# 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. from __future__ import print_function import os, sys # add python path of PadleDetection to sys.path parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 4))) if parent_path not in sys.path: sys.path.append(parent_path) import unittest import numpy as np import paddle import paddle.fluid as fluid from paddle.fluid.framework import Program, program_guard from paddle.fluid.dygraph import base import ppdet.modeling.ops as ops from ppdet.modeling.tests.test_base import LayerTest def make_rois(h, w, rois_num, output_size): rois = np.zeros((0, 4)).astype('float32') for roi_num in rois_num: roi = np.zeros((roi_num, 4)).astype('float32') roi[:, 0] = np.random.randint(0, h - output_size[0], size=roi_num) roi[:, 1] = np.random.randint(0, w - output_size[1], size=roi_num) roi[:, 2] = np.random.randint(roi[:, 0] + output_size[0], h) roi[:, 3] = np.random.randint(roi[:, 1] + output_size[1], w) rois = np.vstack((rois, roi)) return rois def softmax(x): # clip to shiftx, otherwise, when calc loss with # log(exp(shiftx)), may get log(0)=INF shiftx = (x - np.max(x)).clip(-64.) exps = np.exp(shiftx) return exps / np.sum(exps) class TestCollectFpnProposals(LayerTest): def test_collect_fpn_proposals(self): multi_bboxes_np = [] multi_scores_np = [] rois_num_per_level_np = [] for i in range(4): bboxes_np = np.random.rand(5, 4).astype('float32') scores_np = np.random.rand(5, 1).astype('float32') rois_num = np.array([2, 3]).astype('int32') multi_bboxes_np.append(bboxes_np) multi_scores_np.append(scores_np) rois_num_per_level_np.append(rois_num) with self.static_graph(): multi_bboxes = [] multi_scores = [] rois_num_per_level = [] for i in range(4): bboxes = paddle.static.data( name='rois' + str(i), shape=[5, 4], dtype='float32', lod_level=1) scores = paddle.static.data( name='scores' + str(i), shape=[5, 1], dtype='float32', lod_level=1) rois_num = paddle.static.data( name='rois_num' + str(i), shape=[None], dtype='int32') multi_bboxes.append(bboxes) multi_scores.append(scores) rois_num_per_level.append(rois_num) fpn_rois, rois_num = ops.collect_fpn_proposals( multi_bboxes, multi_scores, 2, 5, 10, rois_num_per_level=rois_num_per_level) feed = {} for i in range(4): feed['rois' + str(i)] = multi_bboxes_np[i] feed['scores' + str(i)] = multi_scores_np[i] feed['rois_num' + str(i)] = rois_num_per_level_np[i] fpn_rois_stat, rois_num_stat = self.get_static_graph_result( feed=feed, fetch_list=[fpn_rois, rois_num], with_lod=True) fpn_rois_stat = np.array(fpn_rois_stat) rois_num_stat = np.array(rois_num_stat) with self.dynamic_graph(): multi_bboxes_dy = [] multi_scores_dy = [] rois_num_per_level_dy = [] for i in range(4): bboxes_dy = base.to_variable(multi_bboxes_np[i]) scores_dy = base.to_variable(multi_scores_np[i]) rois_num_dy = base.to_variable(rois_num_per_level_np[i]) multi_bboxes_dy.append(bboxes_dy) multi_scores_dy.append(scores_dy) rois_num_per_level_dy.append(rois_num_dy) fpn_rois_dy, rois_num_dy = ops.collect_fpn_proposals( multi_bboxes_dy, multi_scores_dy, 2, 5, 10, rois_num_per_level=rois_num_per_level_dy) fpn_rois_dy = fpn_rois_dy.numpy() rois_num_dy = rois_num_dy.numpy() self.assertTrue(np.array_equal(fpn_rois_stat, fpn_rois_dy)) self.assertTrue(np.array_equal(rois_num_stat, rois_num_dy)) def test_collect_fpn_proposals_error(self): def generate_input(bbox_type, score_type, name): multi_bboxes = [] multi_scores = [] for i in range(4): bboxes = paddle.static.data( name='rois' + name + str(i), shape=[10, 4], dtype=bbox_type, lod_level=1) scores = paddle.static.data( name='scores' + name + str(i), shape=[10, 1], dtype=score_type, lod_level=1) multi_bboxes.append(bboxes) multi_scores.append(scores) return