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predict_TTA_2.py
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predict_TTA_2.py
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"""
Predict
"""
from datetime import datetime
from tqdm import tqdm
import numpy as np
import random, os, sys, torch, cv2, warnings
from glob import glob
from torch.utils.data import DataLoader
prj_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(prj_dir)
from modules.utils import load_yaml, save_yaml, get_logger
from modules.scalers import get_image_scaler
from modules.datasets import SegDataset_TTA
from models.utils import get_model
warnings.filterwarnings('ignore')
if __name__ == '__main__':
#! Load config
config = load_yaml(os.path.join(prj_dir, 'config', 'predict_2.yaml'))
train_config = load_yaml(os.path.join(prj_dir, 'results', 'train', config['train_serial'], 'train.yaml'))
train_config_2 = load_yaml(os.path.join(prj_dir, 'results', 'train', config['train_serial_2'], 'train.yaml'))
#! Set predict serial
pred_serial = config['train_serial'] + '_' + datetime.now().strftime("%Y%m%d_%H%M%S") + '_2'
# Set random seed, deterministic
torch.cuda.manual_seed(train_config['seed'])
torch.manual_seed(train_config['seed'])
np.random.seed(train_config['seed'])
random.seed(train_config['seed'])
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Set device(GPU/CPU)
os.environ['CUDA_VISIBLE_DEVICES'] = str(config['gpu_num'])
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Create train result directory and set logger
pred_result_dir = os.path.join(prj_dir, 'results', 'pred', pred_serial)
pred_result_dir_mask = os.path.join(prj_dir, 'results', 'pred', pred_serial, 'mask')
os.makedirs(pred_result_dir, exist_ok=True)
os.makedirs(pred_result_dir_mask, exist_ok=True)
# Set logger
logging_level = 'debug' if config['verbose'] else 'info'
logger = get_logger(name='train',
file_path=os.path.join(pred_result_dir, 'pred.log'),
level=logging_level)
# Set data directory
test_dirs = os.path.join(prj_dir, 'data', 'test')
test_img_paths = glob(os.path.join(test_dirs, 'x', '*.png'))
#! Load data & create dataset for train
test_dataset = SegDataset_TTA(paths=test_img_paths,
input_size=[train_config['input_width'], train_config['input_height']],
scaler=get_image_scaler(train_config['scaler']),
mode='test',
logger=logger)
# Create data loader
test_dataloader = DataLoader(dataset=test_dataset,
batch_size=config['batch_size'],
num_workers=config['num_workers'],
shuffle=False,
drop_last=False)
logger.info(f"Load test dataset: {len(test_dataset)}")
# Load architecture
model = get_model(model_str=train_config['architecture'])
if train_config['architecture'] == 'Lawin':
model.to(device)
else:
model = model(
classes=train_config['n_classes'],
encoder_name=train_config['encoder'],
encoder_weights=train_config['encoder_weight'],
activation=train_config['activation']).to(device)
logger.info(f"Load model architecture: {train_config['architecture']}")
#! Load weight
check_point_path = os.path.join(prj_dir, 'results', 'train', config['train_serial'], 'model.pt')
check_point = torch.load(check_point_path)
model.load_state_dict(check_point['model'])
logger.info(f"Load model weight, {check_point_path}")
