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xautodl/exps/NATS-algos/search-cell.py
D-X-Y 8d0799dfb1 To answer issue #119
2022-03-20 23:12:12 -07:00

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##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
######################################################################################
# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --rand_seed 777
# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --drop_path_rate 0.3
# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1
####
# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v2 --rand_seed 777
# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v2
# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v2
####
# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo gdas --rand_seed 777
# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo gdas
# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo gdas
####
# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo setn --rand_seed 777
# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo setn
# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo setn
####
# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo random --rand_seed 777
# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo random
# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo random
####
# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
####
# The following scripts are added in 20 Mar 2022
# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo gdas_v1 --rand_seed 777
######################################################################################
import os, sys, time, random, argparse
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
from xautodl.config_utils import load_config, dict2config, configure2str
from xautodl.datasets import get_datasets, get_nas_search_loaders
from xautodl.procedures import (
prepare_seed,
prepare_logger,
save_checkpoint,
copy_checkpoint,
get_optim_scheduler,
)
from xautodl.utils import count_parameters_in_MB, obtain_accuracy
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
from xautodl.models import get_cell_based_tiny_net, get_search_spaces
from nats_bench import create
# The following three functions are used for DARTS-V2
def _concat(xs):
return torch.cat([x.view(-1) for x in xs])
def _hessian_vector_product(
vector, network, criterion, base_inputs, base_targets, r=1e-2
):
R = r / _concat(vector).norm()
for p, v in zip(network.weights, vector):
p.data.add_(R, v)
_, logits = network(base_inputs)
loss = criterion(logits, base_targets)
grads_p = torch.autograd.grad(loss, network.alphas)
for p, v in zip(network.weights, vector):
p.data.sub_(2 * R, v)
_, logits = network(base_inputs)
loss = criterion(logits, base_targets)
grads_n = torch.autograd.grad(loss, network.alphas)
for p, v in zip(network.weights, vector):
p.data.add_(R, v)
return [(x - y).div_(2 * R) for x, y in zip(grads_p, grads_n)]
def backward_step_unrolled(
network,
criterion,
base_inputs,
base_targets,
w_optimizer,
arch_inputs,
arch_targets,
):
# _compute_unrolled_model
_, logits = network(base_inputs)
loss = criterion(logits, base_targets)
LR, WD, momentum = (
w_optimizer.param_groups[0]["lr"],
w_optimizer.param_groups[0]["weight_decay"],
w_optimizer.param_groups[0]["momentum"],
)
with torch.no_grad():
theta = _concat(network.weights)
try:
moment = _concat(
w_optimizer.state[v]["momentum_buffer"] for v in network.weights
)
moment = moment.mul_(momentum)
except:
moment = torch.zeros_like(theta)
dtheta = _concat(torch.autograd.grad(loss, network.weights)) + WD * theta
params = theta.sub(LR, moment + dtheta)
unrolled_model = deepcopy(network)
model_dict = unrolled_model.state_dict()
new_params, offset = {}, 0
for k, v in network.named_parameters():
if "arch_parameters" in k:
continue
v_length = np.prod(v.size())
new_params[k] = params[offset : offset + v_length].view(v.size())
offset += v_length
model_dict.update(new_params)
unrolled_model.load_state_dict(model_dict)
unrolled_model.zero_grad()
_, unrolled_logits = unrolled_model(arch_inputs)
