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Ludwig unet image decoder v5 #6

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Ludwig unet image decoder v5 #6

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4 changes: 3 additions & 1 deletion ludwig/api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -899,7 +899,7 @@ def predict(

# preprocessing
logger.debug("Preprocessing")
dataset, _ = preprocess_for_prediction( # TODO (Connor): Refactor to use self.config_obj
dataset, _, postproc_dataset = preprocess_for_prediction( # TODO (Connor): Refactor to use self.config_obj
self.config_obj.to_dict(),
dataset=dataset,
training_set_metadata=self.training_set_metadata,
Expand Down Expand Up @@ -933,6 +933,8 @@ def predict(
makedirs(output_directory, exist_ok=True)

logger.debug("Postprocessing")
if postproc_dataset is not None:
predictions = self.backend.df_engine.df_lib.concat([predictions, postproc_dataset], axis=1)
postproc_predictions = postprocess(
predictions,
self.model.output_features,
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4 changes: 3 additions & 1 deletion ludwig/constants.py
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Expand Up @@ -41,12 +41,14 @@
INFER_IMAGE_MAX_HEIGHT = "infer_image_max_height"
INFER_IMAGE_MAX_WIDTH = "infer_image_max_width"
INFER_IMAGE_SAMPLE_SIZE = "infer_image_sample_size"
NUM_CLASSES = "num_classes"
INFER_IMAGE_NUM_CLASSES = "infer_image_num_classes"
IMAGE_MAX_CLASSES = 128
NUM_CHANNELS = "num_channels"
REQUIRES_EQUAL_DIMENSIONS = "requires_equal_dimensions"
USE_PRETRAINED = "use_pretrained"
TRAINABLE = "trainable"
CLASS_WEIGHTS = "class_weights"
DICE = "dice"
LOSS = "loss"
ROC_AUC = "roc_auc"
EVAL_LOSS = "eval_loss"
Expand Down
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