def __init__(self, in_planes, planes, stride=1):
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,
padding=1,
stride=1,
bias=False)
self.bn2 = nn.BatchNorm2D(planes)
self.downsample = []
if stride != 1 or in_planes != self.expansion * planes:
self.downsample = [
nn.Conv2d(in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False),
nn.BatchNorm2D(self.expansion * planes)
]
def __init__(self, block, num_blocks, num_classes=10, url=None):
self.url = url
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, bias=False, padding=3)
self.bn1 = nn.BatchNorm2D(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=2)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.fc = {"weight": Tensor.uniform(512 * block.expansion, num_classes), "bias": Tensor.zeros(num_classes)}
def __init__(self, block, num_blocks, num_classes=10, pretrained=False):
self.in_planes = 64
self.conv1 = nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
bias=False,
padding=3)
self.bn1 = nn.BatchNorm2D(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=2)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.fc = nn.Linear(512 * block.expansion, num_classes)
def __init__(self, num, num_classes):
self.num = num
self.block = {
18: BasicBlock,
34: BasicBlock,
50: Bottleneck,
101: Bottleneck,
152: Bottleneck
}[num]
self.num_blocks = {
18: [2, 2, 2, 2],
34: [3, 4, 6, 3],
50: [3, 4, 6, 3],
101: [3, 4, 23, 3],
152: [3, 8, 36, 3]
}[num]
self.in_planes = 64
self.conv1 = nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
bias=False,
padding=3)
self.bn1 = nn.BatchNorm2D(64)
self.layer1 = self._make_layer(self.block,
64,
self.num_blocks[0],
stride=2)
self.layer2 = self._make_layer(self.block,
128,
self.num_blocks[1],
stride=2)
self.layer3 = self._make_layer(self.block,
256,
self.num_blocks[2],
stride=2)
self.layer4 = self._make_layer(self.block,
512,
self.num_blocks[3],
stride=2)
self.fc = {
"weight": Tensor.uniform(512 * self.block.expansion, num_classes),
"bias": Tensor.zeros(num_classes)
}