From baf9d09f673278555718b99557fca815a7f7c1a8 Mon Sep 17 00:00:00 2001 From: Hobe Date: Sat, 21 Aug 2021 14:29:16 +0800 Subject: [PATCH] Upload 8 class CCCB version --- .gitignore | 2 + BinaryNetpytorch/.DS_Store | Bin 0 -> 6148 bytes BinaryNetpytorch/README.md | 8 + BinaryNetpytorch/__pycache__/data.cpython-38.pyc | Bin 0 -> 972 bytes .../__pycache__/preprocess.cpython-38.pyc | Bin 0 -> 5913 bytes BinaryNetpytorch/__pycache__/utils.cpython-38.pyc | Bin 0 -> 4979 bytes BinaryNetpytorch/data.py | 37 ++ BinaryNetpytorch/main_binary.py | 309 +++++++++++++++ BinaryNetpytorch/main_binary_hinge.py | 332 ++++++++++++++++ BinaryNetpytorch/main_mnist.py | 150 ++++++++ BinaryNetpytorch/models/__init__.py | 6 + .../models/__pycache__/__init__.cpython-38.pyc | Bin 0 -> 275 bytes .../models/__pycache__/alexnet.cpython-38.pyc | Bin 0 -> 2178 bytes .../__pycache__/alexnet_binary.cpython-38.pyc | Bin 0 -> 2299 bytes .../__pycache__/binarized_modules.cpython-38.pyc | Bin 0 -> 4078 bytes 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model_error.ckpt create mode 100644 simcim_model_8classes_BNN_ginger.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..4c489a5 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +models.ckpt +training_state.bin diff --git a/BinaryNetpytorch/.DS_Store b/BinaryNetpytorch/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..1642ef92f2b4e0f87d99dc466a6efd18e88678a3 GIT binary patch literal 6148 zcmeHKO>Wab6n@i&)(Hw^QBjuOAhC$5WC2;AN+zU>E|3sLumF_WwbfX1Jdy1Xg%IQo zXP{T$BpiW*Z~*w`C&)=$HkA;n=0!8#^UV96J)g%j86sl+Y0xEV5s`%=u+~6TVWMA@ zE!)v8g2!ZzBbw0sH0I}iycKO4i~>f1|4jk$?e0@R9_1*d@0a`N5zE38l!w2h!6Uju zV?;)j(2#tI9;Gy|;s*7wr4 z;_8K$k9_UF;ynK2d;aSvoDEwC&v=r1Q4*$-90egl-o1&EfY17Tngp55)vAVsM~4Von_lz^d9%xcCXi4F6-9egYNO^*?9hO@oD+_D~1DOSem8X)wqI7IBRlz zaML8_$pzX4r-{Wpg;1jAbPg}WuR>%wMJ`KIq)spCgr3rXo&%2{d9W_Ee4%BAMN_=M zNW7G)JIIMtj$e(z;58J`6T`ZFjU0w*6fg?hOa;XHV510hG!_cw(t%1I0f03$OM@@| zGzgBZ(a~5aL=TK9Do|0GK4LIMN4u@#9F2uSMJJ{YA57nw=@SZ*cSn9(h7)rXn%XE} 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literal 0 HcmV?d00001 diff --git a/BinaryNetpytorch/data.py b/BinaryNetpytorch/data.py new file mode 100644 index 0000000..ddfea89 --- /dev/null +++ b/BinaryNetpytorch/data.py @@ -0,0 +1,37 @@ +import os +import torchvision.datasets as datasets +import torchvision.transforms as transforms + +_DATASETS_MAIN_PATH = '/home/Datasets' +_dataset_path = { + 'cifar10': os.path.join(_DATASETS_MAIN_PATH, 'CIFAR10'), + 'cifar100': os.path.join(_DATASETS_MAIN_PATH, 'CIFAR100'), + 'stl10': os.path.join(_DATASETS_MAIN_PATH, 'STL10'), + 'mnist': os.path.join(_DATASETS_MAIN_PATH, 'MNIST'), + 'imagenet': { + 'train': os.path.join(_DATASETS_MAIN_PATH, 'ImageNet/train'), + 'val': os.path.join(_DATASETS_MAIN_PATH, 'ImageNet/val') + } +} + + +def get_dataset(name, split='train', transform=None, + target_transform=None, download=True): + train = (split == 'train') + if name == 'cifar10': + return datasets.CIFAR10(root=_dataset_path['cifar10'], + train=train, + transform=transform, + target_transform=target_transform, + download=download) + elif name == 'cifar100': + return datasets.CIFAR100(root=_dataset_path['cifar100'], + train=train, + transform=transform, + target_transform=target_transform, + download=download) + elif name == 'imagenet': + path = _dataset_path[name][split] + return datasets.ImageFolder(root=path, + transform=transform, + target_transform=target_transform) diff --git a/BinaryNetpytorch/main_binary.py b/BinaryNetpytorch/main_binary.py new file mode 100644 index 0000000..91d8093 --- /dev/null +++ b/BinaryNetpytorch/main_binary.py @@ -0,0 +1,309 @@ +import argparse +import os +import time +import logging +import torch +import torch.nn as nn +import torch.nn.parallel +import torch.backends.cudnn as cudnn +import torch.optim +import torch.utils.data +import models +from torch.autograd import Variable +from data import get_dataset +from preprocess import get_transform +from utils import * +from datetime import datetime +from ast import literal_eval +from torchvision.utils import save_image + + +model_names = sorted(name for name in models.__dict__ + if name.islower() and not name.startswith("__") + and callable(models.__dict__[name])) +<<<<<<< HEAD +print(model_names) +======= +>>>>>>> 0d30f7b8e44285531022cdc05b2c11c22db27e3a + +parser = argparse.ArgumentParser(description='PyTorch ConvNet Training') + +parser.add_argument('--results_dir', metavar='RESULTS_DIR', default='./results', + help='results dir') +parser.add_argument('--save', metavar='SAVE', default='', + help='saved folder') +parser.add_argument('--dataset', metavar='DATASET', default='imagenet', + help='dataset name or folder') +parser.add_argument('--model', '-a', metavar='MODEL', default='alexnet', + choices=model_names, + help='model architecture: ' + + ' | '.join(model_names) + + ' (default: alexnet)') +parser.add_argument('--input_size', type=int, default=None, + help='image input size') +parser.add_argument('--model_config', default='', + help='additional architecture configuration') +parser.add_argument('--type', default='torch.cuda.FloatTensor', + help='type of tensor - e.g torch.cuda.HalfTensor') +parser.add_argument('--gpus', default='0', + help='gpus used for training - e.g 0,1,3') +parser.add_argument('-j', '--workers', default=8, type=int, metavar='N', + help='number of data loading workers (default: 8)') +parser.add_argument('--epochs', default=2500, 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=256, type=int, + metavar='N', help='mini-batch size (default: 256)') +parser.add_argument('--optimizer', default='SGD', type=str, metavar='OPT', + help='optimizer function used') +parser.add_argument('--lr', '--learning_rate', default=0.1, type=float, + metavar='LR', help='initial learning rate') +parser.add_argument('--momentum', default=0.9, type=float, metavar='M', + help='momentum') +parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, + metavar='W', help='weight decay (default: 1e-4)') +parser.add_argument('--print-freq', '-p', default=10, 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', type=str, metavar='FILE', + help='evaluate model FILE on validation set') + + +def main(): + global args, best_prec1 + best_prec1 = 0 + args = parser.parse_args() + + if args.evaluate: + args.results_dir = '/tmp' + if args.save is '': + args.save = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + save_path = os.path.join(args.results_dir, args.save) + if not os.path.exists(save_path): + os.makedirs(save_path) + + setup_logging(os.path.join(save_path, 'log.txt')) + results_file = os.path.join(save_path, 'results.%s') + results = ResultsLog(results_file % 'csv', results_file % 'html') + + logging.info("saving to %s", save_path) + logging.debug("run arguments: %s", args) + + if 'cuda' in args.type: + args.gpus = [int(i) for i in args.gpus.split(',')] + torch.cuda.set_device(args.gpus[0]) + cudnn.benchmark = True + else: + args.gpus = None + + # create model + logging.info("creating model %s", args.model) + model = models.__dict__[args.model] + model_config = {'input_size': args.input_size, 'dataset': args.dataset} + + if args.model_config is not '': + model_config = dict(model_config, **literal_eval(args.model_config)) + + model = model(**model_config) + logging.info("created model with configuration: %s", model_config) + + # optionally resume from a checkpoint + if args.evaluate: + if not os.path.isfile(args.evaluate): + parser.error('invalid checkpoint: {}'.format(args.evaluate)) + checkpoint = torch.load(args.evaluate) + model.load_state_dict(checkpoint['state_dict']) + logging.info("loaded checkpoint '%s' (epoch %s)", + args.evaluate, checkpoint['epoch']) + elif args.resume: + checkpoint_file = args.resume + if os.path.isdir(checkpoint_file): + results.load(os.path.join(checkpoint_file, 'results.csv')) + checkpoint_file = os.path.join( + checkpoint_file, 'model_best.pth.tar') + if os.path.isfile(checkpoint_file): + logging.info("loading checkpoint '%s'", args.resume) + checkpoint = torch.load(checkpoint_file) + args.start_epoch = checkpoint['epoch'] - 1 + best_prec1 = checkpoint['best_prec1'] + model.load_state_dict(checkpoint['state_dict']) + logging.info("loaded checkpoint '%s' (epoch %s)", + checkpoint_file, checkpoint['epoch']) + else: + logging.error("no checkpoint found at '%s'", args.resume) + + num_parameters = sum([l.nelement() for l in model.parameters()]) + logging.info("number of parameters: %d", num_parameters) + + # Data loading code + default_transform = { + 'train': get_transform(args.dataset, + input_size=args.input_size, augment=True), + 'eval': get_transform(args.dataset, + input_size=args.input_size, augment=False) + } + transform = getattr(model, 'input_transform', default_transform) + regime = getattr(model, 'regime', {0: {'optimizer': args.optimizer, + 'lr': args.lr, + 'momentum': args.momentum, + 'weight_decay': args.weight_decay}}) + # define loss function (criterion) and optimizer + criterion = getattr(model, 'criterion', nn.CrossEntropyLoss)() + criterion.type(args.type) + model.type(args.type) + + val_data = get_dataset(args.dataset, 'val', transform['eval']) + val_loader = torch.utils.data.DataLoader( + val_data, + batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True) + + if args.evaluate: + validate(val_loader, model, criterion, 0) + return + + train_data = get_dataset(args.dataset, 'train', transform['train']) + train_loader = torch.utils.data.DataLoader( + train_data, + batch_size=args.batch_size, shuffle=True, + num_workers=args.workers, pin_memory=True) + + optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) + logging.info('training regime: %s', regime) + + + for epoch in range(args.start_epoch, args.epochs): + optimizer = adjust_optimizer(optimizer, epoch, regime) + + # train for one epoch + train_loss, train_prec1, train_prec5 = train( + train_loader, model, criterion, epoch, optimizer) + + # evaluate on validation set + val_loss, val_prec1, val_prec5 = validate( + val_loader, model, criterion, epoch) + + # remember best prec@1 and save checkpoint + is_best = val_prec1 > best_prec1 + best_prec1 = max(val_prec1, best_prec1) + + save_checkpoint({ + 'epoch': epoch + 1, + 'model': args.model, + 'config': args.model_config, + 'state_dict': model.state_dict(), + 'best_prec1': best_prec1, + 'regime': regime + }, is_best, path=save_path) + logging.info('\n Epoch: {0}\t' + 'Training Loss {train_loss:.4f} \t' + 'Training Prec@1 {train_prec1:.3f} \t' + 'Training Prec@5 {train_prec5:.3f} \t' + 'Validation Loss {val_loss:.4f} \t' + 'Validation Prec@1 {val_prec1:.3f} \t' + 'Validation Prec@5 {val_prec5:.3f} \n' + .format(epoch + 1, train_loss=train_loss, val_loss=val_loss, + train_prec1=train_prec1, val_prec1=val_prec1, + train_prec5=train_prec5, val_prec5=val_prec5)) + + results.add(epoch=epoch + 1, train_loss=train_loss, val_loss=val_loss, + train_error1=100 - train_prec1, val_error1=100 - val_prec1, + train_error5=100 - train_prec5, val_error5=100 - val_prec5) + #results.plot(x='epoch', y=['train_loss', 'val_loss'], + # title='Loss', ylabel='loss') + #results.plot(x='epoch', y=['train_error1', 'val_error1'], + # title='Error@1', ylabel='error %') + #results.plot(x='epoch', y=['train_error5', 'val_error5'], + # title='Error@5', ylabel='error %') + results.save() + + +def forward(data_loader, model, criterion, epoch=0, training=True, optimizer=None): + if args.gpus and len(args.gpus) > 1: + model = torch.nn.DataParallel(model, args.gpus) + batch_time = AverageMeter() + data_time = AverageMeter() + losses = AverageMeter() + top1 = AverageMeter() + top5 = AverageMeter() + + end = time.time() + for i, (inputs, target) in enumerate(data_loader): + # measure data loading time + data_time.update(time.time() - end) + if args.gpus is not None: + target = target.cuda() + + if not training: + with torch.no_grad(): + input_var = Variable(inputs.type(args.type), volatile=not training) + target_var = Variable(target) + # compute output + output = model(input_var) + else: + input_var = Variable(inputs.type(args.type), volatile=not training) + target_var = Variable(target) + # compute output + output = model(input_var) + + + loss = criterion(output, target_var) + if type(output) is list: + output = output[0] + + # measure accuracy and record loss + prec1, prec5 = accuracy(output.data, target, topk=(1, 5)) + losses.update(loss.item(), inputs.size(0)) + top1.update(prec1.item(), inputs.size(0)) + top5.update(prec5.item(), inputs.size(0)) + + if training: + # compute gradient and do SGD step + optimizer.zero_grad() + loss.backward() + for p in list(model.parameters()): + if hasattr(p,'org'): + p.data.copy_(p.org) + optimizer.step() + for p in list(model.parameters()): + if hasattr(p,'org'): + p.org.copy_(p.data.clamp_(-1,1)) + + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + logging.info('{phase} - Epoch: [{0}][{1}/{2}]\t' + 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' + 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' + 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' + 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' + 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( + epoch, i, len(data_loader), + phase='TRAINING' if training else 'EVALUATING', + batch_time=batch_time, + data_time=data_time, loss=losses, top1=top1, top5=top5)) + + return losses.avg, top1.avg, top5.avg + + +def train(data_loader, model, criterion, epoch, optimizer): + # switch to train mode + model.train() + return forward(data_loader, model, criterion, epoch, + training=True, optimizer=optimizer) + + +def validate(data_loader, model, criterion, epoch): + # switch to evaluate mode + model.eval() + return forward(data_loader, model, criterion, epoch, + training=False, optimizer=None) + + +if __name__ == '__main__': + main() diff --git a/BinaryNetpytorch/main_binary_hinge.py b/BinaryNetpytorch/main_binary_hinge.py new file mode 100644 index 0000000..f922422 --- /dev/null +++ b/BinaryNetpytorch/main_binary_hinge.py @@ -0,0 +1,332 @@ +import argparse +import os +import time +import logging +import torch +import torch.nn as nn +import torch.nn.parallel +import torch.backends.cudnn as cudnn +import torch.optim +import torch.utils.data +import models +from torch.autograd import Variable +from data import get_dataset +from preprocess import get_transform +from utils import * +from datetime import datetime +from ast import literal_eval +from torchvision.utils import save_image +from models.binarized_modules import HingeLoss + +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 ConvNet Training') + +parser.add_argument('--results_dir', metavar='RESULTS_DIR', default='/media/hdd/ihubara/BinaryNet.pytorch/results', + help='results dir') +parser.add_argument('--save', metavar='SAVE', default='', + help='saved folder') +parser.add_argument('--dataset', metavar='DATASET', default='imagenet', + help='dataset name or folder') +parser.add_argument('--model', '-a', metavar='MODEL', default='alexnet', + choices=model_names, + help='model architecture: ' + + ' | '.join(model_names) + + ' (default: alexnet)') +parser.add_argument('--input_size', type=int, default=None, + help='image input size') +parser.add_argument('--model_config', default='', + help='additional architecture configuration') +parser.add_argument('--type', default='torch.cuda.FloatTensor', + help='type of tensor - e.g torch.cuda.HalfTensor') +parser.add_argument('--gpus', default='0', + help='gpus used for training - e.g 0,1,3') +parser.add_argument('-j', '--workers', default=8, type=int, metavar='N', + help='number of data loading workers (default: 8)') +parser.add_argument('--epochs', default=900, 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=256, type=int, + metavar='N', help='mini-batch size (default: 256)') +parser.add_argument('--optimizer', default='SGD', type=str, metavar='OPT', + help='optimizer function used') +parser.add_argument('--lr', '--learning_rate', default=0.1, type=float, + metavar='LR', help='initial learning rate') +parser.add_argument('--momentum', default=0.9, type=float, metavar='M', + help='momentum') +parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, + metavar='W', help='weight decay (default: 1e-4)') +parser.add_argument('--print-freq', '-p', default=10, 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', type=str, metavar='FILE', + help='evaluate model FILE on validation set') + +torch.cuda.random.manual_seed_all(10) + +output_dim = 0 + + +def main(): + global args, best_prec1, output_dim + best_prec1 = 0 + args = parser.parse_args() + output_dim = {'cifar10': 10, 'cifar100':100, 'imagenet': 1000}[args.dataset] + #import pdb; pdb.set_trace() + #torch.save(args.batch_size/(len(args.gpus)/2+1),'multi_gpu_batch_size') + if args.evaluate: + args.results_dir = '/tmp' + if args.save is '': + args.save = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + save_path = os.path.join(args.results_dir, args.save) + if not os.path.exists(save_path): + os.makedirs(save_path) + + setup_logging(os.path.join(save_path, 'log.txt')) + results_file = os.path.join(save_path, 'results.%s') + results = ResultsLog(results_file % 'csv', results_file % 'html') + + logging.info("saving to %s", save_path) + logging.debug("run arguments: %s", args) + + if 'cuda' in args.type: + args.gpus = [int(i) for i in args.gpus.split(',')] + torch.cuda.set_device(args.gpus[0]) + cudnn.benchmark = True + else: + args.gpus = None + + # create model + logging.info("creating model %s", args.model) + model = models.