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import torch
import torch.nn as nn
import torch._utils
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
def __init__(self, cls_num=0, scale=1):
super(SpatialGather_Module, self).__init__()
self.cls_num = cls_num
self.scale = scale
def forward(self, feats, probs):
batch_size, c, h, w = probs.size(0), probs.size(1), probs.size(2), probs.size(3)
probs = probs.view(batch_size, c, -1)
feats = feats.view(batch_size, feats.size(1), -1)
feats = feats.permute(0, 2, 1) # batch x hw x c
probs = F.softmax(self.scale * probs, dim=2) # batch x k x hw
ocr_context = torch.matmul(probs, feats) \
.permute(0, 2, 1).unsqueeze(3) # batch x k x c
return ocr_context
class SpatialOCR_Module(nn.Module):
"""
Implementation of the OCR module:
We aggregate the global object representation to update the representation for each pixel.
"""
def __init__(self,
in_channels,
key_channels,
out_channels,
scale=1,
dropout=0.1,
norm_layer=nn.BatchNorm2d,
align_corners=True):
super(SpatialOCR_Module, self).__init__()
self.object_context_block = ObjectAttentionBlock2D(in_channels, key_channels, scale,
norm_layer, align_corners)
_in_channels = 2 * in_channels
self.conv_bn_dropout = nn.Sequential(
nn.Conv2d(_in_channels, out_channels, kernel_size=1, padding=0, bias=False),
nn.Sequential(norm_layer(out_channels), nn.ReLU(inplace=True)),
nn.Dropout2d(dropout)
)
def forward(self, feats, proxy_feats):
context = self.object_context_block(feats, proxy_feats)
output = self.conv_bn_dropout(torch.cat([context, feats], 1))
return output
class ObjectAttentionBlock2D(nn.Module):
'''
The basic implementation for object context 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
scale : choose the scale to downsample the input feature maps (save memory cost)
bn_type : specify the bn type
Return:
N X C X H X W
'''
def __init__(self,
in_channels,
key_channels,
scale=1,
norm_layer=nn.BatchNorm2d,
align_corners=True):
super(ObjectAttentionBlock2D, self).__init__()
self.scale = scale
self.in_channels = in_channels
self.key_channels = key_channels
self.align_corners = align_corners
self.pool = nn.MaxPool2d(kernel_size=(scale, scale))
self.f_pixel = nn.Sequential(
nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels,
kernel_size=1, stride=1, padding=0, bias=False),
nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True)),
nn.Conv2d(in_channels=self.key_channels, out_channels=self.key_channels,
kernel_size=1, stride=1, padding=0, bias=False),
nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True))
)
self.f_object = nn.Sequential(
nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels,
kernel_size=1, stride=1, padding=0, bias=False),
nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True)),
nn.Conv2d(in_channels=self.key_channels, out_channels=self.key_channels,
kernel_size=1, stride=1, padding=0, bias=False),
nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True))
)
self.f_down = nn.Sequential(
nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels,
kernel_size=1, stride=1, padding=0, bias=False),
nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True))
)
self.f_up = nn.Sequential(
nn.Conv2d(in_channels=self.key_channels, out_channels=self.in_channels,
kernel_size=1, stride=1, padding=0, bias=False),
nn.Sequential(norm_layer(self.in_channels), nn.ReLU(inplace=True))
)
def forward(self, x, proxy):
batch_size, h, w = x.size(0), x.size(2), x.size(3)
if self.scale > 1:
x = self.pool(x)
query = self.f_pixel(x).view(batch_size, self.key_channels, -1)
query = query.permute(0, 2, 1)
key = self.f_object(proxy).view(batch_size, self.key_channels, -1)
value = self.f_down(proxy).view(batch_size, self.key_channels, -1)
value = value.permute(0, 2, 1)
sim_map = torch.matmul(query, key)
sim_map = (self.key_channels ** -.5) * sim_map
sim_map = F.softmax(sim_map, dim=-1)
# add bg context ...
context = torch.matmul(sim_map, value)
context = context.permute(0, 2, 1).contiguous()
context = context.view(batch_size, self.key_channels, *x.size()[2:])
context = self.f_up(context)
if self.scale > 1:
context = F.interpolate(input=context, size=(h, w),
mode='bilinear', align_corners=self.align_corners)
return context
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