From dd33a7512105dcd0d452faa98b2f26108a60c208 Mon Sep 17 00:00:00 2001
From: sunzhe1996328 <545249803@qq.com>
Date: Mon, 28 Mar 2022 11:29:35 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E7=B2=BE=E5=BA=A6=E5=92=8C?=
=?UTF-8?q?=E6=80=A7=E8=83=BD=E6=95=B0=E6=8D=AE=E4=BB=A5=E5=8F=8AACL?=
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---
.../cv/BlitzNet_ID0948_for_ACL/LICENSE | 284 ++++++++++++++++++
.../cv/BlitzNet_ID0948_for_ACL/README.md | 70 +++++
.../BlitzNet_ID0948_for_ACL/freeze_graph.py | 90 ++++++
.../cv/BlitzNet_ID0948_for_ACL/imageToBin.py | 70 +++++
.../modelzoo_level.txt | 6 +
.../BlitzNet_ID0948_for_ACL/requirements.txt | 0
.../testBliznetPb_OM_Data.py | 233 ++++++++++++++
.../BlitzNet_ID0948_for_TensorFlow/README.md | 61 +++-
.../freeze_graph.py | 90 ++++++
.../testBliznetPb_OM_Data.py | 233 ++++++++++++++
10 files changed, 1130 insertions(+), 7 deletions(-)
create mode 100644 ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/LICENSE
create mode 100644 ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/README.md
create mode 100644 ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/freeze_graph.py
create mode 100644 ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/imageToBin.py
create mode 100644 ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/modelzoo_level.txt
create mode 100644 ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/requirements.txt
create mode 100644 ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/testBliznetPb_OM_Data.py
create mode 100644 TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/freeze_graph.py
create mode 100644 TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/testBliznetPb_OM_Data.py
diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/LICENSE b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/LICENSE
new file mode 100644
index 000000000..8389e23f5
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/LICENSE
@@ -0,0 +1,284 @@
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\ No newline at end of file
diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/README.md
new file mode 100644
index 000000000..c07aedecf
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/README.md
@@ -0,0 +1,70 @@
+
+## 推理过程
+环境
+- Tensorflow 1.15
+- python 3.7
+
+1. ckpt文件
+
+- ckpt文件下载地址:
+
+ https://sharegua.obs.cn-north-4.myhuaweicloud.com:443/checkpoint65.zip?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667698491&Signature=Ltfv5%2B5VbaFSklW3pI6W6oTh73A%3D
+
+ 通过freeze_graph.py转换成pb文件bliznet_tf_310.pb
+
+- pb文件下载地址:
+
+ https://sharegua.obs.myhuaweicloud.com:443/bliznet_tf_310.pb?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667656586&Signature=JhBRfk5dpeDFE%2BPy1jQg6Q4mvHY%3D
+
+2. om模型
+
+- om模型下载地址:
+
+ https://sharegua.obs.myhuaweicloud.com:443/bliznet_tf_310.om?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667656644&Signature=Z7DyzKRGPd27pYipfD2Ke/KSGAo%3D
+
+ 使用ATC模型转换工具进行模型转换时可以参考如下指令:
+
+```
+atc --model=/home/HwHiAiUser/atc/bliznet_tf_310.pb --framework=3 --output=/home/HwHiAiUser/atc/bliznet_tf_310 --soc_version=Ascend310 \
+ --input_shape="input:1,300,300,3" \
+ --log=info \
+ --out_nodes="concat_1:0;concat_2:0;ssd_2/Conv_7/BiasAdd:0"
+```
+
+3. 使用msame工具推理
+
+ 参考 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，将待检测om文件放在model文件夹，然后进行性能测试。
+
+ msame推理可以参考如下指令:
+```
+./msame --model "/home/HwHiAiUser/msame/bliznet_tf_310.om" --input "/home/HwHiAiUser/msame/data" --output "/home/HwHiAiUser/msame/out/" --outfmt TXT
+```
+- 将测试集数据转为bin文件:
+```
+ imageToBin.py
+```
+
+- 测试数据bin文件下载地址:
+
+ https://sharegua.obs.cn-north-4.myhuaweicloud.com:443/img.zip?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667698452&Signature=f3aLaUdPnodF8PKtCaI5Ox4wb6c%3D
+
+
+4. 性能测试
+
+ 使用testBliznetPb_OM_Data.py对推理完成后获得的txt文件进行测试
+
+精度测试
+
+训练集：VOC12 train-seg-aug
+
+测试集：VOC12 val
+
+| | mIoU | mAP |
+| ---------- | -------- | -------- |
+| 论文精度 | 72.8 | 80.0 |
+| GPU精度32 | 72.8 | 80.0 |
+| GPU精度16 | 72.0 | 78.3 |
+| NPU精度 | 70.1 | 77.6 |
+| 推理精度 | 70.1 | 77.6 |
\ No newline at end of file
diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/freeze_graph.py b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/freeze_graph.py
new file mode 100644
index 000000000..775d0e09f
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/freeze_graph.py
@@ -0,0 +1,90 @@
+# Copyright 2017 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.
