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model_segmentation: document; remove unused args
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1 changed files with 6 additions and 8 deletions
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@ -6,20 +6,18 @@ import tensorflow as tf
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from .components.convnext_inverse import do_convnext_inverse
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def model_rainfallwater_segmentation(metadata, shape_water_out, model_arch="convnext_i_xtiny", batch_size=64, summary_file=None, water_bins=2):
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def model_rainfallwater_segmentation(metadata, shape_water_out, model_arch="convnext_i_xtiny", batch_size=64, water_bins=2):
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"""Makes a new rainfall / waterdepth segmentation head model.
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Args:
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metadata (dict): A dictionary of metadata about the dataset to use to build the model with.
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feature_dim_in (int): The size of the feature dimension
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shape_water_out (_type_): _description_
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model_arch (str, optional): _description_. Defaults to "convnext_i_xtiny".
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batch_size (int, optional): _description_. Defaults to 64.
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summary_file (_type_, optional): _description_. Defaults to None.
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water_bins (int, optional): _description_. Defaults to 2.
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shape_water_out (int[]): The width and height (in that order) that should dictate the output shape of the segmentation head. CURRENTLY NOT USED.
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model_arch (str, optional): The architecture code for the underlying (inverted) ConvNeXt model. Defaults to "convnext_i_xtiny".
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batch_size (int, optional): The batch size. Reduce to save memory. Defaults to 64.
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water_bins (int, optional): The number of classes that the water depth output oft he segmentation head should be binned into. Defaults to 2.
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Returns:
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_type_: _description_
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tf.keras.Model: The new model, freshly compiled for your convenience! :D
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"""
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out_water_width, out_water_height = shape_water_out
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feature_dim_in = metadata["rainfallradar"][0]
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