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#!/usr/bin/env python3
# Example command:
# ./bin/predict.py \
# model.path=<path to checkpoint, prepared by make_checkpoint.py> \
# indir=<path to input data> \
# outdir=<where to store predicts>
import logging
import os
import sys
import traceback
from saicinpainting.evaluation.utils import move_to_device
from saicinpainting.evaluation.refinement import refine_predict
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
os.environ['NUMEXPR_NUM_THREADS'] = '1'
import cv2
import hydra
import numpy as np
import torch
import tqdm
import yaml
from omegaconf import OmegaConf
from torch.utils.data._utils.collate import default_collate
from saicinpainting.training.data.datasets import make_default_val_dataset
from saicinpainting.training.trainers import load_checkpoint
from saicinpainting.utils import register_debug_signal_handlers
LOGGER = logging.getLogger(__name__)
@hydra.main(config_path='../configs/prediction', config_name='default.yaml')
def main(predict_config: OmegaConf):
try:
register_debug_signal_handlers() # kill -10 <pid> will result in traceback dumped into log
device = torch.device(predict_config.device)
train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
with open(train_config_path, 'r') as f:
train_config = OmegaConf.create(yaml.safe_load(f))
train_config.training_model.predict_only = True
train_config.visualizer.kind = 'noop'
out_ext = predict_config.get('out_ext', '.png')
checkpoint_path = os.path.join(predict_config.model.path,
'models',
predict_config.model.checkpoint)
model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu')
model.freeze()
if not predict_config.get('refine', False):
model.to(device)
if not predict_config.indir.endswith('/'):
predict_config.indir += '/'
dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
for img_i in tqdm.trange(len(dataset)):
mask_fname = dataset.mask_filenames[img_i]
cur_out_fname = os.path.join(
predict_config.outdir,
os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext
)
os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
batch = default_collate([dataset[img_i]])
if predict_config.get('refine', False):
assert 'unpad_to_size' in batch, "Unpadded size is required for the refinement"
# image unpadding is taken care of in the refiner, so that output image
# is same size as the input image
cur_res = refine_predict(batch, model, **predict_config.refiner)
cur_res = cur_res[0].permute(1,2,0).detach().cpu().numpy()
else:
with torch.no_grad():
batch = move_to_device(batch, device)
batch['mask'] = (batch['mask'] > 0) * 1
batch = model(batch)
cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()
unpad_to_size = batch.get('unpad_to_size', None)
if unpad_to_size is not None:
orig_height, orig_width = unpad_to_size
cur_res = cur_res[:orig_height, :orig_width]
cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
cv2.imwrite(cur_out_fname, cur_res)
except KeyboardInterrupt:
LOGGER.warning('Interrupted by user')
except Exception as ex:
LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
sys.exit(1)
if __name__ == '__main__':
main() |