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batch_geochat_sceneclassification.py
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batch_geochat_sceneclassification.py
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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
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 PIL import Image
import math
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 calculate_precision_recall(gt_class, pred_class):
gt_rels = set(gt_class)
pred_rels = set(pred_class)
# Calculate the number of true positives (tp), false positives (fp), and false negatives (fn)
tp = len(gt_rels & pred_rels)
fp = len(pred_rels - gt_rels)
fn = len(gt_rels - pred_rels)
# Calculate precision and recall
precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
return precision, recall
def calculate_tpfpfn(gt_class, pred_class):
gt_rels = set(gt_class)
pred_rels = set(pred_class)
# Calculate the number of true positives (tp), false positives (fp), and false negatives (fn)
tp = len(gt_rels & pred_rels)
fp = len(pred_rels - gt_rels)
fn = len(gt_rels - pred_rels)
return tp, fp, fn
def calculate_PRF1(tp, fp, fn):
precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
return precision, recall, f1
def evaluation_metrics(data_path):
base = [json.loads(q) for q in open(data_path, "r")]
correct_single=0
incorrect_single=0
count = 0
tp_total = 0
fp_total = 0
fn_total = 0
for answers in tqdm(base):
question_text = answers['question']
if question_text.endswith("Answer in one word or a short phrase."):
mode = "single"
elif question_text.endswith("Answer with all applicable classes separated by commas."):
mode = "multi"
gt=answers['ground_truth'].lower()
if mode == "single":
if gt==answers['answer'].lower():
correct_single=correct_single+1
else:
incorrect_single=incorrect_single+1
elif mode == "multi":
gt_obj = [label.strip() for label in gt.split(",")]
answer_obj = [an.strip() for an in answers['answer'].lower().split(",")]
tp, fp, fn = calculate_tpfpfn(gt_obj, answer_obj)
tp_total+=tp
fp_total+=fp
fn_total+=fn
count += 1
print('correct_scene:',correct_single)
print('incorrect_scene:',incorrect_single)
print('Total:',correct_single+incorrect_single)
if (correct_single+incorrect_single)>0:
print('Scene Classify Accuracy:',(correct_single/(correct_single+incorrect_single)))
precision_total, recall_total, f1_total = calculate_PRF1(tp_total, fp_total, fn_total)
print(f'New Average Precision: {precision_total:.4f}')
print(f'New Average Recall: {recall_total:.4f}')
print(f'New F1 score: {f1_total:.4f}')
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)
# print(model)
questions=[]
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")
print(f'answer file:{answers_file}')
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):
image_file=questions[j]['image']
qs=questions[j]['text']
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)
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({
"question_id": questions[count]["question_id"],
"image_id": questions[count]["image"],
"question":questions[count]['text'],
"answer": output,
"ground_truth": questions[count]['ground_truth']
}) + "\n")
count=count+1
ans_file.flush()
ans_file.close()
evaluation_metrics(answers_file)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str,
default=None)
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-folder", type=str,
default=None)
parser.add_argument("--question-file", type=str,
default=None)
parser.add_argument("--answers-file", type=str,
default=None)
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)
args = parser.parse_args()
eval_model(args)