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一、山东土地集团杯-遥感图像语义分割
比赛地址：www.heywhale.com/home/compet…
1.大赛背景
滨州市按照中央和省相关要求，以智慧城市建设为统领，切实推进数据资源整合，创新开展各类数据应用，建成滨州市公共数据开放网站，实现54个部门单位、7个县（市、区）共11592个目录，约4亿余条数据依法依规向社会开放，覆盖经济、科技、教育、卫生、文化、体育等20余个主题。
本次滨州市“山东土地集团杯”数据应用创新创业大赛旨在进一步提升滨州市大数据应用范围和效果，提高部门和社会大众对大数据的理解和认识，发掘大数据优秀案例和人才，加快数据要素资源与产业发展融合，推动赛事成果转化，培育发展数据产业，推动产数融合，为经济社会发展提供数据支撑，服务经济提质增效升级，真正做到数据取之于民、造福于民，努力实现数据开放、数据应用、产业发展三位一体的目标。
地物要素分类是地表第五要素观测与测绘的重要手段之一，然而目前地物要素的提取方法主要依赖人工，效率低且成本高昂，急需通过先进的算法提高精度并使其自动化。充分运用智能算法与大数据技术突破遥感影像的信息提取与分析瓶颈，不仅是业务端的迫切需要，更是一个企业在数据时代打造数字化业务的重要标杆。
基于赛事官方提供的数据及建模分析平台，参赛者需要对光学遥感图像中各类光谱信息和空间信息进行分析，将遥感图像进行土地类型语义分割处理，为图像中具有语义信息的各个像元赋予语义类别标签。
2.数据描述
此次算法赛采用了1.5 万+遥感影像语义分割样本数据，遥感数据为GF1-WFV拍摄的山东滨州附近地区的影像，预处理过程为正射校正、配准、裁剪。分类目标是山东省土地利用类型，经过处理合并得到以下六类：耕地、林地、草地、水域、城乡、工矿、居民用地及未利用土地。
原始影像数据格式为 tif（000001_GF.tif），包含红绿蓝（RGB) 三个波段，影像尺寸为 256*256 标注文件格式为 tif（000001_LT.tif），每个像素的标签值由 1～6 表示：
| 类别 | 类别标签 |
|---|---|
| 耕地 | 1 |
| 林地 | 2 |
| 草地 | 3 |
| 水域 | 4 |
| 城乡、工矿、居民用地 | 5 |
| 未利用土地 | 6 |
3.数据下载
| 数据名称 | 数据描述 | 下载链接（成功报名后可下载） | 开放时间 |
|---|---|---|---|
| 提交样例 | 「results.zip」：提交样例文件，输出值为随机结果，共参赛人员参考 | 点击下载 | 2021-12-31中午 12:00 |
| 初赛训练集 | 5,000 原始影像和标注文件 | 点击下载 | 2021-12-31中午 12:00 |
| 初赛A榜测试集 | 2,000 原始影像 | 点击下载 | 2021-12-31中午 12:00 |
| 初赛B榜测试集 | 1,000 原始影像 | 点击下载 | 2022-1-27中午 12:00 |
| 复赛相关数据 | 训练集、A榜测试集和B榜测试集 | 赛事页面↗前往组织-工作台-查看数据源，新建项目通过“添加数据源”选项挂载使用数据 | 2022-2-15 中午12:00 |
二、环境准备
本项目采用paddleseg套件实现了遥感影像地块的分割，paddleseg版本号为v2.3版本
1.PaddleSeg下载
# 下载paddleseg
%cd ~
!git clone -b release/2.3 https://gitee.com/paddlepaddle/PaddleSeg.git --depth=1
2.PaddleSeg安装
%cd ~/PaddleSeg/
!pip install -U pip
!pip install -r requirements.txt
!pip install -e .