+# ============================================================================
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from npu_bridge.npu_init import *
+import tensorflow as tf
+from tensorflow.python.tools import freeze_graph
+import os
+from Train.config import args
+from help_modelarts import modelarts_result2obs
+
+from Train.resnet import ResNet
+from Train.config import config as net_config
+
+INIT_CKPT_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'checkpoint65')
+ckpt_path = os.path.join(INIT_CKPT_DIR, 'model.ckpt-65000')
+
+def main():
+ print("start ckpt To pb")
+ print("ckpt_path")
+ tf.reset_default_graph()
+ img_ph = tf.placeholder(tf.float32, shape=[1, 300, 300, 3], name="input")
+ dataset_num_classes = 21
+
+ net = ResNet
+ depth = 50
+ net = net(config=net_config, depth=depth, training=False)
+
+ net.create_trunk(img_ph)
+
+ if args.detect:
+ net.create_multibox_head(dataset_num_classes)
+ confidence = net.outputs['confidence']
+ location = net.outputs['location']
+ else:
+ location, confidence = None, None
+
+ if args.segment:
+ net.create_segmentation_head(dataset_num_classes)
+ seg_logits = net.outputs['segmentation']
+ else:
+ seg_logits = None
+
+ print("confidence = ", confidence)
+ print("location = ", location)
+ print("seg_logits = ", seg_logits)
+
+ with tf.Session() as sess:
+ tf.train.write_graph(sess.graph_def, args.result_dir, 'model.pb')
+ modelarts_result2obs(args)
+ freeze_graph.freeze_graph(
+ input_graph=os.path.join(args.result_dir, 'model.pb'),
+ input_saver='',
+ input_binary=False,
+ input_checkpoint=ckpt_path,
+ output_node_names="concat_1, concat_2, ssd_2/Conv_7/BiasAdd", # graph outputs node
+ restore_op_name='save/restore_all',
+ filename_tensor_name='save/Const:0',
+ output_graph=os.path.join(args.result_dir, 'bliznet_tf_310.pb'), # graph outputs name
+ clear_devices=False,
+ initializer_nodes="")
+ print("done")
+
+ modelarts_result2obs(args)
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/imageToBin.py b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/imageToBin.py
new file mode 100644
index 000000000..23635dd95
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/imageToBin.py
@@ -0,0 +1,70 @@
+# Copyright 2017 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.
