Add MMdetection FCOS && Yolov3 support (#595)

* add BatchToSpaceND and SpaceToBatchND op convert

* add Less and fixed Resize RoiAlign Greater Tile op

* add nms custom layer

* fix topk int64 bug

* update model_zoo

* deal with comments
Co-authored-by: Nchanningss <chen_lingchi@163.com>

docs/introduction/x2paddle_model_zoo.md

@@ -71,7 +71,9 @@
 |Ultra-Light-Fast-Generic-Face-Detector-1MB| [onnx_model](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/models/onnx)|9 |
 |BERT| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](../inference_model_convertor/FAQ.md)|
 |GPT2| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](../inference_model_convertor/FAQ.md)|
-|CifarNet | [tensorflow](https://github.com/tensorflow/models/blob/master/research/slim/nets/cifarnet.py)|9||
+|CifarNet | [tensorflow](https://github.com/tensorflow/models/blob/master/research/slim/nets/cifarnet.py)|9|
+|Fcos | [pytorch(mmdetection)](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py)|11|
+|Yolov3 | [pytorch(mmdetection)](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py)|11||
 
 
 ## PyTorch预测模型

x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/__init__.py

@@ -12,10 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 from .one_hot import OneHot
 from .pad_two_input import PadWithTwoInput
 from .pad_all_dim2 import PadAllDim2
 from .pad_all_dim4 import PadAllDim4
 from .pad_all_dim4_one_input import PadAllDim4WithOneInput
 from .lrn import LocalResponseNorm
+from .nms import NMS

x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/nms.py

+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+from paddle.fluid import core
+from paddle.fluid.framework import Variable, in_dygraph_mode
+from paddle.fluid.layer_helper import LayerHelper
+
+
+def multiclass_nms(bboxes,
+                   scores,
+                   score_threshold,
+                   nms_top_k,
+                   keep_top_k,
+                   nms_threshold=0.3,
+                   normalized=True,
+                   nms_eta=1.,
+                   background_label=-1,
+                   return_index=False,
+                   return_rois_num=True,
+                   rois_num=None,
+                   name=None):
+    helper = LayerHelper('multiclass_nms3', **locals())
+
+    if in_dygraph_mode():
+        attrs = ('background_label', background_label, 'score_threshold',
+                 score_threshold, 'nms_top_k', nms_top_k, 'nms_threshold',
+                 nms_threshold, 'keep_top_k', keep_top_k, 'nms_eta', nms_eta,
+                 'normalized', normalized)
+        output, index, nms_rois_num = core.ops.multiclass_nms3(bboxes, scores,
+                                                               rois_num, *attrs)
+        if not return_index:
+            index = None
+        return output, nms_rois_num, index
+
+    else:
+        output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
+        index = helper.create_variable_for_type_inference(dtype='int')
+
+        inputs = {'BBoxes': bboxes, 'Scores': scores}
+        outputs = {'Out': output, 'Index': index}
+
+        if rois_num is not None:
+            inputs['RoisNum'] = rois_num
+
+        if return_rois_num:
+            nms_rois_num = helper.create_variable_for_type_inference(
+                dtype='int32')
+            outputs['NmsRoisNum'] = nms_rois_num
+
+        helper.append_op(
+            type="multiclass_nms3",
+            inputs=inputs,
+            attrs={
+                'background_label': background_label,
+                'score_threshold': score_threshold,
+                'nms_top_k': nms_top_k,
+                'nms_threshold': nms_threshold,
+                'keep_top_k': keep_top_k,
+                'nms_eta': nms_eta,
+                'normalized': normalized
+            },
+            outputs=outputs)
+        output.stop_gradient = True
+        index.stop_gradient = True
+        if not return_index:
+            index = None
+        if not return_rois_num:
+            nms_rois_num = None
+
+        return output, nms_rois_num, index
+
+
+class NMS(object):
+    def __init__(self, score_threshold, nms_top_k, nms_threshold):
+        self.score_threshold = score_threshold
+        self.nms_top_k = nms_top_k
+        self.nms_threshold = nms_threshold
+
+    def __call__(self, bboxes, scores):
+        attrs = {
+            'background_label': -1,
+            'score_threshold': self.score_threshold,
+            'nms_top_k': self.nms_top_k,
+            'nms_threshold': self.nms_threshold,
+            'keep_top_k': -1,
+            'nms_eta': 1.0,
+            'normalized': False,
+            'return_index': True
+        }
+        output, nms_rois_num, index = multiclass_nms(bboxes, scores, **attrs)
+        clas = paddle.slice(output, axes=[1], starts=[0], ends=[1])
+        clas = paddle.cast(clas, dtype="int64")
+        index = paddle.cast(index, dtype="int64")
+        if bboxes.shape[0] == 1:
+            batch = paddle.zeros_like(clas, dtype="int64")
+        else:
+            bboxes_count = bboxes.shape[1]
+            batch = paddle.divide(index, bboxes_count)
+            index = paddle.mod(index, bboxes_count)
+        res = paddle.concat([batch, clas, index], axis=1)
+        return res