Squeeze the extra dimensions in a grayscale image and pass the model name to the response objects
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287b6303ae
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2 changed files with 10 additions and 5 deletions
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@ -10,12 +10,12 @@ class TensorflowResponse(Response):
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"""
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Generic Tensorflow response.
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"""
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def __init__(self, *args, model: Model, **kwargs):
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def __init__(self, *args, model: Model, model_name: Optional[str] = None, **kwargs):
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"""
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:param model: Name of the model.
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"""
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super().__init__(*args, output={
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'model': model.name,
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'model': model_name or model.name,
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}, **kwargs)
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self.model = model
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@ -610,7 +610,11 @@ class TensorflowPlugin(Plugin):
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'Found: {}'.format(colors[0]))
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img = image.load_img(image_file, target_size=size, color_mode=color_mode)
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return image.img_to_array(img)
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data = image.img_to_array(img)
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if data.shape[-1] == 1:
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# Squeeze extra color channels
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data = np.squeeze(data)
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return data
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@classmethod
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def _get_dir(cls, directory: str, model: Model) -> Dict[str, Iterable]:
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@ -847,7 +851,7 @@ class TensorflowPlugin(Plugin):
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use_multiprocessing=use_multiprocessing,
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)
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return TensorflowTrainResponse(model=model, epochs=ret.epoch, history=ret.history)
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return TensorflowTrainResponse(model=model, model_name=name, epochs=ret.epoch, history=ret.history)
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@action
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def evaluate(self,
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@ -1071,7 +1075,8 @@ class TensorflowPlugin(Plugin):
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use_multiprocessing=use_multiprocessing
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)
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return TensorflowPredictResponse(model=model, prediction=ret, output_labels=model.output_labels)
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return TensorflowPredictResponse(model=model, model_name=name, prediction=ret,
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output_labels=model.output_labels)
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@action
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def save(self, model: str, overwrite: bool = True, **opts) -> None:
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