+# ============================================================================
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+import tensorflow as tf
+from config import args
+from getData.voc_loader import VOCLoader
+
+import progressbar
+import logging
+log = logging.getLogger()
+import numpy as np
+
+def main(argv=None):
+ if args.dataset == 'voc07' or args.dataset == 'voc07+12':
+ loader = VOCLoader('07', 'test')
+ if args.dataset == 'voc12-val':
+ loader = VOCLoader('12', 'val', segmentation=args.segment)
+
+ filenames = loader.get_filenames()
+ image_list = []
+
+ inputs = tf.placeholder(tf.float32, shape=[None, None, 3], name="input")
+ img_ph = tf.image.resize_bilinear(tf.expand_dims(inputs, 0), (300, 300))# 增加一维，并reshape
+
+ with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess0:
+ bar = progressbar.ProgressBar()# 显示进度条
+ for i in bar(range(len(filenames))):
+ name = filenames[i]
+ img = loader.load_image(name) # 获取图片
+ image = sess0.run(img_ph, feed_dict={inputs: img})
+
+ image_list.append(image)
+ gt_bboxes, seg_gt, gt_cats, w, h, difficulty = loader.read_annotations(name) # 获取图片信息
+ image.tofile("./binFile/img/{0:05d}.bin".format(i))
+ # im = np.fromfile("./binFile/img/{0:05d}.bin".format(i), dtype=np.float32)
+ # print(im)
+ gt_bboxes.tofile("./binFile/gt_bboxes/{0:05d}.bin".format(i))
+ seg_gt.tofile("./binFile/seg_gt/{0:05d}.bin".format(i))
+ gt_cats.tofile("./binFile/gt_cats/{0:05d}.bin".format(i))
+ # w.tofile("./binFile/w/{0:05d}.bin".format(i))
+ # h.tofile("./binFile/h/{0:05d}.bin".format(i))
+ difficulty.tofile("./binFile/difficulty/{0:05d}.bin".format(i))
+
+
+if __name__ == '__main__':
+ tf.app.run()
\ No newline at end of file
diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/modelzoo_level.txt b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/modelzoo_level.txt
new file mode 100644
index 000000000..1badb843c
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/modelzoo_level.txt
@@ -0,0 +1,6 @@
+FuncStatus:OK
+PrecisionStatus:POK
+AutoTune:POK
+PerfStatus:POK
+ModelConvert:OK
+QuantStatus:OK
\ No newline at end of file
diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/requirements.txt b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/requirements.txt
new file mode 100644
index 000000000..e69de29bb
diff --git a/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/testBliznetPb_OM_Data.py b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/testBliznetPb_OM_Data.py
new file mode 100644
index 000000000..9b9564af6
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/BlitzNet_ID0948_for_ACL/testBliznetPb_OM_Data.py
@@ -0,0 +1,233 @@
+# Copyright 2017 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.
+# ============================================================================
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+import tensorflow as tf
+from config import args
+from getData.voc_loader import VOCLoader
+
+from tensorflow.python.ops.metrics_impl import mean_iou as streaming_mean_iou
+from utils import decode_bboxes
+from getData.boxer import PriorBoxGrid
+from config import config as net_config
+from detector import Detector
+from tabulate import tabulate
+import progressbar
+import numpy as np
+import logging
+log = logging.getLogger()
+
+def eval_category(gt, dets, cid):
+ """Computes average precision for one category"""
+ cgt = gt[cid]
+ cdets = np.array(dets[cid])
+ if (cdets.shape == (0, )):
+ return None, None
+ scores = cdets[:, 1]
+ sorted_inds = np.argsort(-scores)
+ image_ids = cdets[sorted_inds, 0].astype(int)
+ BB = cdets[sorted_inds]
+
+ npos = 0
+ for img_gt in cgt.values():
+ img_gt['ignored'] = np.array(img_gt['difficult'])
+ img_gt['det'] = np.zeros(len(img_gt['difficult']), dtype=np.bool)
+ npos += np.sum(~img_gt['ignored'])
+
+ nd = len(image_ids)
+ tp = np.zeros(nd)
+ fp = np.zeros(nd)
+ for d in range(nd):
+ ovmax = -np.inf
+ if image_ids[d] in cgt:
+ R = cgt[image_ids[d]]
+ bb = BB[d, 2:].astype(float)
+
+ BBGT = R['bbox'].astype(float)
+
+ # compute overlaps
+ # intersection
+ ixmin = np.maximum(BBGT[:, 0], bb[0])
+ iymin = np.maximum(BBGT[:, 1], bb[1])
+ ixmax = np.minimum(BBGT[:, 0] + BBGT[:, 2], bb[0] + bb[2])
+ iymax = np.minimum(BBGT[:, 1] + BBGT[:, 3], bb[1] + bb[3])
+ iw = np.maximum(ixmax - ixmin, 0.)
+ ih = np.maximum(iymax - iymin, 0.)
+ inters = iw * ih
+
+ # union
+ uni = (bb[2] * bb[3] + BBGT[:, 2] * BBGT[:, 3] - inters)
+
+ overlaps = inters / uni
+ ovmax = np.max(overlaps)
+ jmax = np.argmax(overlaps)
+
+ if ovmax > args.voc_iou_thresh:
+ if not R['ignored'][jmax]:
+ if not R['det'][jmax]:
+ tp[d] = 1.
