From 68d9505ec5c2a37767df920cc94480f08d357a6b Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:20:16 +0000 Subject: [PATCH 01/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20SimpleHumanPose=5FID?= =?UTF-8?q?0956=5Ffor=5FACL?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 1cddb2c9e24edf3b782d623b12d21a0e1c32dacf Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:33:04 +0000 Subject: [PATCH 02/59] add ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/LICENSE. --- .../cv/SimpleHumanPose_ID0956_for_ACL/LICENSE | 21 +++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/LICENSE diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/LICENSE b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/LICENSE new file mode 100644 index 000000000..7208551df --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/LICENSE @@ -0,0 +1,21 @@ + MIT License + + Copyright (c) Microsoft Corporation. All rights reserved. + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE \ No newline at end of file -- Gitee From 30e17b2266f6caf80ff24117c10513dc924a8c2f Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:33:28 +0000 Subject: [PATCH 03/59] add ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/requirements.txt. --- .../requirements.txt | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/requirements.txt diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/requirements.txt b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/requirements.txt new file mode 100644 index 000000000..1d32e97b3 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/requirements.txt @@ -0,0 +1,19 @@ +termcolor>=1.1 +tabulate>=0.7.7 +tqdm>4.11.1 +pyarrow>=0.9.0 +pyzmq>=16 + +msgpack +msgpack_numpy +datetime +setproctitle +pillow +matplotlib + +Cython>=0.19.2 +numpy>=1.7.1 +scipy>=0.13.2 +opencv-python + +pycocotools \ No newline at end of file -- Gitee From 108bf3f7efc1dd93ee89a229b652c203632d0239 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:34:45 +0000 Subject: [PATCH 04/59] add ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/author.txt. --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/author.txt | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/author.txt diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/author.txt b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/author.txt new file mode 100644 index 000000000..20dd1837b --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/author.txt @@ -0,0 +1,4 @@ +Fang Wang, Lei Xie +Nanjing University +Nanjing, Jiangsu, China +mf20330077@smail.nju.edu.cn, lxie@nju.edu.cn \ No newline at end of file -- Gitee From 8dcb95409e07c745a6ed624a1562de07915f064f Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:35:23 +0000 Subject: [PATCH 05/59] add ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/modelzoo_level.txt. --- .../cv/SimpleHumanPose_ID0956_for_ACL/modelzoo_level.txt | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/modelzoo_level.txt diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/modelzoo_level.txt b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/modelzoo_level.txt new file mode 100644 index 000000000..62ed12347 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/modelzoo_level.txt @@ -0,0 +1,6 @@ +ModelConvert:OK +QuantStatus:OK +FuncStatus:OK +PrecisionStatus:OK +AutoTune:OK +PerfStatus:OK \ No newline at end of file -- Gitee From 6183bf1f6187c2888fcec41ab311fef365cff06a Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:36:24 +0000 Subject: [PATCH 06/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20src?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 65e935a00cad9f2c5e5f019d99505f139ec00ecb Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:38:23 +0000 Subject: [PATCH 07/59] add ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/pip-requirements.txt. --- .../src/pip-requirements.txt | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/pip-requirements.txt diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/pip-requirements.txt b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/pip-requirements.txt new file mode 100644 index 000000000..1d32e97b3 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/pip-requirements.txt @@ -0,0 +1,19 @@ +termcolor>=1.1 +tabulate>=0.7.7 +tqdm>4.11.1 +pyarrow>=0.9.0 +pyzmq>=16 + +msgpack +msgpack_numpy +datetime +setproctitle +pillow +matplotlib + +Cython>=0.19.2 +numpy>=1.7.1 +scipy>=0.13.2 +opencv-python + +pycocotools \ No newline at end of file -- Gitee From 1eae63fdc79449bcd4e27f953a1cc08b3fd9cd62 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:38:57 +0000 Subject: [PATCH 08/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20assets?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 52fb33f6f0ddd268cc06deecd372a453b049d519 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:40:50 +0000 Subject: [PATCH 09/59] first commit --- .../src/assets/1.jpg | Bin 0 -> 117209 bytes .../src/assets/2.jpg | Bin 0 -> 223163 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 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Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From e883f68337b65c05c51c5c09276740b3303bd917 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:42:56 +0000 Subject: [PATCH 11/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20data?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 3e4c50b8ac2acdb52c104c4818dc238a7edbb9a4 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:43:33 +0000 Subject: [PATCH 12/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20COCO?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From fa2095837127c10bf7e8869561fe429ea7e1ab43 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:43:46 +0000 Subject: [PATCH 13/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/data/COCO/.keep?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From 67eea6dd8bac9e3b34215e12ecbd15722016b7ab Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:44:37 +0000 Subject: [PATCH 14/59] first commit --- .../src/data/dataset.py | 197 ++++++++++++++++++ 1 file changed, 197 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/dataset.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/dataset.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/dataset.py new file mode 100644 index 000000000..4b78780ca --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/dataset.py @@ -0,0 +1,197 @@ +#!/usr/bin/python3 +# coding=utf-8 + +from npu_bridge.npu_init import * +import os +import os.path as osp +import numpy as np +import cv2 +import json +import pickle +import matplotlib.pyplot as plt +import tensorflow as tf + +import sys + +current_path = os.path.dirname(os.path.realpath(__file__)) # 对应于COCO +project_root = os.path.dirname(os.path.dirname(current_path)) # 对应于TF-Simple +main_path = os.path.join(project_root, "main", "train.py") +print("main_path:", main_path) + + +cur_dir = os.path.dirname(__file__) +# print("========>>>>>>cur_dir:{}".format(cur_dir)) +sys.path.insert(0, osp.join(cur_dir, 'PythonAPI')) +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + + +class Dataset(object): + dataset_name = 'COCO' + num_kps = 17 + kps_names = ['nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear', 'l_shoulder', + 'r_shoulder', 'l_elbow', 'r_elbow', 'l_wrist', 'r_wrist', + 'l_hip', 'r_hip', 'l_knee', 'r_knee', 'l_ankle', 'r_ankle'] + kps_symmetry = [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12), (13, 14), (15, 16)] + kps_lines = [(1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13), + (13, 15), (5, 6), (11, 12)] + + # human_det_path = osp.join('..', 'data', dataset_name, 'dets', 'human_detection.json') # human detection result + # img_path = osp.join('..', 'data', dataset_name, 'images') + # train_annot_path = osp.join('..', 'data', dataset_name, 'annotations', 'person_keypoints_train2017.json') + # train_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_train2017.json') + # val_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_val2017.json') + # test_annot_path = osp.join(cur_dir, 'data', dataset_name, 'annotations', 'image_info_test-dev2017.json') + + # tf_flags = tf.app.flags.FLAGS + data_path = "cache/dataset" + img_path = osp.join(data_path, 'images') + human_det_path = osp.join(data_path, 'dets', 'human_detection.json') + train_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_train2017.json') + val_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_val2017.json') + test_annot_path = osp.join(data_path, 'annotations', 'image_info_test-dev2017.json') + + def load_train_data(self, score=False): + coco = COCO(self.train_annot_path) + train_data = [] + for aid in coco.anns.keys(): + ann = coco.anns[aid] + imgname = 'train2017/' + coco.imgs[ann['image_id']]['file_name'] + joints = ann['keypoints'] + + if (ann['image_id'] not in coco.imgs) or ann['iscrowd'] or (np.sum(joints[2::3]) == 0) or ( + ann['num_keypoints'] == 0): + continue + + # sanitize bboxes + x, y, w, h = ann['bbox'] + img = coco.loadImgs(ann['image_id'])[0] + width, height = img['width'], img['height'] + x1 = np.max((0, x)) + y1 = np.max((0, y)) + x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) + y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) + if ann['area'] > 0 and x2 >= x1 and y2 >= y1: + bbox = [x1, y1, x2 - x1, y2 - y1] + else: + continue + + if score: + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) + else: + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints) + + train_data.append(data) + + return train_data + + def load_val_data_with_annot(self): + coco = COCO(self.val_annot_path) + val_data = [] + for aid in coco.anns.keys(): + ann = coco.anns[aid] + if ann['image_id'] not in coco.imgs: + continue + imgname = 'val2017/' + coco.imgs[ann['image_id']]['file_name'] + bbox = ann['bbox'] + joints = ann['keypoints'] + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) + val_data.append(data) + + return val_data + + def load_annot(self, db_set): + if db_set == 'train': + coco = COCO(self.train_annot_path) + elif db_set == 'val': + coco = COCO(self.val_annot_path) + elif db_set == 'test': + coco = COCO(self.test_annot_path) + else: + print('Unknown db_set') + assert 0 + + return coco + + def load_imgid(self, annot): + return annot.imgs + + def imgid_to_imgname(self, annot, imgid, db_set): + imgs = annot.loadImgs(imgid) + imgname = [db_set + '2017/' + i['file_name'] for i in imgs] + return imgname + + def evaluation(self, result, gt, result_dir, db_set): + result_path = osp.join(result_dir, 'result.json') + with open(result_path, 'w') as f: + json.dump(result, f) # 把result中的东西写到result.json里面 + + result = gt.loadRes(result_path) + cocoEval = COCOeval(gt, result, iouType='keypoints') + + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + result_path = osp.join(result_dir, 'result.pkl') + with open(result_path, 'wb') as f: + pickle.dump(cocoEval, f, 2) + print("Saved result file to " + result_path) + + def vis_keypoints(self, img, kps, kp_thresh=0.4, alpha=1): + + # Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv. + cmap = plt.get_cmap('rainbow') + colors = [cmap(i) for i in np.linspace(0, 1, len(self.kps_lines) + 2)] + colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors] + + # Perform the drawing on a copy of the image, to allow for blending. + kp_mask = np.copy(img) + + # Draw mid shoulder / mid hip first for better visualization. + mid_shoulder = ( + kps[:2, 5] + + kps[:2, 6]) / 2.0 + sc_mid_shoulder = np.minimum( + kps[2, 5], + kps[2, 6]) + mid_hip = ( + kps[:2, 11] + + kps[:2, 12]) / 2.0 + sc_mid_hip = np.minimum( + kps[2, 11], + kps[2, 12]) + nose_idx = 0 + if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh: + cv2.line( + kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(kps[:2, nose_idx].astype(np.int32)), + color=colors[len(self.kps_lines)], thickness=2, lineType=cv2.LINE_AA) + if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: + cv2.line( + kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(mid_hip.astype(np.int32)), + color=colors[len(self.kps_lines) + 1], thickness=2, lineType=cv2.LINE_AA) + + # Draw the keypoints. + for l in range(len(self.kps_lines)): + i1 = self.kps_lines[l][0] + i2 = self.kps_lines[l][1] + p1 = kps[0, i1].astype(np.int32), kps[1, i1].astype(np.int32) + p2 = kps[0, i2].astype(np.int32), kps[1, i2].astype(np.int32) + if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: + cv2.line( + kp_mask, p1, p2, + color=colors[l], thickness=2, lineType=cv2.LINE_AA) + if kps[2, i1] > kp_thresh: + cv2.circle( + kp_mask, p1, + radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + if kps[2, i2] > kp_thresh: + cv2.circle( + kp_mask, p2, + radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + + # Blend the keypoints. + return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) + + +dbcfg = Dataset() -- Gitee From cba991b031d482e416bef88a0c6c2082166f1d7c Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:45:32 +0000 Subject: [PATCH 15/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20lib?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 29da46ef6fe82d84ee20309f36e7ceb492d9d3d6 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:45:59 +0000 Subject: [PATCH 16/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20nets?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 55d37bc1609ce036e080bf1310adcb061ffdad19 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:46:08 +0000 Subject: [PATCH 17/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20nms?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 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a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 711f3eec9eae43402b517404978d15d2fea2e29d Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:46:32 +0000 Subject: [PATCH 19/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20utils?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 1016183c9ccdcfe7a2a74f5d9cf71928a6df390c Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:46:56 +0000 Subject: [PATCH 20/59] first commit --- .../cv/SimpleHumanPose_ID0956_for_ACL/src/lib/Makefile | 4 ++++ .../cv/SimpleHumanPose_ID0956_for_ACL/src/lib/__init__.py | 3 +++ 2 files changed, 7 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/Makefile create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/__init__.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/Makefile b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/Makefile new file mode 100644 index 000000000..57d580440 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/Makefile @@ -0,0 +1,4 @@ +all: + cd nms; python setup.py build_ext --inplace; rm -rf build; cd ../../ +clean: + cd nms; rm *.so; cd ../../ diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/__init__.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/__init__.py new file mode 100644 index 000000000..faaaf799c --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/__init__.py @@ -0,0 +1,3 @@ +# -*- coding: utf-8 -*- + + -- Gitee From 459b3e92d8bcc65575841e49f416328a081bce73 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:47:22 +0000 Subject: [PATCH 21/59] first commit --- .../src/lib/nets/__init__.py | 0 .../src/lib/nets/basemodel.py | 179 ++++++++++ .../src/lib/nets/resnet_utils.py | 290 +++++++++++++++ .../src/lib/nets/resnet_v1.py | 333 ++++++++++++++++++ 4 files changed, 802 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/__init__.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/basemodel.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/resnet_utils.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/resnet_v1.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/__init__.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/basemodel.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/basemodel.py new file mode 100644 index 000000000..475da8674 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/basemodel.py @@ -0,0 +1,179 @@ +from npu_bridge.npu_init import * +import tensorflow as tf +import tensorflow.contrib.slim as slim +from . import resnet_v1, resnet_utils +from tensorflow.contrib.slim import arg_scope +from tensorflow.python.framework import ops +from tensorflow.python.ops import nn_ops +from tensorflow.contrib.layers.python.layers import regularizers, \ + initializers, layers +from config import cfg +import numpy as np + +def resnet_arg_scope(bn_is_training, + bn_trainable, + trainable=True, + weight_decay=cfg.weight_decay, + weight_init = initializers.variance_scaling_initializer(), + batch_norm_decay=0.99, + batch_norm_epsilon=1e-9, + batch_norm_scale=True): + batch_norm_params = { + 'is_training': bn_is_training, + 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, + 'scale': batch_norm_scale, + 'trainable': bn_trainable, + 'updates_collections': ops.GraphKeys.UPDATE_OPS + } + + with arg_scope( + [slim.conv2d, slim.conv2d_transpose], + weights_regularizer=regularizers.l2_regularizer(weight_decay), + weights_initializer=weight_init, + trainable=trainable, + activation_fn=nn_ops.relu, + normalizer_fn=layers.batch_norm, + normalizer_params=batch_norm_params): + with arg_scope([layers.batch_norm], **batch_norm_params) as arg_sc: + return arg_sc + +def resnet50(inp, bn_is_training, bn_trainable): + bottleneck = resnet_v1.bottleneck + blocks = [ + resnet_utils.Block('block1', bottleneck, + [(256, 64, 1)] * 2 + [(256, 64, 1)]), + resnet_utils.Block('block2', bottleneck, + [(512, 128, 2)] + [(512, 128, 1)] * 3), + resnet_utils.Block('block3', bottleneck, + [(1024, 256, 2)] + [(1024, 256, 1)] * 5), + resnet_utils.Block('block4', bottleneck, + [(2048, 512, 2)] + [(2048, 512, 1)] * 2) + ] + + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + + with tf.variable_scope('resnet_v1_50', 'resnet_v1_50'): + + net = resnet_utils.conv2d_same( + tf.concat(inp,axis=3), 64, 7, stride=2, scope='conv1') + + net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.max_pool2d( + net, [3, 3], stride=2, padding='VALID', scope='pool1') + net, _ = resnet_v1.resnet_v1( # trainable ????? + net, blocks[0:1], + global_pool=False, include_root_block=False, + scope='resnet_v1_50') + + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + net2, _ = resnet_v1.resnet_v1( + net, blocks[1:2], + global_pool=False, include_root_block=False, + scope='resnet_v1_50') + + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + net3, _ = resnet_v1.resnet_v1( + net2, blocks[2:3], + global_pool=False, include_root_block=False, + scope='resnet_v1_50') + + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + net4, _ = resnet_v1.resnet_v1( + net3, blocks[3:4], + global_pool=False, include_root_block=False, + scope='resnet_v1_50') + + resnet_features = [net, net2, net3, net4] + return resnet_features + +def resnet101(inp, bn_is_training, bn_trainable): + bottleneck = resnet_v1.bottleneck + blocks = [ + resnet_utils.Block('block1', bottleneck, + [(256, 64, 1)] * 2 + [(256, 64, 1)]), + resnet_utils.Block('block2', bottleneck, + [(512, 128, 2)] + [(512, 128, 1)] * 3), + resnet_utils.Block('block3', bottleneck, + [(1024, 256, 2)] + [(1024, 256, 1)] * 22), + resnet_utils.Block('block4', bottleneck, + [(2048, 512, 2)] + [(2048, 512, 1)] * 2) + ] + + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + with tf.variable_scope('resnet_v1_101', 'resnet_v1_101'): + + net = resnet_utils.conv2d_same( + tf.concat(inp,axis=3), 64, 7, stride=2, scope='conv1') + net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.max_pool2d( + net, [3, 3], stride=2, padding='VALID', scope='pool1') + net, _ = resnet_v1.resnet_v1( # trainable ????? + net, blocks[0:1], + global_pool=False, include_root_block=False, + scope='resnet_v1_101') + + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + net2, _ = resnet_v1.resnet_v1( + net, blocks[1:2], + global_pool=False, include_root_block=False, + scope='resnet_v1_101') + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + net3, _ = resnet_v1.resnet_v1( + net2, blocks[2:3], + global_pool=False, include_root_block=False, + scope='resnet_v1_101') + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + net4, _ = resnet_v1.resnet_v1( + net3, blocks[3:4], + global_pool=False, include_root_block=False, + scope='resnet_v1_101') + + resnet_features = [net, net2, net3, net4] + return resnet_features + +def resnet152(inp, bn_is_training, bn_trainable): + bottleneck = resnet_v1.bottleneck + blocks = [ + resnet_utils.Block('block1', bottleneck, + [(256, 64, 1)] * 2 + [(256, 64, 1)]), + resnet_utils.Block('block2', bottleneck, + [(512, 128, 2)] + [(512, 128, 1)] * 7), + resnet_utils.Block('block3', bottleneck, + [(1024, 256, 2)] + [(1024, 256, 1)] * 35), + resnet_utils.Block('block4', bottleneck, + [(2048, 512, 2)] + [(2048, 512, 1)] * 2) + ] + + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + with tf.variable_scope('resnet_v1_152', 'resnet_v1_152'): + net = resnet_utils.conv2d_same( + tf.concat(inp,axis=3), 64, 7, stride=2, scope='conv1') + net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.max_pool2d( + net, [3, 3], stride=2, padding='VALID', scope='pool1') + net, _ = resnet_v1.resnet_v1( # trainable ????? + net, blocks[0:1], + global_pool=False, include_root_block=False, + scope='resnet_v1_152') + + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + net2, _ = resnet_v1.resnet_v1( + net, blocks[1:2], + global_pool=False, include_root_block=False, + scope='resnet_v1_152') + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + net3, _ = resnet_v1.resnet_v1( + net2, blocks[2:3], + global_pool=False, include_root_block=False, + scope='resnet_v1_152') + with slim.arg_scope(resnet_arg_scope(bn_is_training=bn_is_training, bn_trainable=bn_trainable)): + net4, _ = resnet_v1.resnet_v1( + net3, blocks[3:4], + global_pool=False, include_root_block=False, + scope='resnet_v1_152') + + resnet_features = [net, net2, net3, net4] + return resnet_features + + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/resnet_utils.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/resnet_utils.py new file mode 100644 index 000000000..b1783a2ff --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/resnet_utils.py @@ -0,0 +1,290 @@ +# Copyright 2016 The TensorFlow 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. +# ============================================================================== +"""Contains building blocks for various versions of Residual Networks. + +Residual networks (ResNets) were proposed in: + Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Deep Residual Learning for Image Recognition. arXiv:1512.03385, 2015 + +More variants were introduced in: + Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Identity Mappings in Deep Residual Networks. arXiv: 1603.05027, 2016 + +We can obtain different ResNet variants by changing the network depth, width, +and form of residual unit. This module implements the infrastructure for +building them. Concrete ResNet units and full ResNet networks are implemented in +the accompanying resnet_v1.py and resnet_v2.py modules. + +Compared to https://github.com/KaimingHe/deep-residual-networks, in the current +implementation we subsample the output activations in the last residual unit of +each block, instead of subsampling the input activations in the first residual +unit of each block. The two implementations give identical results but our +implementation is more memory efficient. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from npu_bridge.npu_init import * + +import collections + +from tensorflow.contrib import layers as layers_lib +from tensorflow.contrib.framework.python.ops import add_arg_scope +from tensorflow.contrib.framework.python.ops import arg_scope +from tensorflow.contrib.layers.python.layers import initializers +from tensorflow.contrib.layers.python.layers import layers +from tensorflow.contrib.layers.python.layers import regularizers +from tensorflow.contrib.layers.python.layers import utils +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import variable_scope + + +class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])): + """A named tuple describing a ResNet block. + + Its parts are: + scope: The scope of the `Block`. + unit_fn: The ResNet unit function which takes as input a `Tensor` and + returns another `Tensor` with the output of the ResNet unit. + args: A list of length equal to the number of units in the `Block`. The list + contains one (depth, depth_bottleneck, stride) tuple for each unit in the + block to serve as argument to unit_fn. + """ + + +def subsample(inputs, factor, scope=None): + """Subsamples the input along the spatial dimensions. + + Args: + inputs: A `Tensor` of size [batch, height_in, width_in, channels]. + factor: The subsampling factor. + scope: Optional variable_scope. + + Returns: + output: A `Tensor` of size [batch, height_out, width_out, channels] with the + input, either intact (if factor == 1) or subsampled (if factor > 1). + """ + if factor == 1: + return inputs + else: + return layers.max_pool2d(inputs, [1, 1], stride=factor, scope=scope) + + +def conv2d_same(inputs, num_outputs, kernel_size, stride, w_init=initializers.variance_scaling_initializer(), rate=1, scope=None): + """Strided 2-D convolution with 'SAME' padding. + + When stride > 1, then we do explicit zero-padding, followed by conv2d with + 'VALID' padding. + + Note that + + net = conv2d_same(inputs, num_outputs, 3, stride=stride) + + is equivalent to + + net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=1, + padding='SAME') + net = subsample(net, factor=stride) + + whereas + + net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=stride, + padding='SAME') + + is different when the input's height or width is even, which is why we add the + current function. For more details, see ResnetUtilsTest.testConv2DSameEven(). + + Args: + inputs: A 4-D tensor of size [batch, height_in, width_in, channels]. + num_outputs: An integer, the number of output filters. + kernel_size: An int with the kernel_size of the filters. + stride: An integer, the output stride. + rate: An integer, rate for atrous convolution. + scope: Scope. + + Returns: + output: A 4-D tensor of size [batch, height_out, width_out, channels] with + the convolution output. + """ + if stride == 1: + return layers_lib.conv2d( + inputs, + num_outputs, + kernel_size, + stride=1, + rate=rate, + padding='SAME', + weights_initializer=w_init, + scope=scope) + else: + kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1) + pad_total = kernel_size_effective - 1 + pad_beg = pad_total // 2 + pad_end = pad_total - pad_beg + inputs = array_ops.pad( + inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]]) + return layers_lib.conv2d( + inputs, + num_outputs, + kernel_size, + stride=stride, + rate=rate, + padding='VALID', + weights_initializer=w_init, + scope=scope) + + +@add_arg_scope +def stack_blocks_dense(net, + blocks, + output_stride=None, + outputs_collections=None): + """Stacks ResNet `Blocks` and controls output feature density. + + First, this function creates scopes for the ResNet in the form of + 'block_name/unit_1', 'block_name/unit_2', etc. + + Second, this function allows the user to explicitly control the ResNet + output_stride, which is the ratio of the input to output spatial resolution. + This is useful for dense prediction tasks such as semantic segmentation or + object detection. + + Most ResNets consist of 4 ResNet blocks and subsample the activations by a + factor of 2 when transitioning between consecutive ResNet blocks. This results + to a nominal ResNet output_stride equal to 8. If we set the output_stride to + half the nominal network stride (e.g., output_stride=4), then we compute + responses twice. + + Control of the output feature density is implemented by atrous convolution. + + Args: + net: A `Tensor` of size [batch, height, width, channels]. + blocks: A list of length equal to the number of ResNet `Blocks`. Each + element is a ResNet `Block` object describing the units in the `Block`. + output_stride: If `None`, then the output will be computed at the nominal + network stride. If output_stride is not `None`, it specifies the requested + ratio of input to output spatial resolution, which needs to be equal to + the product of unit strides from the start up to some level of the ResNet. + For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1, + then valid values for the output_stride are 1, 2, 6, 24 or None (which + is equivalent to output_stride=24). + outputs_collections: Collection to add the ResNet block outputs. + + Returns: + net: Output tensor with stride equal to the specified output_stride. + + Raises: + ValueError: If the target output_stride is not valid. + """ + # The current_stride variable keeps track of the effective stride of the + # activations. This allows us to invoke atrous convolution whenever applying + # the next residual unit would result in the activations having stride larger + # than the target output_stride. + current_stride = 1 + + # The atrous convolution rate parameter. + rate = 1 + + for block in blocks: + with variable_scope.variable_scope(block.scope, 'block', [net]) as sc: + for i, unit in enumerate(block.args): + if output_stride is not None and current_stride > output_stride: + raise ValueError('The target output_stride cannot be reached.') + + with variable_scope.variable_scope('unit_%d' % (i + 1), values=[net]): + unit_depth, unit_depth_bottleneck, unit_stride = unit + + # If we have reached the target output_stride, then we need to employ + # atrous convolution with stride=1 and multiply the atrous rate by the + # current unit's stride for use in subsequent layers. + if output_stride is not None and current_stride == output_stride: + net = block.unit_fn( + net, + depth=unit_depth, + depth_bottleneck=unit_depth_bottleneck, + stride=1, + rate=rate) + rate *= unit_stride + + else: + net = block.unit_fn( + net, + depth=unit_depth, + depth_bottleneck=unit_depth_bottleneck, + stride=unit_stride, + rate=1) + current_stride *= unit_stride + net = utils.collect_named_outputs(outputs_collections, sc.name, net) + + if output_stride is not None and current_stride != output_stride: + raise ValueError('The target output_stride cannot be reached.') + + return net + + +def resnet_arg_scope(is_training=True, + weight_decay=0.0001, + batch_norm_decay=0.997, + batch_norm_epsilon=1e-5, + batch_norm_scale=True): + """Defines the default ResNet arg scope. + + TODO(gpapan): The batch-normalization related default values above are + appropriate for use in conjunction with the reference ResNet models + released at https://github.com/KaimingHe/deep-residual-networks. When + training ResNets from scratch, they might need to be tuned. + + Args: + is_training: Whether or not we are training the parameters in the batch + normalization layers of the model. + weight_decay: The weight decay to use for regularizing the model. + batch_norm_decay: The moving average decay when estimating layer activation + statistics in batch normalization. + batch_norm_epsilon: Small constant to prevent division by zero when + normalizing activations by their variance in batch normalization. + batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the + activations in the batch normalization layer. + + Returns: + An `arg_scope` to use for the resnet models. + """ + batch_norm_params = { + 'is_training': is_training, + 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, + 'scale': batch_norm_scale, + 'updates_collections': ops.GraphKeys.UPDATE_OPS, + } + + with arg_scope( + [layers_lib.conv2d], + weights_regularizer=regularizers.l2_regularizer(weight_decay), + weights_initializer=initializers.variance_scaling_initializer(), + activation_fn=nn_ops.relu, + normalizer_fn=layers.batch_norm, + normalizer_params=batch_norm_params): + with arg_scope([layers.batch_norm], **batch_norm_params): + # The following implies padding='SAME' for pool1, which makes feature + # alignment easier for dense prediction tasks. This is also used in + # https://github.com/facebook/fb.resnet.torch. However the accompanying + # code of 'Deep Residual Learning for Image Recognition' uses + # padding='VALID' for pool1. You can switch to that choice by setting + # tf.contrib.framework.arg_scope([tf.contrib.layers.max_pool2d], padding='VALID'). + with arg_scope([layers.max_pool2d], padding='SAME') as arg_sc: + return arg_sc + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/resnet_v1.