Prototype generic nas model.
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D-X-Y
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14
configs/nas-benchmark/algos/weight-sharing.config
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configs/nas-benchmark/algos/weight-sharing.config
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{
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"scheduler": ["str", "cos"],
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"LR" : ["float", "0.025"],
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"eta_min" : ["float", "0.001"],
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"epochs" : ["int", "250"],
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"warmup" : ["int", "0"],
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"optim" : ["str", "SGD"],
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"decay" : ["float", "0.0005"],
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"momentum" : ["float", "0.9"],
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"nesterov" : ["bool", "1"],
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"criterion": ["str", "Softmax"],
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"batch_size": ["int", "64"],
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"test_batch_size": ["int", "512"]
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}
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@ -14,7 +14,7 @@ do
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python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001
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python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space}
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python exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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python ./exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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done
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done
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290
exps/algos-v2/search-cell.py
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exps/algos-v2/search-cell.py
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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######################################################################################
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# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --rand_seed 1
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# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1
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# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1
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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 nas_201_api import NASBench201API 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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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_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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sampled_arch = network.module.dync_genotype(True)
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network.module.set_cal_mode('dynamic', sampled_arch)
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#network.module.set_cal_mode( 'urs' )
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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.module.set_cal_mode( 'joint' )
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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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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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# 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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#print (nn.functional.softmax(network.module.arch_parameters, dim=-1))
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#print (network.module.arch_parameters)
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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 get_best_arch(xloader, network, n_samples):
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with torch.no_grad():
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network.eval()
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archs, valid_accs = network.module.return_topK(n_samples), []
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#print ('obtain the top-{:} architectures'.format(n_samples))
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loader_iter = iter(xloader)
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for i, sampled_arch in enumerate(archs):
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network.module.set_cal_mode('dynamic', sampled_arch)
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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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_, logits = network(inputs)
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val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
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valid_accs.append(val_top1.item())
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best_idx = np.argmax(valid_accs)
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best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
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return best_arch, best_valid_acc
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def valid_func(xloader, network, criterion):
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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)
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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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config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \
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(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, 'nas-bench-301')
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model_config = dict2config(
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dict(name='generic', C=xargs.channel, N=xargs.num_cells, max_nodes=xargs.max_nodes, num_classes=class_num,
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space=search_space, 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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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)
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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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api = API()
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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 = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
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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}, {}
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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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logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
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import pdb; pdb.set_trace()
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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_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, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
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network.module.set_cal_mode('dynamic', genotype)
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valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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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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#search_model.set_cal_mode('urs')
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#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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#logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
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#search_model.set_cal_mode('joint')
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#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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#logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
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#search_model.set_cal_mode('select')
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#valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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#logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
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# check the best accuracy
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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], '200')))
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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, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
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search_time.update(time.time() - start_time)
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network.module.set_cal_mode('dynamic', genotype)
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valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
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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('SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, genotype))
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if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '200') ))
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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='tss', choices=['tss'], help='The search space name.')
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parser.add_argument('--algo' , type=str, choices=['darts-v1', 'darts-v2', 'gdas', 'setn', 'random', 'enas'], help='The search space name.')
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# channels and number-of-cells
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parser.add_argument('--max_nodes' , type=int, default=4, help='The maximum number of nodes.')
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parser.add_argument('--channel' , type=int, default=16, help='The number of channels.')
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parser.add_argument('--num_cells' , type=int, default=5, help='The number of cells in one stage.')
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#
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parser.add_argument('--eval_candidate_num', type=int, help='The number of selected architectures to evaluate.')