+ R['det'][jmax] = True
+ else:
+ fp[d] = 1.
+ else:
+ fp[d] = 1.
+
+ # compute precision recall
+ fp = np.cumsum(fp)
+ tp = np.cumsum(tp)
+ rec = tp / float(npos)
+ N = float(npos)
+ # avoid divide by zero in case the first detection matches a difficult
+ # ground truth
+ prec = rec * N / np.maximum(rec * N + fp, np.finfo(np.float32).eps)
+ return rec, prec
+
+def voc_ap(rec, prec, use_07_metric=False):
+ """ ap = voc_ap(rec, prec, [use_07_metric])
+ Compute VOC AP given precision and recall.
+ If use_07_metric is true, uses the
+ VOC 07 11 point method (default:False).
+ """
+ if use_07_metric:
+ # 11 point metric
+ ap = 0.
+ for t in np.arange(0., 1.1, 0.1):
+ p = 0 if np.sum(rec >= t) == 0 else np.max(prec[rec >= t])
+ ap = ap + p / 11.
+ else:
+ # correct AP calculation
+ # first append sentinel values at the end
+ mrec = np.concatenate(([0.], rec, [1.]))
+ mpre = np.concatenate(([0.], prec, [0.]))
+
+ # compute the precision envelope
+ for i in range(mpre.size - 1, 0, -1):
+ mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
+
+ # to calculate area under PR curve, look for points
+ # where X axis (recall) changes value
+ i = np.where(mrec[1:] != mrec[:-1])[0]
+
+ # and sum (\Delta recall) * prec
+ ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+ return ap
+
+def compute_ap(gt, dets, loader):
+ """computes average precision for all categories"""
+ aps = {}
+ for cid in range(1, loader.num_classes):
+ cat_name = loader.ids_to_cats[cid]
+ rec, prec = eval_category(gt, dets, cid)
+ ap = voc_ap(rec, prec, loader.year == '07')
+ aps[loader.ids_to_cats[cid]] = ap
+ return aps
+
+def make_detection_table(gt, dets, loader):
+ """creates a table with AP per category and mean AP"""
+ aps = compute_ap(gt, dets, loader)
+ print("ap = ", aps)
+ eval_cache = [aps]
+
+ table = []
+ for cid in range(1, loader.num_classes):
+ cat_name = loader.ids_to_cats[cid]
+ table.append((cat_name, ) + tuple(aps.get(cat_name, 'N/A') for aps in eval_cache))
+ mean_ap = np.mean([a for a in list(aps.values()) if a >= 0])
+ table.append(("AVERAGE", ) + tuple(np.mean(list(aps.values())) for aps in eval_cache))
+ x = tabulate(table, headers=(["Category", "mAP (all)"]),
+ tablefmt='orgtbl', floatfmt=".3f")
+ log.info("Eval results:\n%s", x)
+ return table
+
+def compute_mean_iou(detector):
+ iou = detector.get_mean_iou()
+ print(iou)
+ log.info("\n Mean IoU is %f", iou)
+ return iou
+
+def main(argv=None):
+ if args.dataset == 'voc07' or args.dataset == 'voc07+12':
+ loader = VOCLoader('07', 'test')
+ if args.dataset == 'voc12-val':
+ loader = VOCLoader('12', 'val', segmentation=args.segment)
+
+ with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
+ log_device_placement=False)) as sess:
+ detector = Detector(sess, loader, net_config, no_gt=args.no_seg_gt)
+
+ filenames = loader.get_filenames()
+ gt = {cid: {} for cid in range(1, loader.num_classes)}
+ dets = {cid: [] for cid in range(1, loader.num_classes)}
+
+ bar = progressbar.ProgressBar()# 显示进度条
+ # print("filenames = ", filenames)
+
+ init_op = tf.group(tf.local_variables_initializer(), tf.global_variables_initializer())
+ sess.run(init_op)
+ for i in bar(range(len(filenames))):
+ name = filenames[i]
+ # print("name = ", name)
+ img_id = i
+ img = loader.load_image(name) # 获取图片
+ # img = np.fromfile("./binFile/img/{0:05d}.bin".format(i), dtype=np.float32)
