# 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 PaddleDetection 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 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 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 = paddle.vision.ops.roi_align( x=inputs, boxes=rois, boxes_num=rois_num, output_size=output_size) 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 = paddle.to_tensor(inputs_np) rois_dy = paddle.to_tensor(rois_np) rois_num_dy = paddle.to_tensor(rois_num_np) output_dy = paddle.vision.ops.roi_align( x=inputs_dy, boxes=rois_dy, boxes_num=rois_num_dy, output_size=output_size) 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, paddle.vision.ops.roi_align, input=inputs, rois=rois, output_size=(7, 7)) paddle.disable_static() 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 = paddle.vision.ops.roi_pool( x=inputs, boxes=rois, boxes_num=rois_num, output_size=output_size) 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 = paddle.to_tensor(inputs_np) rois_dy = paddle.to_tensor(rois_np) rois_num_dy = paddle.to_tensor(rois_num_np) output_dy = paddle.vision.ops.roi_pool( x=inputs_dy, boxes=rois_dy, boxes_num=rois_num_dy, output_size=output_size) 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, paddle.vision.ops.roi_pool, input=inputs, rois=rois, output_size=(7, 7)) paddle.disable_static() 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 = paddle.to_tensor(input_np) image_dy = paddle.to_tensor(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) paddle.disable_static() class TestMulticlassNms(LayerTest): def test_multiclass_nms(self): boxes_np = np.random.rand(10, 81, 4).astype('float32') scores_np = np.random.rand(10, 81).astype('float32') rois_num_np = np.array([2, 8]).astype('int32') with self.static_graph(): boxes = paddle.static.data( name='bboxes', shape=[None, 81, 4], dtype='float32', lod_level=1) scores = paddle.static.data( name='scores', shape=[None, 81], dtype='float32', lod_level=1) rois_num = paddle.static.data( name='rois_num', shape=[None], 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=True) out_np = np.array(out_np) index_np = np.array(index_np) nms_rois_num_np = np.array(nms_rois_num_np) with self.dynamic_graph(): boxes_dy = paddle.to_tensor(boxes_np) scores_dy = paddle.to_tensor(scores_np) rois_num_dy = paddle.to_tensor(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 = paddle.to_tensor(boxes_np) scores_dy = paddle.to_tensor(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) paddle.disable_static() 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 = paddle.to_tensor(prior_box_np) prior_box_var_dy = paddle.to_tensor(prior_box_var_np) target_box_dy = paddle.to_tensor(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) paddle.disable_static() if __name__ == '__main__': unittest.main()