
import os
import numpy as np

import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler, WeightedRandomSampler

from torchlib.datasets.fersynthetic  import SyntheticFaceDataset
from torchlib.datasets.factory import FactoryDataset
from torchlib.attentionnet import AttentionNeuralNet, AttentionGMMNeuralNet
from torchlib.classnet import ClassNeuralNet
from torchlib.datasets.datasets import Dataset

import datetime
from argparse import ArgumentParser
from aug import get_transforms_aug, get_transforms_det


def arg_parser():
    """Arg parser"""
    parser = ArgumentParser()
    parser.add_argument('data', metavar='DIR',
                        help='path to dataset')
    parser.add_argument('--databack', default=None,
                        help='path to background dataset')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='enables CUDA training')
    parser.add_argument('-g', '--gpu', default=0, type=int, metavar='N',
                        help='divice number (default: 0)')
    parser.add_argument('--seed', type=int, default=1,
                        help='random seed (default: 1)')
    parser.add_argument('-j', '--workers', default=1, type=int, metavar='N',
                        help='number of data loading workers (default: 1)')
    parser.add_argument('--epochs', default=90, type=int, metavar='N',
                        help='number of total epochs to run')
    parser.add_argument('--kfold', default=0, type=int, metavar='N',
                        help='k fold')
    parser.add_argument('--nactor', default=0, type=int, metavar='N',
                        help='number of the actores')
    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('--lr', '--learning-rate', default=0.0001, type=float, metavar='LR',
                        help='initial learning rate')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--print-freq', '-p', default=10, type=int, metavar='N',
                        help='print frequency (default: 10)')
    parser.add_argument('--snapshot', '-sh', default=10, type=int, metavar='N',
                        help='snapshot (default: 10)')
    parser.add_argument('--project', default='./runs', type=str, metavar='PATH',
                        help='path to project (default: ./runs)')
    parser.add_argument('--name', default='exp', type=str,
                        help='name of experiment')
    parser.add_argument('--resume', default=None, type=str, metavar='NAME',
                        help='name to latest checkpoint (default: none)')
    parser.add_argument('--arch', default='simplenet', type=str,
                        help='architecture')
    parser.add_argument('--finetuning', action='store_true', default=False,
                        help='Finetuning')
    parser.add_argument('--loss', default='cross', type=str,
                        help='loss function')
    parser.add_argument('--opt', default='adam', type=str,
                        help='optimize function')
    parser.add_argument('--scheduler', default='fixed', type=str,
                        help='scheduler function for learning rate')
    parser.add_argument('--image-size', default=128, type=int, metavar='N',
                        help='image size')
    parser.add_argument('--channels', default=1, type=int, metavar='N',
                        help='input channel (default: 1)')
    parser.add_argument('--dim', default=64, type=int, metavar='N',
                        help='code size (default: 64)')
    parser.add_argument('--num-classes', '-c', default=10, type=int, metavar='N',
                        help='num classes (default: 10)')
    parser.add_argument('--name-dataset', default='mnist', type=str,
                        help='name dataset')
    parser.add_argument('--name-method', default='attnet', type=str,
                        help='name method')
    parser.add_argument('--parallel', action='store_true', default=False,
                        help='Parallel')
    parser.add_argument('--balance', action='store_true', default=False,
                        help='balance data')
    parser.add_argument('--backbone', default='preactresnet', type=str,
                        help='backbone for classification and representation')
    parser.add_argument('--trainiteration', default=None, type=int, metavar='N',
                        help='train iteration to increase data')
    parser.add_argument('--testiteration', default=None, type=int, metavar='N',
                        help='train iteration to increase data')
    parser.add_argument('--breal', type=str, default='real', help='dataset is real or synthetic')
    parser.add_argument('--num_filters', type=int, default=32, help='reconstruction size')

    parser.add_argument('--alpha', default=2.0, type=float, help='The first parameter of the beta distribution')
    parser.add_argument('--beta', default=2.0, type=float, help="the second parameter of the beta distribution")

    return parser


def main():
    # This model has a lot of variabilty, so it needs a lot of parameters.
    # We use an arg parser to get all the arguments we need.
    # See above for the default values, definitions and information on the datatypes.
    parser = arg_parser()
    args = parser.parse_args()
    imsize = args.image_size
    parallel = args.parallel
    num_classes = args.num_classes
    num_channels = args.channels
    num_filters = args.num_filters
    dim = args.dim
    view_freq = 1
    trainiteration = args.trainiteration
    testiteration = args.testiteration
    alpha = args.alpha
    beta = args.beta

