├── README.md
├── data
├── demo_data
│ └── original
│ │ ├── 000000.bin
│ │ └── 000001.bin
├── interpolation_data.py
├── nuscenes_testlist.txt
├── nuscenes_trainlist.txt
├── scene-split
│ ├── scene-0001.txt
│ ├── scene-0002.txt
│ ├── scene-0003.txt
│ ├── scene-0004.txt
│ ├── scene-0005.txt
│ ├── scene-0006.txt
│ ├── scene-0007.txt
│ ├── scene-0008.txt
│ ├── scene-0009.txt
│ ├── scene-0010.txt
│ ├── scene-0011.txt
│ ├── scene-0012.txt
│ ├── scene-0013.txt
│ ├── scene-0014.txt
│ ├── scene-0015.txt
│ ├── scene-0016.txt
│ ├── scene-0017.txt
│ ├── scene-0018.txt
│ ├── scene-0019.txt
│ ├── scene-0020.txt
│ ├── scene-0021.txt
│ ├── scene-0022.txt
│ ├── scene-0023.txt
│ ├── scene-0024.txt
│ ├── scene-0025.txt
│ ├── scene-0026.txt
│ ├── scene-0027.txt
│ ├── scene-0028.txt
│ ├── scene-0029.txt
│ ├── scene-0030.txt
│ ├── scene-0031.txt
│ ├── scene-0032.txt
│ ├── scene-0033.txt
│ ├── scene-0034.txt
│ ├── scene-0035.txt
│ ├── scene-0036.txt
│ ├── scene-0038.txt
│ ├── scene-0039.txt
│ ├── scene-0041.txt
│ ├── scene-0042.txt
│ ├── scene-0043.txt
│ ├── scene-0044.txt
│ ├── scene-0045.txt
│ ├── scene-0046.txt
│ ├── scene-0047.txt
│ ├── scene-0048.txt
│ ├── scene-0049.txt
│ ├── scene-0050.txt
│ ├── scene-0051.txt
│ ├── scene-0052.txt
│ ├── scene-0053.txt
│ ├── scene-0054.txt
│ ├── scene-0055.txt
│ ├── scene-0056.txt
│ ├── scene-0057.txt
│ ├── scene-0058.txt
│ ├── scene-0059.txt
│ ├── scene-0060.txt
│ ├── scene-0061.txt
│ ├── scene-0062.txt
│ ├── scene-0063.txt
│ ├── scene-0064.txt
│ ├── scene-0065.txt
│ ├── scene-0066.txt
│ ├── scene-0067.txt
│ ├── scene-0068.txt
│ ├── scene-0069.txt
│ ├── scene-0070.txt
│ ├── scene-0071.txt
│ ├── scene-0072.txt
│ ├── scene-0073.txt
│ ├── scene-0074.txt
│ ├── scene-0075.txt
│ ├── scene-0076.txt
│ ├── scene-0092.txt
│ ├── scene-0093.txt
│ ├── scene-0094.txt
│ ├── scene-0095.txt
│ ├── scene-0096.txt
│ ├── scene-0097.txt
│ ├── scene-0098.txt
│ ├── scene-0099.txt
│ ├── scene-0100.txt
│ ├── scene-0101.txt
│ ├── scene-0102.txt
│ ├── scene-0103.txt
│ ├── scene-0104.txt
│ ├── scene-0105.txt
│ ├── scene-0106.txt
│ ├── scene-0107.txt
│ ├── scene-0108.txt
│ ├── scene-0109.txt
│ ├── scene-0110.txt
│ ├── scene-0120.txt
│ ├── scene-0121.txt
│ ├── scene-0122.txt
│ ├── scene-0123.txt
│ ├── scene-0124.txt
│ ├── scene-0125.txt
│ ├── scene-0126.txt
│ ├── scene-0127.txt
│ ├── scene-0128.txt
│ ├── scene-0129.txt
│ ├── scene-0130.txt
│ ├── scene-0131.txt
│ ├── scene-0132.txt
│ ├── scene-0133.txt
│ ├── scene-0134.txt
│ ├── scene-0135.txt
│ ├── scene-0138.txt
│ ├── scene-0139.txt
│ ├── scene-0149.txt
│ ├── scene-0150.txt
│ ├── scene-0151.txt
│ ├── scene-0152.txt
│ ├── scene-0154.txt
│ ├── scene-0155.txt
│ ├── scene-0157.txt
│ ├── scene-0158.txt
│ ├── scene-0159.txt
│ ├── scene-0160.txt
│ ├── scene-0161.txt
│ ├── scene-0162.txt
│ ├── scene-0163.txt
│ ├── scene-0164.txt
│ ├── scene-0165.txt
│ ├── scene-0166.txt
│ ├── scene-0167.txt
│ ├── scene-0168.txt
│ ├── scene-0170.txt
│ ├── scene-0171.txt
│ ├── scene-0172.txt
│ ├── scene-0173.txt
│ ├── scene-0174.txt
│ ├── scene-0175.txt
│ ├── scene-0176.txt
│ ├── scene-0177.txt
│ ├── scene-0178.txt
│ ├── scene-0179.txt
│ ├── scene-0180.txt
│ ├── scene-0181.txt
│ ├── scene-0182.txt
│ ├── scene-0183.txt
│ ├── scene-0184.txt
│ ├── scene-0185.txt
│ ├── scene-0187.txt
│ ├── scene-0188.txt
│ ├── scene-0190.txt
│ ├── scene-0191.txt
│ ├── scene-0192.txt
│ ├── scene-0193.txt
│ ├── scene-0194.txt
│ ├── scene-0195.txt
│ ├── scene-0196.txt
│ ├── scene-0199.txt
│ ├── scene-0200.txt
│ ├── scene-0202.txt
│ ├── scene-0203.txt
│ ├── scene-0204.txt
│ ├── scene-0206.txt
│ ├── scene-0207.txt
│ ├── scene-0208.txt
│ ├── scene-0209.txt
│ ├── scene-0210.txt
│ ├── scene-0211.txt
│ ├── scene-0212.txt
│ ├── scene-0213.txt
│ ├── scene-0214.txt
│ ├── scene-0218.txt
│ ├── scene-0219.txt
│ ├── scene-0220.txt
│ ├── scene-0221.txt
│ ├── scene-0222.txt
│ ├── scene-0224.txt
│ ├── scene-0225.txt
│ ├── scene-0226.txt
│ ├── scene-0227.txt
│ ├── scene-0228.txt
│ ├── scene-0229.txt
│ ├── scene-0230.txt
│ ├── scene-0231.txt
│ ├── scene-0232.txt
│ ├── scene-0233.txt
│ ├── scene-0234.txt
│ ├── scene-0235.txt
│ ├── scene-0236.txt
│ ├── scene-0237.txt
│ ├── scene-0238.txt
│ ├── scene-0239.txt
│ ├── scene-0240.txt
│ ├── scene-0241.txt
│ ├── scene-0242.txt
│ ├── scene-0243.txt
│ ├── scene-0244.txt
│ ├── scene-0245.txt
│ ├── scene-0246.txt
│ ├── scene-0247.txt
│ ├── scene-0248.txt
│ ├── scene-0249.txt
│ ├── scene-0250.txt
│ ├── scene-0251.txt
│ ├── scene-0252.txt
│ ├── scene-0253.txt
│ ├── scene-0254.txt
│ ├── scene-0255.txt
│ ├── scene-0256.txt
│ ├── scene-0257.txt
│ ├── scene-0258.txt
│ ├── scene-0259.txt
│ ├── scene-0260.txt
│ ├── scene-0261.txt
│ ├── scene-0262.txt
│ ├── scene-0263.txt
│ ├── scene-0264.txt
│ ├── scene-0268.txt
│ ├── scene-0269.txt
│ ├── scene-0270.txt
│ ├── scene-0271.txt
│ ├── scene-0272.txt
│ ├── scene-0273.txt
│ ├── scene-0274.txt
│ ├── scene-0275.txt
│ ├── scene-0276.txt
│ ├── scene-0277.txt
│ ├── scene-0278.txt
│ ├── scene-0283.txt
│ ├── scene-0284.txt
│ ├── scene-0285.txt
│ ├── scene-0286.txt