+ # img.shape = 1, 300, 300, 3
+ gt_bboxes, seg_gt, gt_cats, w, h, difficulty = loader.read_annotations(name) # 获取图片信息
+
+ confidence = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_0.txt".format(i))
+ location = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_1.txt".format(i))
+ seg_logits = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_2.txt".format(i))
+ confidence.shape = 1, 45390, 21
+ location.shape = 1, 45390, 4
+ seg_logits.shape = 1, 75, 75, 21
+
+ for cid in np.unique(gt_cats):
+ mask = (gt_cats == cid)
+ bbox = gt_bboxes[mask]
+ diff = difficulty[mask]
+ det = np.zeros(len(diff), dtype=np.bool)
+ gt[cid][img_id] = {'bbox': bbox, 'difficult': diff, 'det': det}
+
+ confidence1 = confidence
+ location1 = location
+ seg_logits1 = seg_logits
+ output = detector.feed_forward(img, seg_gt, confidence1, location1, seg_logits1,
+ w, h, name, gt_bboxes, gt_cats) # result
+
+ if args.detect:
+ det_bboxes, det_probs, det_cats = output[:3]
+ for i in range(len(det_cats)):
+ dets[det_cats[i]].append((img_id, det_probs[i]) + tuple(det_bboxes[i]))
+
+ # print("gt = ", gt)
+ # print("dets = ", dets)
+ print("table result:")
+ table = make_detection_table(gt, dets, loader) if args.detect else None
+ print("iou result:")
+ iou = compute_mean_iou(detector) if args.segment else None
+
+
+if __name__ == '__main__':
+ tf.app.run()
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/README.md b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/README.md
index 4e3f0e500..f51769bdd 100644
--- a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/README.md
+++ b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/README.md
@@ -118,10 +118,11 @@ BlitzNet在一次前向传递中联合执行对象检测和语义分割，从而
快速上手
-- 数据集准备
-
+- 训练数据集准备
+
OBS下载地址：（下载的数据集为处理完的tf数据集）
https://blitznets.obs.myhuaweicloud.com:443/Datasets/voc12-train-seg?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1661686224&Signature=QkWct66ZOwIUfNOYeoWFFZ/FTsk%3D
+
- ResNet预训练模型准备
OBS下载地址：（将下载的resnet50_full.ckpt文件置于Weights_imagenet中）
@@ -198,6 +199,51 @@ data_input_test.py
2. 参考脚本的模型存储路径为test/output/*，训练脚本train_*.log中可查看性能、精度的相关运行状态。
+## 推理过程
+1. ckpt文件
+
+- ckpt文件下载地址:
+
+ https://sharegua.obs.cn-north-4.myhuaweicloud.com:443/checkpoint65.zip?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667698491&Signature=Ltfv5%2B5VbaFSklW3pI6W6oTh73A%3D
+
+ 通过freeze_graph.py转换成pb文件bliznet_tf_310.pb
+
+- pb文件下载地址:
+
+ https://sharegua.obs.myhuaweicloud.com:443/bliznet_tf_310.pb?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667656586&Signature=JhBRfk5dpeDFE%2BPy1jQg6Q4mvHY%3D
+
+2. om模型
+
+- om模型下载地址:
+
+ https://sharegua.obs.myhuaweicloud.com:443/bliznet_tf_310.om?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667656644&Signature=Z7DyzKRGPd27pYipfD2Ke/KSGAo%3D
+
+ 使用ATC模型转换工具进行模型转换时可以参考如下指令:
+
+```
+atc --model=/home/HwHiAiUser/atc/bliznet_tf_310.pb --framework=3 --output=/home/HwHiAiUser/atc/bliznet_tf_310 --soc_version=Ascend310 \
+ --input_shape="input:1,300,300,3" \
+ --log=info \
+ --out_nodes="concat_1:0;concat_2:0;ssd_2/Conv_7/BiasAdd:0"
+```
+
+3. 使用msame工具推理
+
+ 参考 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，将待检测om文件放在model文件夹，然后进行性能测试。
+
+ msame推理可以参考如下指令:
+```
+./msame --model "/home/HwHiAiUser/msame/bliznet_tf_310.om" --input "/home/HwHiAiUser/msame/data" --output "/home/HwHiAiUser/msame/out/" --outfmt TXT
+```
+- 测试数据bin文件下载地址:
+
