880 lines
32 KiB
Python
880 lines
32 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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######################################################################################
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# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --rand_seed 777
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# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1 --drop_path_rate 0.3
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# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1
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####
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# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v2 --rand_seed 777
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# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v2
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# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v2
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####
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# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo gdas --rand_seed 777
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# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo gdas
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# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo gdas
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####
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# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo setn --rand_seed 777
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# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo setn
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# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo setn
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####
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# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo random --rand_seed 777
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# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo random
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# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo random
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####
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# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
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# python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
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# python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001 --rand_seed 777
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####
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# The following scripts are added in 20 Mar 2022
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# python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo gdas_v1 --rand_seed 777
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######################################################################################
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import os, sys, time, 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 count_parameters_in_MB, 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 nats_bench import create
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# The following three functions are used for DARTS-V2
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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.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.alphas)
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for p, v in zip(network.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.alphas)
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for p, v in zip(network.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.weights)
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try:
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moment = _concat(
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w_optimizer.state[v]["momentum_buffer"] for v in network.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 = _concat(torch.autograd.grad(loss, network.weights)) + WD * theta
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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.arch_parameters.grad
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vector = [v.grad.data for v in unrolled_model.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.arch_parameters.grad is None:
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network.arch_parameters.grad = deepcopy(dalpha)
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else:
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network.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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algo,
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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(
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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_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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if algo == "setn":
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sampled_arch = network.dync_genotype(True)
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network.set_cal_mode("dynamic", sampled_arch)
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elif algo == "gdas":
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network.set_cal_mode("gdas", None)
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elif algo == "gdas_v1":
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network.set_cal_mode("gdas_v1", None)
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elif algo.startswith("darts"):
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network.set_cal_mode("joint", None)
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elif algo == "random":
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network.set_cal_mode("urs", None)
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elif algo == "enas":
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with torch.no_grad():
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network.controller.eval()
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_, _, sampled_arch = network.controller()
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network.set_cal_mode("dynamic", sampled_arch)
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else:
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raise ValueError("Invalid algo name : {:}".format(algo))
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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(
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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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# update the architecture-weight
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if algo == "setn":
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network.set_cal_mode("joint")
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elif algo == "gdas":
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network.set_cal_mode("gdas", None)
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elif algo == "gdas_v1":
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network.set_cal_mode("gdas_v1", None)
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elif algo.startswith("darts"):
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network.set_cal_mode("joint", None)
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elif algo == "random":
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network.set_cal_mode("urs", None)
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elif algo != "enas":
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raise ValueError("Invalid algo name : {:}".format(algo))
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network.zero_grad()
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if algo == "darts-v2":
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arch_loss, 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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elif algo == "random" or algo == "enas":
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with torch.no_grad():
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_, logits = network(arch_inputs)
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arch_loss = criterion(logits, arch_targets)
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else:
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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(