│ ├── scene-0287.txt
│ ├── scene-0288.txt
│ ├── scene-0289.txt
│ ├── scene-0290.txt
│ ├── scene-0291.txt
│ ├── scene-0292.txt
│ ├── scene-0293.txt
│ ├── scene-0294.txt
│ ├── scene-0295.txt
│ ├── scene-0296.txt
│ ├── scene-0297.txt
│ ├── scene-0298.txt
│ ├── scene-0299.txt
│ ├── scene-0300.txt
│ ├── scene-0301.txt
│ ├── scene-0302.txt
│ ├── scene-0303.txt
│ ├── scene-0304.txt
│ ├── scene-0305.txt
│ ├── scene-0306.txt
│ ├── scene-0315.txt
│ ├── scene-0316.txt
│ ├── scene-0317.txt
│ ├── scene-0318.txt
│ ├── scene-0321.txt
│ ├── scene-0323.txt
│ ├── scene-0324.txt
│ ├── scene-0328.txt
│ ├── scene-0329.txt
│ ├── scene-0330.txt
│ ├── scene-0331.txt
│ ├── scene-0332.txt
│ ├── scene-0344.txt
│ ├── scene-0345.txt
│ ├── scene-0346.txt
│ ├── scene-0347.txt
│ ├── scene-0348.txt
│ ├── scene-0349.txt
│ ├── scene-0350.txt
│ ├── scene-0351.txt
│ ├── scene-0352.txt
│ ├── scene-0353.txt
│ ├── scene-0354.txt
│ ├── scene-0355.txt
│ ├── scene-0356.txt
│ ├── scene-0357.txt
│ ├── scene-0358.txt
│ ├── scene-0359.txt
│ ├── scene-0360.txt
│ ├── scene-0361.txt
│ ├── scene-0362.txt
│ ├── scene-0363.txt
│ ├── scene-0364.txt
│ ├── scene-0365.txt
│ ├── scene-0366.txt
│ ├── scene-0367.txt
│ ├── scene-0368.txt
│ ├── scene-0369.txt
│ ├── scene-0370.txt
│ ├── scene-0371.txt
│ ├── scene-0372.txt
│ ├── scene-0373.txt
│ ├── scene-0374.txt
│ ├── scene-0375.txt
│ ├── scene-0376.txt
│ ├── scene-0377.txt
│ ├── scene-0378.txt
│ ├── scene-0379.txt
│ ├── scene-0380.txt
│ ├── scene-0381.txt
│ ├── scene-0382.txt
│ ├── scene-0383.txt
│ ├── scene-0384.txt
│ ├── scene-0385.txt
│ ├── scene-0386.txt
│ ├── scene-0388.txt
│ ├── scene-0389.txt
│ ├── scene-0390.txt
│ ├── scene-0391.txt
│ ├── scene-0392.txt
│ ├── scene-0393.txt
│ ├── scene-0394.txt
│ ├── scene-0395.txt
│ ├── scene-0396.txt
│ ├── scene-0397.txt
│ ├── scene-0398.txt
│ ├── scene-0399.txt
│ ├── scene-0400.txt
│ ├── scene-0401.txt
│ ├── scene-0402.txt
│ ├── scene-0403.txt
│ ├── scene-0405.txt
│ ├── scene-0406.txt
│ ├── scene-0407.txt
│ ├── scene-0408.txt
│ ├── scene-0410.txt
│ ├── scene-0411.txt
│ ├── scene-0412.txt
│ ├── scene-0413.txt
│ ├── scene-0414.txt
│ ├── scene-0415.txt
│ ├── scene-0416.txt
│ ├── scene-0417.txt
│ ├── scene-0418.txt
│ ├── scene-0419.txt
│ ├── scene-0420.txt
│ ├── scene-0421.txt
│ ├── scene-0422.txt
│ ├── scene-0423.txt
│ ├── scene-0424.txt
│ ├── scene-0425.txt
│ ├── scene-0426.txt
│ ├── scene-0427.txt
│ ├── scene-0428.txt
│ ├── scene-0429.txt
│ ├── scene-0430.txt
│ ├── scene-0431.txt
│ ├── scene-0432.txt
│ ├── scene-0433.txt
│ ├── scene-0434.txt
│ ├── scene-0435.txt
│ ├── scene-0436.txt
│ ├── scene-0437.txt
│ ├── scene-0438.txt
│ ├── scene-0439.txt
│ ├── scene-0440.txt
│ ├── scene-0441.txt
│ ├── scene-0442.txt
│ ├── scene-0443.txt
│ ├── scene-0444.txt
│ ├── scene-0445.txt
│ ├── scene-0446.txt
│ ├── scene-0447.txt
│ ├── scene-0448.txt
│ ├── scene-0449.txt
│ ├── scene-0450.txt
│ ├── scene-0451.txt
│ ├── scene-0452.txt
│ ├── scene-0453.txt
│ ├── scene-0454.txt
│ ├── scene-0455.txt
│ ├── scene-0456.txt
│ ├── scene-0457.txt
│ ├── scene-0458.txt
│ ├── scene-0459.txt
│ ├── scene-0461.txt
│ ├── scene-0462.txt
│ ├── scene-0463.txt
│ ├── scene-0464.txt
│ ├── scene-0465.txt
│ ├── scene-0467.txt
│ ├── scene-0468.txt
│ ├── scene-0469.txt
│ ├── scene-0471.txt
│ ├── scene-0472.txt
│ ├── scene-0474.txt
│ ├── scene-0475.txt
│ ├── scene-0476.txt
│ ├── scene-0477.txt
│ ├── scene-0478.txt
│ ├── scene-0479.txt
│ ├── scene-0480.txt
│ ├── scene-0499.txt
│ ├── scene-0500.txt
│ ├── scene-0501.txt
│ ├── scene-0502.txt
│ ├── scene-0504.txt
│ ├── scene-0505.txt
│ ├── scene-0506.txt
│ ├── scene-0507.txt
│ ├── scene-0508.txt
│ ├── scene-0509.txt
│ ├── scene-0510.txt
│ ├── scene-0511.txt
│ ├── scene-0512.txt
│ ├── scene-0513.txt
│ ├── scene-0514.txt
│ ├── scene-0515.txt
│ ├── scene-0517.txt
│ ├── scene-0518.txt
│ ├── scene-0519.txt
│ ├── scene-0520.txt
│ ├── scene-0521.txt
│ ├── scene-0522.txt
│ ├── scene-0523.txt
│ ├── scene-0524.txt
│ ├── scene-0525.txt
│ ├── scene-0526.txt
│ ├── scene-0527.txt
│ ├── scene-0528.txt
│ ├── scene-0529.txt
│ ├── scene-0530.txt
│ ├── scene-0531.txt
│ ├── scene-0532.txt
│ ├── scene-0533.txt
│ ├── scene-0534.txt
│ ├── scene-0535.txt
│ ├── scene-0536.txt
│ ├── scene-0537.txt
│ ├── scene-0538.txt
│ ├── scene-0539.txt
│ ├── scene-0541.txt
│ ├── scene-0542.txt
│ ├── scene-0543.txt
│ ├── scene-0544.txt
│ ├── scene-0545.txt
│ ├── scene-0546.txt
│ ├── scene-0552.txt
│ ├── scene-0553.txt
│ ├── scene-0554.txt
│ ├── scene-0555.txt
│ ├── scene-0556.txt
│ ├── scene-0557.txt
│ ├── scene-0558.txt
│ ├── scene-0559.txt
│ ├── scene-0560.txt
│ ├── scene-0561.txt
│ ├── scene-0562.txt
│ ├── scene-0563.txt
│ ├── scene-0564.txt
│ ├── scene-0565.txt
│ ├── scene-0566.txt
│ ├── scene-0568.txt
│ ├── scene-0570.txt
│ ├── scene-0571.txt
│ ├── scene-0572.txt
│ ├── scene-0573.txt
│ ├── scene-0574.txt
│ ├── scene-0575.txt
│ ├── scene-0576.txt
│ ├── scene-0577.txt
│ ├── scene-0578.txt
│ ├── scene-0580.txt
│ ├── scene-0582.txt
│ ├── scene-0583.txt
│ ├── scene-0584.txt
│ ├── scene-0585.txt
