From 34ba8053dedda61e4676d93669751bd0b777f4f4 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Sat, 9 Nov 2019 01:36:31 +1100 Subject: [PATCH] update ENAS --- configs/nas-benchmark/algos/ENAS.config | 17 + exps/algos/ENAS.py | 347 ++++++++++++++++++ lib/models/__init__.py | 16 +- lib/models/cell_searchs/__init__.py | 7 + lib/models/cell_searchs/_test_module.py | 9 + lib/models/cell_searchs/search_model_enas.py | 94 +++++ .../cell_searchs/search_model_enas_utils.py | 55 +++ 7 files changed, 533 insertions(+), 12 deletions(-) create mode 100644 configs/nas-benchmark/algos/ENAS.config create mode 100644 exps/algos/ENAS.py create mode 100644 lib/models/cell_searchs/_test_module.py create mode 100644 lib/models/cell_searchs/search_model_enas.py create mode 100644 lib/models/cell_searchs/search_model_enas_utils.py diff --git a/configs/nas-benchmark/algos/ENAS.config b/configs/nas-benchmark/algos/ENAS.config new file mode 100644 index 0000000..d2c0cf6 --- /dev/null +++ b/configs/nas-benchmark/algos/ENAS.config @@ -0,0 +1,17 @@ +{ + "scheduler": ["str", "cos"], + "LR" : ["float", "0.05"], + "eta_min" : ["float", "0.0005"], + "epochs" : ["int", "310"], + "T_max" : ["int", "10"], + "warmup" : ["int", "0"], + "optim" : ["str", "SGD"], + "decay" : ["float", "0.00025"], + "momentum" : ["float", "0.9"], + "nesterov" : ["bool", "1"], + "controller_lr" : ["float", "0.001"], + "controller_betas": ["float", [0, 0.999]], + "controller_eps" : ["float", 0.001], + "criterion": ["str", "Softmax"], + "batch_size": ["int", "128"] +} diff --git a/exps/algos/ENAS.py b/exps/algos/ENAS.py new file mode 100644 index 0000000..d2937b9 --- /dev/null +++ b/exps/algos/ENAS.py @@ -0,0 +1,347 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +################################################## +import os, sys, time, glob, random, argparse +import numpy as np +from copy import deepcopy +import torch +import torch.nn as nn +from pathlib import Path +lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve() +if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir)) +from config_utils import load_config, dict2config, configure2str +from datasets import get_datasets, SearchDataset +from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler +from utils import get_model_infos, obtain_accuracy +from log_utils import AverageMeter, time_string, convert_secs2time +from models import get_cell_based_tiny_net, get_search_spaces + + +def train_shared_cnn(xloader, shared_cnn, controller, criterion, scheduler, optimizer, epoch_str, print_freq, logger): + data_time, batch_time = AverageMeter(), AverageMeter() + losses, top1s, top5s, xend = AverageMeter(), AverageMeter(), AverageMeter(), time.time() + + shared_cnn.train() + controller.eval() + + for step, (inputs, targets) in enumerate(xloader): + scheduler.update(None, 1.0 * step / len(xloader)) + targets = targets.cuda(non_blocking=True) + # measure data loading time + data_time.update(time.time() - xend) + + with torch.no_grad(): + _, _, sampled_arch = controller() + + optimizer.zero_grad() + shared_cnn.module.update_arch(sampled_arch) + _, logits = shared_cnn(inputs) + loss = criterion(logits, targets) + loss.backward() + torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), 5) + optimizer.step() + # record + prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5)) + losses.update(loss.item(), inputs.size(0)) + top1s.update (prec1.item(), inputs.size(0)) + top5s.update (prec5.item(), inputs.size(0)) + + # measure elapsed time + batch_time.update(time.time() - xend) + xend = time.time() + + if step % print_freq == 0 or step + 1 == len(xloader): + Sstr = '*Train-Shared-CNN* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader)) + Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time) + Wstr = '[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=losses, top1=top1s, top5=top5s) + logger.log(Sstr + ' ' + Tstr + ' ' + Wstr) + return losses.avg, top1s.avg, top5s.avg + + +def train_controller(xloader, shared_cnn, controller, criterion, optimizer, config, epoch_str, print_freq, logger): + # config. (containing some necessary arg) + # baseline: The baseline score (i.e. average val_acc) from the previous epoch + data_time, batch_time = AverageMeter(), AverageMeter() + GradnormMeter, LossMeter, ValAccMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time() + + shared_cnn.eval() + controller.train() + controller.zero_grad() + #for step, (inputs, targets) in enumerate(xloader): + loader_iter = iter(xloader) + for step in range(config.ctl_train_steps * config.ctl_num_aggre): + try: + inputs, targets = next(loader_iter) + except: + loader_iter = iter(xloader) + inputs, targets = next(loader_iter) + targets = targets.cuda(non_blocking=True) + # measure data loading time + data_time.update(time.time() - xend) + + log_prob, entropy, sampled_arch = controller() + with torch.no_grad(): + shared_cnn.module.update_arch(sampled_arch) + _, logits = shared_cnn(inputs) + val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5)) + val_top1 = val_top1.view(-1) / 100 + reward = val_top1 + config.ctl_entropy_w * entropy + if config.baseline is None: + baseline = val_top1 + else: + baseline = config.baseline - (1 - config.ctl_bl_dec) * (config.baseline - reward) + + loss = -1 * log_prob * (reward - baseline) + + # account + RewardMeter.update(reward.item()) + BaselineMeter.update(baseline.item()) + ValAccMeter.update(val_top1.item()) + LossMeter.update(loss.item()) + + # Average gradient over controller_num_aggregate samples + loss = loss / config.ctl_num_aggre + loss.backward(retain_graph=True) + + # measure elapsed time + batch_time.update(time.time() - xend) + xend = time.time() + if (step+1) % config.ctl_num_aggre == 0: + grad_norm = torch.nn.utils.clip_grad_norm_(controller.parameters(), 5.0) + GradnormMeter.update(grad_norm) + optimizer.step() + controller.zero_grad() + + if step % print_freq == 0: + Sstr = '*Train-Controller* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, config.ctl_train_steps * config.ctl_num_aggre) + Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time) + Wstr = '[Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Reward {reward.val:.2f} ({reward.avg:.2f})] Baseline {basel.val:.2f} ({basel.avg:.2f})'.format(loss=LossMeter, top1=ValAccMeter, reward=RewardMeter, basel=BaselineMeter) + logger.log(Sstr + ' ' + Tstr + ' ' + Wstr) + + return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg, baseline.item() + + +def get_best_arch(controller, shared_cnn, xloader, n_samples=10): + with torch.no_grad(): + controller.eval() + shared_cnn.eval() + archs, valid_accs = [], [] + loader_iter = iter(xloader) + for i in range(n_samples): + try: + inputs, targets = next(loader_iter) + except: + loader_iter = iter(xloader) + inputs, targets = next(loader_iter) + + _, _, sampled_arch = controller() + arch = shared_cnn.module.update_arch(sampled_arch) + _, logits = shared_cnn(inputs) + val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5)) + + archs.append( arch ) + valid_accs.append( val_top1.item() ) + + best_idx = np.argmax(valid_accs) + best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx] + return best_arch, best_valid_acc + + +def valid_func(xloader, network, criterion): + data_time, batch_time = AverageMeter(), AverageMeter() + arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter() + network.eval() + end = time.time() + with torch.no_grad(): + for step, (arch_inputs, arch_targets) in enumerate(xloader): + arch_targets = arch_targets.cuda(non_blocking=True) + # measure data loading time + data_time.update(time.time() - end) + # prediction + _, logits = network(arch_inputs) + arch_loss = criterion(logits, arch_targets) + # record + arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5)) + arch_losses.update(arch_loss.item(), arch_inputs.size(0)) + arch_top1.update (arch_prec1.item(), arch_inputs.size(0)) + arch_top5.update (arch_prec5.item(), arch_inputs.size(0)) + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + return arch_losses.avg, arch_top1.avg, arch_top5.avg + + +def main(xargs): + assert torch.cuda.is_available(), 'CUDA is not available.' + torch.backends.cudnn.enabled = True + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + torch.set_num_threads( xargs.workers ) + prepare_seed(xargs.rand_seed) + logger = prepare_logger(args) + + train_data, test_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1) + if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100': + split_Fpath = 'configs/nas-benchmark/cifar-split.txt' + cifar_split = load_config(split_Fpath, None, None) + train_split, valid_split = cifar_split.train, cifar_split.valid + logger.log('Load split file from {:}'.format(split_Fpath)) + elif xargs.dataset.startswith('ImageNet16'): + split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset) + imagenet16_split = load_config(split_Fpath, None, None) + train_split, valid_split = imagenet16_split.train, imagenet16_split.valid + logger.log('Load split file from {:}'.format(split_Fpath)) + else: + raise ValueError('invalid dataset : {:}'.format(xargs.dataset)) + logger.log('use config from : {:}'.format(xargs.config_path)) + config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger) + logger.log('config: {:}'.format(config)) + # To split data + train_data_v2 = deepcopy(train_data) + train_data_v2.transform = test_data.transform + valid_data = train_data_v2 + # data loader + train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split), num_workers=xargs.workers, pin_memory=True) + valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True) + logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size)) + logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config)) + + search_space = get_search_spaces('cell', xargs.search_space_name) + model_config = dict2config({'name': 'ENAS', 'C': xargs.channel, 'N': xargs.num_cells, + 'max_nodes': xargs.max_nodes, 'num_classes': class_num, + 'space' : search_space}, None) + shared_cnn = get_cell_based_tiny_net(model_config) + controller = shared_cnn.create_controller() + + w_optimizer, w_scheduler, criterion = get_optim_scheduler(shared_cnn.parameters(), config) + a_optimizer = torch.optim.Adam(controller.parameters(), lr=config.controller_lr, betas=config.controller_betas, eps=config.controller_eps) + logger.log('w-optimizer : {:}'.format(w_optimizer)) + logger.log('a-optimizer : {:}'.format(a_optimizer)) + logger.log('w-scheduler : {:}'.format(w_scheduler)) + logger.log('criterion : {:}'.format(criterion)) + #flop, param = get_model_infos(shared_cnn, xshape) + #logger.log('{:}'.format(shared_cnn)) + #logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param)) + shared_cnn, controller, criterion = torch.nn.DataParallel(shared_cnn).cuda(), controller.cuda(), criterion.cuda() + + last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best') + + if last_info.exists(): # automatically resume from previous checkpoint + logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info)) + last_info = torch.load(last_info) + start_epoch = last_info['epoch'] + checkpoint = torch.load(last_info['last_checkpoint']) + genotypes = checkpoint['genotypes'] + baseline = checkpoint['baseline'] + valid_accuracies = checkpoint['valid_accuracies'] + shared_cnn.load_state_dict( checkpoint['shared_cnn'] ) + controller.load_state_dict( checkpoint['controller'] ) + w_scheduler.load_state_dict ( checkpoint['w_scheduler'] ) + w_optimizer.load_state_dict ( checkpoint['w_optimizer'] ) + a_optimizer.load_state_dict ( checkpoint['a_optimizer'] ) + logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch)) + else: + logger.log("=> do not find the last-info file : {:}".format(last_info)) + start_epoch, valid_accuracies, genotypes, baseline = 0, {'best': -1}, {}, None + + # start training + start_time, epoch_time, total_epoch = time.time(), AverageMeter(), config.epochs + config.warmup + for epoch in range(start_epoch, total_epoch): + w_scheduler.update(epoch, 0.0) + need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) ) + epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch) + logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()))) + + cnn_loss, cnn_top1, cnn_top5 = train_shared_cnn(train_loader, shared_cnn, controller, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, logger) + logger.log('[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, cnn_loss, cnn_top1, cnn_top5)) + ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline \ + = train_controller(valid_loader, shared_cnn, controller, criterion, a_optimizer, \ + dict2config({'baseline': baseline, + 'ctl_train_steps': xargs.controller_train_steps, 'ctl_num_aggre': xargs.controller_num_aggregate, + 'ctl_entropy_w': xargs.controller_entropy_weight, + 'ctl_bl_dec' : xargs.controller_bl_dec}, None), \ + epoch_str, xargs.print_freq, logger) + logger.log('[{:}] controller : loss={:.2f}, accuracy={:.2f}%, baseline={:.2f}, reward={:.2f}, current-baseline={:.4f}'.format(epoch_str, ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline)) + best_arch, _ = get_best_arch(controller, shared_cnn, valid_loader) + shared_cnn.module.update_arch(best_arch) + best_valid_acc = valid_func(valid_loader, shared_cnn, criterion) + + genotypes[epoch] = best_arch + # check the best accuracy + valid_accuracies[epoch] = best_valid_acc + if best_valid_acc > valid_accuracies['best']: + valid_accuracies['best'] = best_valid_acc + genotypes['best'] = best_arch + find_best = True + else: find_best = False + + logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch])) + # save checkpoint + save_path = save_checkpoint({'epoch' : epoch + 1, + 'args' : deepcopy(xargs), + 'baseline' : baseline, + 'shared_cnn' : shared_cnn.state_dict(), + 'controller' : controller.state_dict(), + 'w_optimizer' : w_optimizer.state_dict(), + 'a_optimizer' : a_optimizer.state_dict(), + 'w_scheduler' : w_scheduler.state_dict(), + 'genotypes' : genotypes, + 'valid_accuracies' : valid_accuracies}, + model_base_path, logger) + last_info = save_checkpoint({ + 'epoch': epoch + 1, + 'args' : deepcopy(args), + 'last_checkpoint': save_path, + }, logger.path('info'), logger) + if