change core.ops to _C_ops (#5663)

ppdet/modeling/ops.py

@@ -17,6 +17,7 @@ import paddle.nn.functional as F
 import paddle.nn as nn
 from paddle import ParamAttr
 from paddle.regularizer import L2Decay
+from paddle import _C_ops
 
 from paddle.fluid.framework import Variable, in_dygraph_mode
 from paddle.fluid import core
@@ -174,7 +175,7 @@ def roi_pool(input,
     pooled_height, pooled_width = output_size
     if in_dygraph_mode():
         assert rois_num is not None, "rois_num should not be None in dygraph mode."
-        pool_out, argmaxes = core.ops.roi_pool(
+        pool_out, argmaxes = _C_ops.roi_pool(
             input, rois, rois_num, "pooled_height", pooled_height,
             "pooled_width", pooled_width, "spatial_scale", spatial_scale)
         return pool_out, argmaxes
@@ -281,7 +282,7 @@ def roi_align(input,
 
     if in_dygraph_mode():
         assert rois_num is not None, "rois_num should not be None in dygraph mode."
-        align_out = core.ops.roi_align(
+        align_out = _C_ops.roi_align(
             input, rois, rois_num, "pooled_height", pooled_height,
             "pooled_width", pooled_width, "spatial_scale", spatial_scale,
             "sampling_ratio", sampling_ratio, "aligned", aligned)
@@ -364,7 +365,7 @@ def iou_similarity(x, y, box_normalized=True, name=None):
     """
 
     if in_dygraph_mode():
-        out = core.ops.iou_similarity(x, y, 'box_normalized', box_normalized)
+        out = _C_ops.iou_similarity(x, y, 'box_normalized', box_normalized)
         return out
     else:
         helper = LayerHelper("iou_similarity", **locals())
@@ -460,7 +461,7 @@ def collect_fpn_proposals(multi_rois,
     if in_dygraph_mode():
         assert rois_num_per_level is not None, "rois_num_per_level should not be None in dygraph mode."
         attrs = ('post_nms_topN', post_nms_top_n)
-        output_rois, rois_num = core.ops.collect_fpn_proposals(
+        output_rois, rois_num = _C_ops.collect_fpn_proposals(
             input_rois, input_scores, rois_num_per_level, *attrs)
         return output_rois, rois_num
 
@@ -575,7 +576,7 @@ def distribute_fpn_proposals(fpn_rois,
         attrs = ('min_level', min_level, 'max_level', max_level, 'refer_level',
                  refer_level, 'refer_scale', refer_scale, 'pixel_offset',
                  pixel_offset)
-        multi_rois, restore_ind, rois_num_per_level = core.ops.distribute_fpn_proposals(
+        multi_rois, restore_ind, rois_num_per_level = _C_ops.distribute_fpn_proposals(
             fpn_rois, rois_num, num_lvl, num_lvl, *attrs)
         return multi_rois, restore_ind, rois_num_per_level
 
@@ -729,7 +730,7 @@ def yolo_box(
         attrs = ('anchors', anchors, 'class_num', class_num, 'conf_thresh',
                  conf_thresh, 'downsample_ratio', downsample_ratio, 'clip_bbox',
                  clip_bbox, 'scale_x_y', scale_x_y)
-        boxes, scores = core.ops.yolo_box(x, origin_shape, *attrs)
+        boxes, scores = _C_ops.yolo_box(x, origin_shape, *attrs)
         return boxes, scores
     else:
         boxes = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -867,7 +868,7 @@ def prior_box(input,
                  'min_max_aspect_ratios_order', min_max_aspect_ratios_order)
         if cur_max_sizes is not None:
             attrs += ('max_sizes', cur_max_sizes)
-        box, var = core.ops.prior_box(input, image, *attrs)
+        box, var = _C_ops.prior_box(input, image, *attrs)
         return box, var
     else:
         attrs = {
@@ -1010,7 +1011,7 @@ def multiclass_nms(bboxes,
                  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,
+        output, index, nms_rois_num = _C_ops.multiclass_nms3(bboxes, scores,
                                                                rois_num, *attrs)
         if not return_index:
             index = None
@@ -1152,7 +1153,7 @@ def matrix_nms(bboxes,
                  nms_top_k, 'gaussian_sigma', gaussian_sigma, 'use_gaussian',
                  use_gaussian, 'keep_top_k', keep_top_k, 'normalized',
                  normalized)
-        out, index, rois_num = core.ops.matrix_nms(bboxes, scores, *attrs)
+        out, index, rois_num = _C_ops.matrix_nms(bboxes, scores, *attrs)
         if not return_index:
             index = None
         if not return_rois_num:
@@ -1273,7 +1274,7 @@ def bipartite_match(dist_matrix,
                              ['float32', 'float64'], 'bipartite_match')
 
     if in_dygraph_mode():
-        match_indices, match_distance = core.ops.bipartite_match(
+        match_indices, match_distance = _C_ops.bipartite_match(
             dist_matrix, "match_type", match_type, "dist_threshold",
             dist_threshold)
         return match_indices, match_distance
@@ -1410,12 +1411,12 @@ def box_coder(prior_box,
 
     if in_dygraph_mode():
         if isinstance(prior_box_var, Variable):
-            output_box = core.ops.box_coder(
+            output_box = _C_ops.box_coder(
                 prior_box, prior_box_var, target_box, "code_type", code_type,
                 "box_normalized", box_normalized, "axis", axis)
 
         elif isinstance(prior_box_var, list):
-            output_box = core.ops.box_coder(
+            output_box = _C_ops.box_coder(
                 prior_box, None, target_box, "code_type", code_type,
                 "box_normalized", box_normalized, "axis", axis, "variance",
                 prior_box_var)
@@ -1538,7 +1539,7 @@ def generate_proposals(scores,
         attrs = ('pre_nms_topN', pre_nms_top_n, 'post_nms_topN', post_nms_top_n,
                  'nms_thresh', nms_thresh, 'min_size', min_size, 'eta', eta,
                  'pixel_offset', pixel_offset)
-        rpn_rois, rpn_roi_probs, rpn_rois_num = core.ops.generate_proposals_v2(
+        rpn_rois, rpn_roi_probs, rpn_rois_num = _C_ops.generate_proposals_v2(
             scores, bbox_deltas, im_shape, anchors, variances, *attrs)
         if not return_rois_num:
             rpn_rois_num = None