python pytorch opencv代码积累

目录

• print两种写法
• 打印pytorch权重
• python os.path glob
• np.unique(b) numpy 去重
• 根据标签图和颜色表显示标签图
• pytorch继承的Dataset类的自己定义的OrientClassifyDataset，可以用iter next来迭代访问
• to_tensor || transforms.functional.to_tensor
• pytorch数据处理，继承的Dataset类的函数的单元测试
• pytorch保存模型两种方式
• shape[1,6] + shape[6,1] = shape[6,6]
• numpy 数组，[x1,y1,x2,y2,score,cls] 比如我有形状[100,6]这样的一个numpy数组，需要保留score大于0.8的，其余不要。如何写？
• 图片保持长宽比，缩放到固定尺寸

print两种写法

  print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0]))
  print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(Acc, Acc_class, mIoU, FWIoU))
  print('Loss: %.3f' % test_loss)
  print(f'The corresponding prune ratio is {percent_limit:.3f}.')

打印pytorch权重

model = model.cuda()
print("Model's state_dict:")
for param_tensor in model.state_dict():
    print(param_tensor, "	", model.state_dict()[param_tensor].size())

print(model.state_dict()['backbone.layer1.0.conv1.weight'][0])

打印如下：

Model's state_dict:
backbone.conv1.weight 	 torch.Size([64, 3, 7, 7])
backbone.bn1.weight 	 torch.Size([64])
backbone.bn1.bias 	 torch.Size([64])
backbone.bn1.running_mean 	 torch.Size([64])
backbone.bn1.running_var 	 torch.Size([64])
backbone.bn1.num_batches_tracked 	 torch.Size([])
backbone.layer1.0.conv1.weight 	 torch.Size([64, 64, 1, 1])
backbone.layer1.0.bn1.weight 	 torch.Size([64])
backbone.layer1.0.bn1.bias 	 torch.Size([64])
backbone.layer1.0.bn1.running_mean 	 torch.Size([64])
backbone.layer1.0.bn1.running_var 	 torch.Size([64])
backbone.layer1.0.bn1.num_batches_tracked 	 torch.Size([])
backbone.layer1.0.conv2.weight 	 torch.Size([64, 64, 3, 3])
backbone.layer1.0.bn2.weight 	 torch.Size([64])
backbone.layer1.0.bn2.bias 	 torch.Size([64])
backbone.layer1.0.bn2.running_mean 	 torch.Size([64])
backbone.layer1.0.bn2.running_var 	 torch.Size([64])
backbone.layer1.0.bn2.num_batches_tracked 	 torch.Size([])
backbone.layer1.0.conv3.weight 	 torch.Size([256, 64, 1, 1])
backbone.layer1.0.bn3.weight 	 torch.Size([256])
backbone.layer1.0.bn3.bias 	 torch.Size([256])
backbone.layer1.0.bn3.running_mean 	 torch.Size([256])
backbone.layer1.0.bn3.running_var 	 torch.Size([256])
backbone.layer1.0.bn3.num_batches_tracked 	 torch.Size([])
backbone.layer1.0.downsample.0.weight 	 torch.Size([256, 64, 1, 1])
backbone.layer1.0.downsample.1.weight 	 torch.Size([256])
backbone.layer1.0.downsample.1.bias 	 torch.Size([256])
backbone.layer1.0.downsample.1.running_mean 	 torch.Size([256])
backbone.layer1.0.downsample.1.running_var 	 torch.Size([256])
backbone.layer1.0.downsample.1.num_batches_tracked 	 torch.Size([])
backbone.layer1.1.conv1.weight 	 torch.Size([64, 256, 1, 1])
backbone.layer1.1.bn1.weight 	 torch.Size([64])
backbone.layer1.1.bn1.bias 	 torch.Size([64])
backbone.layer1.1.bn1.running_mean 	 torch.Size([64])
...
tensor([[[-1.8738e-01]],

        [[ 2.3853e-02]],

        [[-2.1837e-02]],

        [[-5.4712e-02]],

        [[ 2.1917e-02]],

        [[ 1.4151e-09]],

        [[ 8.2899e-02]],

        [[-1.8761e-08]],

        [[-1.2909e-08]],
            ...

