please, could you check my model summary:

```
[VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace)
(6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(9): ReLU(inplace)
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(12): ReLU(inplace)
(13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(16): ReLU(inplace)
(17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(19): ReLU(inplace)
(20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(22): ReLU(inplace)
(23): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(25): ReLU(inplace)
(26): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(27): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(29): ReLU(inplace)
(30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(32): ReLU(inplace)
(33): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(35): ReLU(inplace)
(36): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(38): ReLU(inplace)
(39): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(42): ReLU(inplace)
(43): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(44): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(45): ReLU(inplace)
(46): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(48): ReLU(inplace)
(49): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(50): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(51): ReLU(inplace)
(52): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU(inplace)
(2): Dropout(p=0.5)
(3): Linear(in_features=4096, out_features=4096, bias=True)
(4): ReLU(inplace)
(5): Dropout(p=0.5)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
), GlobalPool(
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(maxpool): AdaptiveMaxPool2d(output_size=(1, 1))
(exp_pool): ExpPool()
(linear_pool): LinearPool()
(lse_pool): LogSumExpPool()
), Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)), Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)), Conv2d(1
024, 1, kernel_size=(1, 1), stride=(1, 1)), Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)), Conv2d(1024, 1, ker
nel_size=(1, 1), stride=(1, 1)), Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)), Conv2d(1024, 1, kernel_size=(1
, 1), stride=(1, 1)), Conv2d(1024, 1, kernel_size=(1, 1), stride=(1, 1)), BatchNorm2d(1024, eps=1e-05, momentum=0.1
, affine=True, track_running_stats=True), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_sta
ts=True), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), BatchNorm2d(1024, eps=
1e-05, momentum=0.1, affine=True, track_running_stats=True), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True
, track_running_stats=True), BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Bat
chNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), BatchNorm2d(1024, eps=1e-05, moment
um=0.1, affine=True, track_running_stats=True), AttentionMap(
(channel_attention): CAModule(
(fc1): Linear(in_features=512, out_features=256, bias=True)
(fc2): Linear(in_features=256, out_features=512, bias=True)
(relu): ReLU()
(sigmoid): Sigmoid()
)
(spatial_attention): SAModule(
(conv1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))
(conv2): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))
(conv3): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
)
(pyramid_attention): FPAModule(
(gap_branch): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2dNormRelu(
(conv): Sequential(
(0): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
)
)
(mid_branch): Conv2dNormRelu(
(conv): Sequential(
(0): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
)
(downsample1): Conv2dNormRelu(
(conv): Sequential(
(0): Conv2d(512, 1, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
(1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
)
(downsample2): Conv2dNormRelu(
(conv): Sequential(
(0): Conv2d(1, 1, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
(1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
)
(downsample3): Conv2dNormRelu(
(conv): Sequential(
(0): Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
)
(scale1): Conv2dNormRelu(
(conv): Sequential(
(0): Conv2d(1, 1, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))
(1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
)
(scale2): Conv2dNormRelu(
(conv): Sequential(
(0): Conv2d(1, 1, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
)
(scale3): Conv2dNormRelu(
(conv): Sequential(
(0): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
)
)
)
)]
```