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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data as data
import numpy as np
import random
import tqdm
import os
from pathlib import Path
from data_utils.data_tribology import TribologyDataset
from utils.experiment_utils import get_model, get_name, get_logger, train, evaluate, evaluate_vote, evaluate_vote_analysis
from utils.arg_utils import get_args
def main(args):
'''Reproducibility'''
SEED = args.seed
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
'''Folder Creation'''
basepath=os.getcwd()
experiment_dir = Path(os.path.join(basepath,'experiments',args.model,args.resolution,args.magnification,args.modality,args.pretrained,args.frozen,args.vote))
experiment_dir.mkdir(parents=True, exist_ok=True)
checkpoint_dir = Path(os.path.join(experiment_dir,'checkpoints'))
checkpoint_dir.mkdir(parents=True, exist_ok=True)
'''Logging'''
model_name = get_name(args)
print(model_name, 'STARTED')
logger = get_logger(experiment_dir, 'vote_analysis')
'''Data Loading'''
train_csv_path = f"./LUA_Dataset/CSV/{args.resolution}_{args.magnification}_6w_train.csv"
test_csv_path = f"./LUA_Dataset/CSV/{args.resolution}_{args.magnification}_6w_test.csv"
img_path = f"./LUA_Dataset/{args.resolution}/{args.magnification}/{args.modality}"
# results_acc_1 = {}
# results_acc_3 = {}
# classes_num = 6
BATCHSIZE = args.batch_size
train_dataset = TribologyDataset(csv_path = train_csv_path, img_path = img_path)
test_dataset = TribologyDataset(csv_path = test_csv_path, img_path = img_path)
# prepare the data augmentation
means, stds = train_dataset.get_statistics()
train_dataset.prepare_transform(means, stds, mode='train')
test_dataset.prepare_transform(means, stds, mode='test')
VALID_RATIO = 0.1
num_train = len(train_dataset)
num_valid = int(VALID_RATIO * num_train)
train_dataset, valid_dataset = data.random_split(train_dataset, [num_train - num_valid, num_valid])
logger.info(f'Number of training samples: {len(train_dataset)}')
logger.info(f'Number of validation samples: {len(valid_dataset)}')
test_iterator = torch.utils.data.DataLoader(test_dataset,
batch_size=BATCHSIZE,
num_workers=4,
shuffle=False,
pin_memory=True,
drop_last=False)
print('DATA LOADED')
# Define model
model = get_model(args)
print('MODEL LOADED')
# Define device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
print('SETUP DONE')
# train our model
print('TRAINING STARTED')
model.load_state_dict(torch.load(checkpoint_dir / f'epoch{args.epochs}.pth'))
logger.info('-------------------Beginning of Testing-------------------')
print('TESTING STARTED')
vote_accuracy, correct_case_accuracy, incorrect_case_accuracy, incorrect_most_common, novote_accuracy = evaluate_vote_analysis(model, test_iterator, device)
logger.info(f'Test Acc @1: {vote_accuracy * 100:6.2f}%')
logger.info(f'No Vote Accuracy @1: {novote_accuracy * 100:6.2f}%')
logger.info(f'Correct Case Consistency @1: {correct_case_accuracy * 100:6.2f}%')
logger.info(f'Incorrect Case Consistency @1: {incorrect_case_accuracy * 100:6.2f}%')
logger.info(f'Incorrect Most Common: {incorrect_most_common* 100:6.2f}%')
logger.info('-------------------End of Testing-------------------')
print('TESTING DONE')
if __name__ == '__main__':
args = get_args()
main(args)
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