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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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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 (
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base_losses.avg,
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base_top1.avg,
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base_top5.avg,
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arch_losses.avg,
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arch_top1.avg,
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arch_top5.avg,
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)
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def train_controller(
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xloader, network, criterion, optimizer, prev_baseline, epoch_str, print_freq, logger
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):
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# config. (containing some necessary arg)
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# baseline: The baseline score (i.e. average val_acc) from the previous epoch
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data_time, batch_time = AverageMeter(), AverageMeter()
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(
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GradnormMeter,
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LossMeter,
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ValAccMeter,
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EntropyMeter,
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BaselineMeter,
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RewardMeter,
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xend,
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) = (
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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AverageMeter(),
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time.time(),
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)
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controller_num_aggregate = 20
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controller_train_steps = 50
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controller_bl_dec = 0.99
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controller_entropy_weight = 0.0001
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network.eval()
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network.controller.train()
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network.controller.zero_grad()
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loader_iter = iter(xloader)
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for step in range(controller_train_steps * controller_num_aggregate):
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try:
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inputs, targets = next(loader_iter)
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except:
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loader_iter = iter(xloader)
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inputs, targets = next(loader_iter)
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inputs = inputs.cuda(non_blocking=True)
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targets = targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - xend)
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log_prob, entropy, sampled_arch = network.controller()
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with torch.no_grad():
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network.set_cal_mode("dynamic", sampled_arch)
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_, logits = network(inputs)
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val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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val_top1 = val_top1.view(-1) / 100
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reward = val_top1 + controller_entropy_weight * entropy
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if prev_baseline is None:
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baseline = val_top1
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else:
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baseline = prev_baseline - (1 - controller_bl_dec) * (
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prev_baseline - reward
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)
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loss = -1 * log_prob * (reward - baseline)
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# account
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RewardMeter.update(reward.item())
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BaselineMeter.update(baseline.item())
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ValAccMeter.update(val_top1.item() * 100)
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LossMeter.update(loss.item())
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EntropyMeter.update(entropy.item())
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# Average gradient over controller_num_aggregate samples
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loss = loss / controller_num_aggregate
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loss.backward(retain_graph=True)
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# measure elapsed time
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batch_time.update(time.time() - xend)
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xend = time.time()
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if (step + 1) % controller_num_aggregate == 0:
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grad_norm = torch.nn.utils.clip_grad_norm_(
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network.controller.parameters(), 5.0
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)
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GradnormMeter.update(grad_norm)
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optimizer.step()
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network.controller.zero_grad()
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if step % print_freq == 0:
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Sstr = (
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"*Train-Controller* "
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+ time_string()
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+ " [{:}][{:03d}/{:03d}]".format(
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epoch_str, step, controller_train_steps * controller_num_aggregate
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)
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)
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Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
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batch_time=batch_time, data_time=data_time
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)
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Wstr = "[Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Reward {reward.val:.2f} ({reward.avg:.2f})] Baseline {basel.val:.2f} ({basel.avg:.2f})".format(
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loss=LossMeter,
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top1=ValAccMeter,
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reward=RewardMeter,
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basel=BaselineMeter,
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)
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Estr = "Entropy={:.4f} ({:.4f})".format(EntropyMeter.val, EntropyMeter.avg)
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logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Estr)
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return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg
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def get_best_arch(xloader, network, n_samples, algo):
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with torch.no_grad():
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network.eval()
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if algo == "random":
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archs, valid_accs = network.return_topK(n_samples, True), []
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elif algo == "setn":
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archs, valid_accs = network.return_topK(n_samples, False), []
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elif algo.startswith("darts") or algo == "gdas" or algo == "gdas_v1":
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arch = network.genotype
