non_leaking.py

import math

import torch
from torch.nn import functional as F

from GANs.ops import upfirdn2d


SYM6 = (
    0.015404109327027373,
    0.0034907120842174702,
    -0.11799011114819057,
    -0.048311742585633,
    0.4910559419267466,
    0.787641141030194,
    0.3379294217276218,
    -0.07263752278646252,
    -0.021060292512300564,
    0.04472490177066578,
    0.0017677118642428036,
    -0.007800708325034148,
)


def translate_mat(t_x, t_y):
    batch = t_x.shape[0]

    mat = torch.eye(3).unsqueeze(0).repeat(batch, 1, 1)
    translate = torch.stack((t_x, t_y), 1)
    mat[:, :2, 2] = translate

    return mat


def rotate_mat(theta):
    batch = theta.shape[0]

    mat = torch.eye(3).unsqueeze(0).repeat(batch, 1, 1)
    sin_t = torch.sin(theta)
    cos_t = torch.cos(theta)
    rot = torch.stack((cos_t, -sin_t, sin_t, cos_t), 1).view(batch, 2, 2)
    mat[:, :2, :2] = rot

    return mat


def scale_mat(s_x, s_y):
    batch = s_x.shape[0]

    mat = torch.eye(3).unsqueeze(0).repeat(batch, 1, 1)
    mat[:, 0, 0] = s_x
    mat[:, 1, 1] = s_y

    return mat


def translate3d_mat(t_x, t_y, t_z):
    batch = t_x.shape[0]

    mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
    translate = torch.stack((t_x, t_y, t_z), 1)
    mat[:, :3, 3] = translate

    return mat


def rotate3d_mat(axis, theta):
    batch = theta.shape[0]

    u_x, u_y, u_z = axis

    eye = torch.eye(3).unsqueeze(0)
    cross = torch.tensor([(0, -u_z, u_y), (u_z, 0, -u_x), (-u_y, u_x, 0)]).unsqueeze(0)
    outer = torch.tensor(axis)
    outer = (outer.unsqueeze(1) * outer).unsqueeze(0)

    sin_t = torch.sin(theta).view(-1, 1, 1)
    cos_t = torch.cos(theta).view(-1, 1, 1)

    rot = cos_t * eye + sin_t * cross + (1 - cos_t) * outer

    eye_4 = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
    eye_4[:, :3, :3] = rot

    return eye_4


def scale3d_mat(s_x, s_y, s_z):
    batch = s_x.shape[0]

    mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
    mat[:, 0, 0] = s_x
    mat[:, 1, 1] = s_y
    mat[:, 2, 2] = s_z

    return mat


def luma_flip_mat(axis, i):
    batch = i.shape[0]

    eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
    axis = torch.tensor(axis + (0,))
    flip = 2 * torch.ger(axis, axis) * i.view(-1, 1, 1)

    return eye - flip


def saturation_mat(axis, i):
    batch = i.shape[0]

    eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
    axis = torch.tensor(axis + (0,))
    axis = torch.ger(axis, axis)
    saturate = axis + (eye - axis) * i.view(-1, 1, 1)

    return saturate


def lognormal_sample(size, mean=0, std=1):
    return torch.empty(size).log_normal_(mean=mean, std=std)


def category_sample(size, categories):
    category = torch.tensor(categories)
    sample = torch.randint(high=len(categories), size=(size,))

    return category[sample]


def uniform_sample(size, low, high):
    return torch.empty(size).uniform_(low, high)


def normal_sample(size, mean=0, std=1):
    return torch.empty(size).normal_(mean, std)


def bernoulli_sample(size, p):
    return torch.empty(size).bernoulli_(p)


def random_mat_apply(p, transform, prev, eye):
    size = transform.shape[0]
    select = bernoulli_sample(size, p).view(size, 1, 1)
    select_transform = select * transform + (1 - select) * eye

    return select_transform @ prev


def sample_affine(p, size, height, width):
    G = torch.eye(3).unsqueeze(0).repeat(size, 1, 1)
    eye = G