+ https://sharegua.obs.cn-north-4.myhuaweicloud.com:443/img.zip?AccessKeyId=UC40X3U4Z2RUPSTV8ADH&Expires=1667698452&Signature=f3aLaUdPnodF8PKtCaI5Ox4wb6c%3D
+
+4. 性能测试
+
+ 使用testBliznetPb_OM_Data.py对推理完成后获得的txt文件进行测试
精度测试
@@ -205,8 +251,9 @@ data_input_test.py
测试集：VOC12 val
-| | mIoU | mAP |
-| ---------- | -------- | -------- |
-| 论文精度 | 72.8 | 80.0 |
-| GPU精度 | 72.8 | 80.0 |
-| NPU精度 | 待测 | 待测 |
\ No newline at end of file
+| | mIoU | mAP | 性能|
+| ---------- | -------- | -------- | -------- |
+| 论文精度 | 72.8 | 80.0 | / |
+| GPU精度32 | 72.8 | 80.0 | 0.35 sec/batch |
+| GPU精度16 | 72.0 | 78.3 | 0.35 sec/batch |
+| NPU精度 | 70.1 | 77.6 | 0.5 sec/batch |
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/freeze_graph.py b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/freeze_graph.py
new file mode 100644
index 000000000..775d0e09f
--- /dev/null
+++ b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/freeze_graph.py
@@ -0,0 +1,90 @@
+# Copyright 2017 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.
+# ============================================================================
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from npu_bridge.npu_init import *
+import tensorflow as tf
+from tensorflow.python.tools import freeze_graph
+import os
+from Train.config import args
+from help_modelarts import modelarts_result2obs
+
+from Train.resnet import ResNet
+from Train.config import config as net_config
+
+INIT_CKPT_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'checkpoint65')
+ckpt_path = os.path.join(INIT_CKPT_DIR, 'model.ckpt-65000')
+
+def main():
+ print("start ckpt To pb")
+ print("ckpt_path")
+ tf.reset_default_graph()
+ img_ph = tf.placeholder(tf.float32, shape=[1, 300, 300, 3], name="input")
+ dataset_num_classes = 21
+
+ net = ResNet
+ depth = 50
+ net = net(config=net_config, depth=depth, training=False)
+
+ net.create_trunk(img_ph)
+
+ if args.detect:
+ net.create_multibox_head(dataset_num_classes)
+ confidence = net.outputs['confidence']
+ location = net.outputs['location']
+ else:
+ location, confidence = None, None
+
+ if args.segment:
+ net.create_segmentation_head(dataset_num_classes)
+ seg_logits = net.outputs['segmentation']
+ else:
+ seg_logits = None
+
+ print("confidence = ", confidence)
+ print("location = ", location)
+ print("seg_logits = ", seg_logits)
+
+ with tf.Session() as sess:
+ tf.train.write_graph(sess.graph_def, args.result_dir, 'model.pb')
+ modelarts_result2obs(args)
+ freeze_graph.freeze_graph(
+ input_graph=os.path.join(args.result_dir, 'model.pb'),
+ input_saver='',
+ input_binary=False,
+ input_checkpoint=ckpt_path,
+ output_node_names="concat_1, concat_2, ssd_2/Conv_7/BiasAdd", # graph outputs node
+ restore_op_name='save/restore_all',
+ filename_tensor_name='save/Const:0',
+ output_graph=os.path.join(args.result_dir, 'bliznet_tf_310.pb'), # graph outputs name
+ clear_devices=False,
+ initializer_nodes="")
+ print("done")
+
+ modelarts_result2obs(args)
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/testBliznetPb_OM_Data.py b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/testBliznetPb_OM_Data.py
new file mode 100644
index 000000000..9b9564af6
--- /dev/null
+++ b/TensorFlow/contrib/cv/BlitzNet_ID0948_for_TensorFlow/testBliznetPb_OM_Data.py
@@ -0,0 +1,233 @@
+# Copyright 2017 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.
