"""
StyleMapGAN
Copyright (c) 2021-present NAVER Corp.

This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""

import datetime
import random, os
import argparse
import numpy as np
import torch
from torch import nn
from torch.utils import data
from torchvision import transforms, utils
from training.model import Generator, Encoder
from training.dataset import MultiResolutionDataset
from tqdm import tqdm
from sklearn import svm
import cv2

seed = 0
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)


def data_sampler(dataset, shuffle):
    if shuffle:
        return data.RandomSampler(dataset)

    else:
        return data.SequentialSampler(dataset)


class Sampler_with_index(data.Sampler):
    def __init__(self, indexes):
        self.indexes = indexes

    def __iter__(self):
        return (i for i in self.indexes)

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


class Model(nn.Module):
    def __init__(self, device="cuda"):
        super(Model, self).__init__()

        self.g_ema = Generator(
            train_args.size,
            train_args.mapping_layer_num,
            train_args.latent_channel_size,
            train_args.latent_spatial_size,
            lr_mul=train_args.lr_mul,
            channel_multiplier=train_args.channel_multiplier,
            normalize_mode=train_args.normalize_mode,
            small_generator=train_args.small_generator,
        )
        self.e_ema = Encoder(
            train_args.size,
            train_args.latent_channel_size,
            train_args.latent_spatial_size,
            channel_multiplier=train_args.channel_multiplier,
        )

    def forward(self, images, mode, direction_vector=None, mask=None):

        if mode == "project_latent":
            fake_stylecode = self.e_ema(images)
            return fake_stylecode
        elif mode == "transform_to_other_part":
            get_cuda_device = images.get_device()

            fake_stylecode = self.e_ema(images)

            direction_vector = (
                direction_vector.to(get_cuda_device)
                .unsqueeze(0)
                .repeat(images.shape[0], 1, 1, 1)
            )

            transformed_images = []

            for distance in range(-20, 1, 5):
                modified_stylecode = fake_stylecode + distance * direction_vector
                transformed_image, _ = self.g_ema(
                    modified_stylecode, input_is_stylecode=True
                )
                transformed_images.append(transformed_image)

            direction_vector[mask == 0] = 0

            for distance in range(-20, 1, 5):
                modified_stylecode = fake_stylecode + distance * direction_vector
                transformed_image, _ = self.g_ema(
                    modified_stylecode, input_is_stylecode=True
                )
                transformed_images.append(transformed_image)

            return torch.cat(transformed_images)


def sample_data(loader):
    while True:
        for batch in loader:
            yield batch


if __name__ == "__main__":
    device = "cuda"

    parser = argparse.ArgumentParser()
    parser.add_argument("--ckpt", type=str)
    parser.add_argument("--save_dir", type=str, default="expr/semantic_manipulation")
    parser.add_argument("--LMDB", type=str, default="data/celeba_hq/LMDB")
    parser.add_argument("--batch", type=int, default=16)
    parser.add_argument("--num_workers", type=int, default=2)
    parser.add_argument(
        "--part", type=str, default="Heavy_Makeup"
    )  # No_Beard Smiling ..
    parser.add_argument("--svm_train_iter", type=int, default=None)

    args = parser.parse_args()

    args.attr_celeba_hq = "semantic_manipulation/list_attr_celeba_hq.txt"

    args.train_lmdb = f"{args.LMDB}_train"
    args.val_lmdb = f"{args.LMDB}_val"
    args.test_lmdb = f"{args.LMDB}_test"

    model_name = args.ckpt.split("/")[-1].replace(".pt", "")

    os.makedirs(os.path.join(args.save_dir, args.part), exist_ok=True)
    args.inverted_npy = os.path.join(args.save_dir, f"{model_name}_inverted.npy")
    args.boundary = os.path.join(
        args.save_dir, args.part, f"{model_name}_{args.part}_boundary.npy"
    )
    print(args)

    ckpt = torch.load(args.ckpt)

    train_args = ckpt["train_args"]  # load arguments!

