continue:
(10): Reduction_A(
(branch0): BasicConv2d(
(conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1): Sequential(
(0): BasicConv2d(
(conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(3, 3), stride=(2, 2), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(11): Inception_B(
(branch0): BasicConv2d(
(conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch2): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): BasicConv2d(
(conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(12): Inception_B(
(branch0): BasicConv2d(
(conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch2): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): BasicConv2d(
(conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(13): Inception_B(
(branch0): BasicConv2d(
(conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch2): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): BasicConv2d(
(conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(14): Inception_B(
(branch0): BasicConv2d(
(conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch2): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): BasicConv2d(
(conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(15): Inception_B(
(branch0): BasicConv2d(
(conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch2): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): BasicConv2d(
(conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(16): Inception_B(
(branch0): BasicConv2d(
(conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch2): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): BasicConv2d(
(conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(17): Inception_B(
(branch0): BasicConv2d(
(conv): Conv2d(1024, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch2): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(192, 224, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): BasicConv2d(
(conv): Conv2d(224, 224, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(4): BasicConv2d(
(conv): Conv2d(224, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(18): Reduction_B(
(branch0): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), bias=False)
(bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch1): Sequential(
(0): BasicConv2d(
(conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(1): BasicConv2d(
(conv): Conv2d(256, 256, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(2): BasicConv2d(
(conv): Conv2d(256, 320, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)
(bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(3): BasicConv2d(
(conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), bias=False)
(bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(branch2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(19): Inception_C(
(branch0): BasicConv2d(
(conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1_0): BasicConv2d(
(conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1_1a): BasicConv2d(
(conv): Conv2d(384, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1_1b): BasicConv2d(
(conv): Conv2d(384, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_0): BasicConv2d(
(conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_1): BasicConv2d(
(conv): Conv2d(384, 448, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
(bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_2): BasicConv2d(
(conv): Conv2d(448, 512, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_3a): BasicConv2d(
(conv): Conv2d(512, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_3b): BasicConv2d(
(conv): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(20): Inception_C(
(branch0): BasicConv2d(
(conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1_0): BasicConv2d(
(conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1_1a): BasicConv2d(
(conv): Conv2d(384, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1_1b): BasicConv2d(
(conv): Conv2d(384, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_0): BasicConv2d(
(conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_1): BasicConv2d(
(conv): Conv2d(384, 448, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
(bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_2): BasicConv2d(
(conv): Conv2d(448, 512, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_3a): BasicConv2d(
(conv): Conv2d(512, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_3b): BasicConv2d(
(conv): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
(21): Inception_C(
(branch0): BasicConv2d(
(conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1_0): BasicConv2d(
(conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1_1a): BasicConv2d(
(conv): Conv2d(384, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch1_1b): BasicConv2d(
(conv): Conv2d(384, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_0): BasicConv2d(
(conv): Conv2d(1536, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_1): BasicConv2d(
(conv): Conv2d(384, 448, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
(bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_2): BasicConv2d(
(conv): Conv2d(448, 512, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_3a): BasicConv2d(
(conv): Conv2d(512, 256, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch2_3b): BasicConv2d(
(conv): Conv2d(512, 256, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
(branch3): Sequential(
(0): AvgPool2d(kernel_size=3, stride=1, padding=1)
(1): BasicConv2d(
(conv): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
)
)
(avg_pool): AvgPool2d(kernel_size=8, stride=8, padding=0)
(last_linear): Linear(in_features=1536, out_features=28, bias=True)
)