p60975123
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

modify dataset creating procedure for CenterNet

This commit is contained in:
shibeiji 2020-12-23 10:19:01 +08:00
parent cfc9c740b1
commit 8fc4705d67
11 changed files with 141 additions and 85 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

42
model_zoo/research/cv/centernet/README.md
View File

@ -117,18 +117,21 @@ Dataset used: [COCO2017](<https://cocodataset.org/>)
After installing MindSpore via the official website, you can start training and evaluation as follows:
Note: 1.the first run will generate the mindrecord file, which will take a long time.
2.VALIDATION_JSON_FILE is ground truth label file. CHECKPOINT_PATH is a checkpoint file after training.
Note: 1.the first run of training will generate the mindrecord file, which will take a long time.
2.MINDRECORD_DATASET_PATH is the mindrecord dataset directory.
```shell
# create dataset in mindrecord format
bash scripts/convert_dataset_to_mindrecord.sh
# standalone training
bash run_standalone_train_ascend.sh [DEVICE_ID] [EPOCH_SIZE]
bash scripts/run_standalone_train_ascend.sh [DEVICE_ID] [EPOCH_SIZE]
# distributed training
bash run_distributed_train_ascend.sh [COCO_DATASET_PATH] [MINDRECORD_DATASET_PATH] [RANK_TABLE_FILE]
bash scripts/run_distributed_train_ascend.sh [MINDRECORD_DATASET_PATH] [RANK_TABLE_FILE]
# eval
bash run_standalone_eval_ascend.sh [DEVICE_ID]
bash scripts/run_standalone_eval_ascend.sh [DEVICE_ID]
```
# [Script Description](#contents)
@ -149,6 +152,7 @@ bash run_standalone_eval_ascend.sh [DEVICE_ID]
│ │ ├──hyper_parameter_config.ini // hyper parameter for distributed pretraining
│ │ ├──get_distribute_pretrain_cmd.py // script for distributed pretraining
│ │ ├──README.md
│ ├──convert_dataset_to_mindrecord.sh // shell script for converting coco type dataset to mindrecord
│ ├──run_standalone_train_ascend.sh // shell script for standalone pretrain on ascend
│ ├──run_distributed_train_ascend.sh // shell script for distributed pretrain on ascend
│ ├──run_standalone_eval_ascend.sh // shell script for standalone evaluation on ascend
@ -168,6 +172,19 @@ bash run_standalone_eval_ascend.sh [DEVICE_ID]
## [Script Parameters](#contents)
### Create MindRecord type dataset
```text
usage: dataset.py [--coco_data_dir COCO_DATA_DIR]
[--mindrecord_dir MINDRECORD_DIR]
[--mindrecord_prefix MINDRECORD_PREFIX]
options:
--coco_data_dir path to coco dataset directory: PATH, default is ""
--mindrecord_dir path to mindrecord dataset directory: PATH, default is ""
--mindrecord_prefix prefix of MindRecord dataset filename: STR, default is "coco_hp.train.mind"
```
### Training
```text
@ -180,7 +197,8 @@ usage: train.py [--device_target DEVICE_TARGET] [--distribute DISTRIBUTE]
[--save_checkpoint_path SAVE_CHECKPOINT_PATH]
[--load_checkpoint_path LOAD_CHECKPOINT_PATH]
[--save_checkpoint_steps N] [--save_checkpoint_num N]
[--data_dir DATA_DIR] [--mindrecord_dir MINDRECORD_DIR]
[--mindrecord_dir MINDRECORD_DIR]
[--mindrecord_prefix MINDRECORD_PREFIX]
[--visual_image VISUAL_IMAGE] [--save_result_dir SAVE_RESULT_DIR]
options:
@ -201,8 +219,8 @@ options:
--load_checkpoint_path path to load checkpoint files: PATH, default is ""
--save_checkpoint_steps steps for saving checkpoint files: N, default is 1000
--save_checkpoint_num number for saving checkpoint files: N, default is 1
--data_dir path to original dataset directory: PATH, default is ""
--mindrecord_dir path to mindrecord dataset directory: PATH, default is ""
--mindrecord_prefix prefix of MindRecord dataset filename: STR, default is "coco_hp.train.mind"
--visual_image whether visualize the image and annotation info: "true" | "false", default is "false"
--save_result_dir path to save the visualization results: PATH, default is ""
```
@ -214,7 +232,7 @@ usage: eval.py [--device_target DEVICE_TARGET] [--device_id N]
[--load_checkpoint_path LOAD_CHECKPOINT_PATH]
[--data_dir DATA_DIR] [--run_mode RUN_MODE]
[--visual_image VISUAL_IMAGE]
[enable_eval ENABLE_EVAL] [--save_result_dir SAVE_RESULT_DIR]
[--enable_eval ENABLE_EVAL] [--save_result_dir SAVE_RESULT_DIR]
options:
--device_target device where the code will be implemented: "Ascend" | "CPU", default is "Ascend"
--device_id device id to run task, default is 0
@ -324,6 +342,14 @@ Parameters for optimizer and learning rate:
## [Training Process](#contents)
Before your first training, convert coco type dataset to mindrecord files is needed to improve performance on host.
```bash
bash scripts/convert_dataset_to_mindrecord.sh
```
The command above will run in the background, after converting mindrecord files will be located in path specified by yourself.
### Training
#### Running on Ascend