│ ├── scene-0586.txt
│ ├── scene-0587.txt
│ ├── scene-0588.txt
│ ├── scene-0589.txt
│ ├── scene-0590.txt
│ ├── scene-0591.txt
│ ├── scene-0592.txt
│ ├── scene-0593.txt
│ ├── scene-0594.txt
│ ├── scene-0595.txt
│ ├── scene-0596.txt
│ ├── scene-0597.txt
│ ├── scene-0598.txt
│ ├── scene-0599.txt
│ ├── scene-0600.txt
│ ├── scene-0625.txt
│ ├── scene-0626.txt
│ ├── scene-0627.txt
│ ├── scene-0629.txt
│ ├── scene-0630.txt
│ ├── scene-0632.txt
│ ├── scene-0633.txt
│ ├── scene-0634.txt
│ ├── scene-0635.txt
│ ├── scene-0636.txt
│ ├── scene-0637.txt
│ ├── scene-0638.txt
│ ├── scene-0639.txt
│ ├── scene-0640.txt
│ ├── scene-0641.txt
│ ├── scene-0642.txt
│ ├── scene-0643.txt
│ ├── scene-0644.txt
│ ├── scene-0645.txt
│ ├── scene-0646.txt
│ ├── scene-0647.txt
│ ├── scene-0648.txt
│ ├── scene-0649.txt
│ ├── scene-0650.txt
│ ├── scene-0651.txt
│ ├── scene-0652.txt
│ ├── scene-0653.txt
│ ├── scene-0654.txt
│ ├── scene-0655.txt
│ ├── scene-0656.txt
│ ├── scene-0657.txt
│ ├── scene-0658.txt
│ ├── scene-0659.txt
│ ├── scene-0660.txt
│ ├── scene-0661.txt
│ ├── scene-0662.txt
│ ├── scene-0663.txt
│ ├── scene-0664.txt
│ ├── scene-0665.txt
│ ├── scene-0666.txt
│ ├── scene-0667.txt
│ ├── scene-0668.txt
│ ├── scene-0669.txt
│ ├── scene-0670.txt
│ ├── scene-0671.txt
│ ├── scene-0672.txt
│ ├── scene-0673.txt
│ ├── scene-0674.txt
│ ├── scene-0675.txt
│ ├── scene-0676.txt
│ ├── scene-0677.txt
│ ├── scene-0678.txt
│ ├── scene-0679.txt
│ ├── scene-0681.txt
│ ├── scene-0683.txt
│ ├── scene-0684.txt
│ ├── scene-0685.txt
│ ├── scene-0686.txt
│ ├── scene-0687.txt
│ ├── scene-0688.txt
│ ├── scene-0689.txt
│ ├── scene-0695.txt
│ ├── scene-0696.txt
│ ├── scene-0697.txt
│ ├── scene-0698.txt
│ ├── scene-0700.txt
│ ├── scene-0701.txt
│ ├── scene-0703.txt
│ ├── scene-0704.txt
│ ├── scene-0705.txt
│ ├── scene-0706.txt
│ ├── scene-0707.txt
│ ├── scene-0708.txt
│ ├── scene-0709.txt
│ ├── scene-0710.txt
│ ├── scene-0711.txt
│ ├── scene-0712.txt
│ ├── scene-0713.txt
│ ├── scene-0714.txt
│ ├── scene-0715.txt
│ ├── scene-0716.txt
│ ├── scene-0717.txt
│ ├── scene-0718.txt
│ ├── scene-0719.txt
│ ├── scene-0726.txt
│ ├── scene-0727.txt
│ ├── scene-0728.txt
│ ├── scene-0730.txt
│ ├── scene-0731.txt
│ ├── scene-0733.txt
│ ├── scene-0734.txt
│ ├── scene-0735.txt
│ ├── scene-0736.txt
│ ├── scene-0737.txt
│ ├── scene-0738.txt
│ ├── scene-0739.txt
│ ├── scene-0740.txt
│ ├── scene-0741.txt
│ ├── scene-0744.txt
│ ├── scene-0746.txt
│ ├── scene-0747.txt
│ ├── scene-0749.txt
│ ├── scene-0750.txt
│ ├── scene-0751.txt
│ ├── scene-0752.txt
│ ├── scene-0757.txt
│ ├── scene-0758.txt
│ ├── scene-0759.txt
│ ├── scene-0760.txt
│ ├── scene-0761.txt
│ ├── scene-0762.txt
│ ├── scene-0763.txt
│ ├── scene-0764.txt
│ ├── scene-0765.txt
│ ├── scene-0767.txt
│ ├── scene-0768.txt
│ ├── scene-0769.txt
│ ├── scene-0770.txt
│ ├── scene-0771.txt
│ ├── scene-0775.txt
│ ├── scene-0777.txt
│ ├── scene-0778.txt
│ ├── scene-0780.txt
│ ├── scene-0781.txt
│ ├── scene-0782.txt
│ ├── scene-0783.txt
│ ├── scene-0784.txt
│ ├── scene-0786.txt
│ ├── scene-0787.txt
│ ├── scene-0789.txt
│ ├── scene-0790.txt
│ ├── scene-0791.txt
│ ├── scene-0792.txt
│ ├── scene-0794.txt
│ ├── scene-0795.txt
│ ├── scene-0796.txt
│ ├── scene-0797.txt
│ ├── scene-0798.txt
│ ├── scene-0799.txt
│ ├── scene-0800.txt
│ ├── scene-0802.txt
│ ├── scene-0803.txt
│ ├── scene-0804.txt
│ ├── scene-0805.txt
│ ├── scene-0806.txt
│ ├── scene-0808.txt
│ ├── scene-0809.txt
│ ├── scene-0810.txt
│ ├── scene-0811.txt
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│ ├── scene-0819.txt
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│ ├── scene-0822.txt
│ ├── scene-0847.txt
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│ ├── scene-0862.txt
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│ ├── scene-0864.txt
│ ├── scene-0865.txt
│ ├── scene-0866.txt
│ ├── scene-0868.txt
│ ├── scene-0869.txt
│ ├── scene-0870.txt
│ ├── scene-0871.txt
│ ├── scene-0872.txt
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│ ├── scene-0916.txt
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│ ├── scene-0919.txt
│ ├── scene-0920.txt
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│ ├── scene-0923.txt
│ ├── scene-0924.txt
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│ ├── scene-0931.txt
│ ├── scene-0945.txt
│ ├── scene-0947.txt
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│ ├── scene-1107.txt
│ ├── scene-1108.txt
│ ├── scene-1109.txt
│ └── scene-1110.txt
└── sceneflow_data.py
├── demo.py
├── figs
└── interpolation.png
├── models
├── layers.py
├── models.py
└── utils.py
├── pretrain_model
├── flownet3d_kitti_odometry_maxbias1.pth
└── interp_kitti.pth
├── test.py
├── train_interp.py
└── train_sceneflow.py
/README.md:
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1 | # PointINet: Point Cloud Frame Interpolation Network
2 |
3 | ## Introduction
4 |
5 | The repository contains the source code and pre-trained models of our paper (published on AAAI 2021): `PointINet: Point Cloud Frame Interpolation Network`.
