#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
import os, time, copy, torch, pathlib
import datasets
from config_utils import load_config
from procedures import prepare_seed, get_optim_scheduler
from utils import get_model_infos, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_cell_based_tiny_net
__all__ = ["evaluate_for_seed", "pure_evaluate", "get_nas_bench_loaders"]
def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
latencies, device = [], torch.cuda.current_device()
network.eval()
with torch.no_grad():
end = time.time()
for i, (inputs, targets) in enumerate(xloader):
targets = targets.cuda(device=device, non_blocking=True)
inputs = inputs.cuda(device=device, non_blocking=True)
data_time.update(time.time() - end)
# forward
features, logits = network(inputs)
loss = criterion(logits, targets)
batch_time.update(time.time() - end)
if batch is None or batch == inputs.size(0):
batch = inputs.size(0)
latencies.append(batch_time.val - data_time.val)
# record loss and accuracy
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
end = time.time()
if len(latencies) > 2:
latencies = latencies[1:]
return losses.avg, top1.avg, top5.avg, latencies
def procedure(xloader, network, criterion, scheduler, optimizer, mode: str):
losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
if mode == "train":
network.train()
elif mode == "valid":
network.eval()
else:
raise ValueError("The mode is not right : {:}".format(mode))
device = torch.cuda.current_device()
data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
for i, (inputs, targets) in enumerate(xloader):
if mode == "train":
scheduler.update(None, 1.0 * i / len(xloader))
targets = targets.cuda(device=device, non_blocking=True)
if mode == "train":
optimizer.zero_grad()
# forward
features, logits = network(inputs)
loss = criterion(logits, targets)
# backward
if mode == "train":
loss.backward()
optimizer.step()
# record loss and accuracy
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
# count time
batch_time.update(time.time() - end)
end = time.time()
return losses.avg, top1.avg, top5.avg, batch_time.sum
def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed: int, logger):
prepare_seed(seed) # random seed
net = get_cell_based_tiny_net(arch_config)
# net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
flop, param = get_model_infos(net, opt_config.xshape)
logger.log("Network : {:}".format(net.get_message()), False)
logger.log("{:} Seed-------------------------- {:} --------------------------".format(time_string(), seed))
logger.log("FLOP = {:} MB, Param = {:} MB".format(flop, param))
# train and valid
optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), opt_config)
default_device = torch.cuda.current_device()
network = torch.nn.DataParallel(net, device_ids=[default_device]).cuda(device=default_device)
criterion = criterion.cuda(device=default_device)
# start training
start_time, epoch_time, total_epoch = time.time(), AverageMeter(), opt_config.epochs + opt_config.warmup
train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {}
train_times, valid_times, lrs = {}, {}, {}
for epoch in range(total_epoch):
scheduler.update(epoch, 0.0)
lr = min(scheduler.get_lr())
train_loss, train_acc1, train_acc5, train_tm = procedure(
train_loader, network, criterion, scheduler, optimizer, "train"
)
train_losses[epoch] = train_loss
train_acc1es[epoch] = train_acc1
train_acc5es[epoch] = train_acc5
train_times[epoch] = train_tm
lrs[epoch] = lr
with torch.no_grad():
for key, xloder in valid_loaders.items():
valid_loss, valid_acc1, valid_acc5, valid_tm = procedure(
xloder, network, criterion, None, None, "valid"
)
valid_losses["{:}@{:}".format(key, epoch)] = valid_loss
valid_acc1es["{:}@{:}".format(key, epoch)] = valid_acc1
valid_acc5es["{:}@{:}".format(key, epoch)] = valid_acc5
valid_times["{:}@{:}".format(key, epoch)] = valid_tm
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
need_time = "Time Left: {:}".format(convert_secs2time(epoch_time.avg * (total_epoch - epoch - 1), True))
logger.log(
"{:} {:} epoch={:03d}/{:03d} :: Train [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%] Valid [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%], lr={:}".format(
time_string(),
need_time,
epoch,
total_epoch,
train_loss,
train_acc1,
train_acc5,
valid_loss,
valid_acc1,
valid_acc5,
lr,
)
)
info_seed = {
"flop": flop,
"param": param,
"arch_config": arch_config._asdict(),
"opt_config": opt_config._asdict(),
"total_epoch": total_epoch,
"train_losses": train_losses,
"train_acc1es": train_acc1es,
"train_acc5es": train_acc5es,
"train_times": train_times,
"valid_losses": valid_losses,
"valid_acc1es": valid_acc1es,
"valid_acc5es": valid_acc5es,
"valid_times": valid_times,
"learning_rates": lrs,
"net_state_dict": net.state_dict(),
"net_string": "{:}".format(net),
"finish-train": True,
}
return info_seed
def get_nas_bench_loaders(workers):
torch.set_num_threads(workers)
root_dir = (pathlib.Path(__file__).parent / ".." / "..").resolve()
torch_dir = pathlib.Path(os.environ["TORCH_HOME"])
# cifar
cifar_config_path = root_dir / "configs" / "nas-benchmark" / "CIFAR.config"
cifar_config = load_config(cifar_config_path, None, None)
get_datasets = datasets.get_datasets # a function to return the dataset
break_line = "-" * 150
print("{:} Create data-loader for all datasets".format(time_string()))
print(break_line)
TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets("cifar10", str(torch_dir / "cifar.python"), -1)
print(
"original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num
)
)
cifar10_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar-split.txt", None, None)
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,
]
temp_dataset = copy.deepcopy(TRAIN_CIFAR10)
temp_dataset.transform = VALID_CIFAR10.transform
# data loader
trainval_cifar10_loader = torch.utils.data.DataLoader(
TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
)
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,
)
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,
)
test__cifar10_loader = torch.utils.data.DataLoader(
VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True
)
print(
"CIFAR-10 : trval-loader has {:3d} batch with {:} per batch".format(
len(trainval_cifar10_loader), cifar_config.batch_size
)
)
print(
"CIFAR-10 : train-loader has {:3d} batch with {:} per batch".format(
len(train_cifar10_loader), cifar_config.batch_size
)
)
print(
"CIFAR-10 : valid-loader has {:3d} batch with {:} per batch".format(
len(valid_cifar10_loader), cifar_config.batch_size
)
)
print(
"CIFAR-10 : test--loader has {:3d} batch with {:} per batch".format(
len(test__cifar10_loader), cifar_config.batch_size
)
)
print(break_line)
# CIFAR-100
TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets("cifar100", str(torch_dir / "cifar.python"), -1)
print(
"original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes".format(
len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num
)
)
cifar100_splits = load_config(root_dir / "configs" / "nas-benchmark" / "cifar100-test-split.txt", None, None)
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,
]
train_cifar100_loader = torch.utils.data.DataLoader(
TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
)
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,
)
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,
)
print("CIFAR-100 : train-loader has {:3d} batch".format(len(train_cifar100_loader)))
print("CIFAR-100 : valid-loader has {:3d} batch".format(len(valid_cifar100_loader)))
print("CIFAR-100 : test--loader has {:3d} batch".format(len(test__cifar100_loader)))
print(break_line)
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