6
model_zoo/research/cv/centernet/eval.py
View File

@ -55,9 +55,9 @@ def predict():
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
logger.info("Begin creating {} dataset".format(args_opt.run_mode))
coco = COCOHP(args_opt.data_dir, dataset_config, net_config, run_mode=args_opt.run_mode)
coco.init(enable_visual_image=(args_opt.visual_image == "true"), save_path=args_opt.save_result_dir,
keep_res=eval_config.keep_res, flip_test=eval_config.flip_test)
coco = COCOHP(dataset_config, run_mode=args_opt.run_mode, net_opt=net_config,
enable_visual_image=(args_opt.visual_image == "true"), save_path=args_opt.save_result_dir,)
coco.init(args_opt.data_dir, keep_res=eval_config.keep_res, flip_test=eval_config.flip_test)
dataset = coco.create_eval_dataset()
net_for_eval = CenterNetMultiPoseEval(net_config, eval_config.flip_test, eval_config.K)

18
model_zoo/research/cv/centernet/scripts/ascend_distributed_launcher/get_distribute_train_cmd.py
View File

@ -38,12 +38,9 @@ def parse_args():
help="Run script path, it is better to use absolute path")
parser.add_argument("--hyper_parameter_config_dir", type=str, default="",
help="Hyper Parameter config path, it is better to use absolute path")
parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_train",
help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by "
"data_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir "
"rather than data_dir and anno_path. Default is ./Mindrecord_train")
parser.add_argument("--data_dir", type=str, default="",
help="Data path, it is better to use absolute path")
parser.add_argument("--mindrecord_dir", type=str, default="", help="Mindrecord dataset directory")
parser.add_argument("--mindrecord_prefix", type=str, default="coco_hp.train.mind",
help="Prefix of MindRecord dataset filename.")
parser.add_argument("--hccl_config_dir", type=str, default="",
help="Hccl config path, it is better to use absolute path")
parser.add_argument("--cmd_file", type=str, default="distributed_cmd.sh",
@ -74,8 +71,8 @@ def distribute_train():
args = parse_args()
run_script = args.run_script_dir
data_dir = args.data_dir
mindrecord_dir = args.mindrecord_dir
mindrecord_prefix = args.mindrecord_prefix
cf = configparser.ConfigParser()
cf.read(args.hyper_parameter_config_dir)
cfg = dict(cf.items("config"))
@ -142,7 +139,6 @@ def distribute_train():
print("core_nums:", cmdopt)
print("epoch_size:", str(cfg['epoch_size']))
print("data_dir:", data_dir)
print("mindrecord_dir:", mindrecord_dir)
print("log_file_dir: " + cur_dir + "/LOG" + str(device_id) + "/training_log.txt")
@ -150,12 +146,12 @@ def distribute_train():
run_cmd = 'taskset -c ' + cmdopt + ' nohup python ' + run_script + " "
opt = " ".join(["--" + key + "=" + str(cfg[key]) for key in cfg.keys()])
if ('device_id' in opt) or ('device_num' in opt) or ('data_dir' in opt):
if ('device_id' in opt) or ('device_num' in opt) or ('mindrecord_dir' in opt):
raise ValueError("hyper_parameter_config.ini can not setting 'device_id',"
" 'device_num' or 'data_dir'! ")
" 'device_num' or 'mindrecord_dir'! ")
run_cmd += opt
run_cmd += " --data_dir=" + data_dir
run_cmd += " --mindrecord_dir=" + mindrecord_dir
run_cmd += " --mindrecord_prefix=" + mindrecord_prefix
run_cmd += ' --device_id=' + str(device_id) + ' --device_num=' \
+ str(rank_size) + ' >./training_log.txt 2>&1 &'

