Reformulate via black
This commit is contained in:
D-X-Y
@@ -7,232 +7,330 @@ import sys, time, random, argparse
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from copy import deepcopy
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import torch
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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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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, dict2config, configure2str
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from datasets import get_datasets, get_nas_search_loaders
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from utils import 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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from nas_201_api import NASBench201API as API
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from datasets import get_datasets, get_nas_search_loaders
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from utils import 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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from nas_201_api import NASBench201API as API
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def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger, gradient_clip):
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data_time, batch_time = AverageMeter(), AverageMeter()
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base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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network.train()
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end = time.time()
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for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
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scheduler.update(None, 1.0 * step / len(xloader))
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base_targets = base_targets.cuda(non_blocking=True)
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arch_targets = arch_targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - end)
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# update the weights
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w_optimizer.zero_grad()
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_, logits = network(base_inputs)
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base_loss = criterion(logits, base_targets)
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base_loss.backward()
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if gradient_clip > 0: torch.nn.utils.clip_grad_norm_(network.parameters(), gradient_clip)
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w_optimizer.step()
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# record
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base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
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base_losses.update(base_loss.item(), base_inputs.size(0))
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base_top1.update (base_prec1.item(), base_inputs.size(0))
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base_top5.update (base_prec5.item(), base_inputs.size(0))
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# update the architecture-weight
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a_optimizer.zero_grad()
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_, logits = network(arch_inputs)
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arch_loss = criterion(logits, arch_targets)
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arch_loss.backward()
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a_optimizer.step()
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# record
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arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
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arch_losses.update(arch_loss.item(), arch_inputs.size(0))
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arch_top1.update (arch_prec1.item(), arch_inputs.size(0))
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arch_top5.update (arch_prec5.item(), arch_inputs.size(0))
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# measure elapsed time
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batch_time.update(time.time() - end)
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def search_func(
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xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger, gradient_clip
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):
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data_time, batch_time = AverageMeter(), AverageMeter()
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base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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network.train()
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end = time.time()
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for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
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scheduler.update(None, 1.0 * step / len(xloader))
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base_targets = base_targets.cuda(non_blocking=True)
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arch_targets = arch_targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - end)
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if step % print_freq == 0 or step + 1 == len(xloader):
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Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader))
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Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
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Wstr = 'Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=base_losses, top1=base_top1, top5=base_top5)
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Astr = 'Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=arch_losses, top1=arch_top1, top5=arch_top5)
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logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
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return base_losses.avg, base_top1.avg, base_top5.avg
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# update the weights
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w_optimizer.zero_grad()
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_, logits = network(base_inputs)
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base_loss = criterion(logits, base_targets)
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base_loss.backward()
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if gradient_clip > 0:
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torch.nn.utils.clip_grad_norm_(network.parameters(), gradient_clip)
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w_optimizer.step()
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# record
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base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
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base_losses.update(base_loss.item(), base_inputs.size(0))
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base_top1.update(base_prec1.item(), base_inputs.size(0))
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base_top5.update(base_prec5.item(), base_inputs.size(0))
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# update the architecture-weight
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a_optimizer.zero_grad()
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_, logits = network(arch_inputs)
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arch_loss = criterion(logits, arch_targets)
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arch_loss.backward()
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a_optimizer.step()
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# record
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arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
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arch_losses.update(arch_loss.item(), arch_inputs.size(0))
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arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
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arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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if step % print_freq == 0 or step + 1 == len(xloader):
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Sstr = "*SEARCH* " + time_string() + " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
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Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
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batch_time=batch_time, data_time=data_time
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)
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Wstr = "Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
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loss=base_losses, top1=base_top1, top5=base_top5
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)
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Astr = "Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
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loss=arch_losses, top1=arch_top1, top5=arch_top5
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)
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logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Astr)
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return base_losses.avg, base_top1.avg, base_top5.avg
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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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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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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads(xargs.workers)
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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#config_path = 'configs/nas-benchmark/algos/DARTS.config'
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config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', config.batch_size, xargs.workers)
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logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
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logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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# config_path = 'configs/nas-benchmark/algos/DARTS.config'
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config = load_config(xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger)
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search_loader, _, valid_loader = get_nas_search_loaders(
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train_data, valid_data, xargs.dataset, "configs/nas-benchmark/", config.batch_size, xargs.workers
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)
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logger.log(
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"||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
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xargs.dataset, len(search_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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if xargs.model_config is None:
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model_config = dict2config({'name': 'DARTS-V1', '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,
