Create predict.py

predict.py

@@ -0,0 +1,86 @@
+# Credit for this script goes to @ariG23498 who opened a PR on the apple/ml-fastvlm repo to add this model to hugging face transformers. The apple team still needs to convert the weights in order for it to be officially available.
+
+import os
+import argparse
+
+import torch
+from PIL import Image
+
+from llava.conversation import conv_templates
+from llava.mm_utils import tokenizer_image_token, process_images
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, CLIPImageProcessor
+
+def predict(args):
+    model_id = args.model_path.split("/")[-1]
+    print(f"{model_id=}")
+
+    # Remove generation config from model folder
+    # to read generation parameters from args
+    model_path = os.path.expanduser(args.model_path)
+    generation_config = None
+    if os.path.exists(os.path.join(model_path, 'generation_config.json')):
+        generation_config = os.path.join(model_path, '.generation_config.json')
+        os.rename(os.path.join(model_path, 'generation_config.json'),
+                  generation_config)
+
+    tokenizer = AutoTokenizer.from_pretrained(f"riddhimanrana/{model_id}")
+    model = AutoModelForCausalLM.from_pretrained(f"riddhimanrana/{model_id}", torch_dtype=torch.float16, device_map="cuda")
+    image_processor = CLIPImageProcessor.from_pretrained(f"riddhimanrana/{model_id}")
+
+    # Construct prompt
+    qs = args.prompt
+    if model.config.mm_use_im_start_end:
+        qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
+    else:
+        qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
+    conv = conv_templates[args.conv_mode].copy()
+    conv.append_message(conv.roles[0], qs)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+
+    # Set the pad token id for generation
+    model.generation_config.pad_token_id = tokenizer.pad_token_id
+
+    # Tokenize prompt
+    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(torch.device("cuda"))
+
+    # Load and preprocess image
+    image = Image.open(args.image_file).convert('RGB')
+    image_tensor = process_images([image], image_processor, model.config)[0]
+
+    # Run inference
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=image_tensor.unsqueeze(0).half(),
+            image_sizes=[image.size],
+            do_sample=True if args.temperature > 0 else False,
+            temperature=args.temperature,
+            top_p=args.top_p,
+            num_beams=args.num_beams,
+            max_new_tokens=256,
+            use_cache=True)
+
+        outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+        print(outputs)
+
+    # Restore generation config
+    if generation_config is not None:
+        os.rename(generation_config, os.path.join(model_path, 'generation_config.json'))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="./llava-v1.5-0.5b")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-file", type=str, default=None, help="location of image file")
+    parser.add_argument("--prompt", type=str, default="Describe the image.", help="Prompt for VLM.")
+    parser.add_argument("--conv-mode", type=str, default="qwen_2")
+    parser.add_argument("--temperature", type=float, default=0.0)
+    parser.add_argument("--top_p", type=float, default=None)
+    parser.add_argument("--num_beams", type=int, default=1)
+    args = parser.parse_args()
+
+    predict(args)