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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 warnings
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_, constant_
from .dcnv3_func import 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 DCNv3_pytorch(nn.Module):
def __init__(
self, channels=64, kernel_size=3, stride=1,
pad=1, dilation=1, group=4, offset_scale=1.0,
act_layer='GELU', norm_layer='LN'):
"""
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
# 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.stride = stride
self.dilation = 1
self.pad = pad
self.group = group
self.group_channels = channels // group
self.offset_scale = offset_scale
self.dw_conv = nn.Sequential(
nn.Conv2d(
channels,
channels,
kernel_size=kernel_size,
stride=1,
padding=(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()
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)
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)
x = self.output_proj(x)
return x
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