三、 数据准备
1.解压缩数据
%cd ~
# 解压训练数据集到work目录下
!unzip -oaq -d /home/aistudio/train_dataset data/data125219/初赛训练集.zip
# 解压测试数据集到work目录下
!unzip -oaq -d /home/aistudio/test_dataset data/data125219/初赛A榜_GF.zip
/home/aistudio
2.划分出训练集和验证集文件列表
# 生成文件列表文件
%cd ~
import os
import numpy as np
DATA_ROOT_DIR = '/home/aistudio/train_dataset/'
def make_list():
img_list = [img.split('.')[0].strip('_GF') for img in os.listdir(os.path.join(DATA_ROOT_DIR, '初赛训练_GF'))]
data_path_list = []
for image_id in img_list:
image_path = os.path.join(DATA_ROOT_DIR, '初赛训练_GF', f"{image_id}_GF.tif")
label_path = os.path.join(DATA_ROOT_DIR, '初赛训练_LT', f"{image_id}_LT.tif")
data_path_list.append((image_path, label_path))
np.random.seed(5)
np.random.shuffle(data_path_list)
total_len = len(data_path_list)
train_data_len = int(total_len*0.8)
train_data = data_path_list[0 : train_data_len]
val_data = data_path_list[train_data_len : ]
with open(os.path.join(DATA_ROOT_DIR, 'train_list.txt'), "w") as f:
for image, label in train_data:
f.write(f"{image} {label}\n")
with open(os.path.join(DATA_ROOT_DIR, 'val_list.txt'), "w") as f:
for image, label in val_data:
f.write(f"{image} {label}\n")
if __name__ == '__main__':
make_list()
/home/aistudio
3.标签修改
由于原始label图像是从 1开始编码，不符合PaddleSeg要修，故进行修改。
# 修改label从0开始起算
import numpy as np
from PIL import Image
def convert_img_lab(img_path):
img=Image.open(img_path).convert("L")
img=np.array(img)
# 修改label从0开始起算
# 惯性思维用循环了，多谢嘟嘟赐教
img=img-1
# for i in range(256):
# for j in range(256):
# img[i][j]=img[i][j]-1
img=Image.fromarray(img.astype('uint8'))
img.save(img_path,quality=95)
import os
help(os.listdir)
Help on built-in function listdir in module posix:
listdir(path=None)
Return a list containing the names of the files in the directory.
path can be specified as either str, bytes, or a path-like object. If path is bytes,
the filenames returned will also be bytes; in all other circumstances
the filenames returned will be str.
If path is None, uses the path='.'.
On some platforms, path may also be specified as an open file descriptor;\
the file descriptor must refer to a directory.
If this functionality is unavailable, using it raises NotImplementedError.
The list is in arbitrary order. It does not include the special
entries '.' and '..' even if they are present in the directory.
# 开始修改label
%cd ~
for file in os.listdir('/home/aistudio/train_dataset/初赛训练_LT/'):
convert_img_lab(os.path.join('/home/aistudio/train_dataset/初赛训练_LT/', file))
%cd ~
!head -n 10 train_dataset/train_list.txt
/home/aistudio
/home/aistudio/train_dataset/初赛训练_GF/003938_GF.tif /home/aistudio/train_dataset/初赛训练_LT/003938_LT.tif
/home/aistudio/train_dataset/初赛训练_GF/009635_GF.tif /home/aistudio/train_dataset/初赛训练_LT/009635_LT.tif
/home/aistudio/train_dataset/初赛训练_GF/002443_GF.tif /home/aistudio/train_dataset/初赛训练_LT/002443_LT.tif
/home/aistudio/train_dataset/初赛训练_GF/010992_GF.tif /home/aistudio/train_dataset/初赛训练_LT/010992_LT.tif
