update ENAS
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D-X-Y
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17
configs/nas-benchmark/algos/ENAS.config
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configs/nas-benchmark/algos/ENAS.config
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{
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"scheduler": ["str", "cos"],
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"LR" : ["float", "0.05"],
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"eta_min" : ["float", "0.0005"],
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"epochs" : ["int", "310"],
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"T_max" : ["int", "10"],
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"warmup" : ["int", "0"],
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"optim" : ["str", "SGD"],
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"decay" : ["float", "0.00025"],
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"momentum" : ["float", "0.9"],
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"nesterov" : ["bool", "1"],
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"controller_lr" : ["float", "0.001"],
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"controller_betas": ["float", [0, 0.999]],
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"controller_eps" : ["float", 0.001],
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"criterion": ["str", "Softmax"],
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"batch_size": ["int", "128"]
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}
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347
exps/algos/ENAS.py
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exps/algos/ENAS.py
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time, glob, 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, SearchDataset
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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 get_model_infos, 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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def train_shared_cnn(xloader, shared_cnn, controller, criterion, scheduler, optimizer, epoch_str, print_freq, logger):
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data_time, batch_time = AverageMeter(), AverageMeter()
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losses, top1s, top5s, xend = AverageMeter(), AverageMeter(), AverageMeter(), time.time()
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shared_cnn.train()
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controller.eval()
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for step, (inputs, targets) in enumerate(xloader):
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scheduler.update(None, 1.0 * step / len(xloader))
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targets = targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - xend)
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with torch.no_grad():
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_, _, sampled_arch = controller()
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optimizer.zero_grad()
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shared_cnn.module.update_arch(sampled_arch)
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_, logits = shared_cnn(inputs)
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loss = criterion(logits, targets)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), 5)
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optimizer.step()
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# record
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prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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losses.update(loss.item(), inputs.size(0))
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top1s.update (prec1.item(), inputs.size(0))
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top5s.update (prec5.item(), inputs.size(0))
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# measure elapsed time
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batch_time.update(time.time() - xend)
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xend = time.time()
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if step % print_freq == 0 or step + 1 == len(xloader):
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Sstr = '*Train-Shared-CNN* ' + 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 = '[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)
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logger.log(Sstr + ' ' + Tstr + ' ' + Wstr)
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return losses.avg, top1s.avg, top5s.avg
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def train_controller(xloader, shared_cnn, controller, criterion, optimizer, config, epoch_str, print_freq, logger):
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# config. (containing some necessary arg)
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# baseline: The baseline score (i.e. average val_acc) from the previous epoch
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data_time, batch_time = AverageMeter(), AverageMeter()
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GradnormMeter, LossMeter, ValAccMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time()
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shared_cnn.eval()
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controller.train()
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controller.zero_grad()
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#for step, (inputs, targets) in enumerate(xloader):
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loader_iter = iter(xloader)
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for step in range(config.ctl_train_steps * config.ctl_num_aggre):
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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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targets = targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - xend)
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log_prob, entropy, sampled_arch = controller()
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with torch.no_grad():
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shared_cnn.module.update_arch(sampled_arch)
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_, logits = shared_cnn(inputs)
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val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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val_top1 = val_top1.view(-1) / 100
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reward = val_top1 + config.ctl_entropy_w * entropy
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if config.baseline is None:
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baseline = val_top1
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else:
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baseline = config.baseline - (1 - config.ctl_bl_dec) * (config.baseline - reward)
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loss = -1 * log_prob * (reward - baseline)
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# account
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RewardMeter.update(reward.item())
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BaselineMeter.update(baseline.item())
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ValAccMeter.update(val_top1.item())
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LossMeter.update(loss.item())
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# Average gradient over controller_num_aggregate samples
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loss = loss / config.ctl_num_aggre
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loss.backward(retain_graph=True)
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# measure elapsed time
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batch_time.update(time.time() - xend)
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xend = time.time()
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if (step+1) % config.ctl_num_aggre == 0:
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grad_norm = torch.nn.utils.clip_grad_norm_(controller.parameters(), 5.0)
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GradnormMeter.update(grad_norm)
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optimizer.step()
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controller.zero_grad()
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if step % print_freq == 0:
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Sstr = '*Train-Controller* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, config.ctl_train_steps * config.ctl_num_aggre)
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Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
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Wstr = '[Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Reward {reward.val:.2f} ({reward.avg:.2f})] Baseline {basel.val:.2f} ({basel.avg:.2f})'.format(loss=LossMeter, top1=ValAccMeter, reward=RewardMeter, basel=BaselineMeter)
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logger.log(Sstr + ' ' + Tstr + ' ' + Wstr)
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return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg, baseline.item()
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def get_best_arch(controller, shared_cnn, xloader, n_samples=10):
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with torch.no_grad():
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controller.eval()
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shared_cnn.eval()
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archs, valid_accs = [], []
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loader_iter = iter(xloader)
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for i in range(n_samples):
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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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_, _, sampled_arch = controller()
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arch = shared_cnn.module.update_arch(sampled_arch)
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_, logits = shared_cnn(inputs)
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val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
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archs.append( arch )
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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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network.eval()