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/resnet_v1.py new file mode 100644 index 000000000..de5eb991f --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nets/resnet_v1.py @@ -0,0 +1,333 @@ +# Copyright 2016 The TensorFlow 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. +# ============================================================================== +"""Contains definitions for the original form of Residual Networks. + +The 'v1' residual networks (ResNets) implemented in this module were proposed +by: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Deep Residual Learning for Image Recognition. arXiv:1512.03385 + +Other variants were introduced in: +[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Identity Mappings in Deep Residual Networks. arXiv: 1603.05027 + +The networks defined in this module utilize the bottleneck building block of +[1] with projection shortcuts only for increasing depths. They employ batch +normalization *after* every weight layer. This is the architecture used by +MSRA in the Imagenet and MSCOCO 2016 competition models ResNet-101 and +ResNet-152. See [2; Fig. 1a] for a comparison between the current 'v1' +architecture and the alternative 'v2' architecture of [2] which uses batch +normalization *before* every weight layer in the so-called full pre-activation +units. + +Typical use: + + from tensorflow.contrib.slim.python.slim.nets import + resnet_v1 + +ResNet-101 for image classification into 1000 classes: + + # inputs has shape [batch, 224, 224, 3] + with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training)): + net, end_points = resnet_v1.resnet_v1_101(inputs, 1000) + +ResNet-101 for semantic segmentation into 21 classes: + + # inputs has shape [batch, 513, 513, 3] + with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training)): + net, end_points = resnet_v1.resnet_v1_101(inputs, + 21, + global_pool=False, + output_stride=16) +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from npu_bridge.npu_init import * + +from tensorflow.contrib import layers +from tensorflow.contrib.framework.python.ops import add_arg_scope +from tensorflow.contrib.framework.python.ops import arg_scope +from tensorflow.contrib.layers.python.layers import layers as layers_lib +from tensorflow.contrib.layers.python.layers import utils +#from tensorflow.contrib.slim.python.slim.nets import resnet_utils +from . import resnet_utils +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import variable_scope +import tensorflow as tf + +resnet_arg_scope = resnet_utils.resnet_arg_scope + + +@add_arg_scope +def bottleneck(inputs, + depth, + depth_bottleneck, + stride, + rate=1, + outputs_collections=None, + scope=None): + """Bottleneck residual unit variant with BN after convolutions. + + This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for + its definition. Note that we use here the bottleneck variant which has an + extra bottleneck layer. + + When putting together two consecutive ResNet blocks that use this unit, one + should use stride = 2 in the last unit of the first block. + + Args: + inputs: A tensor of size [batch, height, width, channels]. + depth: The depth of the ResNet unit output. + depth_bottleneck: The depth of the bottleneck layers. + stride: The ResNet unit's stride. Determines the amount of downsampling of + the units output compared to its input. + rate: An integer, rate for atrous convolution. + outputs_collections: Collection to add the ResNet unit output. + scope: Optional variable_scope. + + Returns: + The ResNet unit's output. + """ + with variable_scope.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc: + depth_in = utils.last_dimension(inputs.get_shape(), min_rank=4) + if depth == depth_in: + shortcut = resnet_utils.subsample(inputs, stride, 'shortcut') + else: + shortcut = layers.conv2d( + inputs, + depth, [1, 1], + stride=stride, + activation_fn=None, + scope='shortcut') + + residual = layers.conv2d( + inputs, depth_bottleneck, [1, 1], stride=1, scope='conv1') + residual = resnet_utils.conv2d_same( + residual, depth_bottleneck, 3, stride, rate=rate, scope='conv2') + residual = layers.conv2d( + residual, depth, [1, 1], stride=1, activation_fn=None, scope='conv3') + + output = nn_ops.relu(shortcut + residual) + + return utils.collect_named_outputs(outputs_collections, sc.name, output) + + +def resnet_v1(inputs, + blocks, + num_classes=None, + global_pool=True, + output_stride=None, + include_root_block=True, + reuse=None, # 修改 + scope=None): + """Generator for v1 ResNet models. + + This function generates a family of ResNet v1 models. See the resnet_v1_*() + methods for specific model instantiations, obtained by selecting different + block instantiations that produce ResNets of various depths. + + Training for image classification on Imagenet is usually done with [224, 224] + inputs, resulting in [7, 7] feature maps at the output of the last ResNet + block for the ResNets defined in [1] that have nominal stride equal to 32. + However, for dense prediction tasks we advise that one uses inputs with + spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In + this case the feature maps at the ResNet output will have spatial shape + [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1] + and corners exactly aligned with the input image corners, which greatly + facilitates alignment of the features to the image. Using as input [225, 225] + images results in [8, 8] feature maps at the output of the last ResNet block. + + For dense prediction tasks, the ResNet needs to run in fully-convolutional + (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all + have nominal stride equal to 32 and a good choice in FCN mode is to use + output_stride=16 in order to increase the density of the computed features at + small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915. + + Args: + inputs: A tensor of size [batch, height_in, width_in, channels]. + blocks: A list of length equal to the number of ResNet blocks. Each element + is a resnet_utils.Block object describing the units in the block. + num_classes: Number of predicted classes for classification tasks. If None + we return the features before the logit layer. + global_pool: If True, we perform global average pooling before computing the + logits. Set to True for image classification, False for dense prediction. + output_stride: If None, then the output will be computed at the nominal + network stride. If output_stride is not None, it specifies the requested + ratio of input to output spatial resolution. + include_root_block: If True, include the initial convolution followed by + max-pooling, if False excludes it. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + + Returns: + net: A rank-4 tensor of size [batch, height_out, width_out, channels_out]. + If global_pool is False, then height_out and width_out are reduced by a + factor of output_stride compared to the respective height_in and width_in, + else both height_out and width_out equal one. If num_classes is None, then + net is the output of the last ResNet block, potentially after global + average pooling. If num_classes is not None, net contains the pre-softmax + activations. + end_points: A dictionary from components of the network to the corresponding + activation. + + Raises: + ValueError: If the target output_stride is not valid. + """ + with variable_scope.variable_scope( + scope, 'resnet_v1', [inputs], reuse=reuse) as sc: # 修改 + end_points_collection = sc.original_name_scope + '_end_points' + with arg_scope( + [layers.conv2d, bottleneck, resnet_utils.stack_blocks_dense], + outputs_collections=end_points_collection): + net = inputs + if include_root_block: + if output_stride is not None: + if output_stride % 4 != 0: + raise ValueError('The output_stride needs to be a multiple of 4.') + output_stride /= 4 + net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1') + net = layers_lib.max_pool2d(net, [3, 3], stride=2, scope='pool1') + net = resnet_utils.stack_blocks_dense(net, blocks, output_stride) + if global_pool: + # Global average pooling. + net = math_ops.reduce_mean(net, [1, 2], name='pool5', keep_dims=True) + if num_classes is not None: + net = layers.conv2d( + net, + num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + scope='logits') + # Convert end_points_collection into a dictionary of end_points. + end_points = utils.convert_collection_to_dict(end_points_collection) + if num_classes is not None: + end_points['predictions'] = layers_lib.softmax(net, scope='predictions') + return net, end_points + + +resnet_v1.default_image_size = 224 + + +def resnet_v1_50(inputs, + num_classes=None, + global_pool=True, + output_stride=None, + reuse=None, # 修改 + scope='resnet_v1_50'): + """ResNet-50 model of [1]. See resnet_v1() for arg and return description.""" + blocks = [ + resnet_utils.Block('block1', bottleneck, + [(256, 64, 1)] * 2 + [(256, 64, 2)]), + resnet_utils.Block('block2', bottleneck, + [(512, 128, 1)] * 3 + [(512, 128, 2)]), + resnet_utils.Block('block3', bottleneck, + [(1024, 256, 1)] * 5 + [(1024, 256, 2)]), + resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) + ] + return resnet_v1( + inputs, + blocks, + num_classes, + global_pool, + output_stride, + include_root_block=True, + reuse=reuse, # 修改 + scope=scope) + + +def resnet_v1_101(inputs, + num_classes=None, + global_pool=True, + output_stride=None, + reuse=None, + scope='resnet_v1_101'): + """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" + blocks = [ + resnet_utils.Block('block1', bottleneck, + [(256, 64, 1)] * 2 + [(256, 64, 2)]), + resnet_utils.Block('block2', bottleneck, + [(512, 128, 1)] * 3 + [(512, 128, 2)]), + resnet_utils.Block('block3', bottleneck, + [(1024, 256, 1)] * 22 + [(1024, 256, 2)]), + resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) + ] + return resnet_v1( + inputs, + blocks, + num_classes, + global_pool, + output_stride, + include_root_block=True, + reuse=reuse, + scope=scope) + + +def resnet_v1_152(inputs, + num_classes=None, + global_pool=True, + output_stride=None, + reuse=None, + scope='resnet_v1_152'): + """ResNet-152 model of [1]. See resnet_v1() for arg and return description.""" + blocks = [ + resnet_utils.Block('block1', bottleneck, + [(256, 64, 1)] * 2 + [(256, 64, 2)]), + resnet_utils.Block('block2', bottleneck, + [(512, 128, 1)] * 7 + [(512, 128, 2)]), + resnet_utils.Block('block3', bottleneck, + [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), + resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) + ] + return resnet_v1( + inputs, + blocks, + num_classes, + global_pool, + output_stride, + include_root_block=True, + reuse=reuse, + scope=scope) + + +def resnet_v1_200(inputs, + num_classes=None, + global_pool=True, + output_stride=None, + reuse=None, + scope='resnet_v1_200'): + """ResNet-200 model of [2]. See resnet_v1() for arg and return description.""" + blocks = [ + resnet_utils.Block('block1', bottleneck, + [(256, 64, 1)] * 2 + [(256, 64, 2)]), + resnet_utils.Block('block2', bottleneck, + [(512, 128, 1)] * 23 + [(512, 128, 2)]), + resnet_utils.Block('block3', bottleneck, + [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), + resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) + ] + return resnet_v1( + inputs, + blocks, + num_classes, + global_pool, + output_stride, + include_root_block=True, + reuse=reuse, + scope=scope) + -- Gitee From 35db0f882da99c4c2c4d7e85ad796247abacda97 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:49:12 +0000 Subject: [PATCH 22/59] first commit --- .../src/lib/nms/__init__.py | 0 .../src/lib/nms/cpu_nms.pyx | 67 ++++++++ .../src/lib/nms/gpu_nms.hpp | 2 + .../src/lib/nms/gpu_nms.pyx | 30 ++++ .../src/lib/nms/nms.py | 121 +++++++++++++++ .../src/lib/nms/nms_kernel.cu | 143 ++++++++++++++++++ .../src/lib/nms/nms_my.py | 121 +++++++++++++++ .../src/lib/nms/setup.py | 142 +++++++++++++++++ 8 files changed, 626 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/__init__.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/cpu_nms.pyx create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/gpu_nms.hpp create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/gpu_nms.pyx create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms_kernel.cu create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms_my.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/setup.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/__init__.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/cpu_nms.pyx b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/cpu_nms.pyx new file mode 100644 index 000000000..59388fe78 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/cpu_nms.pyx @@ -0,0 +1,67 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Modified from py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn) +# ------------------------------------------------------------------------------ + +import numpy as np +cimport numpy as np + +cdef inline np.float32_t max(np.float32_t a, np.float32_t b): + return a if a >= b else b + +cdef inline np.float32_t min(np.float32_t a, np.float32_t b): + return a if a <= b else b + +def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): + cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] + cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] + cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] + cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] + cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] + + cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) + cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1].astype('i') + + cdef int ndets = dets.shape[0] + cdef np.ndarray[np.int_t, ndim=1] suppressed = \ + np.zeros((ndets), dtype=np.int) + + # nominal indices + cdef int _i, _j + # sorted indices + cdef int i, j + # temp variables for box i's (the box currently under consideration) + cdef np.float32_t ix1, iy1, ix2, iy2, iarea + # variables for computing overlap with box j (lower scoring box) + cdef np.float32_t xx1, yy1, xx2, yy2 + cdef np.float32_t w, h + cdef np.float32_t inter, ovr + + keep = [] + for _i in range(ndets): + i = order[_i] + if suppressed[i] == 1: + continue + keep.append(i) + ix1 = x1[i] + iy1 = y1[i] + ix2 = x2[i] + iy2 = y2[i] + iarea = areas[i] + for _j in range(_i + 1, ndets): + j = order[_j] + if suppressed[j] == 1: + continue + xx1 = max(ix1, x1[j]) + yy1 = max(iy1, y1[j]) + xx2 = min(ix2, x2[j]) + yy2 = min(iy2, y2[j]) + w = max(0.0, xx2 - xx1 + 1) + h = max(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (iarea + areas[j] - inter) + if ovr >= thresh: + suppressed[j] = 1 + + return keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/gpu_nms.hpp b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/gpu_nms.hpp new file mode 100644 index 000000000..68b6d42cd --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/gpu_nms.hpp @@ -0,0 +1,2 @@ +void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, + int boxes_dim, float nms_overlap_thresh, int device_id); diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/gpu_nms.pyx b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/gpu_nms.pyx new file mode 100644 index 000000000..b6ff660cf --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/gpu_nms.pyx @@ -0,0 +1,30 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Modified from py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn) +# ------------------------------------------------------------------------------ + +import numpy as np +cimport numpy as np + +assert sizeof(int) == sizeof(np.int32_t) + +cdef extern from "gpu_nms.hpp": + void _nms(np.int32_t*, int*, np.float32_t*, int, int, float, int) + +def gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh, + np.int32_t device_id=0): + cdef int boxes_num = dets.shape[0] + cdef int boxes_dim = dets.shape[1] + cdef int num_out + cdef np.ndarray[np.int32_t, ndim=1] \ + keep = np.zeros(boxes_num, dtype=np.int32) + cdef np.ndarray[np.float32_t, ndim=1] \ + scores = dets[:, 4] + cdef np.ndarray[np.int32_t, ndim=1] \ + order = scores.argsort()[::-1].astype(np.int32) + cdef np.ndarray[np.float32_t, ndim=2] \ + sorted_dets = dets[order, :] + _nms(&keep[0], &num_out, &sorted_dets[0, 0], boxes_num, boxes_dim, thresh, device_id) + keep = keep[:num_out] + return list(order[keep]) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms.py new file mode 100644 index 000000000..bd86ed0e5 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms.py @@ -0,0 +1,121 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Modified from py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from npu_bridge.npu_init import * + +import numpy as np + +from .cpu_nms import cpu_nms +from .gpu_nms import gpu_nms + + +def py_nms_wrapper(thresh): + def _nms(dets): + return nms(dets, thresh) + return _nms + + +def cpu_nms_wrapper(thresh): + def _nms(dets): + return cpu_nms(dets, thresh) + return _nms + + +def gpu_nms_wrapper(thresh, device_id): + def _nms(dets): + return gpu_nms(dets, thresh, device_id) + return _nms + + +def nms(dets, thresh): + """ + greedily select boxes with high confidence and overlap with current maximum <= thresh + rule out overlap >= thresh + :param dets: [[x1, y1, x2, y2 score]] + :param thresh: retain overlap < thresh + :return: indexes to keep + """ + if dets.shape[0] == 0: + return [] + + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + +def oks_iou(g, d, a_g, a_d, sigmas=None, in_vis_thre=None): + if not isinstance(sigmas, np.ndarray): + sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0 + vars = (sigmas * 2) ** 2 + xg = g[0::3] + yg = g[1::3] + vg = g[2::3] + ious = np.zeros((d.shape[0])) + for n_d in range(0, d.shape[0]): + xd = d[n_d, 0::3] + yd = d[n_d, 1::3] + vd = d[n_d, 2::3] + dx = xd - xg + dy = yd - yg + e = (dx ** 2 + dy ** 2) / vars / ((a_g + a_d[n_d]) / 2 + np.spacing(1)) / 2 + if in_vis_thre is not None: + ind = list(vg > in_vis_thre) and list(vd > in_vis_thre) + e = e[ind] + ious[n_d] = np.sum(np.exp(-e)) / e.shape[0] if e.shape[0] != 0 else 0.0 + return ious + +def oks_nms(kpts, scores, areas, thresh, sigmas=None, in_vis_thre=None): + """ + greedily select boxes with high confidence and overlap with current maximum <= thresh + rule out overlap >= thresh, overlap = oks + :param kpts_db + :param thresh: retain overlap < thresh + :return: indexes to keep + """ + if len(kpts) == 0: + return [] + + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + + oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]], sigmas, in_vis_thre) + + inds = np.where(oks_ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms_kernel.cu b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms_kernel.cu new file mode 100644 index 000000000..f6176c6de --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms_kernel.cu @@ -0,0 +1,143 @@ +// ------------------------------------------------------------------ +// Copyright (c) Microsoft +// Licensed under The MIT License +// Modified from MATLAB Faster R-CNN (https://github.com/shaoqingren/faster_rcnn) +// ------------------------------------------------------------------ + +#include "gpu_nms.hpp" +#include +#include + +#define CUDA_CHECK(condition) \ + /* Code block avoids redefinition of cudaError_t error */ \ + do { \ + cudaError_t error = condition; \ + if (error != cudaSuccess) { \ + std::cout << cudaGetErrorString(error) << std::endl; \ + } \ + } while (0) + +#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) +int const threadsPerBlock = sizeof(unsigned long long) * 8; + +__device__ inline float devIoU(float const * const a, float const * const b) { + float left = max(a[0], b[0]), right = min(a[2], b[2]); + float top = max(a[1], b[1]), bottom = min(a[3], b[3]); + float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); + float interS = width * height; + float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); + float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); + return interS / (Sa + Sb - interS); +} + +__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh, + const float *dev_boxes, unsigned long long *dev_mask) { + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + __shared__ float block_boxes[threadsPerBlock * 5]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const float *cur_box = dev_boxes + cur_box_idx * 5; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = DIVUP(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +void _set_device(int device_id) { + int current_device; + CUDA_CHECK(cudaGetDevice(¤t_device)); + if (current_device == device_id) { + return; + } + // The call to cudaSetDevice must come before any calls to Get, which + // may perform initialization using the GPU. + CUDA_CHECK(cudaSetDevice(device_id)); +} + +void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num, + int boxes_dim, float nms_overlap_thresh, int device_id) { + _set_device(device_id); + + float* boxes_dev = NULL; + unsigned long long* mask_dev = NULL; + + const int col_blocks = DIVUP(boxes_num, threadsPerBlock); + + CUDA_CHECK(cudaMalloc(&boxes_dev, + boxes_num * boxes_dim * sizeof(float))); + CUDA_CHECK(cudaMemcpy(boxes_dev, + boxes_host, + boxes_num * boxes_dim * sizeof(float), + cudaMemcpyHostToDevice)); + + CUDA_CHECK(cudaMalloc(&mask_dev, + boxes_num * col_blocks * sizeof(unsigned long long))); + + dim3 blocks(DIVUP(boxes_num, threadsPerBlock), + DIVUP(boxes_num, threadsPerBlock)); + dim3 threads(threadsPerBlock); + nms_kernel<<>>(boxes_num, + nms_overlap_thresh, + boxes_dev, + mask_dev); + + std::vector mask_host(boxes_num * col_blocks); + CUDA_CHECK(cudaMemcpy(&mask_host[0], + mask_dev, + sizeof(unsigned long long) * boxes_num * col_blocks, + cudaMemcpyDeviceToHost)); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + int num_to_keep = 0; + for (int i = 0; i < boxes_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long *p = &mask_host[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + *num_out = num_to_keep; + + CUDA_CHECK(cudaFree(boxes_dev)); + CUDA_CHECK(cudaFree(mask_dev)); +} diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms_my.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms_my.py new file mode 100644 index 000000000..f1a3acc0c --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/nms_my.py @@ -0,0 +1,121 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Modified from py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from npu_bridge.npu_init import * + +import numpy as np + +# from .cpu_nms import cpu_nms +# from .gpu_nms import gpu_nms + + +def py_nms_wrapper(thresh): + def _nms(dets): + return nms(dets, thresh) + return _nms + + +# def cpu_nms_wrapper(thresh): +# def _nms(dets): +# return cpu_nms(dets, thresh) +# return _nms +# +# +# def gpu_nms_wrapper(thresh, device_id): +# def _nms(dets): +# return gpu_nms(dets, thresh, device_id) +# return _nms + + +def nms(dets, thresh): + """ + greedily select boxes with high confidence and overlap with current maximum <= thresh + rule out overlap >= thresh + :param dets: [[x1, y1, x2, y2 score]] + :param thresh: retain overlap < thresh + :return: indexes to keep + """ + if dets.shape[0] == 0: + return [] + + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + +def oks_iou(g, d, a_g, a_d, sigmas=None, in_vis_thre=None): + if not isinstance(sigmas, np.ndarray): + sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0 + vars = (sigmas * 2) ** 2 + xg = g[0::3] + yg = g[1::3] + vg = g[2::3] + ious = np.zeros((d.shape[0])) + for n_d in range(0, d.shape[0]): + xd = d[n_d, 0::3] + yd = d[n_d, 1::3] + vd = d[n_d, 2::3] + dx = xd - xg + dy = yd - yg + e = (dx ** 2 + dy ** 2) / vars / ((a_g + a_d[n_d]) / 2 + np.spacing(1)) / 2 + if in_vis_thre is not None: + ind = list(vg > in_vis_thre) and list(vd > in_vis_thre) + e = e[ind] + ious[n_d] = np.sum(np.exp(-e)) / e.shape[0] if e.shape[0] != 0 else 0.0 + return ious + +def oks_nms(kpts, scores, areas, thresh, sigmas=None, in_vis_thre=None): + """ + greedily select boxes with high confidence and overlap with current maximum <= thresh + rule out overlap >= thresh, overlap = oks + :param kpts_db + :param thresh: retain overlap < thresh + :return: indexes to keep + """ + if len(kpts) == 0: + return [] + + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + + oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]], sigmas, in_vis_thre) + + inds = np.where(oks_ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/setup.