    # Which network do we want to use? Initialize it.
    fname = args.name_method
    fnet = {
        'attnet': AttentionNeuralNet, # network for
        'attgmmnet': AttentionGMMNeuralNet,
        'classnet': ClassNeuralNet,
    }
    network = fnet[fname](
        patchproject=args.project,
        nameproject=args.name,
        no_cuda=args.no_cuda,
        parallel=parallel,
        seed=args.seed,
        print_freq=args.print_freq,
        gpu=args.gpu,
        view_freq=view_freq,
    )
    network.create(
        arch=args.arch,
        num_output_channels=dim,
        num_input_channels=num_channels,
        loss=args.loss,
        lr=args.lr,
        momentum=args.momentum,
        optimizer=args.opt,
        lrsch=args.scheduler,
        pretrained=args.finetuning,
        size_input=imsize,
        num_classes=num_classes,
        breal=args.breal,
        alpha=alpha,
        beta=beta
    )


    # resume if a model can be loaded
    if args.resume is not None:
        network.resume(os.path.join(network.pathmodels, args.resume))
    cudnn.benchmark = True

    kfold = args.kfold
    nactores = args.nactor
    idenselect = np.arange(nactores) + kfold * nactores

    # choose and load our datasets
    # training dataset
    if args.breal == 'real':
        train_data = Dataset(
            data=FactoryDataset.factory(
                pathname=args.data,
                name=args.name_dataset,
                subset=FactoryDataset.training,
                idenselect=idenselect,
                download=True
            ),
            num_channels=3,
            transform=get_transforms_det(args.image_size),
        )

        val_data = Dataset(
            data=FactoryDataset.factory(
                pathname=args.data,
                name=args.name_dataset,
                subset=FactoryDataset.validation,
                idenselect=idenselect,
                download=True
            ),
            num_channels=3,
            transform=get_transforms_det(args.image_size),
        )
    else:
        # SyntheticFaceDataset
        train_data = SyntheticFaceDataset(
            data=FactoryDataset.factory(
                pathname=args.data,
                name=args.name_dataset,
                subset=FactoryDataset.training,
                idenselect=idenselect,
                download=True
            ),
            pathnameback=args.databack,
            ext='jpg',
            count=trainiteration,
            num_channels=num_channels,
            iluminate=True, angle=30, translation=0.2, warp=0.1, factor=0.2,
            transform_data=get_transforms_aug(imsize),
            transform_image=get_transforms_det(imsize),
        )

        # validate dataset
        val_data = SyntheticFaceDataset(
            data=FactoryDataset.factory(
                pathname=args.data,
                name=args.name_dataset,
                idenselect=idenselect,
                subset=FactoryDataset.validation,
                download=True
            ),
            pathnameback=args.databack,
            ext='jpg',
            count=testiteration,
            num_channels=num_channels,
            iluminate=True, angle=30, translation=0.2, warp=0.1, factor=0.2,
            transform_data=get_transforms_aug(imsize),
            transform_image=get_transforms_det(imsize),
        )

    num_train = len(train_data)
    # sample to balance the dataset
    if args.balance:
        labels, counts = np.unique(train_data.labels, return_counts=True)
        weights = 1 / (counts / counts.sum())
        samples_weights = np.array([weights[x] for x in train_data.labels])
        sampler = WeightedRandomSampler(weights=samples_weights, num_samples=len(samples_weights), replacement=True)
    else:
        sampler = SubsetRandomSampler(np.random.permutation(num_train))

    # Now that we have our dataset, make loaders to facilitate training and validation
    train_loader = DataLoader(train_data, batch_size=args.batch_size,
                              num_workers=args.workers, pin_memory=network.cuda, drop_last=True,
                              sampler=sampler)  # shuffle=True,

    val_loader = DataLoader(val_data, batch_size=args.batch_size, shuffle=True,
                            num_workers=args.workers, pin_memory=network.cuda, drop_last=False)

    print(f"Train size: {len(train_data)}, test size: {len(val_data)}")

    # print neural net class
    start = datetime.datetime.now()
    print('SEG-Torch: {}'.format(start))
    print(network)

    # fit the neural net - this function performs both training and validation,
    # printing loss values to the screen and saving the best model for use later.
    network.fit(train_loader, val_loader, args.epochs, args.snapshot)

    print("Optimization Finished!")
    end = datetime.datetime.now()
    print("End time is", end)
    print("Time duration is:", round((end - start).total_seconds()/60, 2))
    print("DONE!!!")


