Reformulate via black
This commit is contained in:
D-X-Y
@@ -32,294 +32,420 @@ 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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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 count_parameters_in_MB, 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 nats_bench import create
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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 count_parameters_in_MB, 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 nats_bench import create
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# Ad-hoc for RL algorithms.
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class ExponentialMovingAverage(object):
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"""Class that maintains an exponential moving average."""
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"""Class that maintains an exponential moving average."""
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def __init__(self, momentum):
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self._numerator = 0
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self._denominator = 0
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self._momentum = momentum
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def __init__(self, momentum):
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self._numerator = 0
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self._denominator = 0
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self._momentum = momentum
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def update(self, value):
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self._numerator = self._momentum * self._numerator + (1 - self._momentum) * value
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self._denominator = self._momentum * self._denominator + (1 - self._momentum)
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def update(self, value):
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self._numerator = self._momentum * self._numerator + (1 - self._momentum) * value
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self._denominator = self._momentum * self._denominator + (1 - self._momentum)
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@property
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def value(self):
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"""Return the current value of the moving average"""
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return self._numerator / self._denominator
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@property
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def value(self):
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"""Return the current value of the moving average"""
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return self._numerator / self._denominator
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RL_BASELINE_EMA = ExponentialMovingAverage(0.95)
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def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, enable_controller, algo, epoch_str, print_freq, logger):
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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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end = time.time()
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network.train()
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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_inputs = base_inputs.cuda(non_blocking=True)
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arch_inputs = arch_inputs.cuda(non_blocking=True)
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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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network.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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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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network.zero_grad()
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a_optimizer.zero_grad()
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_, logits, log_probs = network(arch_inputs)
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arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
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if algo == 'mask_rl':
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with torch.no_grad():
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RL_BASELINE_EMA.update(arch_prec1.item())
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rl_advantage = arch_prec1 - RL_BASELINE_EMA.value
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rl_log_prob = sum(log_probs)
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arch_loss = - rl_advantage * rl_log_prob
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elif algo == 'tas' or algo == 'mask_gumbel':
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arch_loss = criterion(logits, arch_targets)
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else:
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raise ValueError('invalid algorightm name: {:}'.format(algo))
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if enable_controller:
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arch_loss.backward()
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a_optimizer.step()
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# record
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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,
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network,
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criterion,
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scheduler,
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w_optimizer,
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a_optimizer,
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enable_controller,
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algo,
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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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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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end = time.time()
