def __init__(self, num_classes=10, classifier_activation='softmax'):
super(LeNetDygraph, self).__init__()
self.num_classes = num_classes
self.features = nn.Sequential(nn.Conv2d(1, 6, 3, stride=1, padding=1),
nn.ReLU(), nn.Pool2D(2, 'max', 2),
nn.Conv2d(6, 16, 5, stride=1, padding=0),
nn.ReLU(), nn.Pool2D(2, 'max', 2))
if num_classes > 0:
self.fc = nn.Sequential(nn.Linear(400, 120), nn.Linear(120, 84),
nn.Linear(84, 10),
nn.Softmax()) #Todo: accept any activation
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2D(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias_attr=False)
self.bn1 = nn.BatchNorm(64)
self.relu = nn.ReLU()
self.maxpool = nn.Pool2D(pool_size=3, pool_stride=2, pool_padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.Pool2D(7, pool_stride=1, pool_type='avg')
self.fc = nn.Linear(512 * block.expansion, num_classes)
def __init__(self):
super(Model, self).__init__()
with supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2,
4]) as ofa_super:
models = []
models += [nn.Conv2D(1, 6, 3)]
models += [ReLU()]
models += [nn.Pool2D(2, 'max', 2)]
models += [nn.Conv2D(6, 16, 5, padding=0)]
models += [ReLU()]
models += [nn.Pool2D(2, 'max', 2)]
models += [
nn.Linear(784, 120),
nn.Linear(120, 84),
nn.Linear(84, 10)
]
models = ofa_super.convert(models)
self.models = paddle.nn.Sequential(*models)
def __init__(self,
ni: int,
nf: int = None,
scale: int = 2,
blur: bool = False,
norm_type='Weight',
leaky: float = None):
super().__init__()
nf = ifnone(nf, ni)
self.conv = conv_layer(ni,
nf * (scale**2),
ks=1,
norm_type=norm_type,
use_activ=False)
self.shuf = PixelShuffle(scale)
self.pad = ReplicationPad2d((1, 0, 1, 0))
self.blur = nn.Pool2D(2, pool_stride=1, pool_type='avg')
self.relu = relu(True, leaky=leaky)
def __init__(self,
ni: int,
nf: int = None,
scale: int = 2,
blur: bool = False,
leaky: float = None,
**kwargs):
super().__init__()
nf = ifnone(nf, ni)
self.conv = custom_conv_layer(ni,
nf * (scale**2),
ks=1,
use_activ=False,
**kwargs)
self.shuf = PixelShuffle(scale)
self.pad = ReplicationPad2d((1, 0, 1, 0))
self.blur = nn.Pool2D(2, pool_stride=1, pool_type='avg')
self.relu = nn.LeakyReLU(leaky) if leaky is not None else nn.ReLU(
) #relu(True, leaky=leaky)
def __init__(self):
super(LeNetDygraph, self).__init__()
self.features = nn.Sequential(nn.Conv2d(1, 6, 3, stride=1, padding=1),
nn.ReLU(), nn.Pool2D(2, 'max', 2),
nn.Conv2d(6, 16, 5, stride=1, padding=0),
nn.ReLU(), nn.Pool2D(2, 'max', 2))