    model = Model()
    model.g_ema.load_state_dict(ckpt["g_ema"])
    model.e_ema.load_state_dict(ckpt["e_ema"])
    model = model.to(device)

    transform = transforms.Compose(
        [
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True),
        ]
    )

    train_dataset = MultiResolutionDataset(args.train_lmdb, transform, train_args.size)
    val_dataset = MultiResolutionDataset(args.val_lmdb, transform, train_args.size)
    test_dataset = MultiResolutionDataset(args.test_lmdb, transform, train_args.size)
    dataset = data.ConcatDataset([train_dataset, val_dataset, test_dataset])

    with torch.no_grad():
        # if inverted latent does not exist, save inverted latent as npy format
        if os.path.isfile(args.inverted_npy):
            print("inverted_npy exists!")
            latent_codes = np.load(args.inverted_npy)
        else:
            loader = data.DataLoader(
                dataset,
                batch_size=args.batch,
                sampler=data_sampler(dataset, shuffle=False),
                num_workers=args.num_workers,
                pin_memory=True,
            )
            latent_codes_enc = []
            for images in tqdm(loader):
                images = images.to(device)
                project_latent = model(images, "project_latent")

                latent_codes_enc.append(project_latent.cpu().numpy())
            latent_codes = np.concatenate(latent_codes_enc, axis=0)
            np.save(f"{args.inverted_npy}", latent_codes)

        latent_code_shape = latent_codes[0].shape
        print(f"latent_code_shape {latent_code_shape}")

        # flatten latent: Nx8x8x64 -> N x 8*8*64
        latent_codes = latent_codes.reshape(len(latent_codes), -1)

        # get boundary
        part = args.part

        if os.path.isfile(args.boundary):
            print(f"{part} boundary exists!")
            boundary_infos = torch.load(args.boundary)
            boundary = boundary_infos["boundary"]
            part1_indexes = boundary_infos["part1_indexes"]
            part2_indexes = boundary_infos["part2_indexes"]

        else:
            ############################################
            # get positive and negative indices by ranking using pretrained network (stylegan1)
            # refer to https://github.com/NVlabs/stylegan/tree/master/metrics/linear_separability.py

            part_pos_neg_indices = [
                "Male",
                "Smiling",
                "Attractive",
                "Wavy_Hair",
                "Young",
                "5_o_Clock_Shadow",
                "Arched_Eyebrows",
                "Bags_Under_Eyes",
                "Bald",
                "Bangs",
                "Big_Lips",
                "Big_Nose",
                "Black_Hair",
                "Blond_Hair",
                "Blurry",
                "Brown_Hair",
                "Bushy_Eyebrows",
                "Chubby",
                "Double_Chin",
                "Eyeglasses",
                "Goatee",
                "Gray_Hair",
                "Heavy_Makeup",
                "High_Cheekbones",
                "Mouth_Slightly_Open",
                "Mustache",
                "Narrow_Eyes",
                "No_Beard",
                "Oval_Face",
                "Pale_Skin",
                "Pointy_Nose",
                "Receding_Hairline",
                "Rosy_Cheeks",
                "Sideburns",
                "Straight_Hair",
                "Wearing_Earrings",
                "Wearing_Hat",
                "Wearing_Lipstick",
                "Wearing_Necklace",
                "Wearing_Necktie",
            ]

            pruned_index = part_pos_neg_indices.index(part)
            part1_indexes = np.load(
                f"semantic_manipulation/{pruned_index}_pos_indices.npy"
            )
            part2_indexes = np.load(
                f"semantic_manipulation/{pruned_index}_neg_indices.npy"
            )

            # get boundary using two parts.
            testset_ratio = 0.1
            np.random.shuffle(part1_indexes)
            np.random.shuffle(part2_indexes)

            positive_len = len(part1_indexes)
            negative_len = len(part2_indexes)

            positive_train = latent_codes[
                part1_indexes[int(positive_len * testset_ratio) :]
            ]
            positive_val = latent_codes[
                part1_indexes[: int(positive_len * testset_ratio)]
            ]

            negative_train = latent_codes[
                part2_indexes[int(negative_len * testset_ratio) :]
            ]
            negative_val = latent_codes[
                part2_indexes[: int(negative_len * testset_ratio)]
            ]

