579 lines
20 KiB
Python
579 lines
20 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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##########################################################################
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# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
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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 xautodl.config_utils import load_config, dict2config, configure2str
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from xautodl.datasets import get_datasets, get_nas_search_loaders
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from xautodl.procedures import (
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prepare_seed,
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prepare_logger,
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save_checkpoint,
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copy_checkpoint,
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get_optim_scheduler,
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)
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from xautodl.utils import get_model_infos, obtain_accuracy
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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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from xautodl.models import get_cell_based_tiny_net, get_search_spaces
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from nas_201_api import NASBench201API as API
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def train_shared_cnn(
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xloader,
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shared_cnn,
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controller,
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criterion,
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scheduler,
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optimizer,
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epoch_str,
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print_freq,
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logger,
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):
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data_time, batch_time = AverageMeter(), AverageMeter()
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losses, top1s, top5s, xend = (
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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time.time(),
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)
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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 = (
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"*Train-Shared-CNN* "
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+ time_string()
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+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
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)
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Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
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batch_time=batch_time, data_time=data_time
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)
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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(
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loss=losses, top1=top1s, top5=top5s
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)
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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(
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xloader,
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shared_cnn,
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controller,
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criterion,
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optimizer,
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config,
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epoch_str,
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print_freq,
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logger,
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):
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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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(
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GradnormMeter,
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LossMeter,
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ValAccMeter,
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EntropyMeter,
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BaselineMeter,
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RewardMeter,
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xend,
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) = (
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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time.time(),
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)
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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) * (
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config.baseline - reward
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)
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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() * 100)
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LossMeter.update(loss.item())
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EntropyMeter.update(entropy.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 = (
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"*Train-Controller* "
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+ time_string()
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+ " [{:}][{:03d}/{:03d}]".format(
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epoch_str, step, config.ctl_train_steps * config.ctl_num_aggre
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)
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)
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Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
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batch_time=batch_time, data_time=data_time
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)
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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(
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loss=LossMeter,
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top1=ValAccMeter,
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reward=RewardMeter,
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basel=BaselineMeter,
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)
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Estr = "Entropy={:.4f} ({:.4f})".format(EntropyMeter.val, EntropyMeter.avg)
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logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Estr)
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return (
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LossMeter.avg,
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ValAccMeter.avg,
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BaselineMeter.avg,
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RewardMeter.avg,
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baseline.item(),
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)
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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(
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logits.cpu().data, targets.data, topk=(1, 5)
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)
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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(
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logits.data, arch_targets.data, topk=(1, 5)
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)
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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(
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xargs.dataset, xargs.data_path, -1
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)
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logger.log("use config from : {:}".format(xargs.config_path))
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config = load_config(
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xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger
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)
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_, train_loader, valid_loader = get_nas_search_loaders(
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train_data,
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test_data,
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xargs.dataset,
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"configs/nas-benchmark/",
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config.batch_size,
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xargs.workers,
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)
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# since ENAS will train the controller on valid-loader, we need to use train transformation for valid-loader
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valid_loader.dataset.transform = deepcopy(train_loader.dataset.transform)
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if hasattr(valid_loader.dataset, "transforms"):
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valid_loader.dataset.transforms = deepcopy(train_loader.dataset.transforms)
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# data loader
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logger.log(
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"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
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xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
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)
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)
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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(
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{
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"name": "ENAS",
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"C": xargs.channel,
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"N": xargs.num_cells,
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"max_nodes": xargs.max_nodes,