1
model_zoo/research/cv/centernet/scripts/ascend_distributed_launcher/hyper_parameter_config.ini
View File

@ -9,6 +9,7 @@ load_checkpoint_path=""
save_checkpoint_path=./
save_checkpoint_steps=3000
save_checkpoint_num=1
mindrecord_prefix="coco_hp.train.mind"
need_profiler=false
profiler_path=./profiler
visual_image=false

28
model_zoo/research/cv/centernet/scripts/convert_dataset_to_mindrecord.sh Normal file
View File

@ -0,0 +1,28 @@
#!/bin/bash
# Copyright 2020 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.
# ============================================================================
echo "=============================================================================================================="
echo "Please run the scipt as: "
echo "bash convert_dataset_to_mindrecord.sh"
echo "=============================================================================================================="
export GLOG_v=1
PROJECT_DIR=$(cd "$(dirname "$0")" || exit; pwd)
python ${PROJECT_DIR}/../src/dataset.py \
--coco_data_dir="" \
--mindrecord_dir="" \
--mindrecord_prefix="coco_hp.train.mind" > create_dataset.log 2>&1 &

5
model_zoo/research/cv/centernet/scripts/run_distributed_train_ascend.sh
View File

@ -27,9 +27,8 @@ CUR_DIR=`pwd`
python ${CUR_DIR}/scripts/ascend_distributed_launcher/get_distribute_train_cmd.py \
--run_script_dir=${CUR_DIR}/train.py \
--hyper_parameter_config_dir=${CUR_DIR}/scripts/ascend_distributed_launcher/hyper_parameter_config.ini \
--data_dir=$1 \
--mindrecord_dir=$2 \
--hccl_config_dir=$3 \
--mindrecord_dir=$1 \
--hccl_config_dir=$2 \
--hccl_time_out=1200 \
--cmd_file=distributed_cmd.sh

2
model_zoo/research/cv/centernet/scripts/run_standalone_eval_ascend.sh
View File

@ -48,4 +48,4 @@ python ${PROJECT_DIR}/../eval.py \
--visual_image=true \
--enable_eval=true \
--save_result_dir="" \
--run_mode=val > log.txt 2>&1 &
--run_mode=val > eval_log.txt 2>&1 &

4
model_zoo/research/cv/centernet/scripts/run_standalone_train_ascend.sh
View File

@ -42,7 +42,7 @@ python ${PROJECT_DIR}/../train.py \
--load_checkpoint_path="" \
--save_checkpoint_steps=10000 \
--save_checkpoint_num=1 \
--data_dir="" \
--mindrecord_dir="" \
--mindrecord_prefix="coco_hp.train.mind" \
--visual_image=false \
--save_result_dir=""> log.txt 2>&1 &
--save_result_dir="" > training_log.txt 2>&1 &

