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eval.py
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import os
import pprint
from collections import OrderedDict, defaultdict
import sys
import numpy as np
import torch
from torch.utils.data import DataLoader
import time
from torch import nn, optim
from batch_engine import valid_trainer
from config import argument_parser
from dataset.AttrDataset import MultiModalAttrDataset, get_transform
from loss.CE_loss import *
from models.base_block import *
from tools.function import get_pedestrian_metrics, get_signle_metrics
from tools.utils import time_str, save_ckpt, ReDirectSTD, set_seed, select_gpus
import torch.nn.functional as F
from CLIP.clip import clip
from CLIP.clip.model import *
from tensorboardX import SummaryWriter
import torch.distributed as dist
import torch.nn.parallel
from torch.utils.data.distributed import DistributedSampler
from peft import LoraConfig, get_peft_model,AdaLoraConfig
def main(args):
ViT_model, ViT_preprocess = clip.load("ViT-B/16", device=device,download_root='/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/yanghaoxiang/VBT/model')
Event_ViT_model,Event_ViT_preprocess = clip.load("ViT-B/16", device=device,download_root='/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/yanghaoxiang/VBT/model')
frame_ViT_model,frame_ViT_preprocess = clip.load("ViT-B/16", device=device,download_root='/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/yanghaoxiang/VBT/model')
lora_config_ViT_model = LoraConfig(
r=4,
lora_alpha=8,
# target_modules=["out_proj","c_fc","c_proj"],
target_modules=["c_fc","c_proj"],
#在这里怎么确定需要加的模块？
lora_dropout=0.01,
task_type="TOKEN_CLS",
bias="none"
)
ViT_model = get_peft_model(ViT_model,lora_config_ViT_model)
Event_ViT_model = get_peft_model(Event_ViT_model,lora_config_ViT_model)
frame_ViT_model = get_peft_model(frame_ViT_model,lora_config_ViT_model)
ViT_model = ViT_model.float()
Event_ViT_model = Event_ViT_model.float()
frame_ViT_model = frame_ViT_model.float()
ViT_model = ViT_model.to(args.local_rank)
Event_ViT_model = Event_ViT_model.to(args.local_rank)
frame_ViT_model = frame_ViT_model.to(args.local_rank)
dist.init_process_group(backend='nccl')
torch.cuda.set_device(args.local_rank)
valid_tsfm = get_transform(args)[1]
valid_set = MultiModalAttrDataset(args=args, split=args.valid_split, transform=valid_tsfm)
valid_sampler = DistributedSampler(valid_set)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=8,
pin_memory=True,
sampler=valid_sampler
)
model = TransformerClassifier(valid_set.attr_num, attr_words=valid_set.attributes)
model = torch.nn.parallel.DistributedDataParallel(model.cuda(), find_unused_parameters=True)
checkpoint = torch.load('/media/amax/c08a625b-023d-436f-b33e-9652dc1bc7c0/DATA/yanghaoxiang/VBT/poker_frame8_checkpoint/best_checkpoint.pth',map_location='cuda:0')
#可视化
#checkpoint = torch.load('/media/amax/836e911f-c5c3-4c4b-91f2-41bb8f3f5cb6/DATA/lidong/VTF_PAR-main/Visualization/VTF/best_checkpoint.pth',map_location='cuda:0')
model.load_state_dict(checkpoint['model_state_dict'], strict=False)
ViT_model.load_state_dict(checkpoint['ViT_model_state_dict'], strict=False)
Event_ViT_model.load_state_dict(checkpoint['Event_ViT_model_state_dict'],strict=False)
frame_ViT_model.load_state_dict(checkpoint['frame_ViT_model_state_dict'],strict=False)
criterion = nn.CrossEntropyLoss()
valid_loss, valid_gt, valid_probs = valid_trainer(
epoch=1,
model=model,
ViT_model=ViT_model,
Event_ViT_model=Event_ViT_model,
frame_ViT_model=frame_ViT_model,
valid_loader=valid_loader,
criterion=criterion,
)
if args.dataset == 'MARS':
valid_preds = valid_probs.argmax(axis=1)
valid_gt = valid_gt.argmax(axis=1)
valid_correct_predictions = (valid_preds == valid_gt).sum()
valid_accuracy = valid_correct_predictions / len(valid_gt)
#######
print('===>>valid_accuracy = ', valid_accuracy)
print('===>>Testing Complete...')
if __name__ == '__main__':
parser = argument_parser()
parser.add_argument('--local_rank', type=int, help='Local rank for distributed training')
args = parser.parse_args()
main(args)