Batch encode images to keep memory consumption manageable

- Issue:
  Process would get killed while encoding images
  for consuming too much memory

- Fix:
  - Encode images in batches and append to image_embeddings
  - No need to use copy or deep_copy anymore with batch processing.
    It would earlier throw too many files open error

Other Changes:
  - Use tqdm to see progress even when using batch
  - See progress bar of encoding independent of verbosity (for now)

src/search_type/image_search.py

@@ -6,6 +6,7 @@ import copy
 # External Packages
 from sentence_transformers import SentenceTransformer, util
 from PIL import Image
+from tqdm import trange
 import torch
 
 # Internal Packages
@@ -50,23 +51,20 @@ def compute_embeddings(image_names, model, embeddings_file, regenerate=False, ve
         if verbose > 0:
             print(f"Loading the {len(image_names)} images into memory")
 
+    batch_size = 50
     if image_embeddings is None:
-        image_embeddings = model.encode(
-            [Image.open(image_name).copy() for image_name in image_names],
-            batch_size=128, convert_to_tensor=True, show_progress_bar=verbose > 0)
-
+        image_embeddings = []
+        for index in trange(0, len(image_names), batch_size):
+            images = [Image.open(image_name) for image_name in image_names[index:index+batch_size]]
+            image_embeddings += model.encode(images, convert_to_tensor=True, batch_size=batch_size)
         torch.save(image_embeddings, embeddings_file)
-
         if verbose > 0:
             print(f"Saved computed embeddings to {embeddings_file}")
 
     if image_metadata_embeddings is None:
-        image_metadata_embeddings = model.encode(
-            [extract_metadata(image_name, verbose) for image_name in image_names],
-            batch_size=128, convert_to_tensor=True, show_progress_bar=verbose > 0)
-
+        image_metadata = [extract_metadata(image_name, verbose) for image_name in image_names],
+        image_metadata_embeddings = model.encode(image_metadata, batch_size=batch_size, convert_to_tensor=True, show_progress_bar=True)
         torch.save(image_metadata_embeddings, f"{embeddings_file}_metadata")
-
         if verbose > 0:
             print(f"Saved computed metadata embeddings to {embeddings_file}_metadata")