Delete test_molmo2.py

test_molmo2.py

@@ -1,133 +0,0 @@
-import argparse
-from PIL import Image
-import requests
-
-import torch
-
-from transformers import AutoProcessor, AutoModelForImageTextToText
-
-
-video_path = "https://storage.googleapis.com/oe-training-public/demo_videos/many_penguins.mp4"
-image1_path = "https://picsum.photos/id/237/536/354"
-image2_path = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg"
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Test Molmo2 HF-compatible model."
-    )
-    parser.add_argument("checkpoint_dir", help="Location of Molmo2 checkpoint.")
-    args = parser.parse_args()
-
-    processor = AutoProcessor.from_pretrained(
-        args.checkpoint_dir,
-        trust_remote_code=True,
-        dtype="auto",
-        device_map="auto",
-        padding_side="left",
-    )
-
-    model = AutoModelForImageTextToText.from_pretrained(
-        args.checkpoint_dir,
-        trust_remote_code=True,
-        dtype="auto",
-        device_map="auto",
-    )
-
-    single_image_messages = [
-        {
-            "role": "user",
-            "content": [
-                dict(type="text", text="Describe this image."),
-                dict(type="image", image=Image.open(requests.get(image1_path, stream=True).raw)),
-            ]
-        }
-    ]
-
-    multi_image_messages = [
-        {
-            "role": "user",
-            "content": [
-                dict(type="text", text="Compare these images."),
-                dict(
-                    type="image",
-                    image=Image.open(requests.get(image1_path, stream=True).raw),
-                ),
-                dict(
-                    type="image",
-                    image=Image.open(requests.get(image2_path, stream=True).raw),
-                ),
-            ],
-        }
-    ]
-
-    video_messages = [
-        {
-            "role": "user",
-            "content": [
-                dict(type="text", text="Which animal appears in the video?"),
-                dict(type="video", video=video_path),
-            ]
-        }
-    ]
-
-    single_image_inputs = processor.apply_chat_template(
-        single_image_messages,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_tensors="pt",
-        return_dict=True,
-    )
-
-    single_image_inputs = {k: v.to(model.device) for k, v in single_image_inputs.items()}
-
-    with torch.inference_mode():
-        with torch.autocast("cuda", enabled=True, dtype=torch.bfloat16):
-            single_image_generated_ids = model.generate(**single_image_inputs, max_new_tokens=448)
-    single_image_generated_tokens = single_image_generated_ids[:, single_image_inputs['input_ids'].size(1):]
-    single_image_generated_text = processor.post_process_image_text_to_text(
-        single_image_generated_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )[0]
-    print(single_image_generated_text)
-
-    multi_image_inputs = processor.apply_chat_template(
-        multi_image_messages,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_tensors="pt",
-        return_dict=True,
-    )
-
-    multi_image_inputs = {k: v.to(model.device) for k, v in multi_image_inputs.items()}
-
-    with torch.inference_mode():
-        with torch.autocast("cuda", enabled=True, dtype=torch.bfloat16):
-            multi_image_generated_ids = model.generate(**multi_image_inputs, max_new_tokens=448)
-    multi_image_generated_tokens = multi_image_generated_ids[:, multi_image_inputs['input_ids'].size(1):]
-    multi_image_generated_text = processor.post_process_image_text_to_text(
-        multi_image_generated_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )[0]
-    print(multi_image_generated_text)
-
-    video_inputs = processor.apply_chat_template(
-        video_messages,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_tensors="pt",
-        return_dict=True,
-    )
-
-    video_inputs = {k: v.to(model.device) for k, v in video_inputs.items()}
-    
-    with torch.inference_mode():
-        with torch.autocast("cuda", enabled=True, dtype=torch.bfloat16):
-            video_generated_ids = model.generate(**video_inputs, max_new_tokens=2048)
-    video_generated_tokens = video_generated_ids[:, video_inputs['input_ids'].size(1):]
-    video_generated_text = processor.post_process_image_text_to_text(
-        video_generated_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )[0]
-    print(video_generated_text)
-
-
-if __name__ == "__main__":
-    main()