88
model_zoo/research/cv/centernet/src/dataset.py
View File

@ -19,6 +19,7 @@ Data operations, will be used in train.py
import os
import copy
import math
import argparse
import cv2
import numpy as np
import pycocotools.coco as coco
@ -26,10 +27,9 @@ import pycocotools.coco as coco
import mindspore.dataset.engine.datasets as de
from mindspore import log as logger
from mindspore.mindrecord import FileWriter
from .image import color_aug
from .image import get_affine_transform, affine_transform
from .image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian, draw_dense_reg
from .visual import visual_image
from src.image import color_aug, get_affine_transform, affine_transform
from src.image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian, draw_dense_reg
from src.visual import visual_image
_current_dir = os.path.dirname(os.path.realpath(__file__))
@ -47,40 +47,39 @@ class COCOHP(de.Dataset):
Returns:
Prepocessed training or testing dataset for CenterNet network.
"""
def __init__(self, data_dir, data_opt, net_opt, run_mode):
def __init__(self, data_opt, run_mode="train", net_opt=None, enable_visual_image=False, save_path=None):
super(COCOHP, self).__init__()
if not os.path.isdir(data_dir):
raise RuntimeError("Invalid dataset path")
assert run_mode in ["train", "test", "val"], "only train/test/val mode are supported"
self.run_mode = run_mode
if self.run_mode != "test":
self.annot_path = os.path.join(data_dir, 'annotations',
'person_keypoints_{}2017.json').format(self.run_mode)
else:
self.annot_path = os.path.join(data_dir, 'annotations', 'image_info_test-dev2017.json')
self.image_path = os.path.join(data_dir, '{}2017').format(self.run_mode)
self._data_rng = np.random.RandomState(123)
self.data_opt = data_opt
self.data_opt.mean = self.data_opt.mean.reshape(1, 1, 3)
self.data_opt.std = self.data_opt.std.reshape(1, 1, 3)
self.net_opt = net_opt
self.coco = coco.COCO(self.annot_path)
def init(self, enable_visual_image=False, save_path=None, keep_res=False, flip_test=False):
"""initailize additional info"""
logger.info('Initializing coco 2017 {} data.'.format(self.run_mode))
logger.info('Image path: {}'.format(self.image_path))
logger.info('Annotations: {}'.format(self.annot_path))
assert run_mode in ["train", "test", "val"], "only train/test/val mode are supported"
self.run_mode = run_mode
if net_opt is not None:
self.net_opt = net_opt
self.enable_visual_image = enable_visual_image
if self.enable_visual_image:
self.save_path = os.path.join(save_path, self.run_mode, "input_image")
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
def init(self, data_dir, keep_res=False, flip_test=False):
"""initailize additional info"""
logger.info('Initializing coco 2017 {} data.'.format(self.run_mode))
if not os.path.isdir(data_dir):
raise RuntimeError("Invalid dataset path")
if self.run_mode != "test":
self.annot_path = os.path.join(data_dir, 'annotations',
'person_keypoints_{}2017.json').format(self.run_mode)
else:
self.annot_path = os.path.join(data_dir, 'annotations', 'image_info_test-dev2017.json')
self.image_path = os.path.join(data_dir, '{}2017').format(self.run_mode)
logger.info('Image path: {}'.format(self.image_path))
logger.info('Annotations: {}'.format(self.annot_path))
self.coco = coco.COCO(self.annot_path)
image_ids = self.coco.getImgIds()
if self.run_mode != "test":
self.images = []
@ -102,8 +101,15 @@ class COCOHP(de.Dataset):
def __len__(self):
return self.num_samples
def transfer_coco_to_mindrecord(self, mindrecord_dir, file_name, shard_num=1):
def transfer_coco_to_mindrecord(self, mindrecord_dir, file_name="coco_hp.train.mind", shard_num=1):
"""Create MindRecord file by image_dir and anno_path."""
if not os.path.isdir(mindrecord_dir):
os.makedirs(mindrecord_dir)
if os.path.isdir(self.image_path) and os.path.exists(self.annot_path):
logger.info("Create MindRecord based on COCO_HP dataset")
else:
raise ValueError('data_dir {} or anno_path {} does not exist'.format(self.image_path, self.annot_path))
mindrecord_path = os.path.join(mindrecord_dir, file_name)
writer = FileWriter(mindrecord_path, shard_num)
centernet_json = {
@ -139,6 +145,7 @@ class COCOHP(de.Dataset):
"category_id": np.array(category_id, np.int32)}
writer.write_raw_data([row])
writer.commit()
logger.info("Create Mindrecord Done, at {}".format(mindrecord_dir))
def _coco_box_to_bbox(self, box):
@ -393,19 +400,11 @@ class COCOHP(de.Dataset):
return ret
def create_train_dataset(self, mindrecord_dir, prefix, batch_size=1,
def create_train_dataset(self, mindrecord_dir, prefix="coco_hp.train.mind", batch_size=1,
device_num=1, rank=0, num_parallel_workers=1, do_shuffle=True):
"""create train dataset based on mindrecord file"""
if not os.path.isdir(mindrecord_dir):
os.makedirs(mindrecord_dir)
if os.path.isdir(self.image_path) and os.path.exists(self.annot_path):
logger.info("Create MindRecord based on COCO_HP dataset")
self.transfer_coco_to_mindrecord(mindrecord_dir, prefix, shard_num=8)
logger.info("Create Mindrecord Done, at {}".format(mindrecord_dir))
else:
raise ValueError('data_dir {} or anno_path {} does not exist'.format(self.image_path, self.annot_path))
else:
logger.info("MindRecord dataset already exists, dir: {}".format(mindrecord_dir))
raise ValueError('MindRecord data_dir {} does not exist'.format(mindrecord_dir))
files = os.listdir(mindrecord_dir)
data_files = []
@ -415,7 +414,6 @@ class COCOHP(de.Dataset):
if not data_files:
raise ValueError('data_dir {} have no data files'.format(mindrecord_dir))
columns = ["image", "num_objects", "keypoints", "bbox", "category_id"]
ds = de.MindDataset(data_files,
columns_list=columns,
@ -447,3 +445,17 @@ class COCOHP(de.Dataset):
ds = de.GeneratorDataset(generator, column, num_parallel_workers=num_parallel_workers)
ds = ds.batch(batch_size, drop_remainder=True, num_parallel_workers=8)
return ds
if __name__ == '__main__':
# Convert coco2017 dataset to mindrecord to improve performance on host
from src.config import dataset_config
parser = argparse.ArgumentParser(description='CenterNet MindRecord dataset')
parser.add_argument("--coco_data_dir", type=str, default="", help="Coco dataset directory.")
parser.add_argument("--mindrecord_dir", type=str, default="", help="MindRecord dataset dir.")
parser.add_argument("--mindrecord_prefix", type=str, default="coco_hp.train.mind",
help="Prefix of MindRecord dataset filename.")
args_opt = parser.parse_args()
dsc = COCOHP(dataset_config, run_mode="train")
dsc.init(args_opt.coco_data_dir)
dsc.transfer_coco_to_mindrecord(args_opt.mindrecord_dir, args_opt.mindrecord_prefix, shard_num=8)

