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)
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')
from gluoncv.auto.estimators import FasterRCNNEstimator
from gluoncv.auto.tasks.utils import config_to_nested
from d8.object_detection import Dataset
if __name__ == '__main__':
# specify hyperparameters
config = {
'dataset': 'sheep',
'gpus': [0, 1, 2, 3, 4, 5, 6, 7],
'estimator': 'faster_rcnn',
'base_network': 'resnet50_v1b',
'batch_size': 8, # range [8, 16, 32, 64]
'epochs': 3
}
config = config_to_nested(config)
config.pop('estimator')
# specify dataset
dataset = Dataset.get('sheep')
train_data, valid_data = dataset.split(0.8)
# specify estimator
estimator = FasterRCNNEstimator(config)
# fit estimator
estimator.fit(train_data, valid_data)
# evaluate auto estimator
eval_map = estimator.evaluate(valid_data)
logging.info('evaluation: mAP={}'.format(eval_map[-1][-1]))