wehub-resource-sync
259 lines
9.1 KiB
Python
259 lines
9.1 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import tempfile
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import unittest
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import numpy as np
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from op_test import get_device, get_device_place, is_custom_device
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sys.path.append("../../legacy_test")
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from test_nms_op import nms
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import paddle
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def _find(condition):
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"""
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Find the indices of elements saticfied the condition.
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Args:
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condition(Tensor[N] or np.ndarray([N,])): Element should be bool type.
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Returns:
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Tensor: Indices of True element.
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"""
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res = []
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for i in range(condition.shape[0]):
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if condition[i]:
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res.append(i)
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return np.array(res)
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def multiclass_nms(boxes, scores, category_idxs, iou_threshold, top_k):
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mask = np.zeros_like(scores)
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for category_id in np.unique(category_idxs):
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cur_category_boxes_idxs = _find(category_idxs == category_id)
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cur_category_boxes = boxes[cur_category_boxes_idxs]
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cur_category_scores = scores[cur_category_boxes_idxs]
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cur_category_sorted_indices = np.argsort(-cur_category_scores)
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cur_category_sorted_boxes = cur_category_boxes[
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cur_category_sorted_indices
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]
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cur_category_keep_boxes_sub_idxs = cur_category_sorted_indices[
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nms(cur_category_sorted_boxes, iou_threshold)
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]
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mask[cur_category_boxes_idxs[cur_category_keep_boxes_sub_idxs]] = True
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keep_boxes_idxs = _find(mask)
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topK_sub_indices = np.argsort(-scores[keep_boxes_idxs])[:top_k]
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return keep_boxes_idxs[topK_sub_indices]
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def gen_args(num_boxes, dtype):
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boxes = np.random.rand(num_boxes, 4).astype(dtype)
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boxes[:, 2] = boxes[:, 0] + boxes[:, 2]
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boxes[:, 3] = boxes[:, 1] + boxes[:, 3]
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scores = np.random.rand(num_boxes).astype(dtype)
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categories = [0, 1, 2, 3]
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category_idxs = np.random.choice(categories, num_boxes)
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return boxes, scores, category_idxs, categories
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class TestOpsNMS(unittest.TestCase):
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def setUp(self):
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self.num_boxes = 64
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self.threshold = 0.5
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self.topk = 20
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self.dtypes = ['float32']
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self.devices = ['cpu']
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if paddle.is_compiled_with_cuda() or is_custom_device():
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self.devices.append(get_device())
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self.temp_dir = tempfile.TemporaryDirectory()
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self.path = os.path.join(self.temp_dir.name, './net')
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def tearDown(self):
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self.temp_dir.cleanup()
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def test_nms(self):
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for device in self.devices:
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for dtype in self.dtypes:
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boxes, scores, category_idxs, categories = gen_args(
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self.num_boxes, dtype
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)
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paddle.set_device(device)
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out = paddle.vision.ops.nms(
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paddle.to_tensor(boxes),
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self.threshold,
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paddle.to_tensor(scores),
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)
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out = paddle.vision.ops.nms(
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paddle.to_tensor(boxes), self.threshold
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)
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out_py = nms(boxes, self.threshold)
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np.testing.assert_array_equal(
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out.numpy(),
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out_py,
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err_msg=f'paddle out: {out}\n py out: {out_py}\n',
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)
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def test_multiclass_nms_dynamic(self):
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for device in self.devices:
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for dtype in self.dtypes:
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boxes, scores, category_idxs, categories = gen_args(
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self.num_boxes, dtype
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)
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paddle.set_device(device)
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out = paddle.vision.ops.nms(
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paddle.to_tensor(boxes),
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self.threshold,
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paddle.to_tensor(scores),
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paddle.to_tensor(category_idxs),
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categories,
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self.topk,
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)
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out_py = multiclass_nms(
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boxes, scores, category_idxs, self.threshold, self.topk
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)
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np.testing.assert_array_equal(
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out.numpy(),
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out_py,
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err_msg=f'paddle out: {out}\n py out: {out_py}\n',
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)
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def test_multiclass_nms_static(self):
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for device in self.devices:
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for dtype in self.dtypes:
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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paddle.enable_static()
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boxes, scores, category_idxs, categories = gen_args(
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self.num_boxes, dtype
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)
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boxes_static = paddle.static.data(
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shape=boxes.shape, dtype=boxes.dtype, name="boxes"
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)
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scores_static = paddle.static.data(
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shape=scores.shape, dtype=scores.dtype, name="scores"
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)
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category_idxs_static = paddle.static.data(
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shape=category_idxs.shape,
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dtype=category_idxs.dtype,
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name="category_idxs",
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)
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out = paddle.vision.ops.nms(
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boxes_static,
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self.threshold,
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scores_static,
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category_idxs_static,
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categories,
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self.topk,
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)
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place = paddle.CPUPlace()
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if device == 'gpu':
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place = get_device_place()
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exe = paddle.static.Executor(place)
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out = exe.run(
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paddle.static.default_main_program(),
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feed={
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'boxes': boxes,
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'scores': scores,
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'category_idxs': category_idxs,
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},
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fetch_list=[out],
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)
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paddle.disable_static()
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out_py = multiclass_nms(
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boxes, scores, category_idxs, self.threshold, self.topk
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)
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out = np.array(out)
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out = np.squeeze(out)
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np.testing.assert_array_equal(
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out,
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out_py,
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err_msg=f'paddle out: {out}\n py out: {out_py}\n',
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)
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def test_multiclass_nms_dynamic_to_static(self):
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for device in self.devices:
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for dtype in self.dtypes:
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paddle.set_device(device)
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def fun(x):
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scores = np.arange(0, 64).astype('float32')
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categories = np.array([0, 1, 2, 3])
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category_idxs = categories.repeat(16)
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out = paddle.vision.ops.nms(
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x,
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0.1,
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paddle.to_tensor(scores),
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paddle.to_tensor(category_idxs),
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categories,
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10,
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)
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return out
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boxes = np.random.rand(64, 4).astype('float32')
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boxes[:, 2] = boxes[:, 0] + boxes[:, 2]
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boxes[:, 3] = boxes[:, 1] + boxes[:, 3]
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origin = fun(paddle.to_tensor(boxes))
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paddle.jit.save(
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fun,
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self.path,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None, 4], dtype='float32', name='x'
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)
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],
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)
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load_func = paddle.jit.load(self.path)
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res = load_func(paddle.to_tensor(boxes))
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np.testing.assert_array_equal(
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origin,
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res,
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err_msg=f'origin out: {origin}\n inference model out: {res}\n',
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)
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def test_matrix_nms_dynamic(self):
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for device in self.devices:
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for dtype in self.dtypes:
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boxes, scores, category_idxs, categories = gen_args(
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self.num_boxes, dtype
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)
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scores = np.random.rand(1, 4, self.num_boxes).astype(dtype)
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paddle.set_device(device)
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out = paddle.vision.ops.matrix_nms(
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paddle.to_tensor(boxes).unsqueeze(0),
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paddle.to_tensor(scores),
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self.threshold,
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post_threshold=0.0,
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nms_top_k=400,
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keep_top_k=100,
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)
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if __name__ == '__main__':
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unittest.main()
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