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FCN8s

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Zhu Wenyong 2021-01-14 15:30:56 +08:00
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# Contents
- [FCN 介绍](#FCN-介绍)
- [模型架构](#模型架构)
- [数据集](#数据集)
- [环境要求](#环境要求)
- [快速开始](#快速开始)
- [脚本介绍](#脚本介绍)
- [脚本以及简单代码](#脚本以及简单代码)
- [脚本参数](#脚本参数)
- [训练步骤](#训练步骤)
- [训练](#训练)
- [评估步骤](#评估步骤)
- [评估](#评估)
- [模型介绍](#模型介绍)
- [性能](#性能)
- [评估性能](#评估性能)
- [如何使用](#如何使用)
- [教程](#教程)
- [随机事件介绍](#随机事件介绍)
- [ModelZoo 主页](#ModelZoo-主页)
# [FCN 介绍](#contents)
FCN主要用用于图像分割领域，是一种端到端的分割方法。FCN丢弃了全连接层，使得其能够处理任意大小的图像，且减少了模型的参数量，提高了模型的分割速度。FCN在编码部分使用了VGG的结构，在解码部分中使用反卷积/上采样操作恢复图像的分辨率。FCN-8s最后使用8倍的反卷积/上采样操作将输出分割图恢复到与输入图像相同大小。
[Paper]: Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
# [模型架构](#contents)
FCN-8s使用丢弃全连接操作的VGG16作为编码部分，并分别融合VGG16中第3,4,5个池化层特征，最后使用stride=8的反卷积获得分割图像。
# [数据集](#contents)
Dataset used:
[PASCAL VOC 2012](<http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html>)
[SBD](<http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz>)
# [环境要求](#contents)
- 硬件（Ascend/GPU）
- 需要准备具有Ascend或GPU处理能力的硬件环境. 如需使用Ascend，可以发送 [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) 到ascend@huawei.com。一旦批准，你就可以使用此资源
- 框架
- [MindSpore](https://www.mindspore.cn/install/en)
- 如需获取更多信息，请查看如下链接：
- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
# [快速开始](#contents)
在通过官方网站安装MindSpore之后，你可以通过如下步骤开始训练以及评估：
- runing on Ascend with default paramaters
```python
# run training example
python train.py --device_id device_id
# run evaluation example with default paramaters
python eval.py --device_id device_id
```
# [脚本介绍](#contents)
## [脚本以及简单代码](#contents)
```python
├── model_zoo
├── README.md // descriptions about all the models
├── FCN8s
├── README.md // descriptions about FCN
├── scripts
├── run_train.sh
├── run_eval.sh
├── build_data.sh
├── src
│ ├──data
│ ├──build_seg_data.py // creating dataset
│ ├──dataset.py // loading dataset
│ ├──nets
│ ├──FCN8s.py // FCN-8s architecture
│ ├──loss
│ ├──loss.py // loss function
│ ├──utils
│ ├──lr_scheduler.py // getting learning_rateFCN-8s
├── train.py // training script
├── eval.py // evaluation script
```
## [脚本参数](#contents)
训练以及评估的参数可以在config.py中设置
- config for FCN8s
```python
# dataset
'data_file': '/data/workspace/mindspore_dataset/FCN/FCN/dataset/MINDRECORED_NAME.mindrecord', # path and name of one mindrecord file
'batch_size': 32,
'crop_size': 512,
'image_mean': [103.53, 116.28, 123.675],
'image_std': [57.375, 57.120, 58.395],
'min_scale': 0.5,
'max_scale': 2.0,
'ignore_label': 255,
'num_classes': 21,
# optimizer
'train_epochs': 500,
'base_lr': 0.015,
'loss_scale': 1024.0,
# model
'model': 'FCN8s',
'ckpt_vgg16': '/data/workspace/mindspore_dataset/FCN/FCN/model/0-150_5004.ckpt',
'ckpt_pre_trained': '/data/workspace/mindspore_dataset/FCN/FCN/model_new/FCN8s-500_82.ckpt',
# train
'save_steps': 330,
'keep_checkpoint_max': 500,
'train_dir': '/data/workspace/mindspore_dataset/FCN/FCN/model_new/',
```
如需获取更多信息，请查看`config.py`.
## [生成数据步骤](#contents)
### 训练数据
- build mindrecord training data
```python
sh build_data.sh
or
python src/data/build_seg_data.py --data_root=/home/sun/data/Mindspore/benchmark_RELEASE/dataset \
--data_lst=/home/sun/data/Mindspore/benchmark_RELEASE/dataset/trainaug.txt \
--dst_path=dataset/MINDRECORED_NAME.mindrecord \
--num_shards=1 \
--shuffle=True
data_root: 训练数据集的总目录包含两个子目录img和cls_png，img目录下存放训练图像，cls_png目录下存放标签mask图像，
data_lst: 存放训练样本的名称列表文档，每行一个样本。
dst_path: 生成mindrecord数据的目标位置
```
## [训练步骤](#contents)
### 训练
- running on Ascend with default parameters
```python
python train.py --device_id device_id
```
训练时，训练过程中的epch和step以及此时的loss和精确度会呈现在终端上：
```python
epoch: * step: **, loss is ****
...
