fix(detection): fix random features in py3.5 (#52)

* feat(detection): add new models and weights

* fix(detection): fix random features in py3.5

.gitignore

 __pycache__/
 *log*/
 *.so
+*.pkl

README.md

@@ -79,9 +79,11 @@ export PYTHONPATH=/path/to/models:$PYTHONPATH
 | retinanet-res50-coco-1x-800size          | 36.4         |
 | retinanet-res50-coco-1x-800size-syncbn   | 37.1         |
 | retinanet-res101-coco-2x-800size         | 40.8         |
+| retinanet-resx101-coco-2x-800size        | 41.8         |
 | faster-rcnn-res50-coco-1x-800size        | 38.8         |
 | faster-rcnn-res50-coco-1x-800size-syncbn | 39.3         |
 | faster-rcnn-res101-coco-2x-800size       | 43.0         |
+| faster-rcnn-resx101-coco-2x-800size      | 44.7         |
 
 ### 图像分割
 

hubconf.py

@@ -31,9 +31,11 @@ from official.vision.detection.configs import (
     faster_rcnn_res50_coco_1x_800size,
     faster_rcnn_res50_coco_1x_800size_syncbn,
     faster_rcnn_res101_coco_2x_800size,
+    faster_rcnn_resx101_coco_2x_800size,
     retinanet_res50_coco_1x_800size,
     retinanet_res50_coco_1x_800size_syncbn,
     retinanet_res101_coco_2x_800size,
+    retinanet_resx101_coco_2x_800size,
 )
 from official.vision.detection.models import FasterRCNN, RetinaNet
 from official.vision.detection.tools.utils import DetEvaluator

official/vision/detection/README.md

@@ -6,14 +6,16 @@
 
 网络的性能在COCO2017数据集上的测试结果如下：
 
-| 模型                                     | mAP<br>@5-95 | batch<br>/gpu | gpu    | trainging speed<br>(8gpu) |
-| ---                                      | :---:        | :---:         | :---:  | :---:                     |
-| retinanet-res50-coco-1x-800size          | 36.4         | 2             | 2080Ti | 3.1(it/s)                 |
-| retinanet-res50-coco-1x-800size-syncbn   | 37.1         | 2             | 2080Ti | 1.7(it/s)                 |
-| retinanet-res101-coco-2x-800size         | 40.8         | 2             | 2080Ti | 2.1(it/s)                 |
-| faster-rcnn-res50-coco-1x-800size        | 38.8         | 2             | 2080Ti | 3.3(it/s)                 |
-| faster-rcnn-res50-coco-1x-800size-syncbn | 39.3         | 2             | 2080Ti | 1.8(it/s)                 |
-| faster-rcnn-res101-coco-2x-800size       | 43.0         | 2             | 2080Ti | 2.3(it/s)                 |
+| 模型                                     | mAP<br>@5-95 | batch<br>/gpu | gpu    | trainging speed<br>(8gpu)    |
+| ---                                      | :---:        | :---:         | :---:  | :---:                        |
+| retinanet-res50-coco-1x-800size          | 36.4         | 2             | 2080Ti | 3.1(it/s)                    |
+| retinanet-res50-coco-1x-800size-syncbn   | 37.1         | 2             | 2080Ti | 1.7(it/s)                    |
+| retinanet-res101-coco-2x-800size         | 40.8         | 2             | 2080Ti | 2.1(it/s)                    |
+| retinanet-resx101-coco-2x-800size        | 41.8         | 2             | 2080Ti | 0.8(it/s) (enable sublinear) |
+| faster-rcnn-res50-coco-1x-800size        | 38.8         | 2             | 2080Ti | 3.3(it/s)                    |
+| faster-rcnn-res50-coco-1x-800size-syncbn | 39.3         | 2             | 2080Ti | 1.8(it/s)                    |
+| faster-rcnn-res101-coco-2x-800size       | 43.0         | 2             | 2080Ti | 2.3(it/s)                    |
+| faster-rcnn-resx101-coco-2x-800size      | 44.7         | 2             | 2080Ti | 0.9(it/s) (enable sublinear) |
 
 * MegEngine v0.5.1
 

official/vision/detection/configs/__init__.py

 from .faster_rcnn_res50_coco_1x_800size import faster_rcnn_res50_coco_1x_800size
 from .faster_rcnn_res50_coco_1x_800size_syncbn import faster_rcnn_res50_coco_1x_800size_syncbn
 from .faster_rcnn_res101_coco_2x_800size import faster_rcnn_res101_coco_2x_800size
+from .faster_rcnn_resx101_coco_2x_800size import faster_rcnn_resx101_coco_2x_800size
 from .retinanet_res50_coco_1x_800size import retinanet_res50_coco_1x_800size
 from .retinanet_res50_coco_1x_800size_syncbn import retinanet_res50_coco_1x_800size_syncbn
 from .retinanet_res101_coco_2x_800size import retinanet_res101_coco_2x_800size
+from .retinanet_resx101_coco_2x_800size import retinanet_resx101_coco_2x_800size
 