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/setup.py new file mode 100644 index 000000000..70221d182 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/nms/setup.py @@ -0,0 +1,142 @@ +# -------------------------------------------------------- +# Copyright (c) Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Modified from py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn) +# -------------------------------------------------------- + +from npu_bridge.npu_init import * +import os +from os.path import join as pjoin +from setuptools import setup +from distutils.extension import Extension +from Cython.Distutils import build_ext +import numpy as np + + +def find_in_path(name, path): + "Find a file in a search path" + # Adapted fom + # http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ + for dir in path.split(os.pathsep): + binpath = pjoin(dir, name) + if os.path.exists(binpath): + return os.path.abspath(binpath) + return None + + +def locate_cuda(): + """Locate the CUDA environment on the system + Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' + and values giving the absolute path to each directory. + Starts by looking for the CUDAHOME env variable. If not found, everything + is based on finding 'nvcc' in the PATH. + """ + + # first check if the CUDAHOME env variable is in use + if 'CUDAHOME' in os.environ: + home = os.environ['CUDAHOME'] + nvcc = pjoin(home, 'bin', 'nvcc') + else: + # otherwise, search the PATH for NVCC + default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') + nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) + if nvcc is None: + raise EnvironmentError('The nvcc binary could not be ' + 'located in your $PATH. Either add it to your path, or set $CUDAHOME') + home = os.path.dirname(os.path.dirname(nvcc)) + + cudaconfig = {'home': home, 'nvcc': nvcc, + 'include': pjoin(home, 'include'), + 'lib64': pjoin(home, 'lib64')} + for k, v in cudaconfig.items(): + if not os.path.exists(v): + raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) + + return cudaconfig + + +CUDA = locate_cuda() + +# Obtain the numpy include directory. This logic works across numpy versions. +try: + numpy_include = np.get_include() +except AttributeError: + numpy_include = np.get_numpy_include() + + +def customize_compiler_for_nvcc(self): + """inject deep into distutils to customize how the dispatch + to gcc/nvcc works. + If you subclass UnixCCompiler, it's not trivial to get your subclass + injected in, and still have the right customizations (i.e. + distutils.sysconfig.customize_compiler) run on it. So instead of going + the OO route, I have this. Note, it's kindof like a wierd functional + subclassing going on.""" + + # tell the compiler it can processes .cu + self.src_extensions.append('.cu') + + # save references to the default compiler_so and _comple methods + default_compiler_so = self.compiler_so + super = self._compile + + # now redefine the _compile method. This gets executed for each + # object but distutils doesn't have the ability to change compilers + # based on source extension: we add it. + def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): + if os.path.splitext(src)[1] == '.cu': + # use the cuda for .cu files + self.set_executable('compiler_so', CUDA['nvcc']) + # use only a subset of the extra_postargs, which are 1-1 translated + # from the extra_compile_args in the Extension class + postargs = extra_postargs['nvcc'] + else: + postargs = extra_postargs['gcc'] + + super(obj, src, ext, cc_args, postargs, pp_opts) + # reset the default compiler_so, which we might have changed for cuda + self.compiler_so = default_compiler_so + + # inject our redefined _compile method into the class + self._compile = _compile + + +# run the customize_compiler +class custom_build_ext(build_ext): + def build_extensions(self): + customize_compiler_for_nvcc(self.compiler) + build_ext.build_extensions(self) + + +ext_modules = [ + Extension( + "cpu_nms", + ["cpu_nms.pyx"], + extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + include_dirs=[numpy_include] + ), + Extension('gpu_nms', + ['nms_kernel.cu', 'gpu_nms.pyx'], + library_dirs=[CUDA['lib64']], + libraries=['cudart'], + language='c++', + runtime_library_dirs=[CUDA['lib64']], + # this syntax is specific to this build system + # we're only going to use certain compiler args with nvcc and not with + # gcc the implementation of this trick is in customize_compiler() below + extra_compile_args={'gcc': ["-Wno-unused-function"], + 'nvcc': ['-arch=sm_35', + '--ptxas-options=-v', + '-c', + '--compiler-options', + "'-fPIC'"]}, + include_dirs=[numpy_include, CUDA['include']] + ), +] + +setup( + name='nms', + ext_modules=ext_modules, + # inject our custom trigger + cmdclass={'build_ext': custom_build_ext}, +) -- Gitee From 77fbfbcd1be38978b786983555728be4bc67e614 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:49:34 +0000 Subject: [PATCH 23/59] first commit --- .../src/lib/tfflat/__init__.py | 0 .../src/lib/tfflat/base.py | 431 ++++++++++++++++++ .../src/lib/tfflat/data_provider.py | 374 +++++++++++++++ .../src/lib/tfflat/dpflow.py | 40 ++ .../src/lib/tfflat/logger.py | 51 +++ .../src/lib/tfflat/mp_utils.py | 135 ++++++ .../src/lib/tfflat/mp_utils_my.py | 135 ++++++ .../src/lib/tfflat/net_utils.py | 120 +++++ .../src/lib/tfflat/saver.py | 69 +++ .../src/lib/tfflat/serialize.py | 80 ++++ .../src/lib/tfflat/timer.py | 40 ++ .../src/lib/tfflat/utils.py | 54 +++ 12 files changed, 1529 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/__init__.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/base.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/data_provider.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/dpflow.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/logger.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/mp_utils.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/mp_utils_my.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/net_utils.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/saver.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/serialize.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/timer.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/utils.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/__init__.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/base.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/base.py new file mode 100644 index 000000000..d6756bd89 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/base.py @@ -0,0 +1,431 @@ +from npu_bridge.npu_init import * +import tensorflow as tf +import tensorflow.contrib.slim as slim +import numpy as np +from collections import OrderedDict as dict +import setproctitle +import os +import os.path as osp +import glob +import abc +import math + +from .net_utils import average_gradients, aggregate_batch, get_optimizer, get_tower_summary_dict +from .saver import load_model, Saver +from .timer import Timer +from .logger import colorlogger +from .utils import approx_equal + + +class ModelDesc(object): + __metaclass__ = abc.ABCMeta + + def __init__(self): + self._loss = None + self._inputs = [] + self._outputs = [] + self._tower_summary = [] + + def set_inputs(self, *vars): + self._inputs = vars + + def set_outputs(self, *vars): + self._outputs = vars + + def set_loss(self, var): + if not isinstance(var, tf.Tensor): + raise ValueError("Loss must be an single tensor.") + # assert var.get_shape() == [], 'Loss tensor must be a scalar shape but got {} shape'.format(var.get_shape()) + self._loss = var + + def get_loss(self, include_wd=False): + if self._loss is None: + raise ValueError("Network doesn't define the final loss") + + if include_wd: + weight_decay = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) + weight_decay = tf.add_n(weight_decay) + return self._loss + weight_decay + else: + return self._loss + + def get_inputs(self): + if len(self._inputs) == 0: + raise ValueError("Network doesn't define the inputs") + return self._inputs + + def get_outputs(self): + if len(self._outputs) == 0: + raise ValueError("Network doesn't define the outputs") + return self._outputs + + def add_tower_summary(self, name, vars, reduced_method='mean'): + assert reduced_method == 'mean' or reduced_method == 'sum', \ + "Summary tensor only supports sum- or mean- reduced method" + if isinstance(vars, list): + for v in vars: + if vars.get_shape() == None: + print('Summary tensor {} got an unknown shape.'.format(name)) + else: + assert v.get_shape().as_list() == [], \ + "Summary tensor only supports scalar but got {}".format(v.get_shape().as_list()) + tf.add_to_collection(name, v) + else: + if vars.get_shape() == None: + print('Summary tensor {} got an unknown shape.'.format(name)) + else: + assert vars.get_shape().as_list() == [], \ + "Summary tensor only supports scalar but got {}".format(vars.get_shape().as_list()) + tf.add_to_collection(name, vars) + self._tower_summary.append([name, reduced_method]) + + @abc.abstractmethod + def make_network(self, is_train): + pass + + +class Base(object): + __metaclass__ = abc.ABCMeta + """ + build graph: + _make_graph + make_inputs + make_network + add_tower_summary + get_summary + + train/test + """ + + def __init__(self, net, cfg, data_iter=None, log_name='logs.txt'): + self._input_list = [] + self._output_list = [] + self._outputs = [] + self.graph_ops = None + + self.net = net + self.cfg = cfg + + self.cur_epoch = 0 + + self.summary_dict = {} + + # timer + self.tot_timer = Timer() + self.gpu_timer = Timer() + self.read_timer = Timer() + + # logger + self.logger = colorlogger(cfg.log_dir, log_name=log_name) + + # initialize tensorflow + tfconfig = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) + tfconfig.gpu_options.allow_growth = True + self.sess = tf.Session(config=npu_config_proto(config_proto=tfconfig)) + + # build_graph + self.build_graph() + + # get data iter + self._data_iter = data_iter + + @abc.abstractmethod + def _make_data(self): + return + + @abc.abstractmethod + def _make_graph(self): + return + + def build_graph(self): + # all variables should be in the same graph and stored in cpu. + with tf.device('/cpu:0'): + tf.set_random_seed(2333) + self.graph_ops = self._make_graph() + if not isinstance(self.graph_ops, list) and not isinstance(self.graph_ops, tuple): + self.graph_ops = [self.graph_ops] + self.summary_dict.update(get_tower_summary_dict(self.net._tower_summary)) + + def load_weights(self, model=None): + + if model == 'last_epoch': + sfiles = os.path.join(self.cfg.model_dump_dir, 'snapshot_*.ckpt.meta') + sfiles = glob.glob(sfiles) + if len(sfiles) > 0: + sfiles.sort(key=os.path.getmtime) + sfiles = [i[:-5] for i in sfiles if i.endswith('.meta')] + model = sfiles[-1] + else: + self.logger.critical('No snapshot model exists.') + return + + if isinstance(model, int): + model = os.path.join(self.cfg.model_dump_dir_test, 'snapshot_%d.ckpt' % model) # 修改 + + if isinstance(model, str) and (osp.exists(model + '.meta') or osp.exists(model)): + self.logger.info('Initialized model weights from {} ...'.format(model)) + load_model(self.sess, model) + if model.split('/')[-1].startswith('snapshot_'): + self.cur_epoch = int(model[model.find('snapshot_') + 9:model.find('.ckpt')]) + self.logger.info('Current epoch is %d.' % self.cur_epoch) + else: + self.logger.critical('Load nothing. There is no model in path {}.'.format(model)) + + def next_feed(self): + if self._data_iter is None: + raise ValueError('No input data.') + feed_dict = dict() + for inputs in self._input_list: + # print("inputs:", self.sess.run(inputs)) + blobs = next(self._data_iter) + for i, inp in enumerate(inputs): + inp_shape = inp.get_shape().as_list() + if None in inp_shape: + feed_dict[inp] = blobs[i] + else: + feed_dict[inp] = blobs[i].reshape(*inp_shape) + return feed_dict + + +class Trainer(Base): + def __init__(self, net, cfg, data_iter=None): + self.lr_eval = cfg.lr + self.lr = tf.Variable(cfg.lr, trainable=False) + self._optimizer = get_optimizer(self.lr, cfg.optimizer) + + super(Trainer, self).__init__(net, cfg, data_iter, log_name='train_logs.txt') + + # make data + self._data_iter, self.itr_per_epoch = self._make_data() + + def _make_data(self): + from dataset import Dataset + from gen_batch import generate_batch + + d = Dataset() + train_data = d.load_train_data() + + from tfflat.data_provider import DataFromList, MultiProcessMapDataZMQ, BatchData, MapData + data_load_thread = DataFromList(train_data) + if self.cfg.multi_thread_enable: + data_load_thread = MultiProcessMapDataZMQ(data_load_thread, self.cfg.num_thread, generate_batch, + strict=True) + else: + data_load_thread = MapData(data_load_thread, generate_batch) + data_load_thread = BatchData(data_load_thread, self.cfg.batch_size) + + data_load_thread.reset_state() + dataiter = data_load_thread.get_data() + + return dataiter, math.ceil(len(train_data) / self.cfg.batch_size / self.cfg.num_gpus) + + def _make_graph(self): + self.logger.info("Generating training graph on {} GPUs ...".format(self.cfg.num_gpus)) + + weights_initializer = slim.xavier_initializer() + biases_initializer = tf.constant_initializer(0.) + biases_regularizer = tf.no_regularizer + weights_regularizer = tf.contrib.layers.l2_regularizer(self.cfg.weight_decay) + + tower_grads = [] + with tf.variable_scope(tf.get_variable_scope()): + for i in range(self.cfg.num_gpus): + with tf.device('/cpu:0'): + with tf.name_scope('tower_%d' % i) as name_scope: + # Force all Variables to reside on the CPU. + with slim.arg_scope([slim.model_variable, slim.variable], device='/device:CPU:0'): + with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, \ + slim.conv2d_transpose, slim.separable_conv2d, + slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + weights_initializer=weights_initializer, + biases_initializer=biases_initializer): + # loss over single GPU + self.net.make_network(is_train=True) + if i == self.cfg.num_gpus - 1: + loss = self.net.get_loss(include_wd=True) + else: + loss = self.net.get_loss() + self._input_list.append(self.net.get_inputs()) + + tf.get_variable_scope().reuse_variables() + + if i == 0: + if self.cfg.num_gpus > 1 and self.cfg.bn_train is True: + self.logger.warning("BN is calculated only on single GPU.") + extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, name_scope) + with tf.control_dependencies(extra_update_ops): + grads = self._optimizer.compute_gradients(loss) + else: + grads = self._optimizer.compute_gradients(loss) + final_grads = [] + with tf.variable_scope('Gradient_Mult') as scope: + for grad, var in grads: + final_grads.append((grad, var)) + tower_grads.append(final_grads) + + if len(tower_grads) > 1: + grads = average_gradients(tower_grads) + else: + grads = tower_grads[0] + + apply_gradient_op = self._optimizer.apply_gradients(grads) + train_op = tf.group(apply_gradient_op, *extra_update_ops) + + return train_op + + def train(self): + + # saver + self.logger.info('Initialize saver ...') + train_saver = Saver(self.sess, tf.global_variables(), self.cfg.model_dump_dir) + + # initialize weights + self.logger.info('Initialize all variables ...') + self.sess.run(tf.variables_initializer(tf.global_variables(), name='init')) + self.load_weights('last_epoch' if self.cfg.continue_train else self.cfg.init_model) + + self.logger.info('Start training ...') + start_itr = self.cur_epoch * self.itr_per_epoch + 1 + end_itr = self.itr_per_epoch * self.cfg.end_epoch + 1 + for itr in range(start_itr, end_itr): + self.tot_timer.tic() + + self.cur_epoch = itr // self.itr_per_epoch + setproctitle.setproctitle('train epoch:' + str(self.cur_epoch)) + + # apply current learning policy + cur_lr = self.cfg.get_lr(self.cur_epoch) + if not approx_equal(cur_lr, self.lr_eval): + print(self.lr_eval, cur_lr) + self.sess.run(tf.assign(self.lr, cur_lr)) + + # input data + self.read_timer.tic() + feed_dict = self.next_feed() + self.read_timer.toc() + + # train one step + self.gpu_timer.tic() + _, self.lr_eval, *summary_res = self.sess.run( + [self.graph_ops[0], self.lr, *self.summary_dict.values()], feed_dict=feed_dict) + self.gpu_timer.toc() + + itr_summary = dict() + for i, k in enumerate(self.summary_dict.keys()): + itr_summary[k] = summary_res[i] + + screen = [ + 'Epoch %d itr %d/%d:' % (self.cur_epoch, itr, self.itr_per_epoch), + 'lr: %g' % (self.lr_eval), + 'speed: %.2f(%.2fs r%.2f)s/itr' % ( + self.tot_timer.average_time, self.gpu_timer.average_time, self.read_timer.average_time), + '%.2fh/epoch' % (self.tot_timer.average_time / 3600. * self.itr_per_epoch), + ' '.join(map(lambda x: '%s: %.4f' % (x[0], x[1]), itr_summary.items())), + ] + + # TODO(display stall?) + if itr % self.cfg.display == 0: + self.logger.info(' '.join(screen)) + + if itr % self.itr_per_epoch == 0: + train_saver.save_model(self.cur_epoch) + + self.tot_timer.toc() + + +class Tester(Base): + def __init__(self, net, cfg, data_iter=None): + super(Tester, self).__init__(net, cfg, data_iter, log_name='test_logs.txt') + + def next_feed(self, batch_data=None): + if self._data_iter is None and batch_data is None: + raise ValueError('No input data.') + feed_dict = dict() + if batch_data is None: + for inputs in self._input_list: + blobs = next(self._data_iter) + for i, inp in enumerate(inputs): + inp_shape = inp.get_shape().as_list() + if None in inp_shape: + feed_dict[inp] = blobs[i] + else: + feed_dict[inp] = blobs[i].reshape(*inp_shape) + else: + assert isinstance(batch_data, list) or isinstance(batch_data, tuple), "Input data should be list-type." + assert len(batch_data) == len(self._input_list[0]), "Input data is incomplete." + + batch_size = self.cfg.batch_size + if self._input_list[0][0].get_shape().as_list()[0] is None: + # fill batch + for i in range(len(batch_data)): + batch_size = (len(batch_data[i]) + self.cfg.num_gpus - 1) // self.cfg.num_gpus + total_batches = batch_size * self.cfg.num_gpus + left_batches = total_batches - len(batch_data[i]) + if left_batches > 0: + batch_data[i] = np.append(batch_data[i], np.zeros((left_batches, *batch_data[i].shape[1:])), + axis=0) + self.logger.warning("Fill some blanks to fit batch_size which wastes %d%% computation" % ( + left_batches * 100. / total_batches)) + else: + assert self.cfg.batch_size * self.cfg.num_gpus == len(batch_data[0]), \ + "Input batch doesn't fit placeholder batch." + + for j, inputs in enumerate(self._input_list): + for i, inp in enumerate(inputs): + feed_dict[inp] = batch_data[i][j * batch_size: (j + 1) * batch_size] + + # @TODO(delete) + assert (j + 1) * batch_size == len(batch_data[0]), 'check batch' + return feed_dict, batch_size + + def _make_graph(self): + self.logger.info("Generating testing graph on {} GPUs ...".format(self.cfg.num_gpus)) + + with tf.variable_scope(tf.get_variable_scope()): + for i in range(self.cfg.num_gpus): + with tf.device('/cpu:0'): + with tf.name_scope('tower_%d' % i) as name_scope: + with slim.arg_scope([slim.model_variable, slim.variable], device='/device:CPU:0'): + self.net.make_network(is_train=False) + self._input_list.append(self.net.get_inputs()) + self._output_list.append(self.net.get_outputs()) + + tf.get_variable_scope().reuse_variables() + + self._outputs = aggregate_batch(self._output_list) + + # run_meta = tf.RunMetadata() + # opts = tf.profiler.ProfileOptionBuilder.float_operation() + # flops = tf.profiler.profile(self.sess.graph, run_meta=run_meta, cmd='op', options=opts) + # + # opts = tf.profiler.ProfileOptionBuilder.trainable_variables_parameter() + # params = tf.profiler.profile(self.sess.graph, run_meta=run_meta, cmd='op', options=opts) + + # print("{:,} --- {:,}".format(flops.total_float_ops, params.total_parameters)) + # from IPython import embed; embed() + + return self._outputs + + def predict_one(self, data=None): + # TODO(reduce data in limited batch) + assert len(self.summary_dict) == 0, "still not support scalar summary in testing stage" + setproctitle.setproctitle('test epoch:' + str(self.cur_epoch)) + + self.read_timer.tic() + feed_dict, batch_size = self.next_feed(data) + self.read_timer.toc() + + self.gpu_timer.tic() + res = self.sess.run([*self.graph_ops, *self.summary_dict.values()], feed_dict=feed_dict) + self.gpu_timer.toc() + + if data is not None and len(data[0]) < self.cfg.num_gpus * batch_size: + for i in range(len(res)): + res[i] = res[i][:len(data[0])] + + return res + + def test(self): + pass diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/data_provider.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/data_provider.py new file mode 100644 index 000000000..1d94c6f5b --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/data_provider.py @@ -0,0 +1,374 @@ +# copy from tensorpack: https://github.com/ppwwyyxx/tensorpack +from npu_bridge.npu_init import * +import numpy as np +import threading +import multiprocessing as mp +import weakref +from contextlib import contextmanager +from .serialize import loads, dumps +import errno +import uuid +import os +import zmq +import atexit +from itertools import cycle +from copy import copy +from .utils import get_rng +from setproctitle import setproctitle + +def del_weakref(x): + o = x() + if o is not None: + o.__del__() + +@contextmanager +def _zmq_catch_error(name): + try: + yield + except zmq.ContextTerminated: + print("[{}] Context terminated.".format(name)) + raise Exception + except zmq.ZMQError as e: + if e.errno == errno.ENOTSOCK: # socket closed + print("[{}] Socket closed.".format(name)) + raise Exception + else: + raise + except Exception: + raise + +class DataFlowReentrantGuard(object): + """ + A tool to enforce non-reentrancy. + Mostly used on DataFlow whose :meth:`get_data` is stateful, + so that multiple instances of the iterator cannot co-exist. + """ + def __init__(self): + self._lock = threading.Lock() + + def __enter__(self): + self._succ = self._lock.acquire(False) + if not self._succ: + raise threading.ThreadError("This DataFlow is not reentrant!") + + def __exit__(self, exc_type, exc_val, exc_tb): + self._lock.release() + return False + +class DataFromList(object): + def __init__(self, datalist, is_train=True, shuffle=True): + self.rng = get_rng() + self._datalist = datalist + self._shuffle = shuffle + self._is_train = is_train + + def get_data(self): + if self._is_train: + while True: + idxs = np.arange(len(self._datalist)) + if self._shuffle: + self.rng.shuffle(idxs) + for i in idxs: + yield self._datalist[i] + else: + idxs = np.arange(len(self._datalist)) + if self._shuffle: + self.rng.shuffle(idxs) + for i in idxs: + yield self._datalist[i] + + def reset_state(self): + self.rng = get_rng() + +class _ParallelMapData(object): + def __init__(self, ds, buffer_size): + assert buffer_size > 0, buffer_size + self._buffer_size = buffer_size + self._buffer_occupancy = 0 # actual #elements in buffer + + self.ds = ds + + def _recv(self): + pass + + def _send(self, dp): + pass + + def _recv_filter_none(self): + ret = self._recv() + assert ret is not None, \ + "[{}] Map function cannot return None when strict mode is used.".format(type(self).__name__) + return ret + + def _fill_buffer(self, cnt=None): + if cnt is None: + cnt = self._buffer_size - self._buffer_occupancy + try: + for _ in range(cnt): + dp = next(self._iter) + self._send(dp) + except StopIteration: + print( + "[{}] buffer_size cannot be larger than the size of the DataFlow!".format(type(self).__name__)) + raise + self._buffer_occupancy += cnt + + def get_data_non_strict(self): + for dp in self._iter: + self._send(dp) + ret = self._recv() + if ret is not None: + yield ret + + self._iter = self.ds.get_data() # refresh + for _ in range(self._buffer_size): + self._send(next(self._iter)) + ret = self._recv() + if ret is not None: + yield ret + + def get_data_strict(self): + self._fill_buffer() + for dp in self._iter: + self._send(dp) + yield self._recv_filter_none() + self._iter = self.ds.get_data() # refresh + + # first clear the buffer, then fill + for k in range(self._buffer_size): + dp = self._recv_filter_none() + self._buffer_occupancy -= 1 + if k == self._buffer_size - 1: + self._fill_buffer() + yield dp + +class MapData(object): + """ + Apply a mapper/filter on the DataFlow. + + Note: + 1. Please make sure func doesn't modify the components + unless you're certain it's safe. + 2. If you discard some datapoints, ``ds.size()`` will be incorrect. + """ + + def __init__(self, ds, func): + """ + Args: + ds (DataFlow): input DataFlow + func (datapoint -> datapoint | None): takes a datapoint and returns a new + datapoint. Return None to discard this datapoint. + """ + self.ds = ds + self.func = func + + def get_data(self): + for dp in self.ds.get_data(): + ret = self.func(copy(dp)) # shallow copy the list + if ret is not None: + yield ret + + def reset_state(self): + pass + +class MultiProcessMapDataZMQ(_ParallelMapData): + """ + Same as :class:`MapData`, but start processes to run the mapping function, + and communicate with ZeroMQ pipe. + + Note: + 1. Processes run in parallel and can take different time to run the + mapping function. Therefore the order of datapoints won't be + preserved, and datapoints from one pass of `df.get_data()` might get + mixed with datapoints from the next pass. + + You can use **strict mode**, where `MultiProcessMapData.get_data()` + is guranteed to produce the exact set which `df.get_data()` + produces. Although the order of data still isn't preserved. + """ + class _Worker(mp.Process): + def __init__(self, identity, map_func, pipename, hwm): + super(MultiProcessMapDataZMQ._Worker, self).__init__() + self.identity = identity + self.map_func = map_func + self.pipename = pipename + self.hwm = hwm + + def run(self): + print('Start data provider {}-{}'.format(self.pipename, self.identity)) + setproctitle('data provider {}-{}'.format(self.pipename, self.identity)) + ctx = zmq.Context() + socket = ctx.socket(zmq.DEALER) + socket.setsockopt(zmq.IDENTITY, self.identity) + socket.set_hwm(self.hwm) + socket.connect(self.pipename) + + while True: + dp = loads(socket.recv(copy=False).bytes) + dp = self.map_func(dp) + socket.send(dumps(dp), copy=False) + + def __init__(self, ds, nr_proc, map_func, buffer_size=200, strict=False): + """ + Args: + ds (DataFlow): the dataflow to map + nr_proc(int): number of threads to use + map_func (callable): datapoint -> datapoint | None + buffer_size (int): number of datapoints in the buffer + strict (bool): use "strict mode", see notes above. + """ + _ParallelMapData.__init__(self, ds, buffer_size) + self.nr_proc = nr_proc + self.map_func = map_func + self._strict = strict + self._procs = [] + self._guard = DataFlowReentrantGuard() + + self._reset_done = False + self._procs = [] + + def _reset_once(self): + self.context = zmq.Context() + self.socket = self.context.socket(zmq.ROUTER) + self.socket.set_hwm(self._buffer_size * 2) + pipename = "ipc://@{}-pipe-{}".format('dataflow-map', str(uuid.uuid1())[:8]) + + try: + self.socket.bind(pipename) + except zmq.ZMQError: + print( + "ZMQError in socket.bind(). Perhaps you're \ + using pipes on a non-local file system. See documentation of PrefetchDataZMQ for more information.") + raise + + self._proc_ids = [u'{}'.format(k).encode('utf-8') for k in range(self.nr_proc)] + worker_hwm = int(self._buffer_size * 2 // self.nr_proc) + self._procs = [MultiProcessMapDataZMQ._Worker( + self._proc_ids[k], self.map_func, pipename, worker_hwm) + for k in range(self.nr_proc)] + + self.ds.reset_state() + self._iter = self.ds.get_data() + self._iter_worker = cycle(iter(self._proc_ids)) + + for p in self._procs: + p.deamon = True + p.start() + self._fill_buffer() # pre-fill the bufer + + def reset_state(self): + if self._reset_done: + return + self._reset_done = True + + # __del__ not guranteed to get called at exit + atexit.register(del_weakref, weakref.ref(self)) + + self._reset_once() # build processes + + def _send(self, dp): + # round-robin assignment + worker = next(self._iter_worker) + msg = [worker, dumps(dp)] + self.socket.send_multipart(msg, copy=False) + + def _recv(self): + msg = self.socket.recv_multipart(copy=False) + dp = loads(msg[1].bytes) + return dp + + def get_data(self): + with self._guard, _zmq_catch_error('MultiProcessMapData'): + if self._strict: + for dp in self.get_data_strict(): + yield dp + else: + for dp in self.get_data_non_strict(): + yield dp + + def __del__(self): + try: + if not self._reset_done: + return + if not self.context.closed: + self.socket.close(0) + self.context.destroy(0) + for x in self._procs: + x.terminate() + x.join(5) + print("{} successfully cleaned-up.".format(type(self).