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!20622 push icnet code 

Merge pull request !20622 from bigpingping/master
This commit is contained in:
i-robot 2021-07-26 07:38:02 +00:00 committed by Gitee
parent fd7c0c1514 f4eebf243c
commit aed52ca7c5
12 changed files with 79 additions and 46 deletions
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30
model_zoo/research/cv/ICNet/README.md
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@ -8,6 +8,7 @@
- [Script and Sample Code](#script-and-sample-code)
- [Script Parameters](#script-parameters)
- [Training Process](#training-process)
- [Prepare Dataset](#prepare-dataset)
- [Distributed Training](#distributed-training)
- [Training Results](#training-results)
- [Evaluation Process](#evaluation-process)
@ -41,8 +42,8 @@ It contains 5,000 finely annotated images split into training, validation and te
- frame:
- [Mindspore](https://www.mindspore.cn/install)
- For details, please refer to the following resources:
- [MindSpore course](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
- [MindSpore course](https://www.mindspore.cn/tutorial/training/zh-CN/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/zh-CN/master/index.html)
# [Scription Description](#Content)
@ -88,7 +89,7 @@ It contains 5,000 finely annotated images split into training, validation and te
## Script Parameters
Set script parameters in configs/icnet.yaml .
Set script parameters in src/model_utils/icnet.yaml .
### Model
@ -112,22 +113,29 @@ weight_decay: 0.0001
```bash
train_batch_size_percard: 4
valid_batch_size: 1
cityscapes_root: "/data/cityscapes/"
cityscapes_root: "/data/cityscapes/" # set dataset path
epochs: 160
val_epoch: 1 # run validation every val-epoch
ckpt_dir: "./ckpt/" # ckpt and training log will be saved here
mindrecord_dir: '/root/bigpingping/mindrecord'
val_epoch: 1
ckpt_dir: "./ckpt/" # ckpt and training log will be saved here
mindrecord_dir: '' # set mindrecord path
pretrained_model_path: '/root/ResNet50V1B-150_625.ckpt' # set the pretrained model path correctly
save_checkpoint_epochs: 5
keep_checkpoint_max: 10
```
### Valid
## Training Process
```bash
ckpt_path: "" # set the pretrained model path correctly
### Prepare Datast
- Convert dataset to Mindrecord
```python
python cityscapes_mindrecord.py [DATASET_PATH] [MINDRECORD_PATH]
```
## Training Process
- Note:
[MINDRCORD_PATH] in script should be consistent with 'mindrecord_dir' in config file.
### Distributed Training

6
model_zoo/research/cv/ICNet/eval.py
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@ -47,7 +47,7 @@ class Evaluator:
self.image_paths, self.mask_paths = _get_city_pairs(config["train"]["cityscapes_root"], "val")
# create network
self.model = ICNet(nclass=19, backbone='resnet50', istraining=False)
self.model = ICNet(nclass=19, pretrained_path=cfg["train"]["pretrained_model_path"], istraining=False)
# load ckpt
ckpt_file_name = args_opt.checkpoint_path
@ -74,6 +74,7 @@ class Evaluator:
mask = self._mask_transform(mask) # mask shape: (H,w)
image = Tensor(image)
print(image)
expand_dims = ops.ExpandDims()
image = expand_dims(image, 0)
@ -163,7 +164,8 @@ if __name__ == '__main__':
from src.metric import SegmentationMetric
from src.logger import SetupLogger
# Set config file
config_path = args_opt.project_path + "/src/model_utils/icnet.yaml"
config_file = "src/model_utils/icnet.yaml"
config_path = os.path.join(args_opt.project_path, config_file)
with open(config_path, "r") as yaml_file:
cfg = yaml.load(yaml_file.read())
logger = SetupLogger(name="semantic_segmentation",

2
model_zoo/research/cv/ICNet/postprocess.py
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@ -106,7 +106,7 @@ if __name__ == '__main__':
from src.metric import SegmentationMetric
from src.logger import SetupLogger
# Set config file
config_file = "/src/model_utils/icnet.yaml"
config_file = "src/model_utils/icnet.yaml"
config_path = os.path.join(args_opt.project_path, config_file)
with open(config_path, "r") as yaml_file:
cfg = yaml.load(yaml_file.read())

24
model_zoo/research/cv/ICNet/scripts/run_distribute_train8p.sh
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@ -17,20 +17,28 @@
if [ $# != 2 ]
then
echo "=============================================================================================================="
