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Oct. updates 2d2c1cf Oct 30, 2018
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import torch
import torch.nn as nn
import torch.nn.functional as F
from blocks import ConvBlock, LinearAttentionBlock, ProjectorBlock
from initialize import *
'''
attention before max-pooling
'''
class AttnVGG_before(nn.Module):
def __init__(self, im_size, num_classes, attention=True, normalize_attn=True, init='xavierUniform'):
super(AttnVGG_before, self).__init__()
self.attention = attention
# conv blocks
self.conv_block1 = ConvBlock(3, 64, 2)
self.conv_block2 = ConvBlock(64, 128, 2)
self.conv_block3 = ConvBlock(128, 256, 3)
self.conv_block4 = ConvBlock(256, 512, 3)
self.conv_block5 = ConvBlock(512, 512, 3)
self.conv_block6 = ConvBlock(512, 512, 2, pool=True)
self.dense = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=int(im_size/32), padding=0, bias=True)
# Projectors & Compatibility functions
if self.attention:
self.projector = ProjectorBlock(256, 512)
self.attn1 = LinearAttentionBlock(in_features=512, normalize_attn=normalize_attn)
self.attn2 = LinearAttentionBlock(in_features=512, normalize_attn=normalize_attn)
self.attn3 = LinearAttentionBlock(in_features=512, normalize_attn=normalize_attn)
# final classification layer
if self.attention:
self.classify = nn.Linear(in_features=512*3, out_features=num_classes, bias=True)
else:
self.classify = nn.Linear(in_features=512, out_features=num_classes, bias=True)
# initialize
if init == 'kaimingNormal':
weights_init_kaimingNormal(self)
elif init == 'kaimingUniform':
weights_init_kaimingUniform(self)
elif init == 'xavierNormal':
weights_init_xavierNormal(self)
elif init == 'xavierUniform':
weights_init_xavierUniform(self)
else:
raise NotImplementedError("Invalid type of initialization!")
def forward(self, x):
# feed forward
x = self.conv_block1(x)
x = self.conv_block2(x)
l1 = self.conv_block3(x) # /1
x = F.max_pool2d(l1, kernel_size=2, stride=2, padding=0) # /2
l2 = self.conv_block4(x) # /2
x = F.max_pool2d(l2, kernel_size=2, stride=2, padding=0) # /4
l3 = self.conv_block5(x) # /4
x = F.max_pool2d(l3, kernel_size=2, stride=2, padding=0) # /8
x = self.conv_block6(x) # /32
g = self.dense(x) # batch_sizex512x1x1
# pay attention
if self.attention:
c1, g1 = self.attn1(self.projector(l1), g)
c2, g2 = self.attn2(l2, g)
c3, g3 = self.attn3(l3, g)
g = torch.cat((g1,g2,g3), dim=1) # batch_sizexC
# classification layer
x = self.classify(g) # batch_sizexnum_classes
else:
c1, c2, c3 = None, None, None
x = self.classify(torch.squeeze(g))
return [x, c1, c2, c3]
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