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| """
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| @Author : Peike Li
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| @Contact : peike.li@yahoo.com
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| @File : ocnet.py
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| @Time : 8/4/19 3:36 PM
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| @Desc :
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| @License : This source code is licensed under the license found in the
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| LICENSE file in the root directory of this source tree.
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| """
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| import functools
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| import torch
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| import torch.nn as nn
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| from torch.autograd import Variable
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| from torch.nn import functional as F
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| from modules import InPlaceABNSync
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| BatchNorm2d = functools.partial(InPlaceABNSync, activation='none')
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| class _SelfAttentionBlock(nn.Module):
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| '''
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| The basic implementation for self-attention block/non-local block
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| Input:
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| N X C X H X W
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| Parameters:
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| in_channels : the dimension of the input feature map
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| key_channels : the dimension after the key/query transform
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| value_channels : the dimension after the value transform
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| scale : choose the scale to downsample the input feature maps (save memory cost)
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| Return:
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| N X C X H X W
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| position-aware context features.(w/o concate or add with the input)
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| '''
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| def __init__(self, in_channels, key_channels, value_channels, out_channels=None, scale=1):
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| super(_SelfAttentionBlock, self).__init__()
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| self.scale = scale
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| self.in_channels = in_channels
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| self.out_channels = out_channels
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| self.key_channels = key_channels
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| self.value_channels = value_channels
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| if out_channels == None:
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| self.out_channels = in_channels
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| self.pool = nn.MaxPool2d(kernel_size=(scale, scale))
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| self.f_key = nn.Sequential(
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| nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels,
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| kernel_size=1, stride=1, padding=0),
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| InPlaceABNSync(self.key_channels),
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| )
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| self.f_query = self.f_key
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| self.f_value = nn.Conv2d(in_channels=self.in_channels, out_channels=self.value_channels,
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| kernel_size=1, stride=1, padding=0)
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| self.W = nn.Conv2d(in_channels=self.value_channels, out_channels=self.out_channels,
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| kernel_size=1, stride=1, padding=0)
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| nn.init.constant(self.W.weight, 0)
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| nn.init.constant(self.W.bias, 0)
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| def forward(self, x):
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| batch_size, h, w = x.size(0), x.size(2), x.size(3)
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| if self.scale > 1:
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| x = self.pool(x)
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| value = self.f_value(x).view(batch_size, self.value_channels, -1)
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| value = value.permute(0, 2, 1)
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| query = self.f_query(x).view(batch_size, self.key_channels, -1)
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| query = query.permute(0, 2, 1)
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| key = self.f_key(x).view(batch_size, self.key_channels, -1)
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| sim_map = torch.matmul(query, key)
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| sim_map = (self.key_channels ** -.5) * sim_map
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| sim_map = F.softmax(sim_map, dim=-1)
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| context = torch.matmul(sim_map, value)
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| context = context.permute(0, 2, 1).contiguous()
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| context = context.view(batch_size, self.value_channels, *x.size()[2:])
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| context = self.W(context)
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| if self.scale > 1:
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| context = F.upsample(input=context, size=(h, w), mode='bilinear', align_corners=True)
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| return context
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| class SelfAttentionBlock2D(_SelfAttentionBlock):
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| def __init__(self, in_channels, key_channels, value_channels, out_channels=None, scale=1):
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| super(SelfAttentionBlock2D, self).__init__(in_channels,
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| key_channels,
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| value_channels,
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| out_channels,
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| scale)
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| class BaseOC_Module(nn.Module):
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| """
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| Implementation of the BaseOC module
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| Parameters:
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| in_features / out_features: the channels of the input / output feature maps.
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| dropout: we choose 0.05 as the default value.
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| size: you can apply multiple sizes. Here we only use one size.
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| Return:
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| features fused with Object context information.
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| """
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| def __init__(self, in_channels, out_channels, key_channels, value_channels, dropout, sizes=([1])):
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| super(BaseOC_Module, self).__init__()
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| self.stages = []
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| self.stages = nn.ModuleList(
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| [self._make_stage(in_channels, out_channels, key_channels, value_channels, size) for size in sizes])
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| self.conv_bn_dropout = nn.Sequential(
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| nn.Conv2d(2 * in_channels, out_channels, kernel_size=1, padding=0),
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| InPlaceABNSync(out_channels),
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| nn.Dropout2d(dropout)
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| )
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| def _make_stage(self, in_channels, output_channels, key_channels, value_channels, size):
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| return SelfAttentionBlock2D(in_channels,
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| key_channels,
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| value_channels,
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| output_channels,
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| size)
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| def forward(self, feats):
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| priors = [stage(feats) for stage in self.stages]
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| context = priors[0]
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| for i in range(1, len(priors)):
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| context += priors[i]
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| output = self.conv_bn_dropout(torch.cat([context, feats], 1))
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| return output
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| class BaseOC_Context_Module(nn.Module):
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| """
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| Output only the context features.
