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- Date : 09 Aug 2023, 16:00:24
- Path : /Users/camille.brianceau/aramis/clinicadl/out_test5
- Model : Sequential(
(0): Sequential(
(0): Conv3d(1, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
(1): BatchNorm3d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): PadMaxPool3d(
(pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(pad): ConstantPad3d(padding=(1, 0, 0, 0, 1, 0), value=0)
)
(4): Conv3d(8, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
(5): BatchNorm3d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): ReLU()
(7): PadMaxPool3d(
(pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(pad): ConstantPad3d(padding=(0, 0, 0, 0, 1, 0), value=0)
)
(8): Conv3d(16, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
(9): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): ReLU()
(11): PadMaxPool3d(
(pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(pad): ConstantPad3d(padding=(1, 0, 0, 0, 1, 0), value=0)
)
(12): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
(13): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(14): ReLU()
(15): PadMaxPool3d(
(pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(pad): ConstantPad3d(padding=(1, 0, 0, 0, 0, 0), value=0)
)
(16): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
(17): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(18): ReLU()
(19): PadMaxPool3d(
(pool): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(pad): ConstantPad3d(padding=(0, 0, 1, 0, 1, 0), value=0)
)
)
(1): Sequential(
(0): Flatten(start_dim=1, end_dim=-1)
(1): Dropout(p=0.0, inplace=False)
(2): Linear(in_features=32256, out_features=1300, bias=True)
(3): ReLU()
(4): Linear(in_features=1300, out_features=50, bias=True)
(5): ReLU()
(6): Linear(in_features=50, out_features=1, bias=True)
)
)
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