forked from mrq/DL-Art-School
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
import os.path as osp
|
|
import logging
|
|
import random
|
|
import time
|
|
import argparse
|
|
from collections import OrderedDict
|
|
|
|
import utils
|
|
import utils.options as option
|
|
import utils.util as util
|
|
from trainer.ExtensibleTrainer import ExtensibleTrainer
|
|
from data import create_dataset, create_dataloader
|
|
from tqdm import tqdm
|
|
import torch
|
|
import numpy as np
|
|
|
|
current_batch = None
|
|
output_file = open('find_faulty_files_results.tsv', 'a')
|
|
|
|
class LossWrapper:
|
|
def __init__(self, lwrap):
|
|
self.lwrap = lwrap
|
|
self.opt = lwrap.opt
|
|
|
|
def is_stateful(self):
|
|
return self.lwrap.is_stateful()
|
|
|
|
def extra_metrics(self):
|
|
return self.lwrap.extra_metrics()
|
|
|
|
def clear_metrics(self):
|
|
self.lwrap.clear_metrics()
|
|
|
|
def __call__(self, m, state):
|
|
global current_batch
|
|
global output_file
|
|
val = state[self.lwrap.key]
|
|
assert val.shape[0] == len(current_batch['path'])
|
|
val = val.view(val.shape[0], -1)
|
|
val = val.mean(dim=1)
|
|
errant = torch.nonzero(val > 8)
|
|
for i in errant:
|
|
print(f"ERRANT FOUND: {val[i]} path: {current_batch['path'][i]}")
|
|
output_file.write(current_batch['path'][i] + "\n")
|
|
output_file.flush()
|
|
return self.lwrap(m, state)
|
|
|
|
|
|
# Script that builds an ExtensibleTrainer, then a pertinent loss with the above LossWrapper. The
|
|
# LossWrapper then croaks when it finds an input that produces a divergent loss
|
|
if __name__ == "__main__":
|
|
# Set seeds
|
|
torch.manual_seed(5555)
|
|
random.seed(5555)
|
|
np.random.seed(5555)
|
|
|
|
#### options
|
|
torch.backends.cudnn.benchmark = True
|
|
want_metrics = False
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../experiments/clean_with_lrdvae.yml')
|
|
opt = option.parse(parser.parse_args().opt, is_train=True)
|
|
opt = option.dict_to_nonedict(opt)
|
|
utils.util.loaded_options = opt
|
|
|
|
util.mkdirs(
|
|
(path for key, path in opt['path'].items()
|
|
if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
|
|
util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
|
|
screen=True, tofile=True)
|
|
logger = logging.getLogger('base')
|
|
logger.info(option.dict2str(opt))
|
|
|
|
#### Create test dataset and dataloader
|
|
dataset = create_dataset(opt['datasets']['train'])
|
|
dataloader = create_dataloader(dataset, opt['datasets']['train'])
|
|
logger.info('Number of test images in [{:s}]: {:d}'.format(opt['datasets']['train']['name'], len(dataset)))
|
|
|
|
model = ExtensibleTrainer(opt)
|
|
assert len(model.steps) == 1
|
|
|
|
step = model.steps[0]
|
|
step.losses['reconstruction_loss'] = LossWrapper(step.losses['reconstruction_loss'])
|
|
|
|
for i, data in enumerate(tqdm(dataloader)):
|
|
current_batch = data
|
|
model.feed_data(data, i)
|
|
model.optimize_parameters(i, optimize=False)
|
|
|
|
|