def __init__(self):
super(ResidualAttentionModel_92, 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.mpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.residual_block1 = ResidualBlock(64, 256)
self.attention_module1 = AttentionModule_stage1(256, 256)
self.residual_block2 = ResidualBlock(256, 512, 2)
self.attention_module2 = AttentionModule_stage2(512, 512)
self.attention_module2_2 = AttentionModule_stage2(512, 512) # tbq add
self.residual_block3 = ResidualBlock(512, 1024, 2)
self.attention_module3 = AttentionModule_stage3(1024, 1024)
self.attention_module3_2 = AttentionModule_stage3(1024, 1024) # tbq add
self.attention_module3_3 = AttentionModule_stage3(1024, 1024) # tbq add
self.residual_block4 = ResidualBlock(1024, 2048, 2)
self.residual_block5 = ResidualBlock(2048, 2048)
self.residual_block6 = ResidualBlock(2048, 2048)
self.mpool2 = nn.Sequential(
nn.BatchNorm2d(2048),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=7, stride=1)
)
self.fc = nn.Linear(2048,14)
self.sigmoid_end = nn.Sigmoid()
def __init__(self, num_classes):
super(ResidualAttentionModel_448input, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2D(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias_attr=False), nn.BatchNorm2D(64), nn.ReLU())
self.mpool1 = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
# tbq add
# 112*112
self.residual_block0 = ResidualBlock(64, 128)
self.attention_module0 = AttentionModule_stage0(128, 128)
# tbq add end
self.residual_block1 = ResidualBlock(128, 256, 2)
# 56*56
self.attention_module1 = AttentionModule_stage1(256, 256)
self.residual_block2 = ResidualBlock(256, 512, 2)
self.attention_module2 = AttentionModule_stage2(512, 512)
self.attention_module2_2 = AttentionModule_stage2(512, 512) # tbq add
self.residual_block3 = ResidualBlock(512, 1024, 2)
self.attention_module3 = AttentionModule_stage3(1024, 1024)
self.attention_module3_2 = AttentionModule_stage3(1024,
1024) # tbq add
self.attention_module3_3 = AttentionModule_stage3(1024,
1024) # tbq add
self.residual_block4 = ResidualBlock(1024, 2048, 2)
self.residual_block5 = ResidualBlock(2048, 2048)
self.residual_block6 = ResidualBlock(2048, 2048)
self.mpool2 = nn.Sequential(nn.BatchNorm2D(2048), nn.ReLU(),
nn.AvgPool2D(kernel_size=7, stride=1))
self.fc = nn.Linear(2048, num_classes)
def __init__(self):
super(ResidualAttentionModel_448input, 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.mpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# tbq add
# 112*112
self.residual_block0 = ResidualBlock(64, 128)
self.attention_module0 = AttentionModule_stage0(128, 128)
# tbq add end
self.residual_block1 = ResidualBlock(128, 256, 2)
# 56*56
self.attention_module1 = AttentionModule_stage1(256, 256)
self.residual_block2 = ResidualBlock(256, 512, 2)
self.attention_module2 = AttentionModule_stage2(512, 512)
self.attention_module2_2 = AttentionModule_stage2(512, 512) # tbq add
self.residual_block3 = ResidualBlock(512, 1024, 2)
self.attention_module3 = AttentionModule_stage3(1024, 1024)
self.attention_module3_2 = AttentionModule_stage3(1024, 1024) # tbq add
self.attention_module3_3 = AttentionModule_stage3(1024, 1024) # tbq add
self.residual_block4 = ResidualBlock(1024, 2048, 2)
self.residual_block5 = ResidualBlock(2048, 2048)
self.residual_block6 = ResidualBlock(2048, 2048)
self.mpool2 = nn.Sequential(
nn.BatchNorm2d(2048),
nn.ReLU(inplace=True),
nn.AvgPool2d(kernel_size=7, stride=1)
)
#-----self.fc = nn.Linear(2048,10)
#-----channel should be 14 because there are 14 classes
self.fc = nn.Linear(2048,14)
