GemRic-18K clip score fixed!

.gitignore

@@ -3,4 +3,7 @@ ehthumbs.db
 Icon?
 Thumbs.db
 *.DS_Store
-secret.sh
+
+secret.sh
+
+.idea/

Archive/test.py

@@ -1,45 +1,124 @@
 import streamlit as st
 from streamlit_sortables import sort_items
+from torchvision import transforms
+from transformers import CLIPProcessor, CLIPModel
+from torchmetrics.multimodal import CLIPScore
+import torch
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from datasets import load_dataset, Dataset, load_from_disk
+import os
 
-if __name__ == "__main__":
-    # if 'check_dict' not in st.session_state:
-    #     st.session_state.check_dict = {'check1': False, 'check2': False, 'check3': False}
-    #
-    # with st.form('my_form'):
-    #     st.session_state.check_dict['check1'] = st.checkbox('Check 1 out')
-    #     st.session_state.check_dict['check2'] = st.checkbox('Check 2 out')
-    #     st.session_state.check_dict['check3'] = st.checkbox('Check 3 out')
-    #
-    #     check21 = st.checkbox('Check 21 out')
-    #     if check21:
-    #         st.write('check21 is checked')
-    #     check22 = st.checkbox('Check 22 out')
-    #     if check22:
-    #         st.write('check22 is checked')
-    #     check23 = st.checkbox('Check 23 out')
-    #     if check23:
-    #         st.write('check23 is checked')
-    #
-    #     # Every form must have a submit button.
-    #     submitted = st.form_submit_button('Submit')
-    #
-    #     items = ['a', 'b', 'c']
-    #     sorted_items = sort_items(items)
-    #
-    # for key, value in st.session_state.check_dict.items():
-    #     st.write(key, value)
+import clip
+
+def compute_clip_score(promptbook, device, drop_negative=False):
+    # if 'clip_score' in promptbook.columns:
+    #     print('==> Skipping CLIP-Score computation')
+    #     return
+    print('==> CLIP-Score computation started')
+    clip_scores = []
+    to_tensor = transforms.ToTensor()
+    # metric = CLIPScore(model_name_or_path='openai/clip-vit-base-patch16').to(DEVICE)
+    metric = CLIPScore(model_name_or_path='openai/clip-vit-large-patch14').to(device)
+    for i in tqdm(range(0, len(promptbook), BATCH_SIZE)):
+        images = []
+        prompts = list(promptbook.prompt.values[i:i+BATCH_SIZE])
+        for image in promptbook.image.values[i:i+BATCH_SIZE]:
+            images.append(to_tensor(image))
+        with torch.no_grad():
+            x = metric.processor(text=prompts, images=images, return_tensors='pt', padding=True)
+            img_features = metric.model.get_image_features(x['pixel_values'].to(device))
+            img_features = img_features / img_features.norm(p=2, dim=-1, keepdim=True)
+            txt_features = metric.model.get_text_features(x['input_ids'].to(device), x['attention_mask'].to(device))
+            txt_features = txt_features / txt_features.norm(p=2, dim=-1, keepdim=True)
+            scores = 100 * (img_features * txt_features).sum(axis=-1).detach().cpu()
+        if drop_negative:
+            scores = torch.max(scores, torch.zeros_like(scores))
+        clip_scores += [round(s.item(), 4) for s in scores]
+    promptbook['clip_score'] = np.asarray(clip_scores)
+    print('==> CLIP-Score computation completed')
+    return promptbook
+
+
+def compute_clip_score_hmd(promptbook):
+
+    metric_cpu = CLIPScore(model_name_or_path="openai/clip-vit-large-patch14").to('cpu')
+    metric_gpu = CLIPScore(model_name_or_path="openai/clip-vit-large-patch14").to('mps')
+
+    for idx in promptbook.index:
+        clip_score_hm = promptbook.loc[idx, 'clip_score']
+
+        with torch.no_grad():
+            image = promptbook.loc[idx, 'image']
+            image.save(f"./tmp/{promptbook.loc[idx, 'image_id']}.png")
+            image = transforms.ToTensor()(image)
+            image_cpu = torch.unsqueeze(image, dim=0).to('cpu')
+            image_gpu = torch.unsqueeze(image, dim=0).to('mps')
+
+            prompts = [promptbook.loc[idx, 'prompt']]
+            clip_score_cpu = metric_cpu(image_cpu, prompts)
+            clip_score_gpu = metric_gpu(image_gpu, prompts)
 