find_best: + logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, best_valid_acc)) + copy_checkpoint(model_base_path, model_best_path, logger) + # measure elapsed time + epoch_time.update(time.time() - start_time) + start_time = time.time() + + logger.log('\n' + '-'*100) + # check the performance from the architecture dataset + #if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset): + # logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset)) + #else: + # nas_bench = NASBenchmarkAPI(xargs.arch_nas_dataset) + # geno = genotypes[total_epoch-1] + # logger.log('The last model is {:}'.format(geno)) + # info = nas_bench.query_by_arch( geno ) + # if info is None: logger.log('Did not find this architecture : {:}.'.format(geno)) + # else : logger.log('{:}'.format(info)) + # logger.log('-'*100) + # geno = genotypes['best'] + # logger.log('The best model is {:}'.format(geno)) + # info = nas_bench.query_by_arch( geno ) + # if info is None: logger.log('Did not find this architecture : {:}.'.format(geno)) + # else : logger.log('{:}'.format(info)) + logger.close() + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser("ENAS") + parser.add_argument('--data_path', type=str, help='Path to dataset') + parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.') + # channels and number-of-cells + parser.add_argument('--search_space_name', type=str, help='The search space name.') + parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.') + parser.add_argument('--channel', type=int, help='The number of channels.') + parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.') + parser.add_argument('--config_path', type=str, help='The config file to train ENAS.') + parser.add_argument('--controller_train_steps', type=int, help='.') + parser.add_argument('--controller_num_aggregate', type=int, help='.') + parser.add_argument('--controller_entropy_weight', type=float, help='The weight for the entropy of the controller.') + parser.add_argument('--controller_bl_dec' , type=float, help='.') + parser.add_argument('--controller_num_samples' , type=int, help='.') + # log + parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)') + parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.') + parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (nas-benchmark).') + 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) + main(args) diff --git a/lib/models/__init__.py b/lib/models/__init__.py index 43cb354..fb761ff 100644 --- a/lib/models/__init__.py +++ b/lib/models/__init__.py @@ -16,18 +16,10 @@ from .cell_searchs import CellStructure, CellArchitectures # Cell-based NAS Models def get_cell_based_tiny_net(config): - if config.name == 'DARTS-V1': - from .cell_searchs import TinyNetworkDartsV1 - return TinyNetworkDartsV1(config.C, config.N, config.max_nodes, config.num_classes, config.space) - elif config.name == 'DARTS-V2': - from .cell_searchs import TinyNetworkDartsV2 - return TinyNetworkDartsV2(config.C, config.N, config.max_nodes, config.num_classes, config.space) - elif config.name == 'GDAS': - from .cell_searchs import TinyNetworkGDAS - return TinyNetworkGDAS(config.C, config.N, config.max_nodes, config.num_classes, config.space) - elif config.name == 'SETN': - from .cell_searchs import TinyNetworkSETN - return TinyNetworkSETN(config.C, config.N, config.max_nodes, config.num_classes, config.space) + group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS'] + from .cell_searchs import nas_super_nets + if config.name in group_names: + return nas_super_nets[config.name](config.C, config.N, config.max_nodes, config.num_classes, config.space) elif config.name == 'infer.tiny': from .cell_infers import TinyNetwork return TinyNetwork(config.C, config.N, config.genotype, config.num_classes) diff --git a/lib/models/cell_searchs/__init__.py b/lib/models/cell_searchs/__init__.py index 42e542d..82ed39e 100644 --- a/lib/models/cell_searchs/__init__.py +++ b/lib/models/cell_searchs/__init__.py @@ -2,4 +2,11 @@ from .search_model_darts_v1 import TinyNetworkDartsV1 from .search_model_darts_v2 import TinyNetworkDartsV2 from .search_model_gdas import TinyNetworkGDAS from .search_model_setn import TinyNetworkSETN +from .search_model_enas import TinyNetworkENAS from .genotypes import Structure as CellStructure, architectures as CellArchitectures + +nas_super_nets = {'DARTS-V1': TinyNetworkDartsV1, + 'DARTS-V2': TinyNetworkDartsV2, + 'GDAS' : TinyNetworkGDAS, + 'SETN' : TinyNetworkSETN, + 'ENAS' : TinyNetworkENAS} diff --git a/lib/models/cell_searchs/_test_module.py b/lib/models/cell_searchs/_test_module.py new file mode 100644 index 0000000..7261cd4 --- /dev/null +++ b/lib/models/cell_searchs/_test_module.py @@ -0,0 +1,9 @@ +import torch +from