318 lines
18 KiB
Python
318 lines
18 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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######################################################################################
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# In this file, we aims to evaluate three kinds of channel searching strategies:
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# -
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####
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# python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --warmup_ratio 0.25
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####
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# python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
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# python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
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# python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --rand_seed 777
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####
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# python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
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# python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
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# python ./exps/NATS-algos/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/NATS-algos/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/NATS-algos/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/NATS-algos/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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from copy import deepcopy
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import torch
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import torch.nn as nn
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, dict2config, configure2str
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from datasets import get_datasets, get_nas_search_loaders
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from utils import count_parameters_in_MB, obtain_accuracy
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from log_utils import AverageMeter, time_string, convert_secs2time
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from models import get_cell_based_tiny_net, get_search_spaces
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from nats_bench import create
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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, enable_controller, 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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end = time.time()
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network.train()
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for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
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scheduler.update(None, 1.0 * step / len(xloader))
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base_inputs = base_inputs.cuda(non_blocking=True)
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arch_inputs = arch_inputs.cuda(non_blocking=True)
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base_targets = base_targets.cuda(non_blocking=True)
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arch_targets = arch_targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - end)
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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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base_loss = criterion(logits, base_targets)
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base_loss.backward()
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w_optimizer.step()
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# record
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base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
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base_losses.update(base_loss.item(), base_inputs.size(0))
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base_top1.update (base_prec1.item(), base_inputs.size(0))
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base_top5.update (base_prec5.item(), base_inputs.size(0))
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# update the architecture-weight
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network.zero_grad()
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a_optimizer.zero_grad()
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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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if enable_controller:
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arch_loss.backward()
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a_optimizer.step()
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# record
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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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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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if step % print_freq == 0 or step + 1 == len(xloader):
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Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader))
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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 = '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)
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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)
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logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
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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 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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end = time.time()
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with torch.no_grad():
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network.eval()
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for step, (arch_inputs, arch_targets) in enumerate(xloader):
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arch_targets = arch_targets.cuda(non_blocking=True)
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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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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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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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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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return arch_losses.avg, arch_top1.avg, arch_top5.avg
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def main(xargs):
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assert torch.cuda.is_available(), 'CUDA is not available.'
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads( xargs.workers )
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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if xargs.overwite_epochs is None:
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extra_info = {'class_num': class_num, 'xshape': xshape}
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else:
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extra_info = {'class_num': class_num, 'xshape': xshape, 'epochs': xargs.overwite_epochs}
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config = load_config(xargs.config_path, extra_info, logger)
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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)
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logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
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logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
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search_space = get_search_spaces(xargs.search_space, 'nats-bench')
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model_config = dict2config(
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dict(name='generic', super_type='search-shape', candidate_Cs=search_space['candidates'], max_num_Cs=search_space['numbers'], num_classes=class_num,
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genotype=args.genotype, affine=bool(xargs.affine), track_running_stats=bool(xargs.track_running_stats)), None)
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logger.log('search space : {:}'.format(search_space))
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logger.log('model config : {:}'.format(model_config))
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search_model = get_cell_based_tiny_net(model_config)
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search_model.set_algo(xargs.algo)
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logger.log('{:}'.format(search_model))
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w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config)
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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)
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logger.log('w-optimizer : {:}'.format(w_optimizer))
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logger.log('a-optimizer : {:}'.format(a_optimizer))
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logger.log('w-scheduler : {:}'.format(w_scheduler))
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logger.log('criterion : {:}'.format(criterion))
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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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if bool(xargs.use_api):
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api = create(None, 'size', fast_mode=True, verbose=False)