6 |
7 |
8 |

9 |
10 |
11 | ## Environment
12 |
13 | Our code is developed and tested on the following environment:
14 |
15 | - Python 3.6
16 | - PyTorch 1.4.0
17 | - Cuda 10.1
18 | - Numpy 1.19
19 |
20 | We utilized several open source library to implement the code:
21 |
22 | - [kaolin](https://github.com/NVIDIAGameWorks/kaolin)
23 | - [pytorch3d](https://github.com/facebookresearch/pytorch3d)
24 | - [PyTorchEMD](https://github.com/daerduoCarey/PyTorchEMD) (only for evaluation)
25 | - [Mayavi](https://docs.enthought.com/mayavi/mayavi/) (only for visualization of demo)
26 | - [wandb](https://app.wandb.ai/) (to record training process)
27 |
28 | ## Usage
29 |
30 | ### Dataset
31 |
32 | We utilize two large scale outdoor LiDAR dataset:
33 |
34 | - [Kitti odometry dataset](http://www.cvlibs.net/datasets/kitti/eval_odometry.php)
35 | - [nuScenes dataset](https://www.nuscenes.org/)
36 |
37 | To facilitate the implementation, we split the LiDAR point clouds in nuScenes dataset by scenes and the results are saved in `data/scene-split`. Besides, all of the LiDAR files in nuScenes dataset are stored in one single folder (include sweeps and samples).
38 |
39 | For the pre-training of FlowNet3D, please refer to [FlowNet3D](https://github.com/xingyul/flownet3d) to download the pre-processed dataset (Flythings3D and Kitti scene flow dataset).
40 |
41 | ### Demo
42 |
43 | We provide a demo to visualize the result, please run
44 |
45 | ```bash
46 | python demo.py --is_save IS_SAVE --visualize VISUALIZE
47 | ```
48 |
49 | ### Training
50 |
51 | #### Training of FlowNet3D
52 |
53 | To train FlowNet3D, firstly train on Flythings3D dataset
54 |
55 | ```bash
56 | python train_sceneflow.py --batch_size BATCH_SIZE --gpu GPU --dataset Flythings3D --root DATAROOT --save_dir CHECKPOINTS_SAVE_DIR --train_type init
57 | ```
58 |
59 | Then train it on Kitti scene flow dataset
60 |
61 | ```bash
62 | python train_sceneflow.py --batch_size BATCH_SIZE --gpu GPU --dataset Kitti --root DATAROOT --pretrain_model PRETRAIN_MODEL --save_dir CHECKPOINTS_SAVE_DIR --train_type init
63 | ```
64 |
65 | After that train it on Kitti odometry dataset based on the model pretrained on Kitti scene flow dataset.
66 |
67 | ```bash
68 | python train_sceneflow.py --batch_size BATCH_SIZE --gpu GPU --dataset Kitti --root DATAROOT --pretrain_model PRETRAIN_MODEL --save_dir CHECKPOINTS_SAVE_DIR --train_type refine
69 | ```
70 |
71 | Also train it on nuScenes dataset based on the model pretrained on Kitti scene flow dataset.
72 |
73 | ```bash
74 | python train_sceneflow.py --batch_size BATCH_SIZE --gpu GPU --dataset nuscenes --root DATAROOT --pretrain_model PRETRAIN_MODEL --save_dir CHECKPOINTS_SAVE_DIR --train_type refine
75 | ```
76 |
77 | #### Training of PointINet
78 |
79 | We only train the PointINet on Kitti odometry dataset, run
80 |
81 | ```bash
82 | python train_interp.py --batch_size BATCH_SIZE --gpu GPU --dataset kitti --root DATAROOT --pretrain_model FLOWNET3D_PRETRAIN_MODEL --freeze 1
83 | ```
84 |
85 | ### Testing
86 |
87 | To test on Kitti odometry dataset, run
88 |
89 | ```bash
90 | python test.py --gpu GPU --dataset kitti --root DATAROOT --pretrain_model POINTINET_PRETRAIN_MODEL --pretrain_flow_model FLOWNET3D_PRETRAIN_MODEL
91 | ```
92 |
93 | To test on nuScenes dataset, run
94 |
95 | ```bash
96 | python test.py --gpu GPU --dataset nuscenes --root DATAROOT --pretrain_model POINTINET_PRETRAIN_MODEL --pretrain_flow_model FLOWNET3D_PRETRAIN_MODEL --scenelist TEST_SCENE_LIST
97 | ```
98 |
99 | ## Citation
100 |
101 | @InProceedings{Lu2020_PointINet,
102 | author = {Lu, Fan and Chen, Guang and Qu, Sanqing and Li, Zhijun and Liu, Yinlong and Knoll, Alois},
103 | title = {PointINet: Point Cloud Frame Interpolation Network},
104 | booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
105 | year = {2021}
106 | }
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/data/demo_data/original/000000.bin:
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https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/data/demo_data/original/000000.bin
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/data/demo_data/original/000001.bin:
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https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/data/demo_data/original/000001.bin
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/data/interpolation_data.py:
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1 | import torch
2 | import torch.nn as nn
3 | from torch.utils.data import Dataset
4 | from torch.utils.data import DataLoader
5 | import glob
6 | import numpy as np
7 | import os
8 | from tqdm import tqdm
9 |
10 | class KittiInterpolationDataset(Dataset):
11 | def __init__(self, root, npoints, interval, train=True, use_intensity=True):
12 | super(KittiInterpolationDataset, self).__init__()
13 | self.root = root
14 | self.npoints = npoints
15 | self.dataroot = os.path.join(self.root, 'velodyne')
16 | self.timeroot = os.path.join(self.root, 'times.txt')
17 | self.train = train
18 | self.use_intensity = use_intensity
19 | self.times = []
20 | self.read_times()
21 | self.datapath = glob.glob(os.path.join(self.dataroot, '*.bin'))
22 | self.datapath = sorted(self.datapath)
23 | self.interval = interval
24 | self.dataset = self.make_dataset()
25 |
26 | def read_times(self):
27 | with open(self.timeroot) as f:
28 | for line in f.readlines():
29 | line = line.strip()
30 | time = float(line)
31 | self.times.append(time)
32 |
33 | def get_cloud(self, file_name):
34 | pc = np.fromfile(file_name, dtype=np.float32, count=-1).reshape([-1,4])
35 | return pc
36 |
37 | def make_dataset(self):
38 | max_ind = len(self.datapath)
39 | ini_index = 0
40 | end_index = 0
41 | index_lists = []
42 | while (ini_index < max_ind - self.interval):
43 | end_index = ini_index + self.interval
44 | if self.train:
45 | mid_index = np.random.randint(1,self.interval) + ini_index
46 | triple = [ini_index, mid_index, end_index]
47 | index_lists.append(triple)
48 | else:
49 | for bias in range(1, self.interval):
50 | mid_index = bias + ini_index
51 | triple = [ini_index, mid_index, end_index]
52 | index_lists.append(triple)
53 | ini_index = end_index
54 | return index_lists
55 |
56 | def __getitem__(self, index):
57 | ini_index, mid_index, end_index = self.dataset[index]