multi_bboxes, multi_scores with self.static_graph(): bbox1 = paddle.static.data( name='rois', shape=[5, 10, 4], dtype='float32', lod_level=1) score1 = paddle.static.data( name='scores', shape=[5, 10, 1], dtype='float32', lod_level=1) bbox2, score2 = generate_input('int32', 'float32', '2') self.assertRaises( TypeError, ops.collect_fpn_proposals, multi_rois=bbox1, multi_scores=score1, min_level=2, max_level=5, post_nms_top_n=2000) self.assertRaises( TypeError, ops.collect_fpn_proposals, multi_rois=bbox2, multi_scores=score2, min_level=2, max_level=5, post_nms_top_n=2000) class TestDistributeFpnProposals(LayerTest): def test_distribute_fpn_proposals(self): rois_np = np.random.rand(10, 4).astype('float32') rois_num_np = np.array([4, 6]).astype('int32') with self.static_graph(): rois = paddle.static.data( name='rois', shape=[10, 4], dtype='float32') rois_num = paddle.static.data( name='rois_num', shape=[None], dtype='int32') multi_rois, restore_ind, rois_num_per_level = ops.distribute_fpn_proposals( fpn_rois=rois, min_level=2, max_level=5, refer_level=4, refer_scale=224, rois_num=rois_num) fetch_list = multi_rois + [restore_ind] + rois_num_per_level output_stat = self.get_static_graph_result( feed={'rois': rois_np, 'rois_num': rois_num_np}, fetch_list=fetch_list, with_lod=True) output_stat_np = [] for output in output_stat: output_np = np.array(output) if len(output_np) > 0: output_stat_np.append(output_np) with self.dynamic_graph(): rois_dy = base.to_variable(rois_np) rois_num_dy = base.to_variable(rois_num_np) multi_rois_dy, restore_ind_dy, rois_num_per_level_dy = ops.distribute_fpn_proposals( fpn_rois=rois_dy, min_level=2, max_level=5, refer_level=4, refer_scale=224, rois_num=rois_num_dy) output_dy = multi_rois_dy + [restore_ind_dy] + rois_num_per_level_dy output_dy_np = [] for output in output_dy: output_np = output.numpy() if len(output_np) > 0: output_dy_np.append(output_np) for res_stat, res_dy in zip(output_stat_np, output_dy_np): self.assertTrue(np.array_equal(res_stat, res_dy)) def test_distribute_fpn_proposals_error(self): with self.static_graph(): fpn_rois = paddle.static.data( name='data_error', shape=[10, 4], dtype='int32', lod_level=1) self.assertRaises( TypeError, ops.distribute_fpn_proposals, fpn_rois=fpn_rois, min_level=2, max_level=5, refer_level=4, refer_scale=224) class TestROIAlign(LayerTest): def test_roi_align(self): b, c, h, w = 2, 12, 20, 20 inputs_np = np.random.rand(b, c, h, w).astype('float32') rois_num = [4, 6] output_size = (7, 7) rois_np = make_rois(h, w, rois_num, output_size) rois_num_np = np.array(rois_num).astype('int32') with self.static_graph(): inputs = paddle.static.data( name='inputs', shape=[b, c, h, w], dtype='float32') rois = paddle.static.data( name='rois', shape=[10, 4], dtype='float32') rois_num = paddle.static.data( name='rois_num', shape=[None], dtype='int32') output = ops.roi_align( input=inputs, rois=rois, output_size=output_size, rois_num=rois_num) output_np, = self.get_static_graph_result( feed={ 'inputs': inputs_np, 'rois': rois_np, 'rois_num': rois_num_np }, fetch_list=output, with_lod=False) with self.dynamic_graph(): inputs_dy = base.to_variable(inputs_np) rois_dy = base.to_variable(rois_np) rois_num_dy = base.to_variable(rois_num_np) output_dy = ops.roi_align( input=inputs_dy, rois=rois_dy, output_size=output_size, rois_num=rois_num_dy) output_dy_np = output_dy.numpy() self.assertTrue(np.array_equal(output_np, output_dy_np)) def test_roi_align_error(self): with self.static_graph(): inputs = paddle.static.data( name='inputs', shape=[2, 12, 20, 20], dtype='float32') rois = paddle.static.data( name='data_error', shape=[10, 