```
此模型的checkpoint会在默认路径下存储
## [评估步骤](#contents)
### 评估
- 在Ascend上使用PASCAL VOC 2012 验证集进行评估
在使用命令运行前，请检查用于评估的checkpoint的路径。请设置路径为到checkpoint的绝对路径，如 "/data/workspace/mindspore_dataset/FCN/FCN/model_new/FCN8s-500_82.ckpt"。
```python
python eval.py
```
以上的python命令会在终端上运行，你可以在终端上查看此次评估的结果。测试集的精确度会以如下方式呈现：
```python
mean IoU 0.6467
```
# [模型介绍](#contents)
## [性能](#contents)
### 评估性能
#### FCN8s on PASCAL VOC 2012
| Parameters | Ascend
| -------------------------- | -----------------------------------------------------------
| Model Version | FCN-8s
| Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G
| uploaded Date | 12/30/2020 (month/day/year)
| MindSpore Version | 1.1.0-alpha
| Dataset | PASCAL VOC 2012 and SBD
| Training Parameters | epoch=500, steps=330, batch_size = 32, lr=0.015
| Optimizer | Momentum
| Loss Function | Softmax Cross Entropy
| outputs | probability
| Loss | 0.038
| Speed | 1pc: 564.652 ms/step;
| Scripts | [FCN script](https://gitee.com/mindspore/mindspore/tree/r1.0/model_zoo/official/cv/FCN)
### Inference Performance
#### FCN8s on PASCAL VOC
| Parameters | Ascend
| ------------------- | ---------------------------
| Model Version | FCN-8s
| Resource | Ascend 910
| Uploaded Date | 10/29/2020 (month/day/year)
| MindSpore Version | 1.1.0-alpha
| Dataset | PASCAL VOC 2012
| batch_size | 16
| outputs | probability
| mean IoU | 64.67
## [如何使用](#contents)
### 教程
如果你需要在不同硬件平台（如GPU，Ascend 910 或者 Ascend 310）使用训练好的模型，你可以参考这个 [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/migrate_3rd_scripts.html)。以下是一个简单例子的步骤介绍：
- Running on Ascend
```
# Set context
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
context.set_auto_parallel_context(device_num=device_num,parallel_mode=ParallelMode.DATA_PARALLEL)
init()
# Load dataset
dataset = data_generator.SegDataset(image_mean=cfg.image_mean,
image_std=cfg.image_std,
data_file=cfg.data_file,
batch_size=cfg.batch_size,
crop_size=cfg.crop_size,
max_scale=cfg.max_scale,
min_scale=cfg.min_scale,
ignore_label=cfg.ignore_label,
num_classes=cfg.num_classes,
num_readers=2,
num_parallel_calls=4,
shard_id=args.rank,
shard_num=args.group_size)
dataset = dataset.get_dataset(repeat=1)
# Define model
net = FCN8s(n_class=cfg.num_classes)
loss_ = loss.SoftmaxCrossEntropyLoss(cfg.num_classes, cfg.ignore_label)
# optimizer
iters_per_epoch = dataset.get_dataset_size()
total_train_steps = iters_per_epoch * cfg.train_epochs
lr_scheduler = CosineAnnealingLR(cfg.base_lr,
cfg.train_epochs,
iters_per_epoch,
cfg.train_epochs,
warmup_epochs=0,
eta_min=0)
lr = Tensor(lr_scheduler.get_lr())
# loss scale
manager_loss_scale = FixedLossScaleManager(cfg.loss_scale, drop_overflow_update=False)
optimizer = nn.Momentum(params=net.trainable_params(), learning_rate=lr, momentum=0.9, weight_decay=0.0001,
loss_scale=cfg.loss_scale)
model = Model(net, loss_fn=loss_, loss_scale_manager=manager_loss_scale, optimizer=optimizer, amp_level="O3")
# callback for saving ckpts
time_cb = TimeMonitor(data_size=iters_per_epoch)
loss_cb = LossMonitor()
cbs = [time_cb, loss_cb]
if args.rank == 0:
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix=cfg.model, directory=cfg.train_dir, config=config_ck)
cbs.append(ckpoint_cb)
model.train(cfg.train_epochs, dataset, callbacks=cbs)
# [随机事件介绍](#contents)
我们在train.py中设置了随机种子
# [ModelZoo 主页](#contents)
请查看官方网站 [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

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# 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.
# ============================================================================
"""eval FCN8s."""
import argparse
import numpy as np
import cv2
from PIL import Image
from mindspore import Tensor
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.nets.FCN8s import FCN8s
def parse_args():
parser = argparse.ArgumentParser('mindspore FCN8s eval')
# val data
parser.add_argument('--data_root', type=str, default='../VOCdevkit/VOC2012/', help='root path of val data')
parser.add_argument('--batch_size', type=int, default=16, help='batch size')
parser.add_argument('--data_lst', type=str, default='../VOCdevkit/VOC2012/ImageSets/Segmentation/val.txt',
help='list of val data')
parser.add_argument('--crop_size', type=int, default=512, help='crop size')
parser.add_argument('--image_mean', type=list, default=[103.53, 116.28, 123.675], help='image mean')
parser.add_argument('--image_std', type=list, default=[57.375, 57.120, 58.395], help='image std')
parser.add_argument('--scales', type=float, default=[1.0], action='append', help='scales of evaluation')
parser.add_argument('--flip', type=bool, default=False, help='perform left-right flip')
parser.add_argument('--ignore_label', type=int, default=255, help='ignore label')
parser.add_argument('--num_classes', type=int, default=21, help='number of classes')
# model
parser.add_argument('--model', type=str, default='FCN8s', help='select model')
parser.add_argument('--freeze_bn', action='store_true', default=False, help='freeze bn')
parser.add_argument('--ckpt_path', type=str, default='model_new/FCN8s-500_82.ckpt', help='model to evaluate')
parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU'],
help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--device_id', type=int, default=0, help='device id of GPU or Ascend. (Default: None)')
args, _ = parser.parse_known_args()
return args
def cal_hist(a, b, n):
k = (a >= 0) & (a < n)
return np.bincount(n * a[k].astype(np.int32) + b[k], minlength=n ** 2).reshape(n, n)
def resize_long(img, long_size=513):
h, w, _ = img.shape
if h > w:
new_h = long_size
new_w = int(1.0 * long_size * w / h)
else:
new_w = long_size
new_h = int(1.0 * long_size * h / w)
imo = cv2.resize(img, (new_w, new_h))
return imo
class BuildEvalNetwork(nn.Cell):
def __init__(self, network):
super(BuildEvalNetwork, self).__init__()
self.network = network
self.softmax = nn.Softmax(axis=1)
def construct(self, input_data):
output = self.network(input_data)
output = self.softmax(output)
return output
def pre_process(args, img_, crop_size=512):
# resize
img_ = resize_long(img_, crop_size)
resize_h, resize_w, _ = img_.shape
# mean, std
image_mean = np.array(args.image_mean)
image_std = np.array(args.image_std)
img_ = (img_ - image_mean) / image_std
# pad to crop_size
pad_h = crop_size - img_.shape[0]
pad_w = crop_size - img_.shape[1]
if pad_h > 0 or pad_w > 0:
img_ = cv2.copyMakeBorder(img_, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=0)
# hwc to chw
img_ = img_.transpose((2, 0, 1))
return img_, resize_h, resize_w
def eval_batch(args, eval_net, img_lst, crop_size=512, flip=True):
result_lst = []
batch_size = len(img_lst)
batch_img = np.zeros((args.batch_size, 3, crop_size, crop_size), dtype=np.float32)
resize_hw = []
for l in range(batch_size):
img_ = img_lst[l]
img_, resize_h, resize_w = pre_process(args, img_, crop_size)
batch_img[l] = img_
resize_hw.append([resize_h, resize_w])
batch_img = np.ascontiguousarray(batch_img)
net_out = eval_net(Tensor(batch_img, mstype.float32))
net_out = net_out.asnumpy()
if flip:
batch_img = batch_img[:, :, :, ::-1]
net_out_flip = eval_net(Tensor(batch_img, mstype.float32))
net_out += net_out_flip.asnumpy()[:, :, :, ::-1]
for bs in range(batch_size):
probs_ = net_out[bs][:, :resize_hw[bs][0], :resize_hw[bs][1]].transpose((1, 2, 0))
ori_h, ori_w = img_lst[bs].shape[0], img_lst[bs].shape[1]
probs_ = cv2.resize(probs_, (ori_w, ori_h))
result_lst.append(probs_)
return result_lst
