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generate_masks.py
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"""
Script generates predictions, splitting original images into tiles, and assembling prediction back together
"""
import argparse
from prepare_train_val import get_split
from dataset import RoboticsDataset
import cv2
from models import UNet16, LinkNet34, UNet11, UNet, AlbuNet
import torch
from pathlib import Path
from tqdm import tqdm
import numpy as np
import utils
import prepare_data
from torch.utils.data import DataLoader
from torch.nn import functional as F
from prepare_data import (original_height,
original_width,
h_start, w_start
)
from albumentations import Compose, Normalize
def img_transform(p=1):
return Compose([
Normalize(p=1)
], p=p)
def get_model(model_path, model_type='UNet11', problem_type='binary'):
"""
:param model_path:
:param model_type: 'UNet', 'UNet16', 'UNet11', 'LinkNet34', 'AlbuNet'
:param problem_type: 'binary', 'parts', 'instruments'
:return:
"""
if problem_type == 'binary':
num_classes = 1
elif problem_type == 'parts':
num_classes = 4
elif problem_type == 'instruments':
num_classes = 8
if model_type == 'UNet16':
model = UNet16(num_classes=num_classes)
elif model_type == 'UNet11':
model = UNet11(num_classes=num_classes)
elif model_type == 'LinkNet34':
model = LinkNet34(num_classes=num_classes)
elif model_type == 'AlbuNet':
model = AlbuNet(num_classes=num_classes)
elif model_type == 'UNet':
model = UNet(num_classes=num_classes)
state = torch.load(str(model_path))
state = {key.replace('module.', ''): value for key, value in state['model'].items()}
model.load_state_dict(state)
if torch.cuda.is_available():
return model.cuda()
model.eval()
return model
def predict(model, from_file_names, batch_size, to_path, problem_type, img_transform):
loader = DataLoader(
dataset=RoboticsDataset(from_file_names, transform=img_transform, mode='predict', problem_type=problem_type),
shuffle=False,
batch_size=batch_size,
num_workers=args.workers,
pin_memory=torch.cuda.is_available()
)
with torch.no_grad():
for batch_num, (inputs, paths) in enumerate(tqdm(loader, desc='Predict')):
inputs = utils.cuda(inputs)
outputs = model(inputs)
for i, image_name in enumerate(paths):
if problem_type == 'binary':
factor = prepare_data.binary_factor
t_mask = (F.sigmoid(outputs[i, 0]).data.cpu().numpy() * factor).astype(np.uint8)
elif problem_type == 'parts':
factor = prepare_data.parts_factor
t_mask = (outputs[i].data.cpu().numpy().argmax(axis=0) * factor).astype(np.uint8)
elif problem_type == 'instruments':
factor = prepare_data.instrument_factor
t_mask = (outputs[i].data.cpu().numpy().argmax(axis=0) * factor).astype(np.uint8)
h, w = t_mask.shape
full_mask = np.zeros((original_height, original_width))
full_mask[h_start:h_start + h, w_start:w_start + w] = t_mask
instrument_folder = Path(paths[i]).parent.parent.name
(to_path / instrument_folder).mkdir(exist_ok=True, parents=True)
cv2.imwrite(str(to_path / instrument_folder / (Path(paths[i]).stem + '.png')), full_mask)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--model_path', type=str, default='data/models/UNet', help='path to model folder')
arg('--model_type', type=str, default='UNet', help='network architecture',
choices=['UNet', 'UNet11', 'UNet16', 'LinkNet34', 'AlbuNet'])
arg('--output_path', type=str, help='path to save images', default='1')
arg('--batch-size', type=int, default=4)
arg('--fold', type=int, default=-1, choices=[0, 1, 2, 3, -1], help='-1: all folds')
arg('--problem_type', type=str, default='binary', choices=['binary', 'parts', 'instruments'])
arg('--workers', type=int, default=12)
args = parser.parse_args()
if args.fold == -1:
for fold in [0, 1, 2, 3]:
_, file_names = get_split(fold)
model = get_model(str(Path(args.model_path).joinpath('model_{fold}.pt'.format(fold=fold))),
model_type=args.model_type, problem_type=args.problem_type)
print('num file_names = {}'.format(len(file_names)))
output_path = Path(args.output_path)
output_path.mkdir(exist_ok=True, parents=True)
predict(model, file_names, args.batch_size, output_path, problem_type=args.problem_type,
img_transform=img_transform(p=1))
else:
_, file_names = get_split(args.fold)
model = get_model(str(Path(args.model_path).joinpath('model_{fold}.pt'.format(fold=args.fold))),
model_type=args.model_type, problem_type=args.problem_type)
print('num file_names = {}'.format(len(file_names)))
output_path = Path(args.output_path)
output_path.mkdir(exist_ok=True, parents=True)
predict(model, file_names, args.batch_size, output_path, problem_type=args.problem_type,
img_transform=img_transform(p=1))