def test_prediction_extended_and_positions(self):
# With actual model to evaluate correct positions
args = predict_args()
args.checkpoint = [
os.path.join(this_dir, "models",
f"version{SavedCalamariModel.VERSION}", "0.ckpt")
]
args.extended_prediction_data = True
run(args)
jsons = [
os.path.join(this_dir, "data", "uw3_50lines", "test", "*.json")
]
run_compute_avg_pred(ExtendedPredictionDataParams(files=jsons))
def assert_pos_in_interval(p, start, end):
self.assertGreaterEqual(p.global_start, start)
self.assertGreaterEqual(p.global_end, start)
self.assertLessEqual(p.global_start, end)
self.assertLessEqual(p.global_end, end)
with open(sorted(glob_all(jsons[0]))[0]) as f:
first_pred: Predictions = Predictions.from_json(f.read())
for p in first_pred.predictions:
# Check for correct prediction string (models is trained!)
self.assertEqual(
p.sentence,
"The problem, simplified for our purposes, is set up as")
# Check for correct character positions
assert_pos_in_interval(p.positions[0], 0, 24) # T
assert_pos_in_interval(p.positions[1], 24, 43) # h
assert_pos_in_interval(p.positions[2], 45, 63) # e
# ...
assert_pos_in_interval(p.positions[-2], 1062, 1081) # a
assert_pos_in_interval(p.positions[-1], 1084, 1099) # s
def test_prediction_extended(self):
args = predict_args()
args.extended_prediction_data = True
run(args)
jsons = [
os.path.join(this_dir, "data", "uw3_50lines", "test", "*.json")
]
run_compute_avg_pred(ExtendedPredictionDataParams(files=jsons))
def test_prediction_abbyy(self):
run(
predict_args(data=Abbyy(images=[
os.path.join(
this_dir,
"data",
"hiltl_die_bank_des_verderbens_abbyyxml",
"*.jpg",
)
], )))
def test_prediction_extended_pagexml_with_voting(self):
# With actual model to evaluate correct positions
args = predict_args(data=pagexml_dataset())
args.checkpoint = [
os.path.join(this_dir, "models",
f"version{SavedCalamariModel.VERSION}", "0.ckpt")
] * 2
args.extended_prediction_data = True
run(args)
jsons = [
os.path.join(this_dir, "data", "uw3_50lines", "test", "*.json")
]
run_compute_avg_pred(ExtendedPredictionDataParams(files=jsons))
def test_prediction_voter(self):
args = PredictionAttrs()
run(args)
def test_prediction(self):
args = PredictionAttrs()
args.checkpoint = args.checkpoint[0:1]
run(args)
def test_prediction_files(self):
run(predict_args())
def test_prediction_hdf5(self):
run(
predict_args(data=Hdf5(files=[
os.path.join(this_dir, "data", "uw3_50lines", "uw3-50lines.h5")
], )))
def test_prediction_pagexml(self):
run(
predict_args(data=PageXML(images=[
os.path.join(this_dir, "data", "avicanon_pagexml",
"008.nrm.png")
], )))
def test_prediction_voter_files(self):
run(predict_args(n_models=3))
def test_prediction_voter(self):
args = PredictionAttrs()
self.checkpoint = [os.path.join(this_dir, "test_models", "uw3_50lines_best.ckpt")] * 2
run(args)
def test_prediction(self):
args = PredictionAttrs()
run(args)
