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7052265501
xautodl/exps/NATS-algos/search-cell.py
D-X-Y 7052265501 Update docs of NATS-Bench
2020-09-16 09:04:22 +00:00

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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
######################################################################################
import os, sys, time, random, argparse
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config, configure2str
from datasets import get_datasets, get_nas_search_loaders
from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
from utils import count_parameters_in_MB, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from 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.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.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':
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':
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.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.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', '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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