58 | ini_pc = self.get_cloud(os.path.join(self.dataroot, self.datapath[ini_index]))
59 | mid_pc = self.get_cloud(os.path.join(self.dataroot, self.datapath[mid_index]))
60 | end_pc = self.get_cloud(os.path.join(self.dataroot, self.datapath[end_index]))
61 |
62 | ini_n = ini_pc.shape[0]
63 | mid_n = mid_pc.shape[0]
64 | end_n = end_pc.shape[0]
65 |
66 | if ini_n >= self.npoints:
67 | sample_idx_ini = np.random.choice(ini_n, self.npoints, replace=False)
68 | else:
69 | sample_idx_ini = np.concatenate((np.arange(ini_n), np.random.choice(ini_n, self.npoints - ini_n, replace=True)), axis=-1)
70 | if mid_n >= self.npoints:
71 | sample_idx_mid = np.random.choice(mid_n, self.npoints, replace=False)
72 | else:
73 | sample_idx_mid = np.concatenate((np.arange(mid_n), np.random.choice(mid_n, self.npoints - mid_n, replace=True)), axis=-1)
74 | if end_n >= self.npoints:
75 | sample_idx_end = np.random.choice(end_n, self.npoints, replace=False)
76 | else:
77 | sample_idx_end = np.concatenate((np.arange(end_n), np.random.choice(end_n, self.npoints - end_n, replace=True)), axis=-1)
78 |
79 | if self.use_intensity:
80 | ini_pc = ini_pc[sample_idx_ini, :].astype('float32')
81 | mid_pc = mid_pc[sample_idx_mid, :].astype('float32')
82 | end_pc = end_pc[sample_idx_end, :].astype('float32')
83 | else:
84 | ini_pc = ini_pc[sample_idx_ini, :3].astype('float32')
85 | mid_pc = mid_pc[sample_idx_mid, :3].astype('float32')
86 | end_pc = end_pc[sample_idx_end, :3].astype('float32')
87 |
88 | ini_color = np.zeros([self.npoints,3]).astype('float32')
89 | mid_color = np.zeros([self.npoints,3]).astype('float32')
90 | end_color = np.zeros([self.npoints,3]).astype('float32')
91 |
92 | ini_pc = torch.from_numpy(ini_pc).t()
93 | mid_pc = torch.from_numpy(mid_pc).t()
94 | end_pc = torch.from_numpy(end_pc).t()
95 |
96 | ini_color = torch.from_numpy(ini_color).t()
97 | mid_color = torch.from_numpy(mid_color).t()
98 | end_color = torch.from_numpy(end_color).t()
99 |
100 | ini_t = self.times[ini_index]
101 | mid_t = self.times[mid_index]
102 | end_t = self.times[end_index]
103 |
104 | t = (mid_t-ini_t)/(end_t-ini_t)
105 |
106 | return ini_pc, mid_pc, end_pc, ini_color, mid_color, end_color, t
107 |
108 | def __len__(self):
109 | return len(self.dataset)
110 |
111 | class NuscenesDataset(Dataset):
112 | def __init__(self, npoints, root, scenes_list, use_intensity, interval, train):
113 | self.npoints = npoints
114 | self.root = root
115 | self.train = train
116 | self.scenes = self.read_scene_list(scenes_list)
117 | self.times_list, self.fns_list = self.load_scene(self.scenes)
118 | self.interval = interval
119 | self.dataset_fns, self.dataset_times = self.make_dataset()
120 | self.use_intensity = use_intensity
121 |
122 |
123 | def read_scene_list(self, scenes_list):
124 | scenes = []
125 | with open(scenes_list, 'r') as f:
126 | for line in f.readlines():
127 | line = line.strip('\n')
128 | scenes.append(line)
129 | return scenes
130 |
131 | def load_scene(self, scenes):
132 | times_list = []
133 | fns_list = []
134 |
135 | for scene in scenes:
136 | scene_file = os.path.join('./data/scene-split', scene+'.txt')
137 | times = []
138 | fns = []
139 | with open(scene_file) as f:
140 | for line in f.readlines():
141 | line = line.strip('\n').split(' ')
142 | fn = line[0]
143 | timestamp = float(line[1])
144 | fns.append(fn)
145 | times.append(timestamp)
146 | times_list.append(times)
147 | fns_list.append(fns)
148 | return times_list, fns_list
149 |
150 | def make_dataset(self):
151 | fns_lists = []
152 | times_lists = []
153 | for i in range(len(self.times_list)):
154 | times = self.times_list[i]
155 | fns = self.fns_list[i]
156 | max_ind = len(times)
157 | ini_index = 0
158 | end_index = 0
159 | while (ini_index < max_ind - self.interval):
160 | end_index = ini_index + self.interval
161 | if self.train:
162 | bias = np.random.randint(1, self.interval)
163 | fns_lists.append([fns[ini_index], fns[ini_index+bias], fns[end_index]])
164 | times_lists.append([times[ini_index], times[ini_index+bias], times[end_index]])
165 | else:
166 | for bias in range(1, self.interval):
167 | fns_lists.append([fns[ini_index], fns[ini_index+bias], fns[end_index]])
168 | times_lists.append([times[ini_index], times[ini_index+bias], times[end_index]])
169 | ini_index = end_index
170 | return fns_lists, times_lists
171 |
172 | def get_lidar(self, fn):
173 | scan_in = np.fromfile(fn, np.float32).reshape(-1,5)
174 | scan = np.zeros((scan_in.shape[0],4),np.float32)
175 | scan = scan_in[:,:4]
176 | return scan
177 |
178 | def __getitem__(self, index):
179 | ini_pc = self.get_lidar(os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][0]))
180 | mid_pc = self.get_lidar(os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][1]))
181 | end_pc = self.get_lidar(os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][2]))
182 |
183 | ini_n = ini_pc.shape[0]
184 | mid_n = mid_pc.shape[0]
185 | end_n = end_pc.shape[0]
186 |
187 | if ini_n >= self.npoints:
188 | sample_idx_ini = np.random.choice(ini_n, self.npoints, replace=False)
189 | else:
190 | sample_idx_ini = np.concatenate((np.arange(ini_n), np.random.choice(ini_n, self.npoints - ini_n, replace=True)), axis=-1)
191 | if mid_n >= self.npoints:
192 | sample_idx_mid = np.random.choice(mid_n, self.npoints, replace=False)
193 | else:
194 | sample_idx_mid = np.concatenate((np.arange(mid_n), np.random.choice(mid_n, self.npoints - mid_n, replace=True)), axis=-1)
195 | if end_n >= self.npoints:
196 | sample_idx_end = np.random.choice(end_n, self.npoints, replace=False)
197 | else:
198 | sample_idx_end = np.concatenate((np.arange(end_n), np.random.choice(end_n, self.npoints - end_n, replace=True)), axis=-1)
199 |
200 | if self.use_intensity:
201 | ini_pc = ini_pc[sample_idx_ini, :].astype('float32')
202 | mid_pc = mid_pc[sample_idx_mid, :].astype('float32')
203 | end_pc = end_pc[sample_idx_end, :].astype('float32')
204 | else:
205 | ini_pc = ini_pc[sample_idx_ini, :3].astype('float32')
206 | mid_pc = mid_pc[sample_idx_mid, :3].astype('float32')
207 | end_pc = end_pc[sample_idx_end, :3].astype('float32')
208 |
209 | ini_color = np.zeros([self.npoints,3]).astype('float32')
210 | mid_color = np.zeros([self.npoints,3]).astype('float32')
211 | end_color = np.zeros([self.npoints,3]).astype('float32')