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archs, valid_accs = [arch], []
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elif algo == "enas":
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archs, valid_accs = [], []
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for _ in range(n_samples):
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_, _, sampled_arch = network.controller()
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archs.append(sampled_arch)
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else:
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raise ValueError("Invalid algorithm name : {:}".format(algo))
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loader_iter = iter(xloader)
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for i, sampled_arch in enumerate(archs):
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network.set_cal_mode("dynamic", sampled_arch)
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try:
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inputs, targets = next(loader_iter)
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except:
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loader_iter = iter(xloader)
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inputs, targets = next(loader_iter)
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_, logits = network(inputs.cuda(non_blocking=True))
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val_top1, val_top5 = obtain_accuracy(
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logits.cpu().data, targets.data, topk=(1, 5)
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)
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valid_accs.append(val_top1.item())
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best_idx = np.argmax(valid_accs)
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best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
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return best_arch, best_valid_acc
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def valid_func(xloader, network, criterion, algo, 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)
|
|
# record
|
|
arch_prec1, arch_prec5 = obtain_accuracy(
|
|
logits.data, arch_targets.data, topk=(1, 5)
|
|
)
|
|
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
|
|
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
|
|
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
|
|
# measure elapsed time
|
|
batch_time.update(time.time() - end)
|
|
end = time.time()
|
|
return arch_losses.avg, arch_top1.avg, arch_top5.avg
|
|
|
|
|
|
def main(xargs):
|
|
assert torch.cuda.is_available(), "CUDA is not available."
|
|
torch.backends.cudnn.enabled = True
|
|
torch.backends.cudnn.benchmark = False
|
|
torch.backends.cudnn.deterministic = True
|
|
torch.set_num_threads(xargs.workers)
|
|
prepare_seed(xargs.rand_seed)
|
|
logger = prepare_logger(args)
|
|
|
|
train_data, valid_data, xshape, class_num = get_datasets(
|
|
xargs.dataset, xargs.data_path, -1
|
|
)
|
|
if xargs.overwite_epochs is None:
|
|
extra_info = {"class_num": class_num, "xshape": xshape}
|
|
else:
|
|
extra_info = {
|
|
"class_num": class_num,
|
|
"xshape": xshape,
|
|
"epochs": xargs.overwite_epochs,
|
|
}
|
|
config = load_config(xargs.config_path, extra_info, logger)
|
|
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,
|
|
)
|
|
logger.log(
|
|
"||||||| {: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))
|
|
|
|
search_space = get_search_spaces(xargs.search_space, "nats-bench")
|
|
|
|
model_config = dict2config(
|
|
dict(
|
|
name="generic",
|
|
C=xargs.channel,
|
|
N=xargs.num_cells,
|
|
max_nodes=xargs.max_nodes,
|
|
num_classes=class_num,
|
|
space=search_space,
|
|
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))
|
|
|
|
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, "topology", fast_mode=True, verbose=False)
|
|
else:
|
|
api = None
|
|
logger.log("{:} create API = {:} done".format(time_string(), api))
|
|
|
|
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"]
|
|
baseline = checkpoint["baseline"]
|
|
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.return_topK(1, True)[0]},
|
|
)
|
|
baseline = None
|
|
|
|
# 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())
|
|
)
|
|
)
|
|
|
|
network.set_drop_path(float(epoch + 1) / total_epoch, xargs.drop_path_rate)
|
|
if xargs.algo == "gdas" or xargs.algo == "gdas_v1":
|
|
network.set_tau(
|
|
xargs.tau_max
|
|
- (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1)
|
|
)
|
|
logger.log(
|
|
"[RESET tau as : {:} and drop_path as {:}]".format(
|
|
network.tau, network.drop_path
|
|
)
|
|
)
|
|
(
|
|
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,
|
|
epoch_str,
|
|
xargs.print_freq,
|
|
xargs.algo,
|
|
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
|
|
)
|
|
)
|
|
if xargs.algo == "enas":
|
|
ctl_loss, ctl_acc, baseline, ctl_reward = train_controller(
|
|
valid_loader,
|
|
network,
|
|
criterion,
|
|
a_optimizer,
|
|
baseline,
|
|
epoch_str,
|
|
xargs.print_freq,
|
|
logger,
|
|
)
|
|
logger.log(
|
|
"[{:}] controller : loss={:}, acc={:}, baseline={:}, reward={:}".format(
|
|
epoch_str, ctl_loss, ctl_acc, baseline, ctl_reward
|
|
)
|
|
)
|
|
|
|
genotype, temp_accuracy = get_best_arch(
|
|
valid_loader, network, xargs.eval_candidate_num, xargs.algo
|
|
)
|
|
if xargs.algo == "setn" or xargs.algo == "enas":
|
|
network.set_cal_mode("dynamic", genotype)
|
|
elif xargs.algo == "gdas":
|
|
network.set_cal_mode("gdas", None)
|
|
elif xargs.algo == "gdas_v1":
|
|
network.set_cal_mode("gdas_v1", None)
|
|
elif xargs.algo.startswith("darts"):
|
|
network.set_cal_mode("joint", None)
|
|
elif xargs.algo == "random":
|
|
network.set_cal_mode("urs", None)
|
|
else:
|
|
raise ValueError("Invalid algorithm name : {:}".format(xargs.algo))
|
|
logger.log(
|
|
"[{:}] - [get_best_arch] : {:} -> {:}".format(
|
|
epoch_str, genotype, temp_accuracy
|
|
)
|
|
)
|
|
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
|
|
valid_loader, network, criterion, xargs.algo, 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),
|
|
"baseline": baseline,
|
|
"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], "200")))
|
|
# 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, temp_accuracy = get_best_arch(
|
|
valid_loader, network, xargs.eval_candidate_num, xargs.algo
|
|
)
|
|
if xargs.algo == "setn" or xargs.algo == "enas":
|
|
network.set_cal_mode("dynamic", genotype)
|
|
elif xargs.algo == "gdas":
|
|
network.set_cal_mode("gdas", None)
|
|
elif xargs.algo == "gdas_v1":
|
|
network.set_cal_mode("gdas_v1", None)
|
|
elif xargs.algo.startswith("darts"):
|
|
network.set_cal_mode("joint", None)
|
|
elif xargs.algo == "random":
|
|
network.set_cal_mode("urs", None)
|
|
else:
|
|
raise ValueError("Invalid algorithm name : {:}".format(xargs.algo))
|
|
search_time.update(time.time() - start_time)
|
|
|
|
valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
|
|
valid_loader, network, criterion, xargs.algo, 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, "200")))
|
|
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="tss",
|
|
choices=["tss"],
|
|
help="The search space name.",
|
|
)
|
|
parser.add_argument(
|
|
"--algo",
|
|
type=str,
|
|
choices=["darts-v1", "darts-v2", "gdas", "gdas_v1", "setn", "random", "enas"],
|
|
help="The search space name.",
|
|
)
|
|
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."
|
|
)
|
|
# channels and number-of-cells
|
|
parser.add_argument(
|
|
"--max_nodes", type=int, default=4, help="The maximum number of nodes."
|
|
)
|
|
parser.add_argument(
|
|
"--channel", type=int, default=16, help="The number of channels."
|
|
)
|
|
parser.add_argument(
|
|
"--num_cells", type=int, default=5, help="The number of cells in one stage."
|
|
)
|
|
#
|
|
parser.add_argument(
|
|
"--eval_candidate_num",
|
|
type=int,
|
|
default=100,
|
|
help="The number of selected architectures to evaluate.",
|
|
)
|
|
#
|
|
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"
|
|
)
|
|
parser.add_argument("--drop_path_rate", type=float, help="The drop path rate.")
|
|
# 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)
|
|
if args.overwite_epochs is None:
|
|
args.save_dir = os.path.join(
|
|
"{:}-{:}".format(args.save_dir, args.search_space),
|
|
args.dataset,
|
|
"{:}-affine{:}_BN{:}-{:}".format(
|
|
args.algo, args.affine, args.track_running_stats, args.drop_path_rate
|
|
),
|
|
)
|
|
else:
|
|
args.save_dir = os.path.join(
|
|
"{:}-{:}".format(args.save_dir, args.search_space),
|
|
args.dataset,
|
|
"{:}-affine{:}_BN{:}-E{:}-{:}".format(
|
|
args.algo,
|
|
args.affine,
|
|
args.track_running_stats,
|
|
args.overwite_epochs,
|
|
args.drop_path_rate,
|
|
),
|
|
)
|
|
|
|
main(args)
|