    # flip
    param = category_sample(size, (0, 1))
    Gc = scale_mat(1 - 2.0 * param, torch.ones(size))
    G = random_mat_apply(p, Gc, G, eye)
    # print('flip', G, scale_mat(1 - 2.0 * param, torch.ones(size)), sep='\n')

    # 90 rotate
    param = category_sample(size, (0, 3))
    Gc = rotate_mat(-math.pi / 2 * param)
    G = random_mat_apply(p, Gc, G, eye)
    # print('90 rotate', G, rotate_mat(-math.pi / 2 * param), sep='\n')

    # integer translate
    param = uniform_sample(size, -0.125, 0.125)
    param_height = torch.round(param * height) / height
    param_width = torch.round(param * width) / width
    Gc = translate_mat(param_width, param_height)
    G = random_mat_apply(p, Gc, G, eye)
    # print('integer translate', G, translate_mat(param_width, param_height), sep='\n')

    # isotropic scale
    param = lognormal_sample(size, std=0.2 * math.log(2))
    Gc = scale_mat(param, param)
    G = random_mat_apply(p, Gc, G, eye)
    # print('isotropic scale', G, scale_mat(param, param), sep='\n')

    p_rot = 1 - math.sqrt(1 - p)

    # pre-rotate
    param = uniform_sample(size, -math.pi, math.pi)
    Gc = rotate_mat(-param)
    G = random_mat_apply(p_rot, Gc, G, eye)
    # print('pre-rotate', G, rotate_mat(-param), sep='\n')

    # anisotropic scale
    param = lognormal_sample(size, std=0.2 * math.log(2))
    Gc = scale_mat(param, 1 / param)
    G = random_mat_apply(p, Gc, G, eye)
    # print('anisotropic scale', G, scale_mat(param, 1 / param), sep='\n')

    # post-rotate
    param = uniform_sample(size, -math.pi, math.pi)
    Gc = rotate_mat(-param)
    G = random_mat_apply(p_rot, Gc, G, eye)
    # print('post-rotate', G, rotate_mat(-param), sep='\n')

    # fractional translate
    param = normal_sample(size, std=0.125)
    Gc = translate_mat(param, param)
    G = random_mat_apply(p, Gc, G, eye)
    # print('fractional translate', G, translate_mat(param, param), sep='\n')

    return G


def sample_color(p, size):
    C = torch.eye(4).unsqueeze(0).repeat(size, 1, 1)
    eye = C
    axis_val = 1 / math.sqrt(3)
    axis = (axis_val, axis_val, axis_val)

    # brightness
    param = normal_sample(size, std=0.2)
    Cc = translate3d_mat(param, param, param)
    C = random_mat_apply(p, Cc, C, eye)

    # contrast
    param = lognormal_sample(size, std=0.5 * math.log(2))
    Cc = scale3d_mat(param, param, param)
    C = random_mat_apply(p, Cc, C, eye)

    # luma flip
    param = category_sample(size, (0, 1))
    Cc = luma_flip_mat(axis, param)
    C = random_mat_apply(p, Cc, C, eye)

    # hue rotation
    param = uniform_sample(size, -math.pi, math.pi)
    Cc = rotate3d_mat(axis, param)
    C = random_mat_apply(p, Cc, C, eye)

    # saturation
    param = lognormal_sample(size, std=1 * math.log(2))
    Cc = saturation_mat(axis, param)
    C = random_mat_apply(p, Cc, C, eye)

    return C


def make_grid(shape, x0, x1, y0, y1, device):
    n, c, h, w = shape
    grid = torch.empty(n, h, w, 3, device=device)
    grid[:, :, :, 0] = torch.linspace(x0, x1, w, device=device)
    grid[:, :, :, 1] = torch.linspace(y0, y1, h, device=device).unsqueeze(-1)
    grid[:, :, :, 2] = 1

    return grid


def affine_grid(grid, mat):
    n, h, w, _ = grid.shape
    return (grid.view(n, h * w, 3) @ mat.transpose(1, 2)).view(n, h, w, 2)


def get_padding(G, height, width):
    extreme = (
        G[:, :2, :]
        @ torch.tensor([(-1.0, -1, 1), (-1, 1, 1), (1, -1, 1), (1, 1, 1)]).t()
    )

