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predict.py
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predict.py
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import argparse
import os
import pandas as pd
import torch
from pytorch_toolbelt.utils import fs
from retinopathy.dataset import get_datasets
from retinopathy.inference import run_model_inference_via_dataset, run_model_inference
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input', nargs='+')
parser.add_argument('--need-features', action='store_true')
parser.add_argument('-b', '--batch-size', type=int, default=None, help='Batch Size during training, e.g. -b 64')
parser.add_argument('-w', '--workers', type=int, default=4, help='')
args = parser.parse_args()
need_features = args.need_features
batch_size = args.batch_size
num_workers = args.workers
checkpoints = args.input
for i, checkpoint_fname in enumerate(checkpoints):
print(i, checkpoint_fname)
# Make OOF predictions
checkpoint = torch.load(checkpoint_fname)
params = checkpoint['checkpoint_data']['cmd_args']
image_size = params['size']
data_dir = params['data_dir']
# train_ds, valid_ds, train_sizes = get_datasets(data_dir=params['data_dir'],
# use_aptos2019=params['use_aptos2019'],
# use_aptos2015=params['use_aptos2015'],
# use_idrid=params['use_idrid'],
# use_messidor=params['use_messidor'],
# use_unsupervised=False,
# image_size=(image_size, image_size),
# augmentation=params['augmentations'],
# preprocessing=params['preprocessing'],
# target_dtype=int,
# coarse_grading=params.get('coarse', False),
# fold=i,
# folds=4)
# print(len(valid_ds))
# oof_predictions = run_model_inference_via_dataset(checkpoint_fname,
# valid_ds,
# apply_softmax=True,
# need_features=need_features,
# batch_size=batch_size,
# workers=num_workers)
# dst_fname = fs.change_extension(checkpoint_fname, '_oof_predictions.pkl')
# oof_predictions.to_pickle(dst_fname)
# Now run inference on holdout IDRID Test dataset
idrid_test = run_model_inference(model_checkpoint=checkpoint_fname,
apply_softmax=True,
need_features=need_features,
test_csv=pd.read_csv(os.path.join(data_dir, 'idrid', 'test_labels.csv')),
data_dir=os.path.join(data_dir, 'idrid'),
images_dir='test_images_768',
batch_size=batch_size,
tta='fliplr',
workers=num_workers,
crop_black=True)
idrid_test.to_pickle(fs.change_extension(checkpoint_fname, '_idrid_test_predictions.pkl'))
# Now run inference on Messidor 2 Test dataset
messidor2_train = run_model_inference(model_checkpoint=checkpoint_fname,
apply_softmax=True,
need_features=need_features,
test_csv=pd.read_csv(os.path.join(data_dir, 'messidor_2', 'train_labels.csv')),
data_dir=os.path.join(data_dir, 'messidor_2'),
images_dir='train_images_768',
batch_size=batch_size,
tta='fliplr',
workers=num_workers,
crop_black=True)
messidor2_train.to_pickle(fs.change_extension(checkpoint_fname, '_messidor2_train_predictions.pkl'))
# Now run inference on Aptos2019 public test
aptos2019_test = run_model_inference(model_checkpoint=checkpoint_fname,
apply_softmax=True,
need_features=need_features,
test_csv=pd.read_csv(os.path.join(data_dir, 'aptos-2019', 'test.csv')),
data_dir=os.path.join(data_dir, 'aptos-2019'),
images_dir='test_images_768',
batch_size=batch_size,
tta='fliplr',
workers=num_workers,
crop_black=True)
aptos2019_test.to_pickle(fs.change_extension(checkpoint_fname, '_aptos2019_test_predictions.pkl'))
# Now run inference on Aptos2015 private test
if True:
aptos2015_df = pd.read_csv(os.path.join(data_dir, 'aptos-2015', 'test_labels.csv'))
aptos2015_df = aptos2015_df[aptos2015_df['Usage'] == 'Private']
aptos2015_test = run_model_inference(model_checkpoint=checkpoint_fname,
apply_softmax=True,
need_features=need_features,
test_csv=aptos2015_df,
data_dir=os.path.join(data_dir, 'aptos-2015'),
images_dir='test_images_768',
batch_size=batch_size,
tta='fliplr',
workers=num_workers,
crop_black=True)
aptos2015_test.to_pickle(fs.change_extension(checkpoint_fname, '_aptos2015_test_private_predictions.pkl'))
if False:
aptos2015_df = pd.read_csv(os.path.join(data_dir, 'aptos-2015', 'test_labels.csv'))
aptos2015_df = aptos2015_df[aptos2015_df['Usage'] == 'Public']
aptos2015_test = run_model_inference(model_checkpoint=checkpoint_fname,
apply_softmax=True,
need_features=need_features,
test_csv=aptos2015_df,
data_dir=os.path.join(data_dir, 'aptos-2015'),
images_dir='test_images_768',
batch_size=batch_size,
tta='fliplr',
workers=num_workers,
crop_black=True)
aptos2015_test.to_pickle(fs.change_extension(checkpoint_fname, '_aptos2015_test_public_predictions.pkl'))
if False:
aptos2015_df = pd.read_csv(os.path.join(data_dir, 'aptos-2015', 'train_labels.csv'))
aptos2015_test = run_model_inference(model_checkpoint=checkpoint_fname,
apply_softmax=True,
need_features=need_features,
test_csv=aptos2015_df,
data_dir=os.path.join(data_dir, 'aptos-2015'),
images_dir='train_images_768',
batch_size=batch_size,
tta='fliplr',
workers=num_workers,
crop_black=True)
aptos2015_test.to_pickle(fs.change_extension(checkpoint_fname, '_aptos2015_train_predictions.pkl'))
if __name__ == '__main__':
main()