def __init__(self,output_dim=6, dropout=False):
super(ResidualAttentionModel_92_Small, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias = False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True)
)
self.dropout = dropout
self.mpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.ResidualBlock1 = ResidualBlock(64, 256)
if self.dropout:
self.dp_1 = nn.Dropout(0.2)
self.dp_2 = nn.Dropout(0.0)
self.dp_3 = nn.Dropout(0.0)
self.attention_module1 = AttentionModule_stage1(256, 256)
self.ResidualBlock2 = ResidualBlock(256, 512, 2)
self.attention_module2 = AttentionModule_stage2(512, 512)
self.attention_module2_2 = AttentionModule_stage2(512, 512) # tbq add
self.ResidualBlock3 = ResidualBlock(512, 1024, 3)
self.mpool2 = nn.Sequential(
nn.BatchNorm2d(1024),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=8, stride=3)
)
self.fc = nn.Linear(1024,output_dim)
#self.softmax = nn.Softmax(dim=1)
for m in self.children():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight.data)
nn.init.xavier_uniforml_(m.bias.data)
def __init__(self,
in_channels,
out_channels,
size1=(28, 28),
size2=(14, 14)):
super(AttentionModule_stage2, self).__init__()
self.first_ResidualBlocks = ResidualBlock(in_channels, out_channels)
self.trunk_branches = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.mpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.softmax1_blocks = ResidualBlock(in_channels, out_channels)
self.skip1_connection_ResidualBlock = ResidualBlock(
in_channels, out_channels)
self.mpool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.softmax2_blocks = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.interpolation2 = nn.UpsamplingBilinear2d(size=size2)
self.softmax3_blocks = ResidualBlock(in_channels, out_channels)
self.interpolation1 = nn.UpsamplingBilinear2d(size=size1)
self.softmax4_blocks = nn.Sequential(
nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True),
nn.Conv2d(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias=False), nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias=False), nn.Sigmoid())
self.last_blocks = ResidualBlock(in_channels, out_channels)
def __init__(self, in_channels, out_channels, size=(8, 8)):
super(AttentionModule_stage3_cifar, self).__init__()
self.first_ResidualBlocks = ResidualBlock(in_channels, out_channels)
self.trunk_branches = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.middle_2r_blocks = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.conv1_1_blocks = nn.Sequential(
nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True),
nn.Conv2d(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias=False), nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias=False), nn.Sigmoid())
self.last_blocks = ResidualBlock(in_channels, out_channels)
def __init__(self, in_channels, out_channels, size=(8, 8)):
super(AttentionModule_stage2_cifar, self).__init__()
self.first_ResidualBlocks = ResidualBlock(in_channels, out_channels)
self.trunk_branches = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.mpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # 4*4
self.middle_2r_blocks = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.interpolation1 = nn.UpsamplingBilinear2d(size=size) # 8*8
self.conv1_1_blocks = nn.Sequential(
nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True),
nn.Conv2d(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias=False), nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias=False), nn.Sigmoid())
self.last_blocks = ResidualBlock(in_channels, out_channels)
def __init__(self, output_dim):
super(ResidualAttentionModel_92_32input_update, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True)
) # 32*32
# self.mpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # 16*16
self.ResidualBlock1 = ResidualBlock(32, 128) # 32*32
self.attention_module1 = AttentionModule_stage1_cifar(128, 128, size1=(32, 32), size2=(16, 16)) # 32*32
self.ResidualBlock2 = ResidualBlock(128, 256, 2) # 16*16
self.attention_module2 = AttentionModule_stage2_cifar(256, 256, size=(16, 16)) # 16*16
self.attention_module2_2 = AttentionModule_stage2_cifar(256, 256, size=(16, 16)) # 16*16 # tbq add
self.ResidualBlock3 = ResidualBlock(256, 512, 2) # 4*4
self.attention_module3 = AttentionModule_stage3_cifar(512, 512) # 8*8
self.attention_module3_2 = AttentionModule_stage3_cifar(512, 512) # 8*8 # tbq add
self.attention_module3_3 = AttentionModule_stage3_cifar(512, 512) # 8*8 # tbq add
self.ResidualBlock4 = ResidualBlock(512, 1024) # 8*8
self.ResidualBlock5 = ResidualBlock(1024, 1024) # 8*8
self.ResidualBlock6 = ResidualBlock(1024, 1024) # 8*8
self.mpool2 = nn.Sequential(
nn.BatchNorm2d(1024),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=8)
)
self.fc = nn.Linear(1024, output_dim)