Spaces:
Paused
Paused
File size: 9,519 Bytes
3d49622 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@Author : Peike Li
@Contact : [email protected]
@File : ocnet.py
@Time : 8/4/19 3:36 PM
@Desc :
@License : This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
import functools
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F
from modules import InPlaceABNSync
BatchNorm2d = functools.partial(InPlaceABNSync, activation='none')
class _SelfAttentionBlock(nn.Module):
'''
The basic implementation for self-attention block/non-local block
Input:
N X C X H X W
Parameters:
in_channels : the dimension of the input feature map
key_channels : the dimension after the key/query transform
value_channels : the dimension after the value transform
scale : choose the scale to downsample the input feature maps (save memory cost)
Return:
N X C X H X W
position-aware context features.(w/o concate or add with the input)
'''
def __init__(self, in_channels, key_channels, value_channels, out_channels=None, scale=1):
super(_SelfAttentionBlock, self).__init__()
self.scale = scale
self.in_channels = in_channels
self.out_channels = out_channels
self.key_channels = key_channels
self.value_channels = value_channels
if out_channels == None:
self.out_channels = in_channels
self.pool = nn.MaxPool2d(kernel_size=(scale, scale))
self.f_key = nn.Sequential(
nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels,
kernel_size=1, stride=1, padding=0),
InPlaceABNSync(self.key_channels),
)
self.f_query = self.f_key
self.f_value = nn.Conv2d(in_channels=self.in_channels, out_channels=self.value_channels,
kernel_size=1, stride=1, padding=0)
self.W = nn.Conv2d(in_channels=self.value_channels, out_channels=self.out_channels,
kernel_size=1, stride=1, padding=0)
nn.init.constant(self.W.weight, 0)
nn.init.constant(self.W.bias, 0)
def forward(self, x):
batch_size, h, w = x.size(0), x.size(2), x.size(3)
if self.scale > 1:
x = self.pool(x)
value = self.f_value(x).view(batch_size, self.value_channels, -1)
value = value.permute(0, 2, 1)
query = self.f_query(x).view(batch_size, self.key_channels, -1)
query = query.permute(0, 2, 1)
key = self.f_key(x).view(batch_size, self.key_channels, -1)
sim_map = torch.matmul(query, key)
sim_map = (self.key_channels ** -.5) * sim_map
sim_map = F.softmax(sim_map, dim=-1)
context = torch.matmul(sim_map, value)
context = context.permute(0, 2, 1).contiguous()
context = context.view(batch_size, self.value_channels, *x.size()[2:])
context = self.W(context)
if self.scale > 1:
context = F.upsample(input=context, size=(h, w), mode='bilinear', align_corners=True)
return context
class SelfAttentionBlock2D(_SelfAttentionBlock):
def __init__(self, in_channels, key_channels, value_channels, out_channels=None, scale=1):
super(SelfAttentionBlock2D, self).__init__(in_channels,
key_channels,
value_channels,
out_channels,
scale)
class BaseOC_Module(nn.Module):
"""
Implementation of the BaseOC module
Parameters:
in_features / out_features: the channels of the input / output feature maps.
dropout: we choose 0.05 as the default value.
size: you can apply multiple sizes. Here we only use one size.
Return:
features fused with Object context information.
"""
def __init__(self, in_channels, out_channels, key_channels, value_channels, dropout, sizes=([1])):
super(BaseOC_Module, self).__init__()
self.stages = []
self.stages = nn.ModuleList(
[self._make_stage(in_channels, out_channels, key_channels, value_channels, size) for size in sizes])
self.conv_bn_dropout = nn.Sequential(
nn.Conv2d(2 * in_channels, out_channels, kernel_size=1, padding=0),
InPlaceABNSync(out_channels),
nn.Dropout2d(dropout)
)
def _make_stage(self, in_channels, output_channels, key_channels, value_channels, size):
return SelfAttentionBlock2D(in_channels,
key_channels,
value_channels,
output_channels,
size)
def forward(self, feats):
priors = [stage(feats) for stage in self.stages]
context = priors[0]
for i in range(1, len(priors)):
context += priors[i]
output = self.conv_bn_dropout(torch.cat([context, feats], 1))
return output
class BaseOC_Context_Module(nn.Module):
"""
Output only the context features.