unrolled_loss = criterion(unrolled_logits, arch_targets)
unrolled_loss.backward()
dalpha = unrolled_model.arch_parameters.grad
vector = [v.grad.data for v in unrolled_model.weights]
[implicit_grads] = _hessian_vector_product(
vector, network, criterion, base_inputs, base_targets
)
dalpha.data.sub_(LR, implicit_grads.data)
if network.arch_parameters.grad is None:
network.arch_parameters.grad = deepcopy(dalpha)
else:
network.arch_parameters.grad.data.copy_(dalpha.data)
return unrolled_loss.detach(), unrolled_logits.detach()
def search_func(
xloader,
network,
criterion,
scheduler,
w_optimizer,
a_optimizer,
epoch_str,
print_freq,
algo,
logger,
):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
end = time.time()
network.train()
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
xloader
):
scheduler.update(None, 1.0 * step / len(xloader))
base_inputs = base_inputs.cuda(non_blocking=True)
arch_inputs = arch_inputs.cuda(non_blocking=True)
base_targets = base_targets.cuda(non_blocking=True)
arch_targets = arch_targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# Update the weights
if algo == "setn":
sampled_arch = network.dync_genotype(True)
network.set_cal_mode("dynamic", sampled_arch)
elif algo == "gdas":
network.set_cal_mode("gdas", None)
elif algo == "gdas_v1":
network.set_cal_mode("gdas_v1", None)
elif algo.startswith("darts"):
network.set_cal_mode("joint", None)
elif algo == "random":
network.set_cal_mode("urs", None)
elif algo == "enas":
with torch.no_grad():
network.controller.eval()
_, _, sampled_arch = network.controller()
network.set_cal_mode("dynamic", sampled_arch)
else:
raise ValueError("Invalid algo name : {:}".format(algo))
network.zero_grad()
_, logits = network(base_inputs)
base_loss = criterion(logits, base_targets)
base_loss.backward()
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(
logits.data, base_targets.data, topk=(1, 5)
)
base_losses.update(base_loss.item(), base_inputs.size(0))
base_top1.update(base_prec1.item(), base_inputs.size(0))
base_top5.update(base_prec5.item(), base_inputs.size(0))
# update the architecture-weight
if algo == "setn":
network.set_cal_mode("joint")
elif algo == "gdas":
network.set_cal_mode("gdas", None)
elif algo == "gdas_v1":
network.set_cal_mode("gdas_v1", None)
elif algo.startswith("darts"):
network.set_cal_mode("joint", None)
elif algo == "random":
network.set_cal_mode("urs", None)
elif algo != "enas":
raise ValueError("Invalid algo name : {:}".format(algo))
network.zero_grad()
if algo == "darts-v2":
arch_loss, logits = backward_step_unrolled(
network,
criterion,
base_inputs,
base_targets,
w_optimizer,
arch_inputs,
arch_targets,
)
a_optimizer.step()
elif algo == "random" or algo == "enas":
with torch.no_grad():
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
else:
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
arch_loss.backward()
a_optimizer.step()
# record
arch_prec1, arch_prec5 = obtain_accuracy(
logits.data, arch_targets.data, topk=(1, 5)
)
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if step % print_freq == 0 or step + 1 == len(xloader):
Sstr = (
"*SEARCH* "
+ time_string()
+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
)
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
batch_time=batch_time, data_time=data_time
)
Wstr = "Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
loss=base_losses, top1=base_top1, top5=base_top5
)
Astr = "Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
loss=arch_losses, top1=arch_top1, top5=arch_top5
)
logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Astr)
return (
base_losses.avg,
base_top1.avg,
base_top5.avg,
arch_losses.avg,
arch_top1.avg,
arch_top5.avg,
)
def train_controller(
xloader, network, criterion, optimizer, prev_baseline, epoch_str, print_freq, logger
):
# config. (containing some necessary arg)
# baseline: The baseline score (i.e. average val_acc) from the previous epoch
data_time, batch_time = AverageMeter(), AverageMeter()
(
GradnormMeter,
LossMeter,
ValAccMeter,
EntropyMeter,
BaselineMeter,
RewardMeter,
xend,
) = (
AverageMeter(),
AverageMeter(),