if __name__ == '__main__':
   main()


import os
import numpy as np

import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler, WeightedRandomSampler

from torchlib.datasets.fersynthetic  import SyntheticFaceDataset
from torchlib.datasets.factory import FactoryDataset
from torchlib.attentionnet import AttentionNeuralNet, AttentionGMMNeuralNet
from torchlib.classnet import ClassNeuralNet
from torchlib.datasets.datasets import Dataset

import datetime
from argparse import ArgumentParser
from aug import get_transforms_aug, get_transforms_det


def arg_parser():
    """Arg parser"""
    parser = ArgumentParser()
    parser.add_argument('data', metavar='DIR',
                        help='path to dataset')
    parser.add_argument('--databack', default=None,
                        help='path to background dataset')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='enables CUDA training')
    parser.add_argument('-g', '--gpu', default=0, type=int, metavar='N',
                        help='divice number (default: 0)')
    parser.add_argument('--seed', type=int, default=1,
                        help='random seed (default: 1)')
    parser.add_argument('-j', '--workers', default=1, type=int, metavar='N',
                        help='number of data loading workers (default: 1)')
    parser.add_argument('--epochs', default=90, type=int, metavar='N',
                        help='number of total epochs to run')
    parser.add_argument('--kfold', default=0, type=int, metavar='N',
                        help='k fold')
    parser.add_argument('--nactor', default=0, type=int, metavar='N',
                        help='number of the actores')
    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('--lr', '--learning-rate', default=0.0001, type=float, metavar='LR',
                        help='initial learning rate')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--print-freq', '-p', default=10, type=int, metavar='N',
                        help='print frequency (default: 10)')
    parser.add_argument('--snapshot', '-sh', default=10, type=int, metavar='N',
                        help='snapshot (default: 10)')
    parser.add_argument('--project', default='./runs', type=str, metavar='PATH',
                        help='path to project (default: ./runs)')
    parser.add_argument('--name', default='exp', type=str,
                        help='name of experiment')
    parser.add_argument('--resume', default=None, type=str, metavar='NAME',
                        help='name to latest checkpoint (default: none)')
    parser.add_argument('--arch', default='simplenet', type=str,
                        help='architecture')
    parser.add_argument('--finetuning', action='store_true', default=False,
                        help='Finetuning')
    parser.add_argument('--loss', default='cross', type=str,
                        help='loss function')
    parser.add_argument('--opt', default='adam', type=str,
                        help='optimize function')
    parser.add_argument('--scheduler', default='fixed', type=str,
                        help='scheduler function for learning rate')
    parser.add_argument('--image-size', default=128, type=int, metavar='N',
                        help='image size')
    parser.add_argument('--channels', default=1, type=int, metavar='N',
                        help='input channel (default: 1)')
    parser.add_argument('--dim', default=64, type=int, metavar='N',
                        help='code size (default: 64)')
    parser.add_argument('--num-classes', '-c', default=10, type=int, metavar='N',
                        help='num classes (default: 10)')
    parser.add_argument('--name-dataset', default='mnist', type=str,
                        help='name dataset')
    parser.add_argument('--name-method', default='attnet', type=str,
                        help='name method')
    parser.add_argument('--parallel', action='store_true', default=False,
                        help='Parallel')
    parser.add_argument('--balance', action='store_true', default=False,
                        help='balance data')
    parser.add_argument('--backbone', default='preactresnet', type=str,
                        help='backbone for classification and representation')
    parser.add_argument('--trainiteration', default=None, type=int, metavar='N',
                        help='train iteration to increase data')
    parser.add_argument('--testiteration', default=None, type=int, metavar='N',
                        help='train iteration to increase data')
    parser.add_argument('--breal', type=str, default='real', help='dataset is real or synthetic')
    parser.add_argument('--num_filters', type=int, default=32, help='reconstruction size')

    parser.add_argument('--alpha', default=2.0, type=float, help='The first parameter of the beta distribution')
    parser.add_argument('--beta', default=2.0, type=float, help="the second parameter of the beta distribution")

    return parser


def main():
    # This model has a lot of variabilty, so it needs a lot of parameters.
    # We use an arg parser to get all the arguments we need.
    # See above for the default values, definitions and information on the datatypes.
    parser = arg_parser()
    args = parser.parse_args()
    imsize = args.image_size
    parallel = args.parallel
    num_classes = args.num_classes
    num_channels = args.channels
    num_filters = args.num_filters
    dim = args.dim
    view_freq = 1
    trainiteration = args.trainiteration
    testiteration = args.testiteration
    alpha = args.alpha
    beta = args.beta