Parameters:
in_features / out_features: the channels of the input / output feature maps.
dropout: specify the dropout ratio
fusion: We provide two different fusion method, "concat" or "add"
size: we find that directly learn the attention weights on even 1/8 feature maps is hard.
Return:
features after "concat" or "add"
"""
def __init__(self, in_channels, out_channels, key_channels, value_channels, dropout, sizes=([1])):
super(BaseOC_Context_Module, self).__init__()
self.stages = []
self.stages = nn.ModuleList(
[self._make_stage(in_channels, out_channels, key_channels, value_channels, size) for size in sizes])
self.conv_bn_dropout = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0),
InPlaceABNSync(out_channels),
)
def _make_stage(self, in_channels, output_channels, key_channels, value_channels, size):
return SelfAttentionBlock2D(in_channels,
key_channels,
value_channels,
output_channels,
size)
def forward(self, feats):
priors = [stage(feats) for stage in self.stages]
context = priors[0]
for i in range(1, len(priors)):
context += priors[i]
output = self.conv_bn_dropout(context)
return output
class ASP_OC_Module(nn.Module):
def __init__(self, features, out_features=256, dilations=(12, 24, 36)):
super(ASP_OC_Module, self).__init__()
self.context = nn.Sequential(nn.Conv2d(features, out_features, kernel_size=3, padding=1, dilation=1, bias=True),
InPlaceABNSync(out_features),
BaseOC_Context_Module(in_channels=out_features, out_channels=out_features,
key_channels=out_features // 2, value_channels=out_features,
dropout=0, sizes=([2])))
self.conv2 = nn.Sequential(nn.Conv2d(features, out_features, kernel_size=1, padding=0, dilation=1, bias=False),
InPlaceABNSync(out_features))
self.conv3 = nn.Sequential(
nn.Conv2d(features, out_features, kernel_size=3, padding=dilations[0], dilation=dilations[0], bias=False),
InPlaceABNSync(out_features))
self.conv4 = nn.Sequential(
nn.Conv2d(features, out_features, kernel_size=3, padding=dilations[1], dilation=dilations[1], bias=False),
InPlaceABNSync(out_features))
self.conv5 = nn.Sequential(
nn.Conv2d(features, out_features, kernel_size=3, padding=dilations[2], dilation=dilations[2], bias=False),
InPlaceABNSync(out_features))
self.conv_bn_dropout = nn.Sequential(
nn.Conv2d(out_features * 5, out_features, kernel_size=1, padding=0, dilation=1, bias=False),
InPlaceABNSync(out_features),
nn.Dropout2d(0.1)
)
def _cat_each(self, feat1, feat2, feat3, feat4, feat5):
assert (len(feat1) == len(feat2))
z = []
for i in range(len(feat1)):
z.append(torch.cat((feat1[i], feat2[i], feat3[i], feat4[i], feat5[i]), 1))
return z
def forward(self, x):
if isinstance(x, Variable):
_, _, h, w = x.size()
elif isinstance(x, tuple) or isinstance(x, list):
_, _, h, w = x[0].size()
else:
raise RuntimeError('unknown input type')
feat1 = self.context(x)
feat2 = self.conv2(x)
feat3 = self.conv3(x)
feat4 = self.conv4(x)
feat5 = self.conv5(x)
if isinstance(x, Variable):
out = torch.cat((feat1, feat2, feat3, feat4, feat5), 1)
elif isinstance(x, tuple) or isinstance(x, list):
out = self._cat_each(feat1, feat2, feat3, feat4, feat5)
else:
raise RuntimeError('unknown input type')
output = self.conv_bn_dropout(out)
return output
|