            # Training set.
            train_data = np.concatenate([positive_train, negative_train], axis=0)
            train_label = np.concatenate(
                [
                    np.ones(len(positive_train), dtype=np.int),
                    np.zeros(len(negative_train), dtype=np.int),
                ],
                axis=0,
            )

            # Validation set.
            val_data = np.concatenate([positive_val, negative_val], axis=0)
            val_label = np.concatenate(
                [
                    np.ones(len(positive_val), dtype=np.int),
                    np.zeros(len(negative_val), dtype=np.int),
                ],
                axis=0,
            )

            print(
                f"positive_train: {len(positive_train)}, negative_train:{len(negative_train)}, positive_val:{len(positive_val)}, negative_val:{len(negative_val)}"
            )

            print(f"Training boundary. {datetime.datetime.now()}")

            if args.svm_train_iter:
                clf = svm.SVC(kernel="linear", max_iter=args.svm_train_iter)
            else:
                clf = svm.SVC(kernel="linear")
            classifier = clf.fit(train_data, train_label)
            print(f"Finish training. {datetime.datetime.now()}")

            print(f"validate boundary.")
            val_prediction = classifier.predict(val_data)
            correct_num = np.sum(val_label == val_prediction)

            print(
                f"Accuracy for validation set: "
                f"{correct_num} / {len(val_data)} = "
                f"{correct_num / (len(val_data)):.6f}"
            )

            print("classifier.coef_.shape", classifier.coef_.shape)
            boundary = classifier.coef_.reshape(1, -1).astype(np.float32)
            boundary = boundary / np.linalg.norm(boundary)
            boundary = boundary.reshape(latent_code_shape)
            print("boundary.shape", boundary.shape)

            boundary = torch.from_numpy(boundary).float()

            torch.save(
                {
                    "boundary": boundary,
                    "part1_indexes": part1_indexes,
                    "part2_indexes": part2_indexes,
                },
                args.boundary,
            )

        with open(args.attr_celeba_hq, "r") as fp:
            total_number = int(fp.readline())
            print(f"{total_number} images, {len(latent_codes)} latent_codes")
            assert total_number == len(latent_codes)
            attrs = fp.readline().strip().split(" ")

            # print(attrs) # ['5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose', 'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace', 'Wearing_Necktie', 'Young']
            # Please note that the order is different from part_pos_neg_indices

            for i, attr in enumerate(attrs):
                if attr == args.part:
                    part_index = i
                    print(part, part_index)
                    break

            read_line = fp.readline()
            GT_part1_indexes = []  # part1: Heavy_Makeup
            GT_part2_indexes = []  # part2: others

            img_i = 0
            pivot = 0
            f_names = sorted(list(os.listdir("data/celeba_hq/raw_images/test/images/")))

            while read_line:
                parse_info = read_line.replace("  ", " ").strip().split(" ")
                attr_info = parse_info[1:]
                f_name = parse_info[0]  # first element is filename

                if f_name == f_names[pivot]:
                    if attr_info[part_index] == "1":
                        GT_part1_indexes.append(pivot)
                    else:
                        GT_part2_indexes.append(pivot)

                    pivot += 1
                    if pivot == 500:
                        break

                img_i += 1
                read_line = fp.readline()

        test_part1_indexes = GT_part1_indexes
        test_part2_indexes = GT_part2_indexes

        part1_loader = data.DataLoader(
            test_dataset,
            batch_size=args.batch,
            sampler=Sampler_with_index(test_part1_indexes),
        )
        part1_loader = sample_data(part1_loader)

        part2_loader = data.DataLoader(
            test_dataset,
            batch_size=args.batch,
            sampler=Sampler_with_index(test_part2_indexes),
        )

        part2_loader = sample_data(part2_loader)
        part2_img = next(part2_loader).to(device)

        # heavy makup
        ref_p_x, ref_p_y, width, height = 2, 5, 4, 2

        mask = (ref_p_x, ref_p_y, width, height)
        mask = torch.zeros(part2_img.shape[0], 64, 8, 8)
        mask[:, :, ref_p_y : ref_p_y + height, ref_p_x : ref_p_x + width] = 1

        part2_to_part1 = model(
            part2_img, "transform_to_other_part", -boundary, mask=mask
        )

        utils.save_image(
            torch.cat([part2_img, part2_to_part1]),
            f"{args.save_dir}/{part}/{model_name}_others_to_{part}.png",
            nrow=args.batch,
            normalize=True,
            range=(-1, 1),
        )

