Kaggle Carvana Image Masking Challenge

1. UNet

Github 项目 - Pytorch-UNet

Pytorch-UNet - U-Net 的 PyTorch 实现,用于二值汽车图像语义分割,包括 dense CRF 后处理.

Pytorch-UNet 用于 Carvana Image Masking Challenge 高分辨率图像的分割. 该项目只输出一个前景目标类,但可以容易地扩展到多前景目标分割任务.

Pytorch-UNet 提供的训练模型 - MODEL.pth,采用 5000 张图片从头开始训练(未进行数据增强),在 100k 测试图片上得到的 dice coefficient 为 0.988423. 虽然结构并不够好,但可以采用更多数据增强,fine-tuning,CRF 后处理,以及对 masks 的边缘添加更多权重等方式,提升分割精度.

2. pydensecrf 库

[[Github - PyDenseCRF]](https://github.com/lucasb-eyer/pydensecrf)

[1] - 安装:

sudo pip install pydensecrf

[2] - 使用:

import numpy as np
import pydensecrf.densecrf as dcrf
d = dcrf.DenseCRF2D(640, 480, 5)  # width, height, nlabels

3. Pytorch-UNet


[1] - 训练的 PyTorch 模型 - MODEL.pth

[2] - U-Net 网络定义 - unet_parts.py

# sub-parts of the U-Net model
import torch
import torch.nn as nn
import torch.nn.functional as F

class double_conv(nn.Module):
    '''(conv => BN => ReLU) * 2'''
    def __init__(self, in_ch, out_ch):
        super(double_conv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, padding=1),
            nn.Conv2d(out_ch, out_ch, 3, padding=1),

    def forward(self, x):
        x = self.conv(x)
        return x

class inconv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(inconv, self).__init__()
        self.conv = double_conv(in_ch, out_ch)

    def forward(self, x):
        x = self.conv(x)
        return x

class down(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(down, self).__init__()
        self.mpconv = nn.Sequential(
            double_conv(in_ch, out_ch)

    def forward(self, x):
        x = self.mpconv(x)
        return x

class up(nn.Module):
    def __init__(self, in_ch, out_ch, bilinear=True):
        super(up, self).__init__()

        #  would be a nice idea if the upsampling could be learned too,
        #  but my machine do not have enough memory to handle all those weights
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)

        self.conv = double_conv(in_ch, out_ch)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        diffX = x1.size()[2] - x2.size()[2]
        diffY = x1.size()[3] - x2.size()[3]
        x2 = F.pad(x2, (diffX // 2, int(diffX / 2),
                        diffY // 2, int(diffY / 2)))
        x = torch.cat([x2, x1], dim=1)
        x = self.conv(x)
        return x

class outconv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(outconv, self).__init__()
        self.conv = nn.Conv2d(in_ch, out_ch, 1)

    def forward(self, x):
        x = self.conv(x)
        return x

[3] - U-Net 网络定义 - unet_model.py

# full assembly of the sub-parts to form the complete net

from .unet_parts import *

class UNet(nn.Module):
    def __init__(self, n_channels, n_classes):
        super(UNet, self).__init__()
        self.inc = inconv(n_channels, 64)
        self.down1 = down(64, 128)
        self.down2 = down(128, 256)
        self.down3 = down(256, 512)
        self.down4 = down(512, 512)
        self.up1 = up(1024, 256)
        self.up2 = up(512, 128)
        self.up3 = up(256, 64)
        self.up4 = up(128, 64)
        self.outc = outconv(64, n_classes)

    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        x = self.outc(x)
        return x

3.1. 测试 demo

import argparse
import os

import numpy as np
import matplotlib.pyplot as plt

import torch
import torch.nn.functional as F

from PIL import Image

from unet import UNet
from utils import resize_and_crop, normalize, split_img_into_squares, hwc_to_chw, merge_masks, dense_crf

from torchvision import transforms

def predict_img(net,
    img_height = full_img.size[1]
    img_width = full_img.size[0]

    img = resize_and_crop(full_img, scale=scale_factor)
    img = normalize(img)

    left_square, right_square = split_img_into_squares(img)

    left_square = hwc_to_chw(left_square)
    right_square = hwc_to_chw(right_square)

    X_left = torch.from_numpy(left_square).unsqueeze(0)
    X_right = torch.from_numpy(right_square).unsqueeze(0)

    if use_gpu:
        X_left = X_left.cuda()
        X_right = X_right.cuda()

    with torch.no_grad():
        output_left = net(X_left)
        output_right = net(X_right)

        left_probs = F.sigmoid(output_left).squeeze(0)
        right_probs = F.sigmoid(output_right).squeeze(0)

        tf = transforms.Compose([transforms.ToPILImage(),
                                 transforms.ToTensor() ])

        left_probs = tf(left_probs.cpu())
        right_probs = tf(right_probs.cpu())

        left_mask_np = left_probs.squeeze().cpu().numpy()
        right_mask_np = right_probs.squeeze().cpu().numpy()

    full_mask = merge_masks(left_mask_np, right_mask_np, img_width)

    if use_dense_crf:
        full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)

    return full_mask > out_threshold

def mask_to_image(mask):
    return Image.fromarray((mask * 255).astype(np.uint8))

def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model', '-m', default='MODEL.pth',
                        help="Specify the file in which is stored the model"
                             " (default : 'MODEL.pth')")
    # parser.add_argument('--input', '-i', metavar='INPUT', nargs='+',
    #                     help='filenames of input images', required=True)

    parser.add_argument('--output', '-o', metavar='INPUT', nargs='+',
                        help='filenames of ouput images')
    parser.add_argument('--cpu', '-c', action='store_true',
                        help="Do not use the cuda version of the net",
    parser.add_argument('--viz', '-v', action='store_true',
                        help="Visualize the images as they are processed",
    parser.add_argument('--no-save', '-n', action='store_true',
                        help="Do not save the output masks",
    parser.add_argument('--no-crf', '-r', action='store_true',
                        help="Do not use dense CRF postprocessing",
    parser.add_argument('--mask-threshold', '-t', type=float,
                        help="Minimum probability value to consider a mask pixel white",
    parser.add_argument('--scale', '-s', type=float,
                        help="Scale factor for the input images",

    return parser.parse_args()

if name == "__main__":
    args = get_args()

    args.cpu = 0
    args.no_crf = True
    args.model = './MODEL.pth'
    net = UNet(n_channels=3, n_classes=1)
    print("Loading model {}".format(args.model))

    if not args.cpu:
        print("Using CUDA version of the net, prepare your GPU !")
        net.load_state_dict(torch.load(args.model, map_location='cpu'))
        print("Using CPU version of the net, this may be very slow")

    print("Model loaded !")

    in_files = os.listdir('/path/to/testcars')

    for i, fn in enumerate(in_files):
        fn = os.path.join('/path/to/testcars', fn)
        print("nPredicting image {} ...".format(fn))

        img = Image.open(fn)
        if img.size[0] < img.size[1]:
            print("Error: image height larger than the width")
            mask = predict_img(net=net,
                               use_dense_crf=not args.no_crf,
                               use_gpu=not args.cpu)

            print("Visualizing results for image {}, close to continue ...".format(fn))
            fig = plt.figure()
            a = fig.add_subplot(1, 2, 1)
            a.set_title('Input image')

            b = fig.add_subplot(1, 2, 2)
            b.set_title('Output mask')

3.2. 未进行 dense crf 后处理

未进行 dense crf 后处理:

dense crf 后处理:

Last modification:December 8th, 2020 at 08:59 pm