 _EXCLUDE = {}
 __all__ = [k for k in globals().keys() if k not in _EXCLUDE and not k.startswith("_")]

official/vision/detection/configs/faster_rcnn_resx101_coco_2x_800size.py

+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+from megengine import hub
+
+from official.vision.detection import models
+
+
+class CustomFasterRCNNConfig(models.FasterRCNNConfig):
+    def __init__(self):
+        super().__init__()
+
+        self.backbone = "resnext101_32x8d"
+
+        # ------------------------ training cfg ---------------------- #
+        self.max_epoch = 36
+        self.lr_decay_stages = [24, 32, 34]
+
+
+@hub.pretrained(
+    "https://data.megengine.org.cn/models/weights/"
+    "faster_rcnn_resx101_coco_2x_800size_44dot7_d03b05b2.pkl"
+)
+def faster_rcnn_resx101_coco_2x_800size(batch_size=1, **kwargs):
+    r"""
+    Faster-RCNN FPN trained from COCO dataset.
+    `"Faster-RCNN" <https://arxiv.org/abs/1506.01497>`_
+    `"FPN" <https://arxiv.org/abs/1612.03144>`_
+    `"COCO" <https://arxiv.org/abs/1405.0312>`_
+    """
+    return models.FasterRCNN(CustomFasterRCNNConfig(), batch_size=batch_size, **kwargs)
+
+
+Net = models.FasterRCNN
+Cfg = CustomFasterRCNNConfig

official/vision/detection/configs/retinanet_resx101_coco_2x_800size.py

+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+from megengine import hub
+
+from official.vision.detection import models
+
+
+class CustomRetinaNetConfig(models.RetinaNetConfig):
+    def __init__(self):
+        super().__init__()
+
+        self.backbone = "resnext101_32x8d"
+
+        # ------------------------ training cfg ---------------------- #
+        self.max_epoch = 36
+        self.lr_decay_stages = [24, 32, 34]
+
+
+@hub.pretrained(
+    "https://data.megengine.org.cn/models/weights/"
+    "retinanet_resx101_coco_2x_800size_41dot8_f92909a1.pkl"
+)
+def retinanet_resx101_coco_2x_800size(batch_size=1, **kwargs):
+    r"""
+    RetinaNet trained from COCO dataset.
+    `"RetinaNet" <https://arxiv.org/abs/1708.02002>`_
+    `"FPN" <https://arxiv.org/abs/1612.03144>`_
+    `"COCO" <https://arxiv.org/abs/1405.0312>`_
+    """
+    return models.RetinaNet(CustomRetinaNetConfig(), batch_size=batch_size, **kwargs)
+
+
+Net = models.RetinaNet
+Cfg = CustomRetinaNetConfig

official/vision/detection/layers/det/fpn.py

@@ -121,7 +121,7 @@ class FPN(M.Module):
             for s in range(stage, stage + self.top_block.num_levels):
                 self._out_feature_strides["p{}".format(s + 1)] = 2 ** (s + 1)
 
-        self._out_features = list(self._out_feature_strides.keys())
+        self._out_features = list(sorted(self._out_feature_strides.keys()))
         self._out_feature_channels = {k: out_channels for k in self._out_features}
 
     def forward(self, x):

official/vision/detection/tools/inference.py

@@ -25,13 +25,9 @@ logger = mge.get_logger(__name__)
 
 def make_parser():
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-f", "--file", default="net.py", type=str, help="net description file"
-    )
-    parser.add_argument(
-        "-w", "--weight_file", default=None, type=str, help="weights file",
-    )
-    parser.add_argument("-i", "--image", default="example.jpg", type=str)
+    parser.add_argument("-f", "--file", type=str, help="net description file")
+    parser.add_argument("-w", "--weight_file", type=str, help="weights file")
+    parser.add_argument("-i", "--image", type=str)
     return parser
 
 
@@ -39,8 +35,6 @@ def main():
     parser = make_parser()
     args = parser.parse_args()
 
-    logger.info("Load Model : %s completed", args.weight_file)
-
     @jit.trace(symbolic=True)
     def val_func():
         pred = model(model.inputs)