        # heavy makup
        s1 = np.array(list(range(13, 16 * 11, 16)))[[0, 3, 6, -1]]
        s2 = np.array(list(range(15, 16 * 11, 16)))[[0, 3, 6, -1]]
        ratio = train_args.size // train_args.latent_spatial_size

        for img, s_n in zip(
            torch.cat([part2_img, part2_to_part1])[
                np.concatenate((s1, s2), axis=0).tolist()
            ],
            [
                "img1_ori",
                "img1_global",
                "img1_local",
                "img1_recon",
                "img2_ori",
                "img2_global",
                "img2_local",
                "img2_recon",
            ],
        ):

            if "local" in s_n:
                img = torch.clamp(img, min=-1.0, max=1.0)
                img = (img + 1) / 2
                img = img.cpu()
                img = transforms.ToPILImage()(img)
                img = np.asarray(img)
                img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
                img = cv2.rectangle(
                    img,
                    (ref_p_x * ratio, ref_p_y * ratio),
                    ((ref_p_x + width) * ratio, (ref_p_y + height) * ratio),
                    (102, 255, 255),
                    1,
                )
                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
                img = transforms.ToTensor()(img)
                img = (img - 0.5) * 2

            utils.save_image(
                img,
                f"{args.save_dir}/{part}/{part}_{s_n}.png",
                nrow=args.batch,
                normalize=True,
                range=(-1, 1),
            )

"""
StyleMapGAN
Copyright (c) 2021-present NAVER Corp.

This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""

import datetime
import random, os
import argparse
import numpy as np
import torch
from torch import nn
from torch.utils import data
from torchvision import transforms, utils
from training.model import Generator, Encoder
from training.dataset import MultiResolutionDataset
from tqdm import tqdm
from sklearn import svm
import cv2

seed = 0
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)


def data_sampler(dataset, shuffle):
    if shuffle:
        return data.RandomSampler(dataset)

    else:
        return data.SequentialSampler(dataset)


class Sampler_with_index(data.Sampler):
    def __init__(self, indexes):
        self.indexes = indexes

    def __iter__(self):
        return (i for i in self.indexes)

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


class Model(nn.Module):
    def __init__(self, device="cuda"):
        super(Model, self).__init__()

        self.g_ema = Generator(
            train_args.size,
            train_args.mapping_layer_num,
            train_args.latent_channel_size,
            train_args.latent_spatial_size,
            lr_mul=train_args.lr_mul,
            channel_multiplier=train_args.channel_multiplier,
            normalize_mode=train_args.normalize_mode,
            small_generator=train_args.small_generator,
        )
        self.e_ema = Encoder(
            train_args.size,
            train_args.latent_channel_size,
            train_args.latent_spatial_size,
            channel_multiplier=train_args.channel_multiplier,
        )

    def forward(self, images, mode, direction_vector=None, mask=None):

        if mode == "project_latent":
            fake_stylecode = self.e_ema(images)
            return fake_stylecode
        elif mode == "transform_to_other_part":
            get_cuda_device = images.get_device()

            fake_stylecode = self.e_ema(images)

            direction_vector = (
                direction_vector.to(get_cuda_device)
                .unsqueeze(0)
                .repeat(images.shape[0], 1, 1, 1)
            )

            transformed_images = []

            for distance in range(-20, 1, 5):
                modified_stylecode = fake_stylecode + distance * direction_vector
                transformed_image, _ = self.g_ema(
                    modified_stylecode, input_is_stylecode=True
                )
                transformed_images.append(transformed_image)

            direction_vector[mask == 0] = 0

            for distance in range(-20, 1, 5):
                modified_stylecode = fake_stylecode + distance * direction_vector
                transformed_image, _ = self.g_ema(
                    modified_stylecode, input_is_stylecode=True
                )
                transformed_images.append(transformed_image)