        [[ 5.3678e-02]],

        [[-1.3887e-08]],

        [[-5.2007e-02]]], device='cuda:0')

model.load_state_dict(checkpoint['state_dict'],strict=False)
strict=False表示名字对不上的就不加载，
默认是true，如果有名字不一致就报错

python os.path glob

directory = os.path.join(path_tmp, 'tmp', 'pascal')
aa = glob.glob(os.path.join(directory, 'experiment_*'))
print(directory)
# /data_2/everyday/0815/tmp/pascal
print(aa)
# ['/data_2/everyday/0815/tmp/pascal/experiment_7', '/data_2/everyday/0815/tmp/pascal/experiment_0', '/data_2/everyday/0815/tmp/pascal/experiment_1']

bb = sorted(glob.glob(os.path.join(directory, 'experiment_*')))
print(bb)
# ['/data_2/everyday/0815/tmp/pascal/experiment_0', '/data_2/everyday/0815/tmp/pascal/experiment_1', '/data_2/everyday/0815/tmp/pascal/experiment_7']

id = 7
experiment_dir = os.path.join(directory, 'experiment_{}'.format(str(id)))
print(experiment_dir)
# /data_2/everyday/0815/tmp/pascal/experiment_7

这里'experiment_{}'.format(str(id) 写法可以代替字符串

2.提取文件名

path = '/data_2/fengjing'
file_name = os.path.basename(path)

np.unique(b) numpy 去重

import numpy as np
b = np.array([(1.5, 2, 3), (3, 5, 6),(1,2,3),(4,5,6)])
print(b)
print(b.shape)

a = np.unique(b)
print(a)

打印如下：

[[1.5 2.  3. ]
 [3.  5.  6. ]
 [1.  2.  3. ]
 [4.  5.  6. ]]
(4, 3)
[1.  1.5 2.  3.  4.  5.  6. ]

这个可以用在：比如训练分割的时候数据检查。
例如：
标签图有16万，分割的标签图看起来都是黑的，里面的像素值代表类别，
说好的只有26个类别，但是训练的时候莫名其妙的报错，报cudnn的错误：

THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1532581333611/work/aten/src/THC/THCCachingHostAllocator.cpp line=271 error=59 : device-side assert triggered
Traceback (most recent call last):
  File "/data_2/tmp_paper/image-segmentation/DeepLab/v3+/pytorch0.41/pytorch-deeplab-xception-master/train.py", line 339, in <module>
    main()
  File "/data_2/tmp_paper/image-segmentation/DeepLab/v3+/pytorch0.41/pytorch-deeplab-xception-master/train.py", line 298, in main
    trainer.training(epoch)
  File "/data_2/tmp_paper/image-segmentation/DeepLab/v3+/pytorch0.41/pytorch-deeplab-xception-master/train.py", line 108, in training
    loss.backward()
  File "/data_1/Yang/software_install/Anaconda1105/envs/pytorch0.4.1/lib/python3.6/site-packages/torch/tensor.py", line 93, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/data_1/Yang/software_install/Anaconda1105/envs/pytorch0.4.1/lib/python3.6/site-packages/torch/autograd/__init__.py", line 90, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: CuDNN error: CUDNN_STATUS_EXECUTION_FAILED

搜cudnn的，都是乱七八糟的，没有可用的。
但是仔细看上面还有一个提示：
line=271 error=59 : device-side assert triggered
这句话百度，有人解答说是标签超过类别数了。
于是我就开始怀疑我的16万数据里面是不是有超过类别数的标签，分割的标签图看起来都是黑的，里面的像素值代表类别，我是26类，那么标签范围是0-25，所以
我就用上了np.unique这个函数，检查数据的代码如下：

import os
from PIL import Image
import numpy as np

txt_list = '/data_2/2019biaozhushuju/seg-chewaiguan/seg_chewaigaun_ding/se_chewaiguan_data/20190618_new/train.txt'

with open(txt_list,'r')as fr:
    lines = fr.readlines()
    for line in lines:
        line = line.strip().split(' ')
        _target = Image.open(line[1]).convert('L')   
        # _target.show()

        targe = np.array(_target)
        a = np.unique(targe)
        max_val = max(a)
        if max_val >= 26:
            print (max_val, "::", line[1])

