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test fix for param_groups
Summary: param_groups only expected on MLPDecoder, not ElementwiseDecoder Reviewed By: shapovalov Differential Revision: D40508539 fbshipit-source-id: ea040ad6f7e26bd7d87e5de2eaadae2cf4b04faf
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@ -43,27 +43,8 @@ class DecoderFunctionBase(ReplaceableBase, torch.nn.Module):
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"""
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Decoding function is a torch.nn.Module which takes the embedding of a location in
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space and transforms it into the required quantity (for example density and color).
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Members:
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param_groups: dictionary where keys are names of individual parameters
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or module members and values are the parameter group where the
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parameter/member will be sorted to. "self" key is used to denote the
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parameter group at the module level. Possible keys, including the "self" key
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do not have to be defined. By default all parameters are put into "default"
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parameter group and have the learning rate defined in the optimizer,
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it can be overridden at the:
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- module level with “self” key, all the parameters and child
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module's parameters will be put to that parameter group
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- member level, which is the same as if the `param_groups` in that
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member has key=“self” and value equal to that parameter group.
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This is useful if members do not have `param_groups`, for
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example torch.nn.Linear.
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- parameter level, parameter with the same name as the key
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will be put to that parameter group.
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"""
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param_groups: Dict[str, str] = field(default_factory=lambda: {})
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def __post_init__(self):
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super().__init__()
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@ -280,11 +261,30 @@ class MLPWithInputSkips(Configurable, torch.nn.Module):
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class MLPDecoder(DecoderFunctionBase):
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"""
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Decoding function which uses `MLPWithIputSkips` to convert the embedding to output.
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If using Implicitron config system `input_dim` of the `network` is changed to the
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value of `input_dim` member and `input_skips` is removed.
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The `input_dim` of the `network` is set from the value of `input_dim` member.
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Members:
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input_dim: dimension of input.
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param_groups: dictionary where keys are names of individual parameters
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or module members and values are the parameter group where the
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parameter/member will be sorted to. "self" key is used to denote the
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parameter group at the module level. Possible keys, including the "self" key
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do not have to be defined. By default all parameters are put into "default"
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parameter group and have the learning rate defined in the optimizer,
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it can be overridden at the:
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- module level with “self” key, all the parameters and child
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module's parameters will be put to that parameter group
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- member level, which is the same as if the `param_groups` in that
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member has key=“self” and value equal to that parameter group.
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This is useful if members do not have `param_groups`, for
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example torch.nn.Linear.
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- parameter level, parameter with the same name as the key
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will be put to that parameter group.
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network_args: configuration for MLPWithInputSkips
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"""
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input_dim: int = 3
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param_groups: Dict[str, str] = field(default_factory=lambda: {})
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network: MLPWithInputSkips
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def __post_init__(self):
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