dcnv3_func.py

# --------------------------------------------------------
# InternImage
# Copyright (c) 2022 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------

from __future__ import absolute_import, division, print_function

import DCNv3
import torch
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.cuda.amp import custom_bwd, custom_fwd


class DCNv3Function(Function):
    @staticmethod
    @custom_fwd
    def forward(
            ctx, input, offset, mask,
            kernel_h, kernel_w, stride_h, stride_w,
            pad_h, pad_w, dilation_h, dilation_w,
            group, group_channels, offset_scale, im2col_step):
        ctx.kernel_h = kernel_h
        ctx.kernel_w = kernel_w
        ctx.stride_h = stride_h
        ctx.stride_w = stride_w
        ctx.pad_h = pad_h
        ctx.pad_w = pad_w
        ctx.dilation_h = dilation_h
        ctx.dilation_w = dilation_w
        ctx.group = group
        ctx.group_channels = group_channels
        ctx.offset_scale = offset_scale
        ctx.im2col_step = im2col_step
        output = DCNv3.dcnv3_forward(
            input, offset, mask, kernel_h,
            kernel_w, stride_h, stride_w, pad_h,
            pad_w, dilation_h, dilation_w, group,
            group_channels, offset_scale, ctx.im2col_step)
        ctx.save_for_backward(input, offset, mask)

        return output

    @staticmethod
    @once_differentiable
    @custom_bwd
    def backward(ctx, grad_output):
        input, offset, mask = ctx.saved_tensors
        grad_input, grad_offset, grad_mask = \
            DCNv3.dcnv3_backward(
                input, offset, mask, ctx.kernel_h,
                ctx.kernel_w, ctx.stride_h, ctx.stride_w, ctx.pad_h,
                ctx.pad_w, ctx.dilation_h, ctx.dilation_w, ctx.group,
                ctx.group_channels, ctx.offset_scale, grad_output.contiguous(), ctx.im2col_step)

        return grad_input, grad_offset, grad_mask, \
            None, None, None, None, None, None, None, None, None, None, None, None

    @staticmethod
    def symbolic(g, input, offset, mask, kernel_h, kernel_w, stride_h,
                 stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
                 group_channels, offset_scale, im2col_step):
        """Symbolic function for mmdeploy::DCNv3.

        Returns:
            DCNv3 op for onnx.
        """
        return g.op(
            'mmdeploy::TRTDCNv3',
            input,
            offset,
            mask,
            kernel_h_i=int(kernel_h),
            kernel_w_i=int(kernel_w),
            stride_h_i=int(stride_h),
            stride_w_i=int(stride_w),
            pad_h_i=int(pad_h),
            pad_w_i=int(pad_w),
            dilation_h_i=int(dilation_h),
            dilation_w_i=int(dilation_w),
            group_i=int(group),
            group_channels_i=int(group_channels),
            offset_scale_f=float(offset_scale),
            im2col_step_i=int(im2col_step),
        )


def _get_reference_points(spatial_shapes, device, kernel_h, kernel_w, dilation_h, dilation_w, pad_h=0, pad_w=0, stride_h=1, stride_w=1):
    _, H_, W_, _ = spatial_shapes
    H_out = (H_ - (dilation_h * (kernel_h - 1) + 1)) // stride_h + 1
    W_out = (W_ - (dilation_w * (kernel_w - 1) + 1)) // stride_w + 1

    ref_y, ref_x = torch.meshgrid(
        torch.linspace(
            # pad_h + 0.5,
            # H_ - pad_h - 0.5,
            (dilation_h * (kernel_h - 1)) // 2 + 0.5,
            (dilation_h * (kernel_h - 1)) // 2 + 0.5 + (H_out - 1) * stride_h,
            H_out,
            dtype=torch.float32,
            device=device),
        torch.linspace(
            # pad_w + 0.5,
            # W_ - pad_w - 0.5,
            (dilation_w * (kernel_w - 1)) // 2 + 0.5,
            (dilation_w * (kernel_w - 1)) // 2 + 0.5 + (W_out - 1) * stride_w,
            W_out,
            dtype=torch.float32,
            device=device))
    ref_y = ref_y.reshape(-1)[None] / H_
    ref_x = ref_x.reshape(-1)[None] / W_