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network.train()
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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_inputs = base_inputs.cuda(non_blocking=True)
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arch_inputs = arch_inputs.cuda(non_blocking=True)
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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, arch_losses.avg, arch_top1.avg, arch_top5.avg
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# Update the weights
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network.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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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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network.zero_grad()
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a_optimizer.zero_grad()
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_, logits, log_probs = network(arch_inputs)
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arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
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if algo == "mask_rl":
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with torch.no_grad():
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RL_BASELINE_EMA.update(arch_prec1.item())
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rl_advantage = arch_prec1 - RL_BASELINE_EMA.value
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rl_log_prob = sum(log_probs)
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arch_loss = -rl_advantage * rl_log_prob
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elif algo == "tas" or algo == "mask_gumbel":
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arch_loss = criterion(logits, arch_targets)
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else:
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raise ValueError("invalid algorightm name: {:}".format(algo))
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if enable_controller:
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arch_loss.backward()
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a_optimizer.step()
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# record
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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, arch_losses.avg, arch_top1.avg, arch_top5.avg
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def valid_func(xloader, network, criterion, logger):
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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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end = time.time()
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with torch.no_grad():
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network.eval()
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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.cuda(non_blocking=True))
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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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end = time.time()
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with torch.no_grad():
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network.eval()
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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.cuda(non_blocking=True))
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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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if xargs.overwite_epochs is None:
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extra_info = {'class_num': class_num, 'xshape': xshape}
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else:
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extra_info = {'class_num': class_num, 'xshape': xshape, 'epochs': xargs.overwite_epochs}
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config = load_config(xargs.config_path, extra_info, logger)
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search_loader, train_loader, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', (config.batch_size, config.test_batch_size), xargs.workers)
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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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search_space = get_search_spaces(xargs.search_space, 'nats-bench')
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model_config = dict2config(
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dict(name='generic', super_type='search-shape', candidate_Cs=search_space['candidates'], max_num_Cs=search_space['numbers'], num_classes=class_num,
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genotype=args.genotype, affine=bool(xargs.affine), track_running_stats=bool(xargs.track_running_stats)), None)
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logger.log('search space : {:}'.format(search_space))
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logger.log('model config : {:}'.format(model_config))
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search_model = get_cell_based_tiny_net(model_config)
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search_model.set_algo(xargs.algo)
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logger.log('{:}'.format(search_model))
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w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config)
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a_optimizer = torch.optim.Adam(search_model.alphas, lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay, eps=xargs.arch_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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params = count_parameters_in_MB(search_model)
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logger.log('The parameters of the search model = {:.2f} MB'.format(params))
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logger.log('search-space : {:}'.format(search_space))
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if bool(xargs.use_api):
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api = create(None, 'size', fast_mode=True, verbose=False)
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else:
