dcnv3.py

# --------------------------------------------------------
# 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 warnings
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_, constant_
from ..functions import DCNv3Function, dcnv3_core_pytorch


class to_channels_first(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x):
        return x.permute(0, 3, 1, 2)


class to_channels_last(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x):
        return x.permute(0, 2, 3, 1)


def build_norm_layer(dim,
                     norm_layer,
                     in_format='channels_last',
                     out_format='channels_last',
                     eps=1e-6):
    layers = []
    if norm_layer == 'BN':
        if in_format == 'channels_last':
            layers.append(to_channels_first())
        layers.append(nn.BatchNorm2d(dim))
        if out_format == 'channels_last':
            layers.append(to_channels_last())
    elif norm_layer == 'LN':
        if in_format == 'channels_first':
            layers.append(to_channels_last())
        layers.append(nn.LayerNorm(dim, eps=eps))
        if out_format == 'channels_first':
            layers.append(to_channels_first())
    else:
        raise NotImplementedError(
            f'build_norm_layer does not support {norm_layer}')
    return nn.Sequential(*layers)


def build_act_layer(act_layer):
    if act_layer == 'ReLU':
        return nn.ReLU(inplace=True)
    elif act_layer == 'SiLU':
        return nn.SiLU(inplace=True)
    elif act_layer == 'GELU':
        return nn.GELU()

    raise NotImplementedError(f'build_act_layer does not support {act_layer}')


def _is_power_of_2(n):
    if (not isinstance(n, int)) or (n < 0):
        raise ValueError(
            "invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))

    return (n & (n - 1) == 0) and n != 0


class CenterFeatureScaleModule(nn.Module):
    def forward(self,
                query,
                center_feature_scale_proj_weight,
                center_feature_scale_proj_bias):
        center_feature_scale = F.linear(query,
                                        weight=center_feature_scale_proj_weight,
                                        bias=center_feature_scale_proj_bias).sigmoid()
        return center_feature_scale


class DCNv3_pytorch(nn.Module):
    def __init__(
            self,
            channels=64,
            kernel_size=3,
            dw_kernel_size=None,
            stride=1,
            pad=1,
            dilation=1,
            group=4,
            offset_scale=1.0,
            act_layer='GELU',
            norm_layer='LN',
            center_feature_scale=False):
        """
        DCNv3 Module
        :param channels
        :param kernel_size
        :param stride
        :param pad
        :param dilation
        :param group
        :param offset_scale
        :param act_layer
        :param norm_layer
        """
        super().__init__()
        if channels % group != 0:
            raise ValueError(
                f'channels must be divisible by group, but got {channels} and {group}')
        _d_per_group = channels // group
        dw_kernel_size = dw_kernel_size if dw_kernel_size is not None else kernel_size
        # you'd better set _d_per_group to a power of 2 which is more efficient in our CUDA implementation
        if not _is_power_of_2(_d_per_group):
            warnings.warn(
                "You'd better set channels in DCNv3 to make the dimension of each attention head a power of 2 "
                "which is more efficient in our CUDA implementation.")

        self.offset_scale = offset_scale
        self.channels = channels
        self.kernel_size = kernel_size
        self.dw_kernel_size = dw_kernel_size
        self.stride = stride
        self.dilation = dilation
        self.pad = pad
        self.group = group
        self.group_channels = channels // group
        self.offset_scale = offset_scale
        self.center_feature_scale = center_feature_scale

        self.dw_conv = nn.Sequential(
            nn.Conv2d(
                channels,
                channels,
                kernel_size=dw_kernel_size,
                stride=1,
                padding=(dw_kernel_size - 1) // 2,
                groups=channels),
            build_norm_layer(
                channels,
                norm_layer,
                'channels_first',
                'channels_last'),
            build_act_layer(act_layer))
        self.offset = nn.Linear(
            channels,
            group * kernel_size * kernel_size * 2)
        self.mask = nn.Linear(
            channels,
            group * kernel_size * kernel_size)
        self.input_proj = nn.Linear(channels, channels)
        self.output_proj = nn.Linear(channels, channels)
        self._reset_parameters()
        
        if center_feature_scale:
            self.center_feature_scale_proj_weight = nn.Parameter(
                torch.zeros((group, channels), dtype=torch.float))
            self.center_feature_scale_proj_bias = nn.Parameter(
                torch.tensor(0.0, dtype=torch.float).view((1,)).repeat(group, ))
            self.center_feature_scale_module = CenterFeatureScaleModule()

    def _reset_parameters(self):
        constant_(self.offset.weight.data, 0.)
        constant_(self.offset.bias.data, 0.)
        constant_(self.mask.weight.data, 0.)
        constant_(self.mask.bias.data, 0.)
        xavier_uniform_(self.input_proj.weight.data)
        constant_(self.input_proj.bias.data, 0.)
        xavier_uniform_(self.output_proj.weight.data)
        constant_(self.output_proj.bias.data, 0.)