__name__)) + except Exception: + pass + +class BatchData(object): + """ + Stack datapoints into batches. + It produces datapoints of the same number of components as ``ds``, but + each component has one new extra dimension of size ``batch_size``. + The batch can be either a list of original components, or (by default) + a numpy array of original components. + """ + + def __init__(self, ds, batch_size, use_list=False): + """ + Args: + ds (DataFlow): When ``use_list=False``, the components of ``ds`` + must be either scalars or :class:`np.ndarray`, and have to be consistent in shapes. + batch_size(int): batch size + use_list (bool): if True, each component will contain a list + of datapoints instead of an numpy array of an extra dimension. + """ + self.ds = ds + self.batch_size = int(batch_size) + self.use_list = use_list + + def get_data(self): + """ + Yields: + Batched data by stacking each component on an extra 0th dimension. + """ + holder = [] + for data in self.ds.get_data(): + holder.append(data) + if len(holder) == self.batch_size: + yield BatchData._aggregate_batch(holder, self.use_list) + del holder[:] + + @staticmethod + def _aggregate_batch(data_holder, use_list=False): + size = len(data_holder[0]) + result = [] + for k in range(size): + if use_list: + result.append( + [x[k] for x in data_holder]) + else: + dt = data_holder[0][k] + if type(dt) in [int, bool]: + tp = 'int32' + elif type(dt) == float: + tp = 'float32' + else: + try: + tp = dt.dtype + except AttributeError: + raise TypeError("Unsupported type to batch: {}".format(type(dt))) + try: + result.append( + np.asarray([x[k] for x in data_holder], dtype=tp)) + except Exception as e: # noqa + logger.exception("Cannot batch data. Perhaps they are of inconsistent shape?") + if isinstance(dt, np.ndarray): + s = pprint.pformat([x[k].shape for x in data_holder]) + logger.error("Shape of all arrays to be batched: " + s) + try: + # open an ipython shell if possible + import IPython as IP; IP.embed() # noqa + except ImportError: + pass + return result + + def reset_state(self): + self.ds.reset_state() + + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/dpflow.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/dpflow.py new file mode 100644 index 000000000..3ec353b38 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/dpflow.py @@ -0,0 +1,40 @@ +from npu_bridge.npu_init import * +import zmq +import multiprocessing as mp +from .serialize import loads, dumps + +def data_sender(id, name, func_iter, *args): + context = zmq.Context() + sender = context.socket(zmq.PUSH) + sender.connect('ipc://@{}'.format(name)) + + print('start data provider {}-{}'.format(name, id)) + while True: + data_iter = func_iter(id, *args) + for msg in data_iter: + # print(id) + sender.send( dumps([id, msg]) ) + +def provider(nr_proc, name, func_iter, *args): + proc_ids = [i for i in range(nr_proc)] + + procs = [] + for i in range(nr_proc): + w = mp.Process(target=data_sender, args=(proc_ids[i], name, func_iter, *args)) + w.deamon = True + procs.append( w ) + + for p in procs: + p.start() + +def receiver(name): + context = zmq.Context() + + receiver = context.socket(zmq.PULL) + receiver.bind('ipc://@{}'.format(name)) + + while True: + id, msg = loads( receiver.recv() ) + # print(id, end='') + yield msg + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/logger.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/logger.py new file mode 100644 index 000000000..1c770e52e --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/logger.py @@ -0,0 +1,51 @@ +from npu_bridge.npu_init import * +import logging +import os + +OK = '\033[92m' +WARNING = '\033[93m' +FAIL = '\033[91m' +END = '\033[0m' + +PINK = '\033[95m' +BLUE = '\033[94m' +GREEN = OK +RED = FAIL +WHITE = END +YELLOW = WARNING + +class colorlogger(): + def __init__(self, log_dir, log_name='train_logs.txt'): + # set log + self._logger = logging.getLogger(log_name) + self._logger.setLevel(logging.INFO) + log_file = os.path.join(log_dir, log_name) + if not os.path.exists(log_dir): + os.makedirs(log_dir) + file_log = logging.FileHandler(log_file, mode='a') + file_log.setLevel(logging.INFO) + console_log = logging.StreamHandler() + console_log.setLevel(logging.INFO) + formatter = logging.Formatter( + "{}%(asctime)s{} %(message)s".format(GREEN, END), + "%m-%d %H:%M:%S") + file_log.setFormatter(formatter) + console_log.setFormatter(formatter) + self._logger.addHandler(file_log) + self._logger.addHandler(console_log) + + def debug(self, msg): + self._logger.debug(str(msg)) + + def info(self, msg): + self._logger.info(str(msg)) + + def warning(self, msg): + self._logger.warning(WARNING + 'WRN: ' + str(msg) + END) + + def critical(self, msg): + self._logger.critical(RED + 'CRI: ' + str(msg) + END) + + def error(self, msg): + self._logger.error(RED + 'ERR: ' + str(msg) + END) + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/mp_utils.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/mp_utils.py new file mode 100644 index 000000000..9a78e05ac --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/mp_utils.py @@ -0,0 +1,135 @@ +from npu_bridge.npu_init import * +import multiprocessing as mp +import numpy as np + +from .serialize import loads, dumps +from .serialize import dump_pkl, load_pkl +from .utils import del_file + + +mp.set_start_method('forkserver', force=True) + + +# reduce_method +LIST = 0 +ITEM = 1 +ITEMS = 2 +ITEMSLIST = 3 + +# func type +FUNC = 0 +# ITER = 1 + +# dump & load +QUEUE = 0 +PICKLE = 1 + + +class Worker(mp.Process): + def __init__(self, id, queue, func, func_type, dump_method=PICKLE, *args, **kwargs): + super(Worker, self).__init__() + self.id = id + self._func = func + self._queue = queue + self.args = args + self.kwargs = kwargs + self._func_type = func_type + self._dump_method = dump_method + + def run(self): + msg = self._func(self.id, *self.args, **self.kwargs) + if self._dump_method == QUEUE: + if self._func_type == FUNC: + self._queue.put(dumps([self.id, msg])) + # elif self._func_type == ITER: + # for i, msg in enumerate(func(self.id, *self.args, **self.kwargs)): + # self._queue.put([self.id, i, dumps(msg)]) + else: + raise ValueError('Invalid func type.') + else: + assert self._func_type == FUNC, 'dump by pickle supports only function that is executed one time.' + dump_pkl('tmp_result_{}'.format(self.id), [self.id, msg]) + print('dump to temp_file: {}'.format('tmp_result_{}'.format(self.id))) + + +class MultiProc(object): + def __init__(self, nr_proc, func, func_type=FUNC, reduce_method=LIST, dump_method=PICKLE, *args, **kwargs): + self._queue = mp.Queue() + self.nr_proc = nr_proc + self._proc_ids = [i for i in range(self.nr_proc)] + self._func_type = func_type + self._reduce_method = reduce_method + self._dump_method = dump_method + + self._procs = [] + for i in range(self.nr_proc): + w = Worker(self._proc_ids[i], self._queue, func, func_type, dump_method=self._dump_method, *args, **kwargs) + w.run() # 添加 + w.deamon = True + self._procs.append(w) + + def work(self): + for p in self._procs: + p.start() + + ret = [[] for i in range(self.nr_proc)] + for i in range(self.nr_proc): + if self._dump_method == QUEUE: + id, msg = loads(self._queue.get(block=True, timeout=None)) + ret[id] = msg + elif self._dump_method == PICKLE: + pass + else: + raise ValueError('Invalid dump method') + + for p in self._procs: + p.join() + + if self._dump_method == PICKLE: + for i in range(self.nr_proc): + id, msg = load_pkl('tmp_result_{}'.format(self._proc_ids[i])) + ret[id] = msg + del_file('tmp_result_{}.pkl'.format(self._proc_ids[i])) + + result = [] + if self._reduce_method == LIST: + for i in range(len(ret)): + result.extend(ret[i]) + elif self._reduce_method == ITEM: + result = ret + elif self._reduce_method == ITEMS: + for i in range(len(ret[0])): + result.append([ret[j][i] for j in range(len(ret))]) + elif self._reduce_method == ITEMSLIST: + for i in range(len(ret[0])): + tmp_res = [] + for j in range(len(ret)): + tmp_res.extend(ret[j][i]) + result.append(tmp_res) + else: + raise ValueError('Invalid reduce method.') + + return result + + +if __name__ == '__main__': + (npu_sess, npu_shutdown) = init_resource() + test_ranges = [0, 100, 200, 300, 400, 500] + + + def test_net(id): + test_range = [test_ranges[id], test_ranges[id + 1]] + x = [] + for i in range(*test_range): + x.append(np.ones((10, 10)) * i) + print('finish {}'.format(id)) + return x + + + x = MultiProc(5, test_net, reduce_method=LIST) + res = x.work() + from IPython import embed; + + embed() + shutdown_resource(npu_sess, npu_shutdown) + close_session(npu_sess) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/mp_utils_my.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/mp_utils_my.py new file mode 100644 index 000000000..d8257ca03 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/mp_utils_my.py @@ -0,0 +1,135 @@ +from npu_bridge.npu_init import * +import multiprocessing as mp +import numpy as np + +from .serialize import loads, dumps +from .serialize import dump_pkl, load_pkl +from .utils import del_file + + +mp.set_start_method('forkserver', force=True) + + +# reduce_method +LIST = 0 +ITEM = 1 +ITEMS = 2 +ITEMSLIST = 3 + +# func type +FUNC = 0 +# ITER = 1 + +# dump & load +# QUEUE = 0 +PICKLE = 1 + + +class Worker(mp.Process): + def __init__(self, id, func, func_type, dump_method=PICKLE, *args, **kwargs): + super(Worker, self).__init__() + self.id = id + self._func = func + # self._queue = queue + self.args = args + self.kwargs = kwargs + self._func_type = func_type + self._dump_method = dump_method + + def run(self): + msg = self._func(self.id, *self.args, **self.kwargs) + # if self._dump_method == QUEUE: + # if self._func_type == FUNC: + # self._queue.put(dumps([self.id, msg])) + # # elif self._func_type == ITER: + # # for i, msg in enumerate(func(self.id, *self.args, **self.kwargs)): + # # self._queue.put([self.id, i, dumps(msg)]) + # else: + # raise ValueError('Invalid func type.') + # else: + assert self._func_type == FUNC, 'dump by pickle supports only function that is executed one time.' + dump_pkl('tmp_result_{}'.format(self.id), [self.id, msg]) + print('dump to temp_file: {}'.format('tmp_result_{}'.format(self.id))) + + +class MultiProc(object): + def __init__(self, nr_proc, func, func_type=FUNC, reduce_method=LIST, dump_method=PICKLE, *args, **kwargs): + # self._queue = mp.Queue() + self.nr_proc = nr_proc + self._proc_ids = [i for i in range(self.nr_proc)] + self._func_type = func_type + self._reduce_method = reduce_method + self._dump_method = dump_method + + self._procs = [] + for i in range(self.nr_proc): + w = Worker(self._proc_ids[i], func, func_type, dump_method=self._dump_method, *args, **kwargs) + w.run() # 添加 + w.deamon = True + self._procs.append(w) + + def work(self): + for p in self._procs: + p.start() + + ret = [[] for i in range(self.nr_proc)] + for i in range(self.nr_proc): + # if self._dump_method == QUEUE: + # id, msg = loads(self._queue.get(block=True, timeout=None)) + # ret[id] = msg + if self._dump_method == PICKLE: + pass + else: + raise ValueError('Invalid dump method') + + for p in self._procs: + p.join() + + if self._dump_method == PICKLE: + for i in range(self.nr_proc): + id, msg = load_pkl('tmp_result_{}'.format(self._proc_ids[i])) + ret[id] = msg + del_file('tmp_result_{}.pkl'.format(self._proc_ids[i])) + + result = [] + if self._reduce_method == LIST: + for i in range(len(ret)): + result.extend(ret[i]) + elif self._reduce_method == ITEM: + result = ret + elif self._reduce_method == ITEMS: + for i in range(len(ret[0])): + result.append([ret[j][i] for j in range(len(ret))]) + elif self._reduce_method == ITEMSLIST: + for i in range(len(ret[0])): + tmp_res = [] + for j in range(len(ret)): + tmp_res.extend(ret[j][i]) + result.append(tmp_res) + else: + raise ValueError('Invalid reduce method.') + + return result + + +if __name__ == '__main__': + (npu_sess, npu_shutdown) = init_resource() + test_ranges = [0, 100, 200, 300, 400, 500] + + + def test_net(id): + test_range = [test_ranges[id], test_ranges[id + 1]] + x = [] + for i in range(*test_range): + x.append(np.ones((10, 10)) * i) + print('finish {}'.format(id)) + return x + + + x = MultiProc(5, test_net, reduce_method=LIST) + res = x.work() + from IPython import embed; + + embed() + shutdown_resource(npu_sess, npu_shutdown) + close_session(npu_sess) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/net_utils.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/net_utils.py new file mode 100644 index 000000000..0d516ea65 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/net_utils.py @@ -0,0 +1,120 @@ +from npu_bridge.npu_init import * +import tensorflow as tf +from collections import namedtuple + +def average_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + average_grads = [] + for grad_and_vars in zip(*tower_grads): + if grad_and_vars[0][0] is None: + print('No gradient on var {}'.format(grad_and_vars[0][1].name)) + continue + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + +def sum_gradients(tower_grads): + """Calculate the sum gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + sum_grads = [] + for grad_and_vars in zip(*tower_grads): + if grad_and_vars[0][0] is None: + print('No gradient on var {}'.format(grad_and_vars[0][1].name)) + continue + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + if g is not None: + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_sum(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + sum_grads.append(grad_and_var) + return sum_grads + +def aggregate_batch(data_holder): + results = [] + if len(data_holder) == 1: + results = data_holder if isinstance(data_holder[0], tf.Tensor) else data_holder[0] + elif isinstance(data_holder[0], tf.Tensor): + results.append( tf.concat(data_holder, axis=0) ) + else: + for i in range(len(data_holder[0])): + results.append( + tf.concat([data_holder[j][i] for j in range(len(data_holder))], axis=0)) + return results + +def get_optimizer(lr, optimizer='momentum'): + if optimizer == 'sgd': + optimizer = tf.train.GradientDescentOptimizer(lr) + elif optimizer == 'momentum': + optimizer = tf.train.MomentumOptimizer(lr, 0.9) + elif optimizer == 'adam': + optimizer = tf.train.AdamOptimizer(lr) + else: + raise ValueError('invalid optimizer') + return optimizer + +def get_tower_summary_dict(summary): + ret = dict() + for v, method in summary: + if len(tf.get_collection(v)) == 1: + ret[v] = tf.get_collection(v)[0] + elif len(tf.get_collection(v)) > 1: + if method == 'mean': + ret[v] = tf.reduce_mean(tf.get_collection(v), axis=0) + elif method == 'sum': + ret[v] = tf.reduce_sum(tf.get_collection(v), axis=0) + elif method == 'concat': + ret[v] = tf.concat(tf.get_collection(v), axis=0) + else: + raise ValueError('Invalid summary reduced method: {}'.format(method)) + return ret + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/saver.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/saver.py new file mode 100644 index 000000000..ec5cd96e0 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/saver.py @@ -0,0 +1,69 @@ +from npu_bridge.npu_init import * +import tensorflow as tf +from tensorflow.python import pywrap_tensorflow +import numpy as np +from config import cfg + +import os +import os.path as osp + +def get_variables_in_checkpoint_file(file_name): + try: + reader = pywrap_tensorflow.NewCheckpointReader(file_name) + var_to_shape_map = reader.get_variable_to_shape_map() + return reader, var_to_shape_map + except Exception as e: # pylint: disable=broad-except + print(str(e)) + if "corrupted compressed block contents" in str(e): + print( + "It's likely that your checkpoint file has been compressed " + "with SNAPPY.") + +class Saver(object): + def __init__(self, sess, var_list, model_dump_dir, name_prefix='snapshot'): + self.sess = sess + self.var_list = var_list + self.model_dump_dir = model_dump_dir + self._name_prefix = name_prefix + + self.saver = tf.train.Saver(var_list=var_list, max_to_keep=100000) + + def save_model(self, iter): + filename = '{}_{:d}'.format(self._name_prefix, iter) + '.ckpt' + if not os.path.exists(self.model_dump_dir): + os.makedirs(self.model_dump_dir) + filename = os.path.join(self.model_dump_dir, filename) + self.saver.save(self.sess, filename) + print('Wrote snapshot to: {:s}'.format(filename)) + +def load_model(sess, model_path): + #TODO(global variables ?? how about _adam weights) + variables = tf.global_variables() + reader, var_keep_dic = get_variables_in_checkpoint_file(model_path) + if 'global_step' in var_keep_dic: + var_keep_dic.pop('global_step') + + # vis_var_keep_dic = [] + variables_to_restore = {} + changed_variables = {} + for v in variables: + + v_name = v.name.split(':')[0] + if v_name in var_keep_dic: + # print('Varibles restored: %s' % v.name) + #variables_to_restore.append(v) + variables_to_restore[v_name] = v + # vis_var_keep_dic.append(v.name.split(':')[0]) + else: + # print('Unrestored Variables: %s' % v.name) + pass + # print('Extra Variables in ckpt', set(var_keep_dic) - set(vis_var_keep_dic)) + + if len(variables_to_restore) > 0: + restorer = tf.train.Saver(variables_to_restore) + restorer.restore(sess, model_path) + + else: + print('No variables in {} fits the network'.format(model_path)) + + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/serialize.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/serialize.py new file mode 100644 index 000000000..3cf9ed5bc --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/serialize.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +### modified from https://github.com/ppwwyyxx/tensorpack + +from npu_bridge.npu_init import * +import os +import sys +import msgpack +import msgpack_numpy +msgpack_numpy.patch() + +# https://github.com/apache/arrow/pull/1223#issuecomment-359895666 +old_mod = sys.modules.get('torch', None) +sys.modules['torch'] = None +try: + import pyarrow as pa +except ImportError: + pa = None +if old_mod is not None: + sys.modules['torch'] = old_mod +else: + del sys.modules['torch'] + +import pickle + +__all__ = ['loads', 'dumps', 'dump_pkl', 'load_pkl'] + + +def dumps_msgpack(obj): + """ + Serialize an object. + Returns: + Implementation-dependent bytes-like object + """ + return msgpack.dumps(obj, use_bin_type=True) + + +def loads_msgpack(buf): + """ + Args: + buf: the output of `dumps`. + """ + return msgpack.loads(buf, raw=False) + + +def dumps_pyarrow(obj): + """ + Serialize an object. + + Returns: + Implementation-dependent bytes-like object + """ + return pa.serialize(obj).to_buffer() + + +def loads_pyarrow(buf): + """ + Args: + buf: the output of `dumps`. + """ + return pa.deserialize(buf) + + +def dump_pkl(name, obj): + with open('{}.pkl'.format(name), 'wb') as f: + pickle.dump( obj, f, pickle.HIGHEST_PROTOCOL ) + +def load_pkl(name): + with open('{}.pkl'.format(name), 'rb') as f: + ret = pickle.load( f ) + return ret + +if pa is None: + loads = loads_msgpack + dumps = dumps_msgpack +else: + loads = loads_pyarrow + dumps = dumps_pyarrow + + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/timer.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/timer.py new file mode 100644 index 000000000..1b2ebaff0 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/timer.py @@ -0,0 +1,40 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +from npu_bridge.npu_init import * +import time + +class Timer(object): + """A simple timer.""" + def __init__(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. + self.warm_up = 0 + + def tic(self): + # using time.time instead of time.clock because time time.clock + # does not normalize for multithreading + self.start_time = time.time() + + def toc(self, average=True): + self.diff = time.time() - self.start_time + if self.warm_up < 10: + self.warm_up += 1 + return self.diff + else: + self.total_time += self.diff + self.calls += 1 + self.average_time = self.total_time / self.calls + + if average: + return self.average_time + else: + return self.diff + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/utils.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/utils.py new file mode 100644 index 000000000..93d5bda19 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/tfflat/utils.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- + +from npu_bridge.npu_init import * +import os +import sys +import numpy as np +from datetime import datetime + +def mem_info(): + import subprocess + dev = subprocess.check_output( + "nvidia-smi | grep MiB | awk -F '|' '{print $3}' | awk -F '/' '{print $1}' | grep -Eo '[0-9]{1,10}'", + shell=True) + dev = dev.decode() + dev_mem = list(map(lambda x: int(x), dev.split('\n')[:-1])) + return dev_mem + +def add_pypath(path): + if path not in sys.path: + sys.path.insert(0, path) + +def make_link(dest_path, link_path): + if os.path.islink(link_path): + os.system('rm {}'.format(link_path)) + os.system('ln -s {} {}'.format(dest_path, link_path)) + +def make_dir(path): + if os.path.exists(path) or os.path.islink(path): + return + os.makedirs(path) + +def del_file(path, msg='{} deleted.'): + if os.path.exists(path): + os.remove(path) + print(msg.format(path)) + +def approx_equal(a, b, eps=1e-9): + return np.fabs(a-b) < eps + +def get_rng(obj=None): + """ + Get a good RNG seeded with time, pid and the object. + + Args: + obj: some object to use to generate random seed. + Returns: + np.random.RandomState: the RNG. + """ + seed = (id(obj) + os.getpid() + + int(datetime.now().strftime("%Y%m%d%H%M%S%f"))) % 4294967295 + return np.random.RandomState(seed) + + -- Gitee From cc4d4ccd593e2dae015a339fe02ece84c1e8701c Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:49:57 +0000 Subject: [PATCH 24/59] first commit --- .../src/lib/utils/__init__.py | 3 + .../src/lib/utils/bbox.c | 8258 +++++++++++++++++ .../src/lib/utils/bbox.pyx | 144 + .../src/lib/utils/blob.py | 49 + .../src/lib/utils/boxes_grid.py | 75 + .../src/lib/utils/mask.py | 42 + .../src/lib/utils/timer.py | 40 + 7 files changed, 8611 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/__init__.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/bbox.c create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/bbox.pyx create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/blob.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/boxes_grid.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/mask.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/timer.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/__init__.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/__init__.py new file mode 100644 index 000000000..faaaf799c --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/__init__.py @@ -0,0 +1,3 @@ +# -*- coding: utf-8 -*- + + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/bbox.c b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/bbox.c new file mode 100644 index 000000000..dcfb03cd4 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/bbox.c @@ -0,0 +1,8258 @@ +/* Generated by Cython 0.23.4 */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. 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PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if PY_VERSION_HEX >= 0x030500B1 +#define __Pyx_PyAsyncMethodsStruct PyAsyncMethods +#define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) +#elif CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 +typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; +} __Pyx_PyAsyncMethodsStruct; +#define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) +#else +#define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) + +#ifndef CYTHON_INLINE + #if defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif + + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__utils__cython_bbox +#define __PYX_HAVE_API__utils__cython_bbox +#include "string.h" +#include "stdio.h" +#include "stdlib.h" +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) && defined (_M_X64) + #define __Pyx_sst_abs(value) _abs64(value) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_COMPILING_IN_CPYTHON +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "pu/lib/utils/bbox.pyx", + "__init__.pxd", + "type.pxd", +}; +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":725 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":726 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":727 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":728 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":732 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":733 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":734 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":735 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":739 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":740 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":749 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":750 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":751 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":753 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":754 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":755 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":757 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":758 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":760 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":761 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "../../../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":762 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; + +/* "utils/bbox.pyx":13 + * + * DTYPE = np.float + * ctypedef np.float_t DTYPE_t # <<<<<<<<<<<<<< + * + * def bbox_overlaps_float( + */ +typedef __pyx_t_5numpy_float_t __pyx_t_5utils_11cython_bbox_DTYPE_t; 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}} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); + +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +static void __Pyx_RaiseBufferIndexError(int axis); + +#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1) +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + value = PyDict_GetItemWithError(d, key); + if (unlikely(!value)) { + if (!PyErr_Occurred()) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) + PyErr_SetObject(PyExc_KeyError, args); + Py_XDECREF(args); + } + return NULL; + } + Py_INCREF(value); + return value; +} +#else + #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#endif + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +typedef struct { + int code_line; + PyCodeObject* code_object; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; +static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value); + +static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *); + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eqf(a, b) ((a)==(b)) + #define __Pyx_c_sumf(a, b) ((a)+(b)) + #define __Pyx_c_difff(a, b) ((a)-(b)) + #define __Pyx_c_prodf(a, b) ((a)*(b)) + #define __Pyx_c_quotf(a, b) ((a)/(b)) + #define __Pyx_c_negf(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zerof(z) ((z)==(float)0) + #define __Pyx_c_conjf(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_absf(z) (::std::abs(z)) + #define __Pyx_c_powf(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zerof(z) ((z)==0) + #define __Pyx_c_conjf(z) (conjf(z)) + #if 1 + #define __Pyx_c_absf(z) (cabsf(z)) + #define __Pyx_c_powf(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq(a, b) ((a)==(b)) + #define __Pyx_c_sum(a, b) ((a)+(b)) + #define __Pyx_c_diff(a, b) ((a)-(b)) + #define __Pyx_c_prod(a, b) ((a)*(b)) + #define __Pyx_c_quot(a, b) ((a)/(b)) + #define __Pyx_c_neg(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero(z) ((z)==(double)0) + #define __Pyx_c_conj(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs(z) (::std::abs(z)) + #define __Pyx_c_pow(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero(z) ((z)==0) + #define __Pyx_c_conj(z) (conj(z)) + #if 