212 |
213 | ini_pc = torch.from_numpy(ini_pc).t()
214 | mid_pc = torch.from_numpy(mid_pc).t()
215 | end_pc = torch.from_numpy(end_pc).t()
216 |
217 | ini_color = torch.from_numpy(ini_color).t()
218 | mid_color = torch.from_numpy(mid_color).t()
219 | end_color = torch.from_numpy(end_color).t()
220 |
221 | ini_t = self.dataset_times[index][0]
222 | mid_t = self.dataset_times[index][1]
223 | end_t = self.dataset_times[index][2]
224 |
225 | t = (mid_t-ini_t)/(end_t-ini_t)
226 |
227 | return ini_pc, mid_pc, end_pc, ini_color, mid_color, end_color, t
228 |
229 | def __len__(self):
230 | return len(self.dataset_fns)
--------------------------------------------------------------------------------
/data/nuscenes_testlist.txt:
--------------------------------------------------------------------------------
1 | scene-0127
2 | scene-0128
3 | scene-0129
4 | scene-0130
5 | scene-0131
6 | scene-0132
7 | scene-0133
8 | scene-0134
9 | scene-0135
10 | scene-0138
11 | scene-0139
12 | scene-0149
13 | scene-0150
14 | scene-0151
15 | scene-0152
16 | scene-0154
17 | scene-0155
18 | scene-0157
19 | scene-0158
20 | scene-0159
21 | scene-0160
22 | scene-0161
23 | scene-0162
24 | scene-0163
25 | scene-0164
26 | scene-0165
27 | scene-0166
28 | scene-0167
29 | scene-0168
30 | scene-0170
31 | scene-0171
32 | scene-0172
33 | scene-0173
34 | scene-0174
35 | scene-0175
36 | scene-0176
37 | scene-0177
38 | scene-0178
39 | scene-0179
40 | scene-0180
41 | scene-0181
42 | scene-0182
43 | scene-0183
44 | scene-0184
45 | scene-0185
46 | scene-0187
47 | scene-0188
48 | scene-0190
49 | scene-0191
50 | scene-0192
51 | scene-0193
52 | scene-0194
53 | scene-0195
54 | scene-0196
55 | scene-0199
56 | scene-0200
57 | scene-0202
58 | scene-0203
59 | scene-0204
60 | scene-0206
61 | scene-0207
62 | scene-0208
63 | scene-0209
64 | scene-0210
65 | scene-0211
66 | scene-0212
67 | scene-0213
68 | scene-0214
69 | scene-0218
70 | scene-0219
71 | scene-0220
72 | scene-0221
73 | scene-0222
74 | scene-0224
75 | scene-0225
76 | scene-0226
77 | scene-0227
78 | scene-0228
79 | scene-0229
80 | scene-0230
81 | scene-0231
82 | scene-0232
83 | scene-0233
84 | scene-0234
85 | scene-0235
86 | scene-0236
87 | scene-0237
88 | scene-0238
89 | scene-0239
90 | scene-0240
91 | scene-0241
92 | scene-0242
93 | scene-0243
94 | scene-0244
95 | scene-0245
96 | scene-0246
97 | scene-0247
98 | scene-0248
99 | scene-0249
100 | scene-0250
101 | scene-0251
102 | scene-0252
103 | scene-0253
104 | scene-0254
105 | scene-0255
106 | scene-0256
107 | scene-0257
108 | scene-0258
109 | scene-0259
110 | scene-0260
111 | scene-0261
112 | scene-0262
113 | scene-0263
114 | scene-0264
115 | scene-0268
116 | scene-0269
117 | scene-0270
118 | scene-0271
119 | scene-0272
120 | scene-0273
121 | scene-0274
122 | scene-0275
123 | scene-0276
124 | scene-0277
125 | scene-0278
126 | scene-0283
127 | scene-0284
128 | scene-0285
129 | scene-0286
130 | scene-0287
131 | scene-0288
132 | scene-0289
133 | scene-0290
134 | scene-0291
135 | scene-0292
136 | scene-0293
137 | scene-0294
138 | scene-0295
139 | scene-0296
140 | scene-0297
141 | scene-0298
142 | scene-0299
143 | scene-0300
144 | scene-0301
145 | scene-0302
146 | scene-0303
147 | scene-0304
148 | scene-0305
149 | scene-0306
150 | scene-0315
151 | scene-0316
152 | scene-0317
153 | scene-0318
154 | scene-0321
155 | scene-0323
156 | scene-0324
157 | scene-0328
158 | scene-0329
159 | scene-0330
160 | scene-0331
161 | scene-0332
162 | scene-0344
163 | scene-0345
164 | scene-0346
165 | scene-0347
166 | scene-0348
167 | scene-0349
168 | scene-0350
169 | scene-0351
170 | scene-0352
171 | scene-0353
172 | scene-0354
173 | scene-0355
174 | scene-0356
175 | scene-0357
176 | scene-0358
177 | scene-0359
178 | scene-0360
179 | scene-0361
180 | scene-0362
181 | scene-0363
182 | scene-0364
183 | scene-0365
184 | scene-0366
185 | scene-0367
186 | scene-0368
187 | scene-0369
188 | scene-0370
189 | scene-0371
190 | scene-0372
191 | scene-0373
192 | scene-0374
193 | scene-0375
194 | scene-0376
195 | scene-0377
196 | scene-0378
197 | scene-0379
198 | scene-0380
199 | scene-0381
200 | scene-0382
201 | scene-0383
202 | scene-0384
203 | scene-0385
204 | scene-0386
205 | scene-0388
206 | scene-0389
207 | scene-0390
208 | scene-0391
209 | scene-0392
210 | scene-0393
211 | scene-0394
212 | scene-0395
213 | scene-0396
214 | scene-0397
215 | scene-0398
216 | scene-0399
217 | scene-0400
218 | scene-0401
219 | scene-0402
220 | scene-0403
221 | scene-0405
222 | scene-0406
223 | scene-0407
224 | scene-0408
225 | scene-0410
226 | scene-0411
227 | scene-0412
228 | scene-0413
229 | scene-0414
230 | scene-0415
231 | scene-0416
232 | scene-0417
233 | scene-0418
234 | scene-0419
235 | scene-0420
236 | scene-0421
237 | scene-0422
238 | scene-0423
239 | scene-0424
240 | scene-0425
241 | scene-0426
242 | scene-0427
243 | scene-0428
244 | scene-0429
245 | scene-0430
246 | scene-0431
247 | scene-0432
248 | scene-0433
249 | scene-0434
250 | scene-0435
251 | scene-0436
252 | scene-0437
253 | scene-0438
254 | scene-0439
255 | scene-0440
256 | scene-0441
257 | scene-0442
258 | scene-0443
259 | scene-0444
260 | scene-0445
261 | scene-0446
262 | scene-0447
263 | scene-0448
264 | scene-0449
265 | scene-0450
266 | scene-0451
267 | scene-0452
268 | scene-0453
269 | scene-0454
270 | scene-0455
271 | scene-0456
272 | scene-0457
273 | scene-0458
274 | scene-0459
275 | scene-0461
276 | scene-0462
277 | scene-0463
278 | scene-0464
279 | scene-0465
280 | scene-0467
281 | scene-0468
282 | scene-0469
283 | scene-0471
284 | scene-0472
285 | scene-0474
286 | scene-0475
287 | scene-0476
288 | scene-0477
289 | scene-0478
290 | scene-0479
291 | scene-0480
292 | scene-0499
293 | scene-0500
294 | scene-0501
295 | scene-0502
296 | scene-0504
297 | scene-0505
298 | scene-0506
299 | scene-0507
300 | scene-0508
301 | scene-0509
302 | scene-0510
303 | scene-0511
304 | scene-0512
305 | scene-0513
306 | scene-0514
307 | scene-0515
308 | scene-0517
309 | scene-0518
310 | scene-0519
311 | scene-0520
312 | scene-0521
313 | scene-0522
314 | scene-0523
315 | scene-0524
316 | scene-0525
317 | scene-0526
318 | scene-0527
319 | scene-0528
320 | scene-0529
321 | scene-0530
322 | scene-0531
323 | scene-0532
324 | scene-0533
325 | scene-0534
326 | scene-0535
327 | scene-0536
328 | scene-0537
329 | scene-0538
330 | scene-0539
331 | scene-0541
332 | scene-0542
333 | scene-0543
334 | scene-0544
335 | scene-0545
336 | scene-0546
337 | scene-0552
338 | scene-0553
339 | scene-0554
340 | scene-0555
341 | scene-0556
342 | scene-0557
343 | scene-0558
344 | scene-0559
345 | scene-0560
346 | scene-0561
347 | scene-0562
348 | scene-0563
349 | scene-0564
350 | scene-0565
351 | scene-0566
352 | scene-0568
353 | scene-0570
354 | scene-0571
355 | scene-0572
356 | scene-0573
357 | scene-0574
358 | scene-0575
359 | scene-0576
360 | scene-0577
361 | scene-0578
362 | scene-0580
363 | scene-0582
364 | scene-0583
365 | scene-0584
366 | scene-0585
367 | scene-0586
368 | scene-0587
369 | scene-0588
370 | scene-0589
371 | scene-0590
372 | scene-0591
373 | scene-0592
374 | scene-0593
375 | scene-0594
376 | scene-0595
377 | scene-0596
378 | scene-0597
379 | scene-0598
380 | scene-0599
381 | scene-0600
382 | scene-0625
383 | scene-0626
384 | scene-0627
385 | scene-0629
386 | scene-0630
387 | scene-0632
388 | scene-0633
389 | scene-0634
390 | scene-0635
391 | scene-0636
392 | scene-0637
393 | scene-0638
394 | scene-0639
395 | scene-0640
396 | scene-0641
397 | scene-0642
398 | scene-0643
399 | scene-0644
400 | scene-0645
401 | scene-0646
402 | scene-0647
403 | scene-0648
404 | scene-0649
405 | scene-0650
406 | scene-0651
407 | scene-0652
408 | scene-0653
409 | scene-0654
410 | scene-0655
411 | scene-0656
412 | scene-0657
413 | scene-0658
414 | scene-0659
415 | scene-0660
416 | scene-0661
417 | scene-0662
418 | scene-0663
419 | scene-0664
420 | scene-0665
421 | scene-0666
422 | scene-0667
423 | scene-0668
424 | scene-0669
425 | scene-0670
426 | scene-0671
427 | scene-0672
428 | scene-0673
429 | scene-0674
430 | scene-0675
431 | scene-0676
432 | scene-0677
433 | scene-0678
434 | scene-0679
435 | scene-0681
436 | scene-0683
437 | scene-0684
438 | scene-0685
439 | scene-0686
440 | scene-0687
441 | scene-0688
442 | scene-0689
443 | scene-0695
444 | scene-0696
445 | scene-0697
446 | scene-0698
447 | scene-0700
448 | scene-0701
449 | scene-0703
450 | scene-0704
451 | scene-0705
452 | scene-0706
453 | scene-0707
454 | scene-0708
455 | scene-0709
456 | scene-0710
457 | scene-0711
458 | scene-0712
459 | scene-0713
460 | scene-0714
461 | scene-0715
462 | scene-0716
463 | scene-0717
464 | scene-0718
465 | scene-0719
466 | scene-0726
467 | scene-0727
468 | scene-0728
469 | scene-0730
470 | scene-0731
471 | scene-0733
472 | scene-0734
473 | scene-0735
474 | scene-0736
475 | scene-0737
476 | scene-0738
477 | scene-0739
478 | scene-0740
479 | scene-0741
480 | scene-0744
481 | scene-0746
482 | scene-0747
483 | scene-0749
484 | scene-0750
485 | scene-0751
486 | scene-0752
487 | scene-0757
488 | scene-0758
489 | scene-0759
490 | scene-0760
491 | scene-0761
492 | scene-0762
493 | scene-0763
494 | scene-0764
495 | scene-0765
496 | scene-0767
497 | scene-0768
498 | scene-0769
499 | scene-0770
500 | scene-0771
501 | scene-0775
502 | scene-0777
503 | scene-0778
504 | scene-0780
505 | scene-0781
506 | scene-0782
507 | scene-0783
508 | scene-0784
509 | scene-0786
510 | scene-0787
511 | scene-0789
512 | scene-0790
513 | scene-0791
514 | scene-0792
515 | scene-0794
516 | scene-0795
517 | scene-0796
518 | scene-0797
519 | scene-0798
520 | scene-0799
521 | scene-0800
522 | scene-0802
523 | scene-0803
524 | scene-0804
525 | scene-0805
526 | scene-0806
527 | scene-0808
528 | scene-0809
529 | scene-0810
530 | scene-0811
531 | scene-0812
532 | scene-0813
533 | scene-0815
534 | scene-0816
535 | scene-0817
536 | scene-0819
537 | scene-0820
538 | scene-0821
539 | scene-0822
540 | scene-0847
541 | scene-0848
542 | scene-0849
543 | scene-0850
544 | scene-0851
545 | scene-0852
546 | scene-0853
547 | scene-0854
548 | scene-0855
549 | scene-0856
550 | scene-0858
551 | scene-0860
552 | scene-0861
553 | scene-0862
554 | scene-0863
555 | scene-0864
556 | scene-0865
557 | scene-0866
558 | scene-0868
559 | scene-0869
560 | scene-0870
561 | scene-0871
562 | scene-0872
563 | scene-0873
564 | scene-0875
565 | scene-0876
566 | scene-0877
567 | scene-0878
568 | scene-0880
569 | scene-0882
570 | scene-0883
571 | scene-0884
572 | scene-0885
573 | scene-0886
574 | scene-0887
575 | scene-0888
576 | scene-0889
577 | scene-0890
578 | scene-0891
579 | scene-0892
580 | scene-0893
581 | scene-0894
582 | scene-0895
583 | scene-0896
584 | scene-0897
585 | scene-0898
586 | scene-0899
587 | scene-0900
588 | scene-0901
589 | scene-0902
590 | scene-0903
591 | scene-0904
592 | scene-0905
593 | scene-0906
594 | scene-0907
595 | scene-0908
596 | scene-0909
597 | scene-0910
598 | scene-0911
599 | scene-0912
600 | scene-0913
601 | scene-0914
602 | scene-0915
603 | scene-0916
604 | scene-0917
605 | scene-0919
606 | scene-0920
607 | scene-0921
608 | scene-0922
609 | scene-0923
610 | scene-0924
611 | scene-0925
612 | scene-0926
613 | scene-0927
614 | scene-0928
615 | scene-0929
616 | scene-0930
617 | scene-0931
618 | scene-0945
619 | scene-0947
620 | scene-0949
621 | scene-0952
622 | scene-0953
623 | scene-0955
624 | scene-0956
625 | scene-0957
626 | scene-0958
627 | scene-0959
628 | scene-0960
629 | scene-0961
630 | scene-0962
631 | scene-0963
632 | scene-0966
633 | scene-0967
634 | scene-0968
635 | scene-0969
636 | scene-0971
637 | scene-0972
638 | scene-0975
639 | scene-0976
640 | scene-0977
641 | scene-0978
642 | scene-0979
643 | scene-0980
644 | scene-0981
645 | scene-0982
646 | scene-0983
647 | scene-0984
648 | scene-0988
649 | scene-0989
650 | scene-0990
651 | scene-0991
652 | scene-0992
653 | scene-0994
654 | scene-0995
655 | scene-0996
656 | scene-0997
657 | scene-0998
658 | scene-0999
659 | scene-1000
660 | scene-1001
661 | scene-1002
662 | scene-1003
663 | scene-1004
664 | scene-1005
665 | scene-1006
666 | scene-1007
667 | scene-1008
668 | scene-1009
669 | scene-1010
670 | scene-1011
671 | scene-1012
672 | scene-1013
673 | scene-1014
674 | scene-1015
675 | scene-1016
676 | scene-1017
677 | scene-1018
678 | scene-1019
679 | scene-1020
680 | scene-1021
681 | scene-1022
682 | scene-1023
683 | scene-1024
684 | scene-1025
685 | scene-1044
686 | scene-1045
687 | scene-1046
688 | scene-1047
689 | scene-1048
690 | scene-1049
691 | scene-1050
692 | scene-1051
693 | scene-1052
694 | scene-1053
695 | scene-1054
696 | scene-1055
697 | scene-1056
698 | scene-1057
699 | scene-1058
700 | scene-1059
