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DeepGazeIII(
(features): FeatureExtractor(
(features): RGBDenseNet201(
(0): Normalizer()
(1): DenseNet(
(features): Sequential(
(conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu0): ReLU(inplace=True)
(pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(denseblock1): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition1): _Transition(
(norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock2): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition2): _Transition(
(norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock3): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer13): _DenseLayer(
(norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer14): _DenseLayer(
(norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer15): _DenseLayer(
(norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer16): _DenseLayer(
(norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer17): _DenseLayer(
(norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer18): _DenseLayer(
(norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer19): _DenseLayer(
(norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer20): _DenseLayer(
(norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer21): _DenseLayer(
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer22): _DenseLayer(
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer23): _DenseLayer(
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer24): _DenseLayer(
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer25): _DenseLayer(
(norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer26): _DenseLayer(
(norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer27): _DenseLayer(
(norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer28): _DenseLayer(
(norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer29): _DenseLayer(
(norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer30): _DenseLayer(
(norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer31): _DenseLayer(
(norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer32): _DenseLayer(
(norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer33): _DenseLayer(
(norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer34): _DenseLayer(
(norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer35): _DenseLayer(
(norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer36): _DenseLayer(
(norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer37): _DenseLayer(
(norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer38): _DenseLayer(
(norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer39): _DenseLayer(
(norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer40): _DenseLayer(
(norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer41): _DenseLayer(
(norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer42): _DenseLayer(
(norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer43): _DenseLayer(
(norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer44): _DenseLayer(
(norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer45): _DenseLayer(
(norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer46): _DenseLayer(
(norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer47): _DenseLayer(
(norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer48): _DenseLayer(
(norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition3): _Transition(
(norm): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv): Conv2d(1792, 896, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock4): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer13): _DenseLayer(
(norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer14): _DenseLayer(
(norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer15): _DenseLayer(
(norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer16): _DenseLayer(
(norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer17): _DenseLayer(
(norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer18): _DenseLayer(
(norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer19): _DenseLayer(
(norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer20): _DenseLayer(
(norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer21): _DenseLayer(
(norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer22): _DenseLayer(
(norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer23): _DenseLayer(
(norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer24): _DenseLayer(
(norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer25): _DenseLayer(
(norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer26): _DenseLayer(
(norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer27): _DenseLayer(
(norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer28): _DenseLayer(
(norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer29): _DenseLayer(
(norm1): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1792, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer30): _DenseLayer(
(norm1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1824, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer31): _DenseLayer(
(norm1): BatchNorm2d(1856, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1856, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer32): _DenseLayer(
(norm1): BatchNorm2d(1888, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(1888, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(norm5): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(classifier): Linear(in_features=1920, out_features=1000, bias=True)
)
)
)
(saliency_networks): ModuleList(
(0): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
(1): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
(2): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
(3): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
(4): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
(5): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
(6): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
(7): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
(8): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
(9): Sequential(
(layernorm0): LayerNorm(2048, eps=1e-12, center=True, scale=True)
(conv0): Conv2d(2048, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias0): Bias(channels=8)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias2): Bias(channels=1)
(softplus2): Softplus(beta=1, threshold=20)
)
)
(scanpath_networks): ModuleList(
(0): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
(1): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
(2): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
(3): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
(4): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
(5): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
(6): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
(7): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
(8): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
(9): Sequential(
(encoding0): FlexibleScanpathHistoryEncoding(
(convolutions): ModuleList(
(0): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(1): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(2): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
(3): Conv2d(3, 128, kernel_size=[1, 1], stride=(1, 1))
)
)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
)
)
(fixation_selection_networks): ModuleList(
(0): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(1): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(2): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(3): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(4): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(5): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(6): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(7): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(8): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(9): Sequential(
(layernorm0): LayerNormMultiInput(
(layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
(layernorm_part1): LayerNorm(16, eps=1e-12, center=True, scale=True)
)
(conv0): Conv2dMultiInput(
(conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(conv_part1): Conv2d(16, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(bias0): Bias(channels=128)
(softplus0): Softplus(beta=1, threshold=20)
(layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
(conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bias1): Bias(channels=16)
(softplus1): Softplus(beta=1, threshold=20)
(conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
)
(finalizers): ModuleList(
(0): Finalizer(
(gauss): GaussianFilterNd()
)
(1): Finalizer(
(gauss): GaussianFilterNd()
)
(2): Finalizer(
(gauss): GaussianFilterNd()
)
(3): Finalizer(
(gauss): GaussianFilterNd()
)
(4): Finalizer(
(gauss): GaussianFilterNd()
)
(5): Finalizer(
(gauss): GaussianFilterNd()
)
(6): Finalizer(
(gauss): GaussianFilterNd()
)
(7): Finalizer(
(gauss): GaussianFilterNd()
)
(8): Finalizer(
(gauss): GaussianFilterNd()
)
(9): Finalizer(
(gauss): GaussianFilterNd()
)
)
)
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