！！！果真，有打印！！！原来真有几张图片超过标签，坑爹啊，这个错误排查了2-3个小时！！！

根据标签图和颜色表显示标签图

import numpy as np
import cv2
import os
from PIL import Image


mmm = cv2.imread("/data_2/fengjing.jpg")
cv2.imshow("m",mmm)
cv2.waitKey(0)



#BGR
color_segmentation=np.asarray([
    [0,0,0],            #[0]背景
    [180,120,120],
    [6,230,230],
    [80,50,50],
    [4,200,3],
    [120,120,80],
    [140,140,140],
    [204,5,255],
    [230,230,230],
    [4,250,7],
    [40,150,255],
    [235,255,7],
    [150,5,61],
    [120,120,70],
    [8,255,51],
    [255,6,82],
    [143,255,140],
    [204,255,4],
    [255,51,7],
    [204,70,3],
    [0,102,200],
    [61,230,250],
    [255,6,51],
    [11,102,255],
    [255,7,71],
    [255,9,224],
    [9,7,230],
    [220,220,220],
    [255, 9, 92]
],dtype=np.uint8)


def decode_segmap(label_mask,n_classes = 21):

    r = label_mask.copy()
    g = label_mask.copy()
    b = label_mask.copy()
    for ll in range(0, n_classes):
        # aaa = color_segmentation[ll, 1]
        # position = label_mask == ll
        b[label_mask == ll] = color_segmentation[ll, 0]
        g[label_mask == ll] = color_segmentation[ll, 1]
        r[label_mask == ll] = color_segmentation[ll, 2]
    rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))
    print(rgb[240][240])

    rgb[:, :, 0] = b
    rgb[:, :, 1] = g
    rgb[:, :, 2] = r

    # rgb[:, :, 0] = b / 255.0
    # rgb[:, :, 1] = g / 255.0
    # rgb[:, :, 2] = r /255.0

    rgb = rgb.astype(np.uint8)  ##重要！ 要不然opencv显示有问题

    print(rgb[240][240])

    return rgb


root_dir = "/data_2/2019biaozhushuju/others/voc/VOCdevkit/VOC2012/SegmentationClassAug/"
list_gray_img = os.listdir(root_dir)
for img_name in list_gray_img:
    path_gray = root_dir + img_name
    laber_mask = cv2.imread(path_gray,0) ##灰度 单通道读取
    # print(laber_mask.shape)
    color_img = decode_segmap(laber_mask)
    # print(color_img.shape)

    # img1 = Image.fromarray(color_img.astype('uint8')).convert('RGB')
    # img1.show()

    cv2.imshow("laber_mask", laber_mask*20)
    cv2.imshow("color",color_img)
    cv2.waitKey()

这里直接放的正确的代码，一开始显示的图片有问题啊，没有颜色

找问题找了半天
aaa = color_segmentation[ll, 1]
print(rgb[240][240])
上面这个在代码里面打印出来的像素值是正常的，都是比如我上面的颜色表里面的，既然像素正常那显示为啥没有颜色呢？难道是我opencv出问题了吗。然后可以看开头我直接读取一张图片显示，是可以正常显示的，然后我加PIL库来显示，
img1 = Image.fromarray(color_img.astype('uint8')).convert('RGB')
显示出来的图像是彩色的，那为啥opencv显示有问题呢，然后我rgb[:, :, 0] = b / 255.0，然后发现opencv显示就正常了，好奇怪啊，/255就变为0-1之间了啊。。。反正很奇怪，找了很久，然后突然想到可以是数据格式的问题！果断打断点：
发现我加载的那张图片格式是：dtype=uint8格式的，而color这个图格式是float64格式的！所以问题就出现在这里！！