4], dtype='int32', lod_level=1) self.assertRaises( TypeError, ops.roi_align, input=inputs, rois=rois, output_size=(7, 7)) class TestROIPool(LayerTest): def test_roi_pool(self): b, c, h, w = 2, 12, 20, 20 inputs_np = np.random.rand(b, c, h, w).astype('float32') rois_num = [4, 6] output_size = (7, 7) rois_np = make_rois(h, w, rois_num, output_size) rois_num_np = np.array(rois_num).astype('int32') with self.static_graph(): inputs = paddle.static.data( name='inputs', shape=[b, c, h, w], dtype='float32') rois = paddle.static.data( name='rois', shape=[10, 4], dtype='float32') rois_num = paddle.static.data( name='rois_num', shape=[None], dtype='int32') output, _ = ops.roi_pool( input=inputs, rois=rois, output_size=output_size, rois_num=rois_num) output_np, = self.get_static_graph_result( feed={ 'inputs': inputs_np, 'rois': rois_np, 'rois_num': rois_num_np }, fetch_list=[output], with_lod=False) with self.dynamic_graph(): inputs_dy = base.to_variable(inputs_np) rois_dy = base.to_variable(rois_np) rois_num_dy = base.to_variable(rois_num_np) output_dy, _ = ops.roi_pool( input=inputs_dy, rois=rois_dy, output_size=output_size, rois_num=rois_num_dy) output_dy_np = output_dy.numpy() self.assertTrue(np.array_equal(output_np, output_dy_np)) def test_roi_pool_error(self): with self.static_graph(): inputs = paddle.static.data( name='inputs', shape=[2, 12, 20, 20], dtype='float32') rois = paddle.static.data( name='data_error', shape=[10, 4], dtype='int32', lod_level=1) self.assertRaises( TypeError, ops.roi_pool, input=inputs, rois=rois, output_size=(7, 7)) class TestIoUSimilarity(LayerTest): def test_iou_similarity(self): b, c, h, w = 2, 12, 20, 20 inputs_np = np.random.rand(b, c, h, w).astype('float32') output_size = (7, 7) x_np = make_rois(h, w, [20], output_size) y_np = make_rois(h, w, [10], output_size) with self.static_graph(): x = paddle.static.data(name='x', shape=[20, 4], dtype='float32') y = paddle.static.data(name='y', shape=[10, 4], dtype='float32') iou = ops.iou_similarity(x=x, y=y) iou_np, = self.get_static_graph_result( feed={ 'x': x_np, 'y': y_np, }, fetch_list=[iou], with_lod=False) with self.dynamic_graph(): x_dy = base.to_variable(x_np) y_dy = base.to_variable(y_np) iou_dy = ops.iou_similarity(x=x_dy, y=y_dy) iou_dy_np = iou_dy.numpy() self.assertTrue(np.array_equal(iou_np, iou_dy_np)) class TestBipartiteMatch(LayerTest): def test_bipartite_match(self): distance = np.random.random((20, 10)).astype('float32') with self.static_graph(): x = paddle.static.data(name='x', shape=[20, 10], dtype='float32') match_indices, match_dist = ops.bipartite_match( x, match_type='per_prediction', dist_threshold=0.5) match_indices_np, match_dist_np = self.get_static_graph_result( feed={'x': distance, }, fetch_list=[match_indices, match_dist], with_lod=False) with self.dynamic_graph(): x_dy = base.to_variable(distance) match_indices_dy, match_dist_dy = ops.bipartite_match( x_dy, match_type='per_prediction', dist_threshold=0.5) match_indices_dy_np = match_indices_dy.numpy() match_dist_dy_np = match_dist_dy.numpy() self.assertTrue(np.array_equal(match_indices_np, match_indices_dy_np)) self.assertTrue(np.array_equal(match_dist_np, match_dist_dy_np)) class TestYoloBox(LayerTest): def test_yolo_box(self): # x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2 np_x = np.random.random([1, 30, 7, 7]).astype('float32') np_origin_shape = np.array([[608, 608]], dtype='int32') class_num = 10 conf_thresh = 0.01 downsample_ratio = 32 scale_x_y = 1.2 # static with self.static_graph(): # x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2 x = paddle.static.data( name='x', shape=[1, 30, 7, 7], dtype='float32') origin_shape = paddle.static.data( name='origin_shape', shape=[1, 2], dtype='int32') boxes, scores = ops.yolo_box( x, origin_shape, [10, 13, 30, 13], class_num, conf_thresh, downsample_ratio, scale_x_y=scale_x_y) boxes_np, scores_np = self.get_static_graph_result( feed={ 'x': np_x, 'origin_shape': np_origin_shape, }, fetch_list=[boxes, scores], with_lod=False) # dygraph with self.dynamic_graph(): x_dy = fluid.layers.assign(np_x) origin_shape_dy = fluid.layers.assign(np_origin_shape) boxes_dy, scores_dy = ops.yolo_box( x_dy, origin_shape_dy, [10, 13, 30, 13], 10, 0.01, 32, scale_x_y=scale_x_y) boxes_dy_np = boxes_dy.numpy() scores_dy_np = scores_dy.numpy() self.assertTrue(np.array_equal(boxes_np, boxes_dy_np)) self.assertTrue(np.array_equal(scores_np, scores_dy_np)) def test_yolo_box_error(self): with self.static_graph(): # x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2 x = paddle.static.data( name='x', shape=[1, 30, 7, 7], dtype='float32') origin_shape = paddle.static.data( name='origin_shape', shape=[1, 2], dtype='int32') self.assertRaises( TypeError, ops.yolo_box, x, origin_shape, [10, 13, 30, 13], 10.123, 0.01, 32, scale_x_y=1.2) class TestPriorBox(LayerTest): def test_prior_box(self): input_np = np.random.rand(2, 10, 32, 32).astype('float32') image_np = np.random.rand(2, 10, 40, 40).astype('float32') min_sizes = [2, 4] with self.static_graph(): input = paddle.static.data( name='input', shape=[2, 10, 32, 32], dtype='float32') image = paddle.static.data( name='image', shape=[2, 10, 40, 40], dtype='float32') box, var = ops.prior_box( input=input, image=image, min_sizes=min_sizes, clip=True, flip=True) box_np, var_np = self.get_static_graph_result( feed={ 'input': input_np, 'image': image_np, }, fetch_list=[box, var], with_lod=False) with self.dynamic_graph(): inputs_dy = base.to_variable(input_np) image_dy = base.to_variable(image_np) box_dy, var_dy = ops.prior_box( input=inputs_dy, image=image_dy, min_sizes=min_sizes, clip=True, flip=True) box_dy_np = box_dy.numpy() var_dy_np = var_dy.numpy() self.assertTrue(np.array_equal(box_np, box_dy_np)) self.assertTrue(np.array_equal(var_np, var_dy_np)) def test_prior_box_error(self): with self.static_graph(): input = paddle.static.data( name='input', shape=[2, 10, 32, 32], dtype='int32') image = paddle.static.data( name='image', shape=[2, 10, 40, 40], dtype='int32') self.assertRaises( TypeError, ops.prior_box, input=input, image=image, min_sizes=[2, 4], clip=True, flip=True) class TestAnchorGenerator(LayerTest): def test_anchor_generator(self): b, c, h, w = 2, 48, 16, 16 input_np = np.random.rand(2, 48, 16, 16).astype('float32') with self.static_graph(): input = paddle.static.data( name='input', shape=[b, c, h, w], dtype='float32') anchor, var = ops.anchor_generator( input=input, anchor_sizes=[64, 128, 256, 512], aspect_ratios=[0.5, 1.0, 2.0], variance=[0.1, 0.1, 0.2, 0.2], stride=[16.0, 16.0], offset=0.5) anchor_np, var_np = self.get_static_graph_result( feed={'input': input_np, }, fetch_list=[anchor, var], with_lod=False) with self.dynamic_graph(): inputs_dy = base.to_variable(input_np) anchor_dy, var_dy = ops.anchor_generator( input=inputs_dy, anchor_sizes=[64, 128, 256, 512], aspect_ratios=[0.5, 1.0, 2.0], variance=[0.1, 0.1, 0.2, 0.2], stride=[16.0, 16.0], offset=0.5) anchor_dy_np = anchor_dy.numpy() var_dy_np = var_dy.numpy() self.assertTrue(np.array_equal(anchor_np, anchor_dy_np)) self.assertTrue(np.array_equal(var_np, var_dy_np)) class TestMulticlassNms(LayerTest): def test_multiclass_nms(self): boxes_np = np.random.rand(81, 4).astype('float32') scores_np = np.random.rand(81).astype('float32') rois_num_np = np.array([40, 41]).astype('int32') with self.static_graph(): boxes = paddle.static.data( name='bboxes', shape=[81, 4], dtype='float32', lod_level=1) scores = paddle.static.data( name='scores', shape=[81], dtype='float32', lod_level=1) rois_num = paddle.static.data( name='rois_num', shape=[40, 41], dtype='int32') output = ops.multiclass_nms( bboxes=boxes, scores=scores, background_label=0, score_threshold=0.5, nms_top_k=400, nms_threshold=0.3, keep_top_k=200, normalized=False, return_index=True, rois_num=rois_num) out_np, index_np, nms_rois_num_np = self.get_static_graph_result( feed={ 'bboxes': boxes_np, 'scores': scores_np, 'rois_num': rois_num_np }, fetch_list=output, with_lod=False) with self.dynamic_graph(): boxes_dy = base.to_variable(boxes_np) scores_dy = base.to_variable(scores_np) rois_num_dy = base.to_variable(rois_num_np) out_dy, index_dy, nms_rois_num_dy = ops.multiclass_nms( bboxes=boxes_dy, scores=scores_dy, background_label=0, score_threshold=0.5, nms_top_k=400, nms_threshold=0.3, keep_top_k=200, normalized=False, return_index=True, rois_num=rois_num_dy) out_dy_np = out_dy.numpy() index_dy_np = index_dy.numpy() nms_rois_num_dy_np = nms_rois_num_dy.numpy() self.assertTrue(np.array_equal(out_np, out_dy_np)) self.assertTrue(np.array_equal(index_np, index_dy_np)) self.assertTrue(np.array_equal(nms_rois_num_np, nms_rois_num_dy_np)) def test_multiclass_nms_error(self): with self.static_graph(): boxes = paddle.static.data( name='bboxes', shape=[81, 4], dtype='float32', lod_level=1) scores = paddle.static.data( name='scores', shape=[81], dtype='float32', lod_level=1) rois_num = paddle.static.data( name='rois_num', shape=[40, 41], dtype='int32') self.assertRaises( TypeError, ops.multiclass_nms, boxes=boxes, scores=scores, background_label=0, score_threshold=0.5, nms_top_k=400, nms_threshold=0.3, keep_top_k=200, normalized=False, return_index=True, rois_num=rois_num) class TestMatrixNMS(LayerTest): def test_matrix_nms(self): N, M, C = 7, 1200, 21 BOX_SIZE = 4 nms_top_k = 400 keep_top_k = 200 score_threshold = 0.01 post_threshold = 0. scores_np = np.random.random((N * M, C)).astype('float32') scores_np = np.apply_along_axis(softmax, 1, scores_np) scores_np = np.reshape(scores_np, (N, M, C)) scores_np = np.transpose(scores_np, (0, 2, 1)) boxes_np = np.random.random((N, M, BOX_SIZE)).astype('float32') boxes_np[:, :, 0:2] = boxes_np[:, :, 0:2] * 0.5 boxes_np[:, :, 2:4] = boxes_np[:, :, 2:4] * 0.5 + 0.5 with self.static_graph(): boxes = paddle.static.data( name='boxes', shape=[N, M, BOX_SIZE], dtype='float32') scores = paddle.static.data( name='scores', shape=[N, C, M], dtype='float32') out, index, _ = ops.matrix_nms( bboxes=boxes, scores=scores, score_threshold=score_threshold, post_threshold=post_threshold, nms_top_k=nms_top_k, keep_top_k=keep_top_k, return_index=True) out_np, index_np = self.get_static_graph_result( feed={'boxes': boxes_np, 'scores': scores_np}, fetch_list=[out, index], with_lod=True) with self.dynamic_graph(): boxes_dy = base.to_variable(boxes_np) scores_dy = base.to_variable(scores_np) out_dy, index_dy, _ = ops.matrix_nms( bboxes=boxes_dy, scores=scores_dy, score_threshold=score_threshold, post_threshold=post_threshold, nms_top_k=nms_top_k, keep_top_k=keep_top_k, return_index=True) out_dy_np = out_dy.numpy() index_dy_np = index_dy.numpy() self.assertTrue(np.array_equal(out_np, out_dy_np)) self.assertTrue(np.array_equal(index_np, index_dy_np)) def test_matrix_nms_error(self): with self.static_graph(): bboxes = paddle.static.data( name='bboxes', shape=[7, 1200, 4], dtype='float32') scores = paddle.static.data( name='data_error', shape=[7, 21, 1200], dtype='int32') self.assertRaises( TypeError, ops.matrix_nms, bboxes=bboxes, scores=scores, score_threshold=0.01, post_threshold=0., nms_top_k=400, keep_top_k=200, return_index=True) class