import os
import torch
import numpy as np
import torchvision.datasets as datasets
from . import transforms
from .folder import ImageFolder


def fast_collate(batch):
    imgs = [img[0] for img in batch]
    targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)
    w = imgs[0].size[0]
    h = imgs[0].size[1]
    tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8)
    for i, img in enumerate(imgs):
        nump_array = np.asarray(img, dtype=np.uint8)
        tens = torch.from_numpy(nump_array)
        if(nump_array.ndim < 3):
            nump_array = np.expand_dims(nump_array, axis=-1)
        nump_array = np.rollaxis(nump_array, 2)

        tensor[i] += torch.from_numpy(nump_array)

    return tensor, targets


class PrefetchedWrapper(object):
    def prefetched_loader(loader):
        mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1,3,1,1)
        std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1,3,1,1)

        stream = torch.cuda.Stream()
        first = True

        for next_input, next_target in loader:
            with torch.cuda.stream(stream):
                next_input = next_input.cuda(non_blocking=True)
                next_target = next_target.cuda(non_blocking=True)
                next_input = next_input.float()
                next_input = next_input.sub_(mean).div_(std)

            if not first:
                yield input, target
            else:
                first = False

            torch.cuda.current_stream().wait_stream(stream)
            input = next_input
            target = next_target

        yield input, target

    def __init__(self, dataloader):
        self.dataloader = dataloader
        self.epoch = 0

    def __iter__(self):
        if (self.dataloader.sampler is not None and
            isinstance(self.dataloader.sampler,
                       torch.utils.data.distributed.DistributedSampler)):

            self.dataloader.sampler.set_epoch(self.epoch)
        self.epoch += 1
        return PrefetchedWrapper.prefetched_loader(self.dataloader)


def get_pytorch_train_loader(data_path, batch_size, sizes, workers=5, _worker_init_fn=None):
    traindir = os.path.join(data_path, 'train')
    train_dataset = ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(),
        ]),
        [transforms.Compose([
            transforms.Resize((size, size)),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
        ]) for size in sizes])

    if torch.distributed.is_initialized():
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    else:
        train_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=batch_size,
        shuffle=(train_sampler is None),
        num_workers=workers,
        worker_init_fn=_worker_init_fn,
        pin_memory=True,
        sampler=train_sampler)

    return train_loader, len(train_loader)


def get_pytorch_val_loader(data_path, batch_size, sizes, workers=5, _worker_init_fn=None):
    valdir = os.path.join(data_path, 'val')
    val_dataset = ImageFolder(
        valdir, None, [transforms.Compose([
            transforms.Resize(int(size/.875)),
            transforms.CenterCrop(size),
            transforms.ToTensor(),
        ]) for size in sizes])

    if torch.distributed.is_initialized():
        val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
    else:
        val_sampler = None

    val_loader = torch.utils.data.DataLoader(
        val_dataset,
        sampler=val_sampler,
        batch_size=batch_size,
        shuffle=False,
        num_workers=workers,
        worker_init_fn=_worker_init_fn,
        pin_memory=True)

    return val_loader, len(val_loader)

c6253205ecca7084123df6652df714b044e780f8
<mask token>


class PrefetchedWrapper(object):

    def prefetched_loader(loader):
        mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda(
            ).view(1, 3, 1, 1)
        std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda(
            ).view(1, 3, 1, 1)
        stream = torch.cuda.Stream()
        first = True
        for next_input, next_target in loader:
            with torch.cuda.stream(stream):
                next_input = next_input.cuda(non_blocking=True)
                next_target = next_target.cuda(non_blocking=True)
                next_input = next_input.float()
                next_input = next_input.sub_(mean).div_(std)
            if not first:
                yield input, target
            else:
                first = False
            torch.cuda.current_stream().wait_stream(stream)
            input = next_input
            target = next_target
        yield input, target
    <mask token>

    def __iter__(self):
        if self.dataloader.sampler is not None and isinstance(self.
            dataloader.sampler, torch.utils.data.distributed.DistributedSampler
            ):
            self.dataloader.sampler.set_epoch(self.epoch)
        self.epoch += 1
        return PrefetchedWrapper.prefetched_loader(self.dataloader)


