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can train aircraft now

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This commit is contained in:
Mhrooz 2024-10-14 23:19:28 +02:00
parent bb33ca9a68
commit ef2608bb42
2 changed files with 76 additions and 37 deletions
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10
exps/NAS-Bench-201/functions.py
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@ -2,11 +2,11 @@
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
import time, torch
from procedures import prepare_seed, get_optim_scheduler
from utils import get_model_infos, obtain_accuracy
from config_utils import dict2config
from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_cell_based_tiny_net
from xautodl.procedures import prepare_seed, get_optim_scheduler
from xautodl.utils import get_model_infos, obtain_accuracy
from xautodl.config_utils import dict2config
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
from xautodl.models import get_cell_based_tiny_net
__all__ = ["evaluate_for_seed", "pure_evaluate"]

103
exps/NAS-Bench-201/main.py
View File

@ -16,8 +16,9 @@ from xautodl.procedures import get_machine_info
from xautodl.datasets import get_datasets
from xautodl.log_utils import Logger, AverageMeter, time_string, convert_secs2time
from xautodl.models import CellStructure, CellArchitectures, get_search_spaces
from xautodl.functions import evaluate_for_seed
from functions import evaluate_for_seed
from torchvision import datasets, transforms
def evaluate_all_datasets(
arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger
@ -46,47 +47,85 @@ def evaluate_all_datasets(
split_info = load_config(
"configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
)
elif dataset.startswith("aircraft"):
if use_less:
config_path = "configs/nas-benchmark/LESS.config"
else:
config_path = "configs/nas-benchmark/aircraft.config"
split_info = load_config(
"configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
)
else:
raise ValueError("invalid dataset : {:}".format(dataset))
config = load_config(
config_path, {"class_num": class_num, "xshape": xshape}, logger
)
# check whether use splited validation set
# if dataset == 'aircraft':
# split = True
if bool(split):
assert dataset == "cifar10"
ValLoaders = {
"ori-test": torch.utils.data.DataLoader(
valid_data,
if dataset == "cifar10" or dataset == "cifar100":
assert dataset == "cifar10"
ValLoaders = {
"ori-test": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
shuffle=False,
num_workers=workers,
pin_memory=True,
)
}
assert len(train_data) == len(split_info.train) + len(
split_info.valid
), "invalid length : {:} vs {:} + {:}".format(
len(train_data), len(split_info.train), len(split_info.valid)
)
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
# data loader
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=config.batch_size,
shuffle=False,
sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train),
num_workers=workers,
pin_memory=True,
)
}
assert len(train_data) == len(split_info.train) + len(
split_info.valid
), "invalid length : {:} vs {:} + {:}".format(
len(train_data), len(split_info.train), len(split_info.valid)
)
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
# data loader
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train),
num_workers=workers,
pin_memory=True,
)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid),
num_workers=workers,
pin_memory=True,
)
ValLoaders["x-valid"] = valid_loader
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid),
num_workers=workers,
pin_memory=True,
)
ValLoaders["x-valid"] = valid_loader
elif dataset == "aircraft":
ValLoaders = {
"ori-test": torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
shuffle=False,
num_workers=workers,
pin_memory=True,
)
}
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
# 使用 DataLoader
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train),
num_workers=workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid),
num_workers=workers,
pin_memory=True)
else:
# data loader
train_loader = torch.utils.data.DataLoader(
@ -103,7 +142,7 @@ def evaluate_all_datasets(
num_workers=workers,
pin_memory=True,
)
if dataset == "cifar10":
if dataset == "cifar10" or dataset == "aircraft":
ValLoaders = {"ori-test": valid_loader}
elif dataset == "cifar100":
cifar100_splits = load_config(