Upload imagebind_eval_altered.py

vinoground_subset/imagebind_eval_altered.py

@@ -0,0 +1,70 @@
+from imagebind import data
+import torch
+from imagebind.models import imagebind_model
+from imagebind.models.imagebind_model import ModalityType
+import pandas as pd
+import os
+
+import argparse
+
+# Create an ArgumentParser object
+parser = argparse.ArgumentParser()
+
+# Add arguments
+parser.add_argument('--data', type=str, default="./Vinoground", help='Path to Vinoground dataset (from Huggingface)')
+
+# Parse arguments
+args = parser.parse_args()
+
+data_path = args.data
+
+vino = pd.read_csv(os.path.join(data_path, "vinoground_hardest.csv"))
+
+num_examples = len(vino.index) # original dataset was 500, but this should be 215 after filtering
+
+# print("num examples:", num_examples) # debug
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Instantiate model
+model = imagebind_model.imagebind_huge(True)
+model.eval()
+model.to(device)
+
+text_correct = 0
+video_correct = 0
+group_correct = 0
+
+from tqdm import tqdm
+for row_num in tqdm(range(num_examples)):
+    video_num = vino["index"][row_num] # after filtering, row number changes from original row number. But keep the index value from the original dataset and extract that so we can append the correct video (in original, row number corresponded to video number)
+    videos = []
+    texts = []
+    videos.append(os.path.join(data_path, f"vinoground_videos/{video_num}_pos.mp4"))
+    videos.append(os.path.join(data_path, f"vinoground_videos/{video_num}_neg.mp4"))
+    texts.append(vino["pos_cap"][row_num])
+    texts.append(vino["neg_cap"][row_num])
+    
+    # debug
+    # print("row num:", row_num)
+    # print("video num:", video_num)
+    # print("texts:", texts)
+    # print("videos:", videos)
+    # debug
+
+    inputs = {
+        ModalityType.TEXT: data.load_and_transform_text(texts, device),
+        ModalityType.VISION: data.load_and_transform_video_data(videos, device),
+    }
+
+    with torch.no_grad():
+        embeddings = model(inputs)
+
+    results = embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T
+    # print(results)
+
+    video_correct += results[0][0] > results[1][0] and results[1][1] > results[0][1]
+    text_correct += results[0][0] > results[0][1] and results[1][1] > results[1][0]
+    group_correct += results[0][0] > results[1][0] and results[1][1] > results[0][1] and results[0][0] > results[0][1] and results[1][1] > results[1][0]
+        
+print(text_correct / num_examples, video_correct / num_examples, group_correct / num_examples)