From ca2f3d395a24c730edb5857178449fe24c497472 Mon Sep 17 00:00:00 2001
From: mirageciel <mirageciel@kookmin.ac.kr>
Date: Wed, 3 Apr 2024 15:04:38 +0900
Subject: [PATCH] Chore: to get just one text input from user

---
 ai/TextReID/lib/data/metrics/evaluation.py | 35 +++++++++++-----------
 ai/TextReID/lib/engine/inference.py        | 10 +++----
 2 files changed, 23 insertions(+), 22 deletions(-)

diff --git a/ai/TextReID/lib/data/metrics/evaluation.py b/ai/TextReID/lib/data/metrics/evaluation.py
index f9c80a4156..6e2eedfc9f 100644
--- a/ai/TextReID/lib/data/metrics/evaluation.py
+++ b/ai/TextReID/lib/data/metrics/evaluation.py
@@ -111,7 +111,9 @@ def evaluation(
             image_ids.append(image_id)
             pids.append(pid)
             image_global.append(prediction[0])
-            if len(prediction) == 2: # text query를 하나만 넣었으므로, text emgedding이 없는 부분이 있을 것임
+            if len(prediction) == 2:
+                # text query를 하나만 넣었으므로, text emgedding은 배치의 제일 처음 이미지에만 들어감
+                # 왜냐하면 유사도 검사 시 배치 별로 검사를 했으니까
                 text_global.append(prediction[1])
 
         pids = list(map(int, pids))
@@ -131,20 +133,19 @@ def evaluation(
 
         writer = SummaryWriter()
         # top 10 results 반환
-        for i in range(4):
-            sorted_indices = torch.argsort(similarity[i], descending=True)
-            sorted_values = similarity[i][sorted_indices]
-            top_k = 10
-            images = []
-            similarities = ""
-            print(cap[i])
-            for index, value in zip(sorted_indices[:top_k], sorted_values[:top_k]):
-                image_id, pid = dataset.get_id_info(idx)
-                img, caption, idx, query = dataset.__getitem__(index)
-                images.append(img)
-                print(f"Index: {index}, Similarity: {value}, pid: {pid}")
-                similarities += str(value) + "\t"
-            grid_img = make_grid(images, nrow=10)
-            writer.add_image(f"Image Grid for Query {i}", grid_img)
-            writer.add_text(f"Captions for Query {i}", cap[i])
+        sorted_indices = torch.argsort(similarity[0], descending=True)
+        sorted_values = similarity[0][sorted_indices]
+        top_k = 10
+        images = []
+        # similarities = ""
+        print(cap[0])
+        for index, value in zip(sorted_indices[:top_k], sorted_values[:top_k]):
+            image_id, pid = dataset.get_id_info(idx)
+            img, caption, idx, query = dataset.__getitem__(index)
+            images.append(img)
+            print(f"Index: {index}, Similarity: {value}, pid: {pid}")
+            # similarities += str(value) + "\t"
+        grid_img = make_grid(images, nrow=10)
+        writer.add_image("Query <%s>"%cap[0], grid_img)
+        # writer.add_text(f"Captions for Query {i}", cap[i])
         writer.close()
\ No newline at end of file
diff --git a/ai/TextReID/lib/engine/inference.py b/ai/TextReID/lib/engine/inference.py
index e0930c5bca..8b09f5d3fd 100644
--- a/ai/TextReID/lib/engine/inference.py
+++ b/ai/TextReID/lib/engine/inference.py
@@ -18,16 +18,16 @@
 def compute_on_dataset(model, data_loader, cap, device):
     model.eval()
     results_dict = defaultdict(list)
+    caption = input("\nText Query Input: ")
+    cap.append(caption)
+    caption = encode(caption)
+    caption = Caption([torch.tensor(caption)])
     for batch in tqdm(data_loader):
         images, captions, image_ids = batch
         images = images.to(device)
         # captions = [captions[0].to(device)] # 첫 번째 캡션만 사용
-        caption = input("\nText Query Input: ")
-        cap.append(caption)
-        caption = encode(caption)
-        caption = torch.tensor(caption)
-        captions = [Caption([caption]).to(device)]
         # captions = [caption.to(device) for caption in captions]
+        captions = [caption.to(device)]
         with torch.no_grad():
             output = model(images, captions)
         for result in output: