imagenet_activations.py

import argparse
import os
import random
import shutil
import time
import warnings
import sys

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import imagenet_dataset as datasets
import torchvision.models as models

import numpy as np
from apoz_policy_imagenet import *
import pdb

model_names = sorted(name for name in models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(models.__dict__[name]))

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--data', metavar='DIR',
                    help='path to imagenet dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=64, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=1, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', default=False, action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')

parser.add_argument('--group', type=int, nargs='+', default=[],
                        help='Generate activations based on the these class indices')
parser.add_argument('--name', type=str, default='Name', help='Set the name id of the group')


global num_layers
num_layers = sys.maxsize
global layer_idx
layer_idx = 0 
num_batches = 0
best_acc1 = 0

def main():
    args = parser.parse_args()
    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    main_worker(args.gpu, args)


def main_worker(gpu, args):
    global best_acc1
    global num_layers
    global apoz_scores_by_layer
    global avg_scores_by_layer
    args.gpu = gpu

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    # create model
    if args.pretrained:
        print("=> using pre-trained model '{}'".format(args.arch))
        model = models.__dict__[args.arch](pretrained=True)
    else:
        print("=> creating model '{}'".format(args.arch))
        model = models.__dict__[args.arch]()

    if args.gpu is not None:
        print("checkpoint 1...")
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
     
    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss()

    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    print("checkpoint 2...")
    apoz_scores_by_layer =  [] 
    avg_scores_by_layer =  []
    model = model.cuda(args.gpu) 

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]), activations=True, group=args.group)

    val_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True, sampler=None)

    print("checkpoint 3...")
    if args.evaluate:
        validate(val_loader, model, criterion, args)
        generate_candidates(args.name)
        return


def validate(val_loader, model, criterion, args):
    global layer_idx
    global num_batches
    global num_layers
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(len(val_loader), batch_time, losses, top1, top5,
                             prefix='Test: ')

    # switch to evaluate mode
    model.apply(apply_hook)
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (input, target) in enumerate(val_loader):
            num_batches += 1
            layer_idx = 0 
            if args.gpu is not None:
                input = input.cuda(args.gpu)
            target = target.cuda(args.gpu)
            # compute output
            output = model(input)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), input.size(0))
            top1.update(acc1[0], input.size(0))
            top5.update(acc5[0], input.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                progress.print(i)

            num_layers = layer_idx
          
        # TODO: this should also be done with the ProgressMeter
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))

    return top1.avg


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')

def parse_activation(relu_reference, feature_map):
    global layer_idx
    global num_layers
    apoz_score = apoz_scoring(feature_map)
    avg_score = avg_scoring(feature_map) 

    if len(apoz_scores_by_layer) < num_layers:
        apoz_scores_by_layer.append(apoz_score)
        avg_scores_by_layer.append(avg_score)
    else:
        apoz_scores_by_layer[layer_idx] = torch.add(apoz_scores_by_layer[layer_idx], apoz_score)
        avg_scores_by_layer[layer_idx] = torch.add(avg_scores_by_layer[layer_idx], avg_score)

    layer_idx += 1
     

"""
    Apply a hook to RelU layer
"""
def hook(self, input, output):
    if self.__class__.__name__ == 'ReLU':
       parse_activation(self, output.data)

def apply_hook(m):
   m.register_forward_hook(hook)

def generate_candidates(name):
     global num_batches
     global apoz_scores_by_layer
     global avg_scores_by_layer
     global num_layers 
     group_id_string = name
     apoz_thresholds = [90] * num_layers
     avg_thresholds = [sys.maxsize] * num_layers   #sys.maxsize to disable avg
     candidates_by_layer = []

     for layer_idx, (apoz_scores, avg_scores) in enumerate(zip(apoz_scores_by_layer, avg_scores_by_layer)):
         apoz_scores *= 1/ float(num_batches)
         apoz_scores = apoz_scores.cpu() 

         avg_scores *= 1/ float(num_batches)
         avg_scores = avg_scores.cpu()
 
         avg_candidates = [idx for idx, score in enumerate(avg_scores) if score >= avg_thresholds[layer_idx]] if avg_scores.dim() != 0 else []
         candidates = [x[0] for x in apoz_scores.gt(apoz_thresholds[layer_idx]).nonzero().tolist()]

         difference_candidates = list(set(candidates).difference(set(avg_candidates)))
         candidates_by_layer.append(difference_candidates)
     print("Total candidates: {}".format(sum([len(l) for l in candidates_by_layer])))
     np.save(open("prune_candidate_logs/group_{}_apoz_layer_thresholds.npy".format( group_id_string), "wb"), candidates_by_layer)
     print(candidates_by_layer)

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)

class ProgressMeter(object):
    def __init__(self, num_batches, *meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def print(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'

def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

if __name__ == '__main__':
    main()