def __init__(self):
super().__init__()
self.obs1 = ModelReportObserver()
self.mod1 = SingleLayerLinearModel()
self.obs2 = ModelReportObserver()
self.fc1 = torch.nn.Linear(5, 5).to(dtype=torch.float)
self.relu = torch.nn.ReLU()
def __init__(self, batch_norm_dim):
super(ModifiedThreeOps, self).__init__()
self.obs1 = ModelReportObserver()
self.linear = torch.nn.Linear(7, 3, 2)
self.obs2 = ModelReportObserver()
if batch_norm_dim == 2:
self.bn = torch.nn.BatchNorm2d(2)
elif batch_norm_dim == 3:
self.bn = torch.nn.BatchNorm3d(4)
else:
raise ValueError("Dim should only be 2 or 3")
self.relu = torch.nn.ReLU()
def __init__(self):
super(HighDimensionNet, self).__init__()
self.obs1 = ModelReportObserver()
self.fc1 = torch.nn.Linear(3, 7)
self.block1 = ModifiedThreeOps(3)
self.fc2 = torch.nn.Linear(3, 7)
self.block2 = ModifiedThreeOps(3)
self.fc3 = torch.nn.Linear(3, 7)
def __init__(self):
super().__init__()
self.obs1 = ModelReportObserver()
self.nested = TinyNestModule()
self.fc1 = SingleLayerLinearModel()
self.relu = torch.nn.ReLU()