search_model_enas_utils import Controller + +def main(): + controller = Controller(6, 4) + predictions = controller() + +if __name__ == '__main__': + main() diff --git a/lib/models/cell_searchs/search_model_enas.py b/lib/models/cell_searchs/search_model_enas.py new file mode 100644 index 0000000..2422b52 --- /dev/null +++ b/lib/models/cell_searchs/search_model_enas.py @@ -0,0 +1,94 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +########################################################################## +# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 # +########################################################################## +import torch +import torch.nn as nn +from copy import deepcopy +from ..cell_operations import ResNetBasicblock +from .search_cells import SearchCell +from .genotypes import Structure +from .search_model_enas_utils import Controller + + +class TinyNetworkENAS(nn.Module): + + def __init__(self, C, N, max_nodes, num_classes, search_space): + super(TinyNetworkENAS, 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) + 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) + # to maintain the sampled architecture + self.sampled_arch = None + + def update_arch(self, _arch): + if _arch is None: + self.sampled_arch = None + elif isinstance(_arch, Structure): + self.sampled_arch = _arch + elif isinstance(_arch, (list, tuple)): + genotypes = [] + for i in range(1, self.max_nodes): + xlist = [] + for j in range(i): + node_str = '{:}<-{:}'.format(i, j) + op_index = _arch[ self.edge2index[node_str] ] + op_name = self.op_names[ op_index ] + xlist.append((op_name, j)) + genotypes.append( tuple(xlist) ) + self.sampled_arch = Structure(genotypes) + else: + raise ValueError('invalid type of input architecture : {:}'.format(_arch)) + return self.sampled_arch + + def create_controller(self): + return Controller(len(self.edge2index), len(self.op_names)) + + def get_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})'.format(name=self.__class__.__name__, **self.__dict__)) + + def forward(self, inputs): + + feature = self.stem(inputs) + for i, cell in enumerate(self.cells): + if isinstance(cell, SearchCell): + feature = cell.forward_dynamic(feature, self.sampled_arch) + 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 diff --git a/lib/models/cell_searchs/search_model_enas_utils.py b/lib/models/cell_searchs/search_model_enas_utils.py new file mode 100644 index 0000000..e03f57b --- /dev/null +++ b/lib/models/cell_searchs/search_model_enas_utils.py @@ -0,0 +1,55 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # +########################################################################## +# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 # +########################################################################## +import torch +import torch.nn as nn +from torch.distributions.categorical import Categorical + +class Controller(nn.Module): + # we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py + def __init__(self, num_edge, num_ops, lstm_size=32, lstm_num_layers=2, tanh_constant=2.5, temperature=5.0): + super(Controller, self).__init__() + # assign the attributes + self.num_edge = num_edge + self.num_ops = num_ops + self.lstm_size = lstm_size + self.lstm_N = lstm_num_layers + self.tanh_constant = tanh_constant + self.temperature = temperature + # create parameters + self.register_parameter('input_vars', nn.Parameter(torch.Tensor(1, 1, lstm_size))) + self.w_lstm = nn.LSTM(input_size=self.lstm_size, hidden_size=self.lstm_size, num_layers=self.lstm_N) + self.w_embd = nn.Embedding(self.num_ops, self.lstm_size) + self.w_pred = nn.Linear(self.lstm_size, self.num_ops) + + nn.init.uniform_(self.input_vars , -0.1, 0.1) + nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1) + nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1) + nn.init.uniform_(self.w_embd.weight , -0.1, 0.1) + nn.init.uniform_(self.w_pred.weight , -0.1, 0.1) + + def forward(self): + + inputs, h0 = self.input_vars, None + log_probs, entropys, sampled_arch = [], [], [] + for iedge in range(self.num_edge): + outputs, h0 = self.w_lstm(inputs, h0) + + logits = self.w_pred(outputs) + logits = logits / self.temperature + logits = self.tanh_constant * torch.tanh(logits) + # distribution + op_distribution = Categorical(logits=logits) + op_index = op_distribution.sample() + sampled_arch.append( op_index.item() ) + + op_log_prob = op_distribution.log_prob(op_index) + log_probs.append( op_log_prob.view(-1) ) + op_entropy = op_distribution.entropy() + entropys.append( op_entropy.view(-1) ) + + # obtain the input embedding for the next step + inputs = self.w_embd(op_index) + return torch.sum(torch.cat(log_probs)), torch.sum(torch.cat(entropys)), sampled_arch