From 68f9d037eb863bad7d86096224fe8c151e002a32 Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 16 Jul 2020 10:34:34 +0000 Subject: [PATCH] Prototype generic nas model. --- .../nas-benchmark/algos/weight-sharing.config | 14 + exps/algos-v2/run-all.sh | 2 +- exps/algos-v2/search-cell.py | 290 ++++++++++++++++++ lib/models/__init__.py | 2 +- lib/models/cell_searchs/__init__.py | 4 +- lib/models/cell_searchs/generic_model.py | 200 ++++++++++++ lib/utils/__init__.py | 2 +- 7 files changed, 510 insertions(+), 4 deletions(-) create mode 100644 configs/nas-benchmark/algos/weight-sharing.config create mode 100644 exps/algos-v2/search-cell.py create mode 100644 lib/models/cell_searchs/generic_model.py diff --git a/configs/nas-benchmark/algos/weight-sharing.config b/configs/nas-benchmark/algos/weight-sharing.config new file mode 100644 index 0000000..e2d956d --- /dev/null +++ b/configs/nas-benchmark/algos/weight-sharing.config @@ -0,0 +1,14 @@ +{ + "scheduler": ["str", "cos"], + "LR" : ["float", "0.025"], + "eta_min" : ["float", "0.001"], + "epochs" : ["int", "250"], + "warmup" : ["int", "0"], + "optim" : ["str", "SGD"], + "decay" : ["float", "0.0005"], + "momentum" : ["float", "0.9"], + "nesterov" : ["bool", "1"], + "criterion": ["str", "Softmax"], + "batch_size": ["int", "64"], + "test_batch_size": ["int", "512"] +} diff --git a/exps/algos-v2/run-all.sh b/exps/algos-v2/run-all.sh index 4b2199b..53cf169 100644 --- a/exps/algos-v2/run-all.sh +++ b/exps/algos-v2/run-all.sh @@ -14,7 +14,7 @@ do python ./exps/algos-v2/reinforce.py --dataset ${dataset} --search_space ${search_space} --learning_rate 0.001 python ./exps/algos-v2/regularized_ea.py --dataset ${dataset} --search_space ${search_space} --ea_cycles 200 --ea_population 10 --ea_sample_size 3 python ./exps/algos-v2/random_wo_share.py --dataset ${dataset} --search_space ${search_space} - python exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 + python ./exps/algos-v2/bohb.py --dataset ${dataset} --search_space ${search_space} --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 done done diff --git a/exps/algos-v2/search-cell.py b/exps/algos-v2/search-cell.py new file mode 100644 index 0000000..568eae9 --- /dev/null +++ b/exps/algos-v2/search-cell.py @@ -0,0 +1,290 @@ +################################################## +# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 # +###################################################################################### +# python ./exps/algos-v2/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --rand_seed 1 +# python ./exps/algos-v2/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1 +# python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1 +###################################################################################### +import os, sys, time, random, argparse +import numpy as np +from copy import deepcopy +import torch +import torch.nn as nn +from pathlib import Path +lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve() +if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir)) +from config_utils import load_config, dict2config, configure2str +from datasets import get_datasets, get_nas_search_loaders +from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler +from utils import count_parameters_in_MB, obtain_accuracy +from log_utils import AverageMeter, time_string, convert_secs2time +from models import get_cell_based_tiny_net, get_search_spaces +from nas_201_api import NASBench201API as API + + +def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger): + data_time, batch_time = AverageMeter(), AverageMeter() + base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter() + arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter() + end = time.time() + network.train() + for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader): + scheduler.update(None, 1.0 * step / len(xloader)) + base_targets = base_targets.cuda(non_blocking=True) + arch_targets = arch_targets.cuda(non_blocking=True) + # measure data loading time + data_time.update(time.time() - end) + + # update the weights + sampled_arch = network.module.dync_genotype(True) + network.module.set_cal_mode('dynamic', sampled_arch) + #network.module.set_cal_mode( 'urs' ) + network.zero_grad() + _, logits = network(base_inputs) + base_loss = criterion(logits, base_targets) + base_loss.backward() + w_optimizer.step() + # record + base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5)) + base_losses.update(base_loss.item(), base_inputs.size(0)) + base_top1.update (base_prec1.item(), base_inputs.size(0)) + base_top5.update (base_prec5.item(), base_inputs.size(0)) + + # update the architecture-weight + network.module.set_cal_mode( 'joint' ) + network.zero_grad() + _, logits = network(arch_inputs) + arch_loss = criterion(logits, arch_targets) + arch_loss.backward() + a_optimizer.step() + # record + arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5)) + arch_losses.update(arch_loss.item(), arch_inputs.size(0)) + arch_top1.update (arch_prec1.item(), arch_inputs.size(0)) + arch_top5.update (arch_prec5.item(), arch_inputs.size(0)) + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if step % print_freq == 0 or step + 1 == len(xloader): + Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader)) + Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time) + Wstr = 'Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=base_losses, top1=base_top1, top5=base_top5) + Astr = 'Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=arch_losses, top1=arch_top1, top5=arch_top5) + logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr) + #print (nn.functional.softmax(network.module.arch_parameters, dim=-1)) + #print (network.module.arch_parameters) + return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg + + +def get_best_arch(xloader, network, n_samples): + with torch.no_grad(): + network.eval() + archs, valid_accs = network.module.return_topK(n_samples), [] + #print ('obtain the top-{:} architectures'.format(n_samples)) + loader_iter = iter(xloader) + for i, sampled_arch in enumerate(archs): + network.module.set_cal_mode('dynamic', sampled_arch) + try: + inputs, targets = next(loader_iter) + except: + loader_iter = iter(xloader) + inputs, targets = next(loader_iter) + + _, logits = network(inputs) + val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5)) + + valid_accs.append(val_top1.item()) + + best_idx = np.argmax(valid_accs) + best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx] + return best_arch, best_valid_acc + + +def valid_func(xloader, network, criterion): + data_time, batch_time = AverageMeter(), AverageMeter() + arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter() + end = time.time() + with torch.no_grad(): + network.eval() + for step, (arch_inputs, arch_targets) in enumerate(xloader): + arch_targets = arch_targets.cuda(non_blocking=True) + # measure data loading time + data_time.update(time.time() - end) + # prediction + _, logits = network(arch_inputs) + arch_loss = criterion(logits, arch_targets) + # record + arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5)) + arch_losses.update(arch_loss.item(), arch_inputs.size(0)) + arch_top1.update (arch_prec1.item(), arch_inputs.size(0)) + arch_top5.update (arch_prec5.item(), arch_inputs.size(0)) + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + return arch_losses.avg, arch_top1.avg, arch_top5.avg + + +def main(xargs): + assert torch.cuda.is_available(), 'CUDA is not available.' + torch.backends.cudnn.enabled = True + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + torch.set_num_threads( xargs.workers ) + prepare_seed(xargs.rand_seed) + logger = prepare_logger(args) + + train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1) + config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger) + search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', \ + (config.batch_size, config.test_batch_size), xargs.workers) + logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size)) + logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config)) + + search_space = get_search_spaces(xargs.search_space, 'nas-bench-301') + + + model_config = dict2config( + dict(name='generic', C=xargs.channel, N=xargs.num_cells, max_nodes=xargs.max_nodes, num_classes=class_num, + space=search_space, affine=bool(xargs.affine), track_running_stats=bool(xargs.track_running_stats)), None) + logger.log('search space : {:}'.format(search_space)) + logger.log('model config : {:}'.format(model_config)) + search_model = get_cell_based_tiny_net(model_config) + search_model.set_algo(xargs.algo) + + w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config) + a_optimizer = torch.optim.Adam(search_model.alphas, lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay) + logger.log('w-optimizer : {:}'.format(w_optimizer)) + logger.log('a-optimizer : {:}'.format(a_optimizer)) + logger.log('w-scheduler : {:}'.format(w_scheduler)) + logger.log('criterion : {:}'.format(criterion)) + params = count_parameters_in_MB(search_model) + logger.log('The parameters of the search model = {:.2f} MB'.format(params)) + logger.log('search-space : {:}'.format(search_space)) + api = API() + logger.log('{:} create API = {:} done'.format(time_string(), api)) + + last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best') + # network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda() + network, criterion = search_model.cuda(), criterion.cuda() # use a single GPU + + last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best') + + if last_info.exists(): # automatically resume from previous checkpoint + logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info)) + last_info = torch.load(last_info) + start_epoch = last_info['epoch'] + checkpoint = torch.load(last_info['last_checkpoint']) + genotypes = checkpoint['genotypes'] + valid_accuracies = checkpoint['valid_accuracies'] + search_model.load_state_dict( checkpoint['search_model'] ) + w_scheduler.load_state_dict ( checkpoint['w_scheduler'] ) + w_optimizer.load_state_dict ( checkpoint['w_optimizer'] ) + a_optimizer.load_state_dict ( checkpoint['a_optimizer'] ) + logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch)) + else: + logger.log("=> do not find the last-info file : {:}".format(last_info)) + start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {} + + # start training + start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup + for epoch in range(start_epoch, total_epoch): + w_scheduler.update(epoch, 0.0) + need_time = 'Time Left: {:}'.format(convert_secs2time(epoch_time.val * (total_epoch-epoch), True)) + epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch) + logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()))) + + import pdb; pdb.set_trace() + + search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \ + = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger) + search_time.update(time.time() - start_time) + logger.log('[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum)) + logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5)) + + genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num) + network.module.set_cal_mode('dynamic', genotype) + valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) + logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype)) + #search_model.set_cal_mode('urs') + #valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) + #logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) + #search_model.set_cal_mode('joint') + #valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) + #logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) + #search_model.set_cal_mode('select') + #valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) + #logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) + # check the best accuracy + valid_accuracies[epoch] = valid_a_top1 + + genotypes[epoch] = genotype + logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch])) + # save checkpoint + save_path = save_checkpoint({'epoch' : epoch + 1, + 'args' : deepcopy(xargs), + 'search_model': search_model.state_dict(), + 'w_optimizer' : w_optimizer.state_dict(), + 'a_optimizer' : a_optimizer.state_dict(), + 'w_scheduler' : w_scheduler.state_dict(), + 'genotypes' : genotypes, + 'valid_accuracies' : valid_accuracies}, + model_base_path, logger) + last_info = save_checkpoint({ + 'epoch': epoch + 1, + 'args' : deepcopy(args), + 'last_checkpoint': save_path, + }, logger.path('info'), logger) + with torch.no_grad(): + logger.log('{:}'.format(search_model.show_alphas())) + if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[epoch], '200'))) + # measure elapsed time + epoch_time.update(time.time() - start_time) + start_time = time.time() + + # the final post procedure : count the time + start_time = time.time() + genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num) + search_time.update(time.time() - start_time) + network.module.set_cal_mode('dynamic', genotype) + valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) + logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1)) + + logger.log('\n' + '-'*100) + # check the performance from the architecture dataset + logger.log('SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, genotype)) + if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '200') )) + logger.close() + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.") + parser.add_argument('--data_path' , type=str, help='Path to dataset') + parser.add_argument('--dataset' , type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.') + parser.add_argument('--search_space', type=str, default='tss', choices=['tss'], help='The search space name.') + parser.add_argument('--algo' , type=str, choices=['darts-v1', 'darts-v2', 'gdas', 'setn', 'random', 'enas'], help='The search space name.') + # channels and number-of-cells + parser.add_argument('--max_nodes' , type=int, default=4, help='The maximum number of nodes.') + parser.add_argument('--channel' , type=int, default=16, help='The number of channels.') + parser.add_argument('--num_cells' , type=int, default=5, help='The number of cells in one stage.') + # + parser.add_argument('--eval_candidate_num', type=int, help='The number of selected architectures to evaluate.') + # + parser.add_argument('--track_running_stats',type=int, default=0, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.') + parser.add_argument('--affine' , type=int, default=0, choices=[0,1],help='Whether use affine=True or False in the BN layer.') + parser.add_argument('--config_path' , type=str, default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.') + # architecture leraning rate + parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding') + parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding') + # 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) diff --git a/lib/models/__init__.py b/lib/models/__init__.py index 481fa74..debdecb 100644 --- a/lib/models/__init__.py +++ b/lib/models/__init__.py @@ -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: diff --git a/lib/models/cell_searchs/__init__.py b/lib/models/cell_searchs/__init__.py index 8454f8e..968eb37 100644 --- a/lib/models/cell_searchs/__init__.py +++ b/lib/models/cell_searchs/__init__.py @@ -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} diff --git a/lib/models/cell_searchs/generic_model.py b/lib/models/cell_searchs/generic_model.py new file mode 100644 index 0000000..5b437cb --- /dev/null +++ b/lib/models/cell_searchs/generic_model.py @@ -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 diff --git a/lib/utils/__init__.py b/lib/utils/__init__.py index 04c3bb6..1cc1647 100644 --- a/lib/utils/__init__.py +++ b/lib/utils/__init__.py @@ -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