One-Prompt/val.py

import os
import sys
import argparse
from datetime import datetime
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.metrics import roc_auc_score, accuracy_score,confusion_matrix
import torchvision
import torchvision.transforms as transforms
from skimage import io
from torch.utils.data import DataLoader
#from dataset import *
from torch.autograd import Variable
from PIL import Image
from tensorboardX import SummaryWriter
#from models.discriminatorlayer import discriminator
from dataset import *
from conf import settings
import time
import cfg
from tqdm import tqdm
from torch.utils.data import DataLoader, random_split
from utils import *
import function


args = cfg.parse_args()

GPUdevice = torch.device('cuda', args.gpu_device)

net = get_network(args, args.net, use_gpu=args.gpu, gpu_device=GPUdevice, distribution = args.distributed)

'''load pretrained model'''
assert args.weights != 0
print(f'=> resuming from {args.weights}')
assert os.path.exists(args.weights)
checkpoint_file = os.path.join(args.weights)
assert os.path.exists(checkpoint_file)
loc = 'cuda:{}'.format(args.gpu_device)
checkpoint = torch.load(checkpoint_file, map_location=loc)
start_epoch = checkpoint['epoch']
best_tol = checkpoint['best_tol']

state_dict = checkpoint['state_dict']
if args.distributed != 'none':
    from collections import OrderedDict
    new_state_dict = OrderedDict()
    for k, v in state_dict.items():
        name = 'module.' + k
        new_state_dict[name] = v
    # load params
else:
    new_state_dict = state_dict

# Filter out problematic weights that depend on token_num (which varies with image_size)
filtered_state_dict = {}
for k, v in new_state_dict.items():
    # Skip mask_decoder.deals.*.p1_tokens.weight and mask_decoder.deals.*.p2_tokens.weight
    if 'mask_decoder.deals' in k and ('p1_tokens.weight' in k or 'p2_tokens.weight' in k):
        continue
    filtered_state_dict[k] = v

# Load the filtered state dict with strict=False to skip the problematic layers
net.load_state_dict(filtered_state_dict, strict=False)

args.path_helper = set_log_dir('logs', args.exp_name)
logger = create_logger(args.path_helper['log_path'])
logger.info(args)

'''segmentation data'''
transform_train = transforms.Compose([
    transforms.Resize((args.image_size,args.image_size)),
    transforms.ToTensor(),
])

transform_train_seg = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize((args.image_size,args.image_size)),
])

transform_test = transforms.Compose([
    transforms.Resize((args.image_size, args.image_size)),
    transforms.ToTensor(),
])

transform_test_seg = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize((args.image_size, args.image_size)),
    
])
'''data end'''
if args.dataset == 'isic':
    '''isic data'''
    isic_train_dataset = ISIC2016(args, args.data_path, transform = transform_train, transform_msk= transform_train_seg, mode = 'Training')
    isic_test_dataset = ISIC2016(args, args.data_path, transform = transform_test, transform_msk= transform_test_seg, mode = 'Test')

    nice_train_loader = DataLoader(isic_train_dataset, batch_size=args.b, shuffle=True, num_workers=8, pin_memory=True)
    nice_test_loader = DataLoader(isic_test_dataset, batch_size=args.b, shuffle=False, num_workers=8, pin_memory=True)
    '''end'''

elif args.dataset == 'oneprompt':
    nice_train_loader, nice_test_loader, transform_train, transform_val, train_list, val_list =get_decath_loader(args)

elif args.dataset == 'REFUGE':
    '''REFUGE data'''
    refuge_train_dataset = REFUGE(args, args.data_path, transform = transform_train, transform_msk= transform_train_seg, mode = 'Training')
    refuge_test_dataset = REFUGE(args, args.data_path, transform = transform_test, transform_msk= transform_test_seg, mode = 'Test')

    nice_train_loader = DataLoader(refuge_train_dataset, batch_size=args.b, shuffle=True, num_workers=8, pin_memory=True)
    nice_test_loader = DataLoader(refuge_test_dataset, batch_size=args.b, shuffle=False, num_workers=8, pin_memory=True)
    '''end'''

'''begain valuation'''
best_acc = 0.0
best_tol = 1e4

# 支持所有mod模式，不仅限于sam_adpt
net.eval()
tol, metrics = function.validation_one(args, nice_test_loader, 0, net)

# Handle both 2-metric and 4-metric cases
if len(metrics) == 2:
    eiou, edice = metrics
    logger.info(f'Total score: {tol}, IOU: {eiou}, DICE: {edice} || @ epoch {start_epoch}.')
elif len(metrics) == 4:
    iou_d, iou_c, disc_dice, cup_dice = metrics
    logger.info(f'Total score: {tol}, Disc IOU: {iou_d}, Cup IOU: {iou_c}, Disc DICE: {disc_dice}, Cup DICE: {cup_dice} || @ epoch {start_epoch}.')
else:
    logger.info(f'Total score: {tol}, Metrics: {metrics} || @ epoch {start_epoch}.')