__dict__[args.model] + + + model_config = {'input_size': args.input_size, 'dataset': args.dataset, 'num_classes': output_dim} + + if args.model_config is not '': + model_config = dict(model_config, **literal_eval(args.model_config)) + model = model(**model_config) + logging.info("created model with configuration: %s", model_config) + + # optionally resume from a checkpoint + if args.evaluate: + if not os.path.isfile(args.evaluate): + parser.error('invalid checkpoint: {}'.format(args.evaluate)) + checkpoint = torch.load(args.evaluate) + model.load_state_dict(checkpoint['state_dict']) + logging.info("loaded checkpoint '%s' (epoch %s)", + args.evaluate, checkpoint['epoch']) + elif args.resume: + checkpoint_file = args.resume + if os.path.isdir(checkpoint_file): + results.load(os.path.join(checkpoint_file, 'results.csv')) + checkpoint_file = os.path.join( + checkpoint_file, 'model_best.pth.tar') + if os.path.isfile(checkpoint_file): + logging.info("loading checkpoint '%s'", args.resume) + checkpoint = torch.load(checkpoint_file) + args.start_epoch = checkpoint['epoch'] - 1 + best_prec1 = checkpoint['best_prec1'] + model.load_state_dict(checkpoint['state_dict']) + logging.info("loaded checkpoint '%s' (epoch %s)", + checkpoint_file, checkpoint['epoch']) + else: + logging.error("no checkpoint found at '%s'", args.resume) + + num_parameters = sum([l.nelement() for l in model.parameters()]) + logging.info("number of parameters: %d", num_parameters) + + # Data loading code + default_transform = { + 'train': get_transform(args.dataset, + input_size=args.input_size, augment=True), + 'eval': get_transform(args.dataset, + input_size=args.input_size, augment=False) + } + transform = getattr(model, 'input_transform', default_transform) + regime = getattr(model, 'regime', {0: {'optimizer': args.optimizer, + 'lr': args.lr, + 'momentum': args.momentum, + 'weight_decay': args.weight_decay}}) + # define loss function (criterion) and optimizer + #criterion = getattr(model, 'criterion', nn.NLLLoss)() + criterion = getattr(model, 'criterion', HingeLoss)() + #criterion.type(args.type) + model.type(args.type) + + val_data = get_dataset(args.dataset, 'val', transform['eval']) + val_loader = torch.utils.data.DataLoader( + val_data, + batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True) + + if args.evaluate: + validate(val_loader, model, criterion, 0) + return + + train_data = get_dataset(args.dataset, 'train', transform['train']) + train_loader = torch.utils.data.DataLoader( + train_data, + batch_size=args.batch_size, shuffle=True, + num_workers=args.workers, pin_memory=True) + + optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) + logging.info('training regime: %s', regime) + #import pdb; pdb.set_trace() + #search_binarized_modules(model) + + for epoch in range(args.start_epoch, args.epochs): + optimizer = adjust_optimizer(optimizer, epoch, regime) + + # train for one epoch + train_loss, train_prec1, train_prec5 = train( + train_loader, model, criterion, epoch, optimizer) + + # evaluate on validation set + val_loss, val_prec1, val_prec5 = validate( + val_loader, model, criterion, epoch) + + # remember best prec@1 and save checkpoint + is_best = val_prec1 > best_prec1 + best_prec1 = max(val_prec1, best_prec1) + save_checkpoint({ + 'epoch': epoch + 1, + 'model': args.model, + 'config': args.model_config, + 'state_dict': model.state_dict(), + 'best_prec1': best_prec1, + 'regime': regime + }, is_best, path=save_path) + logging.info('\n Epoch: {0}\t' + 'Training Loss {train_loss:.4f} \t' + 'Training Prec@1 {train_prec1:.3f} \t' + 'Training Prec@5 {train_prec5:.3f} \t' + 'Validation Loss {val_loss:.4f} \t' + 'Validation Prec@1 {val_prec1:.3f} \t' + 'Validation Prec@5 {val_prec5:.3f} \n' + .format(epoch + 1, train_loss=train_loss, val_loss=val_loss, + train_prec1=train_prec1, val_prec1=val_prec1, + train_prec5=train_prec5, val_prec5=val_prec5)) + + results.add(epoch=epoch + 1, train_loss=train_loss, val_loss=val_loss, + train_error1=100 - train_prec1, val_error1=100 - val_prec1, + train_error5=100 - train_prec5, val_error5=100 - val_prec5) + results.plot(x='epoch', y=['train_loss', 'val_loss'], + title='Loss', ylabel='loss') + results.plot(x='epoch', y=['train_error1', 'val_error1'], + title='Error@1', ylabel='error %') + results.plot(x='epoch', y=['train_error5', 'val_error5'], + title='Error@5', ylabel='error %') + results.save() + +def forward(data_loader, model, criterion, epoch=0, training=True, optimizer=None): + if args.gpus and len(args.gpus) > 1: + model = torch.nn.DataParallel(model, args.gpus) + batch_time = AverageMeter() + data_time = AverageMeter() + losses = AverageMeter() + top1 = AverageMeter() + top5 = AverageMeter() + + end = time.time() + for i, (inputs, target) in enumerate(data_loader): + # measure data loading time + data_time.update(time.time() - end) + if args.gpus is not None: + target = target.cuda() + #import pdb; pdb.set_trace() + if criterion.__class__.__name__=='HingeLoss': + target=target.unsqueeze(1) + target_onehot = torch.cuda.FloatTensor(target.size(0), output_dim) + target_onehot.fill_(-1) + target_onehot.scatter_(1, target, 1) + target=target.squeeze() + if not training: + with torch.no_grad(): + input_var = Variable(inputs.type(args.type)) + target_var = Variable(target_onehot) + + # compute output + output = model(input_var) + else: + input_var = Variable(inputs.type(args.type)) + target_var = Variable(target_onehot) + + # compute output + output = model(input_var) + + #import pdb; pdb.set_trace() + loss = criterion(output, target_onehot) + #import pdb; pdb.set_trace() + if type(output) is list: + output = output[0] + + # measure accuracy and record loss + prec1, prec5 = accuracy(output.data, target, topk=(1, 5)) + losses.update(loss.item(), inputs.size(0)) + top1.update(prec1.item(), inputs.size(0)) + top5.update(prec5.item(), inputs.size(0)) + #import pdb; pdb.set_trace() + #if not training and top1.avg<15: + # import pdb; pdb.set_trace() + if training: + # compute gradient and do SGD step + optimizer.zero_grad() + #add backwoed hook + loss.backward() + for p in list(model.parameters()): + #import pdb; pdb.set_trace() + if hasattr(p,'org'): + #print('before:', p[0][0]) + #gm=max(p.grad.data.max(),-p.grad.data.min()) + #p.grad=p.grad.div(gm+1) + p.data.copy_(p.org) + #print('after:', p[0][0]) + optimizer.step() + for p in list(model.parameters()): + #import pdb; pdb.set_trace() + if hasattr(p,'org'): + #print('before:', p[0][0]) + p.org.copy_(p.data.clamp_(-1,1)) + #if epoch>30: + # import pdb; pdb.set_trace() + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + logging.info('{phase} - Epoch: [{0}][{1}/{2}]\t' + 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' + 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' + 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' + 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' + 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( + epoch, i, len(data_loader), + phase='TRAINING' if training else 'EVALUATING', + batch_time=batch_time, + data_time=data_time, loss=losses, top1=top1, top5=top5)) + + return losses.avg, top1.avg, top5.avg + + +def train(data_loader, model, criterion, epoch, optimizer): + # switch to train mode + model.train() + return forward(data_loader, model, criterion, epoch, + training=True, optimizer=optimizer) + + +def validate(data_loader, model, criterion, epoch): + # switch to evaluate mode + model.eval() + return forward(data_loader, model, criterion, epoch, + training=False, optimizer=None) + + +if __name__ == '__main__': + main() diff --git a/BinaryNetpytorch/main_mnist.py b/BinaryNetpytorch/main_mnist.py new file mode 100644 index 0000000..d2b16f3 --- /dev/null +++ b/BinaryNetpytorch/main_mnist.py @@ -0,0 +1,150 @@ + +from __future__ import print_function +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from torchvision import datasets, transforms +from torch.autograd import Variable +from models.binarized_modules import BinarizeLinear,BinarizeConv2d +from models.binarized_modules import Binarize,HingeLoss +# Training settings +parser = argparse.ArgumentParser(description='PyTorch MNIST Example') +parser.add_argument('--batch-size', type=int, default=64, metavar='N', + help='input batch size for training (default: 256)') +parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', + help='input batch size for testing (default: 1000)') +parser.add_argument('--epochs', type=int, default=100, metavar='N', + help='number of epochs to train (default: 10)') +parser.add_argument('--lr', type=float, default=0.01, metavar='LR', + help='learning rate (default: 0.001)') +parser.add_argument('--momentum', type=float, default=0.5, metavar='M', + help='SGD momentum (default: 0.5)') +parser.add_argument('--no-cuda', action='store_true', default=False, + help='disables CUDA training') +parser.add_argument('--seed', type=int, default=1, metavar='S', + help='random seed (default: 1)') +parser.add_argument('--gpus', default=3, + help='gpus used for training - e.g 0,1,3') +parser.add_argument('--log-interval', type=int, default=10, metavar='N', + help='how many batches to wait before logging training status') +args = parser.parse_args() +args.cuda = not args.no_cuda and torch.cuda.is_available() + +torch.manual_seed(args.seed) +if args.cuda: + torch.cuda.manual_seed(args.seed) + + +kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} +train_loader = torch.utils.data.DataLoader( + datasets.MNIST('../data', train=True, download=True, + transform=transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ])), + batch_size=args.batch_size, shuffle=True, **kwargs) +test_loader = torch.utils.data.DataLoader( + datasets.MNIST('../data', train=False, transform=transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ])), + batch_size=args.test_batch_size, shuffle=True, **kwargs) + + +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.infl_ratio=3 + self.fc1 = BinarizeLinear(784, 2048*self.infl_ratio) + self.htanh1 = nn.Hardtanh() + self.bn1 = nn.BatchNorm1d(2048*self.infl_ratio) + self.fc2 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio) + self.htanh2 = nn.Hardtanh() + self.bn2 = nn.BatchNorm1d(2048*self.infl_ratio) + self.fc3 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio) + self.htanh3 = nn.Hardtanh() + self.bn3 = nn.BatchNorm1d(2048*self.infl_ratio) + self.fc4 = nn.Linear(2048*self.infl_ratio, 10) + self.logsoftmax=nn.LogSoftmax() + self.drop=nn.Dropout(0.5) + + def forward(self, x): + x = x.view(-1, 28*28) + x = self.fc1(x) + x = self.bn1(x) + x = self.htanh1(x) + x = self.fc2(x) + x = self.bn2(x) + x = self.htanh2(x) + x = self.fc3(x) + x = self.drop(x) + x = self.bn3(x) + x = self.htanh3(x) + x = self.fc4(x) + return self.logsoftmax(x) + +model = Net() +if args.cuda: + torch.cuda.set_device(3) + model.cuda() + + +criterion = nn.CrossEntropyLoss() +optimizer = optim.Adam(model.parameters(), lr=args.lr) + + +def train(epoch): + model.train() + for batch_idx, (data, target) in enumerate(train_loader): + if args.cuda: + data, target = data.cuda(), target.cuda() + data, target = Variable(data), Variable(target) + optimizer.zero_grad() + output = model(data) + loss = criterion(output, target) + + if epoch%40==0: + optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1 + + optimizer.zero_grad() + loss.backward() + for p in list(model.parameters()): + if hasattr(p,'org'): + p.data.copy_(p.org) + optimizer.step() + for p in list(model.parameters()): + if hasattr(p,'org'): + p.org.copy_(p.data.clamp_(-1,1)) + + if batch_idx % args.log_interval == 0: + print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( + epoch, batch_idx * len(data), len(train_loader.dataset), + 100. * batch_idx / len(train_loader), loss.item())) + +def test(): + model.eval() + test_loss = 0 + correct = 0 + with torch.no_grad(): + for data, target in test_loader: + if args.cuda: + data, target = data.cuda(), target.cuda() + data, target = Variable(data), Variable(target) + output = model(data) + test_loss += criterion(output, target).item() # sum up batch loss + pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability + correct += pred.eq(target.data.view_as(pred)).cpu().sum() + + test_loss /= len(test_loader.dataset) + print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( + test_loss, correct, len(test_loader.dataset), + 100. * correct / len(test_loader.dataset))) + + +for epoch in range(1, args.epochs + 1): + train(epoch) + test() + if epoch%40==0: + optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1 diff --git a/BinaryNetpytorch/models/__init__.py b/BinaryNetpytorch/models/__init__.py new file mode 100644 index 0000000..1df1fdc --- /dev/null +++ b/BinaryNetpytorch/models/__init__.py @@ -0,0 +1,6 @@ + +from .alexnet import * +from .alexnet_binary import * +from .resnet import * +from .resnet_binary import * +from .vgg_cifar10_binary import * diff --git a/BinaryNetpytorch/models/__pycache__/__init__.cpython-38.pyc b/BinaryNetpytorch/models/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 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BinarizeConv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = BinarizeConv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = BinarizeConv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.tanh = nn.Hardtanh(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + import pdb; pdb.set_trace() + out = self.conv1(x) + out = self.bn1(out) + out = self.tanh(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.tanh(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + if self.do_bntan: + out = self.bn2(out) + out = self.tanh2(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self): + super(ResNet, self).__init__() + + def _make_layer(self, block, planes, blocks, stride=1,do_bntan=True): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + BinarizeConv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks-1): + layers.append(block(self.inplanes, planes)) + layers.append(block(self.inplanes, planes,do_bntan=do_bntan)) + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.maxpool(x) + x = self.bn1(x) + x = self.tanh1(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.bn2(x) + x = self.tanh2(x) + x = self.fc(x) + x = self.bn3(x) + x = self.logsoftmax(x) + + return x + + +class ResNet_imagenet(ResNet): + + def __init__(self, num_classes=1000, + block=Bottleneck, layers=[3, 4, 23, 3]): + super(ResNet_imagenet, self).__init__() + self.inplanes = 64 + self.conv1 = BinarizeConv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.tanh = nn.Hardtanh(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7) + self.fc = BinarizeLinear(512 * block.expansion, num_classes) + + init_model(self) + self.regime = { + 0: {'optimizer': 'SGD', 'lr': 1e-1, + 'weight_decay': 1e-4, 'momentum': 0.9}, + 30: {'lr': 1e-2}, + 60: {'lr': 1e-3, 'weight_decay': 0}, + 90: {'lr': 1e-4} + } + + +class ResNet_cifar10(ResNet): + + def __init__(self, num_classes=10, + block=BasicBlock, depth=18): + super(ResNet_cifar10, self).__init__() + self.inflate = 5 + self.inplanes = 16*self.inflate + n = int((depth - 2) / 6) + self.conv1 = BinarizeConv2d(3, 16*self.inflate, kernel_size=3, stride=1, padding=1, + bias=False) + self.maxpool = lambda x: x + self.bn1 = nn.BatchNorm2d(16*self.inflate) + self.tanh1 = nn.Hardtanh(inplace=True) + self.tanh2 = nn.Hardtanh(inplace=True) + self.layer1 = self._make_layer(block, 16*self.inflate, n) + self.layer2 = self._make_layer(block, 32*self.inflate, n, stride=2) + self.layer3 = self._make_layer(block, 64*self.inflate, n, stride=2,do_bntan=False) + self.layer4 = lambda x: x + self.avgpool = nn.AvgPool2d(8) + self.bn2 = nn.BatchNorm1d(64*self.inflate) + self.bn3 = nn.BatchNorm1d(10) + self.logsoftmax = nn.LogSoftmax() + self.fc = BinarizeLinear(64*self.inflate, num_classes) + + init_model(self) + #self.regime = { + # 0: {'optimizer': 'SGD', 'lr': 1e-1, + # 'weight_decay': 1e-4, 'momentum': 0.9}, + # 81: {'lr': 1e-4}, + # 122: {'lr': 1e-5, 'weight_decay': 0}, + # 164: {'lr': 1e-6} + #} + self.regime = { + 0: {'optimizer': 'Adam', 'lr': 5e-3}, + 101: {'lr': 1e-3}, + 142: {'lr': 5e-4}, + 184: {'lr': 1e-4}, + 220: {'lr': 1e-5} + } + + +def resnet_binary(**kwargs): + num_classes, depth, dataset = map( + kwargs.get, ['num_classes', 'depth', 'dataset']) + if dataset == 'imagenet': + num_classes = num_classes or 1000 + depth = depth or 50 + if depth == 18: + return ResNet_imagenet(num_classes=num_classes, + block=BasicBlock, layers=[2, 2, 2, 2]) + if depth == 34: + return ResNet_imagenet(num_classes=num_classes, + block=BasicBlock, layers=[3, 4, 6, 3]) + if depth == 50: + return ResNet_imagenet(num_classes=num_classes, + block=Bottleneck, layers=[3, 4, 6, 3]) + if depth == 101: + return ResNet_imagenet(num_classes=num_classes, + block=Bottleneck, layers=[3, 4, 23, 3]) + if depth == 152: + return ResNet_imagenet(num_classes=num_classes, + block=Bottleneck, layers=[3, 8, 36, 3]) + + elif dataset == 'cifar10': + num_classes = num_classes or 10 + depth = depth or 18 + return ResNet_cifar10(num_classes=num_classes, + block=BasicBlock, depth=depth) diff --git a/BinaryNetpytorch/models/vgg_cifar10.py b/BinaryNetpytorch/models/vgg_cifar10.py new file mode 100644 index 0000000..1ec23cd --- /dev/null +++ b/BinaryNetpytorch/models/vgg_cifar10.py @@ -0,0 +1,69 @@ +import torch.nn as nn +import torchvision.transforms as transforms + + +class AlexNetOWT_BN(nn.Module): + + def __init__(self, num_classes=1000): + super(AlexNetOWT_BN, self).__init__() + self.features = nn.Sequential( + nn.Conv2d(3, 128, kernel_size=3, stride=1, padding=1, + bias=False), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + + nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.ReLU(inplace=True), + nn.BatchNorm2d(128), + + nn.Conv2d(128, 256, kernel_size=3, padding=1, bias=False), + nn.ReLU(inplace=True), + nn.BatchNorm2d(256), + + nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.ReLU(inplace=True), + nn.BatchNorm2d(256), + + nn.Conv2d(256, 512, kernel_size=3, padding=1, bias=False), + nn.ReLU(inplace=True), + nn.BatchNorm2d(512), + + nn.Conv2d(512, 512, kernel_size=3, padding=1, bias=False), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.ReLU(inplace=True), + nn.BatchNorm2d(512), + ) + self.classifier = nn.Sequential( + nn.Linear(512 * 4 * 4, 1024, bias=False), + nn.BatchNorm1d(1024), + nn.ReLU(inplace=True), + nn.Dropout(0.5), + nn.Linear(1024, 1024, bias=False), + nn.BatchNorm1d(1024), + nn.ReLU(inplace=True), + nn.Dropout(0.5), + nn.Linear(1024, num_classes) + nn.LogSoftMax() + ) + + self.regime = { + 0: {'optimizer': 'SGD', 'lr': 1e-2, + 'weight_decay': 5e-4, 'momentum': 0.9}, + 10: {'lr': 5e-3}, + 15: {'lr': 1e-3, 'weight_decay': 0}, + 20: {'lr': 5e-4}, + 25: {'lr': 1e-4} + } + + def forward(self, x): + x = self.features(x) + x = x.view(-1, 512 * 4 * 4) + x = self.classifier(x) + return x + + +def model(**kwargs): + num_classes = kwargs.get( 'num_classes', 1000) + return AlexNetOWT_BN(num_classes) diff --git a/BinaryNetpytorch/models/vgg_cifar10_binary.py b/BinaryNetpytorch/models/vgg_cifar10_binary.py new file mode 100644 index 0000000..694d6f0 --- /dev/null +++ b/BinaryNetpytorch/models/vgg_cifar10_binary.py @@ -0,0 +1,80 @@ +import torch +import torch.nn as nn +import torchvision.transforms as transforms +from torch.autograd import Function +from .binarized_modules import BinarizeLinear,BinarizeConv2d + + + +class VGG_Cifar10(nn.Module): + + def __init__(self, num_classes=1000): + super(VGG_Cifar10, self).