videollava/eval/geochat_eval_fmow.py

import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
import sys

sys.path.append('/deep/u/emily712/GeoChat')

from geochat.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from geochat.conversation import conv_templates, SeparatorStyle
from geochat.model.builder import load_pretrained_model
from geochat.utils import disable_torch_init
from geochat.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from eval_classification import *

from PIL import Image
import math
import numpy as np

def aggregate_accuracy(answers_file, output_file):
    """
    Parses geochat inference output and aggregates votes on single images
    across an image sequence into the format needed for geovlm-style evaluation. 

    params: 
        - answers_file: path to the file containing geochat inference output
        - output_file: path to the file where the aggregated output will be saved
    """
    with open(answers_file, 'r') as f:
        answers = [json.loads(line) for line in f]
        print(answers)    
    # dictionary that will contain parsed output
    votes = {}

    # parse answers so that predictions with the same linked_id
    # are aggregated into a single item with 'predictions' containing
    # a list of values. All other keys should be the same
    for answer in answers:
        print(answer) 
        print(answer['linked_id'])
        id = answer['linked_id']
        print(id)
        if id not in votes:
            item = {}
            item['predicted'] = [answer['predicted']]
            item['ground_truth'] = answer['ground_truth']
            item['task'] = answer['task']
            item['question'] = answer['question']
            item['id'] = answer['id']
            votes[id] = item
        else: 
            votes['linked_id']['predicted'].append(answer['predicted'])
    
    # implement voting so that each list in 'predicted' attribute
    # is reduced to the most common value
    for linked_id, predicted_dict in votes.items():
        predicted = predicted_dict['predicted']
        unique, counts = np.unique(predicted, return_counts=True)
        index = np.argmax(counts)
        votes[linked_id]['predicted'] = unique[index]

    with open(output_file, 'w') as f:
        json.dump(votes, f)

    
def split_list(lst, n):
    """Split a list into n (roughly) equal-sized chunks"""
    chunk_size = math.ceil(len(lst) / n)  # integer division
    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]


def get_chunk(lst, n, k):
    chunks = split_list(lst, n)
    return chunks[k]
            

def eval_model(args):
    # Model
    disable_torch_init()
    model_path = os.path.expanduser(args.model_path)
    model_name = get_model_name_from_path(model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, cache_dir=args.cache_dir)
    
    with open(args.question_file, 'r') as f:
        questions = json.load(f)
    #questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]

    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
    answers_file = os.path.expanduser(args.answers_file)
    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
    
    ans_file = open(answers_file, "w")

    skipped_count = 0   
 
    for i in tqdm(range(0,len(questions),args.batch_size)):
        input_batch=[]
        input_image_batch=[]
        count=i
        image_folder=[]     
        batch_end = min(i + args.batch_size, len(questions))

        for j in range(i,batch_end):
            if 'image' not in questions[j]:
                print(f"Skipped entry [{skipped_count}]")
                skipped_count += 1
                continue

            print(questions[j])
            image_file=questions[j]['image']
            qs=questions[j]['conversations'][0]['value']
            
            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()

            input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
            input_batch.append(input_ids)

            image = Image.open(os.path.join(args.image_folder, image_file))

            image_folder.append(image)

            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
            keywords = [stop_str]
            stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)

        if len(input_batch) == 0:
            print("All images here were skipped")
            continue

        max_length = max(tensor.size(1) for tensor in input_batch)

        final_input_list = [torch.cat((torch.zeros((1,max_length - tensor.size(1)), dtype=tensor.dtype,device=tensor.get_device()), tensor),dim=1) for tensor in input_batch]
        final_input_tensors=torch.cat(final_input_list,dim=0)
        image_tensor_batch = image_processor.preprocess(image_folder,crop_size ={'height': 504, 'width': 504},size = {'shortest_edge': 504}, return_tensors='pt')['pixel_values']

        with torch.inference_mode():
            output_ids = model.generate( final_input_tensors, images=image_tensor_batch.half().cuda(), do_sample=False , temperature=args.temperature, top_p=args.top_p, num_beams=1, max_new_tokens=256,length_penalty=2.0, use_cache=True)

        input_token_len = final_input_tensors.shape[1]
        n_diff_input_output = (final_input_tensors != 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)
        for k in range(0,len(final_input_list)):
            output = outputs[k].strip()
            if output.endswith(stop_str):
                output = output[:-len(stop_str)]
            output = output.strip()

            ans_id = shortuuid.uuid()
            
            ans_file.write(json.dumps({
                                    "id": questions[count]["id"],
                                    "image_id": questions[count]["image"],
                                    "question": questions[count]['conversations'][0]['value'],
                                    "predicted": output,
                                    "ground_truth": questions[count]['conversations'][1]['value'],
                                    "task": questions[count]['task'],
                                    "linked_id": questions[count]['linked_id']
                                    }) + "\n")
            count=count+1
            ans_file.flush()
    ans_file.close()

    output = [json.loads(q) for q in open((ans_file), "r")]
    output = [{q['id']: q} for q in output]
    with open(ans_file, 'r') as f:
        json.dump(output, f)
    
    agg_ans_file = ans_file.replace('.jsonl', '_agg.jsonl')
    print("Raw Geochat output saved to ", ans_file)
    print("Now parsing and aggregating votes for geovlm evaluation...")
    aggregate_accuracy(ans_file, agg_ans_file)
    print("Aggregated output saved to ", agg_ans_file)

    accuracy_precision_recall(agg_ans_file, 'fmow')

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-folder", type=str, default="")
    parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
    parser.add_argument("--conv-mode", type=str, default="llava_v1")
    parser.add_argument("--num-chunks", type=int, default=1)
    parser.add_argument("--chunk-idx", type=int, default=0)
    parser.add_argument("--temperature", type=float, default=0.2)
    parser.add_argument("--top_p", type=float, default=None)
    parser.add_argument("--num_beams", type=int, default=1)
    parser.add_argument("--batch_size",type=int, default=1)
    parser.add_argument("--cache-dir", type=str, default=None)
    args = parser.parse_args()

    eval_model(args)