1 + #define __Pyx_c_abs(z) (cabs(z)) + #define __Pyx_c_pow(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +static int __Pyx_check_binary_version(void); + +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif + +static PyObject *__Pyx_ImportModule(const char *name); + +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'cython' */ + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'cpython' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'libc.stdlib' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ + +/* Module declarations from 'utils.cython_bbox' */ +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5utils_11cython_bbox_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_5utils_11cython_bbox_DTYPE_t), { 0 }, 0, 'R', 0, 0 }; +#define __Pyx_MODULE_NAME "utils.cython_bbox" +int __pyx_module_is_main_utils__cython_bbox = 0; + +/* Implementation of 'utils.cython_bbox' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_RuntimeError; +static char __pyx_k_B[] = "B"; +static char __pyx_k_H[] = "H"; +static char __pyx_k_I[] = "I"; +static char __pyx_k_K[] = "K"; +static char __pyx_k_L[] = "L"; +static char __pyx_k_N[] = "N"; 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0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static CYTHON_INLINE PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static CYTHON_INLINE int __Pyx_GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + if (obj == Py_None || obj == NULL) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if CYTHON_COMPILING_IN_CPYTHON + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyThreadState *tstate = PyThreadState_GET(); + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_Restore(type, value, tb); +#endif +} +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyThreadState *tstate = PyThreadState_GET(); + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(type, value, tb); +#endif +} + +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + py_frame->f_lineno = py_line; + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } + Py_DECREF(obj); + view->obj = NULL; +} +#endif + + + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value) { + const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(unsigned int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(unsigned int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(unsigned int) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(unsigned int), + little, !is_unsigned); + } +} + +#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" +#endif + +static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *x) { + const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(unsigned int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (unsigned int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (unsigned int) 0; + case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, digits[0]) + case 2: + if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) >= 2 * PyLong_SHIFT) { + return (unsigned int) (((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) >= 3 * PyLong_SHIFT) { + return (unsigned int) (((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) >= 4 * PyLong_SHIFT) { + return (unsigned int) (((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (unsigned int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(unsigned int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (unsigned int) 0; + case -1: __PYX_VERIFY_RETURN_INT(unsigned int, sdigit, -(sdigit) digits[0]) + case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, +digits[0]) + case -2: + if (8 * sizeof(unsigned int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { + return (unsigned int) (((unsigned int)-1)*(((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { + return (unsigned int) ((((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { + return (unsigned int) (((unsigned int)-1)*(((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { + return (unsigned int) ((((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) { + return (unsigned int) (((unsigned int)-1)*(((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) { + return (unsigned int) ((((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(unsigned int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, long, PyLong_AsLong(x)) + } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, PY_LONG_LONG, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + unsigned int val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (unsigned int) -1; + } + } else { + unsigned int val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (unsigned int) -1; + val = __Pyx_PyInt_As_unsigned_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to unsigned int"); + return (unsigned int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned int"); + return (unsigned int) -1; +} + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(a, a); + case 3: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, a); + case 4: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_absf(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(a, a); + case 3: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, a); + case 4: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_abs(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) digits[0]) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { + const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum NPY_TYPES) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(enum NPY_TYPES) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), + little, !is_unsigned); + } +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) digits[0]) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +#ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, + size_t size, int strict) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + py_name = __Pyx_PyIdentifier_FromString(class_name); + if (!py_name) + goto bad; + result = PyObject_GetAttr(py_module, py_name); + Py_DECREF(py_name); + py_name = 0; + Py_DECREF(py_module); + py_module = 0; + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (!strict && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility", + module_name, class_name); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + else if ((size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s has the wrong size, try recompiling", + module_name, class_name); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_module); + Py_XDECREF(result); + return NULL; +} +#endif + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else + if (__Pyx_PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +#endif + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { + PyNumberMethods *m; + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return __Pyx_NewRef(x); + m = Py_TYPE(x)->tp_as_number; +#if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } +#else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(x); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/bbox.pyx b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/bbox.pyx new file mode 100644 index 000000000..936afdd4c --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/bbox.pyx @@ -0,0 +1,144 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Sergey Karayev +# -------------------------------------------------------- + +cimport cython +import numpy as np +cimport numpy as np + +DTYPE = np.float +ctypedef np.float_t DTYPE_t + +def bbox_overlaps_float( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes): + """ + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0]) * + (query_boxes[k, 3] - query_boxes[k, 1]) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + ) + if ih > 0: + ua = float( + (boxes[n, 2] - boxes[n, 0]) * + (boxes[n, 3] - boxes[n, 1]) + + box_area - iw * ih + ) + # if query_boxes[k, 4] == -1: + # ua = float((boxes[n, 2] - boxes[n, 0]) + # *(boxes[n, 3] - boxes[n, 1])) + # else: + # ua = float( + # (boxes[n, 2] - boxes[n, 0]) * + # (boxes[n, 3] - boxes[n, 1]) + + # box_area - iw * ih + # ) + overlaps[n, k] = iw * ih / ua + return overlaps + +def bbox_overlaps( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes): + """ + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0] + 1) * + (query_boxes[k, 3] - query_boxes[k, 1] + 1) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + 1 + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + 1 + ) + if ih > 0: + ua = float( + (boxes[n, 2] - boxes[n, 0] + 1) * + (boxes[n, 3] - boxes[n, 1] + 1) + + box_area - iw * ih + ) + overlaps[n, k] = iw * ih / ua + return overlaps + +def bbox_overlaps_self( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes): + """ + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0] + 1) * + (query_boxes[k, 3] - query_boxes[k, 1] + 1) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + 1 + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + 1 + ) + if ih > 0: + ua = float(box_area) + overlaps[n, k] = iw * ih / ua + return overlaps \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/blob.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/blob.py new file mode 100644 index 000000000..843948934 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/blob.py @@ -0,0 +1,49 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +"""Blob helper functions.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from npu_bridge.npu_init import * + +import numpy as np +import cv2 + + +def im_list_to_blob(ims): + """Convert a list of images into a network input. + + Assumes images are already prepared (means subtracted, BGR order, ...). + """ + max_shape = np.array([im.shape for im in ims]).max(axis=0) + num_images = len(ims) + blob = np.zeros((num_images, max_shape[0], max_shape[1], 3), + dtype=np.float32) + for i in range(num_images): + im = ims[i] + blob[i, 0:im.shape[0], 0:im.shape[1], :] = im + + return blob + + +def prep_im_for_blob(im, pixel_means, target_size, max_size): + """Mean subtract and scale an image for use in a blob.""" + im = im.astype(np.float32, copy=False) + im -= pixel_means + im_shape = im.shape + im_size_min = np.min(im_shape[0:2]) + im_size_max = np.max(im_shape[0:2]) + im_scale = float(target_size) / float(im_size_min) + # Prevent the biggest axis from being more than MAX_SIZE + if np.round(im_scale * im_size_max) > max_size: + im_scale = float(max_size) / float(im_size_max) + im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, + interpolation=cv2.INTER_LINEAR) + + return im, im_scale + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/boxes_grid.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/boxes_grid.py new file mode 100644 index 000000000..3a9628898 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/boxes_grid.py @@ -0,0 +1,75 @@ +# -------------------------------------------------------- +# Subcategory CNN +# Copyright (c) 2015 CVGL Stanford +# Licensed under The MIT License [see LICENSE for details] +# Written by Yu Xiang +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from npu_bridge.npu_init import * + +import numpy as np +import math +from config import cfg + + +def get_boxes_grid(image_height, image_width): + """ + Return the boxes on image grid. + """ + + # height and width of the heatmap + if cfg.NET_NAME == 'CaffeNet': + height = np.floor((image_height * max(cfg.TRAIN.SCALES) - 1) / 4.0 + 1) + height = np.floor((height - 1) / 2.0 + 1 + 0.5) + height = np.floor((height - 1) / 2.0 + 1 + 0.5) + + width = np.floor((image_width * max(cfg.TRAIN.SCALES) - 1) / 4.0 + 1) + width = np.floor((width - 1) / 2.0 + 1 + 0.5) + width = np.floor((width - 1) / 2.0 + 1 + 0.5) + elif cfg.NET_NAME == 'VGGnet': + height = np.floor(image_height * max(cfg.TRAIN.SCALES) / 2.0 + 0.5) + height = np.floor(height / 2.0 + 0.5) + height = np.floor(height / 2.0 + 0.5) + height = np.floor(height / 2.0 + 0.5) + + width = np.floor(image_width * max(cfg.TRAIN.SCALES) / 2.0 + 0.5) + width = np.floor(width / 2.0 + 0.5) + width = np.floor(width / 2.0 + 0.5) + width = np.floor(width / 2.0 + 0.5) + else: + assert (1), 'The network architecture is not supported in utils.get_boxes_grid!' + + # compute the grid box centers + h = np.arange(height) + w = np.arange(width) + y, x = np.meshgrid(h, w, indexing='ij') + centers = np.dstack((x, y)) + centers = np.reshape(centers, (-1, 2)) + num = centers.shape[0] + + # compute width and height of grid box + area = cfg.TRAIN.KERNEL_SIZE * cfg.TRAIN.KERNEL_SIZE + aspect = cfg.TRAIN.ASPECTS # height / width + num_aspect = len(aspect) + widths = np.zeros((1, num_aspect), dtype=np.float32) + heights = np.zeros((1, num_aspect), dtype=np.float32) + for i in range(num_aspect): + widths[0, i] = math.sqrt(area / aspect[i]) + heights[0, i] = widths[0, i] * aspect[i] + + # construct grid boxes + centers = np.repeat(centers, num_aspect, axis=0) + widths = np.tile(widths, num).transpose() + heights = np.tile(heights, num).transpose() + + x1 = np.reshape(centers[:, 0], (-1, 1)) - widths * 0.5 + x2 = np.reshape(centers[:, 0], (-1, 1)) + widths * 0.5 + y1 = np.reshape(centers[:, 1], (-1, 1)) - heights * 0.5 + y2 = np.reshape(centers[:, 1], (-1, 1)) + heights * 0.5 + + boxes_grid = np.hstack((x1, y1, x2, y2)) / cfg.TRAIN.SPATIAL_SCALE + + return boxes_grid, centers[:, 0], centers[:, 1] + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/mask.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/mask.py new file mode 100644 index 000000000..132e2f3d1 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/mask.py @@ -0,0 +1,42 @@ +from npu_bridge.npu_init import * +import json +import numpy as np +import cv2 +from pycocotools import mask as COCOmask + +def showMask(img_obj): + img = cv2.imread(img_obj['fpath']) + img_ori = img.copy() + gtmasks = img_obj['gtmasks'] + n = len(gtmasks) + print(img.shape) + for i, mobj in enumerate(gtmasks): + if not (type(mobj['mask']) is list): + print("Pass a RLE mask") + continue + else: + pts = np.round(np.asarray(mobj['mask'][0])) + pts = pts.reshape(pts.shape[0] // 2, 2) + pts = np.int32(pts) + color = np.uint8(np.random.rand(3) * 255).tolist() + cv2.fillPoly(img, [pts], color) + cv2.addWeighted(img, 0.5, img_ori, 0.5, 0, img) + cv2.imshow("Mask", img) + cv2.waitKey(0) + +def get_seg(height, width, seg_ann): + label = np.zeros((height, width, 1)) + if type(seg_ann) == list or type(seg_ann) == np.ndarray: + for s in seg_ann: + poly = np.array(s, np.int).reshape(len(s)//2, 2) + cv2.fillPoly(label, [poly], 1) + else: + if type(seg_ann['counts']) == list: + rle = COCOmask.frPyObjects([seg_ann], label.shape[0], label.shape[1]) + else: + rle = [seg_ann] + # we set the ground truth as one-hot + m = COCOmask.decode(rle) * 1 + label[label == 0] = m[label == 0] + return label[:, :, 0] + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/timer.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/timer.py new file mode 100644 index 000000000..6e8c27780 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/lib/utils/timer.py @@ -0,0 +1,40 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +from npu_bridge.npu_init import * +import time + +class Timer(object): + """A simple timer.""" + def __init__(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. + self.warm_up = 0 + + def tic(self): + # using time.time instead of time.clock because time time.clock + # does not normalize for multithreading + self.start_time = time.time() + + def toc(self, average=True): + self.diff = time.time() - self.start_time + if self.warm_up < 100: + self.warm_up += 1 + return self.diff + else: + self.total_time += self.diff + self.calls += 1 + self.average_time = self.total_time / self.calls + + if average: + return self.average_time + else: + return self.diff + -- Gitee From d4ccf5ee5718c32bb8d0cc9dfd6d8bb08bcc0350 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:51:41 +0000 Subject: [PATCH 25/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20main?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From a3c2985aebb6640f6cb642d2905125fe0fb0fff9 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 13:56:56 +0000 Subject: [PATCH 26/59] first commit --- .../src/main/config.py | 140 +++++++++++++++++ .../src/main/gen_batch.py | 142 ++++++++++++++++++ .../src/main/model.py | 88 +++++++++++ 3 files changed, 370 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/config.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/gen_batch.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/model.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/config.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/config.py new file mode 100644 index 000000000..42cc9f2fa --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/config.py @@ -0,0 +1,140 @@ +from npu_bridge.npu_init import * +import os +import os.path as osp +import sys +import numpy as np + + +class Config: + ## dataset + dataset = 'COCO' # 'COCO', 'PoseTrack', 'MPII' + testset = 'val' # train, test, val (there is no validation set for MPII) + + ## directory + cur_dir = osp.dirname(os.path.abspath(__file__)) + root_dir = osp.join(cur_dir, '..') + data_dir = osp.join(root_dir, 'data') + # output_dir = osp.join(root_dir, 'output') # 修改成cache/result + + output_dir = "cache/result" + print("========>>>>>output_dir:{}".format(output_dir)) + + # 修改模型路径 + data_path = "cache/dataset" + model_dump_dir_test = osp.join(data_path, 'model_dump', dataset) # test时用的 + model_dump_dir = osp.join(output_dir, 'model_dump', dataset) # train时用的 + vis_dir = osp.join(output_dir, 'vis', dataset) + log_dir = osp.join(output_dir, 'log', dataset) + result_dir = osp.join(output_dir, 'result', dataset) + # 生成bin + bins_dir = osp.join(output_dir, 'bins', dataset) + # 生成pb + pb_dir = osp.join(output_dir, 'pb_model', dataset) + print("========>>>>>config_bins_dir:{}".format(bins_dir)) + print("========>>>>>config_pb_dir:{}".format(pb_dir)) + + ## model setting + backbone = 'resnet50' # 'resnet50', 'resnet101', 'resnet152' + # init_model = osp.join(data_dir, 'imagenet_weights', 'resnet_v1_' + backbone[6:] + '.ckpt') + init_model = osp.join(data_path, 'imagenet_weights', 'resnet_v1_' + backbone[6:] + '.ckpt') + print("========>>>>>ckpt:{}".format(init_model)) + + ## input, output + input_shape = (256, 192) # (256,192), (384,288) + output_shape = (input_shape[0] // 4, input_shape[1] // 4) + if output_shape[0] == 64: + sigma = 2 + elif output_shape[0] == 96: + sigma = 3 + pixel_means = np.array([[[123.68, 116.78, 103.94]]]) + + ## training config + lr_dec_epoch = [90, 120] + end_epoch = 140 # 修改 + lr = 5e-4 + lr_dec_factor = 10 + optimizer = 'adam' + weight_decay = 1e-5 + bn_train = True + batch_size = 32 + scale_factor = 0.3 + rotation_factor = 40 + + ## testing config + useGTbbox = False + flip_test = True + oks_nms_thr = 0.9 + score_thr = 0.2 + test_batch_size = 32 + + ## others + multi_thread_enable = True + num_thread = 10 + gpu_ids = '0' + num_gpus = 1 + continue_train = False + display = 1 + + ## helper functions + def get_lr(self, epoch): + for e in self.lr_dec_epoch: + if epoch < e: + break + if epoch < self.lr_dec_epoch[-1]: + i = self.lr_dec_epoch.index(e) + return self.lr / (self.lr_dec_factor ** i) + else: + return self.lr / (self.lr_dec_factor ** len(self.lr_dec_epoch)) + + def normalize_input(self, img): + return img - self.pixel_means + + def denormalize_input(self, img): + return img + self.pixel_means + + # def set_args(self, gpu_ids, continue_train=False): + # self.gpu_ids = gpu_ids + # self.num_gpus = len(self.gpu_ids.split(',')) + # self.continue_train = continue_train + # os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu_ids + # print('>>> Using /gpu:{}'.format(self.gpu_ids)) + + def set_args(self, continue_train=False): + # self.data_url = data_url + # self.num_gpus = len(self.gpu_ids.split(',')) + + # os.environ['ASCEND_SLOG_PRINT_TO_STDOUT'] = '1' + # os.environ['ASCEND_GLOBAL_LOG_LEVEL'] = '1' + + # os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu_ids + # self.gpu_ids = gpu_ids + # self.num_gpus = len(self.gpu_ids.split(',')) + # os.environ['DEVICE_ID'] = '1' + self.continue_train = continue_train + + +cfg = Config() + +sys.path.insert(0, osp.join(cfg.root_dir, 'lib')) + +from tfflat.utils import add_pypath, make_dir + +add_pypath(osp.join(cfg.data_dir)) +add_pypath(osp.join(cfg.data_dir, cfg.dataset)) +make_dir(cfg.model_dump_dir) +make_dir(cfg.model_dump_dir_test) +make_dir(cfg.vis_dir) +make_dir(cfg.log_dir) +make_dir(cfg.result_dir) +make_dir(cfg.bins_dir) +make_dir(cfg.pb_dir) + +from dataset import dbcfg + +cfg.num_kps = dbcfg.num_kps +cfg.kps_names = dbcfg.kps_names +cfg.kps_lines = dbcfg.kps_lines +cfg.kps_symmetry = dbcfg.kps_symmetry +cfg.img_path = dbcfg.img_path +cfg.human_det_path = dbcfg.human_det_path +cfg.vis_keypoints = dbcfg.vis_keypoints diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/gen_batch.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/gen_batch.py new file mode 100644 index 000000000..6f6e9acfe --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/gen_batch.py @@ -0,0 +1,142 @@ +from npu_bridge.npu_init import * +import os +import os.path as osp +import numpy as np +import cv2 +from config import cfg +import random +import time +import math +from PIL import Image, ImageDraw +import matplotlib.pyplot as plt + +# 仿射变换 +def get_affine_transform(center, + scale, + rot, + output_size, + shift=np.array([0, 0], dtype=np.float32), + inv=0): + if not isinstance(scale, np.ndarray) and not isinstance(scale, list): + print(scale) + scale = np.array([scale, scale]) + + src_w = scale[0] + dst_w = output_size[0] + dst_h = output_size[1] + + rot_rad = np.pi * rot / 180 + src_dir = get_dir([0, src_w * -0.5], rot_rad) + dst_dir = np.array([0, dst_w * -0.5], np.float32) + + src = np.zeros((3, 2), dtype=np.float32) + dst = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale * shift + src[1, :] = center + src_dir + scale * shift + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir + + src[2:, :] = get_3rd_point(src[0, :], src[1, :]) + dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return trans + +def affine_transform(pt, t): + new_pt = np.array([pt[0], pt[1], 1.]).T + new_pt = np.dot(t, new_pt) + return new_pt[:2] + +def get_dir(src_point, rot_rad): + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + + src_result = [0, 0] + src_result[0] = src_point[0] * cs - src_point[1] * sn + src_result[1] = src_point[0] * sn + src_point[1] * cs + + return src_result + +def get_3rd_point(a, b): + direct = a - b + return b + np.array([-direct[1], direct[0]], dtype=np.float32) + +def generate_batch(d, stage='train'): + + img = cv2.imread(os.path.join(cfg.img_path, d['imgpath']), cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION) + if img is None: + print('cannot read ' + os.path.join(cfg.img_path, d['imgpath'])) + assert 0 + + bbox = np.array(d['bbox']).astype(np.float32) + + x, y, w, h = bbox + aspect_ratio = cfg.input_shape[1]/cfg.input_shape[0] + center = np.array([x + w * 0.5, y + h * 0.5]) + if w > aspect_ratio * h: + h = w / aspect_ratio + elif w < aspect_ratio * h: + w = h * aspect_ratio + scale = np.array([w,h]) * 1.25 + rotation = 0 + + if stage == 'train': + + joints = np.array(d['joints']).reshape(cfg.num_kps, 3).astype(np.float32) + + # data augmentation + scale = scale * np.clip(np.random.randn()*cfg.scale_factor + 1, 1-cfg.scale_factor, 1+cfg.scale_factor) + rotation = np.clip(np.random.randn()*cfg.rotation_factor, -cfg.rotation_factor*2, cfg.rotation_factor*2)\ + if random.random() <= 0.6 else 0 + if random.random() <= 0.5: + img = img[:, ::-1, :] + center[0] = img.shape[1] - 1 - center[0] + joints[:,0] = img.shape[1] - 1 - joints[:,0] + for (q, w) in cfg.kps_symmetry: + joints_q, joints_w = joints[q,:].copy(), joints[w,:].copy() + joints[w,:], joints[q,:] = joints_q, joints_w + + trans = get_affine_transform(center, scale, rotation, (cfg.input_shape[1], cfg.input_shape[0])) + cropped_img = cv2.warpAffine(img, trans, (cfg.input_shape[1], cfg.input_shape[0]), flags=cv2.INTER_LINEAR) + #cropped_img = cropped_img[:,:, ::-1] + cropped_img = cfg.normalize_input(cropped_img) + + for i in range(cfg.num_kps): + if joints[i,2] > 0: + joints[i,:2] = affine_transform(joints[i,:2], trans) + joints[i,2] *= ((joints[i,0] >= 0) & (joints[i,0] < cfg.input_shape[1]) & (joints[i,1] >= 0) & (joints[i,1] < cfg.input_shape[0])) + target_coord = joints[:,:2] + target_valid = joints[:,2] + + # for debug + vis = False + if vis: + filename = str(random.randrange(1,500)) + tmpimg = cropped_img.astype(np.float32).copy() + tmpimg = cfg.denormalize_input(tmpimg) + tmpimg = tmpimg.astype(np.uint8).copy() + tmpkps = np.zeros((3,cfg.num_kps)) + tmpkps[:2,:] = target_coord.transpose(1,0) + tmpkps[2,:] = target_valid + tmpimg = cfg.vis_keypoints(tmpimg, tmpkps) + cv2.imwrite(osp.join(cfg.vis_dir, filename + '_gt.jpg'), tmpimg) + + return [cropped_img, + target_coord, + (target_valid > 0)] + + else: + trans = get_affine_transform(center, scale, rotation, (cfg.input_shape[1], cfg.input_shape[0])) + cropped_img = cv2.warpAffine(img, trans, (cfg.input_shape[1], cfg.input_shape[0]), flags=cv2.INTER_LINEAR) # trans是变换矩阵 + #cropped_img = cropped_img[:,:, ::-1] + cropped_img = cfg.normalize_input(cropped_img) + + crop_info = np.asarray([center[0]-scale[0]*0.5, center[1]-scale[1]*0.5, center[0]+scale[0]*0.5, center[1]+scale[1]*0.5]) + + return [cropped_img, crop_info] + + + diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/model.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/model.py new file mode 100644 index 000000000..1f5371898 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/model.py @@ -0,0 +1,88 @@ +from npu_bridge.npu_init import * +import tensorflow as tf +import tensorflow.contrib.slim as slim +import numpy as np +import json +import math +from functools import partial + +from config import cfg +from tfflat.base import ModelDesc + + +from nets.basemodel import resnet50, resnet101, resnet152, resnet_arg_scope, resnet_v1 +resnet_arg_scope = partial(resnet_arg_scope, bn_trainable=cfg.bn_train) +# tf.reset_default_graph() + + +class Model(ModelDesc): + + def head_net(self, blocks, is_training, trainable=True): + + normal_initializer = tf.truncated_normal_initializer(0, 0.01) + msra_initializer = tf.contrib.layers.variance_scaling_initializer() + xavier_initializer = tf.contrib.layers.xavier_initializer() + + with slim.arg_scope(resnet_arg_scope(bn_is_training=is_training)): + + out = slim.conv2d_transpose(blocks[-1], 256, [4, 4], stride=2, + trainable=trainable, weights_initializer=normal_initializer, + padding='SAME', activation_fn=tf.nn.relu, + scope='up1') + out = slim.conv2d_transpose(out, 256, [4, 4], stride=2, + trainable=trainable, weights_initializer=normal_initializer, + padding='SAME', activation_fn=tf.nn.relu, + scope='up2') + out = slim.conv2d_transpose(out, 256, [4, 4], stride=2, + trainable=trainable, weights_initializer=normal_initializer, + padding='SAME', activation_fn=tf.nn.relu, + scope='up3') + + out = slim.conv2d(out, cfg.num_kps, [1, 1], + trainable=trainable, weights_initializer=msra_initializer, + padding='SAME', normalizer_fn=None, activation_fn=None, + scope='out') + + return out + + def render_gaussian_heatmap(self, coord, output_shape, sigma): + + x = [i for i in range(output_shape[1])] + y = [i for i in range(output_shape[0])] + xx,yy = tf.meshgrid(x,y) + xx = tf.reshape(tf.to_float(xx), (1,*output_shape,1)) + yy = tf.reshape(tf.to_float(yy), (1,*output_shape,1)) + + x = tf.floor(tf.reshape(coord[:,:,0],[-1,1,1,cfg.num_kps]) / cfg.input_shape[1] * output_shape[1] + 0.5) + y = tf.floor(tf.reshape(coord[:,:,1],[-1,1,1,cfg.num_kps]) / cfg.input_shape[0] * output_shape[0] + 0.5) + + heatmap = tf.exp(-(((xx-x)/tf.to_float(sigma))**2)/tf.to_float(2) -(((yy-y)/tf.to_float(sigma))**2)/tf.to_float(2)) + + return heatmap * 255. + + def make_network(self, is_train): + global target_coord, valid + if is_train: + image = tf.placeholder(tf.float32, shape=[cfg.batch_size, *cfg.input_shape, 3]) + target_coord = tf.placeholder(tf.float32, shape=[cfg.batch_size, cfg.num_kps, 2]) + valid = tf.placeholder(tf.float32, shape=[cfg.batch_size, cfg.num_kps]) + self.set_inputs(image, target_coord, valid) + else: + image = tf.placeholder(tf.float32, shape=[None, *cfg.input_shape, 3]) + # image = tf.placeholder(tf.float32, shape=[cfg.test_batch_size, *cfg.input_shape, 3]) + self.set_inputs(image) + + backbone = eval(cfg.backbone) + resnet_fms = backbone(image, is_train, bn_trainable=True) + heatmap_outs = self.head_net(resnet_fms, is_train) + + if is_train: + gt_heatmap = tf.stop_gradient(self.render_gaussian_heatmap(target_coord, cfg.output_shape, cfg.sigma)) + valid_mask = tf.reshape(valid, [cfg.batch_size, 1, 1, cfg.num_kps]) + loss = tf.reduce_mean(tf.square(heatmap_outs - gt_heatmap) * valid_mask) + self.add_tower_summary('loss', loss) + self.set_loss(loss) + else: + self.set_outputs(heatmap_outs) + + -- Gitee From 7e4c43e2d7114de82afd0da18b5cc22461982b55 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:08:37 +0000 Subject: [PATCH 27/59] first commit --- .../src/boot_inference.py | 62 +++++++++++++++++++ .../src/boot_pb_frozen.py | 60 ++++++++++++++++++ .../src/boot_test.py | 62 +++++++++++++++++++ 3 files changed, 184 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_inference.