def eval_batch_scales(args, eval_net, img_lst, scales,
base_crop_size=512, flip=True):
sizes_ = [int((base_crop_size - 1) * sc) + 1 for sc in scales]
probs_lst = eval_batch(args, eval_net, img_lst, crop_size=sizes_[0], flip=flip)
print(sizes_)
for crop_size_ in sizes_[1:]:
probs_lst_tmp = eval_batch(args, eval_net, img_lst, crop_size=crop_size_, flip=flip)
for pl, _ in enumerate(probs_lst):
probs_lst[pl] += probs_lst_tmp[pl]
result_msk = []
for i in probs_lst:
result_msk.append(i.argmax(axis=2))
return result_msk
def net_eval():
args = parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id,
save_graphs=False)
# data list
with open(args.data_lst) as f:
img_lst = f.readlines()
net = FCN8s(n_class=args.num_classes)
# load model
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(net, param_dict)
# evaluate
hist = np.zeros((args.num_classes, args.num_classes))
batch_img_lst = []
batch_msk_lst = []
bi = 0
image_num = 0
for i, line in enumerate(img_lst):
img_name = line.strip('\n')
data_root = args.data_root
img_path = data_root + '/JPEGImages/' + str(img_name) + '.jpg'
msk_path = data_root + '/SegmentationClass/' + str(img_name) + '.png'
img_ = np.array(Image.open(img_path), dtype=np.uint8)
msk_ = np.array(Image.open(msk_path), dtype=np.uint8)
batch_img_lst.append(img_)
batch_msk_lst.append(msk_)
bi += 1
if bi == args.batch_size:
batch_res = eval_batch_scales(args, net, batch_img_lst, scales=args.scales,
base_crop_size=args.crop_size, flip=args.flip)
for mi in range(args.batch_size):
hist += cal_hist(batch_msk_lst[mi].flatten(), batch_res[mi].flatten(), args.num_classes)
bi = 0
batch_img_lst = []
batch_msk_lst = []
print('processed {} images'.format(i+1))
image_num = i
if bi > 0:
batch_res = eval_batch_scales(args, net, batch_img_lst, scales=args.scales,
base_crop_size=args.crop_size, flip=args.flip)
for mi in range(bi):
hist += cal_hist(batch_msk_lst[mi].flatten(), batch_res[mi].flatten(), args.num_classes)
print('processed {} images'.format(image_num + 1))
print(hist)
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print('per-class IoU', iu)
print('mean IoU', np.nanmean(iu))
if __name__ == '__main__':
net_eval()

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#!/bin/bash
# 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.
# ============================================================================
export DEVICE_ID=0
python src/data/build_seg_data.py --data_root=/home/sun/data/Mindspore/benchmark_RELEASE/dataset \
--data_lst=/home/sun/data/Mindspore/benchmark_RELEASE/dataset/trainaug.txt \
--dst_path=dataset/MINDRECORED_NAME.mindrecord \
--num_shards=1 \
--shuffle=True

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#!/bin/bash
# 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.
# ============================================================================
echo "=============================================================================================================="
echo "Please run the scipt as: "
echo "sh run_distribute_eval.sh DEVICE_NUM RANK_TABLE_FILE DATASET CKPT_PATH"
echo "for example: sh run_eval.sh [RANK_TABLE_FILE] /path/to/dataset /path/to/ckpt device_id"
echo "It is better to use absolute path."
echo "================================================================================================================="
export DATA_PATH=$1
CKPT_PATH=$2
DEVICE_ID=$3
rm -rf eval
mkdir ./eval
cp ./*.py ./eval
cp -r ./src ./eval
cd ./eval || exit
echo "start testing"
env > env.log
python eval.py \
--device_id=$DEVICE_ID \
--data_path=$DATA_PATH \
--ckpt_path=$CKPT_PATH #> log.txt 2>&1 &
cd ../

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#!/bin/bash
# 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.
# ============================================================================
if [ $# != 2 ]
then
echo "Usage: sh run_train.sh [device_num][RANK_TABLE_FILE]"
exit 1
fi
if [ ! -f $2 ]
then
echo "error: RANK_TABLE_FILE=$2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=$1
export RANK_SIZE=$1
RANK_TABLE_FILE=$(realpath $2)
export RANK_TABLE_FILE
echo "RANK_TABLE_FILE=${RANK_TABLE_FILE}"
export SERVER_ID=0
rank_start=$((DEVICE_NUM * SERVER_ID))
for((i=0; i<$1; i++))
do
export DEVICE_ID=$i
export RANK_ID=$((rank_start + i))
rm -rf ./train_parallel$i
mkdir ./train_parallel$i
cp -r ./src ./train_parallel$i
cp ./train.py ./train_parallel$i
echo "start training for rank $RANK_ID, device $DEVICE_ID"
cd ./train_parallel$i ||exit
env > env.log
python train.py --device_id=$i > log 2>&1 &
cd ..
done

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# 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.
# ============================================================================
"""
network config setting, will be used in train.py
"""
from easydict import EasyDict as edict
FCN8s_VOC2012_cfg = edict({
# dataset
'data_file': '/data/workspace/mindspore_dataset/FCN/FCN/dataset/MINDRECORED_NAME.mindrecord',
'batch_size': 32,
'crop_size': 512,
'image_mean': [103.53, 116.28, 123.675],
'image_std': [57.375, 57.120, 58.395],
'min_scale': 0.5,
'max_scale': 2.0,
'ignore_label': 255,
'num_classes': 21,
# optimizer
'train_epochs': 500,
'base_lr': 0.015,
'loss_scale': 1024.0,
# model
'model': 'FCN8s',
'ckpt_vgg16': '/data/workspace/mindspore_dataset/FCN/FCN/model/0-150_5004.ckpt',
'ckpt_pre_trained': '/data/workspace/mindspore_dataset/FCN/FCN/model_new/FCN8s-500_82.ckpt',
# train
'save_steps': 330,
'keep_checkpoint_max': 500,
'train_dir': '/data/workspace/mindspore_dataset/FCN/FCN/model_new/',
})

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# 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 os
import argparse
import numpy as np
from mindspore.mindrecord import FileWriter
seg_schema = {"file_name": {"type": "string"}, "label": {"type": "bytes"}, "data": {"type": "bytes"}}
def parse_args():
parser = argparse.ArgumentParser('mindrecord')
parser.add_argument('--data_root', type=str, default='', help='root path of data')
parser.add_argument('--data_lst', type=str, default='', help='list of data')
parser.add_argument('--dst_path', type=str, default='', help='save path of mindrecords')
parser.add_argument('--num_shards', type=int, default=8, help='number of shards')
parser.add_argument('--shuffle', type=bool, default=True, help='shuffle or not')
parser_args, _ = parser.parse_known_args()
return parser_args
if __name__ == '__main__':
args = parse_args()
datas = []
with open(args.data_lst) as f:
lines = f.readlines()
if args.shuffle:
np.random.shuffle(lines)
dst_dir = '/'.join(args.dst_path.split('/')[:-1])
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
print('number of samples:', len(lines))
writer = FileWriter(file_name=args.dst_path, shard_num=args.num_shards)
writer.add_schema(seg_schema, "seg_schema")
cnt = 0
for l in lines:
img_name = l.strip('\n')
img_path = 'img/' + str(img_name) + '.jpg'
label_path = 'cls_png/' + str(img_name) + '.png'
sample_ = {"file_name": img_path.split('/')[-1]}
with open(os.path.join(args.data_root, img_path), 'rb') as f:
sample_['data'] = f.read()
with open(os.path.join(args.data_root, label_path), 'rb') as f:
sample_['label'] = f.read()
datas.append(sample_)
cnt += 1
if cnt % 1000 == 0:
writer.write_raw_data(datas)
print('number of samples written:', cnt)
datas = []
if datas:
writer.write_raw_data(datas)
writer.commit()
print('number of samples written:', cnt)

