import os.path as osp

# +
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
import torch
from PIL import ImageEnhance

transformtypedict = dict(
    Brightness=ImageEnhance.Brightness,
    Contrast=ImageEnhance.Contrast,
    Sharpness=ImageEnhance.Sharpness,
    Color=ImageEnhance.Color,
)


class ImageJitter(object):
    def __init__(self, transformdict):
        self.transforms = [
            (transformtypedict[k], transformdict[k]) for k in transformdict
        ]

    def __call__(self, img):
        out = img
        randtensor = torch.rand(len(self.transforms))

        for i, (transformer, alpha) in enumerate(self.transforms):
            r = alpha * (randtensor[i] * 2.0 - 1.0) + 1
            out = transformer(out).enhance(r).convert("RGB")

        return out


# -

THIS_PATH = osp.dirname(__file__)
ROOT_PATH = osp.abspath(osp.join(THIS_PATH, ".."))
IMAGE_PATH = osp.join(
    ROOT_PATH, "/workspace/simple_shot/data/miniimagenet/images"
)  #'data/miniimagenet/images')
SPLIT_PATH = osp.join(
    ROOT_PATH, "/workspace/simple_shot/split/mini"
)  #'data/miniimagenet/split')


class MiniImageNet(Dataset):
    """ Usage: 
    """

    def __init__(self, setname, args):
        csv_path = osp.join(SPLIT_PATH, setname + ".csv")
        lines = [x.strip() for x in open(csv_path, "r").readlines()][1:]

        data = []
        label = []
        lb = -1

        self.wnids = []

        for l in lines:
            name, wnid = l.split(",")
            path = osp.join(IMAGE_PATH, name)
            if wnid not in self.wnids:
                self.wnids.append(wnid)
                lb += 1
            data.append(path)
            label.append(lb)

        self.data = data
        self.label = label
        self.num_class = len(set(label))

        # Transformation

        image_size = 84

        if setname == "train":
            self.transform = transforms.Compose(
                [
                    transforms.RandomResizedCrop(image_size),
                    ImageJitter(dict(Brightness=0.4, Contrast=0.4, Color=0.4)),
                    transforms.RandomHorizontalFlip(),
                    transforms.ToTensor(),
                    transforms.Normalize(
                        np.array([0.485, 0.456, 0.406]), np.array([0.229, 0.224, 0.225])
                    ),
                ]
            )
        else:
            self.transform = transforms.Compose(
                [
                    transforms.Resize(92),
                    transforms.CenterCrop(image_size),
                    transforms.ToTensor(),
                    transforms.Normalize(
                        np.array([0.485, 0.456, 0.406]), np.array([0.229, 0.224, 0.225])
                    ),
                ]
            )

    def __len__(self):
        return len(self.data)

    def __getitem__(self, i):
        path, label = self.data[i], self.label[i]
        image = self.transform(Image.open(path).convert("RGB"))
        return image, label

3bf6dbb04e565760c7990378ba7c5e9bcaa72296
<mask token>


class MiniImageNet(Dataset):
    """ Usage: 
    """

    def __init__(self, setname, args):
        csv_path = osp.join(SPLIT_PATH, setname + '.csv')
        lines = [x.strip() for x in open(csv_path, 'r').readlines()][1:]
        data = []
        label = []
        lb = -1
        self.wnids = []
        for l in lines:
            name, wnid = l.split(',')
            path = osp.join(IMAGE_PATH, name)
            if wnid not in self.wnids:
                self.wnids.append(wnid)
                lb += 1
            data.append(path)
            label.append(lb)
        self.data = data
        self.label = label
        self.num_class = len(set(label))
        image_size = 84
        if setname == 'train':
            self.transform = transforms.Compose([transforms.
                RandomResizedCrop(image_size), ImageJitter(dict(Brightness=
                0.4, Contrast=0.4, Color=0.4)), transforms.
                RandomHorizontalFlip(), transforms.ToTensor(), transforms.
                Normalize(np.array([0.485, 0.456, 0.406]), np.array([0.229,
                0.224, 0.225]))])
        else:
            self.transform = transforms.Compose([transforms.Resize(92),
                transforms.CenterCrop(image_size), transforms.ToTensor(),
                transforms.Normalize(np.array([0.485, 0.456, 0.406]), np.
                array([0.229, 0.224, 0.225]))])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, i):
        path, label = self.data[i], self.label[i]
        image = self.transform(Image.open(path).convert('RGB'))
        return image, label