__init__() + self.infl_ratio=3; + self.features = nn.Sequential( + BinarizeConv2d(3, 128*self.infl_ratio, kernel_size=3, stride=1, padding=1, + bias=True), + nn.BatchNorm2d(128*self.infl_ratio), + nn.Hardtanh(inplace=True), + + BinarizeConv2d(128*self.infl_ratio, 128*self.infl_ratio, kernel_size=3, padding=1, bias=True), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.BatchNorm2d(128*self.infl_ratio), + nn.Hardtanh(inplace=True), + + + BinarizeConv2d(128*self.infl_ratio, 256*self.infl_ratio, kernel_size=3, padding=1, bias=True), + nn.BatchNorm2d(256*self.infl_ratio), + nn.Hardtanh(inplace=True), + + + BinarizeConv2d(256*self.infl_ratio, 256*self.infl_ratio, kernel_size=3, padding=1, bias=True), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.BatchNorm2d(256*self.infl_ratio), + nn.Hardtanh(inplace=True), + + + BinarizeConv2d(256*self.infl_ratio, 512*self.infl_ratio, kernel_size=3, padding=1, bias=True), + nn.BatchNorm2d(512*self.infl_ratio), + nn.Hardtanh(inplace=True), + + + BinarizeConv2d(512*self.infl_ratio, 512, kernel_size=3, padding=1, bias=True), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.BatchNorm2d(512), + nn.Hardtanh(inplace=True) + + ) + self.classifier = nn.Sequential( + BinarizeLinear(512 * 4 * 4, 1024, bias=True), + nn.BatchNorm1d(1024), + nn.Hardtanh(inplace=True), + #nn.Dropout(0.5), + BinarizeLinear(1024, 1024, bias=True), + nn.BatchNorm1d(1024), + nn.Hardtanh(inplace=True), + #nn.Dropout(0.5), + BinarizeLinear(1024, num_classes, bias=True), + nn.BatchNorm1d(num_classes, affine=False), + nn.LogSoftmax() + ) + + self.regime = { + 0: {'optimizer': 'Adam', 'betas': (0.9, 0.999),'lr': 5e-3}, + 40: {'lr': 1e-3}, + 80: {'lr': 5e-4}, + 100: {'lr': 1e-4}, + 120: {'lr': 5e-5}, + 140: {'lr': 1e-5} + } + + def forward(self, x): + x = self.features(x) + x = x.view(-1, 512 * 4 * 4) + x = self.classifier(x) + return x + + +def vgg_cifar10_binary(**kwargs): + num_classes = kwargs.get( 'num_classes', 10) + return VGG_Cifar10(num_classes) diff --git a/BinaryNetpytorch/preprocess.py b/BinaryNetpytorch/preprocess.py new file mode 100644 index 0000000..d904f56 --- /dev/null +++ b/BinaryNetpytorch/preprocess.py @@ -0,0 +1,198 @@ +import torch +import torchvision.transforms as transforms +import random + +__imagenet_stats = {'mean': [0.485, 0.456, 0.406], + 'std': [0.229, 0.224, 0.225]} + +__imagenet_pca = { + 'eigval': torch.Tensor([0.2175, 0.0188, 0.0045]), + 'eigvec': torch.Tensor([ + [-0.5675, 0.7192, 0.4009], + [-0.5808, -0.0045, -0.8140], + [-0.5836, -0.6948, 0.4203], + ]) +} + + +def scale_crop(input_size, scale_size=None, normalize=__imagenet_stats): + t_list = [ + transforms.CenterCrop(input_size), + transforms.ToTensor(), + transforms.Normalize(**normalize), + ] + if scale_size != input_size: + t_list = [transforms.Scale(scale_size)] + t_list + + return transforms.Compose(t_list) + + +def scale_random_crop(input_size, scale_size=None, normalize=__imagenet_stats): + t_list = [ + transforms.RandomCrop(input_size), + transforms.ToTensor(), + transforms.Normalize(**normalize), + ] + if scale_size != input_size: + t_list = [transforms.Scale(scale_size)] + t_list + + transforms.Compose(t_list) + + +def pad_random_crop(input_size, scale_size=None, normalize=__imagenet_stats): + padding = int((scale_size - input_size) / 2) + return transforms.Compose([ + transforms.RandomCrop(input_size, padding=padding), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(**normalize), + ]) + + +def inception_preproccess(input_size, normalize=__imagenet_stats): + return transforms.Compose([ + transforms.RandomSizedCrop(input_size), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(**normalize) + ]) +def inception_color_preproccess(input_size, normalize=__imagenet_stats): + return transforms.Compose([ + transforms.RandomSizedCrop(input_size), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + ColorJitter( + brightness=0.4, + contrast=0.4, + saturation=0.4, + ), + Lighting(0.1, __imagenet_pca['eigval'], __imagenet_pca['eigvec']), + transforms.Normalize(**normalize) + ]) + + +def get_transform(name='imagenet', input_size=None, + scale_size=None, normalize=None, augment=True): + normalize = normalize or __imagenet_stats + if name == 'imagenet': + scale_size = scale_size or 256 + input_size = input_size or 224 + if augment: + return inception_preproccess(input_size, normalize=normalize) + else: + return scale_crop(input_size=input_size, + scale_size=scale_size, normalize=normalize) + elif 'cifar' in name: + input_size = input_size or 32 + if augment: + scale_size = scale_size or 40 + return pad_random_crop(input_size, scale_size=scale_size, + normalize=normalize) + else: + scale_size = scale_size or 32 + return scale_crop(input_size=input_size, + scale_size=scale_size, normalize=normalize) + elif name == 'mnist': + normalize = {'mean': [0.5], 'std': [0.5]} + input_size = input_size or 28 + if augment: + scale_size = scale_size or 32 + return pad_random_crop(input_size, scale_size=scale_size, + normalize=normalize) + else: + scale_size = scale_size or 32 + return scale_crop(input_size=input_size, + scale_size=scale_size, normalize=normalize) + + +class Lighting(object): + """Lighting noise(AlexNet - style PCA - based noise)""" + + def __init__(self, alphastd, eigval, eigvec): + self.alphastd = alphastd + self.eigval = eigval + self.eigvec = eigvec + + def __call__(self, img): + if self.alphastd == 0: + return img + + alpha = img.new().resize_(3).normal_(0, self.alphastd) + rgb = self.eigvec.type_as(img).clone()\ + .mul(alpha.view(1, 3).expand(3, 3))\ + .mul(self.eigval.view(1, 3).expand(3, 3))\ + .sum(1).squeeze() + + return img.add(rgb.view(3, 1, 1).expand_as(img)) + + +class Grayscale(object): + + def __call__(self, img): + gs = img.clone() + gs[0].mul_(0.299).add_(0.587, gs[1]).add_(0.114, gs[2]) + gs[1].copy_(gs[0]) + gs[2].copy_(gs[0]) + return gs + + +class Saturation(object): + + def __init__(self, var): + self.var = var + + def __call__(self, img): + gs = Grayscale()(img) + alpha = random.uniform(0, self.var) + return img.lerp(gs, alpha) + + +class Brightness(object): + + def __init__(self, var): + self.var = var + + def __call__(self, img): + gs = img.new().resize_as_(img).zero_() + alpha = random.uniform(0, self.var) + return img.lerp(gs, alpha) + + +class Contrast(object): + + def __init__(self, var): + self.var = var + + def __call__(self, img): + gs = Grayscale()(img) + gs.fill_(gs.mean()) + alpha = random.uniform(0, self.var) + return img.lerp(gs, alpha) + + +class RandomOrder(object): + """ Composes several transforms together in random order. + """ + + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, img): + if self.transforms is None: + return img + order = torch.randperm(len(self.transforms)) + for i in order: + img = self.transforms[i](img) + return img + + +class ColorJitter(RandomOrder): + + def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4): + self.transforms = [] + if brightness != 0: + self.transforms.append(Brightness(brightness)) + if contrast != 0: + self.transforms.append(Contrast(contrast)) + if saturation != 0: + self.transforms.append(Saturation(saturation)) diff --git a/BinaryNetpytorch/results/2021-04-15_15-36-47/log.txt b/BinaryNetpytorch/results/2021-04-15_15-36-47/log.txt new file mode 100644 index 0000000..47bf526 --- /dev/null +++ b/BinaryNetpytorch/results/2021-04-15_15-36-47/log.txt @@ -0,0 +1,5 @@ +2021-04-15 15:36:47 - INFO - saving to ./results/2021-04-15_15-36-47 +2021-04-15 15:36:47 - DEBUG - run arguments: Namespace(batch_size=256, dataset='imagenet', epochs=2500, evaluate=None, gpus='0', input_size=None, lr=0.1, model='alexnet', model_config='', momentum=0.9, optimizer='SGD', print_freq=10, results_dir='./results', resume='', save='2021-04-15_15-36-47', start_epoch=0, type='torch.cuda.FloatTensor', weight_decay=0.0001, workers=8) +2021-04-15 15:36:47 - INFO - creating model alexnet +2021-04-15 15:36:48 - INFO - created model with configuration: {'input_size': None, 'dataset': 'imagenet'} +2021-04-15 15:36:48 - INFO - number of parameters: 61110184 diff --git a/BinaryNetpytorch/results/2021-04-15_15-37-52/log.txt b/BinaryNetpytorch/results/2021-04-15_15-37-52/log.txt new file mode 100644 index 0000000..503a37e --- /dev/null +++ b/BinaryNetpytorch/results/2021-04-15_15-37-52/log.txt @@ -0,0 +1,5 @@ +2021-04-15 15:37:52 - INFO - saving to ./results/2021-04-15_15-37-52 +2021-04-15 15:37:52 - DEBUG - run arguments: Namespace(batch_size=256, dataset='imagenet', epochs=2500, evaluate=None, gpus='0', input_size=None, lr=0.1, model='resnet', model_config='', momentum=0.9, optimizer='SGD', print_freq=10, results_dir='./results', resume='', save='2021-04-15_15-37-52', start_epoch=0, type='torch.cuda.FloatTensor', weight_decay=0.0001, workers=8) +2021-04-15 15:37:52 - INFO - creating model resnet +2021-04-15 15:37:52 - INFO - created model with configuration: {'input_size': None, 'dataset': 'imagenet'} +2021-04-15 15:37:52 - INFO - number of parameters: 25557032 diff --git a/BinaryNetpytorch/results/2021-04-15_15-38-16/log.txt b/BinaryNetpytorch/results/2021-04-15_15-38-16/log.txt new file mode 100644 index 0000000..5b248e2 --- /dev/null +++ b/BinaryNetpytorch/results/2021-04-15_15-38-16/log.txt @@ -0,0 +1,5 @@ +2021-04-15 15:38:16 - INFO - saving to ./results/2021-04-15_15-38-16 +2021-04-15 15:38:16 - DEBUG - run arguments: Namespace(batch_size=256, dataset='imagenet', epochs=2500, evaluate=None, gpus='0', input_size=None, lr=0.1, model='alexnet', model_config='', momentum=0.9, optimizer='SGD', print_freq=10, results_dir='./results', resume='', save='2021-04-15_15-38-16', start_epoch=0, type='torch.cuda.FloatTensor', weight_decay=0.0001, workers=8) +2021-04-15 15:38:16 - INFO - creating model alexnet +2021-04-15 15:38:17 - INFO - created model with configuration: {'input_size': None, 'dataset': 'imagenet'} +2021-04-15 15:38:17 - INFO - number of parameters: 61110184 diff --git a/BinaryNetpytorch/utils.py b/BinaryNetpytorch/utils.py new file mode 100644 index 0000000..fdd5b17 --- /dev/null +++ b/BinaryNetpytorch/utils.py @@ -0,0 +1,160 @@ +import os +import torch +import logging.config +import shutil +import pandas as pd +from bokeh.io import output_file, save, show +from bokeh.plotting import figure +from bokeh.layouts import column +#from bokeh.charts import Line, defaults +# +#defaults.width = 800 +#defaults.height = 400 +#defaults.tools = 'pan,box_zoom,wheel_zoom,box_select,hover,resize,reset,save' + + +def setup_logging(log_file='log.txt'): + """Setup logging configuration + """ + logging.basicConfig(level=logging.DEBUG, + format="%(asctime)s - %(levelname)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + filename=log_file, + filemode='w') + console = logging.StreamHandler() + console.setLevel(logging.INFO) + formatter = logging.Formatter('%(message)s') + console.setFormatter(formatter) + logging.getLogger('').addHandler(console) + + +class ResultsLog(object): + + def __init__(self, path='results.csv', plot_path=None): + self.path = path + self.plot_path = plot_path or (self.path + '.html') + self.figures = [] + self.results = None + + def add(self, **kwargs): + df = pd.DataFrame([kwargs.values()], columns=kwargs.keys()) + if self.results is None: + self.results = df + else: + self.results = self.results.append(df, ignore_index=True) + + def save(self, title='Training Results'): + if len(self.figures) > 0: + if os.path.isfile(self.plot_path): + os.remove(self.plot_path) + output_file(self.plot_path, title=title) + plot = column(*self.figures) + save(plot) + self.figures = [] + self.results.to_csv(self.path, index=False, index_label=False) + + def load(self, path=None): + path = path or self.path + if os.path.isfile(path): + self.results.read_csv(path) + + def show(self): + if len(self.figures) > 0: + plot = column(*self.figures) + show(plot) + + #def plot(self, *kargs, **kwargs): + # line = Line(data=self.results, *kargs, **kwargs) + # self.figures.append(line) + + def image(self, *kargs, **kwargs): + fig = figure() + fig.image(*kargs, **kwargs) + self.figures.append(fig) + + +def save_checkpoint(state, is_best, path='.', filename='checkpoint.pth.tar', save_all=False): + filename = os.path.join(path, filename) + torch.save(state, filename) + if is_best: + shutil.copyfile(filename, os.path.join(path, 'model_best.pth.tar')) + if save_all: + shutil.copyfile(filename, os.path.join( + path, 'checkpoint_epoch_%s.pth.tar' % state['epoch'])) + + +class AverageMeter(object): + """Computes and stores the average and current value""" + + def __init__(self): + 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 + +__optimizers = { + 'SGD': torch.optim.SGD, + 'ASGD': torch.optim.ASGD, + 'Adam': torch.optim.Adam, + 'Adamax': torch.optim.Adamax, + 'Adagrad': torch.optim.Adagrad, + 'Adadelta': torch.optim.Adadelta, + 'Rprop': torch.optim.Rprop, + 'RMSprop': torch.optim.RMSprop +} + + +def adjust_optimizer(optimizer, epoch, config): + """Reconfigures the optimizer according to epoch and config dict""" + def modify_optimizer(optimizer, setting): + if 'optimizer' in setting: + optimizer = __optimizers[setting['optimizer']]( + optimizer.param_groups) + logging.debug('OPTIMIZER - setting method = %s' % + setting['optimizer']) + for param_group in optimizer.param_groups: + for key in param_group.keys(): + if key in setting: + logging.debug('OPTIMIZER - setting %s = %s' % + (key, setting[key])) + param_group[key] = setting[key] + return optimizer + + if callable(config): + optimizer = modify_optimizer(optimizer, config(epoch)) + else: + for e in range(epoch + 1): # run over all epochs - sticky setting + if e in config: + optimizer = modify_optimizer(optimizer, config[e]) + + return optimizer + + +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.float().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) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + # kernel_img = model.features[0][0].kernel.data.clone() + # kernel_img.add_(-kernel_img.min()) + # kernel_img.mul_(255 / kernel_img.max()) + # save_image(kernel_img, 'kernel%s.jpg' % epoch) diff --git a/SimBinaryNetpytorch/.DS_Store b/SimBinaryNetpytorch/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..1642ef92f2b4e0f87d99dc466a6efd18e88678a3 GIT binary patch literal 6148 zcmeHKO>Wab6n@i&)(Hw^QBjuOAhC$5WC2;AN+zU>E|3sLumF_WwbfX1Jdy1Xg%IQo zXP{T$BpiW*Z~*w`C&)=$HkA;n=0!8#^UV96J)g%j86sl+Y0xEV5s`%=u+~6TVWMA@ zE!)v8g2!ZzBbw0sH0I}iycKO4i~>f1|4jk$?e0@R9_1*d@0a`N5zE38l!w2h!6Uju zV?;)j(2#tI9;Gy|;s*7wr4 z;_8K$k9_UF;ynK2d;aSvoDEwC&v=r1Q4*$-90egl-o1&EfY17Tngp55)vAVsM~4Von_lz^d9%xcCXi4F6-9egYNO^*?9hO@oD+_D~1DOSem8X)wqI7IBRlz 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transform=None, + target_transform=None, download=True): + train = (split == 'train') + if name == 'cifar10': + return datasets.CIFAR10(root=_dataset_path['cifar10'], + train=train, + transform=transform, + target_transform=target_transform, + download=download) + elif name == 'cifar100': + return datasets.CIFAR100(root=_dataset_path['cifar100'], + train=train, + transform=transform, + target_transform=target_transform, + download=download) + elif name == 'imagenet': + path = _dataset_path[name][split] + return datasets.ImageFolder(root=path, + transform=transform, + target_transform=target_transform) diff --git a/SimBinaryNetpytorch/main_binary.py b/SimBinaryNetpytorch/main_binary.py new file mode 100644 index 0000000..91d8093 --- /dev/null +++ b/SimBinaryNetpytorch/main_binary.py @@ -0,0 +1,309 @@ +import argparse +import os +import time +import logging +import torch +import torch.nn as nn +import torch.nn.parallel +import torch.backends.cudnn as cudnn +import torch.optim +import torch.utils.data +import models +from torch.autograd import Variable +from data import get_dataset +from preprocess import get_transform +from utils import * +from datetime import datetime +from ast import literal_eval +from torchvision.utils import save_image + + +model_names = sorted(name for name in models.__dict__ + if name.islower() and not name.startswith("__") + and callable(models.__dict__[name])) +<<<<<<< HEAD +print(model_names) +======= +>>>>>>> 0d30f7b8e44285531022cdc05b2c11c22db27e3a + +parser = argparse.ArgumentParser(description='PyTorch ConvNet Training') + +parser.add_argument('--results_dir', metavar='RESULTS_DIR', default='./results', + help='results dir') +parser.add_argument('--save', metavar='SAVE', default='', + help='saved folder') +parser.add_argument('--dataset', metavar='DATASET', default='imagenet', + help='dataset name or folder') +parser.add_argument('--model', '-a', metavar='MODEL', default='alexnet', + choices=model_names, + help='model architecture: ' + + ' | '.join(model_names) + + ' (default: alexnet)') +parser.add_argument('--input_size', type=int, default=None, + help='image input size') +parser.add_argument('--model_config', default='', + help='additional architecture configuration') +parser.add_argument('--type', default='torch.cuda.FloatTensor', + help='type of tensor - e.g torch.cuda.HalfTensor') +parser.add_argument('--gpus', default='0', + help='gpus used for training - e.g 0,1,3') +parser.add_argument('-j', '--workers', default=8, type=int, metavar='N', + help='number of data loading workers (default: 8)') +parser.add_argument('--epochs', default=2500, 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=256, type=int, + metavar='N', help='mini-batch size (default: 256)') +parser.add_argument('--optimizer', default='SGD', type=str, metavar='OPT', + help='optimizer function used') +parser.add_argument('--lr', '--learning_rate', default=0.1, type=float, + metavar='LR', help='initial learning rate') +parser.add_argument('--momentum', default=0.9, type=float, metavar='M', + help='momentum') +parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, + metavar='W', help='weight decay (default: 1e-4)') +parser.add_argument('--print-freq', '-p', default=10, 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', type=str, metavar='FILE', + help='evaluate model FILE on validation set') + + +def main(): + global args, best_prec1 + best_prec1 = 0 + args = parser.parse_args() + + if args.evaluate: + args.results_dir = '/tmp' + if args.save is '': + args.save = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + save_path = os.path.join(args.results_dir, args.save) + if not os.path.exists(save_path): + os.makedirs(save_path) + + setup_logging(os.path.join(save_path, 'log.txt')) + results_file = os.path.join(save_path, 'results.%s') + results = ResultsLog(results_file % 'csv', results_file % 'html') + + logging.info("saving to %s", save_path) + logging.debug("run arguments: %s", args) + + if 'cuda' in args.type: + args.gpus = [int(i) for i in args.gpus.split(',')] + torch.cuda.set_device(args.gpus[0]) + cudnn.benchmark = True + else: + args.gpus = None + + # create model + logging.info("creating model %s", args.model) + model = models.