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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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# 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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# 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, 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)
|
||||
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, args.algo)
|
||||
|
||||
main(args)
|
||||
@ -20,7 +20,7 @@ from .cell_searchs import CellStructure, CellArchitectures
|
||||
def get_cell_based_tiny_net(config):
|
||||
if isinstance(config, dict): config = dict2config(config, None) # to support the argument being a dict
|
||||
super_type = getattr(config, 'super_type', 'basic')
|
||||
group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM']
|
||||
group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM', 'generic']
|
||||
if super_type == 'basic' and config.name in group_names:
|
||||
from .cell_searchs import nas201_super_nets as nas_super_nets
|
||||
try:
|
||||
|
||||
@ -7,6 +7,7 @@ from .search_model_gdas import TinyNetworkGDAS
|
||||
from .search_model_setn import TinyNetworkSETN
|
||||
from .search_model_enas import TinyNetworkENAS
|
||||
from .search_model_random import TinyNetworkRANDOM
|
||||
from .generic_model import GenericNAS201Model
|
||||
from .genotypes import Structure as CellStructure, architectures as CellArchitectures
|
||||
# NASNet-based macro structure
|
||||
from .search_model_gdas_nasnet import NASNetworkGDAS
|
||||
@ -18,7 +19,8 @@ nas201_super_nets = {'DARTS-V1': TinyNetworkDarts,
|
||||
"GDAS": TinyNetworkGDAS,
|
||||
"SETN": TinyNetworkSETN,
|
||||
"ENAS": TinyNetworkENAS,
|
||||
"RANDOM": TinyNetworkRANDOM}
|
||||
"RANDOM": TinyNetworkRANDOM,
|
||||
"generic": GenericNAS201Model}
|
||||
|
||||
nasnet_super_nets = {"GDAS": NASNetworkGDAS,
|
||||
"DARTS": NASNetworkDARTS}
|
||||
|
||||
200
lib/models/cell_searchs/generic_model.py
Normal file
200
lib/models/cell_searchs/generic_model.py
Normal file
@ -0,0 +1,200 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
|
||||
#####################################################
|
||||
import torch, random
|
||||
import torch.nn as nn
|
||||
from copy import deepcopy
|
||||
from typing import Text
|
||||
|
||||
from ..cell_operations import ResNetBasicblock
|
||||
from .search_cells import NAS201SearchCell as SearchCell
|
||||
from .genotypes import Structure
|
||||
from .search_model_enas_utils import Controller
|
||||
|
||||
|
||||
class GenericNAS201Model(nn.Module):
|
||||
|
||||
def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
|
||||
super(GenericNAS201Model, self).__init__()
|
||||
self._C = C
|
||||
self._layerN = N
|
||||
self._max_nodes = max_nodes
|
||||
self._stem = nn.Sequential(
|
||||
nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
|
||||
nn.BatchNorm2d(C))
|
||||
layer_channels = [C ] * N + [C*2 ] + [C*2 ] * N + [C*4 ] + [C*4 ] * N
|
||||
layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
|
||||
C_prev, num_edge, edge2index = C, None, None
|
||||
self._cells = nn.ModuleList()
|
||||
for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
|
||||
if reduction:
|
||||
cell = ResNetBasicblock(C_prev, C_curr, 2)
|
||||
else:
|
||||
cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, affine, track_running_stats)
|
||||
if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
|
||||
else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
|
||||
self._cells.append(cell)
|
||||
C_prev = cell.out_dim
|
||||
self._op_names = deepcopy(search_space)
|
||||
self._Layer = len(self._cells)
|
||||
self.edge2index = edge2index
|
||||
self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
|
||||
self.global_pooling = nn.AdaptiveAvgPool2d(1)
|
||||
self.classifier = nn.Linear(C_prev, num_classes)
|
||||
self._num_edge = num_edge
|
||||
# algorithm related
|
||||
self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
|
||||
self._mode = None
|
||||
self.dynamic_cell = None
|
||||
self._tau = None
|
||||
self._algo = None
|
||||
|
||||
def set_algo(self, algo: Text):
|
||||
# used for searching
|
||||
assert self._algo is None, 'This functioin can only be called once.'
|
||||
self._algo = algo
|
||||
if algo == 'enas':
|
||||
self.controller = Controller(len(self.edge2index), len(self._op_names))
|
||||
else:
|
||||
self.arch_parameters = nn.Parameter( 1e-3*torch.randn(self._num_edge, len(self._op_names)) )
|
||||
if algo == 'gdas':
|
||||
self._tau = 10
|
||||
|
||||
def set_cal_mode(self, mode, dynamic_cell=None):
|
||||
assert mode in ['gdas', 'enas', 'urs', 'joint', 'select', 'dynamic']
|
||||
self.mode = mode
|
||||
if mode == 'dynamic': self.dynamic_cell = deepcopy(dynamic_cell)
|
||||
else : self.dynamic_cell = None
|
||||
|
||||
@property
|
||||
def mode(self):
|
||||
return self._mode
|
||||
|
||||
@property
|
||||
def weights(self):
|
||||
xlist = list(self._stem.parameters())
|
||||
xlist+= list(self._cells.parameters())
|
||||
xlist+= list(self.lastact.parameters())
|
||||
