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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@Author : Peike Li
@Contact : peike.li@yahoo.com
@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
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