语义分割-地表建筑物识别
赛题和数据下载：零基础入门语义分割-地表建筑物识别-天池大赛-阿里云天池 (aliyun.com)
实验记录
1.赛题理解与baseline
- backbone代码
main.py
import numpy as np
import pandas as pd
import os
import numba, cv2,time
from tqdm import tqdm
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import albumentations as A
import torch
import torch.nn as nn
import torch.utils.data as D
import torchvision
from rle import rle_encode,rle_decode
from Tianchidataset import TianChiDataset
from loss import loss_fn
import argparse
from torchvision import transforms as T
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
def get_model():
model = torchvision.models.segmentation.fcn_resnet101(True)
# pth = torch.load("../input/pretrain-coco-weights-pytorch/fcn_resnet50_coco-1167a1af.pth")
# for key in ["aux_classifier.0.weight", "aux_classifier.1.weight", "aux_classifier.1.bias", "aux_classifier.1.running_mean", "aux_classifier.1.running_var", "aux_classifier.1.num_batches_tracked", "aux_classifier.4.weight", "aux_classifier.4.bias"]:
# del pth[key]
model.classifier[4] = nn.Conv2d(512, 1, kernel_size=(1, 1), stride=(1, 1))
return model
@torch.no_grad()
def validation(model, loader, loss_fn):
losses = []
model.eval()
for image, target in loader:
image, target = image.to(DEVICE), target.float().to(DEVICE)
output = model(image)['out']
loss = loss_fn(output, target)
losses.append(loss.item())
return np.array(losses).mean()
def parse_args():
parser = argparse.ArgumentParser(description='Train semantic segmentation network')
parser.add_argument('--modelDir',
help='saved model path name',
default="./checkpoints/model_best.pth",
type=str)
parser.add_argument('--data_path',
help='dataset path',
default='/home/dzh/Desktop/data/dataset/segmentation/tianchi',
type=str)
parser.add_argument('--epoch',
help='total train epoch num',
default=30,
type=int)
parser.add_argument('--batch_size',
help='total train epoch num',
default=160,
type=int)
parser.add_argument('--image_size',
help='total train epoch num',
default=256,
type=int)
parser.add_argument('--gpu_ids',
help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU',
default=[0,1,2,3],
type=str)
args=parser.parse_args()
return args
def main():
args = parse_args()
#--------------------------加载数据及数据增强----------------------------
train_mask = pd.read_csv(os.path.join(args.data_path,'train_mask.csv'), sep=' ', names=['name', 'mask'])
train_mask['name'] = train_mask['name'].apply(lambda x: os.path.join(args.data_path,'train/') + x)
mask = rle_decode(train_mask['mask'].iloc[0])
print(rle_encode(mask) == train_mask['mask'].iloc[0])
trfm = A.Compose([
A.Resize(args.image_size, args.image_size),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.RandomRotate90(),
])
dataset = TianChiDataset(
train_mask['name'].values,
train_mask['mask'].fillna('').values,
trfm, False
)
valid_idx, train_idx = [], []
for i in range(len(dataset)):
if i % 7 == 0:
valid_idx.append(i)
# else:
elif i % 7 == 1:
train_idx.append(i)
train_ds = D.Subset(dataset, train_idx)
valid_ds = D.Subset(dataset, valid_idx)
# define training and validation data loaders
loader = D.DataLoader(train_ds, batch_size=args.batch_size, shuffle=True, num_workers=0)
vloader = D.DataLoader(valid_ds, batch_size=args.batch_size, shuffle=False, num_workers=0)
#----------------------------加载模型及优化器------------------------------------
model = get_model()
model.to(DEVICE)
model = torch.nn.DataParallel(model, device_ids=args.gpu_ids, output_device=0)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-3)
train_loss = []
if os.path.exists(args.modelDir):
checkpoint=torch.load(args.modelDir)
model.load_state_dict(checkpoint['state_dict'])
if 'epoch' in checkpoint:
start_epoch=checkpoint['epoch']
if 'optimizer' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
if 'train_loss' in checkpoint:
train_loss = checkpoint['train_loss']
print("load model from {}".format(args.modelDir))
else:
start_epoch = 0
print("==> no checkpoint found at '{}'".format(args.modelDir))
# ----------------------------训练-----------------------------------
header = r'''
Train | Valid
Epoch | Loss | Loss | Time, m
'''
# Epoch metrics time
raw_line = '{:6d}' + 'u2502{:7.3f}' * 2 + 'u2502{:6.2f}'
print(header)
best_loss = 10
for epoch in range(start_epoch, args.epoch):
losses = []
start_time = time.time()
model.train()
for image, target in tqdm(loader):
image, target = image.to(DEVICE), target.float().to(DEVICE)
optimizer.zero_grad()