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# 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 numpy as np
import cv2
import mindspore.dataset as de
cv2.setNumThreads(0)
class SegDataset:
def __init__(self,
image_mean,
image_std,
data_file='',
batch_size=32,
crop_size=512,
max_scale=2.0,
min_scale=0.5,
ignore_label=255,
num_classes=21,
num_readers=2,
num_parallel_calls=4,
shard_id=None,
shard_num=None):
self.data_file = data_file
self.batch_size = batch_size
self.crop_size = crop_size
self.image_mean = np.array(image_mean, dtype=np.float32)
self.image_std = np.array(image_std, dtype=np.float32)
self.max_scale = max_scale
self.min_scale = min_scale
self.ignore_label = ignore_label
self.num_classes = num_classes
self.num_readers = num_readers
self.num_parallel_calls = num_parallel_calls
self.shard_id = shard_id
self.shard_num = shard_num
assert max_scale > min_scale
def preprocess_(self, image, label):
# bgr image
image_out = cv2.imdecode(np.frombuffer(image, dtype=np.uint8), cv2.IMREAD_COLOR)
label_out = cv2.imdecode(np.frombuffer(label, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)
sc = np.random.uniform(self.min_scale, self.max_scale)
new_h, new_w = int(sc * image_out.shape[0]), int(sc * image_out.shape[1])
image_out = cv2.resize(image_out, (new_w, new_h), interpolation=cv2.INTER_CUBIC)
label_out = cv2.resize(label_out, (new_w, new_h), interpolation=cv2.INTER_NEAREST)
image_out = (image_out - self.image_mean) / self.image_std
h_, w_ = max(new_h, self.crop_size), max(new_w, self.crop_size)
pad_h, pad_w = h_ - new_h, w_ - new_w
if pad_h > 0 or pad_w > 0:
image_out = cv2.copyMakeBorder(image_out, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=0)
label_out = cv2.copyMakeBorder(label_out, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=self.ignore_label)
offset_h = np.random.randint(0, h_ - self.crop_size + 1)
offset_w = np.random.randint(0, w_ - self.crop_size + 1)
image_out = image_out[offset_h: offset_h + self.crop_size, offset_w: offset_w + self.crop_size, :]
label_out = label_out[offset_h: offset_h + self.crop_size, offset_w: offset_w+self.crop_size]
if np.random.uniform(0.0, 1.0) > 0.5:
image_out = image_out[:, ::-1, :]
label_out = label_out[:, ::-1]
image_out = image_out.transpose((2, 0, 1))
image_out = image_out.copy()
label_out = label_out.copy()
return image_out, label_out
def get_dataset(self, repeat=1):
data_set = de.MindDataset(dataset_file=self.data_file, columns_list=["data", "label"],
shuffle=True, num_parallel_workers=self.num_readers,
num_shards=self.shard_num, shard_id=self.shard_id)
transforms_list = self.preprocess_
data_set = data_set.map(operations=transforms_list, input_columns=["data", "label"],
output_columns=["data", "label"],
num_parallel_workers=self.num_parallel_calls)
data_set = data_set.shuffle(buffer_size=self.batch_size * 10)
data_set = data_set.batch(self.batch_size, drop_remainder=True)
data_set = data_set.repeat(repeat)
return data_set

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# 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 mindspore import Tensor
import mindspore.common.dtype as mstype
import mindspore.nn as nn
from mindspore.ops import operations as P
class SoftmaxCrossEntropyLoss(nn.Cell):
def __init__(self, num_cls=21, ignore_label=255):
super(SoftmaxCrossEntropyLoss, self).__init__()
self.one_hot = P.OneHot(axis=-1)
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.cast = P.Cast()
self.ce = nn.SoftmaxCrossEntropyWithLogits()
self.not_equal = P.NotEqual()
self.num_cls = num_cls
self.ignore_label = ignore_label
self.mul = P.Mul()
self.sum = P.ReduceSum(False)
self.div = P.RealDiv()
self.transpose = P.Transpose()
self.reshape = P.Reshape()
def construct(self, logits, labels):
labels_int = self.cast(labels, mstype.int32)
labels_int = self.reshape(labels_int, (-1,))
logits_ = self.transpose(logits, (0, 2, 3, 1))
logits_ = self.reshape(logits_, (-1, self.num_cls))
weights = self.not_equal(labels_int, self.ignore_label)
weights = self.cast(weights, mstype.float32)
one_hot_labels = self.one_hot(labels_int, self.num_cls, self.on_value, self.off_value)
logits_ = self.cast(logits_, mstype.float32)
loss = self.ce(logits_, one_hot_labels)
loss = self.mul(weights, loss)
loss = self.div(self.sum(loss), self.sum(weights))
return loss

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# 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 mindspore.nn as nn
from mindspore.ops import operations as P
class FCN8s(nn.Cell):
def __init__(self, n_class):
super().__init__()
self.n_class = n_class
self.conv1 = nn.SequentialCell(
nn.Conv2d(in_channels=3,
out_channels=64,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(in_channels=64,
out_channels=64,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(64),
nn.ReLU()
)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.SequentialCell(
nn.Conv2d(in_channels=64,
out_channels=128,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(in_channels=128,
out_channels=128,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(128),
nn.ReLU()
)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.SequentialCell(
nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(in_channels=256,
out_channels=256,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(256),
nn.ReLU()
)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv4 = nn.SequentialCell(
nn.Conv2d(in_channels=256,
out_channels=512,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(512),
nn.ReLU()
)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv5 = nn.SequentialCell(
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
weight_init='xavier_uniform'),
nn.BatchNorm2d(512),
nn.ReLU()
)
self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv6 = nn.SequentialCell(
nn.Conv2d(in_channels=512,
out_channels=4096,
kernel_size=7,
weight_init='xavier_uniform'),
nn.BatchNorm2d(4096),
nn.ReLU(),
)
self.conv7 = nn.SequentialCell(
nn.Conv2d(in_channels=4096,
out_channels=4096,
kernel_size=1,
weight_init='xavier_uniform'),
nn.BatchNorm2d(4096),
nn.ReLU(),
)
self.score_fr = nn.Conv2d(in_channels=4096,
out_channels=self.n_class,
kernel_size=1,
weight_init='xavier_uniform')
self.upscore2 = nn.Conv2dTranspose(in_channels=self.n_class,
out_channels=self.n_class,
kernel_size=4,
stride=2,
weight_init='xavier_uniform')
self.score_pool4 = nn.Conv2d(in_channels=512,
out_channels=self.n_class,
kernel_size=1,
weight_init='xavier_uniform')
self.upscore_pool4 = nn.Conv2dTranspose(in_channels=self.n_class,
out_channels=self.n_class,
kernel_size=4,
stride=2,
weight_init='xavier_uniform')
self.score_pool3 = nn.Conv2d(in_channels=256,
out_channels=self.n_class,
kernel_size=1,
weight_init='xavier_uniform')
self.upscore8 = nn.Conv2dTranspose(in_channels=self.n_class,
out_channels=self.n_class,
kernel_size=16,
stride=8,
weight_init='xavier_uniform')
self.shape = P.Shape()
self.cast = P.Cast()
def construct(self, x):
x1 = self.conv1(x)
p1 = self.pool1(x1)
x2 = self.conv2(p1)
p2 = self.pool2(x2)
x3 = self.conv3(p2)
p3 = self.pool3(x3)
x4 = self.conv4(p3)
p4 = self.pool4(x4)
x5 = self.conv5(p4)
p5 = self.pool5(x5)
x6 = self.conv6(p5)
x7 = self.conv7(x6)
sf = self.score_fr(x7)
u2 = self.upscore2(sf)
s4 = self.score_pool4(p4)
f4 = s4 + u2
u4 = self.upscore_pool4(f4)
s3 = self.score_pool3(p3)
f3 = s3 + u4
out = self.upscore8(f3)
return out

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# 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.