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api = None
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logger.log('{:} create API = {:} done'.format(time_string(), api))
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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 = search_model.cuda(), criterion.cuda() # use a single GPU
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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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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: network.random}
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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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if xargs.warmup_ratio is None or xargs.warmup_ratio <= float(epoch) / total_epoch:
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enable_controller = True
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network.set_warmup_ratio(None)
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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if xargs.overwite_epochs is None:
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extra_info = {"class_num": class_num, "xshape": xshape}
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else:
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enable_controller = False
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network.set_warmup_ratio(1.0 - float(epoch) / total_epoch / xargs.warmup_ratio)
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extra_info = {"class_num": class_num, "xshape": xshape, "epochs": xargs.overwite_epochs}
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config = load_config(xargs.config_path, extra_info, logger)
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search_loader, train_loader, valid_loader = get_nas_search_loaders(
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train_data,
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valid_data,
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xargs.dataset,
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"configs/nas-benchmark/",
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(config.batch_size, config.test_batch_size),
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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(
|
||||
xargs.dataset, len(search_loader), len(valid_loader), config.batch_size
|
||||
)
|
||||
)
|
||||
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
|
||||
|
||||
logger.log('\n[Search the {:}-th epoch] {:}, LR={:}, controller-warmup={:}, enable_controller={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()), network.warmup_ratio, enable_controller))
|
||||
search_space = get_search_spaces(xargs.search_space, "nats-bench")
|
||||
|
||||
if xargs.algo == 'mask_gumbel' or xargs.algo == 'tas':
|
||||
network.set_tau(xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1))
|
||||
logger.log('[RESET tau as : {:}]'.format(network.tau))
|
||||
search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
|
||||
= search_func(search_loader, network, criterion, w_scheduler,
|
||||
w_optimizer, a_optimizer, enable_controller, xargs.algo, epoch_str, xargs.print_freq, logger)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log('[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum))
|
||||
logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5))
|
||||
model_config = dict2config(
|
||||
dict(
|
||||
name="generic",
|
||||
super_type="search-shape",
|
||||
candidate_Cs=search_space["candidates"],
|
||||
max_num_Cs=search_space["numbers"],
|
||||
num_classes=class_num,
|
||||
genotype=args.genotype,
|
||||
affine=bool(xargs.affine),
|
||||
track_running_stats=bool(xargs.track_running_stats),
|
||||
),
|
||||
None,
|
||||
)
|
||||
logger.log("search space : {:}".format(search_space))
|
||||
logger.log("model config : {:}".format(model_config))
|
||||
search_model = get_cell_based_tiny_net(model_config)
|
||||
search_model.set_algo(xargs.algo)
|
||||
logger.log("{:}".format(search_model))
|
||||
|
||||
genotype = network.genotype
|
||||
logger.log('[{:}] - [get_best_arch] : {:}'.format(epoch_str, genotype))
|
||||
valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion, logger)
|
||||
logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config)
|
||||
a_optimizer = torch.optim.Adam(
|
||||
search_model.alphas,
|
||||
lr=xargs.arch_learning_rate,
|
||||
betas=(0.5, 0.999),
|
||||
weight_decay=xargs.arch_weight_decay,
|
||||
eps=xargs.arch_eps,
|
||||
)
|
||||
logger.log("w-optimizer : {:}".format(w_optimizer))
|
||||
logger.log("a-optimizer : {:}".format(a_optimizer))
|
||||
logger.log("w-scheduler : {:}".format(w_scheduler))
|
||||
logger.log("criterion : {:}".format(criterion))
|
||||
params = count_parameters_in_MB(search_model)
|
||||
logger.log("The parameters of the search model = {:.2f} MB".format(params))
|
||||
logger.log("search-space : {:}".format(search_space))
|
||||
if bool(xargs.use_api):
|
||||
api = create(None, "size", fast_mode=True, verbose=False)
|
||||
else:
|
||||
api = None
|
||||
logger.log("{:} create API = {:} done".format(time_string(), api))
|
||||
|
||||
genotypes[epoch] = genotype
|
||||
logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
|
||||
# save checkpoint
|
||||
save_path = save_checkpoint({'epoch' : epoch + 1,
|
||||
'args' : deepcopy(xargs),
|
||||
'search_model': search_model.state_dict(),
|
||||
'w_optimizer' : w_optimizer.state_dict(),
|
||||
'a_optimizer' : a_optimizer.state_dict(),
|
||||
'w_scheduler' : w_scheduler.state_dict(),
|
||||
'genotypes' : genotypes,
|
||||
'valid_accuracies' : valid_accuracies},
|
||||
model_base_path, logger)
|
||||
last_info = save_checkpoint({
|
||||
'epoch': epoch + 1,
|
||||
'args' : deepcopy(args),
|
||||
'last_checkpoint': save_path,
|
||||
}, logger.path('info'), logger)
|
||||
with torch.no_grad():
|
||||
logger.log('{:}'.format(search_model.show_alphas()))
|
||||
if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[epoch], '90')))
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
last_info, model_base_path, model_best_path = logger.path("info"), logger.path("model"), logger.path("best")
|
||||
network, criterion = search_model.cuda(), criterion.cuda() # use a single GPU
|
||||
|
||||