+# ============================================================================
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# 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.
+import tensorflow as tf
+from config import args
+from getData.voc_loader import VOCLoader
+
+from tensorflow.python.ops.metrics_impl import mean_iou as streaming_mean_iou
+from utils import decode_bboxes
+from getData.boxer import PriorBoxGrid
+from config import config as net_config
+from detector import Detector
+from tabulate import tabulate
+import progressbar
+import numpy as np
+import logging
+log = logging.getLogger()
+
+def eval_category(gt, dets, cid):
+ """Computes average precision for one category"""
+ cgt = gt[cid]
+ cdets = np.array(dets[cid])
+ if (cdets.shape == (0, )):
+ return None, None
+ scores = cdets[:, 1]
+ sorted_inds = np.argsort(-scores)
+ image_ids = cdets[sorted_inds, 0].astype(int)
+ BB = cdets[sorted_inds]
+
+ npos = 0
+ for img_gt in cgt.values():
+ img_gt['ignored'] = np.array(img_gt['difficult'])
+ img_gt['det'] = np.zeros(len(img_gt['difficult']), dtype=np.bool)
+ npos += np.sum(~img_gt['ignored'])
+
+ nd = len(image_ids)
+ tp = np.zeros(nd)
+ fp = np.zeros(nd)
+ for d in range(nd):
+ ovmax = -np.inf
+ if image_ids[d] in cgt:
+ R = cgt[image_ids[d]]
+ bb = BB[d, 2:].astype(float)
+
+ BBGT = R['bbox'].astype(float)
+
+ # compute overlaps
+ # intersection
+ ixmin = np.maximum(BBGT[:, 0], bb[0])
+ iymin = np.maximum(BBGT[:, 1], bb[1])
+ ixmax = np.minimum(BBGT[:, 0] + BBGT[:, 2], bb[0] + bb[2])
+ iymax = np.minimum(BBGT[:, 1] + BBGT[:, 3], bb[1] + bb[3])
+ iw = np.maximum(ixmax - ixmin, 0.)
+ ih = np.maximum(iymax - iymin, 0.)
+ inters = iw * ih
+
+ # union
+ uni = (bb[2] * bb[3] + BBGT[:, 2] * BBGT[:, 3] - inters)
+
+ overlaps = inters / uni
+ ovmax = np.max(overlaps)
+ jmax = np.argmax(overlaps)
+
+ if ovmax > args.voc_iou_thresh:
+ if not R['ignored'][jmax]:
+ if not R['det'][jmax]:
+ tp[d] = 1.
+ R['det'][jmax] = True
+ else:
+ fp[d] = 1.
+ else:
+ fp[d] = 1.
+
+ # compute precision recall
+ fp = np.cumsum(fp)
+ tp = np.cumsum(tp)
+ rec = tp / float(npos)
+ N = float(npos)
+ # avoid divide by zero in case the first detection matches a difficult
+ # ground truth
+ prec = rec * N / np.maximum(rec * N + fp, np.finfo(np.float32).eps)
+ return rec, prec
+
+def voc_ap(rec, prec, use_07_metric=False):
+ """ ap = voc_ap(rec, prec, [use_07_metric])
+ Compute VOC AP given precision and recall.
+ If use_07_metric is true, uses the
+ VOC 07 11 point method (default:False).
+ """
+ if use_07_metric:
+ # 11 point metric
+ ap = 0.
+ for t in np.arange(0., 1.1, 0.1):
+ p = 0 if np.sum(rec >= t) == 0 else np.max(prec[rec >= t])
+ ap = ap + p / 11.
+ else:
+ # correct AP calculation
+ # first append sentinel values at the end
+ mrec = np.concatenate(([0.], rec, [1.]))
+ mpre = np.concatenate(([0.], prec, [0.]))