    def forward(self, input):
        """
        :param query                       (N, H, W, C)
        :return output                     (N, H, W, C)
        """
        N, H, W, _ = input.shape

        x = self.input_proj(input)
        x_proj = x

        x1 = input.permute(0, 3, 1, 2)
        x1 = self.dw_conv(x1)
        offset = self.offset(x1)
        mask = self.mask(x1).reshape(N, H, W, self.group, -1)
        mask = F.softmax(mask, -1).reshape(N, H, W, -1)

        x = dcnv3_core_pytorch(
            x, offset, mask,
            self.kernel_size, self.kernel_size,
            self.stride, self.stride,
            self.pad, self.pad,
            self.dilation, self.dilation,
            self.group, self.group_channels,
            self.offset_scale)
        if self.center_feature_scale:
            center_feature_scale = self.center_feature_scale_module(
                x1, self.center_feature_scale_proj_weight, self.center_feature_scale_proj_bias)
            # N, H, W, groups -> N, H, W, groups, 1 -> N, H, W, groups, _d_per_group -> N, H, W, channels
            center_feature_scale = center_feature_scale[..., None].repeat(
                1, 1, 1, 1, self.channels // self.group).flatten(-2)
            x = x * (1 - center_feature_scale) + x_proj * center_feature_scale
        x = self.output_proj(x)

        return x


class DCNv3(nn.Module):
    def __init__(
            self,
            channels=64,
            kernel_size=3,
            dw_kernel_size=None,
            stride=1,
            pad=1,
            dilation=1,
            group=4,
            offset_scale=1.0,
            act_layer='GELU',
            norm_layer='LN',
            center_feature_scale=False):
        """
        DCNv3 Module
        :param channels
        :param kernel_size
        :param stride
        :param pad
        :param dilation
        :param group
        :param offset_scale
        :param act_layer
        :param norm_layer
        """
        super().__init__()
        if channels % group != 0:
            raise ValueError(
                f'channels must be divisible by group, but got {channels} and {group}')
        _d_per_group = channels // group
        dw_kernel_size = dw_kernel_size if dw_kernel_size is not None else kernel_size
        # you'd better set _d_per_group to a power of 2 which is more efficient in our CUDA implementation
        if not _is_power_of_2(_d_per_group):
            warnings.warn(
                "You'd better set channels in DCNv3 to make the dimension of each attention head a power of 2 "
                "which is more efficient in our CUDA implementation.")

        self.offset_scale = offset_scale
        self.channels = channels
        self.kernel_size = kernel_size
        self.dw_kernel_size = dw_kernel_size
        self.stride = stride
        self.dilation = dilation
        self.pad = pad
        self.group = group
        self.group_channels = channels // group
        self.offset_scale = offset_scale
        self.center_feature_scale = center_feature_scale
        
        self.dw_conv = nn.Sequential(
            nn.Conv2d(
                channels,
                channels,
                kernel_size=dw_kernel_size,
                stride=1,
                padding=(dw_kernel_size - 1) // 2,
                groups=channels),
            build_norm_layer(
                channels,
                norm_layer,
                'channels_first',
                'channels_last'),
            build_act_layer(act_layer))
        self.offset = nn.Linear(
            channels,
            group * kernel_size * kernel_size * 2)
        self.mask = nn.Linear(
            channels,
            group * kernel_size * kernel_size)
        self.input_proj = nn.Linear(channels, channels)
        self.output_proj = nn.Linear(channels, channels)
        self._reset_parameters()
        
        if center_feature_scale:
            self.center_feature_scale_proj_weight = nn.Parameter(
                torch.zeros((group, channels), dtype=torch.float))
            self.center_feature_scale_proj_bias = nn.Parameter(
                torch.tensor(0.0, dtype=torch.float).view((1,)).repeat(group, ))
            self.center_feature_scale_module = CenterFeatureScaleModule()

    def _reset_parameters(self):
        constant_(self.offset.weight.data, 0.)
        constant_(self.offset.bias.data, 0.)
        constant_(self.mask.weight.data, 0.)
        constant_(self.mask.bias.data, 0.)
        xavier_uniform_(self.input_proj.weight.data)
        constant_(self.input_proj.bias.data, 0.)
        xavier_uniform_(self.output_proj.weight.data)
        constant_(self.output_proj.bias.data, 0.)

    def forward(self, input):
        """
        :param query                       (N, H, W, C)
        :return output                     (N, H, W, C)
        """
        N, H, W, _ = input.shape

        x = self.input_proj(input)
        x_proj = x
        dtype = x.dtype

        x1 = input.permute(0, 3, 1, 2)
        x1 = self.dw_conv(x1)
        offset = self.offset(x1)
        mask = self.mask(x1).reshape(N, H, W, self.group, -1)
        mask = F.softmax(mask, -1).reshape(N, H, W, -1).type(dtype)

        x = DCNv3Function.apply(
            x, offset, mask,
            self.kernel_size, self.kernel_size,
            self.stride, self.stride,
            self.pad, self.pad,
            self.dilation, self.dilation,
            self.group, self.group_channels,
            self.offset_scale,
            256)
        
        if self.center_feature_scale:
            center_feature_scale = self.center_feature_scale_module(
                x1, self.center_feature_scale_proj_weight, self.center_feature_scale_proj_bias)
            # N, H, W, groups -> N, H, W, groups, 1 -> N, H, W, groups, _d_per_group -> N, H, W, channels
            center_feature_scale = center_feature_scale[..., None].repeat(
                1, 1, 1, 1, self.channels // self.group).flatten(-2)
            x = x * (1 - center_feature_scale) + x_proj * center_feature_scale
        x = self.output_proj(x)

        return x