# ============================================================================
"""
learning rate scheduler
"""
import math
from collections import Counter
import numpy as np
__all__ = ["LambdaLR", "MultiplicativeLR", "StepLR", "MultiStepLR", "ExponentialLR",
"CosineAnnealingLR", "CyclicLR", "CosineAnnealingWarmRestarts", "OneCycleLR"]
class _WarmUp():
def __init__(self, warmup_init_lr):
self.warmup_init_lr = warmup_init_lr
def get_lr(self):
# Get learning rate during warmup
raise NotImplementedError
class _LinearWarmUp(_WarmUp):
"""
linear warmup function
"""
def __init__(self, lr, warmup_epochs, steps_per_epoch, warmup_init_lr=0):
self.base_lr = lr
self.warmup_init_lr = warmup_init_lr
self.warmup_steps = int(warmup_epochs * steps_per_epoch)
super(_LinearWarmUp, self).__init__(warmup_init_lr)
def get_warmup_steps(self):
return self.warmup_steps
def get_lr(self, current_step):
lr_inc = (float(self.base_lr) - float(self.warmup_init_lr)) / float(self.warmup_steps)
lr = float(self.warmup_init_lr) + lr_inc * current_step
return lr
class _ConstWarmUp(_WarmUp):
def get_lr(self):
return self.warmup_init_lr
class _LRScheduler():
def __init__(self, lr, max_epoch, steps_per_epoch):
self.base_lr = lr
self.steps_per_epoch = steps_per_epoch
self.total_steps = int(max_epoch * steps_per_epoch)
def get_lr(self):
# Compute learning rate using chainable form of the scheduler
raise NotImplementedError
class LambdaLR(_LRScheduler):
"""Sets the learning rate to the initial lr times a given function.
Args:
lr (float): Initial learning rate which is the
lower boundary in the cycle.
steps_per_epoch (int): The number of steps per epoch to train for. This is
used along with epochs in order to infer the total number of steps in the cycle.
max_epoch (int): The number of epochs to train for. This is used along
with steps_per_epoch in order to infer the total number of steps in the cycle.
lr_lambda (function or list): A function which computes a multiplicative
factor given an integer parameter epoch.
warmup_epochs (int): The number of epochs to Warmup.
Default: 0
Example:
>>> # Assuming optimizer has two groups.
>>> lambda1 = lambda epoch: epoch // 30
>>> scheduler = LambdaLR(lr=0.1, lr_lambda=lambda1, steps_per_epoch=5000,
>>> max_epoch=90, warmup_epochs=0)
>>> lr = scheduler.get_lr()
"""
def __init__(self, lr, lr_lambda, steps_per_epoch, max_epoch, warmup_epochs=0):
self.lr_lambda = lr_lambda
self.warmup = _LinearWarmUp(lr, warmup_epochs, steps_per_epoch)
super(LambdaLR, self).__init__(lr, max_epoch, steps_per_epoch)
def get_lr(self):
warmup_steps = self.warmup.get_warmup_steps()
lr_each_step = []
for i in range(self.total_steps):
if i < warmup_steps:
lr = self.warmup.get_lr(i+1)
else:
cur_ep = i // self.steps_per_epoch
lr = self.base_lr * self.lr_lambda(cur_ep)
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
class MultiplicativeLR(_LRScheduler):
"""Multiply the learning rate by the factor given
in the specified function.
Args:
lr_lambda (function or list): A function which computes a multiplicative
factor given an integer parameter epoch,.
Example:
>>> lmbda = lambda epoch: 0.95
>>> scheduler = MultiplicativeLR(lr=0.1, lr_lambda=lambda1, steps_per_epoch=5000,
>>> max_epoch=90, warmup_epochs=0)
>>> lr = scheduler.get_lr()
"""
def __init__(self, lr, lr_lambda, steps_per_epoch, max_epoch, warmup_epochs=0):
self.lr_lambda = lr_lambda
self.warmup = _LinearWarmUp(lr, warmup_epochs, steps_per_epoch)
super(MultiplicativeLR, self).__init__(lr, max_epoch, steps_per_epoch)
def get_lr(self):
warmup_steps = self.warmup.get_warmup_steps()
lr_each_step = []
current_lr = self.base_lr
for i in range(self.total_steps):
if i < warmup_steps:
lr = self.warmup.get_lr(i+1)
else:
cur_ep = i // self.steps_per_epoch
if i % self.steps_per_epoch == 0 and cur_ep > 0:
current_lr = current_lr * self.lr_lambda(cur_ep)
lr = current_lr
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
class StepLR(_LRScheduler):
"""Decays the learning rate by gamma every epoch_size epochs.
Args:
lr (float): Initial learning rate which is the
lower boundary in the cycle.
steps_per_epoch (int): The number of steps per epoch to train for. This is
used along with epochs in order to infer the total number of steps in the cycle.
max_epoch (int): The number of epochs to train for. This is used along
with steps_per_epoch in order to infer the total number of steps in the cycle.
epoch_size (int): Period of learning rate decay.
gamma (float): Multiplicative factor of learning rate decay.
Default: 0.1.
warmup_epochs (int): The number of epochs to Warmup.
Default: 0
Example:
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.05 if epoch < 30
>>> # lr = 0.005 if 30 <= epoch < 60
>>> # lr = 0.0005 if 60 <= epoch < 90
>>> # ...
