Reduce memory usage and improve storage efficiency during dataset processing

download.py

@@ -106,18 +106,30 @@ def concatenate_files(animal):
         with open(f"{save_dir}/dataset/image/{animal}.zip", 'wb') as f_out:
             for f_name in part_files:
                 with open(f_name, 'rb') as f_in:
-                    f_out.write(f_in.read())
+                    # Read and write in chunks
+                    CHUNK_SIZE = 8*1024*1024 # 8MB
+                    for chunk in iter(lambda: f_in.read(CHUNK_SIZE), b""):
+                        f_out.write(chunk) 
+                # Delete part files as they are concatenated              
+                os.remove(f_name)
         print(f"Archive for {animal} concatenated.")
     else:
         print(f"No part files found for {animal}.")
 
 with concurrent.futures.ThreadPoolExecutor() as executor:
     executor.map(concatenate_files, animals)
+
+def compute_md5(file_path):
+    hasher = md5()
+    with open(file_path, 'rb') as f:
+        CHUNK_SIZE = 8*1024*1024 # 8MB
+        for chunk in iter(lambda: f.read(CHUNK_SIZE), b""):
+            hasher.update(chunk)
+    return hasher.hexdigest()
     
 def verify_and_extract(animal):
     print(f"Confirming data integrity for {animal}.zip ...")
-    with open(f"{save_dir}/dataset/image/{animal}.zip", 'rb') as f:
-        zip_md5 = md5(f.read()).hexdigest()
+    zip_md5 = compute_md5(f"{save_dir}/dataset/image/{animal}.zip")
     
     with open(f"{save_dir}/dataset/image/{animal}_md5.txt", 'r') as file:
         expected_md5 = file.read().strip().split()[0]
@@ -139,4 +151,4 @@ def verify_and_extract(animal):
 with concurrent.futures.ThreadPoolExecutor() as executor:
     executor.map(verify_and_extract, animals)
 
-print("Download and setup complete.")
+print("Download script finished.")