echo "Usage: bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [PROJECT_PATH]"
echo "Usage: bash scripts/run_distribute_train8p.sh [RANK_TABLE_FILE] [PROJECT_PATH]"
echo "Please run the script as: "
echo "bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [PROJECT_PATH]"
echo "for example: bash run_distribute_train.sh /absolute/path/to/RANK_TABLE_FILE /root/ICNet/"
echo "bash scripts/run_distribute_train8p.sh [RANK_TABLE_FILE] [PROJECT_PATH]"
echo "for example: bash script/run_distribute_train8p.sh /absolute/path/to/RANK_TABLE_FILE /root/ICNet/"
echo "=============================================================================================================="
exit 1
fi
PATH1=$(get_real_path $1)
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
PATH2=$(get_real_path $2)
if [ ! -d $PATH1 ]
if [ ! -d $PATH2 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
echo "error: PROJECT_PATH=$PATH2 is not a directory"
exit 1
fi
@ -51,7 +59,7 @@ do
echo "start training for rank $i, device $DEVICE_ID"
env > env.log
python3 train.py --project_path=$PATH1 > log.txt 2>&1 &
python3 train.py --project_path=$PATH2 > log.txt 2>&1 &
cd ../
done
done

13
model_zoo/research/cv/ICNet/scripts/run_eval.sh
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@ -30,6 +30,7 @@ get_real_path(){
PATH1=$(get_real_path $1)
PATH2=$(get_real_path $2)
PATH3=$(get_real_path $3)
if [ ! -d $PATH1 ]
@ -44,9 +45,15 @@ then
exit 1
fi
if [ ! -d $PATH3 ]
then
echo "error: PROJECT_PATH=$PATH3 is not a directory"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=$5
export DEVICE_ID=0
export RANK_SIZE=1
export RANK_ID=0
@ -61,6 +68,6 @@ cp -r ../src ./eval
cd ./eval || exit
env > env.log
echo "start evaluation for device $DEVICE_ID"
python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
python eval.py --dataset_path=$PATH1 --checkpoint_path=$PATH2 --project_path=$PATH3 &> log &
cd ..
cd ..

17
model_zoo/research/cv/ICNet/src/cityscapes_mindrecord.py
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@ -15,6 +15,7 @@
"""Prepare Cityscapes dataset"""
import os
import random
import argparse
import numpy as np
from PIL import Image
from PIL import ImageOps
@ -27,6 +28,7 @@ import mindspore.dataset.transforms.py_transforms as tc
def _get_city_pairs(folder, split='train'):
"""Return two path arrays of data set img and mask"""
def get_path_pairs(image_folder, masks_folder):
image_paths = []
masks_paths = []
@ -145,11 +147,10 @@ def _img_mask_transform(img, mask):
return (img, mask)
def data_to_mindrecord_img(prefix='cityscapes.mindrecord', file_num=1,
root='/data/cityscapes/', split='train'):
def data_to_mindrecord_img(prefix='cityscapes-2975.mindrecord', file_num=1,
root='./', split='train', mindrecord_dir="./"):
"""to mindrecord"""
mindrecord_dir = '/data/Mindrecord_cityscapes/'
mindrecord_path = os.path.join(mindrecord_dir, prefix + "2975-2")
mindrecord_path = os.path.join(mindrecord_dir, prefix)
writter = FileWriter(mindrecord_path, file_num)
@ -202,4 +203,10 @@ def create_icnet_dataset(mindrecord_file, batch_size=16, device_num=1, rank_id=0
if __name__ == '__main__':
data_to_mindrecord_img()
parser = argparse.ArgumentParser(description="dataset_to_mindrecord")
parser.add_argument("--dataset_path", type=str, default="/data/cityscapes/", help="dataset path")
parser.add_argument("--mindrecord_path", type=str, default="/data/cityscapes_mindrecord/",
help="mindrecord_path")
args_opt = parser.parse_args()
data_to_mindrecord_img(root=args_opt.dataset_path, mindrecord_dir=args_opt.mindrecord_path)

5
model_zoo/research/cv/ICNet/src/model_utils/icnet.yaml
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@ -19,10 +19,11 @@ train:
epochs: 160
val_epoch: 1 # run validation every val-epoch
ckpt_dir: "./ckpt/" # ckpt and training log will be saved here
mindrecord_dir: '/root/ICNet/mindrecord'
mindrecord_dir: '/data/cityscapes_mindrecord'
pretrained_model_path: '/root/ResNet50V1B-150_625.ckpt'
save_checkpoint_epochs: 5
keep_checkpoint_max: 10
### 4.Valid
test:
ckpt_path: "" # set the pretrained model path correctly
ckpt_path: "" # set the pretrained model path correctly

1
model_zoo/research/cv/ICNet/src/models/__init__.py
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@ -1,4 +1,3 @@