    # Which network do we want to use? Initialize it.
    fname = args.name_method
    fnet = {
        'attnet': AttentionNeuralNet, # network for
        'attgmmnet': AttentionGMMNeuralNet,
        'classnet': ClassNeuralNet,
    }
    network = fnet[fname](
        patchproject=args.project,
        nameproject=args.name,
        no_cuda=args.no_cuda,
        parallel=parallel,
        seed=args.seed,
        print_freq=args.print_freq,
        gpu=args.gpu,
        view_freq=view_freq,
    )
    network.create(
        arch=args.arch,
        num_output_channels=dim,
        num_input_channels=num_channels,
        loss=args.loss,
        lr=args.lr,
        momentum=args.momentum,
        optimizer=args.opt,
        lrsch=args.scheduler,
        pretrained=args.finetuning,
        size_input=imsize,
        num_classes=num_classes,
        breal=args.breal,
        alpha=alpha,
        beta=beta
    )


    # resume if a model can be loaded
    if args.resume is not None:
        network.resume(os.path.join(network.pathmodels, args.resume))
    cudnn.benchmark = True

    kfold = args.kfold
    nactores = args.nactor
    idenselect = np.arange(nactores) + kfold * nactores

    # choose and load our datasets
    # training dataset
    if args.breal == 'real':
        train_data = Dataset(
            data=FactoryDataset.factory(
                pathname=args.data,
                name=args.name_dataset,
                subset=FactoryDataset.training,
                idenselect=idenselect,
                download=True
            ),
            num_channels=3,
            transform=get_transforms_det(args.image_size),
        )

        val_data = Dataset(
            data=FactoryDataset.factory(
                pathname=args.data,
                name=args.name_dataset,
                subset=FactoryDataset.validation,
                idenselect=idenselect,
                download=True
            ),
            num_channels=3,
            transform=get_transforms_det(args.image_size),
        )
    else:
        # SyntheticFaceDataset
        train_data = SyntheticFaceDataset(
            data=FactoryDataset.factory(
                pathname=args.data,
                name=args.name_dataset,
                subset=FactoryDataset.training,
                idenselect=idenselect,
                download=True
            ),
            pathnameback=args.databack,
            ext='jpg',
            count=trainiteration,
            num_channels=num_channels,
            iluminate=True, angle=30, translation=0.2, warp=0.1, factor=0.2,
            transform_data=get_transforms_aug(imsize),
            transform_image=get_transforms_det(imsize),
        )

        # validate dataset
        val_data = SyntheticFaceDataset(
            data=FactoryDataset.factory(
                pathname=args.data,
                name=args.name_dataset,
                idenselect=idenselect,
                subset=FactoryDataset.validation,
                download=True
            ),
            pathnameback=args.databack,
            ext='jpg',
            count=testiteration,
            num_channels=num_channels,
            iluminate=True, angle=30, translation=0.2, warp=0.1, factor=0.2,
            transform_data=get_transforms_aug(imsize),
            transform_image=get_transforms_det(imsize),
        )

    num_train = len(train_data)
    # sample to balance the dataset
    if args.balance:
        labels, counts = np.unique(train_data.labels, return_counts=True)
        weights = 1 / (counts / counts.sum())
        samples_weights = np.array([weights[x] for x in train_data.labels])
        sampler = WeightedRandomSampler(weights=samples_weights, num_samples=len(samples_weights), replacement=True)
    else:
        sampler = SubsetRandomSampler(np.random.permutation(num_train))

    # Now that we have our dataset, make loaders to facilitate training and validation
    train_loader = DataLoader(train_data, batch_size=args.batch_size,
                              num_workers=args.workers, pin_memory=network.cuda, drop_last=True,
                              sampler=sampler)  # shuffle=True,

    val_loader = DataLoader(val_data, batch_size=args.batch_size, shuffle=True,
                            num_workers=args.workers, pin_memory=network.cuda, drop_last=False)

    print(f"Train size: {len(train_data)}, test size: {len(val_data)}")

    # print neural net class
    start = datetime.datetime.now()
    print('SEG-Torch: {}'.format(start))
    print(network)

    # fit the neural net - this function performs both training and validation,
    # printing loss values to the screen and saving the best model for use later.
    network.fit(train_loader, val_loader, args.epochs, args.snapshot)

    print("Optimization Finished!")
    end = datetime.datetime.now()
    print("End time is", end)
    print("Time duration is:", round((end - start).total_seconds()/60, 2))
    print("DONE!!!")


if __name__ == '__main__':
   main()

Arg parser # This model has a lot of variabilty, so it needs a lot of parameters. # We use an arg parser to get all the arguments we need. # See above for the default values, definitions and information on the datatypes. # Which network do we want to use? Initialize it. # network for # resume if a model can be loaded # choose and load our datasets # training dataset # SyntheticFaceDataset # validate dataset # sample to balance the dataset # Now that we have our dataset, make loaders to facilitate training and validation # shuffle=True, # print neural net class # fit the neural net - this function performs both training and validation, # printing loss values to the screen and saving the best model for use later.