dev(narugo): update some format (147a93ae) · Commits · git-mirror / Imgutils

zoo/monochrome/resnet1d.py

+4 −8

Original line number	Diff line number	Diff line
		@@ -8,18 +8,15 @@ class BasicBlock(nn.Module):

		def __init__(self, in_planes, planes, stride=1):
		super(BasicBlock, self).__init__()
		self.conv1 = nn.Conv1d(
		in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
		self.conv1 = nn.Conv1d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
		self.bn1 = nn.BatchNorm1d(planes)
		self.conv2 = nn.Conv1d(planes, planes, kernel_size=3,
		stride=1, padding=1, bias=False)
		self.conv2 = nn.Conv1d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
		self.bn2 = nn.BatchNorm1d(planes)

		self.shortcut = nn.Sequential()
		if stride != 1 or in_planes != self.expansion * planes:
		self.shortcut = nn.Sequential(
		nn.Conv1d(in_planes, self.expansion * planes,
		kernel_size=1, stride=stride, bias=False),
		nn.Conv1d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
		nn.BatchNorm1d(self.expansion * planes)
		)

		@@ -64,8 +61,7 @@ class ResNet(nn.Module):
		super(ResNet, self).__init__()
		self.in_planes = 64

		self.conv1 = nn.Conv1d(3, 64, kernel_size=3,
		stride=1, padding=1, bias=False)
		self.conv1 = nn.Conv1d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
		self.bn1 = nn.BatchNorm1d(64)
		self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
		self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)

zoo/monochrome/resnet2d.py

+16 −22

Original line number	Diff line number	Diff line
		@@ -10,18 +10,15 @@ class BasicBlock(nn.Module):

		def __init__(self, in_planes, planes, stride=1):
		super(BasicBlock, self).__init__()
		self.conv1 = nn.Conv2d(
		in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
		self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
		self.bn1 = nn.BatchNorm2d(planes)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
		stride=1, padding=1, bias=False)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
		self.bn2 = nn.BatchNorm2d(planes)

		self.shortcut = nn.Sequential()
		if stride != 1 or in_planes != self.expansion * planes:
		self.shortcut = nn.Sequential(
		nn.Conv2d(in_planes, self.expansion * planes,
		kernel_size=1, stride=stride, bias=False),
		nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
		nn.BatchNorm2d(self.expansion * planes)
		)

		@@ -40,18 +37,15 @@ class Bottleneck(nn.Module):
		super(Bottleneck, self).__init__()
		self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
		self.bn1 = nn.BatchNorm2d(planes)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
		stride=stride, padding=1, bias=False)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
		self.bn2 = nn.BatchNorm2d(planes)
		self.conv3 = nn.Conv2d(planes, self.expansion *
		planes, kernel_size=1, bias=False)
		self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
		self.bn3 = nn.BatchNorm2d(self.expansion * planes)

		self.shortcut = nn.Sequential()
		if stride != 1 or in_planes != self.expansion * planes:
		self.shortcut = nn.Sequential(
		nn.Conv2d(in_planes, self.expansion * planes,
		kernel_size=1, stride=stride, bias=False),
		nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
		nn.BatchNorm2d(self.expansion * planes)
		)

		@@ -104,40 +98,40 @@ class ResNet182D(ResNet):
		__model_name__ = 'resnet18_2d'
		__dims__ = 2

		def __init__(self):
		ResNet.__init__(self, BasicBlock, [2, 2, 2, 2])
		def __init__(self, num_classes: int = 2):
		ResNet.__init__(self, BasicBlock, [2, 2, 2, 2], num_classes)


		class ResNet342D(ResNet):
		__model_name__ = 'resnet34_2d'
		__dims__ = 2

		def __init__(self):
		ResNet.__init__(self, BasicBlock, [3, 4, 6, 3])
		def __init__(self, num_classes: int = 2):
		ResNet.__init__(self, BasicBlock, [3, 4, 6, 3], num_classes)


		class ResNet502D(ResNet):
		__model_name__ = 'resnet50_2d'
		__dims__ = 2

		def __init__(self):
		ResNet.__init__(self, Bottleneck, [3, 4, 6, 3])
		def __init__(self, num_classes: int = 2):
		ResNet.__init__(self, Bottleneck, [3, 4, 6, 3], num_classes)


		class ResNet1012D(ResNet):
		__model_name__ = 'resnet101_2d'
		__dims__ = 2

		def __init__(self):
		ResNet.__init__(self, Bottleneck, [3, 4, 23, 3])
		def __init__(self, num_classes: int = 2):
		ResNet.__init__(self, Bottleneck, [3, 4, 23, 3], num_classes)


		class ResNet1522D(ResNet):
		__model_name__ = 'resnet152_2d'
		__dims__ = 2

		def __init__(self):
		ResNet.__init__(self, Bottleneck, [3, 8, 36, 3])
		def __init__(self, num_classes: int = 2):
		ResNet.__init__(self, Bottleneck, [3, 8, 36, 3], num_classes)


		if __name__ == '__main__':