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_pb_frozen.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_test.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_inference.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_inference.py new file mode 100644 index 000000000..474e11fc7 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_inference.py @@ -0,0 +1,62 @@ +# coding=utf-8 +import os +import argparse +import datetime +import moxing as mox + +import tensorflow as tf +import os +import sys + +# Code dir: /home/work/user-job-dir/code # 在ModelArts上的代码存储目录（父目录均会被重命名为code）。 +# Work dir: /home/work/workspace/device2 # device id因job而异 + +# print(os.system('env')) + +if __name__ == '__main__': + + code_dir = os.path.dirname(os.path.realpath(__file__)) + work_dir = os.getcwd() + print("===>>>code_dir:{}".format(code_dir)) + print("===>>>work_dir{}".format(work_dir)) + + parser = argparse.ArgumentParser() + + # 解析输入参数data_url + parser.add_argument("--data_url", type=str, default="./dataset") # 这里要写绝对路径吗 + parser.add_argument("--train_url", type=str, default="./output") + parser.add_argument('--test_epoch', type=str, default='140') + args = parser.parse_args() + + # 打印config参数 + print("--------config----------") + for k in list(vars(args).keys()): + print("key:{}: value:{}".format(k, vars(args)[k])) + print("--------config----------") + + # 在modelarts创建数据存放目录 + data_dir = "cache/dataset" + # os.makedirs(data_dir) + + # OBS数据拷贝到modelarts容器内 + print("===>>>Copy files from obs:{} to modelarts dir:{}".format(args.data_url, data_dir)) + mox.file.copy_parallel(args.data_url, data_dir) + files = os.listdir(data_dir) + print("===>>>Files:", files) + + print("===>>>Begin booting:") + os.system("python /home/ma-user/modelarts/user-job-dir/code/main/pb_inference.py") + print("===>>>Test finished:") + + # 训练完成, 结果上传到OBS + output_dir = "cache/result" + remote_dir = os.path.join(args.train_url) + if not mox.file.exists(remote_dir): + mox.file.make_dirs(remote_dir) + # start = datetime.datetime.now() + print("===>>>Copy files from local dir:{} to obs:{}".format(output_dir, remote_dir)) + mox.file.copy_parallel(src_url=output_dir, dst_url=remote_dir) + # end = datetime.datetime.now() + # print("===>>>Copy from local to obs, time use:{}(s)".format((end - start).seconds)) + files = os.listdir(output_dir) + print("===>>>Files number:", len(files)) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_pb_frozen.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_pb_frozen.py new file mode 100644 index 000000000..7084f6528 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_pb_frozen.py @@ -0,0 +1,60 @@ +import os +import argparse +import datetime +import moxing as mox + +import tensorflow as tf +import os +import sys + +if __name__ == '__main__': + code_dir = os.path.dirname(os.path.realpath(__file__)) + work_dir = os.getcwd() + print("===>>>code_dir:{}".format(code_dir)) + print("===>>>work_dir{}".format(work_dir)) + + parser = argparse.ArgumentParser() + + # 解析输入参数data_url + parser.add_argument("--data_url", type=str, default="./dataset") + parser.add_argument("--train_url", type=str, default="./output") + + # parser.add_argument('--gpu', type=str, dest='gpu_ids') + parser.add_argument('--continue', dest='continue_train', action='store_true') + args = parser.parse_args() + + # 打印config参数 + print("--------config----------") + for k in list(vars(args).keys()): + print("key:{}: value:{}".format(k, vars(args)[k])) + print("--------config----------") + + # 在modelarts创建数据存放目录 + data_dir = "cache/dataset" + # os.makedirs(data_dir) + + # OBS数据拷贝到modelarts容器内 + print("===>>>Copy files from obs:{} to modelarts dir:{}".format(args.data_url, data_dir)) + mox.file.copy_parallel(args.data_url, data_dir) + for rt, dirs, fl in os.walk(data_dir, topdown=True): + for nm in fl: + filepath = os.path.join(rt, nm) + print("filepath:", filepath) + + print("===>>>Begin booting:") + os.system("python /home/ma-user/modelarts/user-job-dir/code/main/pb_frozen.py") + + # print("===>>>Training finished:") + + # pb完成 + output_dir = "cache/result" + remote_dir = os.path.join(args.train_url) + if not mox.file.exists(remote_dir): + mox.file.make_dirs(remote_dir) + # start = datetime.datetime.now() + print("===>>>Copy files from local dir:{} to obs:{}".format(output_dir, remote_dir)) + mox.file.copy_parallel(src_url=output_dir, dst_url=remote_dir) + # end = datetime.datetime.now() + # print("===>>>Copy from local to obs, time use:{}(s)".format((end - start).seconds)) + files = os.listdir(output_dir) + print("===>>>Files number:", len(files)) \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_test.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_test.py new file mode 100644 index 000000000..041f8c2cd --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/boot_test.py @@ -0,0 +1,62 @@ +# coding=utf-8 +import os +import argparse +import datetime +import moxing as mox + +import tensorflow as tf +import os +import sys + +# Code dir: /home/work/user-job-dir/code # 在ModelArts上的代码存储目录（父目录均会被重命名为code）。 +# Work dir: /home/work/workspace/device2 # device id因job而异 + +# print(os.system('env')) + +if __name__ == '__main__': + + code_dir = os.path.dirname(os.path.realpath(__file__)) + work_dir = os.getcwd() + print("===>>>code_dir:{}".format(code_dir)) + print("===>>>work_dir{}".format(work_dir)) + + parser = argparse.ArgumentParser() + + # 解析输入参数data_url + parser.add_argument("--data_url", type=str, default="./dataset") # 这里要写绝对路径吗 + parser.add_argument("--train_url", type=str, default="./output") + parser.add_argument('--test_epoch', type=str, default='140') + args = parser.parse_args() + + # 打印config参数 + print("--------config----------") + for k in list(vars(args).keys()): + print("key:{}: value:{}".format(k, vars(args)[k])) + print("--------config----------") + + # 在modelarts创建数据存放目录 + data_dir = "cache/dataset" + # os.makedirs(data_dir) + + # OBS数据拷贝到modelarts容器内 + print("===>>>Copy files from obs:{} to modelarts dir:{}".format(args.data_url, data_dir)) + mox.file.copy_parallel(args.data_url, data_dir) + files = os.listdir(data_dir) + print("===>>>Files:", files) + + print("===>>>Begin booting:") + os.system("python /home/ma-user/modelarts/user-job-dir/code/main/test.py") + print("===>>>Test finished:") + + # 训练完成, 结果上传到OBS + output_dir = "cache/result" + remote_dir = os.path.join(args.train_url) + if not mox.file.exists(remote_dir): + mox.file.make_dirs(remote_dir) + # start = datetime.datetime.now() + print("===>>>Copy files from local dir:{} to obs:{}".format(output_dir, remote_dir)) + mox.file.copy_parallel(src_url=output_dir, dst_url=remote_dir) + # end = datetime.datetime.now() + # print("===>>>Copy from local to obs, time use:{}(s)".format((end - start).seconds)) + files = os.listdir(output_dir) + print("===>>>Files number:", len(files)) -- Gitee From 117f36de3e50f086b82cc9ec759a638b8e150f14 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:17:45 +0000 Subject: [PATCH 28/59] first commit --- .../src/main/pb_frozen.py | 29 ++ .../src/main/pb_inference.py | 237 ++++++++++++++++ .../src/main/test.py | 267 ++++++++++++++++++ 3 files changed, 533 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/pb_frozen.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/pb_inference.py create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/test.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/pb_frozen.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/pb_frozen.py new file mode 100644 index 000000000..b43bb7261 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/pb_frozen.py @@ -0,0 +1,29 @@ +import os +import tensorflow as tf +from model import Model +from config import cfg +from tensorflow.python.tools import freeze_graph + + +ckpt_path = "cache/dataset/COCO/snapshot_140.ckpt" +result_dir = cfg.pb_dir + +tf.reset_default_graph() +model = Model() +model.make_network(is_train=False) + +with tf.Session() as sess: + tf.train.write_graph(sess.graph_def, result_dir, 'model.pb') + + freeze_graph.freeze_graph( + input_graph=os.path.join(result_dir, 'model.pb'), + input_saver='', + input_binary=False, + input_checkpoint=ckpt_path, + output_node_names='out/BiasAdd', + restore_op_name='save/restore_all', + filename_tensor_name='save/Const:0', + output_graph=os.path.join(result_dir, 'frozen_model.pb'), + clear_devices=False, + initializer_nodes='' + ) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/pb_inference.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/pb_inference.py new file mode 100644 index 000000000..386950eb1 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/pb_inference.py @@ -0,0 +1,237 @@ +from npu_bridge.npu_init import * +import os +import os.path as osp +import numpy as np +import argparse +from config import cfg +import cv2 +import sys +import time +import json +from PIL import Image +import matplotlib.pyplot as plt +import pickle +from tqdm import tqdm +import math + +import tensorflow as tf + +from tfflat.base import Tester +from tfflat.utils import mem_info +from model import Model + +from gen_batch import generate_batch +from dataset import Dataset +from nms.nms_my import oks_nms + +output_dir = "cache/result" + +msame_out_path = 'cache/dataset/outs' + + +def test_net(tester, dets, det_range, gpu_id): + dump_results = [] + + start_time = time.time() + + img_start = det_range[0] + img_id = 0 + img_id2 = 0 + count = 0 # 计数 + total = 360 + pbar = tqdm(total=det_range[1] - img_start - 1, position=gpu_id) # 进度条 + pbar.set_description("GPU %s" % str(gpu_id)) + while img_start < det_range[1]: + img_end = img_start + 1 + im_info = dets[img_start] + while img_end < det_range[1] and dets[img_end]['image_id'] == im_info['image_id']: + img_end += 1 + + # all human detection results of a certain image + cropped_data = dets[img_start:img_end] # 一个确定的图片中的所有human检测结果 + + pbar.update(img_end - img_start) + img_start = img_end + + kps_result = np.zeros((len(cropped_data), cfg.num_kps, 3)) + area_save = np.zeros(len(cropped_data)) + + # cluster human detection results with test_batch_size + # test_batch_size 改成 1 + for batch_id in range(0, len(cropped_data), 1): + start_id = batch_id + end_id = min(len(cropped_data), batch_id + 1) + + imgs = [] + crop_infos = [] + for i in range(start_id, end_id): + img, crop_info = generate_batch(cropped_data[i], stage='test') + imgs.append(img) + crop_infos.append(crop_info) + + imgs = np.array(imgs) + crop_infos = np.array(crop_infos) + + # forward + if 0 <= count < total: + heatmap = np.loadtxt(osp.join(msame_out_path, "{}_output_0.txt".format(count))) + heatmap = heatmap.reshape((1, 64, 48, 17)) + count += 1 + # print("count:", count) + + if cfg.flip_test: + flip_imgs = imgs[:, :, ::-1, :] + flip_heatmap = tester.predict_one([flip_imgs])[0] + + flip_heatmap = flip_heatmap[:, :, ::-1, :] + for (q, w) in cfg.kps_symmetry: + flip_heatmap_w, flip_heatmap_q = flip_heatmap[:, :, :, w].copy(), flip_heatmap[:, :, :, q].copy() + flip_heatmap[:, :, :, q], flip_heatmap[:, :, :, w] = flip_heatmap_w, flip_heatmap_q + flip_heatmap[:, :, 1:, :] = flip_heatmap.copy()[:, :, 0:-1, :] + heatmap += flip_heatmap + heatmap /= 2 + + # for each human detection from clustered batch + for image_id in range(start_id, end_id): + + for j in range(cfg.num_kps): + hm_j = heatmap[image_id - start_id, :, :, j] + idx = hm_j.argmax() + y, x = np.unravel_index(idx, hm_j.shape) + + px = int(math.floor(x + 0.5)) + py = int(math.floor(y + 0.5)) + if 1 < px < cfg.output_shape[1] - 1 and 1 < py < cfg.output_shape[0] - 1: + diff = np.array([hm_j[py][px + 1] - hm_j[py][px - 1], + hm_j[py + 1][px] - hm_j[py - 1][px]]) + diff = np.sign(diff) + x += diff[0] * .25 + y += diff[1] * .25 + kps_result[image_id, j, :2] = ( + x * cfg.input_shape[1] / cfg.output_shape[1], y * cfg.input_shape[0] / cfg.output_shape[0]) + kps_result[image_id, j, 2] = hm_j.max() / 255 + + # map back to original images + for j in range(cfg.num_kps): + kps_result[image_id, j, 0] = kps_result[image_id, j, 0] / cfg.input_shape[1] * ( + crop_infos[image_id - start_id][2] - crop_infos[image_id - start_id][0]) + \ + crop_infos[image_id - start_id][0] + kps_result[image_id, j, 1] = kps_result[image_id, j, 1] / cfg.input_shape[0] * ( + crop_infos[image_id - start_id][3] - crop_infos[image_id - start_id][1]) + \ + crop_infos[image_id - start_id][1] + + area_save[image_id] = (crop_infos[image_id - start_id][2] - crop_infos[image_id - start_id][0]) * ( + crop_infos[image_id - start_id][3] - crop_infos[image_id - start_id][1]) + + score_result = np.copy(kps_result[:, :, 2]) + kps_result[:, :, 2] = 1 + kps_result = kps_result.reshape(-1, cfg.num_kps * 3) + + # rescoring and oks nms + if cfg.dataset == 'COCO': + rescored_score = np.zeros((len(score_result))) + for i in range(len(score_result)): + score_mask = score_result[i] > cfg.score_thr + if np.sum(score_mask) > 0: + rescored_score[i] = np.mean(score_result[i][score_mask]) * cropped_data[i]['score'] + score_result = rescored_score + keep = oks_nms(kps_result, score_result, area_save, cfg.oks_nms_thr) + if len(keep) > 0: + kps_result = kps_result[keep, :] + score_result = score_result[keep] + area_save = area_save[keep] + elif cfg.dataset == 'PoseTrack': + keep = oks_nms(kps_result, np.mean(score_result, axis=1), area_save, cfg.oks_nms_thr) + if len(keep) > 0: + kps_result = kps_result[keep, :] + score_result = score_result[keep, :] + area_save = area_save[keep] + + # save result + for i in range(len(kps_result)): + if cfg.dataset == 'COCO': + result = dict(image_id=im_info['image_id'], category_id=1, score=float(round(score_result[i], 4)), + keypoints=kps_result[i].round(3).tolist()) + elif cfg.dataset == 'PoseTrack': + result = dict(image_id=im_info['image_id'], category_id=1, track_id=0, + scores=score_result[i].round(4).tolist(), + keypoints=kps_result[i].round(3).tolist()) + elif cfg.dataset == 'MPII': + result = dict(image_id=im_info['image_id'], scores=score_result[i].round(4).tolist(), + keypoints=kps_result[i].round(3).tolist()) + + dump_results.append(result) + + return dump_results + + +def test(test_model): + # annotation load + d = Dataset() + annot = d.load_annot(cfg.testset) # ground truth + gt_img_id = d.load_imgid(annot) # 得到图像id + + # human bbox load + if cfg.useGTbbox and cfg.testset in ['train', 'val']: + if cfg.testset == 'train': + dets = d.load_train_data(score=True) + else: + dets = d.load_val_data_with_annot() + dets.sort(key=lambda x: (x['image_id'])) + else: + with open(cfg.human_det_path, 'r') as f: + dets = json.load(f) + dets = [i for i in dets if i['image_id'] in gt_img_id] + dets = [i for i in dets if i['category_id'] == 1] + dets = [i for i in dets if i['score'] > 0] + dets.sort(key=lambda x: (x['image_id'], x['score']), reverse=True) + + img_id = [] + for i in dets: + img_id.append(i['image_id']) + imgname = d.imgid_to_imgname(annot, img_id, cfg.testset) # 得到图像名 + for i in range(len(dets)): + dets[i]['imgpath'] = imgname[i] # 把图像名作为一项加入dets + + # job assign (multi-gpu) + gpu_ids = "0" + from tfflat.mp_utils_my import MultiProc + img_start = 0 + ranges = [0] + + img_num = len(np.unique([i['image_id'] for i in dets])) + + images_per_gpu = int(img_num / len(gpu_ids.split(','))) + 1 + + for run_img in range(img_num): + img_end = img_start + 1 + while img_end < len(dets) and dets[img_end]['image_id'] == dets[img_start]['image_id']: + img_end += 1 + if (run_img + 1) % images_per_gpu == 0 or (run_img + 1) == img_num: + ranges.append(img_end) + img_start = img_end + print("ranges:", ranges) + + global func + + def func(gpu_id): # 修改 + # cfg.set_args(gpu_ids.split(',')[gpu_id]) + cfg.set_args() + tester = Tester(Model(), cfg) + tester.load_weights(test_model) + range = [ranges[gpu_id], ranges[gpu_id + 1]] + return test_net(tester, dets, range, gpu_id) + + MultiGPUFunc = MultiProc(len(gpu_ids.split(',')), func) + result = MultiGPUFunc.work() + + # evaluation + d.evaluation(result, annot, cfg.result_dir, cfg.testset) + + +if __name__ == '__main__': + (npu_sess, npu_shutdown) = init_resource() + test_epoch = 140 + test(test_epoch) + shutdown_resource(npu_sess, npu_shutdown) + close_session(npu_sess) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/test.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/test.py new file mode 100644 index 000000000..09530874c --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/main/test.py @@ -0,0 +1,267 @@ +from npu_bridge.npu_init import * +import os +import os.path as osp +import numpy as np +import argparse +from config import cfg +import cv2 +import sys +import time +import json +from PIL import Image +import matplotlib.pyplot as plt +import pickle +from tqdm import tqdm +import math + +import tensorflow as tf + +from tfflat.base import Tester +from tfflat.utils import mem_info +from model import Model + +from gen_batch import generate_batch +from dataset import Dataset +from nms.nms_my import oks_nms + +output_dir = "cache/result" + + +def test_net(tester, dets, det_range, gpu_id): + dump_results = [] + + start_time = time.time() + + img_start = det_range[0] + img_id = 0 + img_id2 = 0 + # 计数 + count = 0 + sum = 0 + pbar = tqdm(total=det_range[1] - img_start - 1, position=gpu_id) # 进度条 + pbar.set_description("GPU %s" % str(gpu_id)) + while img_start < det_range[1]: + img_end = img_start + 1 + im_info = dets[img_start] + while img_end < det_range[1] and dets[img_end]['image_id'] == im_info['image_id']: + img_end += 1 + + # all human detection results of a certain image + cropped_data = dets[img_start:img_end] # 一个确定的图片中的所有human检测结果 + + pbar.update(img_end - img_start) + img_start = img_end + + kps_result = np.zeros((len(cropped_data), cfg.num_kps, 3)) + area_save = np.zeros(len(cropped_data)) + + # cluster human detection results with test_batch_size + # test_batch_size 改成 1 + + sum += len(cropped_data) + for batch_id in range(0, len(cropped_data), 1): + start_id = batch_id + end_id = min(len(cropped_data), batch_id + 1) + + imgs = [] + crop_infos = [] + for i in range(start_id, end_id): + img, crop_info = generate_batch(cropped_data[i], stage='test') + imgs.append(img) + crop_infos.append(crop_info) + + image = np.array(imgs) + bin_path = osp.join(cfg.bins_dir, "{}.bin".format(count)) + image.tofile(bin_path) + count += 1 + + imgs = np.array(imgs) + crop_infos = np.array(crop_infos) + + # forward + heatmap = tester.predict_one([imgs])[0] + + if cfg.flip_test: + flip_imgs = imgs[:, :, ::-1, :] + flip_heatmap = tester.predict_one([flip_imgs])[0] + + flip_heatmap = flip_heatmap[:, :, ::-1, :] + for (q, w) in cfg.kps_symmetry: + flip_heatmap_w, flip_heatmap_q = flip_heatmap[:, :, :, w].copy(), flip_heatmap[:, :, :, q].copy() + flip_heatmap[:, :, :, q], flip_heatmap[:, :, :, w] = flip_heatmap_w, flip_heatmap_q + flip_heatmap[:, :, 1:, :] = flip_heatmap.copy()[:, :, 0:-1, :] + heatmap += flip_heatmap + heatmap /= 2 + + # for each human detection from clustered batch + for image_id in range(start_id, end_id): + + for j in range(cfg.num_kps): + hm_j = heatmap[image_id - start_id, :, :, j] + idx = hm_j.argmax() + y, x = np.unravel_index(idx, hm_j.shape) + + px = int(math.floor(x + 0.5)) + py = int(math.floor(y + 0.5)) + if 1 < px < cfg.output_shape[1] - 1 and 1 < py < cfg.output_shape[0] - 1: + diff = np.array([hm_j[py][px + 1] - hm_j[py][px - 1], + hm_j[py + 1][px] - hm_j[py - 1][px]]) + diff = np.sign(diff) + x += diff[0] * .25 + y += diff[1] * .25 + kps_result[image_id, j, :2] = ( + x * cfg.input_shape[1] / cfg.output_shape[1], y * cfg.input_shape[0] / cfg.output_shape[0]) + kps_result[image_id, j, 2] = hm_j.max() / 255 + + vis = False + crop_info = crop_infos[image_id - start_id, :] + area = (crop_info[2] - crop_info[0]) * (crop_info[3] - crop_info[1]) + if vis and np.any(kps_result[image_id, :, 2]) > 0.9 and area > 96 ** 2: + tmpimg = imgs[image_id - start_id].copy() + tmpimg = cfg.denormalize_input(tmpimg) + tmpimg = tmpimg.astype('uint8') + tmpkps = np.zeros((3, cfg.num_kps)) + tmpkps[:2, :] = kps_result[image_id, :, :2].transpose(1, 0) + tmpkps[2, :] = kps_result[image_id, :, 2] + _tmpimg = tmpimg.copy() + _tmpimg = cfg.vis_keypoints(_tmpimg, tmpkps) + cv2.imwrite(osp.join(cfg.vis_dir, str(img_id) + '_output.jpg'), _tmpimg) + img_id += 1 + + # map back to original images + for j in range(cfg.num_kps): + kps_result[image_id, j, 0] = kps_result[image_id, j, 0] / cfg.input_shape[1] * \ + (crop_infos[image_id - start_id][2] - crop_infos[image_id - start_id][ + 0]) + \ + crop_infos[image_id - start_id][0] + kps_result[image_id, j, 1] = kps_result[image_id, j, 1] / cfg.input_shape[0] * \ + (crop_infos[image_id - start_id][3] - crop_infos[image_id - start_id][ + 1]) + \ + crop_infos[image_id - start_id][1] + + area_save[image_id] = (crop_infos[image_id - start_id][2] - crop_infos[image_id - start_id][0]) * ( + crop_infos[image_id - start_id][3] - crop_infos[image_id - start_id][1]) + + # vis + vis = False + if vis and np.any(kps_result[:, :, 2] > 0.9): + tmpimg = cv2.imread(os.path.join(cfg.img_path, cropped_data[0]['imgpath'])) + tmpimg = tmpimg.astype('uint8') + for i in range(len(kps_result)): + tmpkps = np.zeros((3, cfg.num_kps)) + tmpkps[:2, :] = kps_result[i, :, :2].transpose(1, 0) + tmpkps[2, :] = kps_result[i, :, 2] + tmpimg = cfg.vis_keypoints(tmpimg, tmpkps) + cv2.imwrite(osp.join(cfg.vis_dir, str(img_id2) + '.jpg'), tmpimg) + img_id2 += 1 + + score_result = np.copy(kps_result[:, :, 2]) + kps_result[:, :, 2] = 1 + kps_result = kps_result.reshape(-1, cfg.num_kps * 3) + + # rescoring and oks nms + if cfg.dataset == 'COCO': + rescored_score = np.zeros((len(score_result))) + for i in range(len(score_result)): + score_mask = score_result[i] > cfg.score_thr + if np.sum(score_mask) > 0: + rescored_score[i] = np.mean(score_result[i][score_mask]) * cropped_data[i]['score'] + score_result = rescored_score + keep = oks_nms(kps_result, score_result, area_save, cfg.oks_nms_thr) + if len(keep) > 0: + kps_result = kps_result[keep, :] + score_result = score_result[keep] + area_save = area_save[keep] + elif cfg.dataset == 'PoseTrack': + keep = oks_nms(kps_result, np.mean(score_result, axis=1), area_save, cfg.oks_nms_thr) + if len(keep) > 0: + kps_result = kps_result[keep, :] + score_result = score_result[keep, :] + area_save = area_save[keep] + + # save result + for i in range(len(kps_result)): + if cfg.dataset == 'COCO': + result = dict(image_id=im_info['image_id'], category_id=1, score=float(round(score_result[i], 4)), + keypoints=kps_result[i].round(3).tolist()) + elif cfg.dataset == 'PoseTrack': + result = dict(image_id=im_info['image_id'], category_id=1, track_id=0, + scores=score_result[i].round(4).tolist(), + keypoints=kps_result[i].round(3).tolist()) + elif cfg.dataset == 'MPII': + result = dict(image_id=im_info['image_id'], scores=score_result[i].round(4).tolist(), + keypoints=kps_result[i].round(3).tolist()) + + dump_results.append(result) + + return dump_results + + +def test(test_model): + # annotation load + d = Dataset() + annot = d.load_annot(cfg.testset) # ground truth + gt_img_id = d.load_imgid(annot) # 得到图像id + + # human bbox load + if cfg.useGTbbox and cfg.testset in ['train', 'val']: + if cfg.testset == 'train': + dets = d.load_train_data(score=True) + else: + dets = d.load_val_data_with_annot() + dets.sort(key=lambda x: (x['image_id'])) + else: + with open(cfg.human_det_path, 'r') as f: + dets = json.load(f) + dets = [i for i in dets if i['image_id'] in gt_img_id] + dets = [i for i in dets if i['category_id'] == 1] + dets = [i for i in dets if i['score'] > 0] + dets.sort(key=lambda x: (x['image_id'], x['score']), reverse=True) + + img_id = [] + for i in dets: + img_id.append(i['image_id']) + imgname = d.imgid_to_imgname(annot, img_id, cfg.testset) # 得到图像名 + for i in range(len(dets)): + dets[i]['imgpath'] = imgname[i] # 把图像名作为一项加入dets + + # job assign (multi-gpu) + gpu_ids = "0" + from tfflat.mp_utils_my import MultiProc + img_start = 0 + ranges = [0] + + img_num = len(np.unique([i['image_id'] for i in dets])) + + images_per_gpu = int(img_num / len(gpu_ids.split(','))) + 1 + + for run_img in range(img_num): + img_end = img_start + 1 + while img_end < len(dets) and dets[img_end]['image_id'] == dets[img_start]['image_id']: + img_end += 1 + if (run_img + 1) % images_per_gpu == 0 or (run_img + 1) == img_num: + ranges.append(img_end) + img_start = img_end + + global func + + def func(gpu_id): + cfg.set_args() + tester = Tester(Model(), cfg) + tester.load_weights(test_model) + range = [ranges[gpu_id], ranges[gpu_id + 1]] + return test_net(tester, dets, range, gpu_id) + + MultiGPUFunc = MultiProc(len(gpu_ids.split(',')), func) + result = MultiGPUFunc.work() + + # evaluation + d.evaluation(result, annot, cfg.result_dir, cfg.testset) + + +if __name__ == '__main__': + (npu_sess, npu_shutdown) = init_resource() + test_epoch = 140 + test(test_epoch) + shutdown_resource(npu_sess, npu_shutdown) + close_session(npu_sess) -- Gitee From 5de694d4f0bbdacfed387f8713cfcf032cdf4fbb Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:18:34 +0000 Subject: [PATCH 29/59] add ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md. --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md new file mode 100644 index 000000000..e69de29bb -- Gitee From 936e6b9f89b1f86fc68d7ad0860a9d3149095b03 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:30:02 +0000 Subject: [PATCH 30/59] update ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md. --- .../src/README.md | 45 +++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md index e69de29bb..c43e39177 100644 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md @@ -0,0 +1,45 @@ +## SimpleHumanPose +### 基本信息 + +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：Human Pose Estimation** + +**版本（Version）：1.0** + +**修改时间（Modified） ：2022.05.23** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**应用级别（Categories）：Benchmark** + +**描述（Description）：基于TensorFlow框架的SimpleHumanPose网络离线推理代码** + +### 概述 + +SimpleHumanPose模型主要针对人体姿态估计和姿态追踪，提出一种简化的baseline。当前流行的人体姿态估计方法都过于复杂，各种模型在结构上看起来差异性很大，但是性能上又很接近，很难判断究竟是什么在起作用。相比于其他人体姿态估计模型，该模型并没有使用过多的技巧和理论依据。它以简单的技术为基础，通过全面的消融实验进行了验证，提出一个较为简化直观的模型，取得了良好的性能。 + +- 参考论文： + + https://arxiv.org/abs/1804.06208 + +- 参考实现： + + https://github.com/mks0601/TF-SimpleHumanPose + +### 文件结构 +```bash +├── README.md //代码说明文档 +├── LICENSE //许可证 +├── src +| ├── boot_pb_frozen.py //ModelArts启动pb固化代码 +| ├── boot_inference.py //ModelArts启动计算精度代码 +| ├── boot_test.py //ModelArts启动测试代码 +| ├── pip-requirements.txt //配置文件，用于指定依赖包的包名及版本号 +│ ├── data //包含数据加载代码 +│ ├── lib //包含2D多人姿态估计系统的核心代码 +│ ├── main //包含train/test网络、ckpt转pb、数据集转bin等代码 +``` + -- Gitee From f8f064dc090ba08661ee51d0a51a6d960e795a68 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:31:29 +0000 Subject: [PATCH 31/59] add ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md. --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md new file mode 100644 index 000000000..e69de29bb -- Gitee From a4b6e57140eaede6e5f1b24f9aa4d9abc4a4fe12 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:32:10 +0000 Subject: [PATCH 32/59] update README.md. --- .../SimpleHumanPose_ID0956_for_ACL/README.md | 45 +++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md index e69de29bb..c43e39177 100644 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md @@ -0,0 +1,45 @@ +## SimpleHumanPose +### 基本信息 + +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：Human Pose Estimation** + +**版本（Version）：1.0** + +**修改时间（Modified） ：2022.05.23** + +**框架（Framework）：TensorFlow 1.15.0** + +**处理器（Processor）：昇腾910** + +**应用级别（Categories）：Benchmark** + +**描述（Description）：基于TensorFlow框架的SimpleHumanPose网络离线推理代码** + +### 概述 + +SimpleHumanPose模型主要针对人体姿态估计和姿态追踪，提出一种简化的baseline。