一定要加上这句话

rgb = rgb.astype(np.uint8)  ##重要！ 要不然opencv显示有问题

好了，只是把我发现问题，解决问题的思路过程写在这里。
这里主要要学习numpy的几个操作：
1.

r = label_mask.copy()
g = label_mask.copy()
b = label_mask.copy()
 for ll in range(0, n_classes):
      b[label_mask == ll] = color_segmentation[ll, 0]
      g[label_mask == ll] = color_segmentation[ll, 1]
      r[label_mask == ll] = color_segmentation[ll, 2]

这里我一开始的直观理解就是label_mask == ll返回的是位置，b[label_mask == ll]，比如图片像素值等于2的位置都填充后面的值。但是我
position = label_mask == ll
看了一下，其实不是，这里的position是和图一样大小，里面的值都是True或者False
明白了，其实返回的相当于一个mask，只在true的位置上填充后面的值
2.

rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))
rgb[:, :, 0] = b
rgb[:, :, 1] = g
rgb[:, :, 2] = r

这里rgb都是单通道的，就是把这三个合并一个图。首先就是申请了一个三通的。这里需要注意的是opencv的格式都是hwc。
rgb[:, :, 0]代表的第一个通道
还有上面我调试的代码
print(rgb[240][240])
这里打印出来的是这样的
[80. 50. 50.]
想想为啥吧！

@@@@@@@@@@
有个相反操作的，就是把colormap图映射为标签图

def encode_segmap(mask):
    """Encode segmentation label images as pascal classes
    Args:
        mask (np.ndarray): raw segmentation label image of dimension
          (M, N, 3), in which the Pascal classes are encoded as colours.
    Returns:
        (np.ndarray): class map with dimensions (M,N), where the value at
        a given location is the integer denoting the class index.
    """
    mask = mask.astype(int)
    label_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int16)
    for ii, label in enumerate(get_pascal_labels()):
        label_mask[np.where(np.all(mask == label, axis=-1))[:2]] = ii
    label_mask = label_mask.astype(int)
    return label_mask

pytorch继承的Dataset类的自己定义的OrientClassifyDataset，可以用iter next来迭代访问

train_dataset = OrientClassifyDataset(
    train_root,
    transform=train_transform,
)

aa = iter(train_dataset)
a = next(aa)

print('train data:', len(train_dataset))
train_loader = data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True,
                               num_workers=num_workers, drop_last=True, collate_fn=align_collate_train)

to_tensor || transforms.functional.to_tensor

将numpy数组或PIL.Image读的图片转换成(C,H, W)的Tensor格式且/255归一化到[0,1.0]之间

import torchvision.transforms as transforms
class Normalize(object):
    def __init__(self):
        pass
    def __call__(self, img):
        img = transforms.functional.to_tensor(img)
        img.sub_(0.5).div_(0.5)
        return img

pytorch数据处理，继承的Dataset类的函数的单元测试

反向操作，之前减均值，除方差操作现在反着来恢复原图变显示
本文大部分代码参考这个仓库：https://github.com/jfzhang95/pytorch-deeplab-xception

voc_train = VOCSegmentation(args, split='train')

    dataloader = DataLoader(voc_train, batch_size=5, shuffle=True, num_workers=0)

    for ii, sample in enumerate(dataloader):
        for jj in range(sample["image"].size()[0]):
            img = sample['image'].numpy()
            gt = sample['label'].numpy()
            tmp = np.array(gt[jj]).astype(np.uint8)
            segmap = decode_segmap(tmp, dataset='pascal')
            img_tmp = np.transpose(img[jj], axes=[1, 2, 0])
            img_tmp *= (0.229, 0.224, 0.225)
            img_tmp += (0.485, 0.456, 0.406)
            img_tmp *= 255.0
            img_tmp = img_tmp.astype(np.uint8)
            plt.figure()
            plt.title('display')
            plt.subplot(211)
            plt.imshow(img_tmp)
            plt.subplot(212)
            plt.imshow(segmap)