<mask token>


class ImageJitter(object):
    <mask token>
    <mask token>


<mask token>


class MiniImageNet(Dataset):
    """ Usage: 
    """

    def __init__(self, setname, args):
        csv_path = osp.join(SPLIT_PATH, setname + '.csv')
        lines = [x.strip() for x in open(csv_path, 'r').readlines()][1:]
        data = []
        label = []
        lb = -1
        self.wnids = []
        for l in lines:
            name, wnid = l.split(',')
            path = osp.join(IMAGE_PATH, name)
            if wnid not in self.wnids:
                self.wnids.append(wnid)
                lb += 1
            data.append(path)
            label.append(lb)
        self.data = data
        self.label = label
        self.num_class = len(set(label))
        image_size = 84
        if setname == 'train':
            self.transform = transforms.Compose([transforms.
                RandomResizedCrop(image_size), ImageJitter(dict(Brightness=
                0.4, Contrast=0.4, Color=0.4)), transforms.
                RandomHorizontalFlip(), transforms.ToTensor(), transforms.
                Normalize(np.array([0.485, 0.456, 0.406]), np.array([0.229,
                0.224, 0.225]))])
        else:
            self.transform = transforms.Compose([transforms.Resize(92),
                transforms.CenterCrop(image_size), transforms.ToTensor(),
                transforms.Normalize(np.array([0.485, 0.456, 0.406]), np.
                array([0.229, 0.224, 0.225]))])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, i):
        path, label = self.data[i], self.label[i]
        image = self.transform(Image.open(path).convert('RGB'))
        return image, label

<mask token>


class ImageJitter(object):

    def __init__(self, transformdict):
        self.transforms = [(transformtypedict[k], transformdict[k]) for k in
            transformdict]

    def __call__(self, img):
        out = img
        randtensor = torch.rand(len(self.transforms))
        for i, (transformer, alpha) in enumerate(self.transforms):
            r = alpha * (randtensor[i] * 2.0 - 1.0) + 1
            out = transformer(out).enhance(r).convert('RGB')
        return out


<mask token>


class MiniImageNet(Dataset):
    """ Usage: 
    """

    def __init__(self, setname, args):
        csv_path = osp.join(SPLIT_PATH, setname + '.csv')
        lines = [x.strip() for x in open(csv_path, 'r').readlines()][1:]
        data = []
        label = []
        lb = -1
        self.wnids = []
        for l in lines:
            name, wnid = l.split(',')
            path = osp.join(IMAGE_PATH, name)
            if wnid not in self.wnids:
                self.wnids.append(wnid)
                lb += 1
            data.append(path)
            label.append(lb)
        self.data = data
        self.label = label
        self.num_class = len(set(label))
        image_size = 84
        if setname == 'train':
            self.transform = transforms.Compose([transforms.
                RandomResizedCrop(image_size), ImageJitter(dict(Brightness=
                0.4, Contrast=0.4, Color=0.4)), transforms.
                RandomHorizontalFlip(), transforms.ToTensor(), transforms.
                Normalize(np.array([0.485, 0.456, 0.406]), np.array([0.229,
                0.224, 0.225]))])
        else:
            self.transform = transforms.Compose([transforms.Resize(92),
                transforms.CenterCrop(image_size), transforms.ToTensor(),
                transforms.Normalize(np.array([0.485, 0.456, 0.406]), np.
                array([0.229, 0.224, 0.225]))])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, i):
        path, label = self.data[i], self.label[i]
        image = self.transform(Image.open(path).convert('RGB'))
        return image, label

<mask token>
transformtypedict = dict(Brightness=ImageEnhance.Brightness, Contrast=
    ImageEnhance.Contrast, Sharpness=ImageEnhance.Sharpness, Color=
    ImageEnhance.Color)


class ImageJitter(object):

    def __init__(self, transformdict):
        self.transforms = [(transformtypedict[k], transformdict[k]) for k in
            transformdict]

    def __call__(self, img):
        out = img
        randtensor = torch.rand(len(self.transforms))
        for i, (transformer, alpha) in enumerate(self.transforms):
            r = alpha * (randtensor[i] * 2.0 - 1.0) + 1
            out = transformer(out).enhance(r).convert('RGB')
        return out