AverageMeter(),
AverageMeter(),
AverageMeter(),
AverageMeter(),
time.time(),
)
controller_num_aggregate = 20
controller_train_steps = 50
controller_bl_dec = 0.99
controller_entropy_weight = 0.0001
network.eval()
network.controller.train()
network.controller.zero_grad()
loader_iter = iter(xloader)
for step in range(controller_train_steps * controller_num_aggregate):
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
inputs = inputs.cuda(non_blocking=True)
targets = targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - xend)
log_prob, entropy, sampled_arch = network.controller()
with torch.no_grad():
network.set_cal_mode("dynamic", sampled_arch)
_, logits = network(inputs)
val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
val_top1 = val_top1.view(-1) / 100
reward = val_top1 + controller_entropy_weight * entropy
if prev_baseline is None:
baseline = val_top1
else:
baseline = prev_baseline - (1 - controller_bl_dec) * (
prev_baseline - reward
)
loss = -1 * log_prob * (reward - baseline)
# account
RewardMeter.update(reward.item())
BaselineMeter.update(baseline.item())
ValAccMeter.update(val_top1.item() * 100)
LossMeter.update(loss.item())
EntropyMeter.update(entropy.item())
# Average gradient over controller_num_aggregate samples
loss = loss / controller_num_aggregate
loss.backward(retain_graph=True)
# measure elapsed time
batch_time.update(time.time() - xend)
xend = time.time()
if (step + 1) % controller_num_aggregate == 0:
grad_norm = torch.nn.utils.clip_grad_norm_(
network.controller.parameters(), 5.0
)
GradnormMeter.update(grad_norm)
optimizer.step()
network.controller.zero_grad()
if step % print_freq == 0:
Sstr = (
"*Train-Controller* "
+ time_string()
+ " [{:}][{:03d}/{:03d}]".format(
epoch_str, step, controller_train_steps * controller_num_aggregate
)
)
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
batch_time=batch_time, data_time=data_time
)
Wstr = "[Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Reward {reward.val:.2f} ({reward.avg:.2f})] Baseline {basel.val:.2f} ({basel.avg:.2f})".format(
loss=LossMeter,
top1=ValAccMeter,
reward=RewardMeter,
basel=BaselineMeter,
)
Estr = "Entropy={:.4f} ({:.4f})".format(EntropyMeter.val, EntropyMeter.avg)
logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Estr)
return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg
def get_best_arch(xloader, network, n_samples, algo):
with torch.no_grad():
network.eval()
if algo == "random":
archs, valid_accs = network.return_topK(n_samples, True), []
elif algo == "setn":
archs, valid_accs = network.return_topK(n_samples, False), []
elif algo.startswith("darts") or algo == "gdas" or algo == "gdas_v1":
arch = network.genotype
archs, valid_accs = [arch], []
elif algo == "enas":
archs, valid_accs = [], []
for _ in range(n_samples):
_, _, sampled_arch = network.controller()
archs.append(sampled_arch)
else:
raise ValueError("Invalid algorithm name : {:}".format(algo))
loader_iter = iter(xloader)
for i, sampled_arch in enumerate(archs):
network.set_cal_mode("dynamic", sampled_arch)
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
_, logits = network(inputs.cuda(non_blocking=True))
val_top1, val_top5 = obtain_accuracy(
logits.cpu().data, targets.data, topk=(1, 5)
)
valid_accs.append(val_top1.item())
best_idx = np.argmax(valid_accs)
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
return best_arch, best_valid_acc
def valid_func(xloader, network, criterion, algo, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
end = time.time()
with torch.no_grad():
network.eval()
for step, (arch_inputs, arch_targets) in enumerate(xloader):
arch_targets = arch_targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# prediction
_, logits = network(arch_inputs.cuda(non_blocking=True))
arch_loss = criterion(logits, arch_targets)
# record
arch_prec1, arch_prec5 = obtain_accuracy(
logits.data, arch_targets.data, topk=(1, 5)
)
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
return arch_losses.avg, arch_top1.avg, arch_top5.avg
def main(xargs):
assert torch.cuda.is_available(), "CUDA is not available."