# Load architecture2
model2 = get_model(model_str=train_config_2['architecture'])
if train_config_2['architecture'] == 'Lawin':
model2.to(device)
else:
model2 = model2(
classes=train_config_2['n_classes'],
encoder_name=train_config_2['encoder'],
encoder_weights=train_config_2['encoder_weight'],
activation=train_config_2['activation']).to(device)
logger.info(f"Load model2 architecture: {train_config_2['architecture']}")
#! Load weight2
check_point_path = os.path.join(prj_dir, 'results', 'train', config['train_serial_2'], 'model.pt')
check_point = torch.load(check_point_path)
model2.load_state_dict(check_point['model'])
logger.info(f"Load model2 weight, {check_point_path}")
# Save config
save_yaml(os.path.join(pred_result_dir, 'train_config.yml'), train_config)
save_yaml(os.path.join(pred_result_dir, 'train_config_2.yml'), train_config_2)
save_yaml(os.path.join(pred_result_dir, 'predict_config.yml'), config)
# Predict
logger.info(f"START PREDICTION")
model.eval()
model2.eval()
with torch.no_grad():
for batch_id, (x, x2, x3, x4, orig_size, filename) in enumerate(tqdm(test_dataloader)):
x = x.to(device, dtype=torch.float)
x2 = x2.to(device, dtype=torch.float)
x3 = x3.to(device, dtype=torch.float)
x4 = x4.to(device, dtype=torch.float)
y_pred = model(x)
y_pred2 = model(x2)
y_pred3 = model(x3)
y_pred4 = model(x4)
y_pred5 = model2(x)
y_pred6 = model2(x2)
y_pred7 = model2(x3)
y_pred8 = model2(x4)
y_pred = y_pred.cpu().numpy()
y_pred2 = y_pred2.cpu().numpy()
y_pred3 = y_pred3.cpu().numpy()
y_pred4 = y_pred4.cpu().numpy()
y_pred5 = y_pred5.cpu().numpy()
y_pred6 = y_pred6.cpu().numpy()
y_pred7 = y_pred7.cpu().numpy()
y_pred8 = y_pred8.cpu().numpy()
for i in range(y_pred.shape[0]):
for x in range(4):
im1 = y_pred2[i][x][:, :224]
im2 = y_pred2[i][x][:, 224:]
im1 = np.rot90(im1, 1)
im2 = np.rot90(im2, 1)
y_pred2[i][x] = cv2.hconcat([im1, im2])
im1 = y_pred3[i][x][:, :224]
im2 = y_pred3[i][x][:, 224:]
im1 = np.rot90(im1, 2)
im2 = np.rot90(im2, 2)
y_pred3[i][x] = cv2.hconcat([im1, im2])
im1 = y_pred4[i][x][:, :224]
im2 = y_pred4[i][x][:, 224:]
im1 = np.rot90(im1, 3)
im2 = np.rot90(im2, 3)
y_pred4[i][x] = cv2.hconcat([im1, im2])
im1 = y_pred6[i][x][:, :224]
im2 = y_pred6[i][x][:, 224:]
im1 = np.rot90(im1, 1)
im2 = np.rot90(im2, 1)
y_pred6[i][x] = cv2.hconcat([im1, im2])
im1 = y_pred7[i][x][:, :224]
im2 = y_pred7[i][x][:, 224:]
im1 = np.rot90(im1, 2)
im2 = np.rot90(im2, 2)
y_pred7[i][x] = cv2.hconcat([im1, im2])
im1 = y_pred8[i][x][:, :224]
im2 = y_pred8[i][x][:, 224:]
im1 = np.rot90(im1, 3)
im2 = np.rot90(im2, 3)
y_pred8[i][x] = cv2.hconcat([im1, im2])
y_pred += y_pred2
y_pred += y_pred3
y_pred += y_pred4
y_pred += y_pred5
y_pred += y_pred6
y_pred += y_pred7
y_pred += y_pred8
y_pred_argmax = np.argmax(y_pred, axis=1).astype(np.uint8)
orig_size = [(orig_size[0].tolist()[i], orig_size[1].tolist()[i]) for i in range(len(orig_size[0]))]
# Save predict result
for filename_, orig_size_, y_pred_ in zip(filename, orig_size, y_pred_argmax):
resized_img = cv2.resize(y_pred_, [orig_size_[1], orig_size_[0]], interpolation=cv2.INTER_NEAREST)
cv2.imwrite(os.path.join(pred_result_dir_mask, filename_), resized_img)
logger.info(f"END PREDICTION")