pointllm/eval/eval_modelnet_cls.py

import os
import json
import argparse
from torch.utils.data import DataLoader
from pointllm.data import ModelNet
from tqdm import tqdm
import torch
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from llava.conversation import conv_templates
from llava.model.builder import load_pretrained_model
from llava.mm_utils import  tokenizer_image_token, get_model_name_from_path


class MyClass:

    def __init__(self, arg):

        self.vision_tower = None
        self.pretrain_mm_mlp_adapter = arg.pretrain_mm_mlp_adapter
        self.encoder_type = 'pc_encoder'
        self.std=arg.std
        self.pc_encoder_type = arg.pc_encoder_type
        self.pc_feat_dim = 192
        self.embed_dim = 1024
        self.group_size = 64
        self.num_group =512
        self.pc_encoder_dim =512
        self.patch_dropout = 0.0
        self.pc_ckpt_path = arg.pc_ckpt_path
        self.lora_path = arg.lora_path
        self.model_path=arg.model_path
        self.get_pc_tokens_way=arg.get_pc_tokens_way


def init_model(model_arg_):
    model_path = "llava-vicuna_phi_3_finetune_weight"
    model_name = get_model_name_from_path(model_path)
    model_path = model_arg_.model_path
    tokenizer, model, context_len = load_pretrained_model(model_path, None, model_name)

    if model_arg_.lora_path:
        from peft import PeftModel

        model = PeftModel.from_pretrained(model, model_arg_.lora_path)
        print("load lora weight ok")

    model.get_model().initialize_other_modules(model_arg_)
    print("load encoder, mlp ok")
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    # 将模型加载到CUDA设备
    model.to(dtype=torch.bfloat16)
    model.get_model().vision_tower.to(dtype=torch.float)
    model.to(device)

    return tokenizer, model



PROMPT_LISTS = [
    "What is this?",
    "This is an object of "
]


def load_dataset(data_path, config_path, split, subset_nums, use_color):
    print(f"Loading {split} split of ModelNet datasets.")
    dataset = ModelNet(data_path=data_path, config_path=config_path, split=split, subset_nums=subset_nums, use_color=use_color)
    print("Done!")
    return dataset

def get_dataloader(dataset, batch_size, shuffle=False, num_workers=4):
    assert shuffle is False, "Since we using the index of ModelNet as Object ID when evaluation \
        so shuffle shoudl be False and should always set random seed."
    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
    return dataloader


def start_generation(model,  tokenizer, dataloader, prompt_index, output_dir, output_file, args):
    # stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    qs = PROMPT_LISTS[prompt_index]

    results = {"prompt": qs}

    qs = DEFAULT_IMAGE_TOKEN + "\n" + qs

    conv_mode = "phi3_instruct"
    conv = conv_templates[conv_mode].copy()
    conv.append_message(conv.roles[0], qs)
    conv.append_message(conv.roles[1], None)
    qs = conv.get_prompt()

    input_ids = (
        tokenizer_image_token(qs, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
            .unsqueeze(0)
            .cuda()
    )

    responses = []

    for batch in tqdm(dataloader):
        point_clouds = batch["point_clouds"].cuda()  # * tensor of B, N, C(3)
        labels = batch["labels"]
        label_names = batch["label_names"]
        indice = batch["indice"]

        texts = input_ids.repeat(point_clouds.size()[0], 1)

        images_tensor = point_clouds.to(dtype=torch.bfloat16)

        temperature = args.temperature
        top_p = args.top_p

        max_new_tokens = args.max_new_tokens
        min_new_tokens = args.min_new_tokens
        num_beams = args.num_beams

        with torch.inference_mode():
            output_ids = model.generate(
                texts,
                images=images_tensor,
                do_sample=True if temperature > 0 and num_beams == 1 else False,
                temperature=temperature,
                top_p=top_p,
                num_beams=num_beams,
                max_new_tokens=max_new_tokens,
                min_new_tokens=min_new_tokens,
                use_cache=True,
            )

        answers = tokenizer.batch_decode(output_ids, skip_special_tokens=True)

        outputs = []
        for answer in answers:
            answer = answer.strip()
            answer = answer.replace("<|end|>", "").strip()
            outputs.append(answer)