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(
xargs.dataset, xargs.data_path, -1
)
if xargs.overwite_epochs is None:
extra_info = {"class_num": class_num, "xshape": xshape}
else:
extra_info = {
"class_num": class_num,
"xshape": xshape,
"epochs": xargs.overwite_epochs,
}
config = load_config(xargs.config_path, extra_info, logger)
search_loader, train_loader, valid_loader = get_nas_search_loaders(
train_data,
valid_data,
xargs.dataset,
"configs/nas-benchmark/",
(config.batch_size, config.test_batch_size),
xargs.workers,
)
logger.log(
"||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
xargs.dataset, len(search_loader), len(valid_loader), config.batch_size
)
)
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
search_space = get_search_spaces(xargs.search_space, "nats-bench")
model_config = dict2config(
dict(
name="generic",
C=xargs.channel,
N=xargs.num_cells,
max_nodes=xargs.max_nodes,
num_classes=class_num,
space=search_space,
affine=bool(xargs.affine),
track_running_stats=bool(xargs.track_running_stats),
),
None,
)
logger.log("search space : {:}".format(search_space))
logger.log("model config : {:}".format(model_config))
search_model = get_cell_based_tiny_net(model_config)
search_model.set_algo(xargs.algo)
logger.log("{:}".format(search_model))
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
search_model.weights, config
)
a_optimizer = torch.optim.Adam(
search_model.alphas,
lr=xargs.arch_learning_rate,
betas=(0.5, 0.999),
weight_decay=xargs.arch_weight_decay,
eps=xargs.arch_eps,
)
logger.log("w-optimizer : {:}".format(w_optimizer))
logger.log("a-optimizer : {:}".format(a_optimizer))
logger.log("w-scheduler : {:}".format(w_scheduler))
logger.log("criterion : {:}".format(criterion))
params = count_parameters_in_MB(search_model)
logger.log("The parameters of the search model = {:.2f} MB".format(params))
logger.log("search-space : {:}".format(search_space))
if bool(xargs.use_api):
api = create(None, "topology", fast_mode=True, verbose=False)
else:
api = None
logger.log("{:} create API = {:} done".format(time_string(), api))
last_info, model_base_path, model_best_path = (
logger.path("info"),
logger.path("model"),
logger.path("best"),
)
network, criterion = search_model.cuda(), criterion.cuda() # use a single GPU
last_info, model_base_path, model_best_path = (
logger.path("info"),
logger.path("model"),
logger.path("best"),
)
if last_info.exists(): # automatically resume from previous checkpoint
logger.log(
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
)
last_info = torch.load(last_info)
start_epoch = last_info["epoch"]
checkpoint = torch.load(last_info["last_checkpoint"])
genotypes = checkpoint["genotypes"]
baseline = checkpoint["baseline"]
valid_accuracies = checkpoint["valid_accuracies"]
search_model.load_state_dict(checkpoint["search_model"])
w_scheduler.load_state_dict(checkpoint["w_scheduler"])
w_optimizer.load_state_dict(checkpoint["w_optimizer"])
a_optimizer.load_state_dict(checkpoint["a_optimizer"])
logger.log(
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
last_info, start_epoch
)
)
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = (
0,
{"best": -1},
{-1: network.return_topK(1, True)[0]},
)
baseline = None
# start training
start_time, search_time, epoch_time, total_epoch = (
time.time(),
AverageMeter(),
AverageMeter(),
config.epochs + config.warmup,
)
for epoch in range(start_epoch, total_epoch):
w_scheduler.update(epoch, 0.0)
need_time = "Time Left: {:}".format(
convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
)
epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
logger.log(
"\n[Search the {:}-th epoch] {:}, LR={:}".format(
epoch_str, need_time, min(w_scheduler.get_lr())
)
)
network.set_drop_path(float(epoch + 1) / total_epoch, xargs.drop_path_rate)
if xargs.algo == "gdas" or xargs.algo == "gdas_v1":
network.set_tau(
xargs.tau_max
- (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1)
)