            return torch.cat(transformed_images)


def sample_data(loader):
    while True:
        for batch in loader:
            yield batch


if __name__ == "__main__":
    device = "cuda"

    parser = argparse.ArgumentParser()
    parser.add_argument("--ckpt", type=str)
    parser.add_argument("--save_dir", type=str, default="expr/semantic_manipulation")
    parser.add_argument("--LMDB", type=str, default="data/celeba_hq/LMDB")
    parser.add_argument("--batch", type=int, default=16)
    parser.add_argument("--num_workers", type=int, default=2)
    parser.add_argument(
        "--part", type=str, default="Heavy_Makeup"
    )  # No_Beard Smiling ..
    parser.add_argument("--svm_train_iter", type=int, default=None)

    args = parser.parse_args()

    args.attr_celeba_hq = "semantic_manipulation/list_attr_celeba_hq.txt"

    args.train_lmdb = f"{args.LMDB}_train"
    args.val_lmdb = f"{args.LMDB}_val"
    args.test_lmdb = f"{args.LMDB}_test"

    model_name = args.ckpt.split("/")[-1].replace(".pt", "")

    os.makedirs(os.path.join(args.save_dir, args.part), exist_ok=True)
    args.inverted_npy = os.path.join(args.save_dir, f"{model_name}_inverted.npy")
    args.boundary = os.path.join(
        args.save_dir, args.part, f"{model_name}_{args.part}_boundary.npy"
    )
    print(args)

    ckpt = torch.load(args.ckpt)

    train_args = ckpt["train_args"]  # load arguments!

    model = Model()
    model.g_ema.load_state_dict(ckpt["g_ema"])
    model.e_ema.load_state_dict(ckpt["e_ema"])
    model = model.to(device)

    transform = transforms.Compose(
        [
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True),
        ]
    )

    train_dataset = MultiResolutionDataset(args.train_lmdb, transform, train_args.size)
    val_dataset = MultiResolutionDataset(args.val_lmdb, transform, train_args.size)
    test_dataset = MultiResolutionDataset(args.test_lmdb, transform, train_args.size)
    dataset = data.ConcatDataset([train_dataset, val_dataset, test_dataset])

    with torch.no_grad():
        # if inverted latent does not exist, save inverted latent as npy format
        if os.path.isfile(args.inverted_npy):
            print("inverted_npy exists!")
            latent_codes = np.load(args.inverted_npy)
        else:
            loader = data.DataLoader(
                dataset,
                batch_size=args.batch,
                sampler=data_sampler(dataset, shuffle=False),
                num_workers=args.num_workers,
                pin_memory=True,
            )
            latent_codes_enc = []
            for images in tqdm(loader):
                images = images.to(device)
                project_latent = model(images, "project_latent")

                latent_codes_enc.append(project_latent.cpu().numpy())
            latent_codes = np.concatenate(latent_codes_enc, axis=0)
            np.save(f"{args.inverted_npy}", latent_codes)

        latent_code_shape = latent_codes[0].shape
        print(f"latent_code_shape {latent_code_shape}")

        # flatten latent: Nx8x8x64 -> N x 8*8*64
        latent_codes = latent_codes.reshape(len(latent_codes), -1)

        # get boundary
        part = args.part

        if os.path.isfile(args.boundary):
            print(f"{part} boundary exists!")
            boundary_infos = torch.load(args.boundary)
            boundary = boundary_infos["boundary"]
            part1_indexes = boundary_infos["part1_indexes"]
            part2_indexes = boundary_infos["part2_indexes"]

        else:
            ############################################
            # get positive and negative indices by ranking using pretrained network (stylegan1)
            # refer to https://github.com/NVlabs/stylegan/tree/master/metrics/linear_separability.py

