From df4a27c6db2e237c47c63b888905d2a1f4803fba Mon Sep 17 00:00:00 2001 From: hong <43953930+phlrain@users.noreply.github.com> Date: Tue, 12 Apr 2022 10:29:47 +0800 Subject: [PATCH] change core.ops to _C_ops (#5663) --- ppdet/modeling/ops.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/ppdet/modeling/ops.py b/ppdet/modeling/ops.py index 52a4f3396..67e8960ca 100644 --- a/ppdet/modeling/ops.py +++ b/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 -- GitLab