From cfbbe8e8cf6a37d9fc6ecbb66fd6e2ab1ca77c69 Mon Sep 17 00:00:00 2001 From: Starbeamrainbowlabs Date: Thu, 1 Sep 2022 16:06:24 +0100 Subject: [PATCH] ai: global? really? --- aimodel/src/lib/ai/components/convnext.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/aimodel/src/lib/ai/components/convnext.py b/aimodel/src/lib/ai/components/convnext.py index 7858d28..7690c60 100644 --- a/aimodel/src/lib/ai/components/convnext.py +++ b/aimodel/src/lib/ai/components/convnext.py @@ -21,7 +21,7 @@ depths_dims = dict( convnext_xlarge = (dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048])), ) -next_model_number = 0 +__convnext_next_model_number = 0 def make_convnext(input_shape, arch_name="convnext_tiny", **kwargs): """Makes a ConvNeXt model. @@ -30,18 +30,18 @@ def make_convnext(input_shape, arch_name="convnext_tiny", **kwargs): input_shape (int[]): The input shape of the tensor that will be fed to the ConvNeXt model. This is necessary as we make the model using the functional API and thus we need to make an Input layer. arch_name (str, optional): The name of the preset ConvNeXt model architecture to use. Defaults to "convnext_tiny". """ - nonlocal next_model_number + global __convnext_next_model_number layer_in = tf.keras.layers.Input( shape = input_shape ) layer_out = convnext(layer_in, **depths_dims[arch_name], **kwargs) result = tf.keras.Model( - name=f"convnext{next_model_number}", + name=f"convnext{__convnext_next_model_number}", inputs = layer_in, outputs = layer_out ) - next_model_number += 1 + __convnext_next_model_number += 1 return result