            part_pos_neg_indices = [
                "Male",
                "Smiling",
                "Attractive",
                "Wavy_Hair",
                "Young",
                "5_o_Clock_Shadow",
                "Arched_Eyebrows",
                "Bags_Under_Eyes",
                "Bald",
                "Bangs",
                "Big_Lips",
                "Big_Nose",
                "Black_Hair",
                "Blond_Hair",
                "Blurry",
                "Brown_Hair",
                "Bushy_Eyebrows",
                "Chubby",
                "Double_Chin",
                "Eyeglasses",
                "Goatee",
                "Gray_Hair",
                "Heavy_Makeup",
                "High_Cheekbones",
                "Mouth_Slightly_Open",
                "Mustache",
                "Narrow_Eyes",
                "No_Beard",
                "Oval_Face",
                "Pale_Skin",
                "Pointy_Nose",
                "Receding_Hairline",
                "Rosy_Cheeks",
                "Sideburns",
                "Straight_Hair",
                "Wearing_Earrings",
                "Wearing_Hat",
                "Wearing_Lipstick",
                "Wearing_Necklace",
                "Wearing_Necktie",
            ]

            pruned_index = part_pos_neg_indices.index(part)
            part1_indexes = np.load(
                f"semantic_manipulation/{pruned_index}_pos_indices.npy"
            )
            part2_indexes = np.load(
                f"semantic_manipulation/{pruned_index}_neg_indices.npy"
            )

            # get boundary using two parts.
            testset_ratio = 0.1
            np.random.shuffle(part1_indexes)
            np.random.shuffle(part2_indexes)

            positive_len = len(part1_indexes)
            negative_len = len(part2_indexes)

            positive_train = latent_codes[
                part1_indexes[int(positive_len * testset_ratio) :]
            ]
            positive_val = latent_codes[
                part1_indexes[: int(positive_len * testset_ratio)]
            ]

            negative_train = latent_codes[
                part2_indexes[int(negative_len * testset_ratio) :]
            ]
            negative_val = latent_codes[
                part2_indexes[: int(negative_len * testset_ratio)]
            ]

            # Training set.
            train_data = np.concatenate([positive_train, negative_train], axis=0)
            train_label = np.concatenate(
                [
                    np.ones(len(positive_train), dtype=np.int),
                    np.zeros(len(negative_train), dtype=np.int),
                ],
                axis=0,
            )

            # Validation set.
            val_data = np.concatenate([positive_val, negative_val], axis=0)
            val_label = np.concatenate(
                [
                    np.ones(len(positive_val), dtype=np.int),
                    np.zeros(len(negative_val), dtype=np.int),
                ],
                axis=0,
            )

            print(
                f"positive_train: {len(positive_train)}, negative_train:{len(negative_train)}, positive_val:{len(positive_val)}, negative_val:{len(negative_val)}"
            )

            print(f"Training boundary. {datetime.datetime.now()}")

            if args.svm_train_iter:
                clf = svm.SVC(kernel="linear", max_iter=args.svm_train_iter)
            else:
                clf = svm.SVC(kernel="linear")
            classifier = clf.fit(train_data, train_label)
            print(f"Finish training. {datetime.datetime.now()}")

            print(f"validate boundary.")
            val_prediction = classifier.predict(val_data)
            correct_num = np.sum(val_label == val_prediction)

            print(
                f"Accuracy for validation set: "
                f"{correct_num} / {len(val_data)} = "
                f"{correct_num / (len(val_data)):.6f}"
            )

            print("classifier.coef_.shape", classifier.coef_.shape)
            boundary = classifier.coef_.reshape(1, -1).astype(np.float32)
            boundary = boundary / np.linalg.norm(boundary)
            boundary = boundary.reshape(latent_code_shape)
            print("boundary.shape", boundary.shape)

            boundary = torch.from_numpy(boundary).float()

            torch.save(
                {
                    "boundary": boundary,
                    "part1_indexes": part1_indexes,
                    "part2_indexes": part2_indexes,
                },
                args.boundary,
            )

        with open(args.attr_celeba_hq, "r") as fp:
            total_number = int(fp.readline())
            print(f"{total_number} images, {len(latent_codes)} latent_codes")
            assert total_number == len(latent_codes)
            attrs = fp.readline().strip().split(" ")

