pyanomaly.networks package

Subpackages

• pyanomaly.networks.abstract package
  • Module contents
• pyanomaly.networks.auxiliary package
  • Subpackages
    • pyanomaly.networks.auxiliary.flownet2 package
      • Subpackages
      • Submodules
      • pyanomaly.networks.auxiliary.flownet2.FlowNetC module
      • pyanomaly.networks.auxiliary.flownet2.FlowNetFusion module
      • pyanomaly.networks.auxiliary.flownet2.FlowNetS module
      • pyanomaly.networks.auxiliary.flownet2.FlowNetSD module
      • pyanomaly.networks.auxiliary.flownet2.models module
      • pyanomaly.networks.auxiliary.flownet2.submodules module
      • Module contents
    • pyanomaly.networks.auxiliary.liteflownet package
      • Submodules
      • pyanomaly.networks.auxiliary.liteflownet.models module
      • Module contents
  • Module contents
• pyanomaly.networks.meta package
  • Subpackages
    • pyanomaly.networks.meta.base package
      • Submodules
      • pyanomaly.networks.meta.base.commonness module
      • pyanomaly.networks.meta.base.convolution_lstm module
      • pyanomaly.networks.meta.base.prednet module
      • Module contents
    • pyanomaly.networks.meta.pcn_parts package
      • Submodules
      • pyanomaly.networks.meta.pcn_parts.convolution_lstm module
      • pyanomaly.networks.meta.pcn_parts.erm module
      • pyanomaly.networks.meta.pcn_parts.pcm module
      • pyanomaly.networks.meta.pcn_parts.prednet module
      • Module contents
  • Submodules
  • pyanomaly.networks.meta.amc_networks module
  • pyanomaly.networks.meta.anopcn_networks module
  • pyanomaly.networks.meta.anopred_networks module
  • pyanomaly.networks.meta.memae_networks module
  • pyanomaly.networks.meta.ocae_networks module
  • pyanomaly.networks.meta.stae_networks module
  • Module contents

Submodules

pyanomaly.networks.model_api module

@author: Yuhao Cheng @contact: yuhao.cheng[at]outlook.com

class pyanomaly.networks.model_api.ModelAPI(cfg)

Bases: object

For example: If we have a couple in MODEL.parts like this: ['meta_G', 'Generator'], you will get the dict {'G': Generator<object at 0x12345456>}

MODEL_TYPE = ['e2e', 'me2e', 'ae2e', 'ame2e']

pyanomaly.networks.model_api.mode(input, dim=- 1, keepdim=False, values=None, indices=None)

Returns a namedtuple (values, indices) where values is the mode value of each row of the input tensor in the given dimension dim, i.e. a value which appears most often in that row, and indices is the index location of each mode value found.

By default, dim is the last dimension of the input tensor.

If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input.

Note

This function is not defined for torch.cuda.Tensor yet.

Args:

input (Tensor): the input tensor. dim (int): the dimension to reduce. keepdim (bool): whether the output tensor has dim retained or not. values (Tensor, optional): the output tensor indices (Tensor, optional): the output index tensor

Example:

>>> a = torch.randint(10, (5,))
>>> a
tensor([6, 5, 1, 0, 2])
>>> b = a + (torch.randn(50, 1) * 5).long()
>>> torch.mode(b, 0)
torch.return_types.mode(values=tensor([6, 5, 1, 0, 2]), indices=tensor([2, 2, 2, 2, 2]))

pyanomaly.networks.model_registry module

@author: Yuhao Cheng @contact: yuhao.cheng[at]outlook.com

Module contents