"""__init__"""
from .icnet import ICNet
from .icnet_dc import ICNetdc
from .icnet_1p import ICNet1p

8
model_zoo/research/cv/ICNet/src/models/icnet.py
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@ -28,7 +28,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
class ICNet(nn.Cell):
"""Image Cascade Network"""
def __init__(self, nclass=19, backbone='resnet50', pretrained_base=True, istraining=True):
def __init__(self, nclass=19, backbone='resnet50', pretrained_path='', istraining=True):
super(ICNet, self).__init__()
self.conv_sub1 = nn.SequentialCell(
_ConvBNReLU(3, 32, 3, 2),
@ -38,7 +38,7 @@ class ICNet(nn.Cell):
self.istraining = istraining
self.ppm = PyramidPoolingModule()
self.backbone = SegBaseModel()
self.backbone = SegBaseModel(root=pretrained_path)
self.head = _ICHead(nclass)
@ -232,11 +232,11 @@ class CascadeFeatureFusion24(nn.Cell):
class SegBaseModel(nn.Cell):
"""Base Model for Semantic Segmentation"""
def __init__(self, nclass=19, backbone='resnet50', pretrained_base=True, **kwargs):
def __init__(self, nclass=19, backbone='resnet50', root=''):
super(SegBaseModel, self).__init__()
self.nclass = nclass
if backbone == 'resnet50':
self.pretrained = get_resnet50v1b()
self.pretrained = get_resnet50v1b(ckpt_root=root)
def construct(self, x):
"""forwarding pre-trained network"""

8
model_zoo/research/cv/ICNet/src/models/icnet_dc.py
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@ -28,7 +28,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
class ICNetdc(nn.Cell):
"""Image Cascade Network"""
def __init__(self, nclass=19, backbone='resnet50', pretrained_base=True, istraining=True):
def __init__(self, nclass=19, pretrained_path="", istraining=True):
super(ICNetdc, self).__init__()
self.conv_sub1 = nn.SequentialCell(
_ConvBNReLU(3, 32, 3, 2),
@ -38,7 +38,7 @@ class ICNetdc(nn.Cell):
self.istraining = istraining
self.ppm = PyramidPoolingModule()
self.backbone = SegBaseModel()
self.backbone = SegBaseModel(root=pretrained_path)
self.head = _ICHead(nclass)
@ -235,11 +235,11 @@ class CascadeFeatureFusion24(nn.Cell):
class SegBaseModel(nn.Cell):
"""Base Model for Semantic Segmentation"""
def __init__(self, nclass=19, backbone='resnet50', pretrained_base=True, **kwargs):
def __init__(self, nclass=19, backbone='resnet50', root=""):
super(SegBaseModel, self).__init__()
self.nclass = nclass
if backbone == 'resnet50':
self.pretrained = get_resnet50v1b()
self.pretrained = get_resnet50v1b(ckpt_root=root)
def construct(self, x):
"""forwarding pre-trained network"""

2
model_zoo/research/cv/ICNet/src/models/resnet50_v1.py
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@ -258,7 +258,7 @@ class Resnet50v1b(nn.Cell):
return out
def get_resnet50v1b(class_num=1001, ckpt_root='/root/ICNet/ckpt/ResNet50V1B-150_625.ckpt', pretrained=True):
def get_resnet50v1b(class_num=1001, ckpt_root='', pretrained=True):
"""
Get SE-ResNet50 neural network.
Default : GE Theta+ version (best)

9
model_zoo/research/cv/ICNet/train.py
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@ -48,16 +48,16 @@ def train_net():
parameter_broadcast=True,
gradients_mean=True)
init()
prefix = 'cityscapes.mindrecord'
prefix = 'cityscapes-2975.mindrecord'
mindrecord_dir = cfg['train']["mindrecord_dir"]
mindrecord_file = os.path.join(mindrecord_dir, prefix + '2975')
mindrecord_file = os.path.join(mindrecord_dir, prefix)
dataset = create_icnet_dataset(mindrecord_file, batch_size=cfg['train']["train_batch_size_percard"],
device_num=device_num, rank_id=device_id)
train_data_size = dataset.get_dataset_size()
print("data_size", train_data_size)
epoch = cfg["train"]["epochs"]
network = ICNetdc() # __init__
network = ICNetdc(pretrained_path=cfg["train"]["pretrained_model_path"]) # __init__
iters_per_epoch = train_data_size
total_train_steps = iters_per_epoch * epoch
@ -85,7 +85,8 @@ if __name__ == '__main__':
from src.cityscapes_mindrecord import create_icnet_dataset
from src.models.icnet_dc import ICNetdc
from src.lr_scheduler import poly_lr
config_path = args_opt.project_path + "src/model_utils/icnet.yaml"
config_file = "src/model_utils/icnet.yaml"
config_path = os.path.join(args_opt.project_path, config_file)
with open(config_path, "r") as yaml_file:
cfg = yaml.load(yaml_file.read())
logging.basicConfig(level=logging.INFO)