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https://github.com/finegrain-ai/refiners.git
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471ef91d1c
PyTorch chose to make it Any because they expect its users' code to be "highly dynamic": https://github.com/pytorch/pytorch/pull/104321 It is not the case for us, in Refiners having untyped code goes contrary to one of our core principles. Note that there is currently an open PR in PyTorch to return `Module | Tensor`, but in practice this is not always correct either: https://github.com/pytorch/pytorch/pull/115074 I also moved Residuals-related code from SD1 to latent_diffusion because SDXL should not depend on SD1.
31 lines
914 B
Python
31 lines
914 B
Python
import refiners.fluxion.layers as fl
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def test_module_get_path() -> None:
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chain = fl.Chain(
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fl.Sum(
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fl.Linear(1, 1),
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fl.Linear(1, 1),
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),
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fl.Sum(),
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)
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sum_1 = chain.layer("Sum_1", fl.Sum)
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linear_2 = sum_1.layer("Linear_2", fl.Linear)
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assert linear_2.get_path(parent=sum_1) == "Chain.Sum_1.Linear_2"
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assert linear_2.get_path(parent=sum_1, top=sum_1) == "Sum.Linear_2"
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assert sum_1.get_path() == "Chain.Sum_1"
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def test_module_basic_attributes() -> None:
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class MyModule(fl.Module):
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def __init__(self, spam: int = 0, foo: list[str | int] = ["bar", "qux", 42]) -> None:
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self.spam = spam
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self.foo = foo
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self.chunky = "bacon"
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m = MyModule(spam=3995)
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assert str(m) == "MyModule(spam=3995)"
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assert m.basic_attributes() == {"chunky": "bacon", "foo": ["bar", "qux", 42], "spam": 3995}
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