Update TuNAS
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@ -8,6 +8,10 @@
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# python ./exps/algos-v2/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
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# python ./exps/algos-v2/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
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# python ./exps/algos-v2/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo fbv2 --rand_seed 777
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####
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# python ./exps/algos-v2/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777 --use_api 0
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# python ./exps/algos-v2/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777
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# python ./exps/algos-v2/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tunas --arch_weight_decay 0 --rand_seed 777
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######################################################################################
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import os, sys, time, random, argparse
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import numpy as np
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@ -26,7 +30,28 @@ from models import get_cell_based_tiny_net, get_search_spaces
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from nas_201_api import NASBench301API as API
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def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
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# Ad-hoc for TuNAS
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class ExponentialMovingAverage(object):
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"""Class that maintains an exponential moving average."""
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def __init__(self, momentum):
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self._numerator = 0
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self._denominator = 0
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self._momentum = momentum
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def update(self, value):
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self._numerator = self._momentum * self._numerator + (1 - self._momentum) * value
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self._denominator = self._momentum * self._denominator + (1 - self._momentum)
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@property
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def value(self):
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"""Return the current value of the moving average"""
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return self._numerator / self._denominator
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RL_BASELINE_EMA = ExponentialMovingAverage(0.95)
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def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, algo, epoch_str, print_freq, logger):
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data_time, batch_time = AverageMeter(), AverageMeter()
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base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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@ -43,7 +68,7 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
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# Update the weights
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network.zero_grad()
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_, logits = network(base_inputs)
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_, logits, _ = network(base_inputs)
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base_loss = criterion(logits, base_targets)
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base_loss.backward()
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w_optimizer.step()
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@ -55,12 +80,21 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
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# update the architecture-weight
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network.zero_grad()
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_, logits = network(arch_inputs)
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arch_loss = criterion(logits, arch_targets)
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_, logits, log_probs = network(arch_inputs)
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arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
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if algo == 'tunas':
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with torch.no_grad():
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RL_BASELINE_EMA.update(arch_prec1.item())
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rl_advantage = arch_prec1 - RL_BASELINE_EMA.value
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rl_log_prob = sum(log_probs)
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arch_loss = - rl_advantage * rl_log_prob
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elif algo == 'tas' or algo == 'fbv2':
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arch_loss = criterion(logits, arch_targets)
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else:
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raise ValueError('invalid algorightm name: {:}'.format(algo))
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arch_loss.backward()
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a_optimizer.step()
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# record
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arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
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arch_losses.update(arch_loss.item(), arch_inputs.size(0))
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arch_top1.update (arch_prec1.item(), arch_inputs.size(0))
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arch_top5.update (arch_prec5.item(), arch_inputs.size(0))
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@ -78,76 +112,6 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
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return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
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def train_controller(xloader, network, criterion, optimizer, prev_baseline, epoch_str, print_freq, logger):
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# config. (containing some necessary arg)
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# baseline: The baseline score (i.e. average val_acc) from the previous epoch
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data_time, batch_time = AverageMeter(), AverageMeter()
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GradnormMeter, LossMeter, ValAccMeter, EntropyMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time()
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controller_num_aggregate = 20
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controller_train_steps = 50
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controller_bl_dec = 0.99
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controller_entropy_weight = 0.0001
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network.eval()
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network.controller.train()
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network.controller.zero_grad()
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loader_iter = iter(xloader)
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for step in range(controller_train_steps * controller_num_aggregate):
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try:
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inputs, targets = next(loader_iter)
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except:
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loader_iter = iter(xloader)
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inputs, targets = next(loader_iter)
