9729999899864197
0.972000002861023
0.9764999747276306
0.9754999876022339
0.9729999899864197
0.9779999852180481
0.9725000262260437
0.9754999876022339
0.9764999747276306
0.9794999957084656
0.9800000190734863
0.9764999747276306
model= CNN(
(conv1): Sequential(
(0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): ReLU()
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(conv2): Sequential(
(0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): ReLU()
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(conv3): Sequential(
(0): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): ReLU()
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(BasicRFB): ShuffleAttention(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(gn): GroupNorm(2, 2, eps=1e-05, affine=True)
(sigmoid): Sigmoid()
)
(ou