/home/aistudio/train_dataset/初赛训练_GF/004967_GF.tif /home/aistudio/train_dataset/初赛训练_LT/004967_LT.tif
/home/aistudio/train_dataset/初赛训练_GF/011362_GF.tif /home/aistudio/train_dataset/初赛训练_LT/011362_LT.tif
/home/aistudio/train_dataset/初赛训练_GF/001391_GF.tif /home/aistudio/train_dataset/初赛训练_LT/001391_LT.tif
/home/aistudio/train_dataset/初赛训练_GF/003704_GF.tif /home/aistudio/train_dataset/初赛训练_LT/003704_LT.tif
/home/aistudio/train_dataset/初赛训练_GF/015393_GF.tif /home/aistudio/train_dataset/初赛训练_LT/015393_LT.tif
/home/aistudio/train_dataset/初赛训练_GF/004227_GF.tif /home/aistudio/train_dataset/初赛训练_LT/004227_LT.tif
from PIL import Image
img=Image.open('/home/aistudio/train_dataset/初赛训练_GF/003938_GF.tif')
print(img.size)
img
(256, 256)
4.遥感图片转JPG图片
-
- tif可以有8位，24位，32位甚至更高，又分为有符号，无符号等，对于学遥感和GIS的来说，接触到的tif影像往往是高像素深度的，tif影像中的灰度值可以是几千几万。但jpg，一般是8位，rgb都是0到255取值，所以tif到jpg,意味着色彩的压缩。
-
- tif可以是多波段的，例如高分一号影像就有红绿蓝近红外四个波段，而jpg只有RGB三个颜色通道，tif转jpg意味着波段信息的丢失
注意：下面这段代码用于图像转换，不过gdal在win下安装方便，aistudio下安装失败。。。。。。期待大神。 多谢：  大神提醒。
!pip install -U pip
!pip install gdal
# -*- coding: UTF-8 -*-
import numpy as np
import os
from PIL import Image
# from osgeo import gdal
import gdal
def readTif(imgPath, bandsOrder=[3, 2, 1]):
"""
读取GEO tif影像的前三个波段值，并按照R.G.B顺序存储到形状为【原长*原宽*3】的数组中
:param imgPath: 图像存储全路径
:param bandsOrder: RGB对应的波段顺序，如高分二号多光谱包含蓝，绿，红，近红外四个波段，RGB对应的波段为3，2，1
:return: R.G.B三维数组
"""
dataset = gdal.Open(imgPath, gdal.GA_ReadOnly)
cols = dataset.RasterXSize
rows = dataset.RasterYSize
data = np.empty([rows, cols, 3], dtype=float)
for i in range(3):
band = dataset.GetRasterBand(bandsOrder[i])
oneband_data = band.ReadAsArray()
data[:, :, i] = oneband_data
return data
def stretchImg(imgPath, resultPath, lower_percent=0.5, higher_percent=99.5):
"""
#将光谱DN值映射至0-255，并保存
:param imgPath: 需要转换的tif影像路径（***.tif）
:param resultPath: 转换后的文件存储路径(***.jpg)
:param lower_percent: 低值拉伸比率
:param higher_percent: 高值拉伸比率
:return: 无返回参数，直接输出图片
"""
RGB_Array=readTif(imgPath)
band_Num = RGB_Array.shape[2]
JPG_Array = np.zeros_like(RGB_Array, dtype=np.uint8)
for i in range(band_Num):
minValue = 0
maxValue = 255
#获取数组RGB_Array某个百分比分位上的值
low_value = np.percentile(RGB_Array[:, :,i], lower_percent)
high_value = np.percentile(RGB_Array[:, :,i], higher_percent)
temp_value = minValue + (RGB_Array[:, :,i] - low_value) * (maxValue - minValue) / (high_value - low_value)
temp_value[temp_value < minValue] = minValue
temp_value[temp_value > maxValue] = maxValue
JPG_Array[:, :, i] = temp_value
outputImg = Image.fromarray(np.uint8(JPG_Array))
outputImg.save(resultPath)
def Batch_Convert_tif_to_jpg(imgdir,savedir):
#获取文件夹下所有tif文件名称，并存入列表
file_name_list = os.listdir(imgdir)
for name in file_name_list:
# 获取图片文件全路径
img_path = os.path.join(imgdir, name)
#获取文件名，不包含扩展名
filename = os.path.splitext(name)[0]
# savefilename = filename+".jpg"
savefilename = filename+".tif"
#文件存储全路径
savepath = os.path.join(savedir, savefilename)
stretchImg(img_path, savepath)
print("图片:【", filename, "】完成转换")
print("完成所有图片转换!")
