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,
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batch_size=cifar_config.batch_size,
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shuffle=False,
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num_workers=workers,
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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(
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"cifar100", str(torch_dir / "cifar.python"), -1
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)
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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(
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root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None
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)
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assert cifar100_splits.xvalid[:10] == [
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1,
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3,
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4,
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5,
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8,
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10,
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13,
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14,
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15,
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16,
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] 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,
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batch_size=cifar_config.batch_size,
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shuffle=True,
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num_workers=workers,
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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(
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"CIFAR-100 : train-loader has {:3d} batch".format(len(train_cifar100_loader))
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)
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print(
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"CIFAR-100 : valid-loader has {:3d} batch".format(len(valid_cifar100_loader))
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)
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print(
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"CIFAR-100 : test--loader has {:3d} batch".format(len(test__cifar100_loader))
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)
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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(
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"ImageNet16-120", str(torch_dir / "cifar.python" / "ImageNet16"), -1
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)
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print(
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"original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
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len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num
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)
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)
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imagenet_splits = load_config(
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root_dir / "configs" / "nas-benchmark" / "imagenet-16-120-test-split.txt",
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None,
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None,
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)
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assert imagenet_splits.xvalid[:10] == [
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1,
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2,
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3,
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6,
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7,
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8,
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9,
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12,
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16,
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18,
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] and imagenet_splits.xtest[:10] == [
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0,
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4,
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5,
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10,
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11,
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13,
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14,
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15,
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17,
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20,
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]
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train_imagenet_loader = torch.utils.data.DataLoader(
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TRAIN_ImageNet16_120,
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batch_size=imagenet16_config.batch_size,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
|
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)
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valid_imagenet_loader = torch.utils.data.DataLoader(
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VALID_ImageNet16_120,
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batch_size=imagenet16_config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_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__imagenet_loader = torch.utils.data.DataLoader(
|
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VALID_ImageNet16_120,
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batch_size=imagenet16_config.batch_size,
|
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sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest),
|
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num_workers=workers,
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pin_memory=True,
|
||||
)
|
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print(
|
||||
"ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch".format(
|
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len(train_imagenet_loader), imagenet16_config.batch_size
|
||||
)
|
||||
)
|
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print(
|
||||
"ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch".format(
|
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len(valid_imagenet_loader), imagenet16_config.batch_size
|
||||
)
|
||||
)
|
||||
print(
|
||||
"ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch".format(
|
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len(test__imagenet_loader), imagenet16_config.batch_size
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||||
)
|
||||
)
|
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|
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# 'cifar10', 'cifar100', 'ImageNet16-120'
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loaders = {
|
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"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))
|
||||
|
||||
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
|
||||
)
|
||||
)
|
||||
|
||||
dataloader_dict = GET_DataLoaders(6)
|
||||
|
||||
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))
|
||||
|
||||
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("invalid mode : {:}".format(args.mode))
|
||||
Reference in New Issue
Block a user