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import torch |
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import os |
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from ChatUniVi.constants import * |
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from ChatUniVi.conversation import conv_templates, SeparatorStyle |
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from ChatUniVi.model.builder import load_pretrained_model |
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from ChatUniVi.utils import disable_torch_init |
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from ChatUniVi.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria |
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from PIL import Image |
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if __name__ == '__main__': |
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model_path = ${model_path} |
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image_path = ${image_path} |
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qs = "Describe the image." |
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conv_mode = "simple" |
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temperature = 0.2 |
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top_p = None |
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num_beams = 1 |
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disable_torch_init() |
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model_path = os.path.expanduser(model_path) |
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model_name = "ChatUniVi" |
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tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name) |
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mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False) |
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mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True) |
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if mm_use_im_patch_token: |
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tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) |
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if mm_use_im_start_end: |
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tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) |
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model.resize_token_embeddings(len(tokenizer)) |
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vision_tower = model.get_vision_tower() |
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if not vision_tower.is_loaded: |
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vision_tower.load_model() |
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image_processor = vision_tower.image_processor |
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if image_path is not None: |
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cur_prompt = qs |
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if model.config.mm_use_im_start_end: |
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qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs |
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else: |
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qs = DEFAULT_IMAGE_TOKEN + '\n' + qs |
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conv = conv_templates[conv_mode].copy() |
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conv.append_message(conv.roles[0], qs) |
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conv.append_message(conv.roles[1], None) |
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prompt = conv.get_prompt() |
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input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() |
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image = Image.open(image_path) |
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image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] |
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stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 |
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keywords = [stop_str] |
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stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) |
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with torch.inference_mode(): |
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output_ids = model.generate( |
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input_ids, |
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images=image_tensor.unsqueeze(0).half().cuda(), |
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do_sample=True, |
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temperature=temperature, |
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top_p=top_p, |
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num_beams=num_beams, |
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max_new_tokens=1024, |
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use_cache=True, |
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stopping_criteria=[stopping_criteria]) |
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input_token_len = input_ids.shape[1] |
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n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() |
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if n_diff_input_output > 0: |
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print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') |
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outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] |
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outputs = outputs.strip() |
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if outputs.endswith(stop_str): |
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outputs = outputs[:-len(stop_str)] |
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outputs = outputs.strip() |
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print(outputs) |