#主函数，首先调用
if __name__ == '__main__':
imgdir = r"train_dataset/初赛训练_LT" #tif文件所在的【文件夹】
savedir=r"train_dataset/初赛训练_LT" #转为jpg后存储的【文件夹】
Batch_Convert_tif_to_jpg(imgdir, savedir)
imgdir = r"test_dataset" #tif文件所在的【文件夹】
savedir=r"test_dataset" #转为jpg后存储的【文件夹】
Batch_Convert_tif_to_jpg(imgdir, savedir)
四、模型训练
从数据集分析结果来看，各个类别像素占整个图片的面积比例很小
具体训练配置如下remotesensing.yml
batch_size: 60
iters: 150000
train_dataset:
type: Dataset
dataset_root: /home/aistudio
train_path: /home/aistudio/train_dataset/train_list.txt
num_classes: 6
transforms:
- type: ResizeStepScaling
min_scale_factor: 0.75
max_scale_factor: 1.5
scale_step_size: 0.25
- type: RandomHorizontalFlip
- type: RandomVerticalFlip
- type: RandomRotation
max_rotation: 30
- type: RandomDistort
brightness_range: 0.2
contrast_range: 0.2
saturation_range: 0.2
- type: Normalize
- type: Resize
target_size: [256, 256]
mode: train
val_dataset:
type: Dataset
dataset_root: /home/aistudio
val_path: /home/aistudio/train_dataset/val_list.txt
num_classes: 6
transforms:
- type: Resize
target_size: [256, 256]
- type: Normalize
mode: val
optimizer:
type: sgd
momentum: 0.9
weight_decay: 4.0e-5
lr_scheduler:
type: PolynomialDecay
learning_rate: 0.01
end_lr: 0
power: 0.9
loss:
types:
- type: CrossEntropyLoss
coef: [1, 0.4]
model:
type: OCRNet
backbone:
type: HRNet_W48
align_corners: False
pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz
backbone_indices: [-1]
pretrained: https://bj.bcebos.com/paddleseg/dygraph/ccf/fcn_hrnetw48_rs_256x256_160k/model.pdparams
%cd ~/PaddleSeg/
!python train.py \
--config ../remotesensing.yml \
--do_eval \
--use_vdl \
--save_interval 100 \
--save_dir output_ocrnet
五、模型预测
- 因为图片后缀为.tif，所以对
PaddleSeg/paddleseg/utils/utils.py进行了修改，增加.tif格式 - 模型预测时希望将预测的结果直接作为提交结果，但是paddleseg默认预测的结果是增加权重后生成的图片，所以对paddleseg的源码进行了修改。修改的文件为
PaddleSeg/paddleseg/core/predict.py，修改后是下面这段代码
# PaddleSeg/paddleseg/utils/utils.py
def get_image_list(image_path):
"""Get image list"""
valid_suffix = [
'.JPEG', '.jpeg', '.JPG', '.jpg', '.BMP', '.bmp', '.PNG', '.png', '.tif' # 增加tif格式
]
image_list = []
image_dir = None
if os.path.isfile(image_path):
if os.path.splitext(image_path)[-1] in valid_suffix:
image_list.append(image_path)
else:
image_dir = os.path.dirname(image_path)
with open(image_path, 'r') as f:
for line in f:
line = line.strip()
if len(line.split()) > 1:
line = line.split()[0]
image_list.append(os.path.join(image_dir, line))
elif os.path.isdir(image_path):
image_dir = image_path
for root, dirs, files in os.walk(image_path):
for f in files:
if '.ipynb_checkpoints' in root:
continue
if os.path.splitext(f)[-1] in valid_suffix:
image_list.append(os.path.join(root, f))
else:
raise FileNotFoundError(
'`--image_path` is not found. it should be an image file or a directory including images'
)
if len(image_list) == 0:
raise RuntimeError('There are not image file in `--image_path`')
return image_list, image_dir
# 修改predict函数
import os
import math
import cv2
import numpy as np
import paddle
from paddleseg import utils
from paddleseg.core import infer
from paddleseg.utils import logger, progbar, visualize
def mkdir(path):
sub_dir = os.path.dirname(path)
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
def partition_list(arr, m):
"""split the list 'arr' into m pieces"""
n = int(math.ceil(len(arr) / float(m)))
return [arr[i:i + n] for i in range(0, len(arr), n)]
# 修改predict函数
def predict(model,
model_path,
transforms,
image_list,
image_dir=None,
save_dir='output',
aug_pred=False,
scales=1.0,
flip_horizontal=True,
flip_vertical=False,
is_slide=False,
stride=None,
crop_size=None):
"""
predict and visualize the image_list.