__dict__[args.model] + model_config = {'input_size': args.input_size, 'dataset': args.dataset} + + if args.model_config is not '': + model_config = dict(model_config, **literal_eval(args.model_config)) + + model = model(**model_config) + logging.info("created model with configuration: %s", model_config) + + # optionally resume from a checkpoint + if args.evaluate: + if not os.path.isfile(args.evaluate): + parser.error('invalid checkpoint: {}'.format(args.evaluate)) + checkpoint = torch.load(args.evaluate) + model.load_state_dict(checkpoint['state_dict']) + logging.info("loaded checkpoint '%s' (epoch %s)", + args.evaluate, checkpoint['epoch']) + elif args.resume: + checkpoint_file = args.resume + if os.path.isdir(checkpoint_file): + results.load(os.path.join(checkpoint_file, 'results.csv')) + checkpoint_file = os.path.join( + checkpoint_file, 'model_best.pth.tar') + if os.path.isfile(checkpoint_file): + logging.info("loading checkpoint '%s'", args.resume) + checkpoint = torch.load(checkpoint_file) + args.start_epoch = checkpoint['epoch'] - 1 + best_prec1 = checkpoint['best_prec1'] + model.load_state_dict(checkpoint['state_dict']) + logging.info("loaded checkpoint '%s' (epoch %s)", + checkpoint_file, checkpoint['epoch']) + else: + logging.error("no checkpoint found at '%s'", args.resume) + + num_parameters = sum([l.nelement() for l in model.parameters()]) + logging.info("number of parameters: %d", num_parameters) + + # Data loading code + default_transform = { + 'train': get_transform(args.dataset, + input_size=args.input_size, augment=True), + 'eval': get_transform(args.dataset, + input_size=args.input_size, augment=False) + } + transform = getattr(model, 'input_transform', default_transform) + regime = getattr(model, 'regime', {0: {'optimizer': args.optimizer, + 'lr': args.lr, + 'momentum': args.momentum, + 'weight_decay': args.weight_decay}}) + # define loss function (criterion) and optimizer + criterion = getattr(model, 'criterion', nn.CrossEntropyLoss)() + criterion.type(args.type) + model.type(args.type) + + val_data = get_dataset(args.dataset, 'val', transform['eval']) + val_loader = torch.utils.data.DataLoader( + val_data, + batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True) + + if args.evaluate: + validate(val_loader, model, criterion, 0) + return + + train_data = get_dataset(args.dataset, 'train', transform['train']) + train_loader = torch.utils.data.DataLoader( + train_data, + batch_size=args.batch_size, shuffle=True, + num_workers=args.workers, pin_memory=True) + + optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) + logging.info('training regime: %s', regime) + + + for epoch in range(args.start_epoch, args.epochs): + optimizer = adjust_optimizer(optimizer, epoch, regime) + + # train for one epoch + train_loss, train_prec1, train_prec5 = train( + train_loader, model, criterion, epoch, optimizer) + + # evaluate on validation set + val_loss, val_prec1, val_prec5 = validate( + val_loader, model, criterion, epoch) + + # remember best prec@1 and save checkpoint + is_best = val_prec1 > best_prec1 + best_prec1 = max(val_prec1, best_prec1) + + save_checkpoint({ + 'epoch': epoch + 1, + 'model': args.model, + 'config': args.model_config, + 'state_dict': model.state_dict(), + 'best_prec1': best_prec1, + 'regime': regime + }, is_best, path=save_path) + logging.info('\n Epoch: {0}\t' + 'Training Loss {train_loss:.4f} \t' + 'Training Prec@1 {train_prec1:.3f} \t' + 'Training Prec@5 {train_prec5:.3f} \t' + 'Validation Loss {val_loss:.4f} \t' + 'Validation Prec@1 {val_prec1:.3f} \t' + 'Validation Prec@5 {val_prec5:.3f} \n' + .format(epoch + 1, train_loss=train_loss, val_loss=val_loss, + train_prec1=train_prec1, val_prec1=val_prec1, + train_prec5=train_prec5, val_prec5=val_prec5)) + + results.add(epoch=epoch + 1, train_loss=train_loss, val_loss=val_loss, + train_error1=100 - train_prec1, val_error1=100 - val_prec1, + train_error5=100 - train_prec5, val_error5=100 - val_prec5) + #results.plot(x='epoch', y=['train_loss', 'val_loss'], + # title='Loss', ylabel='loss') + #results.plot(x='epoch', y=['train_error1', 'val_error1'], + # title='Error@1', ylabel='error %') + #results.plot(x='epoch', y=['train_error5', 'val_error5'], + # title='Error@5', ylabel='error %') + results.save() + + +def forward(data_loader, model, criterion, epoch=0, training=True, optimizer=None): + if args.gpus and len(args.gpus) > 1: + model = torch.nn.DataParallel(model, args.gpus) + batch_time = AverageMeter() + data_time = AverageMeter() + losses = AverageMeter() + top1 = AverageMeter() + top5 = AverageMeter() + + end = time.time() + for i, (inputs, target) in enumerate(data_loader): + # measure data loading time + data_time.update(time.time() - end) + if args.gpus is not None: + target = target.cuda() + + if not training: + with torch.no_grad(): + input_var = Variable(inputs.type(args.type), volatile=not training) + target_var = Variable(target) + # compute output + output = model(input_var) + else: + input_var = Variable(inputs.type(args.type), volatile=not training) + target_var = Variable(target) + # compute output + output = model(input_var) + + + loss = criterion(output, target_var) + if type(output) is list: + output = output[0] + + # measure accuracy and record loss + prec1, prec5 = accuracy(output.data, target, topk=(1, 5)) + losses.update(loss.item(), inputs.size(0)) + top1.update(prec1.item(), inputs.size(0)) + top5.update(prec5.item(), inputs.size(0)) + + if training: + # compute gradient and do SGD step + optimizer.zero_grad() + loss.backward() + for p in list(model.parameters()): + if hasattr(p,'org'): + p.data.copy_(p.org) + optimizer.step() + for p in list(model.parameters()): + if hasattr(p,'org'): + p.org.copy_(p.data.clamp_(-1,1)) + + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + logging.info('{phase} - Epoch: [{0}][{1}/{2}]\t' + 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' + 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' + 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' + 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' + 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( + epoch, i, len(data_loader), + phase='TRAINING' if training else 'EVALUATING', + batch_time=batch_time, + data_time=data_time, loss=losses, top1=top1, top5=top5)) + + return losses.avg, top1.avg, top5.avg + + +def train(data_loader, model, criterion, epoch, optimizer): + # switch to train mode + model.train() + return forward(data_loader, model, criterion, epoch, + training=True, optimizer=optimizer) + + +def validate(data_loader, model, criterion, epoch): + # switch to evaluate mode + model.eval() + return forward(data_loader, model, criterion, epoch, + training=False, optimizer=None) + + +if __name__ == '__main__': + main() diff --git a/SimBinaryNetpytorch/main_binary_hinge.py b/SimBinaryNetpytorch/main_binary_hinge.py new file mode 100644 index 0000000..f922422 --- /dev/null +++ b/SimBinaryNetpytorch/main_binary_hinge.py @@ -0,0 +1,332 @@ +import argparse +import os +import time +import logging +import torch +import torch.nn as nn +import torch.nn.parallel +import torch.backends.cudnn as cudnn +import torch.optim +import torch.utils.data +import models +from torch.autograd import Variable +from data import get_dataset +from preprocess import get_transform +from utils import * +from datetime import datetime +from ast import literal_eval +from torchvision.utils import save_image +from models.binarized_modules import HingeLoss + +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 ConvNet Training') + +parser.add_argument('--results_dir', metavar='RESULTS_DIR', default='/media/hdd/ihubara/BinaryNet.pytorch/results', + help='results dir') +parser.add_argument('--save', metavar='SAVE', default='', + help='saved folder') +parser.add_argument('--dataset', metavar='DATASET', default='imagenet', + help='dataset name or folder') +parser.add_argument('--model', '-a', metavar='MODEL', default='alexnet', + choices=model_names, + help='model architecture: ' + + ' | '.join(model_names) + + ' (default: alexnet)') +parser.add_argument('--input_size', type=int, default=None, + help='image input size') +parser.add_argument('--model_config', default='', + help='additional architecture configuration') +parser.add_argument('--type', default='torch.cuda.FloatTensor', + help='type of tensor - e.g torch.cuda.HalfTensor') +parser.add_argument('--gpus', default='0', + help='gpus used for training - e.g 0,1,3') +parser.add_argument('-j', '--workers', default=8, type=int, metavar='N', + help='number of data loading workers (default: 8)') +parser.add_argument('--epochs', default=900, 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=256, type=int, + metavar='N', help='mini-batch size (default: 256)') +parser.add_argument('--optimizer', default='SGD', type=str, metavar='OPT', + help='optimizer function used') +parser.add_argument('--lr', '--learning_rate', default=0.1, type=float, + metavar='LR', help='initial learning rate') +parser.add_argument('--momentum', default=0.9, type=float, metavar='M', + help='momentum') +parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, + metavar='W', help='weight decay (default: 1e-4)') +parser.add_argument('--print-freq', '-p', default=10, 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', type=str, metavar='FILE', + help='evaluate model FILE on validation set') + +torch.cuda.random.manual_seed_all(10) + +output_dim = 0 + + +def main(): + global args, best_prec1, output_dim + best_prec1 = 0 + args = parser.parse_args() + output_dim = {'cifar10': 10, 'cifar100':100, 'imagenet': 1000}[args.dataset] + #import pdb; pdb.set_trace() + #torch.save(args.batch_size/(len(args.gpus)/2+1),'multi_gpu_batch_size') + if args.evaluate: + args.results_dir = '/tmp' + if args.save is '': + args.save = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + save_path = os.path.join(args.results_dir, args.save) + if not os.path.exists(save_path): + os.makedirs(save_path) + + setup_logging(os.path.join(save_path, 'log.txt')) + results_file = os.path.join(save_path, 'results.%s') + results = ResultsLog(results_file % 'csv', results_file % 'html') + + logging.info("saving to %s", save_path) + logging.debug("run arguments: %s", args) + + if 'cuda' in args.type: + args.gpus = [int(i) for i in args.gpus.split(',')] + torch.cuda.set_device(args.gpus[0]) + cudnn.benchmark = True + else: + args.gpus = None + + # create model + logging.info("creating model %s", args.model) + model = models.__dict__[args.model] + + + model_config = {'input_size': args.input_size, 'dataset': args.dataset, 'num_classes': output_dim} + + if args.model_config is not '': + model_config = dict(model_config, **literal_eval(args.model_config)) + model = model(**model_config) + logging.info("created model with configuration: %s", model_config) + + # optionally resume from a checkpoint + if args.evaluate: + if not os.path.isfile(args.evaluate): + parser.error('invalid checkpoint: {}'.format(args.evaluate)) + checkpoint = torch.load(args.evaluate) + model.load_state_dict(checkpoint['state_dict']) + logging.info("loaded checkpoint '%s' (epoch %s)", + args.evaluate, checkpoint['epoch']) + elif args.resume: + checkpoint_file = args.resume + if os.path.isdir(checkpoint_file): + results.load(os.path.join(checkpoint_file, 'results.csv')) + checkpoint_file = os.path.join( + checkpoint_file, 'model_best.pth.tar') + if os.path.isfile(checkpoint_file): + logging.info("loading checkpoint '%s'", args.resume) + checkpoint = torch.load(checkpoint_file) + args.start_epoch = checkpoint['epoch'] - 1 + best_prec1 = checkpoint['best_prec1'] + model.load_state_dict(checkpoint['state_dict']) + logging.info("loaded checkpoint '%s' (epoch %s)", + checkpoint_file, checkpoint['epoch']) + else: + logging.error("no checkpoint found at '%s'", args.resume) + + num_parameters = sum([l.nelement() for l in model.parameters()]) + logging.info("number of parameters: %d", num_parameters) + + # Data loading code + default_transform = { + 'train': get_transform(args.dataset, + input_size=args.input_size, augment=True), + 'eval': get_transform(args.dataset, + input_size=args.input_size, augment=False) + } + transform = getattr(model, 'input_transform', default_transform) + regime = getattr(model, 'regime', {0: {'optimizer': args.optimizer, + 'lr': args.lr, + 'momentum': args.momentum, + 'weight_decay': args.weight_decay}}) + # define loss function (criterion) and optimizer + #criterion = getattr(model, 'criterion', nn.NLLLoss)() + criterion = getattr(model, 'criterion', HingeLoss)() + #criterion.type(args.type) + model.type(args.type) + + val_data = get_dataset(args.dataset, 'val', transform['eval']) + val_loader = torch.utils.data.DataLoader( + val_data, + batch_size=args.batch_size, shuffle=False, + num_workers=args.workers, pin_memory=True) + + if args.evaluate: + validate(val_loader, model, criterion, 0) + return + + train_data = get_dataset(args.dataset, 'train', transform['train']) + train_loader = torch.utils.data.DataLoader( + train_data, + batch_size=args.batch_size, shuffle=True, + num_workers=args.workers, pin_memory=True) + + optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) + logging.info('training regime: %s', regime) + #import pdb; pdb.set_trace() + #search_binarized_modules(model) + + for epoch in range(args.start_epoch, args.epochs): + optimizer = adjust_optimizer(optimizer, epoch, regime) + + # train for one epoch + train_loss, train_prec1, train_prec5 = train( + train_loader, model, criterion, epoch, optimizer) + + # evaluate on validation set + val_loss, val_prec1, val_prec5 = validate( + val_loader, model, criterion, epoch) + + # remember best prec@1 and save checkpoint + is_best = val_prec1 > best_prec1 + best_prec1 = max(val_prec1, best_prec1) + save_checkpoint({ + 'epoch': epoch + 1, + 'model': args.model, + 'config': args.model_config, + 'state_dict': model.state_dict(), + 'best_prec1': best_prec1, + 'regime': regime + }, is_best, path=save_path) + logging.info('\n Epoch: {0}\t' + 'Training Loss {train_loss:.4f} \t' + 'Training Prec@1 {train_prec1:.3f} \t' + 'Training Prec@5 {train_prec5:.3f} \t' + 'Validation Loss {val_loss:.4f} \t' + 'Validation Prec@1 {val_prec1:.3f} \t' + 'Validation Prec@5 {val_prec5:.3f} \n' + .format(epoch + 1, train_loss=train_loss, val_loss=val_loss, + train_prec1=train_prec1, val_prec1=val_prec1, + train_prec5=train_prec5, val_prec5=val_prec5)) + + results.add(epoch=epoch + 1, train_loss=train_loss, val_loss=val_loss, + train_error1=100 - train_prec1, val_error1=100 - val_prec1, + train_error5=100 - train_prec5, val_error5=100 - val_prec5) + results.plot(x='epoch', y=['train_loss', 'val_loss'], + title='Loss', ylabel='loss') + results.plot(x='epoch', y=['train_error1', 'val_error1'], + title='Error@1', ylabel='error %') + results.plot(x='epoch', y=['train_error5', 'val_error5'], + title='Error@5', ylabel='error %') + results.save() + +def forward(data_loader, model, criterion, epoch=0, training=True, optimizer=None): + if args.gpus and len(args.gpus) > 1: + model = torch.nn.DataParallel(model, args.gpus) + batch_time = AverageMeter() + data_time = AverageMeter() + losses = AverageMeter() + top1 = AverageMeter() + top5 = AverageMeter() + + end = time.time() + for i, (inputs, target) in enumerate(data_loader): + # measure data loading time + data_time.update(time.time() - end) + if args.gpus is not None: + target = target.cuda() + #import pdb; pdb.set_trace() + if criterion.__class__.__name__=='HingeLoss': + target=target.unsqueeze(1) + target_onehot = torch.cuda.FloatTensor(target.size(0), output_dim) + target_onehot.fill_(-1) + target_onehot.scatter_(1, target, 1) + target=target.squeeze() + if not training: + with torch.no_grad(): + input_var = Variable(inputs.type(args.type)) + target_var = Variable(target_onehot) + + # compute output + output = model(input_var) + else: + input_var = Variable(inputs.type(args.type)) + target_var = Variable(target_onehot) + + # compute output + output = model(input_var) + + #import pdb; pdb.set_trace() + loss = criterion(output, target_onehot) + #import pdb; pdb.set_trace() + if type(output) is list: + output = output[0] + + # measure accuracy and record loss + prec1, prec5 = accuracy(output.data, target, topk=(1, 5)) + losses.update(loss.item(), inputs.size(0)) + top1.update(prec1.item(), inputs.size(0)) + top5.update(prec5.item(), inputs.size(0)) + #import pdb; pdb.set_trace() + #if not training and top1.avg<15: + # import pdb; pdb.set_trace() + if training: + # compute gradient and do SGD step + optimizer.zero_grad() + #add backwoed hook + loss.backward() + for p in list(model.parameters()): + #import pdb; pdb.set_trace() + if hasattr(p,'org'): + #print('before:', p[0][0]) + #gm=max(p.grad.data.max(),-p.grad.data.min()) + #p.grad=p.grad.div(gm+1) + p.data.copy_(p.org) + #print('after:', p[0][0]) + optimizer.step() + for p in list(model.parameters()): + #import pdb; pdb.set_trace() + if hasattr(p,'org'): + #print('before:', p[0][0]) + p.org.copy_(p.data.clamp_(-1,1)) + #if epoch>30: + # import pdb; pdb.set_trace() + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % args.print_freq == 0: + logging.info('{phase} - Epoch: [{0}][{1}/{2}]\t' + 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' + 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' + 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' + 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' + 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( + epoch, i, len(data_loader), + phase='TRAINING' if training else 'EVALUATING', + batch_time=batch_time, + data_time=data_time, loss=losses, top1=top1, top5=top5)) + + return losses.avg, top1.avg, top5.avg + + +def train(data_loader, model, criterion, epoch, optimizer): + # switch to train mode + model.train() + return forward(data_loader, model, criterion, epoch, + training=True, optimizer=optimizer) + + +def validate(data_loader, model, criterion, epoch): + # switch to evaluate mode + model.eval() + return forward(data_loader, model, criterion, epoch, + training=False, optimizer=None) + + +if __name__ == '__main__': + main() diff --git a/SimBinaryNetpytorch/main_mnist.py b/SimBinaryNetpytorch/main_mnist.py new file mode 100644 index 0000000..d2b16f3 --- /dev/null +++ b/SimBinaryNetpytorch/main_mnist.py @@ -0,0 +1,150 @@ + +from __future__ import print_function +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from torchvision import datasets, transforms +from torch.autograd import Variable +from models.binarized_modules import BinarizeLinear,BinarizeConv2d +from models.binarized_modules import Binarize,HingeLoss +# Training settings +parser = argparse.ArgumentParser(description='PyTorch MNIST Example') +parser.add_argument('--batch-size', type=int, default=64, metavar='N', + help='input batch size for training (default: 256)') +parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', + help='input batch size for testing (default: 1000)') +parser.add_argument('--epochs', type=int, default=100, metavar='N', + help='number of epochs to train (default: 10)') +parser.add_argument('--lr', type=float, default=0.01, metavar='LR', + help='learning rate (default: 0.001)') +parser.add_argument('--momentum', type=float, default=0.5, metavar='M', + help='SGD momentum (default: 0.5)') +parser.add_argument('--no-cuda', action='store_true', default=False, + help='disables CUDA training') +parser.add_argument('--seed', type=int, default=1, metavar='S', + help='random seed (default: 1)') +parser.add_argument('--gpus', default=3, + help='gpus used for training - e.g 0,1,3') +parser.add_argument('--log-interval', type=int, default=10, metavar='N', + help='how many batches to wait before logging training status') +args = parser.parse_args() +args.cuda = not args.no_cuda and torch.cuda.is_available() + +torch.manual_seed(args.seed) +if args.cuda: + torch.cuda.manual_seed(args.seed) + + +kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} +train_loader = torch.utils.data.DataLoader( + datasets.MNIST('../data', train=True, download=True, + transform=transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ])), + batch_size=args.batch_size, shuffle=True, **kwargs) +test_loader = torch.utils.data.DataLoader( + datasets.MNIST('../data', train=False, transform=transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ])), + batch_size=args.test_batch_size, shuffle=True, **kwargs) + + +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.infl_ratio=3 + self.fc1 = BinarizeLinear(784, 2048*self.infl_ratio) + self.htanh1 = nn.Hardtanh() + self.bn1 = nn.BatchNorm1d(2048*self.infl_ratio) + self.fc2 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio) + self.htanh2 = nn.Hardtanh() + self.bn2 = nn.BatchNorm1d(2048*self.infl_ratio) + self.fc3 = BinarizeLinear(2048*self.infl_ratio, 2048*self.infl_ratio) + self.htanh3 = nn.Hardtanh() + self.bn3 = nn.BatchNorm1d(2048*self.infl_ratio) + self.fc4 = nn.Linear(2048*self.infl_ratio, 10) + self.logsoftmax=nn.LogSoftmax() + self.drop=nn.Dropout(0.5) + + def forward(self, x): + x = x.view(-1, 28*28) + x = self.fc1(x) + x = self.bn1(x) + x = self.htanh1(x) + x = self.fc2(x) + x = self.bn2(x) + x = self.htanh2(x) + x = self.fc3(x) + x = self.drop(x) + x = self.bn3(x) + x = self.htanh3(x) + x = self.fc4(x) + return self.logsoftmax(x) + +model = Net() +if args.cuda: + torch.cuda.set_device(3) + model.cuda() + + +criterion = nn.CrossEntropyLoss() +optimizer = optim.Adam(model.parameters(), lr=args.lr) + + +def train(epoch): + model.train() + for batch_idx, (data, target) in enumerate(train_loader): + if args.cuda: + data, target = data.cuda(), target.cuda() + data, target = Variable(data), Variable(target) + optimizer.zero_grad() + output = model(data) + loss = criterion(output, target) + + if epoch%40==0: + optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1 + + optimizer.zero_grad() + loss.backward() + for p in list(model.parameters()): + if hasattr(p,'org'): + p.data.copy_(p.org) + optimizer.step() + for p in list(model.parameters()): + if hasattr(p,'org'): + p.org.copy_(p.data.clamp_(-1,1)) + + if batch_idx % args.log_interval == 0: + print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( + epoch, batch_idx * len(data), len(train_loader.dataset), + 100. * batch_idx / len(train_loader), loss.item())) + +def test(): + model.eval() + test_loss = 0 + correct = 0 + with torch.no_grad(): + for data, target in test_loader: + if args.cuda: + data, target = data.cuda(), target.cuda() + data, target = Variable(data), Variable(target) + output = model(data) + test_loss += criterion(output, target).item() # sum up batch loss + pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability + correct += pred.eq(target.data.view_as(pred)).cpu().sum() + + test_loss /= len(test_loader.dataset) + print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( + test_loss, correct, len(test_loader.dataset), + 100. * correct / len(test_loader.dataset))) + + +for epoch in range(1, args.epochs + 1): + train(epoch) + test() + if epoch%40==0: + optimizer.param_groups[0]['lr']=optimizer.param_groups[0]['lr']*0.1 diff --git a/SimBinaryNetpytorch/models/__init__.py b/SimBinaryNetpytorch/models/__init__.py new file mode 100644 index 0000000..1df1fdc --- /dev/null +++ b/SimBinaryNetpytorch/models/__init__.py @@ -0,0 +1,6 @@ + +from .alexnet import * +from .alexnet_binary import * +from .resnet import * +from .resnet_binary import * +from 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padding=1, bias=False), + nn.ReLU(inplace=True), + nn.BatchNorm2d(384), + nn.Conv2d(384, 256, kernel_size=3, padding=1, bias=False), + nn.ReLU(inplace=True), + nn.BatchNorm2d(256), + nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.ReLU(inplace=True), + nn.BatchNorm2d(256) + ) + self.classifier = nn.Sequential( + nn.Linear(256 * 6 * 6, 4096, bias=False), + nn.BatchNorm1d(4096), + nn.ReLU(inplace=True), + nn.Dropout(0.5), + nn.Linear(4096, 4096, bias=False), + nn.BatchNorm1d(4096), + nn.ReLU(inplace=True), + nn.Dropout(0.5), + nn.Linear(4096, num_classes) + ) + + self.regime = { + 0: {'optimizer': 'SGD', 'lr': 1e-2, + 'weight_decay': 5e-4, 'momentum': 0.9}, + 10: {'lr': 5e-3}, + 15: {'lr': 1e-3, 'weight_decay': 0}, + 20: {'lr': 5e-4}, + 25: {'lr': 1e-4} + } + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + self.input_transform = { + 'train': transforms.Compose([ + transforms.Scale(256), + transforms.RandomCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalize + ]), + 'eval': transforms.Compose([ + transforms.Scale(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize + ]) + } + + def forward(self, x): + x = self.features(x) + x = x.view(-1, 256 * 6 * 6) + x = self.classifier(x) + return x + + +def alexnet(**kwargs): + num_classes = kwargs.get( 'num_classes', 1000) + return AlexNetOWT_BN(num_classes) diff --git a/SimBinaryNetpytorch/models/alexnet_binary.py b/SimBinaryNetpytorch/models/alexnet_binary.py new file mode 100644 index 0000000..26d67a5 --- /dev/null +++ b/SimBinaryNetpytorch/models/alexnet_binary.py @@ -0,0 +1,92 @@ +import torch.nn as nn +import torchvision.transforms as transforms +from .binarized_modules import BinarizeLinear,BinarizeConv2d + +__all__ = ['alexnet_binary'] + +class AlexNetOWT_BN(nn.Module): + + def __init__(self, num_classes=1000): + super(AlexNetOWT_BN, self).__init__() + self.ratioInfl=3 + self.features = nn.Sequential( + BinarizeConv2d(3, int(64*self.ratioInfl), kernel_size=11, stride=4, padding=2), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.BatchNorm2d(int(64*self.ratioInfl)), + nn.Hardtanh(inplace=True), + BinarizeConv2d(int(64*self.ratioInfl), int(192*self.ratioInfl), kernel_size=5, padding=2), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.BatchNorm2d(int(192*self.ratioInfl)), + nn.Hardtanh(inplace=True), + + BinarizeConv2d(int(192*self.ratioInfl), int(384*self.ratioInfl), kernel_size=3, padding=1), + nn.BatchNorm2d(int(384*self.ratioInfl)), + nn.Hardtanh(inplace=True), + + BinarizeConv2d(int(384*self.ratioInfl), int(256*self.ratioInfl), kernel_size=3, padding=1), + nn.BatchNorm2d(int(256*self.ratioInfl)), + nn.Hardtanh(inplace=True), + + BinarizeConv2d(int(256*self.ratioInfl), 256, kernel_size=3, padding=1), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.BatchNorm2d(256), + nn.Hardtanh(inplace=True) + + ) + self.classifier = nn.Sequential( + BinarizeLinear(256 * 6 * 6, 4096), + nn.BatchNorm1d(4096), + nn.Hardtanh(inplace=True), + #nn.Dropout(0.5), + BinarizeLinear(4096, 4096), + nn.BatchNorm1d(4096), + nn.Hardtanh(inplace=True), + #nn.Dropout(0.5), + BinarizeLinear(4096, num_classes), + nn.BatchNorm1d(1000), + nn.LogSoftmax() + ) + + #self.regime = { + # 0: {'optimizer': 'SGD', 'lr': 1e-2, + # 'weight_decay': 5e-4, 'momentum': 0.9}, + # 10: {'lr': 5e-3}, + # 15: {'lr': 1e-3, 'weight_decay': 0}, + # 20: {'lr': 5e-4}, + # 25: {'lr': 1e-4} + #} + self.regime = { + 0: {'optimizer': 'Adam', 'lr': 5e-3}, + 20: {'lr': 1e-3}, + 30: {'lr': 5e-4}, + 35: {'lr': 1e-4}, + 40: {'lr': 1e-5} + } + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + self.input_transform = { + 'train': transforms.Compose([ + transforms.Scale(256), + transforms.RandomCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalize + ]), + 'eval': transforms.Compose([ + transforms.Scale(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize + ]) + } + + def forward(self, x): + x = self.features(x) + x = x.view(-1, 256 * 6 * 6) + x = self.classifier(x) + return x + + +def alexnet_binary(**kwargs): + num_classes = kwargs.get( 'num_classes', 1000) + return AlexNetOWT_BN(num_classes) diff --git a/SimBinaryNetpytorch/models/binarized_modules.py b/SimBinaryNetpytorch/models/binarized_modules.py new file mode 100644 index 0000000..9555959 --- /dev/null +++ b/SimBinaryNetpytorch/models/binarized_modules.py @@ -0,0 +1,423 @@ +import torch +import pdb +import torch.nn as nn +import math +from torch.autograd import Variable +from torch.autograd import Function +from decimal import Decimal, ROUND_HALF_UP + +import numpy as np + + +def Binarize(tensor,quant_mode='det'): + if quant_mode=='det': + return tensor.sign() + else: + return tensor.add_(1).div_(2).add_(torch.rand(tensor.size()).add(-0.5)).clamp_(0,1).round().mul_(2).add_(-1) + + + + +class HingeLoss(nn.Module): + def __init__(self): + super(HingeLoss,self).__init__() + self.margin=1.0 + + def hinge_loss(self,input,target): + #import pdb; pdb.set_trace() + output=self.margin-input.mul(target) + output[output.le(0)]=0 + return output.mean() + + def forward(self, input, target): + return self.hinge_loss(input,target) + +class SqrtHingeLossFunction(Function): + def __init__(self): + super(SqrtHingeLossFunction,self).__init__() + self.margin=1.0 + + def forward(self, input, target): + output=self.margin-input.mul(target) + output[output.le(0)]=0 + self.save_for_backward(input, target) + loss=output.mul(output).sum(0).sum(1).div(target.numel()) + return loss + + def backward(self,grad_output): + input, target = self.saved_tensors + output=self.margin-input.mul(target) + output[output.le(0)]=0 + import pdb; pdb.set_trace() + grad_output.resize_as_(input).copy_(target).mul_(-2).mul_(output) + grad_output.mul_(output.ne(0).float()) + grad_output.div_(input.numel()) + return grad_output,grad_output + +def Quantize(tensor,quant_mode='det', params=None, numBits=8): + tensor.clamp_(-2**(numBits-1),2**(numBits-1)) + if quant_mode=='det': + tensor=tensor.mul(2**(numBits-1)).round().div(2**(numBits-1)) + else: + tensor=tensor.mul(2**(numBits-1)).round().add(torch.rand(tensor.size()).add(-0.5)).div(2**(numBits-1)) + quant_fixed(tensor, params) + return tensor + +#import torch.nn._functions as tnnf + + +class BinarizeLinear(nn.Linear): + + def __init__(self, *kargs, **kwargs): + super(BinarizeLinear, self).__init__(*kargs, **kwargs) + + def forward(self, input): + +# if input.size(1) != 784: +# input.data=Binarize(input.data) + if not hasattr(self.weight,'org'): + self.weight.org=self.weight.data.clone() + self.weight.data=Binarize(self.weight.org) + out = nn.functional.linear(input, self.weight) + if not self.bias is None: + self.bias.org=self.bias.data.clone() + out += self.bias.view(1, -1).expand_as(out) + + return out + +class BinarizeConv2d(nn.Conv2d): + + def __init__(self, *kargs, **kwargs): + super(BinarizeConv2d, self).__init__(*kargs, **kwargs) + + + def forward(self, input): +# if input.size(1) != 3: +# input.data = Binarize(input.data) + if not hasattr(self.weight,'org'): + self.weight.org=self.weight.data.clone() + self.weight.data=Binarize(self.weight.org) + #input = torch.round(input) + #input = input*2-1 + #scale = max(torch.max(input), -torch.min(input)) / 63 + #input = torch.round(input*2 / scale) - 63 + #if scale != 0: + # input = torch.round(input / scale) + #print (torch.max(input)) + #print(input) + input = torch.round(input) + #print(input) + #print (torch.max(input)) + out = nn.functional.conv2d(input, self.weight, None, self.stride, + self.padding, self.dilation, self.groups) + + #print (torch.min(out), torch.max(out)) + #out = torch.round(out) + #print (torch.min(out), torch.max(out)) + #print (torch.min(input), torch.max(input)) + #out = torch.round(out / 64 * 36 / 64) + #print (self.weight.size()[1]) + #if self.weight.size()[1] >= 16 and self.weight.size()[1] <= 24: + if self.weight.size()[1] >= 4 and self.weight.size()[2] * self.weight.size()[3] == 9: + out = torch.round(out / 64 * 36 / 64) + elif self.weight.size()[1] == 1: + out = torch.round(out * 7 / 64) + else: + out = torch.round(out / 64) + out = out * 4 + out[out > 63] = 63 + out[out < -63] = -63 + #out = out - torch.round(torch.mean(out)) + # out = out*4 + #out[out > 63] = 63 + #out[out < -63] = -63 + #else: + # out = torch.round(out * 10 / 64) + #print (torch.min(out), torch.max(out)) + + # if not self.bias is None: + # self.bias.org=self.bias.data.clone() + # out += self.bias.view(1, -1, 1, 1).expand_as(out) + + return out + +class IdealCimConv2d(nn.Conv2d): + + def __init__(self, *kargs, **kwargs): + super(IdealCimConv2d, self).__init__(*kargs, **kwargs) + + + def forward(self, input): +# if input.size(1) != 3: +# input.data = Binarize(input.data) + if not hasattr(self.weight,'org'): + self.weight.org=self.weight.data.clone() + self.weight.data=Binarize(self.weight.org) + #input = torch.round(input) + #input = input*2-1 + #scale = max(torch.max(input), -torch.min(input)) / 63 + #input = torch.round(input*2 / scale) - 63 + #if scale != 0: + # input = torch.round(input / scale) + #print (torch.max(input)) + #print(input) + input = torch.round(input) + #print(input) + #print (torch.max(input)) + out = nn.functional.conv2d(input, self.weight, None, self.stride, + self.padding, self.dilation, self.groups) + out = out / 64 + out = out * 4 + out[out > 63] = 63 + out[out < -63] = -63 + return out + + +device = 'cuda:0' +''' +H = [1024, 512] +sim_model = torch.nn.Sequential( + torch.nn.Linear(36, H[0]), + torch.nn.Dropout(p=0.5), + torch.nn.ReLU(), + torch.nn.Linear(H[0], H[1]), + torch.nn.Dropout(p=0.5), + torch.nn.ReLU(), + torch.nn.Linear(H[-1], 1), +) +sim_model.load_state_dict(torch.load('model_error.ckpt', map_location=torch.device('cuda:0'))) +sim_model = sim_model.to(device) +sim_model.eval() +''' + +class CimSimConv2d(nn.Conv2d): + def __init__(self, *kargs, **kwargs): + super(CimSimConv2d, self).__init__(*kargs, **kwargs) + + self.device = device + + def forward(self, input): + if not hasattr(self.weight,'org'): + self.weight.org=self.weight.data.clone() + self.weight.data=Binarize(self.weight.org) + + #scale = max(torch.max(input), -torch.min(input)) / 63 + #if scale != 0: + # input = torch.round(input / scale) + #''' random error + #out = nn.functional.conv2d(input, self.weight, None, self.stride, + # self.padding, self.dilation, self.groups) + #out = torch.round(out / 64 * 36 / 64) + #randrange = (self.weight.size()[1] // 4) + #for _ in range(randrange): + # out += torch.randint(-1, 1, out.size(), device=device) + #out[out>63] = 63 + #out[out<-63] -63 + #''' + input = torch.round(input) + out2 = self.simconv(input, self.weight) + ''' + if torch.max(out2) < 32: + out2 = out2 * 2 + if torch.max(out2) < 32: + out2 = out2 * 2 + if torch.max(out2) < 32: + out2 = out2 * 2 + ''' + out2 = out2 * 4 + out2[out2 > 63] = 63 + out2[out2 < -63] = -63 + #print (self.weight.data.size()) + #print (torch.max(out2), torch.min(out2)) + #print (torch.max(out-out2), torch.min(out-out2)) + #out = nn.functional.conv2d(input, self.weight, None, self.stride, + # self.padding, self.dilation, self.groups) + #print(input.size(), self.weight.size(), out.size()) + + #if not self.bias is None: + # self.bias.org=self.bias.data.clone() + # out += self.bias.view(1, -1, 1, 1).expand_as(out) + + return out2 + + def simconv(self, input_a, weight): + #print(input_a.size(), weight.size()) + batch_size = input_a.size()[0] + out_channel = weight.size()[0] + out_width = input_a.size()[2] - 2 * (weight.size()[2] // 2) + out_height = input_a.size()[3] - 2 * (weight.size()[3] // 2) + simout = torch.zeros(batch_size, out_channel, out_width, out_height, dtype = input_a.dtype).to(device) + first = True + #''' Mapping Table + if weight.size()[2] == 7: + kernel_group = 1 + else: + kernel_group = 4 + Digital_input_split = torch.split(input_a, kernel_group, dim=1) + binary_weight_split = torch.split(weight, kernel_group, dim=1) + for i in range(len(Digital_input_split)): + temp_output = nn.functional.conv2d(Digital_input_split[i], binary_weight_split[i], None, self.stride, self.padding, self.dilation, self.groups) + #temp_output = torch.round(temp_output / 64 * 36 / 64) + temp_output = torch.round(temp_output / 64) + temp_output = Mapping.apply(temp_output) + simout += temp_output + 2 + #print (torch.max(simout), torch.min(simout)) + #''' + ''' Error model + for n in range(batch_size): + for c in range(out_channel): + w = torch.reshape(weight[c], (-1,)).to(device) + inputs = [] + for i in range(out_width): + for j in range(out_height): + input = torch.reshape(input_a[n, :, i: i + weight.size()[2], j: j + weight.size()[3]], (-1,)) + #print (w.size(), input.size()) + # simout[n][c][i][j] = sum(w*input) + # TODO + simout[n][c][i][j] = self.cim_conv_tmp(input, w) + #''' + #print (len(input)) + #print (simout.size()) + # out = nn.functional.conv2d(input_a, weight) + return simout + + def cim_conv_tmp(self, input, weight): + assert len(input) == len(weight) + + raw_sum = 0 + + if len(weight) == 3: + + for i in range((len(input)-1) // 36 + 1): + data_x = input[i*36:i*36+36] * weight[i*36:i*36+36] + + + row = int(Decimal(float(sum(data_x)/64.0)).quantize(0, ROUND_HALF_UP)) + #''' Error model + if len(data_x) < 36: + data_x = torch.cat((data_x, torch.zeros(36 - len(data_x), dtype=data_x.dtype))) + try: + #ensor_x = torch.Tensor(data_x).to(self.device) + tensor_x = data_x.to(device) + except: + print (data_x, len()) + y_pred = sim_model(tensor_x) + if int(y_pred[0]) > 10: + adjust = 10 + elif int(y_pred[0]) < -10: + adjust = -10 + else: + adjust = int(y_pred[0]) + #print (tensor_x, y_pred) + raw_sum += (row + adjust + 2) + #''' + #if row in self.mappingTable: + # row = self.mappingTable[row] + #raw_sum += row + #raw_sum += row + else: + for i in range((len(input)-1) // 49 + 1): + data_x = input[i*49:i*49+49] * weight[i*49:i*49+49] + + + row = int(Decimal(float(sum(data_x)/64.0)).quantize(0, ROUND_HALF_UP)) + #''' Error model + if len(data_x) < 49: + data_x = torch.cat((data_x, torch.zeros(49 - len(data_x), dtype=data_x.dtype))) + try: + #ensor_x = torch.Tensor(data_x).to(self.device) + tensor_x = data_x.to(device) + except: + print (data_x, len()) + y_pred = sim_model(tensor_x) + if int(y_pred[0]) > 10: + adjust = 10 + elif int(y_pred[0]) < -10: + adjust = -10 + else: + adjust = int(y_pred[0]) + #print (tensor_x, y_pred) + raw_sum += (row + adjust + 2) + #print (raw_sum) + return raw_sum + +class Mapping(torch.autograd.Function): + @staticmethod + def forward(ctx, input): + output = input.clone() + + output[input==-1] = -4 + output[input==-2] = -5 + output[input==-3] = -6 + output[input==-4] = -7 + output[input==-5] = -9 + output[input==-6] = -9 + output[input==-7] = -11 + output[input==-8] = -11 + output[input==-9] = -13 + output[input==-10] = -13 + output[input==-11] = -17 + output[input==-12] = -17 + output[input==-13] = -17 + output[input==-14] = -19 + output[input==-15] = -19 + output[input==-16] = -21 + output[input==-17] = -21 + output[input==-18] = -23 + output[input==-19] = -25 + output[input==-20] = -25 + output[input==-21] = -25 + output[input==-22] = -25 + output[input==-23] = -27 + output[input==-24] = -27 + output[input==-25] = -29 + output[input==-26] = -29 + output[input==-27] = -29 + output[input==-28] = -31 + output[input==-29] = -31 + output[input==-30] = -33 + output[input==-31] = -33 + output[input==-32] = -35 + output[input==-33] = -35 + output[input==-34] = -35 + #output[input==-35] = -35 + + output[input==0] = -2 + output[input==1] = -1 + output[input==2] = 1 + output[input==3] = 2 + #output[input==4] = 4 + output[input==5] = 4 + #output[input==6] = 6 + output[input==7] = 8 + #output[input==8] = 8 + output[input==9] = 10 + #output[input==10] = 10 + output[input==11] = 12 + #output[input==12] = 12 + output[input==13] = 16 + output[input==14] = 16 + output[input==15] = 16 + #output[input==16] = 16 + output[input==17] = 18 + output[input==18] = 20 + output[input==19] = 20 + output[input==20] = 24 + output[input==21] = 24 + output[input==22] = 24 + output[input==23] = 26 + output[input==24] = 26 + output[input==25] = 28 + output[input==26] = 28 + output[input==27] = 28 + output[input==28] = 30 + output[input==29] = 30 + output[input==30] = 32 + output[input==31] = 32 + output[input==32] = 34 + output[input==33] = 34 + output[input==34] = 34 + output[input==35] = 34 + return output + def backward(ctx, grad_output): + return grad_output diff --git a/SimBinaryNetpytorch/models/resnet.py b/SimBinaryNetpytorch/models/resnet.py new file mode 100644 index 0000000..774ab12 --- /dev/null +++ b/SimBinaryNetpytorch/models/resnet.py @@ -0,0 +1,217 @@ +import torch.nn as nn +import torchvision.transforms as transforms +import math + +__all__ = ['resnet'] + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +def init_model(model): + for m in model.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self): + super(ResNet, self).__init__() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + + return x + + +class ResNet_imagenet(ResNet): + + def __init__(self, num_classes=1000, + block=Bottleneck, layers=[3, 4, 23, 3]): + super(ResNet_imagenet, self).__init__() + self.inplanes = 64 + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + init_model(self) + self.regime = { + 0: {'optimizer': 'SGD', 'lr': 1e-1, + 'weight_decay': 1e-4, 'momentum': 0.9}, + 30: {'lr': 1e-2}, + 60: {'lr': 1e-3, 'weight_decay': 0}, + 90: {'lr': 1e-4} + } + + +class ResNet_cifar10(ResNet): + + def __init__(self, num_classes=10, + block=BasicBlock, depth=18): + super(ResNet_cifar10, self).