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model.txt
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CVNet_Rerank(
(encoder_q): ResNet(
(stem): ResStemIN(
(conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(pool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
)
(s1): ResStage(
(b1): ResBlock(
(proj): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(f): BottleneckTransform(
(a): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b2): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b3): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
)
(s2): ResStage(
(b1): ResBlock(
(proj): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(f): BottleneckTransform(
(a): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b2): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b3): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b4): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
)
(s3): ResStage(
(b1): ResBlock(
(proj): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(f): BottleneckTransform(
(a): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b2): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b3): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b4): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b5): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b6): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
)
(s4): ResStage(
(b1): ResBlock(
(proj): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(f): BottleneckTransform(
(a): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b2): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
(b3): ResBlock(
(f): BottleneckTransform(
(a): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(a_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(a_relu): relup()
(b): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(b_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(b_relu): relup()
(c): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(c_bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(relu): relup()
(relup): relup()
)
)
(head): GlobalHead(
(fc): Linear(in_features=2048, out_features=2048, bias=True)
(pool): GeneralizedMeanPoolingP(Parameter containing:
tensor([3.], requires_grad=True), output_size=1)
)
(seb1): Conv2d(2048, 512, kernel_size=(7, 7), stride=(1, 1), padding=same)
(seb2): Conv2d(512, 2048, kernel_size=(7, 7), stride=(1, 1), padding=same)
(sefc): Linear(in_features=2048, out_features=1, bias=True)
(pool): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(rgem): rgem(
(lppool): LPPool2d(norm_type=2.5, kernel_size=5, stride=1, ceil_mode=False)
(pad): ReflectionPad2d((2, 2, 2, 2))
)
(gemp): gemp()
(sgem): sgem()
)
(softmax): Softmax(dim=1)
(conv2ds): ModuleList(
(0-2): 3 x Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(cv_learner): CVLearner(
(block1): Sequential(
(0): CenterPivotConv4d(
(conv1): Conv2d(9, 16, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
(conv2): Conv2d(9, 16, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
)
(1): GroupNorm(4, 16, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
)
(block2): Sequential(
(0): CenterPivotConv4d(
(conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): GroupNorm(4, 16, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
(3): CenterPivotConv4d(
(conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
)
(4): GroupNorm(4, 32, eps=1e-05, affine=True)
(5): ReLU(inplace=True)
)
(block3): Sequential(
(0): CenterPivotConv4d(
(conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): GroupNorm(4, 32, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
(3): CenterPivotConv4d(
(conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): GroupNorm(4, 32, eps=1e-05, affine=True)
(5): ReLU(inplace=True)
(6): CenterPivotConv4d(
(conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
)
(7): GroupNorm(4, 64, eps=1e-05, affine=True)
(8): ReLU(inplace=True)
)
(block4): Sequential(
(0): CenterPivotConv4d(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(1): GroupNorm(4, 64, eps=1e-05, affine=True)
(2): ReLU(inplace=True)
(3): CenterPivotConv4d(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(4): GroupNorm(4, 64, eps=1e-05, affine=True)
(5): ReLU(inplace=True)
(6): CenterPivotConv4d(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(7): GroupNorm(4, 128, eps=1e-05, affine=True)
(8): ReLU(inplace=True)
)
(mlp): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=2, bias=True)
)
)
)