Args:
model (nn.Layer): Used to predict for input image.
model_path (str): The path of pretrained model.
transforms (transform.Compose): Preprocess for input image.
image_list (list): A list of image path to be predicted.
image_dir (str, optional): The root directory of the images predicted. Default: None.
save_dir (str, optional): The directory to save the visualized results. Default: 'output'.
aug_pred (bool, optional): Whether to use mulit-scales and flip augment for predition. Default: False.
scales (list|float, optional): Scales for augment. It is valid when `aug_pred` is True. Default: 1.0.
flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when `aug_pred` is True. Default: True.
flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when `aug_pred` is True. Default: False.
is_slide (bool, optional): Whether to predict by sliding window. Default: False.
stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
crop_size (tuple|list, optional): The crop size of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
"""
utils.utils.load_entire_model(model, model_path)
model.eval()
nranks = paddle.distributed.get_world_size()
local_rank = paddle.distributed.get_rank()
if nranks > 1:
img_lists = partition_list(image_list, nranks)
else:
img_lists = [image_list]
added_saved_dir = os.path.join(save_dir, 'added_prediction')
pred_saved_dir = os.path.join(save_dir, 'pseudo_color_prediction')
logger.info("Start to predict...")
progbar_pred = progbar.Progbar(target=len(img_lists[0]), verbose=1)
with paddle.no_grad():
for i, im_path in enumerate(img_lists[local_rank]):
im = cv2.imread(im_path)
ori_shape = im.shape[:2]
im, _ = transforms(im)
im = im[np.newaxis, ...]
im = paddle.to_tensor(im)
if aug_pred:
pred = infer.aug_inference(
model,
im,
ori_shape=ori_shape,
transforms=transforms.transforms,
scales=scales,
flip_horizontal=flip_horizontal,
flip_vertical=flip_vertical,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
pred = infer.inference(
model,
im,
ori_shape=ori_shape,
transforms=transforms.transforms,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
pred = paddle.squeeze(pred)
pred = pred.numpy().astype('uint8')
# get the saved name
if image_dir is not None:
im_file = im_path.replace(image_dir, '')
else:
im_file = os.path.basename(im_path)
if im_file[0] == '/' or im_file[0] == '\\':
im_file = im_file[1:]
# 修改预测后的图片标签
# 修改label从1开始起算
# 惯性思维用循环了，多谢嘟嘟赐教
pred=pred+1
# for jj in range(256):
# for j in range(256):
# pred[jj][j]=pred[jj][j]+1
pred_saved_path = os.path.join(save_dir, im_file.rsplit(".")[0].strip("_GF") + "_LT.tif")
mkdir(pred_saved_path)
cv2.imwrite(pred_saved_path, pred)
progbar_pred.update(i + 1)
# 模型预测
%cd ~/PaddleSeg/
!python predict.py \
--config ../remotesensing.yml \
--model_path ./output_ocrnet/best_model/model.pdparams \
--image_path /home/aistudio/test_dataset \
--save_dir /home/aistudio/result
/home/aistudio/PaddleSeg
2022-01-13 09:32:53 [INFO]
---------------Config Information---------------
batch_size: 60
iters: 150000
loss:
coef:
- 1
- 0.4
types:
- type: CrossEntropyLoss
lr_scheduler:
end_lr: 0
learning_rate: 0.01
power: 0.9
type: PolynomialDecay
model:
backbone:
align_corners: false
pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz
type: HRNet_W48
backbone_indices:
- -1
pretrained: https://bj.bcebos.com/paddleseg/dygraph/ccf/fcn_hrnetw48_rs_256x256_160k/model.pdparams
type: OCRNet
optimizer:
momentum: 0.9
type: sgd
weight_decay: 4.0e-05
train_dataset:
dataset_root: /home/aistudio
mode: train
num_classes: 6
train_path: /home/aistudio/train_dataset/train_list.txt
transforms:
- max_scale_factor: 1.5
min_scale_factor: 0.75
scale_step_size: 0.25
type: ResizeStepScaling
- type: RandomHorizontalFlip
- type: RandomVerticalFlip
- max_rotation: 30
type: RandomRotation
- brightness_range: 0.2
contrast_range: 0.2
saturation_range: 0.2
type: RandomDistort
- type: Normalize
- target_size:
- 256
- 256
type: Resize
type: Dataset
val_dataset:
dataset_root: /home/aistudio
mode: val
num_classes: 6
transforms:
- target_size:
- 256
- 256
type: Resize
- type: Normalize
type: Dataset
val_path: /home/aistudio/train_dataset/val_list.txt
------------------------------------------------
W0113 09:32:53.874372 1340 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0113 09:32:53.874420 1340 device_context.cc:422] device: 0, cuDNN Version: 7.6.
2022-01-13 09:32:59 [INFO] Loading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz
2022-01-13 09:33:02 [INFO] There are 1525/1525 variables loaded into HRNet.
2022-01-13 09:33:02 [INFO] Loading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/ccf/fcn_hrnetw48_rs_256x256_160k/model.pdparams