+
+ # compute the precision envelope
+ for i in range(mpre.size - 1, 0, -1):
+ mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
+
+ # to calculate area under PR curve, look for points
+ # where X axis (recall) changes value
+ i = np.where(mrec[1:] != mrec[:-1])[0]
+
+ # and sum (\Delta recall) * prec
+ ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
+ return ap
+
+def compute_ap(gt, dets, loader):
+ """computes average precision for all categories"""
+ aps = {}
+ for cid in range(1, loader.num_classes):
+ cat_name = loader.ids_to_cats[cid]
+ rec, prec = eval_category(gt, dets, cid)
+ ap = voc_ap(rec, prec, loader.year == '07')
+ aps[loader.ids_to_cats[cid]] = ap
+ return aps
+
+def make_detection_table(gt, dets, loader):
+ """creates a table with AP per category and mean AP"""
+ aps = compute_ap(gt, dets, loader)
+ print("ap = ", aps)
+ eval_cache = [aps]
+
+ table = []
+ for cid in range(1, loader.num_classes):
+ cat_name = loader.ids_to_cats[cid]
+ table.append((cat_name, ) + tuple(aps.get(cat_name, 'N/A') for aps in eval_cache))
+ mean_ap = np.mean([a for a in list(aps.values()) if a >= 0])
+ table.append(("AVERAGE", ) + tuple(np.mean(list(aps.values())) for aps in eval_cache))
+ x = tabulate(table, headers=(["Category", "mAP (all)"]),
+ tablefmt='orgtbl', floatfmt=".3f")
+ log.info("Eval results:\n%s", x)
+ return table
+
+def compute_mean_iou(detector):
+ iou = detector.get_mean_iou()
+ print(iou)
+ log.info("\n Mean IoU is %f", iou)
+ return iou
+
+def main(argv=None):
+ if args.dataset == 'voc07' or args.dataset == 'voc07+12':
+ loader = VOCLoader('07', 'test')
+ if args.dataset == 'voc12-val':
+ loader = VOCLoader('12', 'val', segmentation=args.segment)
+
+ with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
+ log_device_placement=False)) as sess:
+ detector = Detector(sess, loader, net_config, no_gt=args.no_seg_gt)
+
+ filenames = loader.get_filenames()
+ gt = {cid: {} for cid in range(1, loader.num_classes)}
+ dets = {cid: [] for cid in range(1, loader.num_classes)}
+
+ bar = progressbar.ProgressBar()# 显示进度条
+ # print("filenames = ", filenames)
+
+ init_op = tf.group(tf.local_variables_initializer(), tf.global_variables_initializer())
+ sess.run(init_op)
+ for i in bar(range(len(filenames))):
+ name = filenames[i]
+ # print("name = ", name)
+ img_id = i
+ img = loader.load_image(name) # 获取图片
+ # img = np.fromfile("./binFile/img/{0:05d}.bin".format(i), dtype=np.float32)
+ # img.shape = 1, 300, 300, 3
+ gt_bboxes, seg_gt, gt_cats, w, h, difficulty = loader.read_annotations(name) # 获取图片信息
+
+ confidence = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_0.txt".format(i))
+ location = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_1.txt".format(i))
+ seg_logits = np.loadtxt("./binFile/test/2021118_18_51_25_234650/{0:05d}_output_2.txt".format(i))
+ confidence.shape = 1, 45390, 21
+ location.shape = 1, 45390, 4
+ seg_logits.shape = 1, 75, 75, 21
+
+ for cid in np.unique(gt_cats):
+ mask = (gt_cats == cid)
+ bbox = gt_bboxes[mask]
+ diff = difficulty[mask]
+ det = np.zeros(len(diff), dtype=np.bool)
+ gt[cid][img_id] = {'bbox': bbox, 'difficult': diff, 'det': det}
+
+ confidence1 = confidence
+ location1 = location
+ seg_logits1 = seg_logits
+ output = detector.feed_forward(img, seg_gt, confidence1, location1, seg_logits1,
+ w, h, name, gt_bboxes, gt_cats) # result
+
+ if args.detect:
+ det_bboxes, det_probs, det_cats = output[:3]
+ for i in range(len(det_cats)):
+ dets[det_cats[i]].append((img_id, det_probs[i]) + tuple(det_bboxes[i]))
+
+ # print("gt = ", gt)
+ # print("dets = ", dets)
+ print("table result:")
+ table = make_detection_table(gt, dets, loader) if args.detect else None
+ print("iou result:")
+ iou = compute_mean_iou(detector) if args.segment else None
+
+
+if __name__ == '__main__':
+ tf.app.run()
\ No newline at end of file
--
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