>>> scheduler = StepLR(lr=0.1, epoch_size=30, gamma=0.1, steps_per_epoch=5000,
>>> max_epoch=90, warmup_epochs=0)
>>> lr = scheduler.get_lr()
"""
def __init__(self, lr, epoch_size, gamma, steps_per_epoch, max_epoch, warmup_epochs=0):
self.epoch_size = epoch_size
self.gamma = gamma
self.warmup = _LinearWarmUp(lr, warmup_epochs, steps_per_epoch)
super(StepLR, self).__init__(lr, max_epoch, steps_per_epoch)
def get_lr(self):
warmup_steps = self.warmup.get_warmup_steps()
lr_each_step = []
for i in range(self.total_steps):
if i < warmup_steps:
lr = self.warmup.get_lr(i+1)
else:
cur_ep = i // self.steps_per_epoch
lr = self.base_lr * self.gamma**(cur_ep // self.epoch_size)
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
class MultiStepLR(_LRScheduler):
"""Decays the learning rate by gamma once the number of epoch reaches one
of the milestones.
Args:
lr (float): Initial learning rate which is the
lower boundary in the cycle.
steps_per_epoch (int): The number of steps per epoch to train for. This is
used along with epochs in order to infer the total number of steps in the cycle.
max_epoch (int): The number of epochs to train for. This is used along
with steps_per_epoch in order to infer the total number of steps in the cycle.
milestones (list): List of epoch indices. Must be increasing.
gamma (float): Multiplicative factor of learning rate decay.
Default: 0.1.
warmup_epochs (int): The number of epochs to Warmup.
Default: 0
Example:
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.05 if epoch < 30
>>> # lr = 0.005 if 30 <= epoch < 80
>>> # lr = 0.0005 if epoch >= 80
>>> scheduler = MultiStepLR(lr=0.1, milestones=[30,80], gamma=0.1, steps_per_epoch=5000,
>>> max_epoch=90, warmup_epochs=0)
>>> lr = scheduler.get_lr()
"""
def __init__(self, lr, milestones, gamma, steps_per_epoch, max_epoch, warmup_epochs=0):
self.milestones = Counter(milestones)
self.gamma = gamma
self.warmup = _LinearWarmUp(lr, warmup_epochs, steps_per_epoch)
super(MultiStepLR, self).__init__(lr, max_epoch, steps_per_epoch)
def get_lr(self):
warmup_steps = self.warmup.get_warmup_steps()
lr_each_step = []
current_lr = self.base_lr
for i in range(self.total_steps):
if i < warmup_steps:
lr = self.warmup.get_lr(i+1)
else:
cur_ep = i // self.steps_per_epoch
if i % self.steps_per_epoch == 0 and cur_ep in self.milestones:
current_lr = current_lr * self.gamma
lr = current_lr
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
class ExponentialLR(_LRScheduler):
"""Decays the learning rate of each parameter group by gamma every epoch.
Args:
lr (float): Initial learning rate which is the
lower boundary in the cycle.
gamma (float): Multiplicative factor of learning rate decay.
steps_per_epoch (int): The number of steps per epoch to train for. This is
used along with epochs in order to infer the total number of steps in the cycle.
max_epoch (int): The number of epochs to train for. This is used along
with steps_per_epoch in order to infer the total number of steps in the cycle.
warmup_epochs (int): The number of epochs to Warmup.
Default: 0
"""
def __init__(self, lr, gamma, steps_per_epoch, max_epoch, warmup_epochs=0):
self.gamma = gamma
self.warmup = _LinearWarmUp(lr, warmup_epochs, steps_per_epoch)
super(ExponentialLR, self).__init__(lr, max_epoch, steps_per_epoch)
def get_lr(self):
warmup_steps = self.warmup.get_warmup_steps()
lr_each_step = []
current_lr = self.base_lr
for i in range(self.total_steps):
if i < warmup_steps:
lr = self.warmup.get_lr(i+1)
else:
if i % self.steps_per_epoch == 0 and i > 0:
current_lr = current_lr * self.gamma
lr = current_lr
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
class CosineAnnealingLR(_LRScheduler):
r"""Set the learning rate using a cosine annealing schedule, where
:math:`\eta_{max}` is set to the initial lr and :math:`T_{cur}` is the
number of epochs since the last restart in SGDR:
.. math::
\begin{aligned}
\eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1
+ \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right),
& T_{cur} \neq (2k+1)T_{max}; \\
\eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min})
\left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right),
& T_{cur} = (2k+1)T_{max}.
\end{aligned}
It has been proposed in
`SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this only
implements the cosine annealing part of SGDR, and not the restarts.
Args:
lr (float): Initial learning rate which is the
lower boundary in the cycle.
T_max (int): Maximum number of iterations.
eta_min (float): Minimum learning rate. Default: 0.
steps_per_epoch (int): The number of steps per epoch to train for. This is
used along with epochs in order to infer the total number of steps in the cycle.
max_epoch (int): The number of epochs to train for. This is used along
with steps_per_epoch in order to infer the total number of steps in the cycle.
warmup_epochs (int): The number of epochs to Warmup.
Default: 0
.. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
https://arxiv.org/abs/1608.03983
"""
def __init__(self, lr, T_max, steps_per_epoch, max_epoch, warmup_epochs=0, eta_min=0):
self.T_max = T_max
self.eta_min = eta_min
self.warmup = _LinearWarmUp(lr, warmup_epochs, steps_per_epoch)
super(CosineAnnealingLR, self).__init__(lr, max_epoch, steps_per_epoch)
def get_lr(self):
warmup_steps = self.warmup.get_warmup_steps()
lr_each_step = []
current_lr = self.base_lr
for i in range(self.total_steps):
if i < warmup_steps:
lr = self.warmup.get_lr(i+1)
else:
cur_ep = i // self.steps_per_epoch
if i % self.steps_per_epoch == 0 and i > 0:
current_lr = self.eta_min + \
(self.base_lr - self.eta_min) * (1. + math.cos(math.pi*cur_ep / self.T_max)) / 2
lr = current_lr
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
class CyclicLR(_LRScheduler):
r"""Sets the learning rate according to cyclical learning rate policy (CLR).
The policy cycles the learning rate between two boundaries with a constant
frequency, as detailed in the paper `Cyclical Learning Rates for Training
Neural Networks`_. The distance between the two boundaries can be scaled on
a per-iteration or per-cycle basis.
Cyclical learning rate policy changes the learning rate after every batch.
This class has three built-in policies, as put forth in the paper:
* "triangular": A basic triangular cycle without amplitude scaling.
* "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle.
* "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}`
at each cycle iteration.
This implementation was adapted from the github repo: `bckenstler/CLR`_
Args:
lr (float): Initial learning rate which is the
lower boundary in the cycle.
max_lr (float): Upper learning rate boundaries in the cycle.
Functionally, it defines the cycle amplitude (max_lr - base_lr).