logger.log(
"[RESET tau as : {:} and drop_path as {:}]".format(
network.tau, network.drop_path
)
)
(
search_w_loss,
search_w_top1,
search_w_top5,
search_a_loss,
search_a_top1,
search_a_top5,
) = search_func(
search_loader,
network,
criterion,
w_scheduler,
w_optimizer,
a_optimizer,
epoch_str,
xargs.print_freq,
xargs.algo,
logger,
)
search_time.update(time.time() - start_time)
logger.log(
"[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format(
epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum
)
)
logger.log(
"[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
epoch_str, search_a_loss, search_a_top1, search_a_top5
)
)
if xargs.algo == "enas":
ctl_loss, ctl_acc, baseline, ctl_reward = train_controller(
valid_loader,
network,
criterion,
a_optimizer,
baseline,
epoch_str,
xargs.print_freq,
logger,
)
logger.log(
"[{:}] controller : loss={:}, acc={:}, baseline={:}, reward={:}".format(
epoch_str, ctl_loss, ctl_acc, baseline, ctl_reward
)
)
genotype, temp_accuracy = get_best_arch(
valid_loader, network, xargs.eval_candidate_num, xargs.algo
)
if xargs.algo == "setn" or xargs.algo == "enas":
network.set_cal_mode("dynamic", genotype)
elif xargs.algo == "gdas":
network.set_cal_mode("gdas", None)
elif xargs.algo == "gdas_v1":
network.set_cal_mode("gdas_v1", None)
elif xargs.algo.startswith("darts"):
network.set_cal_mode("joint", None)
elif xargs.algo == "random":
network.set_cal_mode("urs", None)
else:
raise ValueError("Invalid algorithm name : {:}".format(xargs.algo))
logger.log(
"[{:}] - [get_best_arch] : {:} -> {:}".format(
epoch_str, genotype, temp_accuracy
)
)
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
valid_loader, network, criterion, xargs.algo, logger
)
logger.log(
"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}".format(
epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype
)
)
valid_accuracies[epoch] = valid_a_top1
genotypes[epoch] = genotype
logger.log(
"<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch])
)
# save checkpoint
save_path = save_checkpoint(
{
"epoch": epoch + 1,
"args": deepcopy(xargs),
"baseline": baseline,
"search_model": search_model.state_dict(),
"w_optimizer": w_optimizer.state_dict(),
"a_optimizer": a_optimizer.state_dict(),
"w_scheduler": w_scheduler.state_dict(),
"genotypes": genotypes,
"valid_accuracies": valid_accuracies,
},
model_base_path,
logger,
)
last_info = save_checkpoint(
{
"epoch": epoch + 1,
"args": deepcopy(args),
"last_checkpoint": save_path,
},
logger.path("info"),
logger,
)
with torch.no_grad():
logger.log("{:}".format(search_model.show_alphas()))
if api is not None:
logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200")))
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
# the final post procedure : count the time
start_time = time.time()
genotype, temp_accuracy = get_best_arch(
valid_loader, network, xargs.eval_candidate_num, xargs.algo
)
if xargs.algo == "setn" or xargs.algo == "enas":
network.set_cal_mode("dynamic", genotype)
elif xargs.algo == "gdas":
network.set_cal_mode("gdas", None)
elif xargs.algo == "gdas_v1":
network.set_cal_mode("gdas_v1", None)
elif xargs.algo.startswith("darts"):
network.set_cal_mode("joint", None)
elif xargs.algo == "random":
network.set_cal_mode("urs", None)
else:
raise ValueError("Invalid algorithm name : {:}".format(xargs.algo))
search_time.update(time.time() - start_time)
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
valid_loader, network, criterion, xargs.algo, logger
)
logger.log(
"Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.".format(
genotype, valid_a_top1
)
)
logger.log("\n" + "-" * 100)
# check the performance from the architecture dataset
logger.log(
"[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
xargs.algo, total_epoch, search_time.sum, genotype
)
)
if api is not None:
logger.log("{:}".format(api.query_by_arch(genotype, "200")))
logger.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.")