9
model_zoo/research/cv/centernet/src/utils.py
View File

@ -20,7 +20,6 @@ import math
import numpy as np
import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import log as logger
from mindspore.common.tensor import Tensor
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
@ -494,11 +493,11 @@ class LossCallBack(Callback):
if percent == 0:
percent = 1
epoch_num -= 1
logger.info("epoch: {}, current epoch percent: {}, step: {}, outputs are {}"
.format(int(epoch_num), "%.3f" % percent, cb_params.cur_step_num, str(cb_params.net_outputs)))
print("epoch: {}, current epoch percent: {}, step: {}, outputs are {}"
.format(int(epoch_num), "%.3f" % percent, cb_params.cur_step_num, str(cb_params.net_outputs)))
else:
logger.info("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
str(cb_params.net_outputs)))
print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
str(cb_params.net_outputs)))
class CenterNetPolynomialDecayLR(LearningRateSchedule):

23
model_zoo/research/cv/centernet/train.py
View File

@ -58,13 +58,9 @@ parser.add_argument("--save_checkpoint_path", type=str, default="", help="Save c
parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path")
parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, default is 1000.")
parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
parser.add_argument("--mindrecord_dir", type=str, default="",
help="Mindrecord files directory. If is empty, mindrecord format files will be generated"
"based on the original dataset and annotation information. If mindrecord_dir isn't empty,"
"mindrecord_dir will be used inplace of data_dir and anno_path.")
parser.add_argument("--data_dir", type=str, default="", help="Dataset directory, "
"the absolute image path is joined by the data_dir "
"and the relative path in anno_path")
parser.add_argument("--mindrecord_dir", type=str, default="", help="Mindrecord dataset files directory")
parser.add_argument("--mindrecord_prefix", type=str, default="coco_hp.train.mind",
help="Prefix of MindRecord dataset filename.")
parser.add_argument("--visual_image", type=str, default="false", help="Visulize the ground truth and predicted image")
parser.add_argument("--save_result_dir", type=str, default="", help="The path to save the predict results")
@ -148,16 +144,15 @@ def train():
else:
rank = 0
device_num = 1
num_workers = device_num * 8
num_workers = 8
# Start create dataset!
# mindrecord files will be generated at args_opt.mindrecord_dir such as centernet.mindrecord0, 1, ... file_num.
logger.info("Begin creating dataset for CenterNet")
prefix = "coco_hp.train.mind"
coco = COCOHP(args_opt.data_dir, dataset_config, net_config, run_mode="train")
coco.init(enable_visual_image=(args_opt.visual_image == "true"), save_path=args_opt.save_result_dir)
dataset = coco.create_train_dataset(args_opt.mindrecord_dir, prefix, batch_size=train_config.batch_size,
device_num=device_num, rank=rank, num_parallel_workers=num_workers,
do_shuffle=args_opt.do_shuffle == 'true')
coco = COCOHP(dataset_config, run_mode="train", net_opt=net_config,
enable_visual_image=(args_opt.visual_image == "true"), save_path=args_opt.save_result_dir)
dataset = coco.create_train_dataset(args_opt.mindrecord_dir, args_opt.mindrecord_prefix,
batch_size=train_config.batch_size, device_num=device_num, rank=rank,
num_parallel_workers=num_workers, do_shuffle=args_opt.do_shuffle == 'true')
dataset_size = dataset.get_dataset_size()
logger.info("Create dataset done!")