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.0._conv.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.0._conv.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.0._batch_norm.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.0._batch_norm.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.0._batch_norm._mean is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.0._batch_norm._variance is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.1._conv.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.1._conv.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.1._batch_norm.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.1._batch_norm.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.1._batch_norm._mean is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_pixel.1._batch_norm._variance is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.0._conv.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.0._conv.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.0._batch_norm.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.0._batch_norm.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.0._batch_norm._mean is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.0._batch_norm._variance is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.1._conv.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.1._conv.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.1._batch_norm.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.1._batch_norm.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.1._batch_norm._mean is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_object.1._batch_norm._variance is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_down._conv.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_down._conv.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_down._batch_norm.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_down._batch_norm.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_down._batch_norm._mean is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_down._batch_norm._variance is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_up._conv.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_up._conv.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_up._batch_norm.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_up._batch_norm.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_up._batch_norm._mean is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.attention_block.f_up._batch_norm._variance is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.conv1x1.0._conv.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.conv1x1.0._conv.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.conv1x1.0._batch_norm.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.conv1x1.0._batch_norm.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.conv1x1.0._batch_norm._mean is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.spatial_ocr.conv1x1.0._batch_norm._variance is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.conv3x3_ocr._conv.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.conv3x3_ocr._conv.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.conv3x3_ocr._batch_norm.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.conv3x3_ocr._batch_norm.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.conv3x3_ocr._batch_norm._mean is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.conv3x3_ocr._batch_norm._variance is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.cls_head.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.cls_head.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.aux_head.0._conv.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.aux_head.0._conv.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.aux_head.0._batch_norm.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.aux_head.0._batch_norm.bias is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.aux_head.0._batch_norm._mean is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.aux_head.0._batch_norm._variance is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.aux_head.1.weight is not in pretrained model
2022-01-13 09:33:04 [WARNING] head.aux_head.1.bias is not in pretrained model
2022-01-13 09:33:05 [INFO] There are 1525/1583 variables loaded into OCRNet.
2022-01-13 09:33:05 [INFO] Number of predict images = 2000
2022-01-13 09:33:05 [INFO] Loading pretrained model from ./output_ocrnet/best_model/model.pdparams
2022-01-13 09:33:06 [INFO] There are 1583/1583 variables loaded into OCRNet.
2022-01-13 09:33:06 [INFO] Start to predict...
256/2000 [==>...........................] - ETA: 1:12:0
六、提交
整体跑了约30个epoch，打包zip文件夹并提交，即可得分。
如需提分可多花一些时间训练。
