587 lines
22 KiB
Python
587 lines
22 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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###########################################################################################################################################
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#
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# In this file, we aims to evaluate three kinds of channel searching strategies:
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# - channel-wise interpolation from "Network Pruning via Transformable Architecture Search, NeurIPS 2019"
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# - masking + Gumbel-Softmax (mask_gumbel) from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020"
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# - masking + sampling (mask_rl) from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020"
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#
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# For simplicity, we use tas, mask_gumbel, and mask_rl to refer these three strategies. Their official implementations are at the following links:
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# - TAS: https://github.com/D-X-Y/AutoDL-Projects/blob/main/docs/NeurIPS-2019-TAS.md
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# - FBNetV2: https://github.com/facebookresearch/mobile-vision
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# - TuNAS: https://github.com/google-research/google-research/tree/master/tunas
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####
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# python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --warmup_ratio 0.25
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####
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# python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
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# python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
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# python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --rand_seed 777
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####
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# python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --rand_seed 777
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# python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --rand_seed 777
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# python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_gumbel --rand_seed 777
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####
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# python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --rand_seed 777 --use_api 0
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# python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --rand_seed 777
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# python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_rl --arch_weight_decay 0 --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 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:
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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 (
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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 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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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 = (
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self._momentum * self._numerator + (1 - self._momentum) * value
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)
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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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RL_BASELINE_EMA = ExponentialMovingAverage(0.95)
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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(
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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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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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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(
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logits.data, arch_targets.data, topk=(1, 5)
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)
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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 = (
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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 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(
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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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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 = {
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"class_num": class_num,
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"xshape": xshape,
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"epochs": xargs.overwite_epochs,
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}
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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(
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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(xargs.search_space, "nats-bench")
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model_config = dict2config(
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dict(
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name="generic",
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super_type="search-shape",
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candidate_Cs=search_space["candidates"],
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max_num_Cs=search_space["numbers"],
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num_classes=class_num,
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genotype=args.genotype,
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affine=bool(xargs.affine),
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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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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(
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search_model.weights, config
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)
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a_optimizer = torch.optim.Adam(
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search_model.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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eps=xargs.arch_eps,
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)
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logger.log("w-optimizer : {:}".format(w_optimizer))
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logger.log("a-optimizer : {:}".format(a_optimizer))
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logger.log("w-scheduler : {:}".format(w_scheduler))
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logger.log("criterion : {:}".format(criterion))
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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 = (
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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 = search_model.cuda(), criterion.cuda() # use a single GPU
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last_info, model_base_path, model_best_path = (
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logger.path("info"),
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logger.path("model"),
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logger.path("best"),
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)
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start".format(last_info)
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)
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last_info = torch.load(last_info)
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start_epoch = last_info["epoch"]
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checkpoint = torch.load(last_info["last_checkpoint"])
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genotypes = checkpoint["genotypes"]
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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 = 0, {"best": -1}, {-1: network.random}
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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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if (
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xargs.warmup_ratio is None
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or xargs.warmup_ratio <= float(epoch) / total_epoch
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):
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enable_controller = True
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network.set_warmup_ratio(None)
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else:
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enable_controller = False
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network.set_warmup_ratio(
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1.0 - float(epoch) / total_epoch / xargs.warmup_ratio
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)
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logger.log(
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"\n[Search the {:}-th epoch] {:}, LR={:}, controller-warmup={:}, enable_controller={:}".format(
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epoch_str,
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need_time,
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min(w_scheduler.get_lr()),
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network.warmup_ratio,
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enable_controller,
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)
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)
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if xargs.algo == "mask_gumbel" or xargs.algo == "tas":
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network.set_tau(
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xargs.tau_max
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- (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1)
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)
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logger.log("[RESET tau as : {:}]".format(network.tau))
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(
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search_w_loss,
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search_w_top1,
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search_w_top5,
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search_a_loss,
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search_a_top1,
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search_a_top5,
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) = 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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enable_controller,
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xargs.algo,
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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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"[{:}] search [base] : 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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logger.log(
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"[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
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epoch_str, search_a_loss, search_a_top1, search_a_top5
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)
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)
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genotype = network.genotype
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logger.log("[{:}] - [get_best_arch] : {:}".format(epoch_str, genotype))
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valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
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valid_loader, network, criterion, logger
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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, genotype
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)
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)
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valid_accuracies[epoch] = valid_a_top1
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genotypes[epoch] = 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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with torch.no_grad():
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|
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)
|
|
|
|
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()
|
|
|
|
|
|
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)
|