Fix auto detectors (#1497)

* fix yolo predictor

* fix predict

gluoncv/auto/estimators/faster_rcnn/faster_rcnn.py

@@ -8,6 +8,7 @@
 import mxnet as mx
 from mxnet import gluon
 
+from ....data.batchify import Tuple
 from ....data.transforms.presets.rcnn import load_test, transform_test
 from ....data.transforms.presets.rcnn import FasterRCNNDefaultTrainTransform, FasterRCNNDefaultValTransform
 from ....model_zoo import get_model

gluoncv/auto/estimators/ssd/default.py

@@ -34,7 +34,7 @@ class SSD:
 @dataclass
 class TrainCfg:
     # Batch size during training
-    batch_size : int = 32
+    batch_size : int = 16
     # starting epoch
     start_epoch : int = 0
     # total epoch for training

gluoncv/auto/estimators/ssd/ssd.py

@@ -13,6 +13,7 @@
 from mxnet.contrib import amp
 
 from .... import utils as gutils
+from ....data.batchify import Tuple, Stack, Pad
 from ....utils.metrics.voc_detection import VOC07MApMetric, VOCMApMetric
 from ....model_zoo import get_model
 from ....model_zoo import custom_ssd

gluoncv/auto/estimators/yolo/default.py

@@ -36,7 +36,7 @@ class YOLOv3:
 @dataclass
 class TrainCfg:
     # Training mini-batch size
-    batch_size : int = 32
+    batch_size : int = 16
     # Training epochs.
     epochs : int = 20
     # Starting epoch for resuming, default is 0 for new training.

gluoncv/auto/estimators/yolo/yolo.py

@@ -11,6 +11,7 @@
 from mxnet.contrib import amp
 
 from .... import utils as gutils
+from ....data.batchify import Tuple, Stack, Pad
 from ....data.transforms.presets.yolo import load_test, transform_test
 from ....model_zoo import get_model
 from ....model_zoo import custom_yolov3

tests/auto/test_auto_estimators.py

@@ -21,38 +21,48 @@
 from gluoncv.auto.tasks import ObjectDetection
 from autogluon.core.scheduler.resource import get_cpu_count, get_gpu_count
 
-IMAGE_CLASS_DATASET, _, _ = ImageClassification.Dataset.from_folders('https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
+IMAGE_CLASS_DATASET, _, IMAGE_CLASS_TEST = ImageClassification.Dataset.from_folders(
+    'https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
 OBJECT_DETCTION_DATASET = ObjectDetection.Dataset.from_voc('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip')
 
 def test_image_classification_estimator():
     from gluoncv.auto.estimators import ImageClassificationEstimator
     est = ImageClassificationEstimator({'train': {'epochs': 1, 'batch_size': 8}, 'gpus': list(range(get_gpu_count()))})
     res = est.fit(IMAGE_CLASS_DATASET)
     assert res.get('valid_acc', 0) > 0
+    test_result = est.predict(IMAGE_CLASS_TEST)
 
 def test_center_net_estimator():
     from gluoncv.auto.estimators import CenterNetEstimator
     est = CenterNetEstimator({'train': {'epochs': 1, 'batch_size': 8}, '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)
 
 def test_ssd_estimator():
     from gluoncv.auto.estimators import SSDEstimator
     est = SSDEstimator({'train': {'epochs': 1, 'batch_size': 8}, '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)
 
 def test_yolo3_estimator():
     from gluoncv.auto.estimators import YOLOv3Estimator
     est = YOLOv3Estimator({'train': {'epochs': 1, 'batch_size': 8}, '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)
 
 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)
 
 if __name__ == '__main__':
     import nose

tests/auto/test_auto_tasks.py

@@ -1,17 +1,21 @@
 from gluoncv.auto.tasks import ImageClassification
 from gluoncv.auto.tasks import ObjectDetection
 
-IMAGE_CLASS_DATASET, _, _ = ImageClassification.Dataset.from_folders('https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
+IMAGE_CLASS_DATASET, _, IMAGE_CLASS_TEST = ImageClassification.Dataset.from_folders(
+    'https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
 OBJECT_DETCTION_DATASET = ObjectDetection.Dataset.from_voc('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip')
 
 def test_image_classification():
     from gluoncv.auto.tasks import ImageClassification
     task = ImageClassification({'num_trials': 1})
     classifier = task.fit(IMAGE_CLASS_DATASET)
     assert task.fit_summary.get('valid_acc', 0) > 0
+    test_result = classifier.predict(IMAGE_CLASS_TEST)
 
 def test_center_net_estimator():
     from gluoncv.auto.tasks import ObjectDetection
     task = ObjectDetection({'num_trials': 1})
     detector = task.fit(OBJECT_DETCTION_DATASET)
     assert task.fit_summary.get('valid_map', 0) > 0
+    _, _, test_data = OBJECT_DETCTION_DATASET.random_split()
+    test_result = detector.predict(test_data)