当前流行的人体姿态估计方法都过于复杂，各种模型在结构上看起来差异性很大，但是性能上又很接近，很难判断究竟是什么在起作用。相比于其他人体姿态估计模型，该模型并没有使用过多的技巧和理论依据。它以简单的技术为基础，通过全面的消融实验进行了验证，提出一个较为简化直观的模型，取得了良好的性能。 + +- 参考论文： + + https://arxiv.org/abs/1804.06208 + +- 参考实现： + + https://github.com/mks0601/TF-SimpleHumanPose + +### 文件结构 +```bash +├── README.md //代码说明文档 +├── LICENSE //许可证 +├── src +| ├── boot_pb_frozen.py //ModelArts启动pb固化代码 +| ├── boot_inference.py //ModelArts启动计算精度代码 +| ├── boot_test.py //ModelArts启动测试代码 +| ├── pip-requirements.txt //配置文件，用于指定依赖包的包名及版本号 +│ ├── data //包含数据加载代码 +│ ├── lib //包含2D多人姿态估计系统的核心代码 +│ ├── main //包含train/test网络、ckpt转pb、数据集转bin等代码 +``` + -- Gitee From 15c8f5e369bc6af2da47b3cd3c74ee1c5722d6d4 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:32:24 +0000 Subject: [PATCH 33/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/README.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../src/README.md | 45 ------------------- 1 file changed, 45 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md deleted file mode 100644 index c43e39177..000000000 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/README.md +++ /dev/null @@ -1,45 +0,0 @@ -## SimpleHumanPose -### 基本信息 - -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：Human Pose Estimation** - -**版本（Version）：1.0** - -**修改时间（Modified） ：2022.05.23** - -**框架（Framework）：TensorFlow 1.15.0** - -**处理器（Processor）：昇腾910** - -**应用级别（Categories）：Benchmark** - -**描述（Description）：基于TensorFlow框架的SimpleHumanPose网络离线推理代码** - -### 概述 - -SimpleHumanPose模型主要针对人体姿态估计和姿态追踪，提出一种简化的baseline。当前流行的人体姿态估计方法都过于复杂，各种模型在结构上看起来差异性很大，但是性能上又很接近，很难判断究竟是什么在起作用。相比于其他人体姿态估计模型，该模型并没有使用过多的技巧和理论依据。它以简单的技术为基础，通过全面的消融实验进行了验证，提出一个较为简化直观的模型，取得了良好的性能。 - -- 参考论文： - - https://arxiv.org/abs/1804.06208 - -- 参考实现： - - https://github.com/mks0601/TF-SimpleHumanPose - -### 文件结构 -```bash -├── README.md //代码说明文档 -├── LICENSE //许可证 -├── src -| ├── boot_pb_frozen.py //ModelArts启动pb固化代码 -| ├── boot_inference.py //ModelArts启动计算精度代码 -| ├── boot_test.py //ModelArts启动测试代码 -| ├── pip-requirements.txt //配置文件，用于指定依赖包的包名及版本号 -│ ├── data //包含数据加载代码 -│ ├── lib //包含2D多人姿态估计系统的核心代码 -│ ├── main //包含train/test网络、ckpt转pb、数据集转bin等代码 -``` - -- Gitee From 797458840ad75d6f5656b2f0ecd100a9b8206949 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:32:45 +0000 Subject: [PATCH 34/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/.keep?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From 0789a064f32527f82c47e54b3abf4d32323c7335 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:32:57 +0000 Subject: [PATCH 35/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?.keep?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From 4891d501857f3560e83dc78e88f7a8f02a28780e Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:34:04 +0000 Subject: [PATCH 36/59] update ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md. --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md index c43e39177..4655dbec5 100644 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md @@ -34,12 +34,12 @@ SimpleHumanPose模型主要针对人体姿态估计和姿态追踪，提出一 ├── README.md //代码说明文档 ├── LICENSE //许可证 ├── src +│ ├── data //包含数据加载代码 +│ ├── lib //包含2D多人姿态估计系统的核心代码 +│ ├── main //包含train/test网络、ckpt转pb、数据集转bin等代码 | ├── boot_pb_frozen.py //ModelArts启动pb固化代码 | ├── boot_inference.py //ModelArts启动计算精度代码 | ├── boot_test.py //ModelArts启动测试代码 -| ├── pip-requirements.txt //配置文件，用于指定依赖包的包名及版本号 -│ ├── data //包含数据加载代码 -│ ├── lib //包含2D多人姿态估计系统的核心代码 -│ ├── main //包含train/test网络、ckpt转pb、数据集转bin等代码 +| ├── pip-requirements.txt //配置文件，用于指定依赖包的包名及版本号 ``` -- Gitee From 57bfea5704d995ead1875c1ca588a6d8c89f6ebc Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Mon, 23 May 2022 14:35:18 +0000 Subject: [PATCH 37/59] update ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md. --- .../cv/SimpleHumanPose_ID0956_for_ACL/README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md index 4655dbec5..0ba36ccdb 100644 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md @@ -34,12 +34,12 @@ SimpleHumanPose模型主要针对人体姿态估计和姿态追踪，提出一 ├── README.md //代码说明文档 ├── LICENSE //许可证 ├── src -│ ├── data //包含数据加载代码 -│ ├── lib //包含2D多人姿态估计系统的核心代码 -│ ├── main //包含train/test网络、ckpt转pb、数据集转bin等代码 -| ├── boot_pb_frozen.py //ModelArts启动pb固化代码 -| ├── boot_inference.py //ModelArts启动计算精度代码 -| ├── boot_test.py //ModelArts启动测试代码 -| ├── pip-requirements.txt //配置文件，用于指定依赖包的包名及版本号 +│ ├── data/ //包含数据加载代码 +│ ├── lib/ //包含2D多人姿态估计系统的核心代码 +│ ├── main/ //包含train/test网络、ckpt转pb、数据集转bin等代码 +| ├── boot_pb_frozen.py //ModelArts启动pb固化代码 +| ├── boot_inference.py //ModelArts启动计算精度代码 +| ├── boot_test.py //ModelArts启动测试代码 +| ├── pip-requirements.txt //配置文件，用于指定依赖包的包名及版本号 ``` -- Gitee From eab0daacc5b2bf94ed1e4ac554c612a55224a676 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:18:50 +0000 Subject: [PATCH 38/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20COCO?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 77621a196bd01af6da5d357305c334ad88ec9b97 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:19:08 +0000 Subject: [PATCH 39/59] first commit --- .../src/data/COCO/dataset.py | 197 ++++++++++++++++++ 1 file changed, 197 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py new file mode 100644 index 000000000..4b78780ca --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py @@ -0,0 +1,197 @@ +#!/usr/bin/python3 +# coding=utf-8 + +from npu_bridge.npu_init import * +import os +import os.path as osp +import numpy as np +import cv2 +import json +import pickle +import matplotlib.pyplot as plt +import tensorflow as tf + +import sys + +current_path = os.path.dirname(os.path.realpath(__file__)) # 对应于COCO +project_root = os.path.dirname(os.path.dirname(current_path)) # 对应于TF-Simple +main_path = os.path.join(project_root, "main", "train.py") +print("main_path:", main_path) + + +cur_dir = os.path.dirname(__file__) +# print("========>>>>>>cur_dir:{}".format(cur_dir)) +sys.path.insert(0, osp.join(cur_dir, 'PythonAPI')) +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + + +class Dataset(object): + dataset_name = 'COCO' + num_kps = 17 + kps_names = ['nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear', 'l_shoulder', + 'r_shoulder', 'l_elbow', 'r_elbow', 'l_wrist', 'r_wrist', + 'l_hip', 'r_hip', 'l_knee', 'r_knee', 'l_ankle', 'r_ankle'] + kps_symmetry = [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12), (13, 14), (15, 16)] + kps_lines = [(1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13), + (13, 15), (5, 6), (11, 12)] + + # human_det_path = osp.join('..', 'data', dataset_name, 'dets', 'human_detection.json') # human detection result + # img_path = osp.join('..', 'data', dataset_name, 'images') + # train_annot_path = osp.join('..', 'data', dataset_name, 'annotations', 'person_keypoints_train2017.json') + # train_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_train2017.json') + # val_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_val2017.json') + # test_annot_path = osp.join(cur_dir, 'data', dataset_name, 'annotations', 'image_info_test-dev2017.json') + + # tf_flags = tf.app.flags.FLAGS + data_path = "cache/dataset" + img_path = osp.join(data_path, 'images') + human_det_path = osp.join(data_path, 'dets', 'human_detection.json') + train_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_train2017.json') + val_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_val2017.json') + test_annot_path = osp.join(data_path, 'annotations', 'image_info_test-dev2017.json') + + def load_train_data(self, score=False): + coco = COCO(self.train_annot_path) + train_data = [] + for aid in coco.anns.keys(): + ann = coco.anns[aid] + imgname = 'train2017/' + coco.imgs[ann['image_id']]['file_name'] + joints = ann['keypoints'] + + if (ann['image_id'] not in coco.imgs) or ann['iscrowd'] or (np.sum(joints[2::3]) == 0) or ( + ann['num_keypoints'] == 0): + continue + + # sanitize bboxes + x, y, w, h = ann['bbox'] + img = coco.loadImgs(ann['image_id'])[0] + width, height = img['width'], img['height'] + x1 = np.max((0, x)) + y1 = np.max((0, y)) + x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) + y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) + if ann['area'] > 0 and x2 >= x1 and y2 >= y1: + bbox = [x1, y1, x2 - x1, y2 - y1] + else: + continue + + if score: + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) + else: + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints) + + train_data.append(data) + + return train_data + + def load_val_data_with_annot(self): + coco = COCO(self.val_annot_path) + val_data = [] + for aid in coco.anns.keys(): + ann = coco.anns[aid] + if ann['image_id'] not in coco.imgs: + continue + imgname = 'val2017/' + coco.imgs[ann['image_id']]['file_name'] + bbox = ann['bbox'] + joints = ann['keypoints'] + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) + val_data.append(data) + + return val_data + + def load_annot(self, db_set): + if db_set == 'train': + coco = COCO(self.train_annot_path) + elif db_set == 'val': + coco = COCO(self.val_annot_path) + elif db_set == 'test': + coco = COCO(self.test_annot_path) + else: + print('Unknown db_set') + assert 0 + + return coco + + def load_imgid(self, annot): + return annot.imgs + + def imgid_to_imgname(self, annot, imgid, db_set): + imgs = annot.loadImgs(imgid) + imgname = [db_set + '2017/' + i['file_name'] for i in imgs] + return imgname + + def evaluation(self, result, gt, result_dir, db_set): + result_path = osp.join(result_dir, 'result.json') + with open(result_path, 'w') as f: + json.dump(result, f) # 把result中的东西写到result.json里面 + + result = gt.loadRes(result_path) + cocoEval = COCOeval(gt, result, iouType='keypoints') + + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + result_path = osp.join(result_dir, 'result.pkl') + with open(result_path, 'wb') as f: + pickle.dump(cocoEval, f, 2) + print("Saved result file to " + result_path) + + def vis_keypoints(self, img, kps, kp_thresh=0.4, alpha=1): + + # Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv. + cmap = plt.get_cmap('rainbow') + colors = [cmap(i) for i in np.linspace(0, 1, len(self.kps_lines) + 2)] + colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors] + + # Perform the drawing on a copy of the image, to allow for blending. + kp_mask = np.copy(img) + + # Draw mid shoulder / mid hip first for better visualization. + mid_shoulder = ( + kps[:2, 5] + + kps[:2, 6]) / 2.0 + sc_mid_shoulder = np.minimum( + kps[2, 5], + kps[2, 6]) + mid_hip = ( + kps[:2, 11] + + kps[:2, 12]) / 2.0 + sc_mid_hip = np.minimum( + kps[2, 11], + kps[2, 12]) + nose_idx = 0 + if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh: + cv2.line( + kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(kps[:2, nose_idx].astype(np.int32)), + color=colors[len(self.kps_lines)], thickness=2, lineType=cv2.LINE_AA) + if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: + cv2.line( + kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(mid_hip.astype(np.int32)), + color=colors[len(self.kps_lines) + 1], thickness=2, lineType=cv2.LINE_AA) + + # Draw the keypoints. + for l in range(len(self.kps_lines)): + i1 = self.kps_lines[l][0] + i2 = self.kps_lines[l][1] + p1 = kps[0, i1].astype(np.int32), kps[1, i1].astype(np.int32) + p2 = kps[0, i2].astype(np.int32), kps[1, i2].astype(np.int32) + if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: + cv2.line( + kp_mask, p1, p2, + color=colors[l], thickness=2, lineType=cv2.LINE_AA) + if kps[2, i1] > kp_thresh: + cv2.circle( + kp_mask, p1, + radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + if kps[2, i2] > kp_thresh: + cv2.circle( + kp_mask, p2, + radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + + # Blend the keypoints. + return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) + + +dbcfg = Dataset() -- Gitee From 0f3d78db1a94afb77ac571ca25da82f5df8e88be Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:19:15 +0000 Subject: [PATCH 40/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/data/COCO/.keep?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From c4335668d4202d8ae33347f63f0a8179bc7c4ea2 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:19:29 +0000 Subject: [PATCH 41/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/data/.keep?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From 1f8200c0e659e80f7f11cc3081ada09be9aae783 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:19:37 +0000 Subject: [PATCH 42/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/data/dataset.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../src/data/dataset.py | 197 ------------------ 1 file changed, 197 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/dataset.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/dataset.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/dataset.py deleted file mode 100644 index 4b78780ca..000000000 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/dataset.py +++ /dev/null @@ -1,197 +0,0 @@ -#!/usr/bin/python3 -# coding=utf-8 - -from npu_bridge.npu_init import * -import os -import os.path as osp -import numpy as np -import cv2 -import json -import pickle -import matplotlib.pyplot as plt -import tensorflow as tf - -import sys - -current_path = os.path.dirname(os.path.realpath(__file__)) # 对应于COCO -project_root = os.path.dirname(os.path.dirname(current_path)) # 对应于TF-Simple -main_path = os.path.join(project_root, "main", "train.py") -print("main_path:", main_path) - - -cur_dir = os.path.dirname(__file__) -# print("========>>>>>>cur_dir:{}".format(cur_dir)) -sys.path.insert(0, osp.join(cur_dir, 'PythonAPI')) -from pycocotools.coco import COCO -from pycocotools.cocoeval import COCOeval - - -class Dataset(object): - dataset_name = 'COCO' - num_kps = 17 - kps_names = ['nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear', 'l_shoulder', - 'r_shoulder', 'l_elbow', 'r_elbow', 'l_wrist', 'r_wrist', - 'l_hip', 'r_hip', 'l_knee', 'r_knee', 'l_ankle', 'r_ankle'] - kps_symmetry = [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12), (13, 14), (15, 16)] - kps_lines = [(1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13), - (13, 15), (5, 6), (11, 12)] - - # human_det_path = osp.join('..', 'data', dataset_name, 'dets', 'human_detection.json') # human detection result - # img_path = osp.join('..', 'data', dataset_name, 'images') - # train_annot_path = osp.join('..', 'data', dataset_name, 'annotations', 'person_keypoints_train2017.json') - # train_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_train2017.json') - # val_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_val2017.json') - # test_annot_path = osp.join(cur_dir, 'data', dataset_name, 'annotations', 'image_info_test-dev2017.json') - - # tf_flags = tf.app.flags.FLAGS - data_path = "cache/dataset" - img_path = osp.join(data_path, 'images') - human_det_path = osp.join(data_path, 'dets', 'human_detection.json') - train_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_train2017.json') - val_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_val2017.json') - test_annot_path = osp.join(data_path, 'annotations', 'image_info_test-dev2017.json') - - def load_train_data(self, score=False): - coco = COCO(self.train_annot_path) - train_data = [] - for aid in coco.anns.keys(): - ann = coco.anns[aid] - imgname = 'train2017/' + coco.imgs[ann['image_id']]['file_name'] - joints = ann['keypoints'] - - if (ann['image_id'] not in coco.imgs) or ann['iscrowd'] or (np.sum(joints[2::3]) == 0) or ( - ann['num_keypoints'] == 0): - continue - - # sanitize bboxes - x, y, w, h = ann['bbox'] - img = coco.loadImgs(ann['image_id'])[0] - width, height = img['width'], img['height'] - x1 = np.max((0, x)) - y1 = np.max((0, y)) - x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) - y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) - if ann['area'] > 0 and x2 >= x1 and y2 >= y1: - bbox = [x1, y1, x2 - x1, y2 - y1] - else: - continue - - if score: - data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) - else: - data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints) - - train_data.append(data) - - return train_data - - def load_val_data_with_annot(self): - coco = COCO(self.val_annot_path) - val_data = [] - for aid in coco.anns.keys(): - ann = coco.anns[aid] - if ann['image_id'] not in coco.imgs: - continue - imgname = 'val2017/' + coco.imgs[ann['image_id']]['file_name'] - bbox = ann['bbox'] - joints = ann['keypoints'] - data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) - val_data.append(data) - - return val_data - - def load_annot(self, db_set): - if db_set == 'train': - coco = COCO(self.train_annot_path) - elif db_set == 'val': - coco = COCO(self.val_annot_path) - elif db_set == 'test': - coco = COCO(self.test_annot_path) - else: - print('Unknown db_set') - assert 0 - - return coco - - def load_imgid(self, annot): - return annot.imgs - - def imgid_to_imgname(self, annot, imgid, db_set): - imgs = annot.loadImgs(imgid) - imgname = [db_set + '2017/' + i['file_name'] for i in imgs] - return imgname - - def evaluation(self, result, gt, result_dir, db_set): - result_path = osp.join(result_dir, 'result.json') - with open(result_path, 'w') as f: - json.dump(result, f) # 把result中的东西写到result.json里面 - - result = gt.loadRes(result_path) - cocoEval = COCOeval(gt, result, iouType='keypoints') - - cocoEval.evaluate() - cocoEval.accumulate() - cocoEval.summarize() - - result_path = osp.join(result_dir, 'result.pkl') - with open(result_path, 'wb') as f: - pickle.dump(cocoEval, f, 2) - print("Saved result file to " + result_path) - - def vis_keypoints(self, img, kps, kp_thresh=0.4, alpha=1): - - # Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv. - cmap = plt.get_cmap('rainbow') - colors = [cmap(i) for i in np.linspace(0, 1, len(self.kps_lines) + 2)] - colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors] - - # Perform the drawing on a copy of the image, to allow for blending. - kp_mask = np.copy(img) - - # Draw mid shoulder / mid hip first for better visualization. - mid_shoulder = ( - kps[:2, 5] + - kps[:2, 6]) / 2.0 - sc_mid_shoulder = np.minimum( - kps[2, 5], - kps[2, 6]) - mid_hip = ( - kps[:2, 11] + - kps[:2, 12]) / 2.0 - sc_mid_hip = np.minimum( - kps[2, 11], - kps[2, 12]) - nose_idx = 0 - if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh: - cv2.line( - kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(kps[:2, nose_idx].astype(np.int32)), - color=colors[len(self.kps_lines)], thickness=2, lineType=cv2.LINE_AA) - if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: - cv2.line( - kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(mid_hip.astype(np.int32)), - color=colors[len(self.kps_lines) + 1], thickness=2, lineType=cv2.LINE_AA) - - # Draw the keypoints. - for l in range(len(self.kps_lines)): - i1 = self.kps_lines[l][0] - i2 = self.kps_lines[l][1] - p1 = kps[0, i1].astype(np.int32), kps[1, i1].astype(np.int32) - p2 = kps[0, i2].astype(np.int32), kps[1, i2].astype(np.int32) - if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: - cv2.line( - kp_mask, p1, p2, - color=colors[l], thickness=2, lineType=cv2.LINE_AA) - if kps[2, i1] > kp_thresh: - cv2.circle( - kp_mask, p1, - radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) - if kps[2, i2] > kp_thresh: - cv2.circle( - kp_mask, p2, - radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) - - # Blend the keypoints. - return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) - - -dbcfg = Dataset() -- Gitee From 8f19e4ee8c0f962040035c03eb8cf776e864c9e7 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:19:58 +0000 Subject: [PATCH 43/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/data/COCO?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../src/data/COCO/dataset.py | 197 ------------------ 1 file changed, 197 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py deleted file mode 100644 index 4b78780ca..000000000 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py +++ /dev/null @@ -1,197 +0,0 @@ -#!/usr/bin/python3 -# coding=utf-8 - -from npu_bridge.npu_init import * -import os -import os.path as osp -import numpy as np -import cv2 -import json -import pickle -import matplotlib.pyplot as plt -import tensorflow as tf - -import sys - -current_path = os.path.dirname(os.path.realpath(__file__)) # 对应于COCO -project_root = os.path.dirname(os.path.dirname(current_path)) # 对应于TF-Simple -main_path = os.path.join(project_root, "main", "train.py") -print("main_path:", main_path) - - -cur_dir = os.path.dirname(__file__) -# print("========>>>>>>cur_dir:{}".format(cur_dir)) -sys.path.insert(0, osp.join(cur_dir, 'PythonAPI')) -from pycocotools.coco import COCO -from pycocotools.cocoeval import COCOeval - - -class Dataset(object): - dataset_name = 'COCO' - num_kps = 17 - kps_names = ['nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear', 'l_shoulder', - 'r_shoulder', 'l_elbow', 'r_elbow', 'l_wrist', 'r_wrist', - 'l_hip', 'r_hip', 'l_knee', 'r_knee', 'l_ankle', 'r_ankle'] - kps_symmetry = [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12), (13, 14), (15, 16)] - kps_lines = [(1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13), - (13, 15), (5, 6), (11, 12)] - - # human_det_path = osp.join('..', 'data', dataset_name, 'dets', 'human_detection.json') # human detection result - # img_path = osp.join('..', 'data', dataset_name, 'images') - # train_annot_path = osp.join('..', 'data', dataset_name, 'annotations', 'person_keypoints_train2017.json') - # train_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_train2017.json') - # val_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_val2017.json') - # test_annot_path = osp.join(cur_dir, 'data', dataset_name, 'annotations', 'image_info_test-dev2017.json') - - # tf_flags = tf.app.flags.FLAGS - data_path = "cache/dataset" - img_path = osp.join(data_path, 'images') - human_det_path = osp.join(data_path, 'dets', 'human_detection.json') - train_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_train2017.json') - val_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_val2017.json') - test_annot_path = osp.join(data_path, 'annotations', 'image_info_test-dev2017.json') - - def load_train_data(self, score=False): - coco = COCO(self.train_annot_path) - train_data = [] - for aid in coco.anns.keys(): - ann = coco.anns[aid] - imgname = 'train2017/' + coco.imgs[ann['image_id']]['file_name'] - joints = ann['keypoints'] - - if (ann['image_id'] not in coco.imgs) or ann['iscrowd'] or (np.sum(joints[2::3]) == 0) or ( - ann['num_keypoints'] == 0): - continue - - # sanitize bboxes - x, y, w, h = ann['bbox'] - img = coco.loadImgs(ann['image_id'])[0] - width, height = img['width'], img['height'] - x1 = np.max((0, x)) - y1 = np.max((0, y)) - x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) - y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) - if ann['area'] > 0 and x2 >= x1 and y2 >= y1: - bbox = [x1, y1, x2 - x1, y2 - y1] - else: - continue - - if score: - data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) - else: - data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints) - - train_data.append(data) - - return train_data - - def load_val_data_with_annot(self): - coco = COCO(self.val_annot_path) - val_data = [] - for aid in coco.anns.keys(): - ann = coco.anns[aid] - if ann['image_id'] not in coco.imgs: - continue - imgname = 'val2017/' + coco.imgs[ann['image_id']]['file_name'] - bbox = ann['bbox'] - joints = ann['keypoints'] - data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) - val_data.append(data) - - return val_data - - def load_annot(self, db_set): - if db_set == 'train': - coco = COCO(self.train_annot_path) - elif db_set == 'val': - coco = COCO(self.val_annot_path) - elif db_set == 'test': - coco = COCO(self.test_annot_path) - else: - print('Unknown db_set') - assert 0 - - return coco - - def load_imgid(self, annot): - return annot.imgs - - def imgid_to_imgname(self, annot, imgid, db_set): - imgs = annot.loadImgs(imgid) - imgname = [db_set + '2017/' + i['file_name'] for i in imgs] - return imgname - - def evaluation(self, result, gt, result_dir, db_set): - result_path = osp.join(result_dir, 'result.json') - with open(result_path, 'w') as f: - json.dump(result, f) # 把result中的东西写到result.json里面 - - result = gt.loadRes(result_path) - cocoEval = COCOeval(gt, result, iouType='keypoints') - - cocoEval.evaluate() - cocoEval.accumulate() - cocoEval.summarize() - - result_path = osp.join(result_dir, 'result.pkl') - with open(result_path, 'wb') as f: - pickle.dump(cocoEval, f, 2) - print("Saved result file to " + result_path) - - def vis_keypoints(self, img, kps, kp_thresh=0.4, alpha=1): - - # Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv. - cmap = plt.get_cmap('rainbow') - colors = [cmap(i) for i in np.linspace(0, 1, len(self.kps_lines) + 2)] - colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors] - - # Perform the drawing on a copy of the image, to allow for blending. - kp_mask = np.copy(img) - - # Draw mid shoulder / mid hip first for better visualization. - mid_shoulder = ( - kps[:2, 5] + - kps[:2, 6]) / 2.0 - sc_mid_shoulder = np.minimum( - kps[2, 5], - kps[2, 6]) - mid_hip = ( - kps[:2, 11] + - kps[:2, 12]) / 2.0 - sc_mid_hip = np.minimum( - kps[2, 11], - kps[2, 12]) - nose_idx = 0 - if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh: - cv2.line( - kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(kps[:2, nose_idx].astype(np.int32)), - color=colors[len(self.kps_lines)], thickness=2, lineType=cv2.LINE_AA) - if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: - cv2.line( - kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(mid_hip.astype(np.int32)), - color=colors[len(self.kps_lines) + 1], thickness=2, lineType=cv2.LINE_AA) - - # Draw the keypoints. - for l in range(len(self.kps_lines)): - i1 = self.kps_lines[l][0] - i2 = self.kps_lines[l][1] - p1 = kps[0, i1].astype(np.int32), kps[1, i1].astype(np.int32) - p2 = kps[0, i2].astype(np.int32), kps[1, i2].astype(np.int32) - if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: - cv2.line( - kp_mask, p1, p2, - color=colors[l], thickness=2, lineType=cv2.LINE_AA) - if kps[2, i1] > kp_thresh: - cv2.circle( - kp_mask, p1, - radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) - if kps[2, i2] > kp_thresh: - cv2.circle( - kp_mask, p2, - radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) - - # Blend the keypoints. - return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) - - -dbcfg = Dataset() -- Gitee From 004e05eab4e46c4163b0756c2938b37db4e7b782 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:20:06 +0000 Subject: [PATCH 44/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20data?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From c17d2c396ea696f297aefdfbc2053cc28a47c751 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:20:17 +0000 Subject: [PATCH 45/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20COCO?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 7ea87ca551d53359d721c765304fc25bfbd2baca Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:20:32 +0000 Subject: [PATCH 46/59] first commit --- .../src/data/COCO/dataset.py | 197 ++++++++++++++++++ 1 file changed, 197 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py new file mode 100644 index 000000000..4b78780ca --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py @@ -0,0 +1,197 @@ +#!