TestBoxCoder(LayerTest): def test_box_coder(self): prior_box_np = np.random.random((81, 4)).astype('float32') prior_box_var_np = np.random.random((81, 4)).astype('float32') target_box_np = np.random.random((20, 81, 4)).astype('float32') # static with self.static_graph(): prior_box = paddle.static.data( name='prior_box', shape=[81, 4], dtype='float32') prior_box_var = paddle.static.data( name='prior_box_var', shape=[81, 4], dtype='float32') target_box = paddle.static.data( name='target_box', shape=[20, 81, 4], dtype='float32') boxes = ops.box_coder( prior_box=prior_box, prior_box_var=prior_box_var, target_box=target_box, code_type="decode_center_size", box_normalized=False) boxes_np, = self.get_static_graph_result( feed={ 'prior_box': prior_box_np, 'prior_box_var': prior_box_var_np, 'target_box': target_box_np, }, fetch_list=[boxes], with_lod=False) # dygraph with self.dynamic_graph(): prior_box_dy = base.to_variable(prior_box_np) prior_box_var_dy = base.to_variable(prior_box_var_np) target_box_dy = base.to_variable(target_box_np) boxes_dy = ops.box_coder( prior_box=prior_box_dy, prior_box_var=prior_box_var_dy, target_box=target_box_dy, code_type="decode_center_size", box_normalized=False) boxes_dy_np = boxes_dy.numpy() self.assertTrue(np.array_equal(boxes_np, boxes_dy_np)) def test_box_coder_error(self): with self.static_graph(): prior_box = paddle.static.data( name='prior_box', shape=[81, 4], dtype='int32') prior_box_var = paddle.static.data( name='prior_box_var', shape=[81, 4], dtype='float32') target_box = paddle.static.data( name='target_box', shape=[20, 81, 4], dtype='float32') self.assertRaises(TypeError, ops.box_coder, prior_box, prior_box_var, target_box) class TestGenerateProposals(LayerTest): def test_generate_proposals(self): scores_np = np.random.rand(2, 3, 4, 4).astype('float32') bbox_deltas_np = np.random.rand(2, 12, 4, 4).astype('float32') im_shape_np = np.array([[8, 8], [6, 6]]).astype('float32') anchors_np = np.reshape(np.arange(4 * 4 * 3 * 4), [4, 4, 3, 4]).astype('float32') variances_np = np.ones((4, 4, 3, 4)).astype('float32') with self.static_graph(): scores = paddle.static.data( name='scores', shape=[2, 3, 4, 4], dtype='float32') bbox_deltas = paddle.static.data( name='bbox_deltas', shape=[2, 12, 4, 4], dtype='float32') im_shape = paddle.static.data( name='im_shape', shape=[2, 2], dtype='float32') anchors = paddle.static.data( name='anchors', shape=[4, 4, 3, 4], dtype='float32') variances = paddle.static.data( name='var', shape=[4, 4, 3, 4], dtype='float32') rois, roi_probs, rois_num = ops.generate_proposals( scores, bbox_deltas, im_shape, anchors, variances, pre_nms_top_n=10, post_nms_top_n=5, return_rois_num=True) rois_stat, roi_probs_stat, rois_num_stat = self.get_static_graph_result( feed={ 'scores': scores_np, 'bbox_deltas': bbox_deltas_np, 'im_shape': im_shape_np, 'anchors': anchors_np, 'var': variances_np }, fetch_list=[rois, roi_probs, rois_num], with_lod=True) with self.dynamic_graph(): scores_dy = base.to_variable(scores_np) bbox_deltas_dy = base.to_variable(bbox_deltas_np) im_shape_dy = base.to_variable(im_shape_np) anchors_dy = base.to_variable(anchors_np) variances_dy = base.to_variable(variances_np) rois, roi_probs, rois_num = ops.generate_proposals( scores_dy, bbox_deltas_dy, im_shape_dy, anchors_dy, variances_dy, pre_nms_top_n=10, post_nms_top_n=5, return_rois_num=True) rois_dy = rois.numpy() roi_probs_dy = roi_probs.numpy() rois_num_dy = rois_num.numpy() self.assertTrue(np.array_equal(np.array(rois_stat), rois_dy)) self.assertTrue(np.array_equal(np.array(roi_probs_stat), roi_probs_dy)) self.assertTrue(np.array_equal(np.array(rois_num_stat), rois_num_dy)) if __name__ == '__main__': unittest.main()