The lr at any cycle is the sum of base_lr and some scaling
of the amplitude; therefore max_lr may not actually be reached
depending on scaling function.
steps_per_epoch (int): The number of steps per epoch to train for. This is
used along with epochs in order to infer the total number of steps in the cycle.
max_epoch (int): The number of epochs to train for. This is used along
with steps_per_epoch in order to infer the total number of steps in the cycle.
step_size_up (int): Number of training iterations in the
increasing half of a cycle. Default: 2000
step_size_down (int): Number of training iterations in the
decreasing half of a cycle. If step_size_down is None,
it is set to step_size_up. Default: None
mode (str): One of {triangular, triangular2, exp_range}.
Values correspond to policies detailed above.
If scale_fn is not None, this argument is ignored.
Default: 'triangular'
gamma (float): Constant in 'exp_range' scaling function:
gamma**(cycle iterations)
Default: 1.0
scale_fn (function): Custom scaling policy defined by a single
argument lambda function, where
0 <= scale_fn(x) <= 1 for all x >= 0.
If specified, then 'mode' is ignored.
Default: None
scale_mode (str): {'cycle', 'iterations'}.
Defines whether scale_fn is evaluated on
cycle number or cycle iterations (training
iterations since start of cycle).
Default: 'cycle'
warmup_epochs (int): The number of epochs to Warmup.
Default: 0
.. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
.. _bckenstler/CLR: https://github.com/bckenstler/CLR
"""
def __init__(self,
lr,
max_lr,
steps_per_epoch,
max_epoch,
step_size_up=2000,
step_size_down=None,
mode='triangular',
gamma=1.,
scale_fn=None,
scale_mode='cycle',
warmup_epochs=0):
self.max_lr = max_lr
step_size_up = float(step_size_up)
step_size_down = float(step_size_down) if step_size_down is not None else step_size_up
self.total_size = step_size_up + step_size_down
self.step_ratio = step_size_up / self.total_size
if mode not in ['triangular', 'triangular2', 'exp_range'] \
and scale_fn is None:
raise ValueError('mode is invalid and scale_fn is None')
self.mode = mode
self.gamma = gamma
if scale_fn is None:
if self.mode == 'triangular':
self.scale_fn = self._triangular_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'triangular2':
self.scale_fn = self._triangular2_scale_fn
self.scale_mode = 'cycle'
elif self.mode == 'exp_range':
self.scale_fn = self._exp_range_scale_fn
self.scale_mode = 'iterations'
else:
self.scale_fn = scale_fn
self.scale_mode = scale_mode
self.warmup = _LinearWarmUp(lr, warmup_epochs, steps_per_epoch)
super(CyclicLR, self).__init__(lr, max_epoch, steps_per_epoch)
def _triangular_scale_fn(self, x):
return 1.
def _triangular2_scale_fn(self, x):
return 1 / (2. ** (x - 1))
def _exp_range_scale_fn(self, x):
return self.gamma**(x)
def get_lr(self):
warmup_steps = self.warmup.get_warmup_steps()
lr_each_step = []
for i in range(self.total_steps):
if i < warmup_steps:
lr = self.warmup.get_lr(i+1)
else:
# Calculates the learning rate at batch index.
cycle = math.floor(1 + i / self.total_size)
x = 1. + i / self.total_size - cycle
if x <= self.step_ratio:
scale_factor = x / self.step_ratio
else:
scale_factor = (x - 1) / (self.step_ratio - 1)
base_height = (self.max_lr - self.base_lr) * scale_factor
if self.scale_mode == 'cycle':
lr = self.base_lr + base_height * self.scale_fn(cycle)
else:
lr = self.base_lr + base_height * self.scale_fn(i)
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
class CosineAnnealingWarmRestarts(_LRScheduler):
r"""Set the learning rate using a cosine annealing schedule, where
:math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` is the
number of epochs since the last restart and :math:`T_{i}` is the number
of epochs between two warm restarts in SGDR:
.. math::
\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
\cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right)
When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`.
When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`.
It has been proposed in
`SGDR: Stochastic Gradient Descent with Warm Restarts`_.
Args:
lr (float): Initial learning rate.
steps_per_epoch (int): The number of steps per epoch to train for. This is
used along with epochs in order to infer the total number of steps in the cycle.
max_epoch (int): The number of epochs to train for. This is used along
with steps_per_epoch in order to infer the total number of steps in the cycle.
T_0 (int): Number of iterations for the first restart.
T_mult (int, optional): A factor increases :math:`T_{i}` after a restart. Default: 1.
eta_min (float, optional): Minimum learning rate. Default: 0.
warmup_epochs (int): The number of epochs to Warmup.
Default: 0
.. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
https://arxiv.org/abs/1608.03983
"""
def __init__(self, lr, steps_per_epoch, max_epoch, T_0, T_mult=1, eta_min=0, warmup_epochs=0):
if T_0 <= 0 or not isinstance(T_0, int):
raise ValueError("Expected positive integer T_0, but got {}".format(T_0))
if T_mult < 1 or not isinstance(T_mult, int):
raise ValueError("Expected integer T_mult >= 1, but got {}".format(T_mult))
self.T_0 = T_0
self.T_i = T_0
self.T_mult = T_mult
self.eta_min = eta_min
self.T_cur = 0
self.warmup = _LinearWarmUp(lr, warmup_epochs, steps_per_epoch)
super(CosineAnnealingWarmRestarts, self).__init__(lr, max_epoch, steps_per_epoch)
def get_lr(self):
warmup_steps = self.warmup.get_warmup_steps()
lr_each_step = []
for i in range(self.total_steps):
if i < warmup_steps:
lr = self.warmup.get_lr(i+1)
else:
if i % self.steps_per_epoch == 0 and i > 0:
self.T_cur += 1
if self.T_cur >= self.T_i:
self.T_cur = self.T_cur - self.T_i
self.T_i = self.T_i * self.T_mult
lr = self.eta_min + (self.base_lr - self.eta_min) * \
(1 + math.cos(math.pi * self.T_cur / self.T_i)) / 2
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
class OneCycleLR(_LRScheduler):
r"""Sets the learning rate of each parameter group according to the
1cycle learning rate policy. The 1cycle policy anneals the learning
rate from an initial learning rate to some maximum learning rate and then
from that maximum learning rate to some minimum learning rate much lower
than the initial learning rate.
This policy was initially described in the paper `Super-Convergence:
Very Fast Training of Neural Networks Using Large Learning Rates`_.
The 1cycle learning rate policy changes the learning rate after every batch.
This scheduler is not chainable.
Args:
lr (float): Initial learning rate.
steps_per_epoch (int): The number of steps per epoch to train for. This is
used along with epochs in order to infer the total number of steps in the cycle.
max_epoch (int): The number of epochs to train for. This is used along
with steps_per_epoch in order to infer the total number of steps in the cycle.
pct_start (float): The percentage of the cycle (in number of steps) spent
increasing the learning rate.