<mask token>

<mask token>


def fast_collate(batch):
    imgs = [img[0] for img in batch]
    targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)
    w = imgs[0].size[0]
    h = imgs[0].size[1]
    tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8)
    for i, img in enumerate(imgs):
        nump_array = np.asarray(img, dtype=np.uint8)
        tens = torch.from_numpy(nump_array)
        if nump_array.ndim < 3:
            nump_array = np.expand_dims(nump_array, axis=-1)
        nump_array = np.rollaxis(nump_array, 2)
        tensor[i] += torch.from_numpy(nump_array)
    return tensor, targets


class PrefetchedWrapper(object):

    def prefetched_loader(loader):
        mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda(
            ).view(1, 3, 1, 1)
        std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda(
            ).view(1, 3, 1, 1)
        stream = torch.cuda.Stream()
        first = True
        for next_input, next_target in loader:
            with torch.cuda.stream(stream):
                next_input = next_input.cuda(non_blocking=True)
                next_target = next_target.cuda(non_blocking=True)
                next_input = next_input.float()
                next_input = next_input.sub_(mean).div_(std)
            if not first:
                yield input, target
            else:
                first = False
            torch.cuda.current_stream().wait_stream(stream)
            input = next_input
            target = next_target
        yield input, target

    def __init__(self, dataloader):
        self.dataloader = dataloader
        self.epoch = 0

    def __iter__(self):
        if self.dataloader.sampler is not None and isinstance(self.
            dataloader.sampler, torch.utils.data.distributed.DistributedSampler
            ):
            self.dataloader.sampler.set_epoch(self.epoch)
        self.epoch += 1
        return PrefetchedWrapper.prefetched_loader(self.dataloader)


def get_pytorch_train_loader(data_path, batch_size, sizes, workers=5,
    _worker_init_fn=None):
    traindir = os.path.join(data_path, 'train')
    train_dataset = ImageFolder(traindir, transforms.Compose([transforms.
        RandomResizedCrop()]), [transforms.Compose([transforms.Resize((size,
        size)), transforms.RandomHorizontalFlip(), transforms.ToTensor()]) for
        size in sizes])
    if torch.distributed.is_initialized():
        train_sampler = torch.utils.data.distributed.DistributedSampler(
            train_dataset)
    else:
        train_sampler = None
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=
        batch_size, shuffle=train_sampler is None, num_workers=workers,
        worker_init_fn=_worker_init_fn, pin_memory=True, sampler=train_sampler)
    return train_loader, len(train_loader)


<mask token>

<mask token>


def fast_collate(batch):
    imgs = [img[0] for img in batch]
    targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)
    w = imgs[0].size[0]
    h = imgs[0].size[1]
    tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8)
    for i, img in enumerate(imgs):
        nump_array = np.asarray(img, dtype=np.uint8)
        tens = torch.from_numpy(nump_array)
        if nump_array.ndim < 3:
            nump_array = np.expand_dims(nump_array, axis=-1)
        nump_array = np.rollaxis(nump_array, 2)
        tensor[i] += torch.from_numpy(nump_array)
    return tensor, targets


class PrefetchedWrapper(object):

    def prefetched_loader(loader):
        mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda(
            ).view(1, 3, 1, 1)
        std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda(
            ).view(1, 3, 1, 1)
        stream = torch.cuda.Stream()
        first = True
        for next_input, next_target in loader:
            with torch.cuda.stream(stream):
                next_input = next_input.cuda(non_blocking=True)
                next_target = next_target.cuda(non_blocking=True)
                next_input = next_input.float()
                next_input = next_input.sub_(mean).div_(std)
            if not first:
                yield input, target
            else:
                first = False
            torch.cuda.current_stream().wait_stream(stream)
            input = next_input
            target = next_target
        yield input, target

    def __init__(self, dataloader):
        self.dataloader = dataloader
        self.epoch = 0

    def __iter__(self):
        if self.dataloader.sampler is not None and isinstance(self.
            dataloader.sampler, torch.utils.data.distributed.DistributedSampler
            ):
            self.dataloader.sampler.set_epoch(self.epoch)
        self.epoch += 1
        return PrefetchedWrapper.prefetched_loader(self.dataloader)


