allow TorchModuleWrapper compute output shape #21160
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## master #21160 +/- ##
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Coverage 82.69% 82.69%
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Files 564 564
Lines 54223 54228 +5
Branches 8424 8425 +1
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+ Hits 44837 44842 +5
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Partials 2076 2076
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fchollet
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Apr 14, 2025
keras/src/utils/torch_utils.py
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| @@ -25,6 +25,8 @@ class TorchModuleWrapper(Layer): | |||
| instance, then its parameters must be initialized before | |||
| passing the instance to `TorchModuleWrapper` (e.g. by calling | |||
| it once). | |||
| out_shape :The shape of the output of this layer. It helps Keras | |||
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Please name it output_shape
fchollet
reviewed
Apr 14, 2025
| @@ -138,13 +141,18 @@ def load_own_variables(self, store): | |||
| state_dict[key] = convert_to_tensor(store[key]) | |||
| self.module.load_state_dict(state_dict) | |||
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| def compute_output_shape(self, input_shape): | |||
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Should we consider validating this in __call__?
fchollet
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Apr 14, 2025
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The TorchModuleWrapper layer does not allow automatic inference of the output. Therefore, the model that owns this layer cannot be used like other keras.Model. For example, you cannot use model.summary().
And now the vast majority of models are based on Torch. By adding this feature, we can let ordinary Torch models enjoy the workflow of Keras without making too many modifications.