__init__() + self.inplanes = 16 + n = int((depth - 2) / 6) + self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(16) + self.relu = nn.ReLU(inplace=True) + self.maxpool = lambda x: x + self.layer1 = self._make_layer(block, 16, n) + self.layer2 = self._make_layer(block, 32, n, stride=2) + self.layer3 = self._make_layer(block, 64, n, stride=2) + self.layer4 = lambda x: x + self.avgpool = nn.AvgPool2d(8) + self.fc = nn.Linear(64, num_classes) + + init_model(self) + self.regime = { + 0: {'optimizer': 'SGD', 'lr': 1e-1, + 'weight_decay': 1e-4, 'momentum': 0.9}, + 81: {'lr': 1e-2}, + 122: {'lr': 1e-3, 'weight_decay': 0}, + 164: {'lr': 1e-4} + } + + +def resnet(**kwargs): + num_classes, depth, dataset = map( + kwargs.get, ['num_classes', 'depth', 'dataset']) + if dataset == 'imagenet': + num_classes = num_classes or 1000 + depth = depth or 50 + if depth == 18: + return ResNet_imagenet(num_classes=num_classes, + block=BasicBlock, layers=[2, 2, 2, 2]) + if depth == 34: + return ResNet_imagenet(num_classes=num_classes, + block=BasicBlock, layers=[3, 4, 6, 3]) + if depth == 50: + return ResNet_imagenet(num_classes=num_classes, + block=Bottleneck, layers=[3, 4, 6, 3]) + if depth == 101: + return ResNet_imagenet(num_classes=num_classes, + block=Bottleneck, layers=[3, 4, 23, 3]) + if depth == 152: + return ResNet_imagenet(num_classes=num_classes, + block=Bottleneck, layers=[3, 8, 36, 3]) + + elif dataset == 'cifar10': + num_classes = num_classes or 10 + depth = depth or 18 #56 + return ResNet_cifar10(num_classes=num_classes, + block=BasicBlock, depth=depth) diff --git a/SimBinaryNetpytorch/models/resnet_binary.py b/SimBinaryNetpytorch/models/resnet_binary.py new file mode 100644 index 0000000..802d54d --- /dev/null +++ b/SimBinaryNetpytorch/models/resnet_binary.py @@ -0,0 +1,248 @@ +import torch.nn as nn +import torchvision.transforms as transforms +import math +from .binarized_modules import BinarizeLinear,BinarizeConv2d + +__all__ = ['resnet_binary'] + +def Binaryconv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def init_model(model): + for m in model.modules(): + if isinstance(m, BinarizeConv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None,do_bntan=True): + super(BasicBlock, self).__init__() + + self.conv1 = Binaryconv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.tanh1 = nn.Hardtanh(inplace=True) + self.conv2 = Binaryconv3x3(planes, planes) + self.tanh2 = nn.Hardtanh(inplace=True) + self.bn2 = nn.BatchNorm2d(planes) + + self.downsample = downsample + self.do_bntan=do_bntan; + self.stride = stride + + def forward(self, x): + + residual = x.clone() + + out = self.conv1(x) + out = self.bn1(out) + out = self.tanh1(out) + + out = self.conv2(out) + + + if self.downsample is not None: + if residual.data.max()>1: + import pdb; pdb.set_trace() + residual = self.downsample(residual) + + out += residual + if self.do_bntan: + out = self.bn2(out) + out = self.tanh2(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = BinarizeConv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = BinarizeConv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = BinarizeConv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.tanh = nn.Hardtanh(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + import pdb; pdb.set_trace() + out = self.conv1(x) + out = self.bn1(out) + out = self.tanh(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.tanh(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + if self.do_bntan: + out = self.bn2(out) + out = self.tanh2(out) + + return out + + +class ResNet(nn.Module): + + def __init__(self): + super(ResNet, self).__init__() + + def _make_layer(self, block, planes, blocks, stride=1,do_bntan=True): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + BinarizeConv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks-1): + layers.append(block(self.inplanes, planes)) + layers.append(block(self.inplanes, planes,do_bntan=do_bntan)) + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.maxpool(x) + x = self.bn1(x) + x = self.tanh1(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.bn2(x) + x = self.tanh2(x) + x = self.fc(x) + x = self.bn3(x) + x = self.logsoftmax(x) + + return x + + +class ResNet_imagenet(ResNet): + + def __init__(self, num_classes=1000, + block=Bottleneck, layers=[3, 4, 23, 3]): + super(ResNet_imagenet, self).__init__() + self.inplanes = 64 + self.conv1 = BinarizeConv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.tanh = nn.Hardtanh(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7) + self.fc = BinarizeLinear(512 * block.expansion, num_classes) + + init_model(self) + self.regime = { + 0: {'optimizer': 'SGD', 'lr': 1e-1, + 'weight_decay': 1e-4, 'momentum': 0.9}, + 30: {'lr': 1e-2}, + 60: {'lr': 1e-3, 'weight_decay': 0}, + 90: {'lr': 1e-4} + } + + +class ResNet_cifar10(ResNet): + + def __init__(self, num_classes=10, + block=BasicBlock, depth=18): + super(ResNet_cifar10, self).__init__() + self.inflate = 5 + self.inplanes = 16*self.inflate + n = int((depth - 2) / 6) + self.conv1 = BinarizeConv2d(3, 16*self.inflate, kernel_size=3, stride=1, padding=1, + bias=False) + self.maxpool = lambda x: x + self.bn1 = nn.BatchNorm2d(16*self.inflate) + self.tanh1 = nn.Hardtanh(inplace=True) + self.tanh2 = nn.Hardtanh(inplace=True) + self.layer1 = self._make_layer(block, 16*self.inflate, n) + self.layer2 = self._make_layer(block, 32*self.inflate, n, stride=2) + self.layer3 = self._make_layer(block, 64*self.inflate, n, stride=2,do_bntan=False) + self.layer4 = lambda x: x + self.avgpool = nn.AvgPool2d(8) + self.bn2 = nn.BatchNorm1d(64*self.inflate) + self.bn3 = nn.BatchNorm1d(10) + self.logsoftmax = nn.LogSoftmax() + self.fc = BinarizeLinear(64*self.inflate, num_classes) + + init_model(self) + #self.regime = { + # 0: {'optimizer': 'SGD', 'lr': 1e-1, + # 'weight_decay': 1e-4, 'momentum': 0.9}, + # 81: {'lr': 1e-4}, + # 122: {'lr': 1e-5, 'weight_decay': 0}, + # 164: {'lr': 1e-6} + #} + self.regime = { + 0: {'optimizer': 'Adam', 'lr': 5e-3}, + 101: {'lr': 1e-3}, + 142: {'lr': 5e-4}, + 184: {'lr': 1e-4}, + 220: {'lr': 1e-5} + } + + +def resnet_binary(**kwargs): + num_classes, depth, dataset = map( + kwargs.get, ['num_classes', 'depth', 'dataset']) + if dataset == 'imagenet': + num_classes = num_classes or 1000 + depth = depth or 50 + if depth == 18: + return ResNet_imagenet(num_classes=num_classes, + block=BasicBlock, layers=[2, 2, 2, 2]) + if depth == 34: + return ResNet_imagenet(num_classes=num_classes, + block=BasicBlock, layers=[3, 4, 6, 3]) + if depth == 50: + return ResNet_imagenet(num_classes=num_classes, + block=Bottleneck, layers=[3, 4, 6, 3]) + if depth == 101: + return ResNet_imagenet(num_classes=num_classes, + block=Bottleneck, layers=[3, 4, 23, 3]) + if depth == 152: + return ResNet_imagenet(num_classes=num_classes, + block=Bottleneck, layers=[3, 8, 36, 3]) + + elif dataset == 'cifar10': + num_classes = num_classes or 10 + depth = depth or 18 + return ResNet_cifar10(num_classes=num_classes, + block=BasicBlock, depth=depth) diff --git a/SimBinaryNetpytorch/models/vgg_cifar10.py b/SimBinaryNetpytorch/models/vgg_cifar10.py new file mode 100644 index 0000000..1ec23cd --- /dev/null +++ b/SimBinaryNetpytorch/models/vgg_cifar10.py @@ -0,0 +1,69 @@ +import torch.nn as nn +import torchvision.transforms as transforms + + +class AlexNetOWT_BN(nn.Module): + + def __init__(self, num_classes=1000): + super(AlexNetOWT_BN, self).__init__() + self.features = nn.Sequential( + nn.Conv2d(3, 128, kernel_size=3, stride=1, padding=1, + bias=False), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + + nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.ReLU(inplace=True), + nn.BatchNorm2d(128), + + nn.Conv2d(128, 256, kernel_size=3, padding=1, bias=False), + nn.ReLU(inplace=True), + nn.BatchNorm2d(256), + + nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.ReLU(inplace=True), + nn.BatchNorm2d(256), + + nn.Conv2d(256, 512, kernel_size=3, padding=1, bias=False), + nn.ReLU(inplace=True), + nn.BatchNorm2d(512), + + nn.Conv2d(512, 512, kernel_size=3, padding=1, bias=False), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.ReLU(inplace=True), + nn.BatchNorm2d(512), + ) + self.classifier = nn.Sequential( + nn.Linear(512 * 4 * 4, 1024, bias=False), + nn.BatchNorm1d(1024), + nn.ReLU(inplace=True), + nn.Dropout(0.5), + nn.Linear(1024, 1024, bias=False), + nn.BatchNorm1d(1024), + nn.ReLU(inplace=True), + nn.Dropout(0.5), + nn.Linear(1024, num_classes) + nn.LogSoftMax() + ) + + self.regime = { + 0: {'optimizer': 'SGD', 'lr': 1e-2, + 'weight_decay': 5e-4, 'momentum': 0.9}, + 10: {'lr': 5e-3}, + 15: {'lr': 1e-3, 'weight_decay': 0}, + 20: {'lr': 5e-4}, + 25: {'lr': 1e-4} + } + + def forward(self, x): + x = self.features(x) + x = x.view(-1, 512 * 4 * 4) + x = self.classifier(x) + return x + + +def model(**kwargs): + num_classes = kwargs.get( 'num_classes', 1000) + return AlexNetOWT_BN(num_classes) diff --git a/SimBinaryNetpytorch/models/vgg_cifar10_binary.py b/SimBinaryNetpytorch/models/vgg_cifar10_binary.py new file mode 100644 index 0000000..694d6f0 --- /dev/null +++ b/SimBinaryNetpytorch/models/vgg_cifar10_binary.py @@ -0,0 +1,80 @@ +import torch +import torch.nn as nn +import torchvision.transforms as transforms +from torch.autograd import Function +from .binarized_modules import BinarizeLinear,BinarizeConv2d + + + +class VGG_Cifar10(nn.Module): + + def __init__(self, num_classes=1000): + super(VGG_Cifar10, self).__init__() + self.infl_ratio=3; + self.features = nn.Sequential( + BinarizeConv2d(3, 128*self.infl_ratio, kernel_size=3, stride=1, padding=1, + bias=True), + nn.BatchNorm2d(128*self.infl_ratio), + nn.Hardtanh(inplace=True), + + BinarizeConv2d(128*self.infl_ratio, 128*self.infl_ratio, kernel_size=3, padding=1, bias=True), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.BatchNorm2d(128*self.infl_ratio), + nn.Hardtanh(inplace=True), + + + BinarizeConv2d(128*self.infl_ratio, 256*self.infl_ratio, kernel_size=3, padding=1, bias=True), + nn.BatchNorm2d(256*self.infl_ratio), + nn.Hardtanh(inplace=True), + + + BinarizeConv2d(256*self.infl_ratio, 256*self.infl_ratio, kernel_size=3, padding=1, bias=True), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.BatchNorm2d(256*self.infl_ratio), + nn.Hardtanh(inplace=True), + + + BinarizeConv2d(256*self.infl_ratio, 512*self.infl_ratio, kernel_size=3, padding=1, bias=True), + nn.BatchNorm2d(512*self.infl_ratio), + nn.Hardtanh(inplace=True), + + + BinarizeConv2d(512*self.infl_ratio, 512, kernel_size=3, padding=1, bias=True), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.BatchNorm2d(512), + nn.Hardtanh(inplace=True) + + ) + self.classifier = nn.Sequential( + BinarizeLinear(512 * 4 * 4, 1024, bias=True), + nn.BatchNorm1d(1024), + nn.Hardtanh(inplace=True), + #nn.Dropout(0.5), + BinarizeLinear(1024, 1024, bias=True), + nn.BatchNorm1d(1024), + nn.Hardtanh(inplace=True), + #nn.Dropout(0.5), + BinarizeLinear(1024, num_classes, bias=True), + nn.BatchNorm1d(num_classes, affine=False), + nn.LogSoftmax() + ) + + self.regime = { + 0: {'optimizer': 'Adam', 'betas': (0.9, 0.999),'lr': 5e-3}, + 40: {'lr': 1e-3}, + 80: {'lr': 5e-4}, + 100: {'lr': 1e-4}, + 120: {'lr': 5e-5}, + 140: {'lr': 1e-5} + } + + def forward(self, x): + x = self.features(x) + x = x.view(-1, 512 * 4 * 4) + x = self.classifier(x) + return x + + +def vgg_cifar10_binary(**kwargs): + num_classes = kwargs.get( 'num_classes', 10) + return VGG_Cifar10(num_classes) diff --git a/SimBinaryNetpytorch/preprocess.py b/SimBinaryNetpytorch/preprocess.py new file mode 100644 index 0000000..d904f56 --- /dev/null +++ b/SimBinaryNetpytorch/preprocess.py @@ -0,0 +1,198 @@ +import torch +import torchvision.transforms as transforms +import random + +__imagenet_stats = {'mean': [0.485, 0.456, 0.406], + 'std': [0.229, 0.224, 0.225]} + +__imagenet_pca = { + 'eigval': torch.Tensor([0.2175, 0.0188, 0.0045]), + 'eigvec': torch.Tensor([ + [-0.5675, 0.7192, 0.4009], + [-0.5808, -0.0045, -0.8140], + [-0.5836, -0.6948, 0.4203], + ]) +} + + +def scale_crop(input_size, scale_size=None, normalize=__imagenet_stats): + t_list = [ + transforms.CenterCrop(input_size), + transforms.ToTensor(), + transforms.Normalize(**normalize), + ] + if scale_size != input_size: + t_list = [transforms.Scale(scale_size)] + t_list + + return transforms.Compose(t_list) + + +def scale_random_crop(input_size, scale_size=None, normalize=__imagenet_stats): + t_list = [ + transforms.RandomCrop(input_size), + transforms.ToTensor(), + transforms.Normalize(**normalize), + ] + if scale_size != input_size: + t_list = [transforms.Scale(scale_size)] + t_list + + transforms.Compose(t_list) + + +def pad_random_crop(input_size, scale_size=None, normalize=__imagenet_stats): + padding = int((scale_size - input_size) / 2) + return transforms.Compose([ + transforms.RandomCrop(input_size, padding=padding), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(**normalize), + ]) + + +def inception_preproccess(input_size, normalize=__imagenet_stats): + return transforms.Compose([ + transforms.RandomSizedCrop(input_size), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(**normalize) + ]) +def inception_color_preproccess(input_size, normalize=__imagenet_stats): + return transforms.Compose([ + transforms.RandomSizedCrop(input_size), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + ColorJitter( + brightness=0.4, + contrast=0.4, + saturation=0.4, + ), + Lighting(0.1, __imagenet_pca['eigval'], __imagenet_pca['eigvec']), + transforms.Normalize(**normalize) + ]) + + +def get_transform(name='imagenet', input_size=None, + scale_size=None, normalize=None, augment=True): + normalize = normalize or __imagenet_stats + if name == 'imagenet': + scale_size = scale_size or 256 + input_size = input_size or 224 + if augment: + return inception_preproccess(input_size, normalize=normalize) + else: + return scale_crop(input_size=input_size, + scale_size=scale_size, normalize=normalize) + elif 'cifar' in name: + input_size = input_size or 32 + if augment: + scale_size = scale_size or 40 + return pad_random_crop(input_size, scale_size=scale_size, + normalize=normalize) + else: + scale_size = scale_size or 32 + return scale_crop(input_size=input_size, + scale_size=scale_size, normalize=normalize) + elif name == 'mnist': + normalize = {'mean': [0.5], 'std': [0.5]} + input_size = input_size or 28 + if augment: + scale_size = scale_size or 32 + return pad_random_crop(input_size, scale_size=scale_size, + normalize=normalize) + else: + scale_size = scale_size or 32 + return scale_crop(input_size=input_size, + scale_size=scale_size, normalize=normalize) + + +class Lighting(object): + """Lighting noise(AlexNet - style PCA - based noise)""" + + def __init__(self, alphastd, eigval, eigvec): + self.alphastd = alphastd + self.eigval = eigval + self.eigvec = eigvec + + def __call__(self, img): + if self.alphastd == 0: + return img + + alpha = img.new().resize_(3).normal_(0, self.alphastd) + rgb = self.eigvec.type_as(img).clone()\ + .mul(alpha.view(1, 3).expand(3, 3))\ + .mul(self.eigval.view(1, 3).expand(3, 3))\ + .sum(1).squeeze() + + return img.add(rgb.view(3, 1, 1).expand_as(img)) + + +class Grayscale(object): + + def __call__(self, img): + gs = img.clone() + gs[0].mul_(0.299).add_(0.587, gs[1]).add_(0.114, gs[2]) + gs[1].copy_(gs[0]) + gs[2].copy_(gs[0]) + return gs + + +class Saturation(object): + + def __init__(self, var): + self.var = var + + def __call__(self, img): + gs = Grayscale()(img) + alpha = random.uniform(0, self.var) + return img.lerp(gs, alpha) + + +class Brightness(object): + + def __init__(self, var): + self.var = var + + def __call__(self, img): + gs = img.new().resize_as_(img).zero_() + alpha = random.uniform(0, self.var) + return img.lerp(gs, alpha) + + +class Contrast(object): + + def __init__(self, var): + self.var = var + + def __call__(self, img): + gs = Grayscale()(img) + gs.fill_(gs.mean()) + alpha = random.uniform(0, self.var) + return img.lerp(gs, alpha) + + +class RandomOrder(object): + """ Composes several transforms together in random order. + """ + + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, img): + if self.transforms is None: + return img + order = torch.randperm(len(self.transforms)) + for i in order: + img = self.transforms[i](img) + return img + + +class ColorJitter(RandomOrder): + + def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4): + self.transforms = [] + if brightness != 0: + self.transforms.append(Brightness(brightness)) + if contrast != 0: + self.transforms.append(Contrast(contrast)) + if saturation != 0: + self.transforms.append(Saturation(saturation)) diff --git a/SimBinaryNetpytorch/results/2021-04-15_15-36-47/log.txt b/SimBinaryNetpytorch/results/2021-04-15_15-36-47/log.txt new file mode 100644 index 0000000..47bf526 --- /dev/null +++ b/SimBinaryNetpytorch/results/2021-04-15_15-36-47/log.txt @@ -0,0 +1,5 @@ +2021-04-15 15:36:47 - INFO - saving to ./results/2021-04-15_15-36-47 +2021-04-15 15:36:47 - DEBUG - run arguments: Namespace(batch_size=256, dataset='imagenet', epochs=2500, evaluate=None, gpus='0', input_size=None, lr=0.1, model='alexnet', model_config='', momentum=0.9, optimizer='SGD', print_freq=10, results_dir='./results', resume='', save='2021-04-15_15-36-47', start_epoch=0, type='torch.cuda.FloatTensor', weight_decay=0.0001, workers=8) +2021-04-15 15:36:47 - INFO - creating model alexnet +2021-04-15 15:36:48 - INFO - created model with configuration: {'input_size': None, 'dataset': 'imagenet'} +2021-04-15 15:36:48 - INFO - number of parameters: 61110184 diff --git a/SimBinaryNetpytorch/results/2021-04-15_15-37-52/log.txt b/SimBinaryNetpytorch/results/2021-04-15_15-37-52/log.txt new file mode 100644 index 0000000..503a37e --- /dev/null +++ b/SimBinaryNetpytorch/results/2021-04-15_15-37-52/log.txt @@ -0,0 +1,5 @@ +2021-04-15 15:37:52 - INFO - saving to ./results/2021-04-15_15-37-52 +2021-04-15 15:37:52 - DEBUG - run arguments: Namespace(batch_size=256, dataset='imagenet', epochs=2500, evaluate=None, gpus='0', input_size=None, lr=0.1, model='resnet', model_config='', momentum=0.9, optimizer='SGD', print_freq=10, results_dir='./results', resume='', save='2021-04-15_15-37-52', start_epoch=0, type='torch.cuda.FloatTensor', weight_decay=0.0001, workers=8) +2021-04-15 15:37:52 - INFO - creating model resnet +2021-04-15 15:37:52 - INFO - created model with configuration: {'input_size': None, 'dataset': 'imagenet'} +2021-04-15 15:37:52 - INFO - number of parameters: 25557032 diff --git a/SimBinaryNetpytorch/results/2021-04-15_15-38-16/log.txt b/SimBinaryNetpytorch/results/2021-04-15_15-38-16/log.txt new file mode 100644 index 0000000..5b248e2 --- /dev/null +++ b/SimBinaryNetpytorch/results/2021-04-15_15-38-16/log.txt @@ -0,0 +1,5 @@ +2021-04-15 15:38:16 - INFO - saving to ./results/2021-04-15_15-38-16 +2021-04-15 15:38:16 - DEBUG - run arguments: Namespace(batch_size=256, dataset='imagenet', epochs=2500, evaluate=None, gpus='0', input_size=None, lr=0.1, model='alexnet', model_config='', momentum=0.9, optimizer='SGD', print_freq=10, results_dir='./results', resume='', save='2021-04-15_15-38-16', start_epoch=0, type='torch.cuda.FloatTensor', weight_decay=0.0001, workers=8) +2021-04-15 15:38:16 - INFO - creating model alexnet +2021-04-15 15:38:17 - INFO - created model with configuration: {'input_size': None, 'dataset': 'imagenet'} +2021-04-15 15:38:17 - INFO - number of parameters: 61110184 diff --git a/SimBinaryNetpytorch/utils.py b/SimBinaryNetpytorch/utils.py new file mode 100644 index 0000000..fdd5b17 --- /dev/null +++ b/SimBinaryNetpytorch/utils.py @@ -0,0 +1,160 @@ +import os +import torch +import logging.config +import shutil +import pandas as pd +from bokeh.io import output_file, save, show +from bokeh.plotting import figure +from bokeh.layouts import column +#from bokeh.charts import Line, defaults +# +#defaults.width = 800 +#defaults.height = 400 +#defaults.tools = 'pan,box_zoom,wheel_zoom,box_select,hover,resize,reset,save' + + +def setup_logging(log_file='log.txt'): + """Setup logging configuration + """ + logging.basicConfig(level=logging.DEBUG, + format="%(asctime)s - %(levelname)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + filename=log_file, + filemode='w') + console = logging.StreamHandler() + console.setLevel(logging.INFO) + formatter = logging.Formatter('%(message)s') + console.setFormatter(formatter) + logging.getLogger('').addHandler(console) + + +class ResultsLog(object): + + def __init__(self, path='results.csv', plot_path=None): + self.path = path + self.plot_path = plot_path or (self.path + '.html') + self.figures = [] + self.results = None + + def add(self, **kwargs): + df = pd.DataFrame([kwargs.values()], columns=kwargs.keys()) + if self.results is None: + self.results = df + else: + self.results = self.results.append(df, ignore_index=True) + + def save(self, title='Training Results'): + if len(self.figures) > 0: + if os.path.isfile(self.plot_path): + os.remove(self.plot_path) + output_file(self.plot_path, title=title) + plot = column(*self.figures) + save(plot) + self.figures = [] + self.results.to_csv(self.path, index=False, index_label=False) + + def load(self, path=None): + path = path or self.path + if os.path.isfile(path): + self.results.read_csv(path) + + def show(self): + if len(self.figures) > 0: + plot = column(*self.figures) + show(plot) + + #def plot(self, *kargs, **kwargs): + # line = Line(data=self.results, *kargs, **kwargs) + # self.figures.append(line) + + def image(self, *kargs, **kwargs): + fig = figure() + fig.image(*kargs, **kwargs) + self.figures.append(fig) + + +def save_checkpoint(state, is_best, path='.', filename='checkpoint.pth.tar', save_all=False): + filename = os.path.join(path, filename) + torch.save(state, filename) + if is_best: + shutil.copyfile(filename, os.path.join(path, 'model_best.pth.tar')) + if save_all: + shutil.copyfile(filename, os.path.join( + path, 'checkpoint_epoch_%s.pth.tar' % state['epoch'])) + + +class AverageMeter(object): + """Computes and stores the average and current value""" + + def __init__(self): + 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 + +__optimizers = { + 'SGD': torch.optim.SGD, + 'ASGD': torch.optim.ASGD, + 'Adam': torch.optim.Adam, + 'Adamax': torch.optim.Adamax, + 'Adagrad': torch.optim.Adagrad, + 'Adadelta': torch.optim.Adadelta, + 'Rprop': torch.optim.Rprop, + 'RMSprop': torch.optim.RMSprop +} + + +def adjust_optimizer(optimizer, epoch, config): + """Reconfigures the optimizer according to epoch and config dict""" + def modify_optimizer(optimizer, setting): + if 'optimizer' in setting: + optimizer = __optimizers[setting['optimizer']]( + optimizer.param_groups) + logging.debug('OPTIMIZER - setting method = %s' % + setting['optimizer']) + for param_group in optimizer.param_groups: + for key in param_group.keys(): + if key in setting: + logging.debug('OPTIMIZER - setting %s = %s' % + (key, setting[key])) + param_group[key] = setting[key] + return optimizer + + if callable(config): + optimizer = modify_optimizer(optimizer, config(epoch)) + else: + for e in range(epoch + 1): # run over all epochs - sticky setting + if e in config: + optimizer = modify_optimizer(optimizer, config[e]) + + return optimizer + + +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.float().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) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + # kernel_img = model.features[0][0].kernel.data.clone() + # kernel_img.add_(-kernel_img.min()) + # kernel_img.mul_(255 / kernel_img.max()) + # save_image(kernel_img, 'kernel%s.jpg' % epoch) diff --git a/activity_recognition_3classes_resnet.py b/activity_recognition_3classes_resnet.py new file mode 100644 index 0000000..4976596 --- /dev/null +++ b/activity_recognition_3classes_resnet.py @@ -0,0 +1,154 @@ +import torch +import numpy as np +import cv2, os, sys +import pandas as pd +from torch.utils.data import Dataset +from matplotlib import pyplot as plt +from torch.utils.data import ConcatDataset, DataLoader, Subset +import torch.nn as nn +import torchvision.transforms as transforms +from torchvision.datasets import DatasetFolder +from PIL import Image +import torchvision.models as models +batch_size = 32 +num_epoch = 10 + +train_tfm = transforms.Compose([ + transforms.Grayscale(), + transforms.RandomResizedCrop((40,30)), + transforms.Resize((40, 30)), + transforms.ToTensor(), + + #transforms.TenCrop((40,30)), + #transforms.Normalize(0.5,0.5), +]) + +test_tfm = transforms.Compose([ + transforms.Grayscale(), + transforms.Resize((40, 30)), + transforms.ToTensor() + ]) +''' +class Classifier(nn.Module): + def __init__(self): + super(Classifier, self).__init__() + + self.cnn_layers = nn.Sequential( + #input_size(1,30,40) + nn.Conv2d(1, 16, 3, 1), #output_size(16,28,38) + nn.BatchNorm2d(16), + nn.ReLU(), + nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #output_size(16,14,19) + + nn.Conv2d(16, 24, 3, 1), #output_size(24,12,17) + nn.BatchNorm2d(24), + nn.ReLU(), + nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #output_size(24,6,8) + + nn.Conv2d(24, 32, 3, 1), #output_size(32,4,6) + nn.BatchNorm2d(32), + nn.ReLU(), + nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2) #ouput_size(32,2,3) + + + ) + self.fc_layers = nn.Sequential( + nn.Linear(32 * 2 * 3, 32), + nn.ReLU(), + nn.Dropout(0.2), + + + nn.Linear(32,8) + ) + + def forward(self, x): + x = self.cnn_layers(x) + + x = x.flatten(1) + + x = self.fc_layers(x) + + return x +''' +def main(): + train_set = DatasetFolder("./dataset/data_0705/lepton/train", loader=lambda x: Image.open(x), extensions="bmp", transform=train_tfm) + test_set = DatasetFolder("./dataset/data_0705/lepton/test", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm) + + train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True) + test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True) + + + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = models.resnet18() + model.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=3, + bias=False) + model.fc = nn.Linear(512, 3) + model = model.to(device) + print(model) + optimizer = torch.optim.Adam(model.parameters(), lr=0.001) + criterion = nn.CrossEntropyLoss() + + for epoch in range(num_epoch): + ##Training + running_loss = 0.0 + total = 0 + correct = 0 + for i, data in enumerate(train_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + optimizer.zero_grad() + + outputs = model(inputs) + + loss = criterion(outputs, labels) + loss.backward() + + optimizer.step() + + running_loss += loss.item() + total += labels.size(0) + _,predicted = torch.max(outputs.data,1) + #print(predicted) + #print("label",labels) + correct += (predicted == labels).sum().item() + train_acc = correct / total + + print(f"[ Train | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {train_acc:.5f}") + + + + + + ##Testing + model.eval() + + with torch.no_grad(): + correct = 0 + total = 0 + + for i, data in enumerate(test_loader): + inputs, labels = data + + inputs = inputs.to(device) + labels = labels.to(device) + + outputs = model(inputs) + _,predicted = torch.max(outputs.data,1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + #print(predicted) + #print("labels:",labels) + print('Test Accuracy:{} %'.format((correct / total) * 100)) + + + + + + +if __name__ == '__main__': + main() diff --git a/activity_recognition_3classes_resnet_binary.py b/activity_recognition_3classes_resnet_binary.py new file mode 100644 index 0000000..d4e5795 --- /dev/null +++ b/activity_recognition_3classes_resnet_binary.py @@ -0,0 +1,211 @@ +import torch +import numpy as np +import cv2, os, sys +import pandas as pd +from torch.utils.data import Dataset +from matplotlib import pyplot as plt +from torch.utils.data import ConcatDataset, DataLoader, Subset +import torch.nn as nn +import torchvision.transforms as transforms +from torchvision.datasets import DatasetFolder +from PIL import Image +import torchvision.models +import BinaryNetpytorch.models as models +from BinaryNetpytorch.models.binarized_modules import BinarizeLinear,BinarizeConv2d +batch_size = 32 +num_epoch = 10 + +train_tfm = transforms.Compose([ +# transforms.RandomHorizontalFlip(), +# transforms.RandomResizedCrop((40,30)), + transforms.Grayscale(), + transforms.Resize((40, 30)), + transforms.ToTensor(), + #transforms.RandomResizedCrop((40,30)), + #transforms.TenCrop((40,30)), +# transforms.Normalize(0.5,0.5), +]) +test_tfm = transforms.Compose([ + transforms.Grayscale(), + transforms.Resize((40, 30)), + transforms.ToTensor() +]) +def Binaryconv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None,do_bntan=True): + super(BasicBlock, self).__init__() + + self.conv1 = Binaryconv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.tanh1 = nn.Hardtanh(inplace=True) + self.conv2 = Binaryconv3x3(planes, planes) + self.tanh2 = nn.Hardtanh(inplace=True) + self.bn2 = nn.BatchNorm2d(planes) + + self.downsample = downsample + self.do_bntan=do_bntan + self.stride = stride + + def forward(self, x): + + residual = x.clone() + + out = self.conv1(x) + out = self.bn1(out) + out = self.tanh1(out) + + out = self.conv2(out) + + + if self.downsample is not None: + if residual.data.max()>1: + import pdb; pdb.set_trace() + residual = self.downsample(residual) + + out += residual + if self.do_bntan: + out = self.bn2(out) + out = self.tanh2(out) + + return out +class ResNet(nn.Module): + + def __init__(self): + super(ResNet, self).__init__() + + def _make_layer(self, block, planes, blocks, stride=1,do_bntan=True): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + BinarizeConv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks-1): + layers.append(block(self.inplanes, planes)) + layers.append(block(self.inplanes, planes,do_bntan=do_bntan)) + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.maxpool(x) + x = self.bn1(x) + x = self.tanh1(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.bn2(x) + x = self.tanh2(x) + x = self.fc(x) + x = self.bn3(x) + x = self.logsoftmax(x) + + return x +class ResNet_cifar10(ResNet): + + def __init__(self, num_classes=3, + block=BasicBlock, depth=18): + super(ResNet_cifar10, self).__init__() + self.inflate = 5 + self.inplanes = 16*self.inflate + n = int((depth - 2) / 6) + self.conv1 = BinarizeConv2d(1, 16*self.inflate, kernel_size=3, stride=1, padding=1, + bias=False) + self.maxpool = lambda x: x + self.bn1 = nn.BatchNorm2d(16*self.inflate) + self.tanh1 = nn.Hardtanh(inplace=True) + self.tanh2 = nn.Hardtanh(inplace=True) + self.layer1 = self._make_layer(block, 16*self.inflate, n) + self.layer2 = self._make_layer(block, 32*self.inflate, n, stride=2) + self.layer3 = self._make_layer(block, 64*self.inflate, n, stride=2,do_bntan=False) + self.layer4 = lambda x: x + self.avgpool = nn.AvgPool2d(8) + self.bn2 = nn.BatchNorm1d(64*self.inflate) + self.bn3 = nn.BatchNorm1d(3) + self.logsoftmax = nn.LogSoftmax() + self.fc = BinarizeLinear(64*self.inflate, 3) +def main(): + train_set = DatasetFolder("pose_data/training/labeled", loader=lambda x: Image.open(x), extensions="bmp", transform=train_tfm) + test_set = DatasetFolder("pose_data/testing", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm) + + train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True) + test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = ResNet_cifar10(num_classes=3,block=BasicBlock,depth=18) + model = model.to(device) + print(model) + optimizer = torch.optim.Adam(model.parameters(), lr=0.001) + criterion = nn.CrossEntropyLoss() + + for epoch in range(num_epoch): + running_loss = 0.0 + total = 0 + correct = 0 + for i, data in enumerate(train_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + optimizer.zero_grad() + + outputs = model(inputs) + + loss = criterion(outputs, labels) + loss.backward() + + optimizer.step() + + running_loss += loss.item() + total += labels.size(0) + _,predicted = torch.max(outputs.data,1) + #print(predicted) + #print("label",labels) + correct += (predicted == labels).sum().item() + train_acc = correct / total + + print(f"[ Train | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {train_acc:.5f}") + + model.eval() + + with torch.no_grad(): + correct = 0 + total = 0 + + for i, data in enumerate(test_loader): + inputs, labels = data + + inputs = inputs.to(device) + labels = labels.to(device) + + outputs = model(inputs) + _,predicted = torch.max(outputs.data,1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + #print(predicted) + #print("labels:",labels) + print('Test Accuracy:{} %'.format((correct / total) * 100)) + + + + + + +if __name__ == '__main__': + main() diff --git a/activity_recognition_8classes_resnet.py b/activity_recognition_8classes_resnet.py new file mode 100644 index 0000000..da3df11 --- /dev/null +++ b/activity_recognition_8classes_resnet.py @@ -0,0 +1,193 @@ +import torch +import numpy as np +import cv2, os, sys +import pandas as pd +from torch.utils.data import Dataset +from matplotlib import pyplot as plt +from torch.utils.data import ConcatDataset, DataLoader, Subset +import torch.nn as nn +import torchvision.transforms as transforms +from torchvision.datasets import DatasetFolder +from PIL import Image +import torchvision.models as models +batch_size = 32 +num_epoch = 1 + +torch.cuda.set_device(1) +train_tfm = transforms.Compose([ + transforms.Grayscale(), + transforms.RandomHorizontalFlip(), + transforms.RandomResizedCrop((68,68)), + transforms.ToTensor(), + #transforms.RandomResizedCrop((40,30)), + #transforms.TenCrop((40,30)), + #transforms.Normalize(0.5,0.5), +]) + +test_tfm = transforms.Compose([ + transforms.Grayscale(), + transforms.ToTensor() + ]) +''' +class Classifier(nn.Module): + def __init__(self): + super(Classifier, self).__init__() + + self.cnn_layers = nn.Sequential( + #input_size(1,30,40) + nn.Conv2d(1, 16, 3, 1), #output_size(16,28,38) + nn.BatchNorm2d(16), + nn.ReLU(), + nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #output_size(16,14,19) + + nn.Conv2d(16, 24, 3, 1), #output_size(24,12,17) + nn.BatchNorm2d(24), + nn.ReLU(), + nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #output_size(24,6,8) + + nn.Conv2d(24, 32, 3, 1), #output_size(32,4,6) + nn.BatchNorm2d(32), + nn.ReLU(), + nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2) #ouput_size(32,2,3) + + + ) + self.fc_layers = nn.Sequential( + nn.Linear(32 * 2 * 3, 32), + nn.ReLU(), + nn.Dropout(0.2), + + + nn.Linear(32,8) + ) + + def forward(self, x): + x = self.cnn_layers(x) + + x = x.flatten(1) + + x = self.fc_layers(x) + + return x +''' +def main(): + train_set = DatasetFolder("pose_data2/train", loader=lambda x: Image.open(x), extensions="bmp", transform=train_tfm) + test_set = DatasetFolder("pose_data2/test", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm) + valid_set = DatasetFolder("pose_data2/val", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm) + + train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True) + test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True) + valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=True) + + model_path = "model.ckpt" + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = models.resnet50() + model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, + bias=False) + model.fc = nn.Linear(2048, 8) + model = model.to(device) + print(model) + optimizer = torch.optim.Adam(model.parameters(), lr=0.001) + criterion = nn.CrossEntropyLoss() + + best_acc = -1 + for epoch in range(num_epoch): + ##Training + running_loss = 0.0 + total = 0 + correct = 0 + for i, data in enumerate(train_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + optimizer.zero_grad() + + outputs = model(inputs) + + loss = criterion(outputs, labels) + loss.backward() + + optimizer.step() + + running_loss += loss.item() + total += labels.size(0) + _,predicted = torch.max(outputs.data,1) + #print(predicted) + #print("label",labels) + correct += (predicted == labels).sum().item() + train_acc = correct / total + + print(f"[ Train | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {train_acc:.5f}") + + ##Validation + model.eval() + + valid_loss = 0.0 + total = 0 + correct = 0 + for i, data in enumerate(valid_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + + with torch.no_grad(): + outputs = model(inputs) + + loss = criterion(outputs, labels) + running_loss += loss.item() + total += labels.size(0) + _,predicted = torch.max(outputs.data,1) + correct += (predicted == labels).sum().item() + + valid_acc = correct / total + print(f"[ Valid | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {valid_acc:.5f}") + if valid_acc > best_acc: + best_acc = valid_acc + torch.save(model.state_dict(), model_path) + print('saving model with acc {:.3f}'.format(valid_acc)) + + + + ##Testing + model = models.resnet50() + model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, + bias=False) + model.fc = nn.Linear(2048, 8) + model = model.to(device) + model.load_state_dict(torch.load(model_path)) + model.eval() + + with torch.no_grad(): + correct = 0 + total = 0 + + for i, data in enumerate(test_loader): + inputs, labels = data + + inputs = inputs.to(device) + labels = labels.to(device) + + outputs = model(inputs) + _,predicted = torch.max(outputs.data,1) + + total += labels.size(0) + correct += (predicted == labels).sum().item() + # for k in range(batch_size): + # if predicted[k] != labels[k]: + # print(inputs[k]) + + #print(predicted) + #print("labels:",labels) + print('Test Accuracy:{} %'.format((correct / total) * 100)) + + + + + + +if __name__ == '__main__': + main() diff --git a/activity_recognition_8classes_resnet_binary.py b/activity_recognition_8classes_resnet_binary.py new file mode 100644 index 0000000..d59ea97 --- /dev/null +++ b/activity_recognition_8classes_resnet_binary.py @@ -0,0 +1,285 @@ +import torch +import numpy as np +import cv2, os, sys +import pandas as pd +from torch.utils.data import Dataset +from matplotlib import pyplot as plt +from torch.utils.data import ConcatDataset, DataLoader, Subset +import torch.nn as nn +import torchvision.transforms as transforms +from torchvision.datasets import DatasetFolder +from PIL import Image +import torchvision.models +import BinaryNetpytorch.models as models +from BinaryNetpytorch.models.binarized_modules import BinarizeLinear,BinarizeConv2d +import progressbar +import seaborn as sns + +batch_size = 32 +num_epoch = 60 + +torch.cuda.set_device(1) + +train_tfm = transforms.Compose([ +# transforms.RandomHorizontalFlip(), +# transforms.RandomResizedCrop((40,30)), + transforms.Grayscale(), + transforms.Resize((68, 68)), + transforms.ToTensor(), + #transforms.RandomResizedCrop((40,30)), + #transforms.TenCrop((40,30)), +# transforms.Normalize(0.5,0.5), +]) +test_tfm = transforms.Compose([ + transforms.Grayscale(), + transforms.Resize((68, 68)), + transforms.ToTensor() +]) + +def Quantize(img): + scaler = torch.div(img, 0.0078125, rounding_mode="floor") + scaler_t1 = scaler * 0.0078125 + scaler_t2 = (scaler + 1) * 0.0078125 + img = torch.where(abs(img - scaler_t1) < abs(img -scaler_t2), scaler_t1 , scaler_t2) + return img + # bar = progressbar.ProgressBar(maxval=img.size(0)*img.size(2)*img.size(3), \ + # widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()]) + # bar.start() + # for p in range(img.size(0)): + # for i in range(img.size(2)): + # for j in range(img.size(3)): + + # scaler = int(img[p][0][i][j] / 0.0078125) + # t1 = scaler * 0.0078125 + # t2 = (scaler + 1) * 0.0078125 + # if(abs(img[p][0][i][j] - t1) < abs(img[p][0][i][j] - t2)): + # img[p][0][i][j] = t1 + # else: + # img[p][0][i][j] = t2 + # bar.finish() + # return img +def Binaryconv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + +def conv3x3(in_planes, out_planes, stride=1): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None,do_bntan=True): + super(BasicBlock, self).