def get_pytorch_train_loader(data_path, batch_size, sizes, workers=5,
    _worker_init_fn=None):
    traindir = os.path.join(data_path, 'train')
    train_dataset = ImageFolder(traindir, transforms.Compose([transforms.
        RandomResizedCrop()]), [transforms.Compose([transforms.Resize((size,
        size)), transforms.RandomHorizontalFlip(), transforms.ToTensor()]) for
        size in sizes])
    if torch.distributed.is_initialized():
        train_sampler = torch.utils.data.distributed.DistributedSampler(
            train_dataset)
    else:
        train_sampler = None
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=
        batch_size, shuffle=train_sampler is None, num_workers=workers,
        worker_init_fn=_worker_init_fn, pin_memory=True, sampler=train_sampler)
    return train_loader, len(train_loader)


def get_pytorch_val_loader(data_path, batch_size, sizes, workers=5,
    _worker_init_fn=None):
    valdir = os.path.join(data_path, 'val')
    val_dataset = ImageFolder(valdir, None, [transforms.Compose([transforms
        .Resize(int(size / 0.875)), transforms.CenterCrop(size), transforms
        .ToTensor()]) for size in sizes])
    if torch.distributed.is_initialized():
        val_sampler = torch.utils.data.distributed.DistributedSampler(
            val_dataset)
    else:
        val_sampler = None
    val_loader = torch.utils.data.DataLoader(val_dataset, sampler=
        val_sampler, batch_size=batch_size, shuffle=False, num_workers=
        workers, worker_init_fn=_worker_init_fn, pin_memory=True)
    return val_loader, len(val_loader)

import os
import torch
import numpy as np
import torchvision.datasets as datasets
from . import transforms
from .folder import ImageFolder


def fast_collate(batch):
    imgs = [img[0] for img in batch]
    targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)
    w = imgs[0].size[0]
    h = imgs[0].size[1]
    tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8)
    for i, img in enumerate(imgs):
        nump_array = np.asarray(img, dtype=np.uint8)
        tens = torch.from_numpy(nump_array)
        if nump_array.ndim < 3:
            nump_array = np.expand_dims(nump_array, axis=-1)
        nump_array = np.rollaxis(nump_array, 2)
        tensor[i] += torch.from_numpy(nump_array)
    return tensor, targets


class PrefetchedWrapper(object):

    def prefetched_loader(loader):
        mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda(
            ).view(1, 3, 1, 1)
        std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda(
            ).view(1, 3, 1, 1)
        stream = torch.cuda.Stream()
        first = True
        for next_input, next_target in loader:
            with torch.cuda.stream(stream):
                next_input = next_input.cuda(non_blocking=True)
                next_target = next_target.cuda(non_blocking=True)
                next_input = next_input.float()
                next_input = next_input.sub_(mean).div_(std)
            if not first:
                yield input, target
            else:
                first = False
            torch.cuda.current_stream().wait_stream(stream)
            input = next_input
            target = next_target
        yield input, target

    def __init__(self, dataloader):
        self.dataloader = dataloader
        self.epoch = 0

    def __iter__(self):
        if self.dataloader.sampler is not None and isinstance(self.
            dataloader.sampler, torch.utils.data.distributed.DistributedSampler
            ):
            self.dataloader.sampler.set_epoch(self.epoch)
        self.epoch += 1
        return PrefetchedWrapper.prefetched_loader(self.dataloader)


def get_pytorch_train_loader(data_path, batch_size, sizes, workers=5,
    _worker_init_fn=None):
    traindir = os.path.join(data_path, 'train')
    train_dataset = ImageFolder(traindir, transforms.Compose([transforms.
        RandomResizedCrop()]), [transforms.Compose([transforms.Resize((size,
        size)), transforms.RandomHorizontalFlip(), transforms.ToTensor()]) for
        size in sizes])
    if torch.distributed.is_initialized():
        train_sampler = torch.utils.data.distributed.DistributedSampler(
            train_dataset)
    else:
        train_sampler = None
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=
        batch_size, shuffle=train_sampler is None, num_workers=workers,
        worker_init_fn=_worker_init_fn, pin_memory=True, sampler=train_sampler)
    return train_loader, len(train_loader)