    ref = torch.stack((ref_x, ref_y), -1).reshape(
        1, H_out, W_out, 1, 2)

    return ref


def _generate_dilation_grids(spatial_shapes, kernel_h, kernel_w, dilation_h, dilation_w, group, device):
    _, H_, W_, _ = spatial_shapes
    points_list = []
    x, y = torch.meshgrid(
        torch.linspace(
            -((dilation_w * (kernel_w - 1)) // 2),
            -((dilation_w * (kernel_w - 1)) // 2) +
            (kernel_w - 1) * dilation_w, kernel_w,
            dtype=torch.float32,
            device=device),
        torch.linspace(
            -((dilation_h * (kernel_h - 1)) // 2),
            -((dilation_h * (kernel_h - 1)) // 2) +
            (kernel_h - 1) * dilation_h, kernel_h,
            dtype=torch.float32,
            device=device))

    points_list.extend([x / W_, y / H_])
    grid = torch.stack(points_list, -1).reshape(-1, 1, 2).\
        repeat(1, group, 1).permute(1, 0, 2)
    grid = grid.reshape(1, 1, 1, group * kernel_h * kernel_w, 2)

    return grid


def dcnv3_core_pytorch(
        input, offset, mask, kernel_h,
        kernel_w, stride_h, stride_w, pad_h,
        pad_w, dilation_h, dilation_w, group,
        group_channels, offset_scale):
    # for debug and test only,
    # need to use cuda version instead
    input = F.pad(
        input,
        [0, 0, pad_h, pad_h, pad_w, pad_w])
    N_, H_in, W_in, _ = input.shape
    _, H_out, W_out, _ = offset.shape

    ref = _get_reference_points(
        input.shape, input.device, kernel_h, kernel_w, dilation_h, dilation_w, pad_h, pad_w, stride_h, stride_w)
    grid = _generate_dilation_grids(
        input.shape, kernel_h, kernel_w, dilation_h, dilation_w, group, input.device)
    spatial_norm = torch.tensor([W_in, H_in]).reshape(1, 1, 1, 2).\
        repeat(1, 1, 1, group*kernel_h*kernel_w).to(input.device)

    sampling_locations = (ref + grid * offset_scale).repeat(N_, 1, 1, 1, 1).flatten(3, 4) + \
        offset * offset_scale / spatial_norm

    P_ = kernel_h * kernel_w
    sampling_grids = 2 * sampling_locations - 1
    # N_, H_in, W_in, group*group_channels -> N_, H_in*W_in, group*group_channels -> N_, group*group_channels, H_in*W_in -> N_*group, group_channels, H_in, W_in
    input_ = input.view(N_, H_in*W_in, group*group_channels).transpose(1, 2).\
        reshape(N_*group, group_channels, H_in, W_in)
    # N_, H_out, W_out, group*P_*2 -> N_, H_out*W_out, group, P_, 2 -> N_, group, H_out*W_out, P_, 2 -> N_*group, H_out*W_out, P_, 2
    sampling_grid_ = sampling_grids.view(N_, H_out*W_out, group, P_, 2).transpose(1, 2).\
        flatten(0, 1)
    # N_*group, group_channels, H_out*W_out, P_
    sampling_input_ = F.grid_sample(
        input_, sampling_grid_, mode='bilinear', padding_mode='zeros', align_corners=False)

    # (N_, H_out, W_out, group*P_) -> N_, H_out*W_out, group, P_ -> (N_, group, H_out*W_out, P_) -> (N_*group, 1, H_out*W_out, P_)
    mask = mask.view(N_, H_out*W_out, group, P_).transpose(1, 2).\
        reshape(N_*group, 1, H_out*W_out, P_)
    output = (sampling_input_ * mask).sum(-1).view(N_,
                                                   group*group_channels, H_out*W_out)

    return output.transpose(1, 2).reshape(N_, H_out, W_out, -1).contiguous()