    size = torch.tensor((width, height))

    pad_low = (
        ((extreme.min(-1).values + 1) * size)
        .clamp(max=0)
        .abs()
        .ceil()
        .max(0)
        .values.to(torch.int64)
        .tolist()
    )
    pad_high = (
        (extreme.max(-1).values * size - size)
        .clamp(min=0)
        .ceil()
        .max(0)
        .values.to(torch.int64)
        .tolist()
    )

    return pad_low[0], pad_high[0], pad_low[1], pad_high[1]


def try_sample_affine_and_pad(img, p, pad_k, G=None):
    batch, _, height, width = img.shape

    G_try = G

    while True:
        if G is None:
            G_try = sample_affine(p, batch, height, width)

        pad_x1, pad_x2, pad_y1, pad_y2 = get_padding(
            torch.inverse(G_try), height, width
        )

        try:
            img_pad = F.pad(
                img,
                (pad_x1 + pad_k, pad_x2 + pad_k, pad_y1 + pad_k, pad_y2 + pad_k),
                mode="reflect",
            )

        except RuntimeError:
            continue

        break

    return img_pad, G_try, (pad_x1, pad_x2, pad_y1, pad_y2)


def random_apply_affine(img, p, G=None, antialiasing_kernel=SYM6):
    kernel = antialiasing_kernel
    len_k = len(kernel)
    pad_k = (len_k + 1) // 2

    kernel = torch.as_tensor(kernel)
    kernel = torch.ger(kernel, kernel).to(img)
    kernel_flip = torch.flip(kernel, (0, 1))

    img_pad, G, (pad_x1, pad_x2, pad_y1, pad_y2) = try_sample_affine_and_pad(
        img, p, pad_k, G
    )

    p_ux1 = pad_x1
    p_ux2 = pad_x2 + 1
    p_uy1 = pad_y1
    p_uy2 = pad_y2 + 1
    w_p = img_pad.shape[3] - len_k + 1
    h_p = img_pad.shape[2] - len_k + 1
    h_o = img.shape[2]
    w_o = img.shape[3]

    img_2x = upfirdn2d(img_pad, kernel_flip, up=2)

    grid = make_grid(
        img_2x.shape,
        -2 * p_ux1 / w_o - 1,
        2 * (w_p - p_ux1) / w_o - 1,
        -2 * p_uy1 / h_o - 1,
        2 * (h_p - p_uy1) / h_o - 1,
        device=img_2x.device,
    ).to(img_2x)
    grid = affine_grid(grid, torch.inverse(G)[:, :2, :].to(img_2x))
    grid = grid * torch.tensor(
        [w_o / w_p, h_o / h_p], device=grid.device
    ) + torch.tensor(
        [(w_o + 2 * p_ux1) / w_p - 1, (h_o + 2 * p_uy1) / h_p - 1], device=grid.device
    )

    img_affine = F.grid_sample(
        img_2x, grid, mode="bilinear", align_corners=False, padding_mode="zeros"
    )

    img_down = upfirdn2d(img_affine, kernel, down=2)

    end_y = -pad_y2 - 1
    if end_y == 0:
        end_y = img_down.shape[2]

    end_x = -pad_x2 - 1
    if end_x == 0:
        end_x = img_down.shape[3]

    img = img_down[:, :, pad_y1:end_y, pad_x1:end_x]

    return img, G


def apply_color(img, mat):
    batch = img.shape[0]
    img = img.permute(0, 2, 3, 1)
    mat_mul = mat[:, :3, :3].transpose(1, 2).view(batch, 1, 3, 3)
    mat_add = mat[:, :3, 3].view(batch, 1, 1, 3)
    img = img @ mat_mul + mat_add
    img = img.permute(0, 3, 1, 2)

    return img


def random_apply_color(img, p, C=None):
    if C is None:
        C = sample_color(p, img.shape[0])

    img = apply_color(img, C.to(img))

    return img, C


def augment(img, p, transform_matrix=(None, None)):
    img, G = random_apply_affine(img, p, transform_matrix[0])
    img, C = random_apply_color(img, p, transform_matrix[1])

    return img, (G, C)