connecting_the_dots/torchext/functions.py

import torch
from . import ext_cpu
from . import ext_cuda


class NNFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, in0, in1):
        args = (in0, in1)
        if in0.is_cuda:
            out = ext_cuda.nn_cuda(*args)
        else:
            out = ext_cpu.nn_cpu(*args)
        return out

    @staticmethod
    def backward(ctx, grad_out):
        return None, None


def nn(in0, in1):
    return NNFunction.apply(in0, in1)


class CrossCheckFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, in0, in1):
        args = (in0, in1)
        if in0.is_cuda:
            out = ext_cuda.crosscheck_cuda(*args)
        else:
            out = ext_cpu.crosscheck_cpu(*args)
        return out

    @staticmethod
    def backward(ctx, grad_out):
        return None, None


def crosscheck(in0, in1):
    return CrossCheckFunction.apply(in0, in1)


class ProjNNFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, xyz0, xyz1, K, patch_size):
        args = (xyz0, xyz1, K, patch_size)
        if xyz0.is_cuda:
            out = ext_cuda.proj_nn_cuda(*args)
        else:
            out = ext_cpu.proj_nn_cpu(*args)
        return out

    @staticmethod
    def backward(ctx, grad_out):
        return None, None, None, None


def proj_nn(xyz0, xyz1, K, patch_size):
    return ProjNNFunction.apply(xyz0, xyz1, K, patch_size)


class XCorrVolFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, in0, in1, n_disps, block_size):
        args = (in0, in1, n_disps, block_size)
        if in0.is_cuda:
            out = ext_cuda.xcorrvol_cuda(*args)
        else:
            out = ext_cpu.xcorrvol_cpu(*args)
        return out

    @staticmethod
    def backward(ctx, grad_out):
        return None, None, None, None


def xcorrvol(in0, in1, n_disps, block_size):
    return XCorrVolFunction.apply(in0, in1, n_disps, block_size)


class PhotometricLossFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, es, ta, block_size, type, eps):
        args = (es, ta, block_size, type, eps)
        ctx.save_for_backward(es, ta)
        ctx.block_size = block_size
        ctx.type = type
        ctx.eps = eps
        if es.is_cuda:
            out = ext_cuda.photometric_loss_forward(*args)
        else:
            out = ext_cpu.photometric_loss_forward(*args)
        return out

    @staticmethod
    def backward(ctx, grad_out):
        es, ta = ctx.saved_tensors
        block_size = ctx.block_size
        type = ctx.type
        eps = ctx.eps
        args = (es, ta, grad_out.contiguous(), block_size, type, eps)
        if grad_out.is_cuda:
            grad_es = ext_cuda.photometric_loss_backward(*args)
        else:
            grad_es = ext_cpu.photometric_loss_backward(*args)
        return grad_es, None, None, None, None


def photometric_loss(es, ta, block_size, type='mse', eps=0.1):
    type = type.lower()
    if type == 'mse':
        type = 0
    elif type == 'sad':
        type = 1
    elif type == 'census_mse':
        type = 2
    elif type == 'census_sad':
        type = 3
    else:
        raise Exception('invalid loss type')
    return PhotometricLossFunction.apply(es, ta, block_size, type, eps)


def photometric_loss_pytorch(es, ta, block_size, type='mse', eps=0.1):
    type = type.lower()
    p = block_size // 2
    es_pad = torch.nn.functional.pad(es, (p, p, p, p), mode='replicate')
    ta_pad = torch.nn.functional.pad(ta, (p, p, p, p), mode='replicate')
    es_uf = torch.nn.functional.unfold(es_pad, kernel_size=block_size)
    ta_uf = torch.nn.functional.unfold(ta_pad, kernel_size=block_size)
    es_uf = es_uf.view(es.shape[0], es.shape[1], -1, es.shape[2], es.shape[3])
    ta_uf = ta_uf.view(ta.shape[0], ta.shape[1], -1, ta.shape[2], ta.shape[3])
    if type == 'mse':
        ref = (es_uf - ta_uf) ** 2
    elif type == 'sad':
        ref = torch.abs(es_uf - ta_uf)
    elif type == 'census_mse' or type == 'census_sad':
        des = es_uf - es.unsqueeze(2)
        dta = ta_uf - ta.unsqueeze(2)
        h_des = 0.5 * (1 + des / torch.sqrt(des * des + eps))
        h_dta = 0.5 * (1 + dta / torch.sqrt(dta * dta + eps))
        diff = h_des - h_dta
        if type == 'census_mse':
            ref = diff * diff
        elif type == 'census_sad':
            ref = torch.abs(diff)
    else:
        raise Exception('invalid loss type')
    ref = ref.view(es.shape[0], -1, es.shape[2], es.shape[3])
    ref = torch.sum(ref, dim=1, keepdim=True) / block_size ** 2
    return ref