fix: rename the classes

jina/executors/__init__.py

@@ -560,20 +560,37 @@ def __call__(self, req_type, *args, **kwargs):
             raise NoDriverForRequest(req_type)
 
 
-class BaseFramewordExecutor(BaseExecutor):
+class BaseFrameworkExecutor(BaseExecutor):
+    """
+    :class:`BaseFrameworkExecutor` is the base class for the executors using other frameworks internally, including
+        `tensorflow`, `pytorch`, `onnx`, and, `paddlepaddle`.
+
+    ..notes:
+        The derived classes must implement `build_model()` and `set_device()` methods.
+    """
     def post_init(self):
         super().post_init()
         self.build_model()
         self.set_device()
 
     def build_model(self):
+        """
+        Build the model with the framework set by `self._backend`.
+        """
         raise NotImplementedError
 
     def set_device(self):
+        """
+        Set the device on which the model will be executed.
+
+        ..notes:
+            In the case of using GPUs, we only use the first gpu from the visible gpus. To specify which gpu to use,
+            please use the environment variable `CUDA_VISIBLE_DEVICES`.
+        """
         raise NotImplementedError
 
 
-class BaseTorchExecutor(BaseFramewordExecutor):
+class BaseTorchExecutor(BaseFrameworkExecutor):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._backend = 'pytorch'
@@ -591,7 +608,7 @@ def set_device(self):
         self.model.set_providers(self._device)
 
 
-class BaseTfExecutor(BaseFramewordExecutor):
+class BaseTFExecutor(BaseFrameworkExecutor):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._backend = 'tensorflow'
@@ -601,7 +618,7 @@ def set_device(self):
         tf.config.experimental.set_visible_devices(self._device)
 
 
-class BasePaddleExecutor(BaseFramewordExecutor):
+class BasePaddleExecutor(BaseFrameworkExecutor):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._backend = 'paddlepaddle'

jina/executors/encoders/image/tfkeras.py

@@ -5,10 +5,10 @@
 
 from .. import BaseImageEncoder
 from ...decorators import batching, as_ndarray
-from ... import BaseTfExecutor
+from ... import BaseTFExecutor
 
 
-class KerasImageEncoder(BaseImageEncoder, BaseTfExecutor):
+class KerasImageEncoder(BaseImageEncoder, BaseTFExecutor):
     """
     :class:`KerasImageEncoder` encodes data from a ndarray, potentially B x (Channel x Height x Width) into a
         ndarray of `B x D`.

jina/executors/encoders/nlp/transformer.py

@@ -8,10 +8,10 @@
 from .. import BaseTextEncoder
 from ..helper import reduce_mean, reduce_max, reduce_min, reduce_cls
 from ...decorators import batching, as_ndarray
-from ... import BaseFramewordExecutor, BaseTfExecutor, BaseTorchExecutor
+from ... import BaseFrameworkExecutor, BaseTFExecutor, BaseTorchExecutor
 
 
-class BaseTransformerEncoder(BaseFramewordExecutor):
+class BaseTransformerEncoder(BaseFrameworkExecutor):
     """
     :class:`TransformerTextEncoder` encodes data from an array of string in size `B` into an ndarray in size `B x D`.
     """
@@ -138,7 +138,7 @@ def _build_model(self):
         raise NotImplementedError
 
 
-class TransformerTFEncoder(BaseTfExecutor, BaseTransformerEncoder):
+class TransformerTFEncoder(BaseTFExecutor, BaseTransformerEncoder):
     """
     Internally, TransformerTFEncoder wraps the tensorflow-version of transformers from huggingface.
     """

tests/executors/encoders/image/test_paddlehub.py

@@ -7,7 +7,8 @@
 class MyTestCase(ImageTestCase):
     def _get_encoder(self):
         self.target_output_dim = 2048
-        return ImagePaddlehubEncoder()
+        self.input_dim = 224
+        return ImagePaddlehubEncoder(on_gpu=True)
 
 
 if __name__ == '__main__':

tests/executors/encoders/image/test_tfkeras.py

@@ -7,6 +7,7 @@
 class MyTestCase(ImageTestCase):
     def _get_encoder(self):
         self.target_output_dim = 1280
+        self.input_dim = 224
         return KerasImageEncoder(channel_axis=1)