-    from tqdm import tqdm
-    import time
+        print(
+            f'==> clip_score_hm: {clip_score_hm:.4f}, clip_score_cpu: {clip_score_cpu:.4f}, clip_score_gpu: {clip_score_gpu:.4f}')
 
-    outer_list = [1, 2, 3, 4]
-    inner_list = [5, 6, 7, 8]
+def compute_clip_score_transformers(promptbook, device='cpu'):
+    model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+    processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
 
-    # Outer loop
-    for item in tqdm(outer_list, desc="Outer Loop", position=0):
-        # Inner loop with nested=True
-        for inner_item in tqdm(inner_list, desc="Inner Loop", leave=False, position=1):
-            # Your nested loop logic here
-            time.sleep(0.1)
+    with torch.no_grad():
+        inputs = processor(text=promptbook.prompt.tolist(), images=promptbook.image.tolist(), return_tensors="pt", padding=True)
+        outputs = model(**inputs)
+        logits_per_image = outputs.logits_per_image
 
+    promptbook.loc[:, 'clip_score'] = logits_per_image[:, 0].tolist()
+    return promptbook
+
+def compute_clip_score_clip(promptbook, device='cpu'):
+    model, preprocess = clip.load("ViT-B/32", device=device)
+    with torch.no_grad():
+
+        for idx in promptbook.index:
+            # image_input = preprocess(promptbook.loc[idx, 'image']).unsqueeze(0).to(device)
+            image_inputs = preprocess(promptbook.image.tolist()).to(device)
+            text_inputs = torch.cat([clip.tokenize(promptbook.prompt.tolist()).to(device)]).to(device)
+
+            image_features = model.encode_image(image_inputs)
+            text_features = model.encode_text(text_inputs)
+
+
+            probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+    promptbook.loc[:, 'clip_score'] = probs[:, 0].tolist()
+    return promptbook
+
+
+if __name__ == "__main__":
+    BATCH_SIZE = 200
+    # DEVICE = 'mps' if torch.has_mps else 'cpu'
+
+    print(torch.__version__)
+
+    images_ds = load_from_disk(os.path.join(os.pardir, 'data', 'promptbook'))
+    images_ds = images_ds.sort(['prompt_id', 'modelVersion_id'])
+    print(images_ds)
+    print(type(images_ds[0]['image']))
+    promptbook_hmd = pd.DataFrame(images_ds[:20])
+    promptbook_new = promptbook_hmd.drop(columns=['clip_score'])
+    promptbook_cpu = compute_clip_score(promptbook_new.copy(deep=True), device='cpu')
+    promptbook_mps = compute_clip_score(promptbook_new.copy(deep=True), device='mps')
+    promptbook_tra_cpu = compute_clip_score_transformers(promptbook_new.copy(deep=True))
+    promptbook_tra_mps = compute_clip_score_transformers(promptbook_new.copy(deep=True), device='mps')
+    #
+    for idx in promptbook_mps.index:
+        print(
+            'image id: ', promptbook_mps['image_id'][idx],
+            'mps: ', promptbook_mps['clip_score'][idx],
+            'cpu: ', promptbook_cpu['clip_score'][idx],
+            'tra cpu: ', promptbook_tra_cpu['clip_score'][idx],
+            'tra mps: ', promptbook_tra_mps['clip_score'][idx],
+            'hmd: ', promptbook_hmd['clip_score'][idx]
+        )
+    #
+    # compute_clip_score_hmd(promptbook_hmd)
 

data/download_script.py

@@ -1,7 +1,9 @@
 from datasets import load_dataset, Dataset, load_from_disk
+import os
 
 
 def main():
+    os.makedirs('./promptbook', exist_ok=True)
     promptbook = load_dataset('NYUSHPRP/ModelCofferPromptBook', split='train')
     print(promptbook)
     promptbook.save_to_disk('./promptbook')

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