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# --------------------------------------------------------
# InternImage
# Copyright (c) 2022 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn.functional as F
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()
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