parser.add_argument("--data_path", type=str, help="Path to dataset")
parser.add_argument(
"--dataset",
type=str,
choices=["cifar10", "cifar100", "ImageNet16-120"],
help="Choose between Cifar10/100 and ImageNet-16.",
)
parser.add_argument(
"--search_space",
type=str,
default="tss",
choices=["tss"],
help="The search space name.",
)
parser.add_argument(
"--algo",
type=str,
choices=["darts-v1", "darts-v2", "gdas", "gdas_v1", "setn", "random", "enas"],
help="The search space name.",
)
parser.add_argument(
"--use_api",
type=int,
default=1,
choices=[0, 1],
help="Whether use API or not (which will cost much memory).",
)
# FOR GDAS
parser.add_argument(
"--tau_min", type=float, default=0.1, help="The minimum tau for Gumbel Softmax."
)
parser.add_argument(
"--tau_max", type=float, default=10, help="The maximum tau for Gumbel Softmax."
)
# channels and number-of-cells
parser.add_argument(
"--max_nodes", type=int, default=4, help="The maximum number of nodes."
)
parser.add_argument(
"--channel", type=int, default=16, help="The number of channels."
)
parser.add_argument(
"--num_cells", type=int, default=5, help="The number of cells in one stage."
)
#
parser.add_argument(
"--eval_candidate_num",
type=int,
default=100,
help="The number of selected architectures to evaluate.",
)
#
parser.add_argument(
"--track_running_stats",
type=int,
default=0,
choices=[0, 1],
help="Whether use track_running_stats or not in the BN layer.",
)
parser.add_argument(
"--affine",
type=int,
default=0,
choices=[0, 1],
help="Whether use affine=True or False in the BN layer.",
)
parser.add_argument(
"--config_path",
type=str,
default="./configs/nas-benchmark/algos/weight-sharing.config",
help="The path of configuration.",
)
parser.add_argument(
"--overwite_epochs",
type=int,
help="The number of epochs to overwrite that value in config files.",
)
# architecture leraning rate
parser.add_argument(
"--arch_learning_rate",
type=float,
default=3e-4,
help="learning rate for arch encoding",
)
parser.add_argument(
"--arch_weight_decay",
type=float,
default=1e-3,
help="weight decay for arch encoding",
)
parser.add_argument(
"--arch_eps", type=float, default=1e-8, help="weight decay for arch encoding"
)
parser.add_argument("--drop_path_rate", type=float, help="The drop path rate.")
# log
parser.add_argument(
"--workers",
type=int,
default=2,
help="number of data loading workers (default: 2)",
)
parser.add_argument(
"--save_dir",
type=str,
default="./output/search",
help="Folder to save checkpoints and log.",
)
parser.add_argument(
"--print_freq", type=int, default=200, help="print frequency (default: 200)"
)
parser.add_argument("--rand_seed", type=int, help="manual seed")
args = parser.parse_args()
if args.rand_seed is None or args.rand_seed < 0:
args.rand_seed = random.randint(1, 100000)
if args.overwite_epochs is None:
args.save_dir = os.path.join(
"{:}-{:}".format(args.save_dir, args.search_space),
args.dataset,
"{:}-affine{:}_BN{:}-{:}".format(
args.algo, args.affine, args.track_running_stats, args.drop_path_rate
),
)
else:
args.save_dir = os.path.join(
"{:}-{:}".format(args.save_dir, args.search_space),
args.dataset,
"{:}-affine{:}_BN{:}-E{:}-{:}".format(
args.algo,
args.affine,
args.track_running_stats,
args.overwite_epochs,
args.drop_path_rate,
),
)
main(args)
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