def test_frcnn_estimator():
from gluoncv.auto.estimators import FasterRCNNEstimator
est = FasterRCNNEstimator({'train': {'epochs': 1}, 'gpus': list(range(get_gpu_count()))})
OBJECT_DETECTION_TRAIN_MINI, OBJECT_DETECTION_VAL_MINI, OBJECT_DETECTION_TEST_MINI = OBJECT_DETECTION_TRAIN.random_split(
val_size=0.3, test_size=0.2)
res = est.fit(OBJECT_DETECTION_TRAIN_MINI)
assert res.get('valid_map', 0) > 0
test_result = est.predict(OBJECT_DETECTION_TEST_MINI)
est.predict(OBJECT_DETECTION_TEST.iloc[0]['image'])
with Image.open(OBJECT_DETECTION_TEST.iloc[0]['image']) as pil_image:
est.predict(pil_image)
evaluate_result = est.evaluate(OBJECT_DETECTION_VAL_MINI)
# test save/load
_save_load_test(est, 'frcnn.pkl')
def test_frcnn_estimator():
from gluoncv.auto.estimators import FasterRCNNEstimator
est = FasterRCNNEstimator({'train': {'epochs': 1}, 'gpus': list(range(get_gpu_count()))})
res = est.fit(OBJECT_DETCTION_DATASET)
assert res.get('valid_map', 0) > 0
_, _, test_data = OBJECT_DETCTION_DATASET.random_split()
test_result = est.predict(test_data)
evaluate_result = est.evaluate(test_data)
def test_frcnn_estimator():
from gluoncv.auto.estimators import FasterRCNNEstimator
est = FasterRCNNEstimator({
'train': {
'epochs': 1
},
'gpus': list(range(get_gpu_count()))
})
OBJECT_DETECTION_TRAIN_MINI, OBJECT_DETECTION_VAL_MINI, OBJECT_DETECTION_TEST_MINI = OBJECT_DETECTION_TRAIN.random_split(
val_size=0.3, test_size=0.2)
res = est.fit(OBJECT_DETECTION_TRAIN_MINI)
assert res.get('valid_map', 0) > 0
test_result = est.predict(OBJECT_DETECTION_TEST_MINI)
evaluate_result = est.evaluate(OBJECT_DETECTION_VAL_MINI)