        # saving results
        for index, output, label, label_name in zip(indice, outputs, labels, label_names):
            responses.append({
                "object_id": index.item(),
                "ground_truth": label.item(),
                "model_output": output,
                "label_name": label_name
            })
    
    results["results"] = responses

    os.makedirs(output_dir, exist_ok=True)
    # save the results to a JSON file
    with open(os.path.join(output_dir, output_file), 'w') as fp:
        json.dump(results, fp, indent=2)

    # * print info
    print(f"Saved results to {os.path.join(output_dir, output_file)}")

    return results

def main(args):
    # * ouptut
    args.output_dir = os.path.join(args.out_path, "evaluation")

    # * output file 
    args.output_file = f"ModelNet_classification_prompt{args.prompt_index}.json"
    args.output_file_path = os.path.join(args.output_dir, args.output_file)

    # * First inferencing, then evaluate
    if not os.path.exists(args.output_file_path):
        # * need to generate results first
        dataset = load_dataset(data_path=args.data_path, config_path=None, split=args.split, subset_nums=args.subset_nums, use_color=args.use_color) # * defalut config
        dataloader = get_dataloader(dataset, args.batch_size, args.shuffle, args.num_workers)

        model_arg = MyClass(args)
        tokenizer, model = init_model(model_arg)


        model.eval()

        # * ouptut
        print(f'[INFO] Start generating results for {args.output_file}.')
        results = start_generation(model, tokenizer, dataloader, args.prompt_index, args.output_dir, args.output_file, args)

        # * release model and tokenizer, and release cuda memory
        del model

        torch.cuda.empty_cache()
    else:
        # * directly load the results
        print(f'[INFO] {args.output_file_path} already exists, directly loading...')
        with open(args.output_file_path, 'r') as fp:
            results = json.load(fp)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--out_path", type=str,  default="./output_json")
    parser.add_argument("--pretrain_mm_mlp_adapter", type=str,  required=True)



    parser.add_argument("--lora_path", type=str, default=None)
    parser.add_argument("--model_path", type=str, default='./lava-vicuna_2024_4_Phi-3-mini-4k-instruct')

    parser.add_argument("--std", type=float, default=0.0)
    parser.add_argument("--pc_ckpt_path",  type=str,  required=True, default="./pretrained_weight/Uni3D_PC_encoder/modelzoo/uni3d-small/model.pt")
    parser.add_argument("--pc_encoder_type", type=str, required=True, default='small')
    parser.add_argument("--get_pc_tokens_way", type=str, required=True)

    # * dataset type
    parser.add_argument("--data_path", type=str, default="./dataset/modelnet40_data", help="train or test.")
    parser.add_argument("--split", type=str, default="test", help="train or test.")
    parser.add_argument("--use_color",  action="store_true", default=True)

    # * data loader, batch_size, shuffle, num_workers
    parser.add_argument("--batch_size", type=int, default=10)
    parser.add_argument("--shuffle", type=bool, default=False)
    parser.add_argument("--num_workers", type=int, default=20)
    parser.add_argument("--subset_nums", type=int, default=-1) # * only use "subset_nums" of samples, mainly for debug 

    # * evaluation setting
    parser.add_argument("--prompt_index", type=int, required=True,  help="0 or 1")

    ############## new add
    parser.add_argument("--max_new_tokens", type=int, default=110, help="max number of generated tokens")
    parser.add_argument("--min_new_tokens", type=int, default=0, help="min number of generated tokens")
    parser.add_argument("--num_beams", type=int, default=1)
    parser.add_argument("--temperature", type=float, default=0.1)
    parser.add_argument("--top_k", type=int, default=1)
    parser.add_argument("--top_p", type=float, default=0.7)
    ############## new add

    args = parser.parse_args()

    main(args)