/usr/bin/python3 +# coding=utf-8 + +from npu_bridge.npu_init import * +import os +import os.path as osp +import numpy as np +import cv2 +import json +import pickle +import matplotlib.pyplot as plt +import tensorflow as tf + +import sys + +current_path = os.path.dirname(os.path.realpath(__file__)) # 对应于COCO +project_root = os.path.dirname(os.path.dirname(current_path)) # 对应于TF-Simple +main_path = os.path.join(project_root, "main", "train.py") +print("main_path:", main_path) + + +cur_dir = os.path.dirname(__file__) +# print("========>>>>>>cur_dir:{}".format(cur_dir)) +sys.path.insert(0, osp.join(cur_dir, 'PythonAPI')) +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + + +class Dataset(object): + dataset_name = 'COCO' + num_kps = 17 + kps_names = ['nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear', 'l_shoulder', + 'r_shoulder', 'l_elbow', 'r_elbow', 'l_wrist', 'r_wrist', + 'l_hip', 'r_hip', 'l_knee', 'r_knee', 'l_ankle', 'r_ankle'] + kps_symmetry = [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12), (13, 14), (15, 16)] + kps_lines = [(1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13), + (13, 15), (5, 6), (11, 12)] + + # human_det_path = osp.join('..', 'data', dataset_name, 'dets', 'human_detection.json') # human detection result + # img_path = osp.join('..', 'data', dataset_name, 'images') + # train_annot_path = osp.join('..', 'data', dataset_name, 'annotations', 'person_keypoints_train2017.json') + # train_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_train2017.json') + # val_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_val2017.json') + # test_annot_path = osp.join(cur_dir, 'data', dataset_name, 'annotations', 'image_info_test-dev2017.json') + + # tf_flags = tf.app.flags.FLAGS + data_path = "cache/dataset" + img_path = osp.join(data_path, 'images') + human_det_path = osp.join(data_path, 'dets', 'human_detection.json') + train_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_train2017.json') + val_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_val2017.json') + test_annot_path = osp.join(data_path, 'annotations', 'image_info_test-dev2017.json') + + def load_train_data(self, score=False): + coco = COCO(self.train_annot_path) + train_data = [] + for aid in coco.anns.keys(): + ann = coco.anns[aid] + imgname = 'train2017/' + coco.imgs[ann['image_id']]['file_name'] + joints = ann['keypoints'] + + if (ann['image_id'] not in coco.imgs) or ann['iscrowd'] or (np.sum(joints[2::3]) == 0) or ( + ann['num_keypoints'] == 0): + continue + + # sanitize bboxes + x, y, w, h = ann['bbox'] + img = coco.loadImgs(ann['image_id'])[0] + width, height = img['width'], img['height'] + x1 = np.max((0, x)) + y1 = np.max((0, y)) + x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) + y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) + if ann['area'] > 0 and x2 >= x1 and y2 >= y1: + bbox = [x1, y1, x2 - x1, y2 - y1] + else: + continue + + if score: + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) + else: + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints) + + train_data.append(data) + + return train_data + + def load_val_data_with_annot(self): + coco = COCO(self.val_annot_path) + val_data = [] + for aid in coco.anns.keys(): + ann = coco.anns[aid] + if ann['image_id'] not in coco.imgs: + continue + imgname = 'val2017/' + coco.imgs[ann['image_id']]['file_name'] + bbox = ann['bbox'] + joints = ann['keypoints'] + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) + val_data.append(data) + + return val_data + + def load_annot(self, db_set): + if db_set == 'train': + coco = COCO(self.train_annot_path) + elif db_set == 'val': + coco = COCO(self.val_annot_path) + elif db_set == 'test': + coco = COCO(self.test_annot_path) + else: + print('Unknown db_set') + assert 0 + + return coco + + def load_imgid(self, annot): + return annot.imgs + + def imgid_to_imgname(self, annot, imgid, db_set): + imgs = annot.loadImgs(imgid) + imgname = [db_set + '2017/' + i['file_name'] for i in imgs] + return imgname + + def evaluation(self, result, gt, result_dir, db_set): + result_path = osp.join(result_dir, 'result.json') + with open(result_path, 'w') as f: + json.dump(result, f) # 把result中的东西写到result.json里面 + + result = gt.loadRes(result_path) + cocoEval = COCOeval(gt, result, iouType='keypoints') + + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + result_path = osp.join(result_dir, 'result.pkl') + with open(result_path, 'wb') as f: + pickle.dump(cocoEval, f, 2) + print("Saved result file to " + result_path) + + def vis_keypoints(self, img, kps, kp_thresh=0.4, alpha=1): + + # Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv. + cmap = plt.get_cmap('rainbow') + colors = [cmap(i) for i in np.linspace(0, 1, len(self.kps_lines) + 2)] + colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors] + + # Perform the drawing on a copy of the image, to allow for blending. + kp_mask = np.copy(img) + + # Draw mid shoulder / mid hip first for better visualization. + mid_shoulder = ( + kps[:2, 5] + + kps[:2, 6]) / 2.0 + sc_mid_shoulder = np.minimum( + kps[2, 5], + kps[2, 6]) + mid_hip = ( + kps[:2, 11] + + kps[:2, 12]) / 2.0 + sc_mid_hip = np.minimum( + kps[2, 11], + kps[2, 12]) + nose_idx = 0 + if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh: + cv2.line( + kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(kps[:2, nose_idx].astype(np.int32)), + color=colors[len(self.kps_lines)], thickness=2, lineType=cv2.LINE_AA) + if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: + cv2.line( + kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(mid_hip.astype(np.int32)), + color=colors[len(self.kps_lines) + 1], thickness=2, lineType=cv2.LINE_AA) + + # Draw the keypoints. + for l in range(len(self.kps_lines)): + i1 = self.kps_lines[l][0] + i2 = self.kps_lines[l][1] + p1 = kps[0, i1].astype(np.int32), kps[1, i1].astype(np.int32) + p2 = kps[0, i2].astype(np.int32), kps[1, i2].astype(np.int32) + if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: + cv2.line( + kp_mask, p1, p2, + color=colors[l], thickness=2, lineType=cv2.LINE_AA) + if kps[2, i1] > kp_thresh: + cv2.circle( + kp_mask, p1, + radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + if kps[2, i2] > kp_thresh: + cv2.circle( + kp_mask, p2, + radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + + # Blend the keypoints. + return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) + + +dbcfg = Dataset() -- Gitee From a6359636429e9d67bf1b53d951541f2a050ddb34 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:20:45 +0000 Subject: [PATCH 47/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/data/COCO/.keep?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From c5445d233260bfc7d4bb981f3c00e6b987f7b09f Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:20:52 +0000 Subject: [PATCH 48/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/data/.keep?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From 5578ff559fa41b228b8ea3dd5f7678378326dd9d Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:21:20 +0000 Subject: [PATCH 49/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/data/COCO?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../src/data/COCO/dataset.py | 197 ------------------ 1 file changed, 197 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py deleted file mode 100644 index 4b78780ca..000000000 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py +++ /dev/null @@ -1,197 +0,0 @@ -#!/usr/bin/python3 -# coding=utf-8 - -from npu_bridge.npu_init import * -import os -import os.path as osp -import numpy as np -import cv2 -import json -import pickle -import matplotlib.pyplot as plt -import tensorflow as tf - -import sys - -current_path = os.path.dirname(os.path.realpath(__file__)) # 对应于COCO -project_root = os.path.dirname(os.path.dirname(current_path)) # 对应于TF-Simple -main_path = os.path.join(project_root, "main", "train.py") -print("main_path:", main_path) - - -cur_dir = os.path.dirname(__file__) -# print("========>>>>>>cur_dir:{}".format(cur_dir)) -sys.path.insert(0, osp.join(cur_dir, 'PythonAPI')) -from pycocotools.coco import COCO -from pycocotools.cocoeval import COCOeval - - -class Dataset(object): - dataset_name = 'COCO' - num_kps = 17 - kps_names = ['nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear', 'l_shoulder', - 'r_shoulder', 'l_elbow', 'r_elbow', 'l_wrist', 'r_wrist', - 'l_hip', 'r_hip', 'l_knee', 'r_knee', 'l_ankle', 'r_ankle'] - kps_symmetry = [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12), (13, 14), (15, 16)] - kps_lines = [(1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13), - (13, 15), (5, 6), (11, 12)] - - # human_det_path = osp.join('..', 'data', dataset_name, 'dets', 'human_detection.json') # human detection result - # img_path = osp.join('..', 'data', dataset_name, 'images') - # train_annot_path = osp.join('..', 'data', dataset_name, 'annotations', 'person_keypoints_train2017.json') - # train_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_train2017.json') - # val_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_val2017.json') - # test_annot_path = osp.join(cur_dir, 'data', dataset_name, 'annotations', 'image_info_test-dev2017.json') - - # tf_flags = tf.app.flags.FLAGS - data_path = "cache/dataset" - img_path = osp.join(data_path, 'images') - human_det_path = osp.join(data_path, 'dets', 'human_detection.json') - train_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_train2017.json') - val_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_val2017.json') - test_annot_path = osp.join(data_path, 'annotations', 'image_info_test-dev2017.json') - - def load_train_data(self, score=False): - coco = COCO(self.train_annot_path) - train_data = [] - for aid in coco.anns.keys(): - ann = coco.anns[aid] - imgname = 'train2017/' + coco.imgs[ann['image_id']]['file_name'] - joints = ann['keypoints'] - - if (ann['image_id'] not in coco.imgs) or ann['iscrowd'] or (np.sum(joints[2::3]) == 0) or ( - ann['num_keypoints'] == 0): - continue - - # sanitize bboxes - x, y, w, h = ann['bbox'] - img = coco.loadImgs(ann['image_id'])[0] - width, height = img['width'], img['height'] - x1 = np.max((0, x)) - y1 = np.max((0, y)) - x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) - y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) - if ann['area'] > 0 and x2 >= x1 and y2 >= y1: - bbox = [x1, y1, x2 - x1, y2 - y1] - else: - continue - - if score: - data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) - else: - data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints) - - train_data.append(data) - - return train_data - - def load_val_data_with_annot(self): - coco = COCO(self.val_annot_path) - val_data = [] - for aid in coco.anns.keys(): - ann = coco.anns[aid] - if ann['image_id'] not in coco.imgs: - continue - imgname = 'val2017/' + coco.imgs[ann['image_id']]['file_name'] - bbox = ann['bbox'] - joints = ann['keypoints'] - data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) - val_data.append(data) - - return val_data - - def load_annot(self, db_set): - if db_set == 'train': - coco = COCO(self.train_annot_path) - elif db_set == 'val': - coco = COCO(self.val_annot_path) - elif db_set == 'test': - coco = COCO(self.test_annot_path) - else: - print('Unknown db_set') - assert 0 - - return coco - - def load_imgid(self, annot): - return annot.imgs - - def imgid_to_imgname(self, annot, imgid, db_set): - imgs = annot.loadImgs(imgid) - imgname = [db_set + '2017/' + i['file_name'] for i in imgs] - return imgname - - def evaluation(self, result, gt, result_dir, db_set): - result_path = osp.join(result_dir, 'result.json') - with open(result_path, 'w') as f: - json.dump(result, f) # 把result中的东西写到result.json里面 - - result = gt.loadRes(result_path) - cocoEval = COCOeval(gt, result, iouType='keypoints') - - cocoEval.evaluate() - cocoEval.accumulate() - cocoEval.summarize() - - result_path = osp.join(result_dir, 'result.pkl') - with open(result_path, 'wb') as f: - pickle.dump(cocoEval, f, 2) - print("Saved result file to " + result_path) - - def vis_keypoints(self, img, kps, kp_thresh=0.4, alpha=1): - - # Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv. - cmap = plt.get_cmap('rainbow') - colors = [cmap(i) for i in np.linspace(0, 1, len(self.kps_lines) + 2)] - colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors] - - # Perform the drawing on a copy of the image, to allow for blending. - kp_mask = np.copy(img) - - # Draw mid shoulder / mid hip first for better visualization. - mid_shoulder = ( - kps[:2, 5] + - kps[:2, 6]) / 2.0 - sc_mid_shoulder = np.minimum( - kps[2, 5], - kps[2, 6]) - mid_hip = ( - kps[:2, 11] + - kps[:2, 12]) / 2.0 - sc_mid_hip = np.minimum( - kps[2, 11], - kps[2, 12]) - nose_idx = 0 - if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh: - cv2.line( - kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(kps[:2, nose_idx].astype(np.int32)), - color=colors[len(self.kps_lines)], thickness=2, lineType=cv2.LINE_AA) - if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: - cv2.line( - kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(mid_hip.astype(np.int32)), - color=colors[len(self.kps_lines) + 1], thickness=2, lineType=cv2.LINE_AA) - - # Draw the keypoints. - for l in range(len(self.kps_lines)): - i1 = self.kps_lines[l][0] - i2 = self.kps_lines[l][1] - p1 = kps[0, i1].astype(np.int32), kps[1, i1].astype(np.int32) - p2 = kps[0, i2].astype(np.int32), kps[1, i2].astype(np.int32) - if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: - cv2.line( - kp_mask, p1, p2, - color=colors[l], thickness=2, lineType=cv2.LINE_AA) - if kps[2, i1] > kp_thresh: - cv2.circle( - kp_mask, p1, - radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) - if kps[2, i2] > kp_thresh: - cv2.circle( - kp_mask, p2, - radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) - - # Blend the keypoints. - return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) - - -dbcfg = Dataset() -- Gitee From 76ba4799abc3a163aafe3d459359da27d5eb0df1 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:21:26 +0000 Subject: [PATCH 50/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20data?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From dcd2a69184db049b6d9db672b485a66567e7d8ac Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:21:36 +0000 Subject: [PATCH 51/59] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20COCO?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From e0886a0dfec721b0ff37f2b1dd9cc8decea666ca Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:21:48 +0000 Subject: [PATCH 52/59] first commit --- .../src/data/COCO/dataset.py | 197 ++++++++++++++++++ 1 file changed, 197 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py new file mode 100644 index 000000000..4b78780ca --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/dataset.py @@ -0,0 +1,197 @@ +#!/usr/bin/python3 +# coding=utf-8 + +from npu_bridge.npu_init import * +import os +import os.path as osp +import numpy as np +import cv2 +import json +import pickle +import matplotlib.pyplot as plt +import tensorflow as tf + +import sys + +current_path = os.path.dirname(os.path.realpath(__file__)) # 对应于COCO +project_root = os.path.dirname(os.path.dirname(current_path)) # 对应于TF-Simple +main_path = os.path.join(project_root, "main", "train.py") +print("main_path:", main_path) + + +cur_dir = os.path.dirname(__file__) +# print("========>>>>>>cur_dir:{}".format(cur_dir)) +sys.path.insert(0, osp.join(cur_dir, 'PythonAPI')) +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + + +class Dataset(object): + dataset_name = 'COCO' + num_kps = 17 + kps_names = ['nose', 'l_eye', 'r_eye', 'l_ear', 'r_ear', 'l_shoulder', + 'r_shoulder', 'l_elbow', 'r_elbow', 'l_wrist', 'r_wrist', + 'l_hip', 'r_hip', 'l_knee', 'r_knee', 'l_ankle', 'r_ankle'] + kps_symmetry = [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12), (13, 14), (15, 16)] + kps_lines = [(1, 2), (0, 1), (0, 2), (2, 4), (1, 3), (6, 8), (8, 10), (5, 7), (7, 9), (12, 14), (14, 16), (11, 13), + (13, 15), (5, 6), (11, 12)] + + # human_det_path = osp.join('..', 'data', dataset_name, 'dets', 'human_detection.json') # human detection result + # img_path = osp.join('..', 'data', dataset_name, 'images') + # train_annot_path = osp.join('..', 'data', dataset_name, 'annotations', 'person_keypoints_train2017.json') + # train_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_train2017.json') + # val_annot_path = osp.join(cur_dir, 'annotations', 'person_keypoints_val2017.json') + # test_annot_path = osp.join(cur_dir, 'data', dataset_name, 'annotations', 'image_info_test-dev2017.json') + + # tf_flags = tf.app.flags.FLAGS + data_path = "cache/dataset" + img_path = osp.join(data_path, 'images') + human_det_path = osp.join(data_path, 'dets', 'human_detection.json') + train_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_train2017.json') + val_annot_path = osp.join(data_path, 'annotations', 'person_keypoints_val2017.json') + test_annot_path = osp.join(data_path, 'annotations', 'image_info_test-dev2017.json') + + def load_train_data(self, score=False): + coco = COCO(self.train_annot_path) + train_data = [] + for aid in coco.anns.keys(): + ann = coco.anns[aid] + imgname = 'train2017/' + coco.imgs[ann['image_id']]['file_name'] + joints = ann['keypoints'] + + if (ann['image_id'] not in coco.imgs) or ann['iscrowd'] or (np.sum(joints[2::3]) == 0) or ( + ann['num_keypoints'] == 0): + continue + + # sanitize bboxes + x, y, w, h = ann['bbox'] + img = coco.loadImgs(ann['image_id'])[0] + width, height = img['width'], img['height'] + x1 = np.max((0, x)) + y1 = np.max((0, y)) + x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) + y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) + if ann['area'] > 0 and x2 >= x1 and y2 >= y1: + bbox = [x1, y1, x2 - x1, y2 - y1] + else: + continue + + if score: + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) + else: + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints) + + train_data.append(data) + + return train_data + + def load_val_data_with_annot(self): + coco = COCO(self.val_annot_path) + val_data = [] + for aid in coco.anns.keys(): + ann = coco.anns[aid] + if ann['image_id'] not in coco.imgs: + continue + imgname = 'val2017/' + coco.imgs[ann['image_id']]['file_name'] + bbox = ann['bbox'] + joints = ann['keypoints'] + data = dict(image_id=ann['image_id'], imgpath=imgname, bbox=bbox, joints=joints, score=1) + val_data.append(data) + + return val_data + + def load_annot(self, db_set): + if db_set == 'train': + coco = COCO(self.train_annot_path) + elif db_set == 'val': + coco = COCO(self.val_annot_path) + elif db_set == 'test': + coco = COCO(self.test_annot_path) + else: + print('Unknown db_set') + assert 0 + + return coco + + def load_imgid(self, annot): + return annot.imgs + + def imgid_to_imgname(self, annot, imgid, db_set): + imgs = annot.loadImgs(imgid) + imgname = [db_set + '2017/' + i['file_name'] for i in imgs] + return imgname + + def evaluation(self, result, gt, result_dir, db_set): + result_path = osp.join(result_dir, 'result.json') + with open(result_path, 'w') as f: + json.dump(result, f) # 把result中的东西写到result.json里面 + + result = gt.loadRes(result_path) + cocoEval = COCOeval(gt, result, iouType='keypoints') + + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + result_path = osp.join(result_dir, 'result.pkl') + with open(result_path, 'wb') as f: + pickle.dump(cocoEval, f, 2) + print("Saved result file to " + result_path) + + def vis_keypoints(self, img, kps, kp_thresh=0.4, alpha=1): + + # Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv. + cmap = plt.get_cmap('rainbow') + colors = [cmap(i) for i in np.linspace(0, 1, len(self.kps_lines) + 2)] + colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors] + + # Perform the drawing on a copy of the image, to allow for blending. + kp_mask = np.copy(img) + + # Draw mid shoulder / mid hip first for better visualization. + mid_shoulder = ( + kps[:2, 5] + + kps[:2, 6]) / 2.0 + sc_mid_shoulder = np.minimum( + kps[2, 5], + kps[2, 6]) + mid_hip = ( + kps[:2, 11] + + kps[:2, 12]) / 2.0 + sc_mid_hip = np.minimum( + kps[2, 11], + kps[2, 12]) + nose_idx = 0 + if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh: + cv2.line( + kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(kps[:2, nose_idx].astype(np.int32)), + color=colors[len(self.kps_lines)], thickness=2, lineType=cv2.LINE_AA) + if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: + cv2.line( + kp_mask, tuple(mid_shoulder.astype(np.int32)), tuple(mid_hip.astype(np.int32)), + color=colors[len(self.kps_lines) + 1], thickness=2, lineType=cv2.LINE_AA) + + # Draw the keypoints. + for l in range(len(self.kps_lines)): + i1 = self.kps_lines[l][0] + i2 = self.kps_lines[l][1] + p1 = kps[0, i1].astype(np.int32), kps[1, i1].astype(np.int32) + p2 = kps[0, i2].astype(np.int32), kps[1, i2].astype(np.int32) + if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: + cv2.line( + kp_mask, p1, p2, + color=colors[l], thickness=2, lineType=cv2.LINE_AA) + if kps[2, i1] > kp_thresh: + cv2.circle( + kp_mask, p1, + radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + if kps[2, i2] > kp_thresh: + cv2.circle( + kp_mask, p2, + radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + + # Blend the keypoints. + return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) + + +dbcfg = Dataset() -- Gitee From c2613ecf0a02d264622b1e946ff8ca1ae3db188c Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 06:21:55 +0000 Subject: [PATCH 53/59] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/SimpleHumanPose=5FID0956=5Ffor=5FACL/?= =?UTF-8?q?src/data/COCO/.keep?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/data/COCO/.keep deleted file mode 100644 index e69de29bb..000000000 -- Gitee From f4621608af461338689498135b4d7c05ae1b6257 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 07:18:58 +0000 Subject: [PATCH 54/59] update ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md. --- .../SimpleHumanPose_ID0956_for_ACL/README.md | 54 ++++++++++++++++++ .../src/assets/acl_result.png | Bin 0 -> 45320 bytes 2 files changed, 54 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/acl_result.png diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md index 0ba36ccdb..83ba2f28c 100644 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md @@ -42,4 +42,58 @@ SimpleHumanPose模型主要针对人体姿态估计和姿态追踪，提出一 | ├── boot_test.py //ModelArts启动测试代码 | ├── pip-requirements.txt //配置文件，用于指定依赖包的包名及版本号 ``` +### ckpt转pb +在main/pb_frozen.py中，将模型和权重转化为pb，可以使用ModelArts启动pb固化代码，我们提供转换好的pb模型文件:[obs链接，提取码：000000](https://e-share.obs-website.cn-north-1.myhuaweicloud.com?token=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) + +pb_frozen.py主要代码如下： + +``` +tf.reset_default_graph() +model = Model() +model.make_network(is_train=False) + +with tf.Session() as sess: + tf.train.write_graph(sess.graph_def, result_dir, 'model.pb') + + freeze_graph.freeze_graph( + input_graph=os.path.join(result_dir, 'model.pb'), + input_saver='', + input_binary=False, + input_checkpoint=ckpt_path, + output_node_names='out/BiasAdd', + restore_op_name='save/restore_all', + filename_tensor_name='save/Const:0', + output_graph=os.path.join(result_dir, 'frozen_model.pb'), + clear_devices=False, + initializer_nodes='' + ) +``` + +### pb转om +使用ATC模型转换工具进行模型转换时可以参考如下指令，我们提供转换好的om模型文件:[obs链接，提取码：000000](https://e-share.obs-website.cn-north-1.myhuaweicloud.com?token=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) + +``` +atc --model=/home/HwHiAiUser/AscendProjects/pb_model/frozen_model.pb --framework=3 --output=/home/HwHiAiUser/AscendProjects/shp/shp_acc --soc_version=Ascend310 --input_shape="Placeholder:1,256,192,3" --log=info --out_nodes="out/BiasAdd:0" --debug_dir=/home/module/out/debug_info +``` +### msame工具 +我们采用msame工具进行离线推理，参考[msame简介](https://gitee.com/ascend/tools/tree/master/msame), 获取msame推理工具及使用方法。 + +获取到msame可执行文件之后，进行推理测试。 + +### 数据集转bin +该过程在原测试代码main/test.py中加入如下代码，直接获取预处理好的图片，并以bin格式存储。我们提供整个测试集的bin文件：[obs链接，提取码：000000](https://e-share.obs-website.cn-north-1.myhuaweicloud.com?token=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) +``` +image = np.array(imgs) +bin_path = osp.join(cfg.bins_dir, "{}.bin".format(count)) +image.tofile(bin_path) +count += 1 +``` +### 推理测试 +使用msame推理工具，参考如下命令，发起推理测试： + +``` +./msame --model "/home/HwHiAiUser/AscendProjects/shp/shp_acc.om" --input "/home/HwHiAiUser/AscendProjects/shp/data" --output "/home/HwHiAiUser/AscendProjects/shp/out/" --outfmt TXT +``` +推理结果如下图所示： +![输入图片说明](src/assets/acl_result.png) \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/acl_result.png b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/src/assets/acl_result.png new file mode 100644 index 0000000000000000000000000000000000000000..0bbda1a29786ff62806ada2d041e7638516cd3b9 GIT binary patch literal 45320 zcmbTeWmFt(*EEVlaCdi?0Kwe}8iKpKySsaU;BEneyADop5AN<5<-c`G5Pq>1d1OhBBEC>h)f|R7_7Z4C|Di9D*1{konPcp~DszE@AL8L^5zPf=P zx9ghWOEd`Re7n^@;qXKN1@;q=;ME4!{*DALR;^8v!|&sLOM>vQ%S%Ry0yn5dp$@?d zy5N<8>4u9DlykY!^S{Z%6M!gKc#gNkuGs?eTQPdXqp?<5erSH~3-EhwTW}BixyGfe 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a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md index 83ba2f28c..5f599a3bb 100644 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md @@ -90,10 +90,18 @@ image.tofile(bin_path) count += 1 ``` ### 推理测试 -使用msame推理工具，参考如下命令，发起推理测试： +使用msame推理工具，参考如下命令，发起推理测试。我们只使用了测试集中的部分数据进行推理（即如下命令中的data文件夹只包含360个bin文件）。 ``` ./msame --model "/home/HwHiAiUser/AscendProjects/shp/shp_acc.om" --input "/home/HwHiAiUser/AscendProjects/shp/data" --output "/home/HwHiAiUser/AscendProjects/shp/out/" --outfmt TXT ``` 推理结果如下图所示： -![输入图片说明](src/assets/acl_result.png) \ No newline at end of file +![输入图片说明](src/assets/acl_result.png) + +然后使用获取到的推理结果计算精度，具体代码在main/pb_inference.py中。 + +### 推理精度 +|Methods|AP|AP.5 |AP.75|AP(M)|AP(L)|AR|AR.5|AR.75|AR(M|AR(L)| +|--|--|--|--|--|--|--|--|--|--|--| 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z3p>@Hp)Op$NHlr;^H9#`xL&=+z>K|hQ%KbW2KjEKy}{;5mi4GqbCflJ4RfiH~)smIJtP2?0)Yr1e6-ymp{ws;3H_1thP~SbjCv|eti8{aLG8- zMW|Ei#h>f0nH&d2dQ^Nar|4vUdl*~&-d4n1!xZzG?;y%t=4{uqBc%E{^w-&c!9<1c q1FfGk(#ABu&mG|W1@jPqud$>f3i(;f82aFV9~nsniK53ZU;hu14<-Qs -- Gitee From b2c5fca192579a8fb082f556d8829c698402a01c Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 11:29:12 +0000 Subject: [PATCH 58/59] update ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md. --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md index 5f599a3bb..9e2605cc9 100644 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md @@ -43,7 +43,7 @@ SimpleHumanPose模型主要针对人体姿态估计和姿态追踪，提出一 | ├── pip-requirements.txt //配置文件，用于指定依赖包的包名及版本号 ``` ### ckpt转pb -在main/pb_frozen.py中，将模型和权重转化为pb，可以使用ModelArts启动pb固化代码，我们提供转换好的pb模型文件:[obs链接，提取码：000000](https://e-share.obs-website.cn-north-1.myhuaweicloud.com?token=0xQYRip2sq0KE/psA/59+BRZKLxwglylSGDkKq9CCOl7T4Nsf80zWftId0Zc+TqRVTMTtR1TiXIDRq+a4W6N5HfkghM5sX1GTCI1T1xyYOLeq6YjcV1RRAHr8/mo/0DjOirO77RhAeQ+32iuuaWzbZU0UkcTvtIBWsYFzQgKOaDkaUUhmut8luIAdb97RhTwk9Xb9YwIvNF70BbD4MIemgJlzCgMIgg41X2PrRKyLa840RwBjNeM/3nRimUVLxPI2w1oFj08R7MPETcc9W5cEHlj8RxzJ2cgr/8F/OEBZQNqTq7fHSnWy2OqCxoD0Vj5QBLMztMGxadgGeuARd1G2Pb2+EXBf3N9mSTofqV6RowjgM5ETuhJuW2HCXEdZSJ6qPqtgwfLE4x6G12qkvA+cxG986L9lTGGrcugfTkTyPnLuSmz78pHBAAXBXiylok4cmRz3IM0BFkNk+C4CEKT30kmKceVYM33inGtR4kKvQk60UiGcRnjJVgDdPN0TtCiDE5Wq7MJ+dc10r8XMctCKVxJuHvt4vliJolTG4HqV9Q=) +在main/pb_frozen.py中，将模型和权重转化为pb，可以使用ModelArts启动pb固化代码，我们提供转换好的pb模型文件: [obs链接，提取码：000000](https://e-share.obs-website.cn-north-1.myhuaweicloud.com?token=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) pb_frozen.py主要代码如下： @@ -71,7 +71,7 @@ with tf.Session() as sess: ``` ### pb转om -使用ATC模型转换工具进行模型转换时可以参考如下指令，我们提供转换好的om模型文件:[obs链接，提取码：000000](https://e-share.obs-website.cn-north-1.myhuaweicloud.com?token=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) +使用ATC模型转换工具进行模型转换时可以参考如下指令，我们提供转换好的om模型文件: [obs链接，提取码：000000](https://e-share.obs-website.cn-north-1.myhuaweicloud.com?token=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) ``` atc --model=/home/HwHiAiUser/AscendProjects/pb_model/frozen_model.pb --framework=3 --output=/home/HwHiAiUser/AscendProjects/shp/shp_acc --soc_version=Ascend310 --input_shape="Placeholder:1,256,192,3" --log=info --out_nodes="out/BiasAdd:0" --debug_dir=/home/module/out/debug_info -- Gitee From 75d231b5cff0c4acc09660a7973f5abc360d3784 Mon Sep 17 00:00:00 2001 From: maoao <1846577244@qq.com> Date: Tue, 24 May 2022 12:26:37 +0000 Subject: [PATCH 59/59] update ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md. --- .../contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md index 9e2605cc9..106529b86 100644 --- a/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md +++ b/ACL_TensorFlow/contrib/cv/SimpleHumanPose_ID0956_for_ACL/README.md @@ -103,5 +103,5 @@ count += 1 ### 推理精度 |Methods|AP|AP.5 |AP.75|AP(M)|AP(L)|AR|AR.5|AR.75|AR(M|AR(L)| |--|--|--|--|--|--|--|--|--|--|--| -|原论文|70.2|89.0|77.6|66.8|76.9|76.0|93.1|82.7|71.7|82.3| +|原论文|70.3|88.8|77.8|67.0|76.7|76.1|93.0|82.9|71.8|82.3| |离线推理|64.0|86.4|71.9|60.8|71.1|72.5|91.6|79.5|67.5|79.4| \ No newline at end of file -- Gitee