497 lines
18 KiB
Python
497 lines
18 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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########################################################
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# DARTS: Differentiable Architecture Search, ICLR 2019 #
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########################################################
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import os, sys, time, glob, random, argparse
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import numpy as np
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from copy import deepcopy
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import torch
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import torch.nn as nn
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from 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 _concat(xs):
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return torch.cat([x.view(-1) for x in xs])
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def _hessian_vector_product(
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vector, network, criterion, base_inputs, base_targets, r=1e-2
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):
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R = r / _concat(vector).norm()
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for p, v in zip(network.module.get_weights(), vector):
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p.data.add_(R, v)
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_, logits = network(base_inputs)
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loss = criterion(logits, base_targets)
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grads_p = torch.autograd.grad(loss, network.module.get_alphas())
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for p, v in zip(network.module.get_weights(), vector):
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p.data.sub_(2 * R, v)
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_, logits = network(base_inputs)
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loss = criterion(logits, base_targets)
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grads_n = torch.autograd.grad(loss, network.module.get_alphas())
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for p, v in zip(network.module.get_weights(), vector):
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p.data.add_(R, v)
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return [(x - y).div_(2 * R) for x, y in zip(grads_p, grads_n)]
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def backward_step_unrolled(
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network,
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criterion,
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base_inputs,
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base_targets,
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w_optimizer,
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arch_inputs,
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arch_targets,
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):
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# _compute_unrolled_model
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_, logits = network(base_inputs)
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loss = criterion(logits, base_targets)
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LR, WD, momentum = (
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w_optimizer.param_groups[0]["lr"],
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w_optimizer.param_groups[0]["weight_decay"],
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w_optimizer.param_groups[0]["momentum"],
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)
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with torch.no_grad():
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theta = _concat(network.module.get_weights())
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try:
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moment = _concat(
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w_optimizer.state[v]["momentum_buffer"]
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for v in network.module.get_weights()
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)
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moment = moment.mul_(momentum)
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except:
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moment = torch.zeros_like(theta)
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dtheta = (
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_concat(torch.autograd.grad(loss, network.module.get_weights()))
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+ WD * theta
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)
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params = theta.sub(LR, moment + dtheta)
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unrolled_model = deepcopy(network)
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model_dict = unrolled_model.state_dict()
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new_params, offset = {}, 0
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for k, v in network.named_parameters():
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if "arch_parameters" in k:
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continue
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v_length = np.prod(v.size())
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new_params[k] = params[offset : offset + v_length].view(v.size())
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offset += v_length
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model_dict.update(new_params)
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unrolled_model.load_state_dict(model_dict)
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unrolled_model.zero_grad()
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_, unrolled_logits = unrolled_model(arch_inputs)
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unrolled_loss = criterion(unrolled_logits, arch_targets)
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unrolled_loss.backward()
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dalpha = unrolled_model.module.arch_parameters.grad
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vector = [v.grad.data for v in unrolled_model.module.get_weights()]
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[implicit_grads] = _hessian_vector_product(
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vector, network, criterion, base_inputs, base_targets
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)
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dalpha.data.sub_(LR, implicit_grads.data)
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if network.module.arch_parameters.grad is None:
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network.module.arch_parameters.grad = deepcopy(dalpha)
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else:
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network.module.arch_parameters.grad.data.copy_(dalpha.data)
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return unrolled_loss.detach(), unrolled_logits.detach()
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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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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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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(
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xloader
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):
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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 architecture-weight
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a_optimizer.zero_grad()
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arch_loss, arch_logits = backward_step_unrolled(
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network,
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criterion,
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base_inputs,
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base_targets,
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w_optimizer,
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arch_inputs,
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arch_targets,
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)
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a_optimizer.step()
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# record
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arch_prec1, arch_prec5 = obtain_accuracy(
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arch_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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# 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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torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
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w_optimizer.step()
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# record
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base_prec1, base_prec5 = obtain_accuracy(
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logits.data, base_targets.data, topk=(1, 5)
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)
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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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# 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 = (
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"*SEARCH* "
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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 = "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(
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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, valid_data, xshape, class_num = get_datasets(
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xargs.dataset, xargs.data_path, -1
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)
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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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search_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,
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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(
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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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model_config = dict2config(
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{
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"name": "DARTS-V2",
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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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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(
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search_model.get_weights(), config
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)
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a_optimizer = torch.optim.Adam(
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search_model.get_alphas(),
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lr=xargs.arch_learning_rate,
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betas=(0.5, 0.999),
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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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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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network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
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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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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(
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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 = (
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0,
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{"best": -1},
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{-1: search_model.genotype()},
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)
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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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min_LR = min(w_scheduler.get_lr())
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logger.log(
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"\n[Search the {:}-th epoch] {:}, LR={:}".format(
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epoch_str, need_time, min_LR
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)
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)
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search_w_loss, search_w_top1, search_w_top5 = search_func(
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search_loader,
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network,
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criterion,
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w_scheduler,
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w_optimizer,
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a_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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search_time.update(time.time() - start_time)
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logger.log(
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"[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format(
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epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum
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)
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)
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valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
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valid_loader, network, criterion
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)
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logger.log(
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"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
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epoch_str, valid_a_loss, valid_a_top1, valid_a_top5
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)
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)
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# check the best accuracy
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valid_accuracies[epoch] = valid_a_top1
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if valid_a_top1 > valid_accuracies["best"]:
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valid_accuracies["best"] = valid_a_top1
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genotypes["best"] = search_model.genotype()
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find_best = True
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else:
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find_best = False
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genotypes[epoch] = search_model.genotype()
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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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"search_model": search_model.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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)
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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),
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"last_checkpoint": save_path,
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},
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logger.path("info"),
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logger,
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)
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if find_best:
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logger.log(
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"<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format(
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epoch_str, valid_a_top1
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)
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)
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copy_checkpoint(model_base_path, model_best_path, logger)
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with torch.no_grad():
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logger.log(
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"arch-parameters :\n{:}".format(
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nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu()
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)
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)
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if api is not None:
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logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200")))
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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logger.log("\n" + "-" * 100)
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# check the performance from the architecture dataset
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logger.log(
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"DARTS-V2 : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
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total_epoch, search_time.sum, genotypes[total_epoch - 1]
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)
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)
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if api is not None:
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logger.log("{:}".format(api.query_by_arch(genotypes[total_epoch - 1], "200")))
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logger.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("DARTS Second Order")
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parser.add_argument("--data_path", type=str, help="The path to dataset")
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parser.add_argument(
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"--dataset",
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type=str,
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choices=["cifar10", "cifar100", "ImageNet16-120"],
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help="Choose between Cifar10/100 and ImageNet-16.",
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)
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# channels and number-of-cells
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parser.add_argument("--config_path", type=str, help="The config path.")
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parser.add_argument("--search_space_name", type=str, help="The search space name.")
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parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
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parser.add_argument("--channel", type=int, help="The number of channels.")
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parser.add_argument(
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"--num_cells", type=int, help="The number of cells in one stage."
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)
|
|
parser.add_argument(
|
|
"--track_running_stats",
|
|
type=int,
|
|
choices=[0, 1],
|
|
help="Whether use track_running_stats or not in the BN layer.",
|
|
)
|
|
# 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 (tiny-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)
|