def get_pytorch_val_loader(data_path, batch_size, sizes, workers=5,
    _worker_init_fn=None):
    valdir = os.path.join(data_path, 'val')
    val_dataset = ImageFolder(valdir, None, [transforms.Compose([transforms
        .Resize(int(size / 0.875)), transforms.CenterCrop(size), transforms
        .ToTensor()]) for size in sizes])
    if torch.distributed.is_initialized():
        val_sampler = torch.utils.data.distributed.DistributedSampler(
            val_dataset)
    else:
        val_sampler = None
    val_loader = torch.utils.data.DataLoader(val_dataset, sampler=
        val_sampler, batch_size=batch_size, shuffle=False, num_workers=
        workers, worker_init_fn=_worker_init_fn, pin_memory=True)
    return val_loader, len(val_loader)

import os
import torch
import numpy as np
import torchvision.datasets as datasets
from . import transforms
from .folder import ImageFolder


def fast_collate(batch):
    imgs = [img[0] for img in batch]
    targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)
    w = imgs[0].size[0]
    h = imgs[0].size[1]
    tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8)
    for i, img in enumerate(imgs):
        nump_array = np.asarray(img, dtype=np.uint8)
        tens = torch.from_numpy(nump_array)
        if(nump_array.ndim < 3):
            nump_array = np.expand_dims(nump_array, axis=-1)
        nump_array = np.rollaxis(nump_array, 2)

        tensor[i] += torch.from_numpy(nump_array)

    return tensor, targets


class PrefetchedWrapper(object):
    def prefetched_loader(loader):
        mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1,3,1,1)
        std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1,3,1,1)

        stream = torch.cuda.Stream()
        first = True

        for next_input, next_target in loader:
            with torch.cuda.stream(stream):
                next_input = next_input.cuda(non_blocking=True)
                next_target = next_target.cuda(non_blocking=True)
                next_input = next_input.float()
                next_input = next_input.sub_(mean).div_(std)

            if not first:
                yield input, target
            else:
                first = False

            torch.cuda.current_stream().wait_stream(stream)
            input = next_input
            target = next_target

        yield input, target

    def __init__(self, dataloader):
        self.dataloader = dataloader
        self.epoch = 0

    def __iter__(self):
        if (self.dataloader.sampler is not None and
            isinstance(self.dataloader.sampler,
                       torch.utils.data.distributed.DistributedSampler)):

            self.dataloader.sampler.set_epoch(self.epoch)
        self.epoch += 1
        return PrefetchedWrapper.prefetched_loader(self.dataloader)


def get_pytorch_train_loader(data_path, batch_size, sizes, workers=5, _worker_init_fn=None):
    traindir = os.path.join(data_path, 'train')
    train_dataset = ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(),
        ]),
        [transforms.Compose([
            transforms.Resize((size, size)),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
        ]) for size in sizes])

    if torch.distributed.is_initialized():
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    else:
        train_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=batch_size,
        shuffle=(train_sampler is None),
        num_workers=workers,
        worker_init_fn=_worker_init_fn,
        pin_memory=True,
        sampler=train_sampler)

    return train_loader, len(train_loader)


def get_pytorch_val_loader(data_path, batch_size, sizes, workers=5, _worker_init_fn=None):
    valdir = os.path.join(data_path, 'val')
    val_dataset = ImageFolder(
        valdir, None, [transforms.Compose([
            transforms.Resize(int(size/.875)),
            transforms.CenterCrop(size),
            transforms.ToTensor(),
        ]) for size in sizes])

    if torch.distributed.is_initialized():
        val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
    else:
        val_sampler = None

    val_loader = torch.utils.data.DataLoader(
        val_dataset,
        sampler=val_sampler,
        batch_size=batch_size,
        shuffle=False,
        num_workers=workers,
        worker_init_fn=_worker_init_fn,
        pin_memory=True)

    return val_loader, len(val_loader)