            # print(attrs) # ['5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose', 'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace', 'Wearing_Necktie', 'Young']
            # Please note that the order is different from part_pos_neg_indices

            for i, attr in enumerate(attrs):
                if attr == args.part:
                    part_index = i
                    print(part, part_index)
                    break

            read_line = fp.readline()
            GT_part1_indexes = []  # part1: Heavy_Makeup
            GT_part2_indexes = []  # part2: others

            img_i = 0
            pivot = 0
            f_names = sorted(list(os.listdir("data/celeba_hq/raw_images/test/images/")))

            while read_line:
                parse_info = read_line.replace("  ", " ").strip().split(" ")
                attr_info = parse_info[1:]
                f_name = parse_info[0]  # first element is filename

                if f_name == f_names[pivot]:
                    if attr_info[part_index] == "1":
                        GT_part1_indexes.append(pivot)
                    else:
                        GT_part2_indexes.append(pivot)

                    pivot += 1
                    if pivot == 500:
                        break

                img_i += 1
                read_line = fp.readline()

        test_part1_indexes = GT_part1_indexes
        test_part2_indexes = GT_part2_indexes

        part1_loader = data.DataLoader(
            test_dataset,
            batch_size=args.batch,
            sampler=Sampler_with_index(test_part1_indexes),
        )
        part1_loader = sample_data(part1_loader)

        part2_loader = data.DataLoader(
            test_dataset,
            batch_size=args.batch,
            sampler=Sampler_with_index(test_part2_indexes),
        )

        part2_loader = sample_data(part2_loader)
        part2_img = next(part2_loader).to(device)

        # heavy makup
        ref_p_x, ref_p_y, width, height = 2, 5, 4, 2

        mask = (ref_p_x, ref_p_y, width, height)
        mask = torch.zeros(part2_img.shape[0], 64, 8, 8)
        mask[:, :, ref_p_y : ref_p_y + height, ref_p_x : ref_p_x + width] = 1

        part2_to_part1 = model(
            part2_img, "transform_to_other_part", -boundary, mask=mask
        )

        utils.save_image(
            torch.cat([part2_img, part2_to_part1]),
            f"{args.save_dir}/{part}/{model_name}_others_to_{part}.png",
            nrow=args.batch,
            normalize=True,
            range=(-1, 1),
        )

        # heavy makup
        s1 = np.array(list(range(13, 16 * 11, 16)))[[0, 3, 6, -1]]
        s2 = np.array(list(range(15, 16 * 11, 16)))[[0, 3, 6, -1]]
        ratio = train_args.size // train_args.latent_spatial_size

        for img, s_n in zip(
            torch.cat([part2_img, part2_to_part1])[
                np.concatenate((s1, s2), axis=0).tolist()
            ],
            [
                "img1_ori",
                "img1_global",
                "img1_local",
                "img1_recon",
                "img2_ori",
                "img2_global",
                "img2_local",
                "img2_recon",
            ],
        ):

            if "local" in s_n:
                img = torch.clamp(img, min=-1.0, max=1.0)
                img = (img + 1) / 2
                img = img.cpu()
                img = transforms.ToPILImage()(img)
                img = np.asarray(img)
                img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
                img = cv2.rectangle(
                    img,
                    (ref_p_x * ratio, ref_p_y * ratio),
                    ((ref_p_x + width) * ratio, (ref_p_y + height) * ratio),
                    (102, 255, 255),
                    1,
                )
                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
                img = transforms.ToTensor()(img)
                img = (img - 0.5) * 2

            utils.save_image(
                img,
                f"{args.save_dir}/{part}/{part}_{s_n}.png",
                nrow=args.batch,
                normalize=True,
                range=(-1, 1),
            )

StyleMapGAN Copyright (c) 2021-present NAVER Corp.  This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. # No_Beard Smiling .. # load arguments! # if inverted latent does not exist, save inverted latent as npy format # flatten latent: Nx8x8x64 -> N x 8*8*64 # get boundary ############################################ # get positive and negative indices by ranking using pretrained network (stylegan1) # refer to https://github.com/NVlabs/stylegan/tree/master/metrics/linear_separability.py # get boundary using two parts. # Training set. # Validation set. # print(attrs) # ['5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose', 'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace', 'Wearing_Necktie', 'Young'] # Please note that the order is different from part_pos_neg_indices # part1: Heavy_Makeup # part2: others # first element is filename # heavy makup # heavy makup