| 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() | |
| ) | |
| ) | |
| ) | |