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4ce9939 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 | import torch
import os
import argparse
from dataclasses import dataclass, field
import transformers
from torch.utils.data import Dataset, DataLoader
from transformers import CLIPImageProcessor
import pdb
import json
from transformers import AutoProcessor, LlavaForConditionalGeneration
from tqdm import tqdm
import random
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from vllm import LLM, SamplingParams
import pdb
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def parse_args():
parser = argparse.ArgumentParser(description="FakeVLM Model Testing")
# Model-specific settings
parser.add_argument("--model_path", default="", type=str)
parser.add_argument("--val_batch_size", default=1, type=int)
parser.add_argument("--workers", default=1, type=int)
parser.add_argument("--data_base_test", default="", type=str)
parser.add_argument("--test_json_file", default="", type=str)
parser.add_argument("--output_path", default="", type=str)
return parser.parse_args()
class legion_cls_dataset(Dataset):
def __init__(self, args, train=True):
super().__init__()
self.args = args
self.train = train
if train == True:
with open(args.train_json_file, 'r') as f:
self.data = json.load(f)
elif train == False:
with open(args.test_json_file, 'r') as f:
self.data = json.load(f)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
if self.train == True:
img_path = os.path.join(self.args.data_base_train, self.data[idx]['image'])
else:
img_path = os.path.join(self.args.data_base_test, self.data[idx]['image'])
label = self.data[idx]['label']
cate = self.data[idx]['cate']
# torch.Size([n, 3, 448, 448]), int, int, str, str
return [self.data[idx]['conversations'][0]['value']], [label], [img_path], [cate]
# change this function to our own to support custom behaviors
def load_model(args):
print("Loading model...")
llm = LLM(model=args.model_path,
dtype="float16",
max_model_len=800,
tensor_parallel_size=torch.cuda.device_count()
)
print("Successfully loaded model from:", args.model_path)
return llm
def calculate_results_acc(results):
acc_results = {}
for cate in results:
data = results[cate]
right_real = data['right']['right_real']
right_fake = data['right']['right_fake']
wrong_real = data['wrong']['wrong_real']
wrong_fake = data['wrong']['wrong_fake']
total_real = right_real + wrong_real
total_fake = right_fake + wrong_fake
total = total_real + total_fake
acc_total = (right_real + right_fake) / total if total != 0 else 0
acc_real = right_real / total_real if total_real != 0 else 0
acc_fake = right_fake / total_fake if total_fake != 0 else 0
acc_results[cate] = {
'total_samples': total,
'total_accuracy': round(acc_total, 4),
'real_accuracy': round(acc_real, 4),
'fake_accuracy': round(acc_fake, 4),
'overall': {
'right_real': right_real,
'wrong_real': wrong_real,
'right_fake': right_fake,
'wrong_fake': wrong_fake,
}
}
global_stats = {
'total_right': sum(r['right']['right_real'] + r['right']['right_fake'] for r in results.values()),
'total_wrong': sum(r['wrong']['wrong_real'] + r['wrong']['wrong_fake'] for r in results.values())
}
global_stats['global_accuracy'] = global_stats['total_right'] / (global_stats['total_right'] + global_stats['total_wrong'])
return {
'category_acc': acc_results,
'global_stats': global_stats
}
def validate(args, model, cls_test_dataloader):
results = {}
output_result = []
sampling_params = SamplingParams(
max_tokens=4096,
temperature=0,
)
with torch.no_grad():
for questions, labels, imgs, cates in tqdm(cls_test_dataloader):
inputs = []
for question, img in zip(questions[0], imgs[0]):
inputs.append({
"prompt": question,
"multi_modal_data": {"image": Image.open(img)},
})
# pdb.set_trace()
outputs = model.generate(inputs, sampling_params=sampling_params)
pred_cls = []
for i, output in enumerate(outputs):
# pdb.set_trace()
# #save result
output_result.append({'id':imgs[0][i], 'caption':output.outputs[0].text})
response = output.outputs[0].text
if 'real' in response.split('.')[0].lower():
pred_cls.append(1)
elif 'fake' in response.split('.')[0].lower():
pred_cls.append(0)
else:
try:
if 'real' in response.split('.')[1].lower():
pred_cls.append(1)
elif 'fake' in response.split('.')[1].lower():
pred_cls.append(0)
else:
print(f"no fake or real in reponse:{response}")
pred_cls.append(random.choice([0, 1]))
except:
print(f"no fake or real in reponse:{response}")
pred_cls.append(random.choice([0, 1]))
for label, pred, cate in zip(labels[0].tolist(), pred_cls, cates[0]):
if cate not in results:
results[cate] = {'right':{'right_fake':0, 'right_real':0}, 'wrong':{'wrong_fake':0, 'wrong_real':0}}
if label == pred:
if label == 1:
results[cate]['right']['right_real'] += 1
else:
results[cate]['right']['right_fake'] += 1
else:
if label == 1:
results[cate]['wrong']['wrong_real'] += 1
else:
results[cate]['wrong']['wrong_fake'] += 1
# save result
os.makedirs("results", exist_ok=True)
with open(args.output_path, "w") as file:
json.dump(output_result, file, indent=2)
acc = calculate_results_acc(results)
print(acc)
def main():
args = parse_args()
model = load_model(args)
cls_test_dataset = legion_cls_dataset(args, train=False)
cls_test_dataloader = DataLoader(
cls_test_dataset,
batch_size=args.val_batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
validate(args, model, cls_test_dataloader)
if __name__ == "__main__":
main()
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