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'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
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else:
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model_config = load_config(xargs.model_config, {'num_classes': class_num, 'space' : search_space,
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'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)
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search_model = get_cell_based_tiny_net(model_config)
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logger.log('search-model :\n{:}'.format(search_model))
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w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
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a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
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logger.log('w-optimizer : {:}'.format(w_optimizer))
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logger.log('a-optimizer : {:}'.format(a_optimizer))
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logger.log('w-scheduler : {:}'.format(w_scheduler))
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logger.log('criterion : {:}'.format(criterion))
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flop, param = get_model_infos(search_model, xshape)
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#logger.log('{:}'.format(search_model))
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logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
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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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search_space = get_search_spaces("cell", xargs.search_space_name)
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if xargs.model_config is None:
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model_config = dict2config(
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{
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"name": "DARTS-V1",
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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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else:
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model_config = load_config(
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xargs.model_config,
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{
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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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search_model = get_cell_based_tiny_net(model_config)
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logger.log("search-model :\n{:}".format(search_model))
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last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
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network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
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w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
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a_optimizer = torch.optim.Adam(
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search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay
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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(search_model, xshape)
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# logger.log('{:}'.format(search_model))
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logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param))
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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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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
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last_info = torch.load(last_info)
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start_epoch = last_info['epoch']
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checkpoint = torch.load(last_info['last_checkpoint'])
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genotypes = checkpoint['genotypes']
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valid_accuracies = checkpoint['valid_accuracies']
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search_model.load_state_dict( checkpoint['search_model'] )
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w_scheduler.load_state_dict ( checkpoint['w_scheduler'] )
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w_optimizer.load_state_dict ( checkpoint['w_optimizer'] )
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a_optimizer.load_state_dict ( checkpoint['a_optimizer'] )
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logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
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else:
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logger.log("=> do not find the last-info file : {:}".format(last_info))
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start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: search_model.genotype()}
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last_info, model_base_path, model_best_path = logger.path("info"), logger.path("model"), logger.path("best")
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network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
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# start training
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start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup
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for epoch in range(start_epoch, total_epoch):
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w_scheduler.update(epoch, 0.0)
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need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) )
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epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
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logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
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last_info = torch.load(last_info)
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start_epoch = last_info["epoch"]
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checkpoint = torch.load(last_info["last_checkpoint"])
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genotypes = checkpoint["genotypes"]
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valid_accuracies = checkpoint["valid_accuracies"]
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search_model.load_state_dict(checkpoint["search_model"])
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w_scheduler.load_state_dict(checkpoint["w_scheduler"])
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w_optimizer.load_state_dict(checkpoint["w_optimizer"])
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a_optimizer.load_state_dict(checkpoint["a_optimizer"])
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch)
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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 = 0, {"best": -1}, {-1: search_model.genotype()}
|
||||
|
||||
search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger, xargs.gradient_clip)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log('[{:}] searching : 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))
|
||||
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))
|
||||
# check the best accuracy
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
if valid_a_top1 > valid_accuracies['best']:
|
||||
valid_accuracies['best'] = valid_a_top1
|
||||
genotypes['best'] = search_model.genotype()
|
||||
find_best = True
|
||||
else: find_best = False
|
||||
# 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())))
|
||||
|
||||
genotypes[epoch] = search_model.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)
|
||||
if find_best:
|
||||
logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1))
|
||||
copy_checkpoint(model_base_path, model_best_path, logger)
|
||||
with torch.no_grad():
|
||||
#logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
|
||||
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()
|
||||
search_w_loss, search_w_top1, search_w_top5 = search_func(
|
||||
search_loader,
|
||||
network,
|
||||
criterion,
|
||||
w_scheduler,
|
||||
w_optimizer,
|
||||
a_optimizer,
|
||||
epoch_str,
|
||||
xargs.print_freq,
|
||||
logger,
|
||||
xargs.gradient_clip,
|
||||
)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log(
|
||||
"[{:}] searching : 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
|
||||
)
|
||||
)
|
||||
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
|
||||
)
|
||||
)
|
||||
# check the best accuracy
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
if valid_a_top1 > valid_accuracies["best"]:
|
||||
valid_accuracies["best"] = valid_a_top1
|
||||
genotypes["best"] = search_model.genotype()
|
||||
find_best = True
|
||||
else:
|
||||
find_best = False
|
||||
|
||||
logger.log('\n' + '-'*100)
|
||||
logger.log('DARTS-V1 : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, genotypes[total_epoch-1]))
|
||||
if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[total_epoch-1], '200')))
|
||||
logger.close()
|
||||
|
||||
genotypes[epoch] = search_model.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,
|
||||
)
|
||||
if find_best:
|
||||
logger.log(
|
||||
"<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format(
|
||||
epoch_str, valid_a_top1
|
||||
)
|
||||
)
|
||||
copy_checkpoint(model_base_path, model_best_path, logger)
|
||||
with torch.no_grad():
|
||||
# logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
|
||||
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()
|
||||
|
||||
logger.log("\n" + "-" * 100)
|
||||
logger.log(
|
||||
"DARTS-V1 : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
|
||||
total_epoch, search_time.sum, genotypes[total_epoch - 1]
|
||||
)
|
||||
)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[total_epoch - 1], "200")))
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser("DARTS first order")
|
||||
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('--track_running_stats',type=int, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
|
||||
parser.add_argument('--config_path', type=str, help='The config path.')
|
||||
parser.add_argument('--model_config', type=str, help='The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.')
|
||||
parser.add_argument('--gradient_clip', type=float, default=5, help='')
|
||||
# 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, 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)
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("DARTS first order")
|
||||
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(
|
||||
"--track_running_stats",
|
||||
type=int,
|
||||
choices=[0, 1],
|
||||
help="Whether use track_running_stats or not in the BN layer.",
|
||||
)
|
||||
parser.add_argument("--config_path", type=str, help="The config path.")
|
||||
parser.add_argument(
|
||||
"--model_config",
|
||||
type=str,
|
||||
help="The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.",
|
||||
)
|
||||
parser.add_argument("--gradient_clip", type=float, default=5, help="")
|
||||
# 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, 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)
|
||||
|
||||
Reference in New Issue
Block a user