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inputs = inputs.cuda(non_blocking=True)
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targets = targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - xend)
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log_prob, entropy, sampled_arch = network.controller()
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with torch.no_grad():
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network.set_cal_mode('dynamic', sampled_arch)
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_, logits = network(inputs)
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val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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val_top1 = val_top1.view(-1) / 100
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reward = val_top1 + controller_entropy_weight * entropy
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if prev_baseline is None:
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baseline = val_top1
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else:
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baseline = prev_baseline - (1 - controller_bl_dec) * (prev_baseline - reward)
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loss = -1 * log_prob * (reward - baseline)
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# account
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RewardMeter.update(reward.item())
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BaselineMeter.update(baseline.item())
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ValAccMeter.update(val_top1.item()*100)
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LossMeter.update(loss.item())
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EntropyMeter.update(entropy.item())
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# Average gradient over controller_num_aggregate samples
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loss = loss / controller_num_aggregate
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loss.backward(retain_graph=True)
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# measure elapsed time
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batch_time.update(time.time() - xend)
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xend = time.time()
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if (step+1) % controller_num_aggregate == 0:
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grad_norm = torch.nn.utils.clip_grad_norm_(network.controller.parameters(), 5.0)
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GradnormMeter.update(grad_norm)
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optimizer.step()
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network.controller.zero_grad()
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if step % print_freq == 0:
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Sstr = '*Train-Controller* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, controller_train_steps * controller_num_aggregate)
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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)
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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)
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Estr = 'Entropy={:.4f} ({:.4f})'.format(EntropyMeter.val, EntropyMeter.avg)
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logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Estr)
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return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg
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def valid_func(xloader, network, criterion, logger):
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data_time, batch_time = AverageMeter(), AverageMeter()
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arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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@ -159,7 +123,7 @@ def valid_func(xloader, network, criterion, logger):
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# measure data loading time
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data_time.update(time.time() - end)
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# prediction
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_, logits = network(arch_inputs.cuda(non_blocking=True))
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_, logits, _ = network(arch_inputs.cuda(non_blocking=True))
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arch_loss = criterion(logits, arch_targets)
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# record
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arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
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@ -211,9 +175,9 @@ def main(xargs):
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params = count_parameters_in_MB(search_model)
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logger.log('The parameters of the search model = {:.2f} MB'.format(params))
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logger.log('search-space : {:}'.format(search_space))
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try:
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if bool(xargs.use_api):
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api = API(verbose=False)
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except:
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else:
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api = None
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logger.log('{:} create API = {:} done'.format(time_string(), api))
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@ -250,7 +214,7 @@ def main(xargs):
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network.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) )
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logger.log('[RESET tau as : {:}]'.format(network.tau))
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search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
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= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
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= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, xargs.algo, epoch_str, xargs.print_freq, logger)
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search_time.update(time.time() - start_time)
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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))
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logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5))
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@ -305,8 +269,9 @@ if __name__ == '__main__':
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parser.add_argument('--data_path' , type=str, help='Path to dataset')
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parser.add_argument('--dataset' , type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
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parser.add_argument('--search_space', type=str, default='sss', choices=['sss'], help='The search space name.')
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parser.add_argument('--algo' , type=str, choices=['tas', 'fbv2', 'enas'], help='The search space name.')
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parser.add_argument('--algo' , type=str, choices=['tas', 'fbv2', 'tunas'], help='The search space name.')
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parser.add_argument('--genotype' , type=str, default='|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|', help='The genotype.')
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parser.add_argument('--use_api' , type=int, default=1, choices=[0,1], help='Whether use API or not (which will cost much memory).')
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# FOR GDAS
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parser.add_argument('--tau_min', type=float, default=0.1, help='The minimum tau for Gumbel Softmax.')
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parser.add_argument('--tau_max', type=float, default=10, help='The maximum tau for Gumbel Softmax.')
