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import argparse
import torch
from torch.utils.data import DataLoader
import os
from pointllm.conversation import conv_templates, SeparatorStyle
from pointllm.utils import disable_torch_init
from pointllm.model import *
from pointllm.model.utils import KeywordsStoppingCriteria
from pointllm.data import ObjectPointCloudDataset
from tqdm import tqdm
from transformers import AutoTokenizer
from pointllm.eval.evaluator import start_evaluation
import os
import json
PROMPT_LISTS = [
"What is this?",
"This is an object of ",
"Caption this 3D model in detail."
]
def init_model(args):
# Model
disable_torch_init()
model_name = os.path.expanduser(args.model_name)
# * print the model_name (get the basename)
print(f'[INFO] Model name: {os.path.basename(model_name)}')
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = PointLLMLlamaForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=False, use_cache=True, torch_dtype=torch.bfloat16).cuda()
model.initialize_tokenizer_point_backbone_config_wo_embedding(tokenizer)
conv_mode = "vicuna_v1_1"
conv = conv_templates[conv_mode].copy()
return model, tokenizer, conv
def load_dataset(data_path, anno_path, pointnum, conversation_types, use_color):
print("Loading validation datasets.")
dataset = ObjectPointCloudDataset(
data_path=data_path,
anno_path=anno_path,
pointnum=pointnum,
conversation_types=conversation_types,
use_color=use_color,
tokenizer=None # * load point cloud only
)
print("Done!")
return dataset
def get_dataloader(dataset, batch_size, shuffle=False, num_workers=4):
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
return dataloader
def generate_outputs(model, tokenizer, input_ids, point_clouds, stopping_criteria, do_sample=True, temperature=1.0, top_k=50, max_length=2048, top_p=0.95):
model.eval()
with torch.inference_mode():
output_ids = model.generate(
input_ids,
point_clouds=point_clouds,
do_sample=do_sample,
temperature=temperature,
top_k=top_k,
max_length=max_length,
top_p=top_p,
stopping_criteria=[stopping_criteria]) # * B, L'
input_token_len = input_ids.shape[1]
n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
if n_diff_input_output > 0:
print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)
outputs = [output.strip() for output in outputs]
return outputs
def start_generation(model, tokenizer, conv, dataloader, annos, prompt_index, output_dir, output_file):
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
qs = PROMPT_LISTS[prompt_index]
results = {"prompt": qs}
point_backbone_config = model.get_model().point_backbone_config
point_token_len = point_backbone_config['point_token_len']
default_point_patch_token = point_backbone_config['default_point_patch_token']
default_point_start_token = point_backbone_config['default_point_start_token']
default_point_end_token = point_backbone_config['default_point_end_token']
mm_use_point_start_end = point_backbone_config['mm_use_point_start_end']
if mm_use_point_start_end:
qs = default_point_start_token + default_point_patch_token * point_token_len + default_point_end_token + '\n' + qs
else:
qs = default_point_patch_token * point_token_len + '\n' + qs
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
inputs = tokenizer([prompt])
input_ids_ = torch.as_tensor(inputs.input_ids).cuda() # * tensor of 1, L
stopping_criteria = KeywordsStoppingCriteria([stop_str], tokenizer, input_ids_)
responses = []
for batch in tqdm(dataloader):
point_clouds = batch["point_clouds"].cuda().to(model.dtype) # * tensor of B, N, C(3)
object_ids = batch["object_ids"] # * list of string
batchsize = len(object_ids)
input_ids = input_ids_.repeat(batchsize, 1) # * tensor of B, L
outputs = generate_outputs(model, tokenizer, input_ids, point_clouds, stopping_criteria) # List of str, length is B
# saving results
for obj_id, output in zip(object_ids, outputs):
responses.append({
"object_id": obj_id,
"ground_truth": annos[obj_id],
"model_output": output
})
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.model_name, "evaluation")
# * output file
anno_file = os.path.splitext(os.path.basename(args.anno_path))[0]
args.output_file = f"{anno_file}_Objaverse_{args.task_type}_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 inferencing
# * load annotation files
with open(args.anno_path, 'r') as fp:
annos = json.load(fp)
dataset = load_dataset(args.data_path, args.anno_path, args.pointnum, ("simple_description",), args.use_color)
dataloader = get_dataloader(dataset, args.batch_size, args.shuffle, args.num_workers)
model, tokenizer, conv = init_model(args)
# * convert annos file from [{"object_id": }] to {"object_id": }
annos = {anno["object_id"]: anno["conversations"][1]['value'] for anno in annos}
print(f'[INFO] Start generating results for {args.output_file}.')
results = start_generation(model, tokenizer, conv, dataloader, annos, args.prompt_index, args.output_dir, args.output_file)
# * release model and tokenizer, and release cuda memory
del model
del tokenizer
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 args.start_eval:
evaluated_output_file = args.output_file.replace(".json", f"_evaluated_{args.gpt_type}.json")
eval_type_mapping = {
"captioning": "object-captioning",
"classification": "open-free-form-classification"
}
start_evaluation(results, output_dir=args.output_dir, output_file=evaluated_output_file, eval_type=eval_type_mapping[args.task_type], model_type=args.gpt_type, parallel=True, num_workers=20)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, \
default="RunsenXu/PointLLM_7B_v1.2")
# * dataset type
parser.add_argument("--data_path", type=str, default="data/objaverse_data", required=False)
parser.add_argument("--anno_path", type=str, default="data/anno_data/PointLLM_brief_description_val_200_GT.json", required=False)
parser.add_argument("--pointnum", type=int, default=8192)
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=6)
parser.add_argument("--shuffle", type=bool, default=False)
parser.add_argument("--num_workers", type=int, default=10)
# * evaluation setting
parser.add_argument("--prompt_index", type=int, default=0)
parser.add_argument("--start_eval", action="store_true", default=False)
parser.add_argument("--gpt_type", type=str, default="gpt-4-0613", choices=["gpt-3.5-turbo-0613", "gpt-3.5-turbo-1106", "gpt-4-0613", "gpt-4-1106-preview"], help="Type of the model used to evaluate.")
parser.add_argument("--task_type", type=str, default="captioning", choices=["captioning", "classification"], help="Type of the task to evaluate.")
args = parser.parse_args()
# * check prompt index
# * * classification: 0, 1 and captioning: 2. Raise Warning otherwise.
if args.task_type == "classification":
if args.prompt_index != 0 and args.prompt_index != 1:
print("[Warning] For classification task, prompt_index should be 0 or 1.")
elif args.task_type == "captioning":
if args.prompt_index != 2:
print("[Warning] For captioning task, prompt_index should be 2.")
else:
raise NotImplementedError
main(args) |