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end = time.time()
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with torch.no_grad():
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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, test_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
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split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
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cifar_split = load_config(split_Fpath, None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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logger.log('Load split file from {:}'.format(split_Fpath))
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elif xargs.dataset.startswith('ImageNet16'):
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split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
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imagenet16_split = load_config(split_Fpath, None, None)
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train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
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logger.log('Load split file from {:}'.format(split_Fpath))
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else:
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raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
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logger.log('use config from : {:}'.format(xargs.config_path))
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config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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logger.log('config: {:}'.format(config))
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# To split data
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = test_data.transform
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valid_data = train_data_v2
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# data loader
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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)
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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)
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logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_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('cell', xargs.search_space_name)
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model_config = dict2config({'name': 'ENAS', 'C': xargs.channel, 'N': xargs.num_cells,
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'max_nodes': xargs.max_nodes, 'num_classes': class_num,
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'space' : search_space}, None)
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shared_cnn = get_cell_based_tiny_net(model_config)
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controller = shared_cnn.create_controller()
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w_optimizer, w_scheduler, criterion = get_optim_scheduler(shared_cnn.parameters(), config)
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a_optimizer = torch.optim.Adam(controller.parameters(), lr=config.controller_lr, betas=config.controller_betas, eps=config.controller_eps)
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logger.log('w-optimizer : {:}'.format(w_optimizer))
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logger.log('a-optimizer : {:}'.format(a_optimizer))
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logger.log('w-scheduler : {:}'.format(w_scheduler))
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logger.log('criterion : {:}'.format(criterion))
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#flop, param = get_model_infos(shared_cnn, xshape)
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#logger.log('{:}'.format(shared_cnn))
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#logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
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shared_cnn, controller, criterion = torch.nn.DataParallel(shared_cnn).cuda(), controller.cuda(), criterion.cuda()
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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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baseline = checkpoint['baseline']
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valid_accuracies = checkpoint['valid_accuracies']
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shared_cnn.load_state_dict( checkpoint['shared_cnn'] )
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controller.load_state_dict( checkpoint['controller'] )
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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, baseline = 0, {'best': -1}, {}, None
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# start training
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start_time, epoch_time, total_epoch = time.time(), 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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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)
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logger.log('[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, cnn_loss, cnn_top1, cnn_top5))
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ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline \
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= train_controller(valid_loader, shared_cnn, controller, criterion, a_optimizer, \
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dict2config({'baseline': baseline,
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'ctl_train_steps': xargs.controller_train_steps, 'ctl_num_aggre': xargs.controller_num_aggregate,
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'ctl_entropy_w': xargs.controller_entropy_weight,
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'ctl_bl_dec' : xargs.controller_bl_dec}, None), \
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epoch_str, xargs.print_freq, logger)
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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))
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best_arch, _ = get_best_arch(controller, shared_cnn, valid_loader)
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shared_cnn.module.update_arch(best_arch)
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best_valid_acc = valid_func(valid_loader, shared_cnn, criterion)
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genotypes[epoch] = best_arch
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# check the best accuracy
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valid_accuracies[epoch] = best_valid_acc
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if best_valid_acc > valid_accuracies['best']:
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valid_accuracies['best'] = best_valid_acc
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genotypes['best'] = best_arch
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find_best = True
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else: find_best = False
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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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'baseline' : baseline,
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'shared_cnn' : shared_cnn.state_dict(),
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'controller' : controller.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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if find_best:
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logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, best_valid_acc))
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copy_checkpoint(model_base_path, model_best_path, logger)
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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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logger.log('\n' + '-'*100)
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# check the performance from the architecture dataset
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#if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset):
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# logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset))
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#else:
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# nas_bench = NASBenchmarkAPI(xargs.arch_nas_dataset)
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# geno = genotypes[total_epoch-1]
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# logger.log('The last model is {:}'.format(geno))
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# info = nas_bench.query_by_arch( geno )
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# if info is None: logger.log('Did not find this architecture : {:}.'.format(geno))
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# else : logger.log('{:}'.format(info))
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# logger.log('-'*100)
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# geno = genotypes['best']
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# logger.log('The best model is {:}'.format(geno))
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# info = nas_bench.query_by_arch( geno )
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# if info is None: logger.log('Did not find this architecture : {:}.'.format(geno))
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# else : logger.log('{:}'.format(info))
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logger.close()
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if __name__ == '__main__':
|
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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)
|
||||
@ -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)
|
||||
|
||||
@ -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}
|
||||
|
||||
9
lib/models/cell_searchs/_test_module.py
Normal file
9
lib/models/cell_searchs/_test_module.py
Normal file
@ -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()
|
||||
94
lib/models/cell_searchs/search_model_enas.py
Normal file
94
lib/models/cell_searchs/search_model_enas.py
Normal file
@ -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
|
||||
55
lib/models/cell_searchs/search_model_enas_utils.py
Normal file
55
lib/models/cell_searchs/search_model_enas_utils.py
Normal file
@ -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
|
||||
Loading…
Reference in New Issue
Block a user