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@ -29,8 +29,8 @@ from log_utils import time_string
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def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
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ss_dir = '{:}-{:}'.format(root_dir, search_space)
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alg2name, alg2path = OrderedDict(), OrderedDict()
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seeds = [777]
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if search_space == 'tss':
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seeds = [777]
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alg2name['GDAS'] = 'gdas-affine0_BN0-None'
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alg2name['RSPS'] = 'random-affine0_BN0-None'
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alg2name['DARTS (1st)'] = 'darts-v1-affine0_BN0-None'
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@ -38,8 +38,10 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None):
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alg2name['ENAS'] = 'enas-affine0_BN0-None'
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alg2name['SETN'] = 'setn-affine0_BN0-None'
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else:
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seeds = [777, 888, 999]
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alg2name['TAS'] = 'tas-affine0_BN0'
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alg2name['FBNetV2'] = 'fbv2-affine0_BN0'
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alg2name['TuNAS'] = 'tunas-affine0_BN0'
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for alg, name in alg2name.items():
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alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth')
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alg2data = OrderedDict()
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@ -84,7 +86,7 @@ def visualize_curve(api, vis_save_dir, search_space):
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alg2data = fetch_data(search_space=search_space, dataset=dataset)
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alg2accuracies = OrderedDict()
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epochs = 100
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colors = ['b', 'g', 'c', 'm', 'y']
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colors = ['b', 'g', 'c', 'm', 'y', 'r']
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ax.set_xlim(0, epochs)
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# ax.set_ylim(y_min_s[(dataset, search_space)], y_max_s[(dataset, search_space)])
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for idx, (alg, data) in enumerate(alg2data.items()):
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@ -47,10 +47,10 @@ class GenericNAS301Model(nn.Module):
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def set_algo(self, algo: Text):
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# used for searching
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assert self._algo is None, 'This functioin can only be called once.'
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assert algo in ['fbv2', 'enas', 'tas'], 'invalid algo : {:}'.format(algo)
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assert algo in ['fbv2', 'tunas', 'tas'], 'invalid algo : {:}'.format(algo)
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self._algo = algo
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self._arch_parameters = nn.Parameter(1e-3*torch.randn(self._max_num_Cs, len(self._candidate_Cs)))
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if algo == 'fbv2' or algo == 'enas':
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if algo == 'fbv2' or algo == 'tunas':
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self.register_buffer('_masks', torch.zeros(len(self._candidate_Cs), max(self._candidate_Cs)))
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for i in range(len(self._candidate_Cs)):
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self._masks.data[i, :self._candidate_Cs[i]] = 1
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@ -106,15 +106,17 @@ class GenericNAS301Model(nn.Module):
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def forward(self, inputs):
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feature = inputs
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log_probs = []
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for i, cell in enumerate(self._cells):
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feature = cell(feature)
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# apply different searching algorithms
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idx = max(0, i-1)
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if self._algo == 'fbv2':
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idx = max(0, i-1)
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weights = nn.functional.gumbel_softmax(self._arch_parameters[idx:idx+1], tau=self.tau, dim=-1)
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mask = torch.matmul(weights, self._masks).view(1, -1, 1, 1)
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feature = feature * mask
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elif self._algo == 'tas':
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idx = max(0, i-1)
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selected_cs, selected_probs = select2withP(self._arch_parameters[idx:idx+1], self.tau, num=2)
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with torch.no_grad():
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i1, i2 = selected_cs.cpu().view(-1).tolist()
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@ -128,6 +130,13 @@ class GenericNAS301Model(nn.Module):
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else:
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miss = torch.zeros(feature.shape[0], feature.shape[1]-out.shape[1], feature.shape[2], feature.shape[3], device=feature.device)
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feature = torch.cat((out, miss), dim=1)
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elif self._algo == 'tunas':
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prob = nn.functional.softmax(self._arch_parameters[idx:idx+1], dim=-1)
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dist = torch.distributions.Categorical(prob)
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action = dist.sample()
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log_probs.append(dist.log_prob(action))
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mask = self._masks[action.item()].view(1, -1, 1, 1)
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feature = feature * mask
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else:
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raise ValueError('invalid algorithm : {:}'.format(self._algo))
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@ -136,4 +145,4 @@ class GenericNAS301Model(nn.Module):
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out = out.view(out.size(0), -1)
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logits = self.classifier(out)
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return out, logits
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return out, logits, log_probs
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@ -60,6 +60,7 @@ class NASBench301API(NASBenchMetaAPI):
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self.reset_time()
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if file_path_or_dict is None:
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file_path_or_dict = os.path.join(os.environ['TORCH_HOME'], ALL_BENCHMARK_FILES[-1])
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print ('Try to use the default NAS-Bench-301 path from {:}.'.format(file_path_or_dict))
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if isinstance(file_path_or_dict, str) or isinstance(file_path_or_dict, Path):
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file_path_or_dict = str(file_path_or_dict)
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if verbose: print('try to create the NAS-Bench-201 api from {:}'.format(file_path_or_dict))
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