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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)
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