Update dimensionality_reduction_api

Author: jaime-cespedes-sisniega

dimensionality_reduction_api/Dockerfile

@@ -1,5 +1,5 @@
 FROM python:3.10-slim
-LABEL author="Jaime Céspedes Sisniega <cespedes@ifca.unican.es>"
+LABEL author="Jaime Céspedes Sisniega <jaime.cespedes@alumnos.unican.es>"
 
 ARG DATASET
 ARG PARENT_DIR
@@ -15,8 +15,8 @@ RUN apt-get -y update && \
     rm requirements.txt
 
 COPY ${PARENT_DIR}/app ./
-COPY ./ml/${DATASET}/encoder.pt ./objects/encoder.pt
-COPY ./ml/${DATASET}/transformer.pt ./objects/transformer.pt
+COPY ./ml/objects/${DATASET}/encoder.pt ./objects/encoder.pt
+COPY ./ml/objects/${DATASET}/transform.pt ./objects/transform.pt
 
 USER app
 

dimensionality_reduction_api/app/api.py

@@ -19,12 +19,12 @@
 from settings import (
     api_settings,
     encoder_settings,
-    transformer_settings,
+    transform_settings,
 )
 
 dr = DimensionalityReduction(
     settings_encoder=encoder_settings,
-    settings_transformer=transformer_settings,
+    settings_transform=transform_settings,
 )
 
 
@@ -33,9 +33,9 @@
     status_code=HTTP_200_OK,
 )
 async def dimensionality_reduction(
-        data: DimensionalityReductionInputData = Body(
-            media_type=RequestEncodingType.MULTI_PART,
-        ),
+    data: DimensionalityReductionInputData = Body(
+        media_type=RequestEncodingType.MULTI_PART,
+    ),
 ) -> DimensionalityReductionResponse:
     """Reduce image.
 
@@ -46,7 +46,7 @@ async def dimensionality_reduction(
     """
     image = await data.image
     logging.info("Transforming image...")
-    transformed_image = dr.transform(
+    transformed_image = dr.apply_transform(
         data=image,
     )
     logging.info("Image transformed.")

dimensionality_reduction_api/app/dr.py

@@ -8,7 +8,7 @@
 import torchvision
 from torch import nn
 
-from settings import EncoderSettings, TransformerSettings
+from settings import EncoderSettings, TransformSettings
 from utils import SingletonMeta
 
 
@@ -70,7 +70,9 @@ def __init__(self, input_size: Tuple[int, int, int], latent_dim: int) -> None:
             ),
         )
 
-    def _get_feature_size(self, input_channels: int, input_size: Tuple[int, int]) -> Tuple[int, int, int]:
+    def _get_feature_size(
+        self, input_channels: int, input_size: Tuple[int, int]
+    ) -> Tuple[int, int, int]:
         # Function to compute the size of the feature maps after convolutional layers
         x = torch.randn(1, input_channels, *input_size)
         x = self.encoder_conv(x)
@@ -89,19 +91,19 @@ class DimensionalityReduction(metaclass=SingletonMeta):
     def __init__(
         self: "DimensionalityReduction",
         settings_encoder: EncoderSettings,
-        settings_transformer: TransformerSettings,
+        settings_transform: TransformSettings,
     ) -> None:
         """Init method."""
         logging.info("Loading image encoder...")
         self.encoder = self.load_encoder(
             settings=settings_encoder,
         )
         logging.info("Image encoder loaded.")
-        logging.info("Loading transformer...")
-        self.transformer = self.load_transformer(
-            settings=settings_transformer,
+        logging.info("Loading transform...")
+        self.transform = self.load_transform(
+            settings=settings_transform,
         )
-        logging.info("Transformer loaded.")
+        logging.info("Transform loaded.")
 
     def load_encoder(
         self: "DimensionalityReduction",
@@ -117,16 +119,16 @@ def load_encoder(
         )
         return encoder
 
-    def load_transformer(
+    def load_transform(
         self: "DimensionalityReduction",
-        settings: EncoderSettings,
+        settings: TransformSettings
     ) -> torchvision.transforms.Compose:
         """Load image encoder.
 
         :return encoder
         :rtype: dict[str, str | None]
         """
-        transformer = self._load_transformer(
+        transformer = self._load_transform(
             settings=settings,
         )
         return transformer
@@ -143,7 +145,7 @@ def encode(self, data: np.ndarray) -> np.ndarray:
             encoded = self.encoder(data).numpy()
         return encoded
 
-    def transform(self, data: np.ndarray) -> torch.Tensor:
+    def apply_transform(self, data: np.ndarray) -> torch.Tensor:
         """Transform data.
 
         :param data: data
@@ -152,7 +154,7 @@ def transform(self, data: np.ndarray) -> torch.Tensor:
         :rtype: np.ndarray
         """
 
-        transformed = self.transformer(data).unsqueeze(0)
+        transformed = self.transform(data).unsqueeze(0)
         return transformed
 
     @staticmethod
@@ -173,8 +175,8 @@ def _load_encoder(settings: EncoderSettings) -> Encoder:
         return encoder
 
     @staticmethod
-    def _load_transformer(
-        settings: TransformerSettings,
+    def _load_transform(
+        settings: TransformSettings,
     ) -> torchvision.transforms.Compose:
         transformer = torch.load(
             f=settings.FILE_PATH,

dimensionality_reduction_api/app/schemas/dr.py

@@ -23,10 +23,10 @@ async def parse_image(cls, data: UploadFile) -> Image:
         """
         data = await data.read()
         image = Image.open(
-                fp=BytesIO(
-                    initial_bytes=data,
-                ),
-            )
+            fp=BytesIO(
+                initial_bytes=data,
+            ),
+        )
         return image
 
     class Config(BaseConfig):

dimensionality_reduction_api/app/settings.py

@@ -22,12 +22,12 @@ class EncoderSettings(BaseSettings):
     FILE_PATH: Path = Path("objects/encoder.pt")
 
 
-class TransformerSettings(BaseSettings):
-    """Transformer settings class."""
+class TransformSettings(BaseSettings):
+    """Transform settings class."""
 
-    FILE_PATH: Path = Path("objects/transformer.pt")
+    FILE_PATH: Path = Path("objects/transform.pt")
 
 
 api_settings = APISettings()
 encoder_settings = EncoderSettings()
-transformer_settings = TransformerSettings()
+transform_settings = TransformSettings()