Spaces:
Sleeping
Sleeping
File size: 5,804 Bytes
251e479 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import CONV_LAYERS, ConvAWS2d, constant_init
from annotator.uniformer.mmcv.ops.deform_conv import deform_conv2d
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
@CONV_LAYERS.register_module(name='SAC')
class SAConv2d(ConvAWS2d):
"""SAC (Switchable Atrous Convolution)
This is an implementation of SAC in DetectoRS
(https://arxiv.org/pdf/2006.02334.pdf).
Args:
in_channels (int): Number of channels in the input image
out_channels (int): Number of channels produced by the convolution
kernel_size (int or tuple): Size of the convolving kernel
stride (int or tuple, optional): Stride of the convolution. Default: 1
padding (int or tuple, optional): Zero-padding added to both sides of
the input. Default: 0
padding_mode (string, optional): ``'zeros'``, ``'reflect'``,
``'replicate'`` or ``'circular'``. Default: ``'zeros'``
dilation (int or tuple, optional): Spacing between kernel elements.
Default: 1
groups (int, optional): Number of blocked connections from input
channels to output channels. Default: 1
bias (bool, optional): If ``True``, adds a learnable bias to the
output. Default: ``True``
use_deform: If ``True``, replace convolution with deformable
convolution. Default: ``False``.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
use_deform=False):
super().__init__(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias)
self.use_deform = use_deform
self.switch = nn.Conv2d(
self.in_channels, 1, kernel_size=1, stride=stride, bias=True)
self.weight_diff = nn.Parameter(torch.Tensor(self.weight.size()))
self.pre_context = nn.Conv2d(
self.in_channels, self.in_channels, kernel_size=1, bias=True)
self.post_context = nn.Conv2d(
self.out_channels, self.out_channels, kernel_size=1, bias=True)
if self.use_deform:
self.offset_s = nn.Conv2d(
self.in_channels,
18,
kernel_size=3,
padding=1,
stride=stride,
bias=True)
self.offset_l = nn.Conv2d(
self.in_channels,
18,
kernel_size=3,
padding=1,
stride=stride,
bias=True)
self.init_weights()
def init_weights(self):
constant_init(self.switch, 0, bias=1)
self.weight_diff.data.zero_()
constant_init(self.pre_context, 0)
constant_init(self.post_context, 0)
if self.use_deform:
constant_init(self.offset_s, 0)
constant_init(self.offset_l, 0)
def forward(self, x):
# pre-context
avg_x = F.adaptive_avg_pool2d(x, output_size=1)
avg_x = self.pre_context(avg_x)
avg_x = avg_x.expand_as(x)
x = x + avg_x
# switch
avg_x = F.pad(x, pad=(2, 2, 2, 2), mode='reflect')
avg_x = F.avg_pool2d(avg_x, kernel_size=5, stride=1, padding=0)
switch = self.switch(avg_x)
# sac
weight = self._get_weight(self.weight)
zero_bias = torch.zeros(
self.out_channels, device=weight.device, dtype=weight.dtype)
if self.use_deform:
offset = self.offset_s(avg_x)
out_s = deform_conv2d(x, offset, weight, self.stride, self.padding,
self.dilation, self.groups, 1)
else:
if (TORCH_VERSION == 'parrots'
or digit_version(TORCH_VERSION) < digit_version('1.5.0')):
out_s = super().conv2d_forward(x, weight)
elif digit_version(TORCH_VERSION) >= digit_version('1.8.0'):
# bias is a required argument of _conv_forward in torch 1.8.0
out_s = super()._conv_forward(x, weight, zero_bias)
else:
out_s = super()._conv_forward(x, weight)
ori_p = self.padding
ori_d = self.dilation
self.padding = tuple(3 * p for p in self.padding)
self.dilation = tuple(3 * d for d in self.dilation)
weight = weight + self.weight_diff
if self.use_deform:
offset = self.offset_l(avg_x)
out_l = deform_conv2d(x, offset, weight, self.stride, self.padding,
self.dilation, self.groups, 1)
else:
if (TORCH_VERSION == 'parrots'
or digit_version(TORCH_VERSION) < digit_version('1.5.0')):
out_l = super().conv2d_forward(x, weight)
elif digit_version(TORCH_VERSION) >= digit_version('1.8.0'):
# bias is a required argument of _conv_forward in torch 1.8.0
out_l = super()._conv_forward(x, weight, zero_bias)
else:
out_l = super()._conv_forward(x, weight)
out = switch * out_s + (1 - switch) * out_l
self.padding = ori_p
self.dilation = ori_d
# post-context
avg_x = F.adaptive_avg_pool2d(out, output_size=1)
avg_x = self.post_context(avg_x)
avg_x = avg_x.expand_as(out)
out = out + avg_x
return out
|