last_info, model_base_path, model_best_path = logger.path("info"), logger.path("model"), logger.path("best")
|
||||
|
||||
if last_info.exists(): # automatically resume from previous checkpoint
|
||||
logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
|
||||
last_info = torch.load(last_info)
|
||||
start_epoch = last_info["epoch"]
|
||||
checkpoint = torch.load(last_info["last_checkpoint"])
|
||||
genotypes = checkpoint["genotypes"]
|
||||
valid_accuracies = checkpoint["valid_accuracies"]
|
||||
search_model.load_state_dict(checkpoint["search_model"])
|
||||
w_scheduler.load_state_dict(checkpoint["w_scheduler"])
|
||||
w_optimizer.load_state_dict(checkpoint["w_optimizer"])
|
||||
a_optimizer.load_state_dict(checkpoint["a_optimizer"])
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch)
|
||||
)
|
||||
else:
|
||||
logger.log("=> do not find the last-info file : {:}".format(last_info))
|
||||
start_epoch, valid_accuracies, genotypes = 0, {"best": -1}, {-1: network.random}
|
||||
|
||||
# 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)
|
||||
|
||||
if xargs.warmup_ratio is None or xargs.warmup_ratio <= float(epoch) / total_epoch:
|
||||
enable_controller = True
|
||||
network.set_warmup_ratio(None)
|
||||
else:
|
||||
enable_controller = False
|
||||
network.set_warmup_ratio(1.0 - float(epoch) / total_epoch / xargs.warmup_ratio)
|
||||
|
||||
logger.log(
|
||||
"\n[Search the {:}-th epoch] {:}, LR={:}, controller-warmup={:}, enable_controller={:}".format(
|
||||
epoch_str, need_time, min(w_scheduler.get_lr()), network.warmup_ratio, enable_controller
|
||||
)
|
||||
)
|
||||
|
||||
if xargs.algo == "mask_gumbel" or xargs.algo == "tas":
|
||||
network.set_tau(xargs.tau_max - (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1))
|
||||
logger.log("[RESET tau as : {:}]".format(network.tau))
|
||||
search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 = search_func(
|
||||
search_loader,
|
||||
network,
|
||||
criterion,
|
||||
w_scheduler,
|
||||
w_optimizer,
|
||||
a_optimizer,
|
||||
enable_controller,
|
||||
xargs.algo,
|
||||
epoch_str,
|
||||
xargs.print_freq,
|
||||
logger,
|
||||
)
|
||||
search_time.update(time.time() - start_time)
|
||||
logger.log(
|
||||
"[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format(
|
||||
epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum
|
||||
)
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
|
||||
epoch_str, search_a_loss, search_a_top1, search_a_top5
|
||||
)
|
||||
)
|
||||
|
||||
genotype = network.genotype
|
||||
logger.log("[{:}] - [get_best_arch] : {:}".format(epoch_str, genotype))
|
||||
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(valid_loader, network, criterion, logger)
|
||||
logger.log(
|
||||
"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}".format(
|
||||
epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype
|
||||
)
|
||||
)
|
||||
valid_accuracies[epoch] = valid_a_top1
|
||||
|
||||
genotypes[epoch] = genotype
|
||||
logger.log("<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch]))
|
||||
# save checkpoint
|
||||
save_path = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(xargs),
|
||||
"search_model": search_model.state_dict(),
|
||||
"w_optimizer": w_optimizer.state_dict(),
|
||||
"a_optimizer": a_optimizer.state_dict(),
|
||||
"w_scheduler": w_scheduler.state_dict(),
|
||||
"genotypes": genotypes,
|
||||
"valid_accuracies": valid_accuracies,
|
||||
},
|
||||
model_base_path,
|
||||
logger,
|
||||
)
|
||||
last_info = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch + 1,
|
||||
"args": deepcopy(args),
|
||||
"last_checkpoint": save_path,
|
||||
},
|
||||
logger.path("info"),
|
||||
logger,
|
||||
)
|
||||
with torch.no_grad():
|
||||
logger.log("{:}".format(search_model.show_alphas()))
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "90")))
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
# the final post procedure : count the time
|
||||
start_time = time.time()
|
||||
genotype = network.genotype
|
||||
search_time.update(time.time() - start_time)
|
||||
|
||||
# the final post procedure : count the time
|
||||
start_time = time.time()
|
||||
genotype = network.genotype
|
||||
search_time.update(time.time() - start_time)
|
||||
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(valid_loader, network, criterion, logger)
|
||||
logger.log("Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.".format(genotype, valid_a_top1))
|
||||
|
||||
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(valid_loader, network, criterion, logger)
|
||||
logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1))
|
||||
logger.log("\n" + "-" * 100)
|
||||
# check the performance from the architecture dataset
|
||||
logger.log(
|
||||
"[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
|
||||
xargs.algo, total_epoch, search_time.sum, genotype
|
||||
)
|
||||
)
|
||||
if api is not None:
|
||||
logger.log("{:}".format(api.query_by_arch(genotype, "90")))
|
||||
logger.close()
|
||||
|
||||
logger.log('\n' + '-'*100)
|
||||
# check the performance from the architecture dataset
|
||||
logger.log('[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(xargs.algo, total_epoch, search_time.sum, genotype))
|
||||
if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '90') ))
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.")
|
||||
parser.add_argument('--data_path' , type=str, help='Path to dataset')
|
||||
parser.add_argument('--dataset' , type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
|
||||
parser.add_argument('--search_space', type=str, default='sss', choices=['sss'], help='The search space name.')
|
||||
parser.add_argument('--algo' , type=str, choices=['tas', 'mask_gumbel', 'mask_rl'], help='The search space name.')
|
||||
parser.add_argument('--genotype' , type=str, default='|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|', help='The genotype.')
|
||||
parser.add_argument('--use_api' , type=int, default=1, choices=[0,1], help='Whether use API or not (which will cost much memory).')
|
||||
# FOR GDAS
|
||||
parser.add_argument('--tau_min', type=float, default=0.1, help='The minimum tau for Gumbel Softmax.')
|
||||
parser.add_argument('--tau_max', type=float, default=10, help='The maximum tau for Gumbel Softmax.')
|
||||
# FOR ALL
|
||||
parser.add_argument('--warmup_ratio', type=float, help='The warmup ratio, if None, not use warmup.')