Default: 0.3
anneal_strategy (str): {'cos', 'linear'}
Specifies the annealing strategy: "cos" for cosine annealing, "linear" for
linear annealing.
Default: 'cos'
div_factor (float): Determines the max learning rate via
max_lr = lr * div_factor
Default: 25
final_div_factor (float): Determines the minimum learning rate via
min_lr = lr / final_div_factor
Default: 1e4
warmup_epochs (int): The number of epochs to Warmup.
Default: 0
.. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
https://arxiv.org/abs/1708.07120
"""
def __init__(self,
lr,
steps_per_epoch,
max_epoch,
pct_start=0.3,
anneal_strategy='cos',
div_factor=25.,
final_div_factor=1e4,
warmup_epochs=0):
self.warmup = _LinearWarmUp(lr, warmup_epochs, steps_per_epoch)
super(OneCycleLR, self).__init__(lr, max_epoch, steps_per_epoch)
self.step_size_up = float(pct_start * self.total_steps) - 1
self.step_size_down = float(self.total_steps - self.step_size_up) - 1
# Validate pct_start
if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
raise ValueError("Expected float between 0 and 1 pct_start, but got {}".format(pct_start))
# Validate anneal_strategy
if anneal_strategy not in ['cos', 'linear']:
raise ValueError("anneal_strategy must by one of 'cos' or 'linear', instead got {}".format(anneal_strategy))
if anneal_strategy == 'cos':
self.anneal_func = self._annealing_cos
elif anneal_strategy == 'linear':
self.anneal_func = self._annealing_linear
# Initialize learning rate variables
self.max_lr = lr * div_factor
self.min_lr = lr / final_div_factor
def _annealing_cos(self, start, end, pct):
"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
cos_out = math.cos(math.pi * pct) + 1
return end + (start - end) / 2.0 * cos_out
def _annealing_linear(self, start, end, pct):
"Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."
return (end - start) * pct + start
def get_lr(self):
warmup_steps = self.warmup.get_warmup_steps()
lr_each_step = []
for i in range(self.total_steps):
if i < warmup_steps:
lr = self.warmup.get_lr(i+1)
else:
if i <= self.step_size_up:
lr = self.anneal_func(self.base_lr, self.max_lr, i / self.step_size_up)
else:
down_step_num = i - self.step_size_up
lr = self.anneal_func(self.max_lr, self.min_lr, down_step_num / self.step_size_down)
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)

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# 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.
# ============================================================================
"""train FCN8s."""
import os
import argparse
from mindspore import context, Tensor
from mindspore.train.model import Model
from mindspore.context import ParallelMode
import mindspore.nn as nn
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.communication.management import init, get_rank, get_group_size
from mindspore.train.callback import LossMonitor, TimeMonitor
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.common import set_seed
from src.data import dataset as data_generator
from src.loss import loss
from src.utils.lr_scheduler import CosineAnnealingLR
from src.nets.FCN8s import FCN8s
from src.config import FCN8s_VOC2012_cfg
set_seed(1)
def parse_args():
parser = argparse.ArgumentParser('mindspore FCN training')
parser.add_argument('--device_id', type=int, default=0, help='device id of GPU or Ascend. (Default: None)')
args, _ = parser.parse_known_args()
return args
def train():
args = parse_args()
cfg = FCN8s_VOC2012_cfg
device_num = int(os.environ.get("DEVICE_NUM", 1))
# init multicards training
if device_num > 1:
parallel_mode = ParallelMode.DATA_PARALLEL
context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=device_num)
init()
args.rank = get_rank()
args.group_size = get_group_size()
context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, save_graphs=False,
device_target="Ascend", device_id=args.device_id)
# dataset
dataset = data_generator.SegDataset(image_mean=cfg.image_mean,
image_std=cfg.image_std,
data_file=cfg.data_file,
batch_size=cfg.batch_size,
crop_size=cfg.crop_size,
max_scale=cfg.max_scale,
min_scale=cfg.min_scale,
ignore_label=cfg.ignore_label,
num_classes=cfg.num_classes,
num_readers=2,
num_parallel_calls=4,
shard_id=args.rank,
shard_num=args.group_size)
dataset = dataset.get_dataset(repeat=1)
net = FCN8s(n_class=cfg.num_classes)
loss_ = loss.SoftmaxCrossEntropyLoss(cfg.num_classes, cfg.ignore_label)
# load pretrained vgg16 parameters to init FCN8s
if cfg.ckpt_vgg16:
param_vgg = load_checkpoint(cfg.ckpt_vgg16)
param_dict = {}
for layer_id in range(1, 6):
sub_layer_num = 2 if layer_id < 3 else 3
for sub_layer_id in range(sub_layer_num):
# conv param
y_weight = 'conv{}.{}.weight'.format(layer_id, 3 * sub_layer_id)
x_weight = 'vgg16_feature_extractor.conv{}_{}.0.weight'.format(layer_id, sub_layer_id + 1)
param_dict[y_weight] = param_vgg[x_weight]
# BatchNorm param
y_gamma = 'conv{}.{}.gamma'.format(layer_id, 3 * sub_layer_id + 1)
y_beta = 'conv{}.{}.beta'.format(layer_id, 3 * sub_layer_id + 1)
x_gamma = 'vgg16_feature_extractor.conv{}_{}.1.gamma'.format(layer_id, sub_layer_id + 1)
x_beta = 'vgg16_feature_extractor.conv{}_{}.1.beta'.format(layer_id, sub_layer_id + 1)
param_dict[y_gamma] = param_vgg[x_gamma]
param_dict[y_beta] = param_vgg[x_beta]
load_param_into_net(net, param_dict)
# load pretrained FCN8s
elif cfg.ckpt_pre_trained:
param_dict = load_checkpoint(cfg.ckpt_pre_trained)
load_param_into_net(net, param_dict)
# optimizer
iters_per_epoch = dataset.get_dataset_size()
lr_scheduler = CosineAnnealingLR(cfg.base_lr,
cfg.train_epochs,
iters_per_epoch,
cfg.train_epochs,
warmup_epochs=0,
eta_min=0)
lr = Tensor(lr_scheduler.get_lr())
# loss scale
manager_loss_scale = FixedLossScaleManager(cfg.loss_scale, drop_overflow_update=False)
optimizer = nn.Momentum(params=net.trainable_params(), learning_rate=lr, momentum=0.9, weight_decay=0.0001,
loss_scale=cfg.loss_scale)
model = Model(net, loss_fn=loss_, loss_scale_manager=manager_loss_scale, optimizer=optimizer, amp_level="O3")
# callback for saving ckpts
time_cb = TimeMonitor(data_size=iters_per_epoch)
loss_cb = LossMonitor()
cbs = [time_cb, loss_cb]
if args.rank == 0:
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix=cfg.model, directory=cfg.train_dir, config=config_ck)
cbs.append(ckpoint_cb)
model.train(cfg.train_epochs, dataset, callbacks=cbs)
if __name__ == '__main__':
train()