THIS_PATH = osp.dirname(__file__)
ROOT_PATH = osp.abspath(osp.join(THIS_PATH, '..'))
IMAGE_PATH = osp.join(ROOT_PATH,
    '/workspace/simple_shot/data/miniimagenet/images')
SPLIT_PATH = osp.join(ROOT_PATH, '/workspace/simple_shot/split/mini')


class MiniImageNet(Dataset):
    """ Usage: 
    """

    def __init__(self, setname, args):
        csv_path = osp.join(SPLIT_PATH, setname + '.csv')
        lines = [x.strip() for x in open(csv_path, 'r').readlines()][1:]
        data = []
        label = []
        lb = -1
        self.wnids = []
        for l in lines:
            name, wnid = l.split(',')
            path = osp.join(IMAGE_PATH, name)
            if wnid not in self.wnids:
                self.wnids.append(wnid)
                lb += 1
            data.append(path)
            label.append(lb)
        self.data = data
        self.label = label
        self.num_class = len(set(label))
        image_size = 84
        if setname == 'train':
            self.transform = transforms.Compose([transforms.
                RandomResizedCrop(image_size), ImageJitter(dict(Brightness=
                0.4, Contrast=0.4, Color=0.4)), transforms.
                RandomHorizontalFlip(), transforms.ToTensor(), transforms.
                Normalize(np.array([0.485, 0.456, 0.406]), np.array([0.229,
                0.224, 0.225]))])
        else:
            self.transform = transforms.Compose([transforms.Resize(92),
                transforms.CenterCrop(image_size), transforms.ToTensor(),
                transforms.Normalize(np.array([0.485, 0.456, 0.406]), np.
                array([0.229, 0.224, 0.225]))])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, i):
        path, label = self.data[i], self.label[i]
        image = self.transform(Image.open(path).convert('RGB'))
        return image, label

import os.path as osp

# +
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
import torch
from PIL import ImageEnhance

transformtypedict = dict(
    Brightness=ImageEnhance.Brightness,
    Contrast=ImageEnhance.Contrast,
    Sharpness=ImageEnhance.Sharpness,
    Color=ImageEnhance.Color,
)


class ImageJitter(object):
    def __init__(self, transformdict):
        self.transforms = [
            (transformtypedict[k], transformdict[k]) for k in transformdict
        ]

    def __call__(self, img):
        out = img
        randtensor = torch.rand(len(self.transforms))

        for i, (transformer, alpha) in enumerate(self.transforms):
            r = alpha * (randtensor[i] * 2.0 - 1.0) + 1
            out = transformer(out).enhance(r).convert("RGB")

        return out


# -

THIS_PATH = osp.dirname(__file__)
ROOT_PATH = osp.abspath(osp.join(THIS_PATH, ".."))
IMAGE_PATH = osp.join(
    ROOT_PATH, "/workspace/simple_shot/data/miniimagenet/images"
)  #'data/miniimagenet/images')
SPLIT_PATH = osp.join(
    ROOT_PATH, "/workspace/simple_shot/split/mini"
)  #'data/miniimagenet/split')


class MiniImageNet(Dataset):
    """ Usage: 
    """

    def __init__(self, setname, args):
        csv_path = osp.join(SPLIT_PATH, setname + ".csv")
        lines = [x.strip() for x in open(csv_path, "r").readlines()][1:]

        data = []
        label = []
        lb = -1

        self.wnids = []

        for l in lines:
            name, wnid = l.split(",")
            path = osp.join(IMAGE_PATH, name)
            if wnid not in self.wnids:
                self.wnids.append(wnid)
                lb += 1
            data.append(path)
            label.append(lb)

        self.data = data
        self.label = label
        self.num_class = len(set(label))

        # Transformation

        image_size = 84

        if setname == "train":
            self.transform = transforms.Compose(
                [
                    transforms.RandomResizedCrop(image_size),
                    ImageJitter(dict(Brightness=0.4, Contrast=0.4, Color=0.4)),
                    transforms.RandomHorizontalFlip(),
                    transforms.ToTensor(),
                    transforms.Normalize(
                        np.array([0.485, 0.456, 0.406]), np.array([0.229, 0.224, 0.225])
                    ),
                ]
            )
        else:
            self.transform = transforms.Compose(
                [
                    transforms.Resize(92),
                    transforms.CenterCrop(image_size),
                    transforms.ToTensor(),
                    transforms.Normalize(
                        np.array([0.485, 0.456, 0.406]), np.array([0.229, 0.224, 0.225])
                    ),
                ]
            )

    def __len__(self):
        return len(self.data)

    def __getitem__(self, i):
        path, label = self.data[i], self.label[i]
        image = self.transform(Image.open(path).convert("RGB"))
        return image, label