|
||||
#
|
||||
parser.add_argument('--track_running_stats',type=int, default=0, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')
|
||||
parser.add_argument('--affine' , type=int, default=0, choices=[0,1],help='Whether use affine=True or False in the BN layer.')
|
||||
parser.add_argument('--config_path' , type=str, default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.')
|
||||
parser.add_argument('--overwite_epochs', type=int, help='The number of epochs to overwrite that value in config files.')
|
||||
# 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')
|
||||
parser.add_argument('--arch_eps' , type=float, default=1e-8, help='weight decay for arch encoding')
|
||||
# log
|
||||
parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
|
||||
parser.add_argument('--save_dir', type=str, default='./output/search', help='Folder to save checkpoints and log.')
|
||||
parser.add_argument('--print_freq', type=int, default=200, 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)
|
||||
dirname = '{:}-affine{:}_BN{:}-AWD{:}-WARM{:}'.format(args.algo, args.affine, args.track_running_stats, args.arch_weight_decay, args.warmup_ratio)
|
||||
if args.overwite_epochs is not None:
|
||||
dirname = dirname + '-E{:}'.format(args.overwite_epochs)
|
||||
args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, dirname)
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.")
|
||||
parser.add_argument("--data_path", type=str, help="Path to dataset")
|
||||
parser.add_argument(
|
||||
"--dataset",
|
||||
type=str,
|
||||
choices=["cifar10", "cifar100", "ImageNet16-120"],
|
||||
help="Choose between Cifar10/100 and ImageNet-16.",
|
||||
)
|
||||
parser.add_argument("--search_space", type=str, default="sss", choices=["sss"], help="The search space name.")
|
||||
parser.add_argument("--algo", type=str, choices=["tas", "mask_gumbel", "mask_rl"], help="The search space name.")
|
||||
parser.add_argument(
|
||||
"--genotype",
|
||||
type=str,
|
||||
default="|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|",
|
||||
help="The genotype.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--use_api", type=int, default=1, choices=[0, 1], help="Whether use API or not (which will cost much memory)."
|
||||
)
|
||||
# FOR GDAS
|
||||
parser.add_argument("--tau_min", type=float, default=0.1, help="The minimum tau for Gumbel Softmax.")
|
||||
parser.add_argument("--tau_max", type=float, default=10, help="The maximum tau for Gumbel Softmax.")
|
||||
# FOR ALL
|
||||
parser.add_argument("--warmup_ratio", type=float, help="The warmup ratio, if None, not use warmup.")
|
||||
#
|
||||
parser.add_argument(
|
||||
"--track_running_stats",
|
||||
type=int,
|
||||
default=0,
|
||||
choices=[0, 1],
|
||||
help="Whether use track_running_stats or not in the BN layer.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--affine", type=int, default=0, choices=[0, 1], help="Whether use affine=True or False in the BN layer."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--config_path",
|
||||
type=str,
|
||||
default="./configs/nas-benchmark/algos/weight-sharing.config",
|
||||
help="The path of configuration.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--overwite_epochs", type=int, help="The number of epochs to overwrite that value in config files."
|
||||
)
|
||||
# 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")
|
||||
parser.add_argument("--arch_eps", type=float, default=1e-8, help="weight decay for arch encoding")
|
||||
# log
|
||||
parser.add_argument("--workers", type=int, default=2, help="number of data loading workers (default: 2)")
|
||||
parser.add_argument("--save_dir", type=str, default="./output/search", help="Folder to save checkpoints and log.")
|
||||
parser.add_argument("--print_freq", type=int, default=200, 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)
|
||||
dirname = "{:}-affine{:}_BN{:}-AWD{:}-WARM{:}".format(
|
||||
args.algo, args.affine, args.track_running_stats, args.arch_weight_decay, args.warmup_ratio
|
||||
)
|
||||
if args.overwite_epochs is not None:
|
||||
dirname = dirname + "-E{:}".format(args.overwite_epochs)
|
||||
args.save_dir = os.path.join("{:}-{:}".format(args.save_dir, args.search_space), args.dataset, dirname)
|
||||
|
||||
main(args)
|
||||
main(args)
|
||||
|
||||
Reference in New Issue
Block a user