def __init__(self):
super(ModelConv, self).__init__()
with supernet(kernel_size=(3, 5, 7),
channel=((4, 8, 12), (8, 12, 16), (8, 12, 16),
(8, 12, 16))) as ofa_super:
models = []
models += [nn.Conv2D(3, 4, 3, padding=1)]
models += [nn.InstanceNorm(4)]
models += [ReLU()]
models += [nn.Conv2D(4, 4, 3, groups=4)]
models += [nn.InstanceNorm(4)]
models += [ReLU()]
models += [
nn.Conv2DTranspose(4,
4,
3,
groups=4,
padding=1,
use_cudnn=True)
]
models += [nn.BatchNorm(4)]
models += [ReLU()]
models += [nn.Conv2D(4, 3, 3)]
models += [ReLU()]
models = ofa_super.convert(models)
models += [
Block(SuperSeparableConv2D(3,
6,
1,
padding=1,
candidate_config={'channel': (3, 6)}),
fixed=True)
]
with supernet(kernel_size=(3, 5, 7),
expand_ratio=(1, 2, 4)) as ofa_super:
models1 = []
models1 += [nn.Conv2D(6, 4, 3)]
models1 += [nn.BatchNorm(4)]
models1 += [ReLU()]
models1 += [nn.Conv2D(4, 4, 3, groups=2)]
models1 += [nn.InstanceNorm(4)]
models1 += [ReLU()]
models1 += [nn.Conv2DTranspose(4, 4, 3, groups=2)]
models1 += [nn.BatchNorm(4)]
models1 += [ReLU()]
models1 += [nn.Conv2DTranspose(4, 4, 3)]
models1 += [nn.BatchNorm(4)]
models1 += [ReLU()]
models1 = ofa_super.convert(models1)
models += models1
self.models = paddle.nn.Sequential(*models)
def __init__(self, in_planes, out_planes, stride=1):
super(Block, self).__init__()
self.conv1 = nn.Conv2D(in_planes,
in_planes,
filter_size=3,
stride=stride,
padding=1,
groups=in_planes,
bias_attr=False)
self.bn1 = nn.InstanceNorm(in_planes)
self.conv2 = nn.Conv2D(in_planes,
out_planes,
filter_size=1,
stride=1,
padding=0)
self.bn2 = nn.InstanceNorm(out_planes)
def __init__(self):
super(MobileNet, self).__init__()
self.conv1 = nn.Conv2D(shape[0],
shape[0],
filter_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn1 = nn.InstanceNorm(shape[0])
self._layers = self._make_layers(in_planes=shape[0])
self.conv_last = nn.Conv2D(shape[0],
shape[0],
filter_size=3,
stride=1,
padding=1,
bias_attr=False)