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| import argparse | |
| import torch | |
| from mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN | |
| from mplug_owl2.conversation import conv_templates, SeparatorStyle | |
| from mplug_owl2.model.builder import load_pretrained_model | |
| from mplug_owl2.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria | |
| from PIL import Image | |
| import requests | |
| from PIL import Image | |
| from io import BytesIO | |
| from transformers import TextStreamer | |
| def disable_torch_init(): | |
| """ | |
| Disable the redundant torch default initialization to accelerate model creation. | |
| """ | |
| import torch | |
| setattr(torch.nn.Linear, "reset_parameters", lambda self: None) | |
| setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) | |
| def load_image(image_file): | |
| if image_file.startswith('http://') or image_file.startswith('https://'): | |
| response = requests.get(image_file) | |
| image = Image.open(BytesIO(response.content)).convert('RGB') | |
| else: | |
| image = Image.open(image_file).convert('RGB') | |
| return image | |
| def main(args): | |
| # Model | |
| disable_torch_init() | |
| model_name = get_model_name_from_path(args.model_path) | |
| tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device) | |
| conv_mode = "mplug_owl2" | |
| if args.conv_mode is not None and conv_mode != args.conv_mode: | |
| print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode)) | |
| else: | |
| args.conv_mode = conv_mode | |
| conv = conv_templates[args.conv_mode].copy() | |
| roles = conv.roles | |
| image = load_image(args.image_file) | |
| # Similar operation in model_worker.py | |
| image_tensor = process_images([image], image_processor, args) | |
| if type(image_tensor) is list: | |
| image_tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor] | |
| else: | |
| image_tensor = image_tensor.to(model.device, dtype=torch.float16) | |
| while True: | |
| try: | |
| inp = input(f"{roles[0]}: ") | |
| except EOFError: | |
| inp = "" | |
| if not inp: | |
| print("exit...") | |
| break | |
| print(f"{roles[1]}: ", end="") | |
| if image is not None: | |
| # first message | |
| inp = DEFAULT_IMAGE_TOKEN + inp | |
| conv.append_message(conv.roles[0], inp) | |
| image = None | |
| else: | |
| # later messages | |
| conv.append_message(conv.roles[0], inp) | |
| conv.append_message(conv.roles[1], None) | |
| prompt = conv.get_prompt() | |
| input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device) | |
| stop_str = conv.sep if conv.sep_style not in [SeparatorStyle.TWO, SeparatorStyle.TWO_NO_SYS] else conv.sep2 | |
| keywords = [stop_str] | |
| stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) | |
| streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) | |
| with torch.inference_mode(): | |
| output_ids = model.generate( | |
| input_ids, | |
| images=image_tensor, | |
| do_sample=True, | |
| temperature=args.temperature, | |
| max_new_tokens=args.max_new_tokens, | |
| streamer=streamer, | |
| use_cache=True, | |
| stopping_criteria=[stopping_criteria]) | |
| outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip() | |
| conv.messages[-1][-1] = outputs | |
| if args.debug: | |
| print("\n", {"prompt": prompt, "outputs": outputs}, "\n") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--model-path", type=str, default="facebook/opt-350m") | |
| parser.add_argument("--model-base", type=str, default=None) | |
| parser.add_argument("--image-file", type=str, required=True) | |
| parser.add_argument("--device", type=str, default="cuda") | |
| parser.add_argument("--conv-mode", type=str, default=None) | |
| parser.add_argument("--temperature", type=float, default=0.2) | |
| parser.add_argument("--max-new-tokens", type=int, default=512) | |
| parser.add_argument("--load-8bit", action="store_true") | |
| parser.add_argument("--load-4bit", action="store_true") | |
| parser.add_argument("--debug", action="store_true") | |
| parser.add_argument("--image-aspect-ratio", type=str, default='pad') | |
| args = parser.parse_args() | |
| main(args) |