Time_TravelRephotography/op/upfirdn2d.py

import os

import torch
from torch.autograd import Function
from torch.utils.cpp_extension import load


module_path = os.path.dirname(__file__)
#upfirdn2d_op = load(
#    'upfirdn2d',
#    sources=[
 #       os.path.join(module_path, 'upfirdn2d.cpp'),
 #       os.path.join(module_path, 'upfirdn2d_kernel.cu'),
 #   ],
#)


class UpFirDn2dBackward(Function):
    @staticmethod
    def forward(
        ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
    ):

        up_x, up_y = up
        down_x, down_y = down
        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad

        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)

        grad_input = upfirdn2d_op.upfirdn2d(
            grad_output,
            grad_kernel,
            down_x,
            down_y,
            up_x,
            up_y,
            g_pad_x0,
            g_pad_x1,
            g_pad_y0,
            g_pad_y1,
        )
        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])

        ctx.save_for_backward(kernel)

        pad_x0, pad_x1, pad_y0, pad_y1 = pad

        ctx.up_x = up_x
        ctx.up_y = up_y
        ctx.down_x = down_x
        ctx.down_y = down_y
        ctx.pad_x0 = pad_x0
        ctx.pad_x1 = pad_x1
        ctx.pad_y0 = pad_y0
        ctx.pad_y1 = pad_y1
        ctx.in_size = in_size
        ctx.out_size = out_size

        return grad_input

    @staticmethod
    def backward(ctx, gradgrad_input):
        kernel, = ctx.saved_tensors

        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)

        gradgrad_out = upfirdn2d_op.upfirdn2d(
            gradgrad_input,
            kernel,
            ctx.up_x,
            ctx.up_y,
            ctx.down_x,
            ctx.down_y,
            ctx.pad_x0,
            ctx.pad_x1,
            ctx.pad_y0,
            ctx.pad_y1,
        )
        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
        gradgrad_out = gradgrad_out.view(
            ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
        )

        return gradgrad_out, None, None, None, None, None, None, None, None


class UpFirDn2d(Function):
    @staticmethod
    def forward(ctx, input, kernel, up, down, pad):
        up_x, up_y = up
        down_x, down_y = down
        pad_x0, pad_x1, pad_y0, pad_y1 = pad

        kernel_h, kernel_w = kernel.shape
        batch, channel, in_h, in_w = input.shape
        ctx.in_size = input.shape

        input = input.reshape(-1, in_h, in_w, 1)

        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))

        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
        ctx.out_size = (out_h, out_w)

        ctx.up = (up_x, up_y)
        ctx.down = (down_x, down_y)
        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)

        g_pad_x0 = kernel_w - pad_x0 - 1
        g_pad_y0 = kernel_h - pad_y0 - 1
        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1

        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)

        out = upfirdn2d_op.upfirdn2d(
            input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
        )
        # out = out.view(major, out_h, out_w, minor)
        out = out.view(-1, channel, out_h, out_w)

        return out

    @staticmethod
    def backward(ctx, grad_output):
        kernel, grad_kernel = ctx.saved_tensors

        grad_input = UpFirDn2dBackward.apply(
            grad_output,
            kernel,
            grad_kernel,
            ctx.up,
            ctx.down,
            ctx.pad,
            ctx.g_pad,
            ctx.in_size,
            ctx.out_size,
        )

        return grad_input, None, None, None, None

@misc.profiled_function
def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
    """
    # Validate arguments.
    assert isinstance(x, torch.Tensor) and x.ndim == 4
    if f is None:
        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
    assert f.dtype == torch.float32 and not f.requires_grad
    batch_size, num_channels, in_height, in_width = x.shape
    upx, upy = _parse_scaling(up)
    downx, downy = _parse_scaling(down)
    padx0, padx1, pady0, pady1 = _parse_padding(padding)

    # Upsample by inserting zeros.
    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])

    # Pad or crop.
    x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
    x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)]

    # Setup filter.
    f = f * (gain ** (f.ndim / 2))
    f = f.to(x.dtype)
    if not flip_filter:
        f = f.flip(list(range(f.ndim)))

    # Convolve with the filter.
    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
    if f.ndim == 4:
        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
    else:
        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)

    # Downsample by throwing away pixels.
    x = x[:, :, ::downy, ::downx]
    return x

def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
    r"""Pad, upsample, filter, and downsample a batch of 2D images.
    Performs the following sequence of operations for each channel:
    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
    2. Pad the image with the specified number of zeros on each side (`padding`).
       Negative padding corresponds to cropping the image.
    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
       so that the footprint of all output pixels lies within the input image.
    4. Downsample the image by keeping every Nth pixel (`down`).
    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
    The fused op is considerably more efficient than performing the same calculation
    using standard PyTorch ops. It supports gradients of arbitrary order.
    Args:
        x:           Float32/float64/float16 input tensor of the shape
                     `[batch_size, num_channels, in_height, in_width]`.
        f:           Float32 FIR filter of the shape
                     `[filter_height, filter_width]` (non-separable),
                     `[filter_taps]` (separable), or
                     `None` (identity).
        up:          Integer upsampling factor. Can be a single int or a list/tuple
                     `[x, y]` (default: 1).
        down:        Integer downsampling factor. Can be a single int or a list/tuple
                     `[x, y]` (default: 1).
        padding:     Padding with respect to the upsampled image. Can be a single number
                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
                     (default: 0).
        flip_filter: False = convolution, True = correlation (default: False).
        gain:        Overall scaling factor for signal magnitude (default: 1).
        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
    Returns:
        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
    """
    assert isinstance(x, torch.Tensor)
    assert impl in ['ref', 'cuda']
    if impl == 'cuda' and x.device.type == 'cuda' and _init():
        return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
    return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)


def upfirdn2d_native(
    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
):
    _, in_h, in_w, minor = input.shape
    kernel_h, kernel_w = kernel.shape

    out = input.view(-1, in_h, 1, in_w, 1, minor)
    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
    out = out.view(-1, in_h * up_y, in_w * up_x, minor)

    out = F.pad(
        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
    )
    out = out[
        :,
        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
        :,
    ]

    out = out.permute(0, 3, 1, 2)
    out = out.reshape(
        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
    )
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(
        -1,
        minor,
        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
    )
    out = out.permute(0, 2, 3, 1)

    return out[:, ::down_y, ::down_x, :]