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"num_classes": class_num,
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"space": search_space,
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"affine": False,
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"track_running_stats": bool(xargs.track_running_stats),
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},
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None,
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)
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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(
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shared_cnn.parameters(), config
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)
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a_optimizer = torch.optim.Adam(
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controller.parameters(),
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lr=config.controller_lr,
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betas=config.controller_betas,
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eps=config.controller_eps,
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)
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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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logger.log("search-space : {:}".format(search_space))
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if xargs.arch_nas_dataset is None:
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api = None
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else:
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api = API(xargs.arch_nas_dataset)
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logger.log("{:} create API = {:} done".format(time_string(), api))
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shared_cnn, controller, criterion = (
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torch.nn.DataParallel(shared_cnn).cuda(),
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controller.cuda(),
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criterion.cuda(),
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)
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last_info, model_base_path, model_best_path = (
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logger.path("info"),
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logger.path("model"),
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logger.path("best"),
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)
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start".format(last_info)
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)
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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(
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"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
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last_info, start_epoch
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)
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)
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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, search_time, epoch_time, total_epoch = (
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time.time(),
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AverageMeter(),
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AverageMeter(),
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config.epochs + config.warmup,
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)
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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(
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convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
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)
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epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
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logger.log(
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"\n[Search the {:}-th epoch] {:}, LR={:}, baseline={:}".format(
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epoch_str, need_time, min(w_scheduler.get_lr()), baseline
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)
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)
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cnn_loss, cnn_top1, cnn_top5 = train_shared_cnn(
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train_loader,
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shared_cnn,
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controller,
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criterion,
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w_scheduler,
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w_optimizer,
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epoch_str,
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xargs.print_freq,
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logger,
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)
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logger.log(
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"[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
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epoch_str, cnn_loss, cnn_top1, cnn_top5
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)
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)
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ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline = train_controller(
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valid_loader,
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shared_cnn,
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controller,
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criterion,
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a_optimizer,
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dict2config(
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{
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"baseline": baseline,
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"ctl_train_steps": xargs.controller_train_steps,
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"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,
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},
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None,
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),
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epoch_str,
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xargs.print_freq,
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logger,
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)
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search_time.update(time.time() - start_time)
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logger.log(
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"[{:}] controller : loss={:.2f}, accuracy={:.2f}%, baseline={:.2f}, reward={:.2f}, current-baseline={:.4f}, time-cost={:.1f} s".format(
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epoch_str,
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ctl_loss,
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ctl_acc,
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ctl_baseline,
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ctl_reward,
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baseline,
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search_time.sum,
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)
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)
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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:
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find_best = False
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|
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logger.log(
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"<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch])
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)
|
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# save checkpoint
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|
save_path = save_checkpoint(
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{
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"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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},
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model_base_path,
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logger,
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)
|
|
last_info = save_checkpoint(
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|
{
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"epoch": epoch + 1,
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"args": deepcopy(args),
|
|
"last_checkpoint": save_path,
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|
},
|
|
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)
|
|
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()
|
|
|
|
logger.log("\n" + "-" * 100)
|
|
logger.log(
|
|
"During searching, the best architecture is {:}".format(genotypes["best"])
|
|
)
|
|
logger.log("Its accuracy is {:.2f}%".format(valid_accuracies["best"]))
|
|
logger.log(
|
|
"Randomly select {:} architectures and select the best.".format(
|
|
xargs.controller_num_samples
|
|
)
|
|
)
|
|
start_time = time.time()
|
|
final_arch, _ = get_best_arch(
|
|
controller, shared_cnn, valid_loader, xargs.controller_num_samples
|
|
)
|
|
search_time.update(time.time() - start_time)
|
|
shared_cnn.module.update_arch(final_arch)
|
|
final_loss, final_top1, final_top5 = valid_func(valid_loader, shared_cnn, criterion)
|
|
logger.log("The Selected Final Architecture : {:}".format(final_arch))
|
|
logger.log(
|
|
"Loss={:.3f}, Accuracy@1={:.2f}%, Accuracy@5={:.2f}%".format(
|
|
final_loss, final_top1, final_top5
|
|
)
|
|
)
|
|
logger.log(
|
|
"ENAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
|
|
total_epoch, search_time.sum, final_arch
|
|
)
|
|
)
|
|
if api is not None:
|
|
logger.log("{:}".format(api.query_by_arch(final_arch)))
|
|
logger.close()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser("ENAS")
|
|
parser.add_argument("--data_path", type=str, help="The 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(
|
|
"--track_running_stats",
|
|
type=int,
|
|
choices=[0, 1],
|
|
help="Whether use track_running_stats or not in the BN layer.",
|
|
)
|
|
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)
|