__init__() + + self.conv1 = Binaryconv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.tanh1 = nn.Hardtanh(inplace=True) + self.conv2 = Binaryconv3x3(planes, planes) + self.tanh2 = nn.Hardtanh(inplace=True) + self.bn2 = nn.BatchNorm2d(planes) + + self.downsample = downsample + self.do_bntan=do_bntan + self.stride = stride + + def forward(self, x): + + residual = x.clone() + x = Quantize(x) + out = self.conv1(x) + out = self.bn1(out) + out = self.tanh1(out) + out = Quantize(out) + out = self.conv2(out) + + + if self.downsample is not None: + if residual.data.max()>1: + import pdb; pdb.set_trace() + residual = self.downsample(residual) + + out += residual + if self.do_bntan: + out = self.bn2(out) + out = self.tanh2(out) + + return out +class ResNet(nn.Module): + + def __init__(self): + super(ResNet, self).__init__() + + def _make_layer(self, block, planes, blocks, stride=1,do_bntan=True): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + BinarizeConv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks-1): + layers.append(block(self.inplanes, planes)) + layers.append(block(self.inplanes, planes,do_bntan=do_bntan)) + return nn.Sequential(*layers) + + def forward(self, x): + x = Quantize(x) + + x = self.conv1(x) + x = self.maxpool(x) + x = self.bn1(x) + x = self.tanh1(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.bn2(x) + x = self.tanh2(x) + #print(x.size()) + x = x.view(32,1280,1,1) + x = self.fc(x) + x = x.view(x.size(0), -1) + x = self.bn3(x) + x = self.logsoftmax(x) + + return x +class ResNet_cifar10(ResNet): + + def __init__(self, num_classes=8, + block=BasicBlock, depth=18): + super(ResNet_cifar10, self).__init__() + self.inflate = 5 + self.inplanes = 16*self.inflate + n = int((depth - 2) / 6) + self.conv1 = BinarizeConv2d(1, 16*self.inflate, kernel_size=3, stride=1, padding=1, + bias=False) + self.maxpool = lambda x: x + self.bn1 = nn.BatchNorm2d(16*self.inflate) + self.tanh1 = nn.Hardtanh(inplace=True) + self.tanh2 = nn.Hardtanh(inplace=True) + self.layer1 = self._make_layer(block, 16*self.inflate, n) + self.layer2 = self._make_layer(block, 32*self.inflate, n, stride=2) + self.layer3 = self._make_layer(block, 64*self.inflate, n, stride=2,do_bntan=False) + self.layer4 = lambda x: x + self.avgpool = nn.AvgPool2d(8) + self.bn2 = nn.BatchNorm1d(256*self.inflate) + self.bn3 = nn.BatchNorm1d(8) + self.logsoftmax = nn.LogSoftmax() + #self.fc = BinarizeLinear(256*self.inflate, 8) + self.fc = BinarizeConv2d(256*self.inflate, 8, kernel_size=1) +def main(): + train_set = DatasetFolder("pose_data2/train", loader=lambda x: Image.open(x), extensions="bmp", transform=train_tfm) + test_set = DatasetFolder("pose_data2/test", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm) + + train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True) + test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = ResNet_cifar10(num_classes=8,block=BasicBlock,depth=18) + model = model.to(device) + print(model) + optimizer = torch.optim.Adam(model.parameters(), lr=0.001) + criterion = nn.CrossEntropyLoss() + + model_path = "model.ckpt" + + for epoch in range(num_epoch): + running_loss = 0.0 + total = 0 + correct = 0 + for i, data in enumerate(train_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + optimizer.zero_grad() + + outputs = model(inputs) + + loss = criterion(outputs, labels) + loss.backward() + + optimizer.step() + + running_loss += loss.item() + total += labels.size(0) + _,predicted = torch.max(outputs.data,1) + #print(predicted) + #print("label",labels) + correct += (predicted == labels).sum().item() + train_acc = correct / total + + print(f"[ Train | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {train_acc:.5f}") + + torch.save(model.state_dict(), model_path) + + model = ResNet_cifar10(num_classes=8,block=BasicBlock,depth=18) + model = model.to(device) + model.load_state_dict(torch.load(model_path)) + model.eval() + + with torch.no_grad(): + correct = 0 + total = 0 + + correct_2 = 0 + stat = np.zeros((8,8)) + for i, data in enumerate(test_loader): + inputs, labels = data + + inputs = inputs.to(device) + labels = labels.to(device) + + outputs = model(inputs) + _,predicted = torch.max(outputs.data,1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + + for b in range(batch_size): + if predicted[b] == 0 or predicted[b] == 1 or predicted[b] == 2 or predicted[b] == 3: + if labels[b] == 0 or labels[b] == 1 or labels[b] == 2 or labels[b] == 3: + correct_2 += 1 + else: + if labels[b] == 4 or labels[b] == 5 or labels[b] == 6 or labels[b] == 7: + correct_2 += 1 + + + + for k in range(batch_size): + if predicted[k] != labels[k]: + img = inputs[k].mul(255).byte() + img = img.cpu().numpy().squeeze(0) + img = np.moveaxis(img, 0, -1) + + predict = predicted[k].cpu().numpy() + label = labels[k].cpu().numpy() + path = "test_result/predict:"+str(predict)+"_labels:"+str(label)+".jpg" + stat[int(label)][int(predict)] += 1 + + + + cv2.imwrite(path,img) + print(stat) + ax = sns.heatmap(stat, linewidth=0.5) + plt.xlabel('Prediction') + plt.ylabel('Label') + plt.savefig('heatmap.jpg') + #print(predicted) + #print("labels:",labels) + print('Test_2clasee Accuracy:{} %'.format((correct_2 / total) * 100)) + print('Test Accuracy:{} %'.format((correct / total) * 100)) + + + + + + +if __name__ == '__main__': + main() diff --git a/cim_model_3classes_BNN.py b/cim_model_3classes_BNN.py new file mode 100644 index 0000000..179aded --- /dev/null +++ b/cim_model_3classes_BNN.py @@ -0,0 +1,207 @@ +import torch +import numpy as np +import cv2, os, sys +from torch.utils.data import Dataset +from matplotlib import pyplot as plt +from torch.utils.data import ConcatDataset, DataLoader, Subset +import torch.nn as nn +import torchvision.transforms as transforms +from torchvision.datasets import DatasetFolder +from PIL import Image +from BinaryNetpytorch.models.binarized_modules import BinarizeLinear,BinarizeConv2d +from BinaryNetpytorch.models.binarized_modules import Binarize,HingeLoss +import seaborn as sns +import random +batch_size = 8 +num_epoch = 10 + +seed = 777 +torch.manual_seed(seed) +torch.cuda.manual_seed(seed) +torch.cuda.manual_seed_all(seed) +np.random.seed(seed) +random.seed(seed) +torch.backends.cudnn.benchmark = False +torch.backends.cudnn.deterministic = True + + +train_tfm = transforms.Compose([ + #transforms.Grayscale(), + #transforms.RandomHorizontalFlip(), + #transforms.RandomResizedCrop((40,30)), + #transforms.RandomCrop((40,30)), + #transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + #transforms.RandomResizedCrop((40,30)), + #transforms.TenCrop((40,30)), + #transforms.Normalize(0.5,0.5), +]) + +test_tfm = transforms.Compose([ + #transforms.Grayscale(), + transforms.ToTensor() + ]) +class Classifier(nn.Module): + def __init__(self): + super(Classifier, self).__init__() + + self.cnn_layers = nn.Sequential( + # BinarizeConv2d(in_channels=1, out_channels=128, kernel_size=9, padding=9//2, bias=False), + # nn.BatchNorm2d(128), + # nn.ReLU(), + # BinarizeConv2d(in_channels=128, out_channels=64, kernel_size=1, padding=1//2, bias=False), + # nn.BatchNorm2d(64), + #input_size(1,30,40) + BinarizeConv2d(1, 128, 3, 1), #output_size(16,28,38) + nn.BatchNorm2d(128), + nn.ReLU(), + #nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #output_size(16,14,19) + + BinarizeConv2d(128, 64, 3, 1), #output_size(24,12,17) + nn.BatchNorm2d(64), + nn.ReLU(), + #nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #output_size(24,6,8) + + BinarizeConv2d(64, 32, 3, 1), #output_size(32,4,6) + nn.BatchNorm2d(32), + nn.ReLU(), + #nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #ouput_size(32,2,3) + #nn.LogSoftmax(), + BinarizeConv2d(32, 3, (3,2), 1) #ouput_size(4,2,3) without max :(32,24,34) + + ) + + + + def forward(self, x): + x = self.cnn_layers(x) + #x = x.flatten(1) + #x = self.fc_layers(x) + #print(x.shape) + x = x.view(x.size(0), -1) + #print(x.shape) + #x = nn.LogSoftmax(x) + #print(x) + + return x + +def main(): + train_set = DatasetFolder("./dataset/data_0711/grideye/train", loader=lambda x: Image.open(x), extensions="bmp", transform=train_tfm) + test_set = DatasetFolder("./dataset/data_0711/grideye/test", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm) + val_set = DatasetFolder("./dataset/data_0711/grideye/train", loader=lambda x: Image.open(x), extensions="bmp", transform=test_tfm) + + train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True) + test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True) + val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=True) + + save_path = 'models.ckpt' + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = Classifier().to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=0.0001) + criterion = nn.CrossEntropyLoss() + best_accuracy = 0.0 + for epoch in range(num_epoch): + running_loss = 0.0 + total = 0 + correct = 0 + for i, data in enumerate(train_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + #print(labels) + optimizer.zero_grad() + + outputs = model(inputs) + #print(outputs.shape) + + loss = criterion(outputs, labels) + loss.backward() + + for p in list(model.parameters()): + if hasattr(p,'org'): + p.data.copy_(p.org) + + optimizer.step() + + for p in list(model.parameters()): + if hasattr(p,'org'): + p.org.copy_(p.data.clamp_(-1,1)) + + running_loss += loss.item() + total += labels.size(0) + _,predicted = torch.max(outputs.data,1) + #print(predicted) + #print("label",labels) + correct += (predicted == labels).sum().item() + train_acc = correct / total + + print(f"[ Train | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {train_acc:.5f}") + + model.eval() + with torch.no_grad(): + correct = 0 + total = 0 + for i, data in enumerate(val_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + outputs = model(inputs) + _,predicted = torch.max(outputs.data,1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + val_acc = correct / total + + if val_acc > best_accuracy: + best_accuracy = val_acc + torch.save(model.state_dict(), save_path) + print("Save Model") + + + print(f"[ Val | {epoch + 1:03d}/{num_epoch:03d} ] acc = {val_acc:.5f}") + + model = Classifier().to(device) + model.load_state_dict(torch.load(save_path)) + model.eval() + stat = np.zeros((3,3)) + with torch.no_grad(): + correct = 0 + total = 0 + print(model) + for i, data in enumerate(test_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + outputs = model(inputs) + #print(outputs.data) + _,predicted = torch.max(outputs.data,1) + #print(predicted) + total += labels.size(0) + correct += (predicted == labels).sum().item() + for k in range(len(predicted)): + if predicted[k] != labels[k]: + img = inputs[k].mul(255).byte() + img = img.cpu().numpy().squeeze(0) + img = np.moveaxis(img, 0, -1) + + predict = predicted[k].cpu().numpy() + label = labels[k].cpu().numpy() + path = 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BinarizeConv2d, IdealCimConv2d +from BinaryNetpytorch.models.binarized_modules import BinarizeConv2d as BConv2d +from BinaryNetpytorch.models.binarized_modules import Binarize,HingeLoss +import seaborn as sns +import random +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = "0" +batch_size = 8 +num_epoch = 150 +LEARN_RATE = 0.001 + +seed = 333 +torch.manual_seed(seed) +torch.cuda.manual_seed(seed) +torch.cuda.manual_seed_all(seed) +np.random.seed(seed) +random.seed(seed) +torch.backends.cudnn.benchmark = False +torch.backends.cudnn.deterministic = True + + +H = [32, 64, 128] +H = [16, 32, 64] + +RANDOM_WEIGHT_PER_EPOCH = 10 +RANDOM_RATE = 1 + + +class Classifier(nn.Module): + def __init__(self): + super(Classifier, self).__init__() + conv = CimSimConv2d + conv2 = BConv2d + conv3 = IdealCimConv2d + conv3 = BinarizeConv2d + #conv = nn.Conv2d + + self.cnn_layers1 = nn.Sequential( + + #conv(in_channels=1, out_channels=128, kernel_size=7), + #nn.BatchNorm2d(128), + #nn.LeakyReLU(0.5), + #conv(in_channels=128, out_channels=64, kernel_size=3), + #nn.BatchNorm2d(64), + #nn.LeakyReLU(0.5), + + #input_size(1,30,40) + conv(1, H[0], 3, 1), #output_size(16,66,66) + #nn.BatchNorm2d(128), + nn.LeakyReLU(0.5), + #nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #output_size(16,33,33) + ) + self.cnn_layers2 = nn.Sequential( + + conv(H[0], H[1], 3, 1), #output_size(24,31,31) + #nn.BatchNorm2d(64), + nn.LeakyReLU(0.5), + #nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #output_size(24,15,15) + ) + + self.cnn_layers3 = nn.Sequential( + + conv(H[1], H[2], 3, 1), #output_size(32,13,13) + #nn.BatchNorm2d(32), + nn.LeakyReLU(0.5), + #nn.Dropout(0.2), + nn.MaxPool2d(kernel_size = 2), #ouput_size(32,6,6) + #nn.LogSoftmax(), + #BinarizeConv2d(H[2], 8, (3,2), 1) #ouput_size(4,2,3) without max :(32,24,34) + conv2(H[2], 8, (3,2), 1) #ouput_size(4,2,3) without max :(32,24,34) + #conv(32, 8, (2,1), 1) #ouput_size(4,2,3) without max :(32,24,34) + + ) + + + + def forward(self, x): + #print(x) + #print("input",float(torch.min(x)),float(torch.max(x))) + x = self.cnn_layers1(x) + #print("layer1",float(torch.min(x)),float(torch.max(x))) + #print(x) + x = self.cnn_layers2(x) + #print("layer2",float(torch.min(x)),float(torch.max(x))) + x = self.cnn_layers3(x) + #print("layer3",float(torch.min(x)),float(torch.max(x))) + #print(x) + #x = x.flatten(1) + #x = self.fc_layers(x) + #print(x.shape) + x = x.view(x.size(0), -1) + #print(x.shape) + #x = nn.LogSoftmax(x) + #print(x) + + return x + +class Cls_Dataset(Dataset): + def __init__(self, input, target): + self.input = input + self.target = target + def __getitem__(self, index): + return self.input[index], self.target[index] + def __len__(self): + return len(self.input) + +def Load_data(path, cls): + data = [] + label = [] + f = 0 + for c in range(cls): + t = 0 + img = [] + data_path = path + "/0"+str(c)+"/" + for filename in os.listdir(data_path): + # with open(data_path + "/" + filename, "rb") as f_in: + + tmp_input = cv2.imread(data_path + filename,cv2.IMREAD_UNCHANGED) + #print(tmp_input) + tmp_input = cv2.resize(tmp_input, (30,40), interpolation = cv2.INTER_AREA) + tmp_input = tmp_input.astype(int) + tmp_input = tmp_input//2 + tmp_input = tmp_input - 63 + tmp_input = np.where(tmp_input > 63, 63, tmp_input) + tmp_input = tmp_input[:, :, np.newaxis] + tmp_input = tmp_input.transpose(2,0,1) + + + + + if img is not None: + img.append(tmp_input) + else: + img = tmp_input + label_tmp = np.full((len(img),1),c) + + if f != 0: + data = np.append(data,img,axis=0) + else: + data = img + if f != 0: + label = np.append(label,label_tmp,axis=0) + else: + label = label_tmp + f = 1 + + + + label = np.squeeze(label) + + label = np.array(label) + #print(data) + data = np.array(data) + #print(label.shape) + data = data.astype('float32') + + return data, label + +def main(): + # test_set = DatasetFolder("./dataset/8cls_srcnnimg/test", loader=lambda x: Image.open(x), extensions="jpg", transform=test_tfm) + # val_set = DatasetFolder("./dataset/8cls_srcnnimg/train", loader=lambda x: Image.open(x), extensions="jpg", transform=test_tfm) + global num_epoch + + train_data, train_label = Load_data("./dataset/8cls_grideye/train",8) + train = Cls_Dataset(train_data, train_label) + train_loader = DataLoader( + train, batch_size=100, shuffle=True, + num_workers=4, pin_memory=True, drop_last=True + ) + + test_data, test_label = Load_data("./dataset/8cls_grideye/test",8) + + test = Cls_Dataset(test_data, test_label) + + test_loader = DataLoader( + test, batch_size=100, shuffle=True, + num_workers=4, pin_memory=True, drop_last=True + ) + + val_data, val_label = Load_data("./dataset/8cls_grideye/val",8) + + val = Cls_Dataset(val_data, val_label) + + val_loader = DataLoader( + val, batch_size=100, shuffle=True, + num_workers=4, pin_memory=True, drop_last=True + ) + + + + + save_path = 'models.ckpt' + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + model = Classifier().to(device) + #optimizer = torch.optim.Adam(model.parameters(), lr=0.1) + optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) + try: + checkpoint = torch.load('training_state_pre.bin') + model.load_state_dict(checkpoint['state_dict']) + #optimizer.load_state_dict(checkpoint['optimizer']) + #model.load_state_dict(torch.load('models_pre.ckpt')) + num_epoch = 150 + except: + print('cannot read from pretrained model.') + criterion = nn.CrossEntropyLoss() + best_accuracy = 0.0 + last_save = 0 + for epoch in range(num_epoch): + model.train() + running_loss = 0.0 + total = 0 + correct = 0 + for i, data in enumerate(train_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + optimizer.zero_grad() + + outputs = model(inputs) + #print(outputs.shape) + + loss = criterion(outputs, labels) + loss.backward() + #print(model) + #print(model.cnn_layers3[3].weight.grad) + + + for p in list(model.parameters()): + if hasattr(p,'org'): + p.data.copy_(p.org) + + optimizer.step() + + for p in list(model.parameters()): + if hasattr(p,'org'): + p.org.copy_(p.data.clamp_(-1,1)) + + running_loss += loss.item() + total += labels.size(0) + _,predicted = torch.max(outputs.data,1) + #print(predicted) + #print("label",labels) + correct += (predicted == labels).sum().item() + train_acc = correct / total + + print(f"[ Train | {epoch + 1:03d}/{num_epoch:03d} ] loss = {running_loss:.5f}, acc = {train_acc:.5f}") + + model.eval() + with torch.no_grad(): + correct = 0 + total = 0 + for i, data in enumerate(val_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + outputs = model(inputs) + _,predicted = torch.max(outputs.data,1) + total += labels.size(0) + correct += (predicted == labels).sum().item() + val_acc = correct / total + + if val_acc > best_accuracy: + last_save = epoch + best_accuracy = val_acc + torch.save(model.state_dict(), save_path) + state = {'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict()} + torch.save(state, 'training_state.bin') + print("Save Model") + + print(f"[ Val | {epoch + 1:03d}/{num_epoch:03d} ] acc = {val_acc:.5f}") + + + model = Classifier().to(device) + model.load_state_dict(torch.load(save_path)) + model.eval() + stat = np.zeros((8,8)) + with torch.no_grad(): + correct = 0 + total = 0 + print(model) + for i, data in enumerate(test_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + outputs = model(inputs) + #print(outputs.data) + _,predicted = torch.max(outputs.data,1) + #print(predicted) + total += labels.size(0) + correct += (predicted == labels).sum().item() + #print(labels) + #print(outputs.size()) + + + print('Test Accuracy:{} %'.format((correct / total) * 100)) + print('Save at', last_save+1) +if __name__ == '__main__': + main()

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