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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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last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
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network, criterion = search_model.cuda(), criterion.cuda() # use a single GPU
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last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
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last_info = torch.load(last_info)
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start_epoch = last_info['epoch']
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checkpoint = torch.load(last_info['last_checkpoint'])
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genotypes = checkpoint['genotypes']
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valid_accuracies = checkpoint['valid_accuracies']
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search_model.load_state_dict( checkpoint['search_model'] )
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w_scheduler.load_state_dict ( checkpoint['w_scheduler'] )
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w_optimizer.load_state_dict ( checkpoint['w_optimizer'] )
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a_optimizer.load_state_dict ( checkpoint['a_optimizer'] )
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logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
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else:
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logger.log("=> do not find the last-info file : {:}".format(last_info))
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start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: network.random}
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# start training
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start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
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for epoch in range(start_epoch, total_epoch):
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w_scheduler.update(epoch, 0.0)
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need_time = 'Time Left: {:}'.format(convert_secs2time(epoch_time.val * (total_epoch-epoch), True))
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epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
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if xargs.warmup_ratio is None or xargs.warmup_ratio <= float(epoch) / total_epoch:
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enable_controller = True
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network.set_warmup_ratio(None)
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else:
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enable_controller = False
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network.set_warmup_ratio(1.0 - float(epoch) / total_epoch / xargs.warmup_ratio)
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logger.log('\n[Search the {:}-th epoch] {:}, LR={:}, controller-warmup={:}, enable_controller={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()), network.warmup_ratio, enable_controller))
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if xargs.algo == 'fbv2' or xargs.algo == 'tas':
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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,
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w_optimizer, a_optimizer, enable_controller, 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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genotype = network.genotype
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logger.log('[{:}] - [get_best_arch] : {:}'.format(epoch_str, genotype))
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valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion, logger)
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logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
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valid_accuracies[epoch] = valid_a_top1
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genotypes[epoch] = genotype
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logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
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# save checkpoint
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save_path = save_checkpoint({'epoch' : epoch + 1,
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'args' : deepcopy(xargs),
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'search_model': search_model.state_dict(),
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'w_optimizer' : w_optimizer.state_dict(),
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'a_optimizer' : a_optimizer.state_dict(),
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'w_scheduler' : w_scheduler.state_dict(),
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'genotypes' : genotypes,
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'valid_accuracies' : valid_accuracies},
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model_base_path, logger)
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last_info = save_checkpoint({
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'epoch': epoch + 1,
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'args' : deepcopy(args),
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'last_checkpoint': save_path,
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}, logger.path('info'), logger)
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with torch.no_grad():
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logger.log('{:}'.format(search_model.show_alphas()))
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if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[epoch], '90')))
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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# the final post procedure : count the time
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start_time = time.time()
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genotype = network.genotype
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search_time.update(time.time() - start_time)
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valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(valid_loader, network, criterion, logger)
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logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1))
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logger.log('\n' + '-'*100)
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# check the performance from the architecture dataset
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logger.log('[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(xargs.algo, total_epoch, search_time.sum, genotype))
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if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '90') ))
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logger.close()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.")
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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', '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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# FOR ALL
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parser.add_argument('--warmup_ratio', type=float, help='The warmup ratio, if None, not use warmup.')
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#
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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.')
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parser.add_argument('--affine' , type=int, default=0, choices=[0,1],help='Whether use affine=True or False in the BN layer.')
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parser.add_argument('--config_path' , type=str, default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.')
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parser.add_argument('--overwite_epochs', type=int, help='The number of epochs to overwrite that value in config files.')
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# architecture leraning rate
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parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
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parser.add_argument('--arch_weight_decay' , type=float, default=1e-3, help='weight decay for arch encoding')
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parser.add_argument('--arch_eps' , type=float, default=1e-8, help='weight decay for arch encoding')
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# log
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parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
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parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
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parser.add_argument('--print_freq', type=int, default=200, help='print frequency (default: 200)')
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parser.add_argument('--rand_seed', type=int, help='manual seed')
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args = parser.parse_args()
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if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
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dirname = '{:}-affine{:}_BN{:}-AWD{:}-WARM{:}'.format(args.algo, args.affine, args.track_running_stats, args.arch_weight_decay, args.warmup_ratio)
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if args.overwite_epochs is not None:
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dirname = dirname + '-E{:}'.format(args.overwite_epochs)
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args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, dirname)
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main(args)
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