xlist+= list(self.global_pooling.parameters())
|
||||
xlist+= list(self.classifier.parameters())
|
||||
return xlist
|
||||
|
||||
def set_tau(self, tau):
|
||||
self._tau = tau
|
||||
|
||||
@property
|
||||
def tau(self):
|
||||
return self._tau
|
||||
|
||||
@property
|
||||
def alphas(self):
|
||||
if self._algo == 'enas':
|
||||
return list(self.controller.parameters())
|
||||
else:
|
||||
return [self.arch_parameters]
|
||||
|
||||
@property
|
||||
def message(self):
|
||||
string = self.extra_repr()
|
||||
for i, cell in enumerate(self._cells):
|
||||
string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self._cells), cell.extra_repr())
|
||||
return string
|
||||
|
||||
def extra_repr(self):
|
||||
return ('{name}(C={_C}, Max-Nodes={_max_nodes}, N={_layerN}, L={_Layer}, alg={_algo})'.format(name=self.__class__.__name__, **self.__dict__))
|
||||
|
||||
@property
|
||||
def genotype(self):
|
||||
genotypes = []
|
||||
for i in range(1, self._max_nodes):
|
||||
xlist = []
|
||||
for j in range(i):
|
||||
node_str = '{:}<-{:}'.format(i, j)
|
||||
with torch.no_grad():
|
||||
weights = self.arch_parameters[ self.edge2index[node_str] ]
|
||||
op_name = self.op_names[ weights.argmax().item() ]
|
||||
xlist.append((op_name, j))
|
||||
genotypes.append(tuple(xlist))
|
||||
return Structure(genotypes)
|
||||
|
||||
def dync_genotype(self, use_random=False):
|
||||
genotypes = []
|
||||
with torch.no_grad():
|
||||
alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
|
||||
for i in range(1, self._max_nodes):
|
||||
xlist = []
|
||||
for j in range(i):
|
||||
node_str = '{:}<-{:}'.format(i, j)
|
||||
if use_random:
|
||||
op_name = random.choice(self.op_names)
|
||||
else:
|
||||
weights = alphas_cpu[ self.edge2index[node_str] ]
|
||||
op_index = torch.multinomial(weights, 1).item()
|
||||
op_name = self.op_names[ op_index ]
|
||||
xlist.append((op_name, j))
|
||||
genotypes.append(tuple(xlist))
|
||||
return Structure(genotypes)
|
||||
|
||||
def get_log_prob(self, arch):
|
||||
with torch.no_grad():
|
||||
logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
|
||||
select_logits = []
|
||||
for i, node_info in enumerate(arch.nodes):
|
||||
for op, xin in node_info:
|
||||
node_str = '{:}<-{:}'.format(i+1, xin)
|
||||
op_index = self.op_names.index(op)
|
||||
select_logits.append( logits[self.edge2index[node_str], op_index] )
|
||||
return sum(select_logits).item()
|
||||
|
||||
def return_topK(self, K):
|
||||
archs = Structure.gen_all(self.op_names, self._max_nodes, False)
|
||||
pairs = [(self.get_log_prob(arch), arch) for arch in archs]
|
||||
if K < 0 or K >= len(archs): K = len(archs)
|
||||
sorted_pairs = sorted(pairs, key=lambda x: -x[0])
|
||||
return_pairs = [sorted_pairs[_][1] for _ in range(K)]
|
||||
return return_pairs
|
||||
|
||||
def normalize_archp(self):
|
||||
if self.mode == 'gdas':
|
||||
while True:
|
||||
gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
|
||||
logits = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau
|
||||
probs = nn.functional.softmax(logits, dim=1)
|
||||
index = probs.max(-1, keepdim=True)[1]
|
||||
one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
|
||||
hardwts = one_h - probs.detach() + probs
|
||||
if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
|
||||
continue
|
||||
else: break
|
||||
with torch.no_grad():
|
||||
hardwts_cpu = hardwts.detach().cpu()
|
||||
return hardwts, hardwts_cpu, index
|
||||
else:
|
||||
alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
|
||||
index = alphas.max(-1, keepdim=True)[1]
|
||||
with torch.no_grad():
|
||||
alphas_cpu = alphas.detach().cpu()
|
||||
return alphas, alphas_cpu, index
|
||||
|
||||
def forward(self, inputs):
|
||||
alphas, alphas_cpu, index = self.normalize_archp()
|
||||
feature = self._stem(inputs)
|
||||
for i, cell in enumerate(self._cells):
|
||||
if isinstance(cell, SearchCell):
|
||||
if self.mode == 'urs':
|
||||
feature = cell.forward_urs(feature)
|
||||
elif self.mode == 'select':
|
||||
feature = cell.forward_select(feature, alphas_cpu)
|
||||
elif self.mode == 'joint':
|
||||
feature = cell.forward_joint(feature, alphas)
|
||||
elif self.mode == 'dynamic':
|
||||
feature = cell.forward_dynamic(feature, self.dynamic_cell)
|
||||
elif self.mode == 'gdas':
|
||||
feature = cell.forward_gdas(feature, alphas, index)
|
||||
else: raise ValueError('invalid mode={:}'.format(self.mode))
|
||||
else: feature = cell(feature)
|
||||
out = self.lastact(feature)
|
||||
out = self.global_pooling(out)
|
||||
out = out.view(out.size(0), -1)
|
||||
logits = self.classifier(out)
|
||||
return out, logits
|
||||
@ -1,5 +1,5 @@
|
||||
from .evaluation_utils import obtain_accuracy
|
||||
from .gpu_manager import GPUManager
|
||||
from .flop_benchmark import get_model_infos
|
||||
from .flop_benchmark import get_model_infos, count_parameters_in_MB
|
||||
from .affine_utils import normalize_points, denormalize_points
|
||||
from .affine_utils import identity2affine, solve2theta, affine2image
|
||||
|
||||
Loading…
Reference in New Issue
Block a user