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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
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, DefaultInpaintingTrainingModule
from saicinpainting.utils import register_debug_signal_handlers, get_shape
LOGGER = logging.getLogger(__name__)
@hydra.main(config_path='../configs/prediction', config_name='default_inner_features.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))
checkpoint_path = os.path.join(predict_config.model.path, 'models', predict_config.model.checkpoint)
model = load_checkpoint(train_config, checkpoint_path, strict=False)
model.freeze()
model.to(device)
assert isinstance(model, DefaultInpaintingTrainingModule), 'Only DefaultInpaintingTrainingModule is supported'
assert isinstance(getattr(model.generator, 'model', None), torch.nn.Sequential)
if not predict_config.indir.endswith('/'):
predict_config.indir += '/'
dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
max_level = max(predict_config.levels)
with torch.no_grad():
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])
os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
batch = move_to_device(default_collate([dataset[img_i]]), device)
img = batch['image']
mask = batch['mask']
mask[:] = 0
mask_h, mask_w = mask.shape[-2:]
mask[:, :,
mask_h // 2 - predict_config.hole_radius : mask_h // 2 + predict_config.hole_radius,
mask_w // 2 - predict_config.hole_radius : mask_w // 2 + predict_config.hole_radius] = 1
masked_img = torch.cat([img * (1 - mask), mask], dim=1)
feats = masked_img
for level_i, level in enumerate(model.generator.model):
feats = level(feats)
if level_i in predict_config.levels:
cur_feats = torch.cat([f for f in feats if torch.is_tensor(f)], dim=1) \
if isinstance(feats, tuple) else feats
if predict_config.slice_channels:
cur_feats = cur_feats[:, slice(*predict_config.slice_channels)]
cur_feat = cur_feats.pow(2).mean(1).pow(0.5).clone()
cur_feat -= cur_feat.min()
cur_feat /= cur_feat.std()
cur_feat = cur_feat.clamp(0, 1) / 1
cur_feat = cur_feat.cpu().numpy()[0]
cur_feat *= 255
cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')
cv2.imwrite(cur_out_fname + f'_lev{level_i:02d}_norm.png', cur_feat)
# for channel_i in predict_config.channels:
#
# cur_feat = cur_feats[0, channel_i].clone().detach().cpu().numpy()
# cur_feat -= cur_feat.min()
# cur_feat /= cur_feat.max()
# cur_feat *= 255
# cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')
# cv2.imwrite(cur_out_fname + f'_lev{level_i}_ch{channel_i}.png', cur_feat)
elif level_i >= max_level:
break
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()
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