# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. """Faster R-CNN and Mask R-CNN operations.""" from . import _make def roi_align(data, rois, pooled_size, spatial_scale, sample_ratio=-1, layout='NCHW'): """ROI align operator. Parameters ---------- data : relay.Expr 4-D tensor with shape [batch, channel, height, width] rois : relay.Expr 2-D tensor with shape [num_roi, 5]. The last dimension should be in format of [batch_index, w_start, h_start, w_end, h_end] pooled_size : list/tuple of two ints output size spatial_scale : float Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers, which should be in range (0.0, 1.0] sample_ratio : int Optional sampling ratio of ROI align, using adaptive size by default. Returns ------- output : relay.Expr 4-D tensor with shape [num_roi, channel, pooled_size, pooled_size] """ return _make.roi_align(data, rois, pooled_size, spatial_scale, sample_ratio, layout) def roi_pool(data, rois, pooled_size, spatial_scale, layout='NCHW'): """ROI pool operator. Parameters ---------- data : relay.Expr 4-D tensor with shape [batch, channel, height, width] rois : relay.Expr 2-D tensor with shape [num_roi, 5]. The last dimension should be in format of [batch_index, w_start, h_start, w_end, h_end] pooled_size : list/tuple of two ints output size spatial_scale : float Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers, which should be in range (0.0, 1.0] Returns ------- output : relay.Expr 4-D tensor with shape [num_roi, channel, pooled_size, pooled_size] """ return _make.roi_pool(data, rois, pooled_size, spatial_scale, layout) def proposal(cls_prob, bbox_pred, im_info, scales, ratios, feature_stride, threshold, rpn_pre_nms_top_n, rpn_post_nms_top_n, rpn_min_size, iou_loss): """Proposal operator. Parameters ---------- cls_prob : relay.Expr 4-D tensor with shape [batch, 2 * num_anchors, height, width]. bbox_pred : relay.Expr 4-D tensor with shape [batch, 4 * num_anchors, height, width]. im_info : relay.Expr 2-D tensor with shape [batch, 3]. The last dimension should be in format of [im_height, im_width, im_scale] scales : list/tuple of float Scales of anchor windoes. ratios : list/tuple of float Ratios of anchor windoes. feature_stride : int The size of the receptive field each unit in the convolution layer of the rpn, for example the product of all stride's prior to this layer. threshold : float Non-maximum suppression threshold. rpn_pre_nms_top_n : int Number of top scoring boxes to apply NMS. -1 to use all boxes. rpn_post_nms_top_n : int Number of top scoring boxes to keep after applying NMS to RPN proposals. rpn_min_size : int Minimum height or width in proposal. iou_loss : bool Usage of IoU loss. Returns ------- output : relay.Expr 2-D tensor with shape [batch * rpn_post_nms_top_n, 5]. The last dimension is in format of [batch_index, w_start, h_start, w_end, h_end]. """ return _make.proposal(cls_prob, bbox_pred, im_info, scales, ratios, feature_stride, threshold, rpn_pre_nms_top_n, rpn_post_nms_top_n, rpn_min_size, iou_loss)