        if ii == 1:
            break

    plt.show(block=True)

pytorch保存模型两种方式

保存和加载模型参数有两种方式：
方式一：
torch.save(net.state_dict(),path):
功能：保存训练完的网络的各层参数（即weights和bias)
其中：net.state_dict()获取各层参数，path是文件存放路径(通常保存文件格式为.pt或.pth)
net2.load_state_dict(torch.load(path)):
功能：加载保存到path中的各层参数到神经网络
注意：不可以直接为torch.load_state_dict(path)，此函数不能直接接收字符串类型参数
方式二：
torch.save(net,path):
功能：保存训练完的整个网络模型（不止weights和bias）
net2=torch.load(path):
功能：加载保存到path中的整个神经网络
说明：官方推荐方式一，原因自然是保存的内容少，速度会更快。

shape[1,6] + shape[6,1] = shape[6,6]

>>> a = np.array([[3,2,1,1,2,3]])
>>> b = np.array([[3],[2],[1],[1],[2],[3]])
>>> print(a.shape),print(b.shape)
(1, 6)
(6, 1)
(None, None)
>>> c = a+b
>>> print(a)
[[3 2 1 1 2 3]]
>>> print(b)
[[3]
 [2]
 [1]
 [1]
 [2]
 [3]]
>>> print(c)
[[6 5 4 4 5 6]
 [5 4 3 3 4 5]
 [4 3 2 2 3 4]
 [4 3 2 2 3 4]
 [5 4 3 3 4 5]
 [6 5 4 4 5 6]]

numpy 数组，[x1,y1,x2,y2,score,cls] 比如我有形状[100,6]这样的一个numpy数组，需要保留score大于0.8的，其余不要。如何写？

问群里人，得到三种答案：

import numpy as np

a = [[1, 2, 3, 4, 0.75, 1],
     [1, 2, 3, 4, 0.85, 2],
     [1, 2, 3, 4, 0.95, 3],
     [1, 2, 3, 4, 0.61, 4],
     [1, 2, 3, 4, 0.97, 5],
     [1, 2, 3, 4, 0.82, 6],]
a = np.array(a)

####11111
s = a[np.apply_along_axis(lambda x:x[4]>0.8,axis=1,arr=a)]
print(s)

print()
####2222
mask = a[:,4] > 0.8
print(mask)
print(a[mask])

print()
####333
print(a[a[:,4]>0.8,:])

#######################################
[[1.   2.   3.   4.   0.85 2.  ]
 [1.   2.   3.   4.   0.95 3.  ]
 [1.   2.   3.   4.   0.97 5.  ]
 [1.   2.   3.   4.   0.82 6.  ]]

[False  True  True False  True  True]
[[1.   2.   3.   4.   0.85 2.  ]
 [1.   2.   3.   4.   0.95 3.  ]
 [1.   2.   3.   4.   0.97 5.  ]
 [1.   2.   3.   4.   0.82 6.  ]]

[[1.   2.   3.   4.   0.85 2.  ]
 [1.   2.   3.   4.   0.95 3.  ]
 [1.   2.   3.   4.   0.97 5.  ]
 [1.   2.   3.   4.   0.82 6.  ]]

Process finished with exit code 0

图片保持长宽比，缩放到固定尺寸

    int input_w = 512；
    int input_h = 512；
    float scale = cv::min(float(input_w)/img.cols,float(input_h)/img.rows);
    auto scaleSize = cv::Size(img.cols * scale,img.rows * scale);

    cv::Mat resized;
    cv::resize(img, resized,scaleSize,0,0);

    cv::Mat cropped = cv::Mat::zeros(input_h,input_w,CV_8UC3);
    cv::Rect rect((input_w- scaleSize.width)/2, (input_h-scaleSize.height)/2, scaleSize.width,scaleSize.height);

    resized.copyTo(cropped(rect));