output = model(image)['out']
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
losses.append(loss.item())
# print(loss.item())
vloss = validation(model, vloader, loss_fn)
print(raw_line.format(epoch, np.array(losses).mean(), vloss,(time.time() - start_time) / 60 ** 1))
train_loss.append(np.array(losses).mean())
if vloss < best_loss:
best_loss = vloss
state={
'epoch':epoch,
'state_dict':model.state_dict(),
'optimizer':optimizer.state_dict(),
'train_loss':train_loss
}
torch.save(state,args.modelDir)
plt.figure(figsize=(10, 5))
plt.title("Loss During Training")
plt.plot(train_loss, label="loss")
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()
plt.savefig('./loss.png')
#--------------------------------验证-----------------------------------
def valid():
args = parse_args()
trfm = T.Compose([
T.ToPILImage(),
T.Resize(args.image_size),
T.ToTensor(),
T.Normalize([0.625, 0.448, 0.688],
[0.131, 0.177, 0.101]),
])
subm = []
model = get_model()
model.to(DEVICE)
model = torch.nn.DataParallel(model, device_ids=args.gpu_ids, output_device=0)
if os.path.exists(args.modelDir):
checkpoint = torch.load(args.modelDir)
model.load_state_dict(checkpoint['state_dict'])
print("load model from {}".format(args.modelDir))
model.eval()
test_mask = pd.read_csv(os.path.join(args.data_path,'test_a_samplesubmit.csv'), sep=' ', names=['name', 'mask'])
test_mask['name'] = test_mask['name'].apply(lambda x: os.path.join(args.data_path,'test_a/') + x)
for idx, name in enumerate(tqdm(test_mask['name'].iloc[:])):
image = cv2.imread(name)
image = trfm(image)
with torch.no_grad():
image = image.to(DEVICE)[None]
score = model(image)['out'][0][0]
score_sigmoid = score.sigmoid().cpu().numpy()
score_sigmoid = (score_sigmoid > 0.5).astype(np.uint8)
score_sigmoid = cv2.resize(score_sigmoid, (512, 512))
# break
subm.append([name.split('/')[-1], rle_encode(score_sigmoid)])
subm = pd.DataFrame(subm)
subm.to_csv('./tmp.csv', index=None, header=None, sep=' ')
# plt.imsave('./output.png',rle_decode(subm[1].fillna('').iloc[0]), cmap='gray')
if __name__ == '__main__':
main()
valid()
Tianchidataset.py
import torch.utils.data as D
import cv2
from torchvision import transforms as T
from rle import rle_decode
IMAGE_SIZE = 256
class TianChiDataset(D.Dataset):
def __init__(self, paths, rles, transform, test_mode=False):
self.paths = paths
self.rles = rles
self.transform = transform
self.test_mode = test_mode
self.len = len(paths)
self.as_tensor = T.Compose([
T.ToPILImage(),
T.Resize(IMAGE_SIZE),
T.ToTensor(),
T.Normalize([0.625, 0.448, 0.688],
[0.131, 0.177, 0.101]),
])
# get data operation
def __getitem__(self, index):
img = cv2.imread(self.paths[index])
if not self.test_mode:
mask = rle_decode(self.rles[index])
augments = self.transform(image=img, mask=mask)
return self.as_tensor(augments['image']), augments['mask'][None]
else:
return self.as_tensor(img), ''
def __len__(self):
"""
Total number of samples in the dataset
"""
return self.len
loss.py
import torch.nn as nn
class SoftDiceLoss(nn.Module):
def __init__(self, smooth=1., dims=(-2, -1)):
super(SoftDiceLoss, self).__init__()
self.smooth = smooth
self.dims = dims
def forward(self, x, y):
tp = (x * y).sum(self.dims)
fp = (x * (1 - y)).sum(self.dims)
fn = ((1 - x) * y).sum(self.dims)
dc = (2 * tp + self.smooth) / (2 * tp + fp + fn + self.smooth)
dc = dc.mean()
return 1 - dc
def loss_fn(y_pred, y_true):
bce_fn = nn.BCEWithLogitsLoss()
dice_fn = SoftDiceLoss()
bce = bce_fn(y_pred, y_true)
dice = dice_fn(y_pred.sigmoid(), y_true)
return 0.8*bce+ 0.2*dice
rle.py
import numpy as np
def rle_encode(im):
'''
im: numpy array, 1 - mask, 0 - background
Returns run length as string formated
'''
pixels = im.flatten(order = 'F')
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return ' '.join(str(x) for x in runs)
def rle_decode(mask_rle, shape=(512, 512)):
'''
mask_rle: run-length as string formated (start length)
shape: (height,width) of array to return
Returns numpy array, 1 - mask, 0 - background
'''
s = mask_rle.split()
starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
starts -= 1
ends = starts + lengths
img = np.zeros(shape[0]*shape[1], dtype=np.uint8)
for lo, hi in zip(starts, ends):
img[lo:hi] = 1
return img.reshape(shape, order='F')
2）提交测试结果
-
数据增强方法
A.Rotate(),
A.ShiftScaleRotate(),
A.Cutout(),
# A.RandomScale(),
A.ShiftScaleRotate(),
本次改进在原程序的基础上增加了以上数据增强方法，并选用了deeplabv3_resnet101进行训练，因为服务器占用只训练了这个版本，达到效果为：