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| Parameters:
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| in_features / out_features: the channels of the input / output feature maps.
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| dropout: specify the dropout ratio
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| fusion: We provide two different fusion method, "concat" or "add"
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| size: we find that directly learn the attention weights on even 1/8 feature maps is hard.
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| Return:
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| features after "concat" or "add"
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| """
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| def __init__(self, in_channels, out_channels, key_channels, value_channels, dropout, sizes=([1])):
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| super(BaseOC_Context_Module, self).__init__()
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| self.stages = []
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| self.stages = nn.ModuleList(
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| [self._make_stage(in_channels, out_channels, key_channels, value_channels, size) for size in sizes])
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| self.conv_bn_dropout = nn.Sequential(
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| nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0),
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| InPlaceABNSync(out_channels),
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| )
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| def _make_stage(self, in_channels, output_channels, key_channels, value_channels, size):
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| return SelfAttentionBlock2D(in_channels,
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| key_channels,
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| value_channels,
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| output_channels,
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| size)
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| def forward(self, feats):
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| priors = [stage(feats) for stage in self.stages]
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| context = priors[0]
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| for i in range(1, len(priors)):
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| context += priors[i]
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| output = self.conv_bn_dropout(context)
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| return output
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| class ASP_OC_Module(nn.Module):
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| def __init__(self, features, out_features=256, dilations=(12, 24, 36)):
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| super(ASP_OC_Module, self).__init__()
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| self.context = nn.Sequential(nn.Conv2d(features, out_features, kernel_size=3, padding=1, dilation=1, bias=True),
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| InPlaceABNSync(out_features),
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| BaseOC_Context_Module(in_channels=out_features, out_channels=out_features,
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| key_channels=out_features // 2, value_channels=out_features,
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| dropout=0, sizes=([2])))
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| self.conv2 = nn.Sequential(nn.Conv2d(features, out_features, kernel_size=1, padding=0, dilation=1, bias=False),
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| InPlaceABNSync(out_features))
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| self.conv3 = nn.Sequential(
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| nn.Conv2d(features, out_features, kernel_size=3, padding=dilations[0], dilation=dilations[0], bias=False),
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| InPlaceABNSync(out_features))
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| self.conv4 = nn.Sequential(
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| nn.Conv2d(features, out_features, kernel_size=3, padding=dilations[1], dilation=dilations[1], bias=False),
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| InPlaceABNSync(out_features))
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| self.conv5 = nn.Sequential(
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| nn.Conv2d(features, out_features, kernel_size=3, padding=dilations[2], dilation=dilations[2], bias=False),
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| InPlaceABNSync(out_features))
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| self.conv_bn_dropout = nn.Sequential(
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| nn.Conv2d(out_features * 5, out_features, kernel_size=1, padding=0, dilation=1, bias=False),
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| InPlaceABNSync(out_features),
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| nn.Dropout2d(0.1)
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| )
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| def _cat_each(self, feat1, feat2, feat3, feat4, feat5):
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| assert (len(feat1) == len(feat2))
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| z = []
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| for i in range(len(feat1)):
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| z.append(torch.cat((feat1[i], feat2[i], feat3[i], feat4[i], feat5[i]), 1))
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| return z
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| def forward(self, x):
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| if isinstance(x, Variable):
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| _, _, h, w = x.size()
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| elif isinstance(x, tuple) or isinstance(x, list):
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| _, _, h, w = x[0].size()
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| else:
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| raise RuntimeError('unknown input type')
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| feat1 = self.context(x)
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| feat2 = self.conv2(x)
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| feat3 = self.conv3(x)
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| feat4 = self.conv4(x)
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| feat5 = self.conv5(x)
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| if isinstance(x, Variable):
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| out = torch.cat((feat1, feat2, feat3, feat4, feat5), 1)
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| elif isinstance(x, tuple) or isinstance(x, list):
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| out = self._cat_each(feat1, feat2, feat3, feat4, feat5)
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| else:
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| raise RuntimeError('unknown input type')
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| output = self.conv_bn_dropout(out)
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| return output
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