701 | scene-1060
702 | scene-1061
703 | scene-1062
704 | scene-1063
705 | scene-1064
706 | scene-1065
707 | scene-1066
708 | scene-1067
709 | scene-1068
710 | scene-1069
711 | scene-1070
712 | scene-1071
713 | scene-1072
714 | scene-1073
715 | scene-1074
716 | scene-1075
717 | scene-1076
718 | scene-1077
719 | scene-1078
720 | scene-1079
721 | scene-1080
722 | scene-1081
723 | scene-1082
724 | scene-1083
725 | scene-1084
726 | scene-1085
727 | scene-1086
728 | scene-1087
729 | scene-1088
730 | scene-1089
731 | scene-1090
732 | scene-1091
733 | scene-1092
734 | scene-1093
735 | scene-1094
736 | scene-1095
737 | scene-1096
738 | scene-1097
739 | scene-1098
740 | scene-1099
741 | scene-1100
742 | scene-1101
743 | scene-1102
744 | scene-1104
745 | scene-1105
746 | scene-1106
747 | scene-1107
748 | scene-1108
749 | scene-1109
750 | scene-1110
751 |
--------------------------------------------------------------------------------
/data/nuscenes_trainlist.txt:
--------------------------------------------------------------------------------
1 | scene-0001
2 | scene-0002
3 | scene-0003
4 | scene-0004
5 | scene-0005
6 | scene-0006
7 | scene-0007
8 | scene-0008
9 | scene-0009
10 | scene-0010
11 | scene-0011
12 | scene-0012
13 | scene-0013
14 | scene-0014
15 | scene-0015
16 | scene-0016
17 | scene-0017
18 | scene-0018
19 | scene-0019
20 | scene-0020
21 | scene-0021
22 | scene-0022
23 | scene-0023
24 | scene-0024
25 | scene-0025
26 | scene-0026
27 | scene-0027
28 | scene-0028
29 | scene-0029
30 | scene-0030
31 | scene-0031
32 | scene-0032
33 | scene-0033
34 | scene-0034
35 | scene-0035
36 | scene-0036
37 | scene-0038
38 | scene-0039
39 | scene-0041
40 | scene-0042
41 | scene-0043
42 | scene-0044
43 | scene-0045
44 | scene-0046
45 | scene-0047
46 | scene-0048
47 | scene-0049
48 | scene-0050
49 | scene-0051
50 | scene-0052
51 | scene-0053
52 | scene-0054
53 | scene-0055
54 | scene-0056
55 | scene-0057
56 | scene-0058
57 | scene-0059
58 | scene-0060
59 | scene-0061
60 | scene-0062
61 | scene-0063
62 | scene-0064
63 | scene-0065
64 | scene-0066
65 | scene-0067
66 | scene-0068
67 | scene-0069
68 | scene-0070
69 | scene-0071
70 | scene-0072
71 | scene-0073
72 | scene-0074
73 | scene-0075
74 | scene-0076
75 | scene-0092
76 | scene-0093
77 | scene-0094
78 | scene-0095
79 | scene-0096
80 | scene-0097
81 | scene-0098
82 | scene-0099
83 | scene-0100
84 | scene-0101
85 | scene-0102
86 | scene-0103
87 | scene-0104
88 | scene-0105
89 | scene-0106
90 | scene-0107
91 | scene-0108
92 | scene-0109
93 | scene-0110
94 | scene-0120
95 | scene-0121
96 | scene-0122
97 | scene-0123
98 | scene-0124
99 | scene-0125
100 | scene-0126
--------------------------------------------------------------------------------
/data/scene-split/scene-0005.txt:
--------------------------------------------------------------------------------
1 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104147859.pcd.bin 1531884104147859.0
2 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104198184.pcd.bin 1531884104198184.0
3 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104247955.pcd.bin 1531884104247955.0
4 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104298285.pcd.bin 1531884104298285.0
5 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104348042.pcd.bin 1531884104348042.0
6 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104398352.pcd.bin 1531884104398352.0
7 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104448133.pcd.bin 1531884104448133.0
8 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104498432.pcd.bin 1531884104498432.0
9 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104548251.pcd.bin 1531884104548251.0
10 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104598531.pcd.bin 1531884104598531.0
11 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104648311.pcd.bin 1531884104648311.0
12 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104698605.pcd.bin 1531884104698605.0
13 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104748401.pcd.bin 1531884104748401.0
14 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104798693.pcd.bin 1531884104798693.0
15 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104848457.pcd.bin 1531884104848457.0
16 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104898777.pcd.bin 1531884104898777.0
17 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104948553.pcd.bin 1531884104948553.0
18 | n015-2018-07-18-11-18-34+0800__LIDAR_TOP__1531884104998868.pcd.bin 1531884104998868.0
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153 | n015-2018-07-27-11-36-48+0800__LIDAR_TOP__1532663149598526.pcd.bin 1532663149598526.0
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155 | n015-2018-07-27-11-36-48+0800__LIDAR_TOP__1532663149698602.pcd.bin 1532663149698602.0
156 | n015-2018-07-27-11-36-48+0800__LIDAR_TOP__1532663149748382.pcd.bin 1532663149748382.0
157 | n015-2018-07-27-11-36-48+0800__LIDAR_TOP__1532663149798703.pcd.bin 1532663149798703.0
158 | n015-2018-07-27-11-36-48+0800__LIDAR_TOP__1532663149848478.pcd.bin 1532663149848478.0
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164 | n015-2018-07-27-11-36-48+0800__LIDAR_TOP__1532663150148720.pcd.bin 1532663150148720.0
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167 | n015-2018-07-27-11-36-48+0800__LIDAR_TOP__1532663150298580.pcd.bin 1532663150298580.0
168 | n015-2018-07-27-11-36-48+0800__LIDAR_TOP__1532663150348337.pcd.bin 1532663150348337.0
169 | n015-2018-07-27-11-36-48+0800__LIDAR_TOP__1532663150398092.pcd.bin 1532663150398092.0
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316 |
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/data/scene-split/scene-0227.txt:
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147 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708055500320.pcd.bin 1532708055500320.0
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149 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708055602946.pcd.bin 1532708055602946.0
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151 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708055699967.pcd.bin 1532708055699967.0
152 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708055748470.pcd.bin 1532708055748470.0
153 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708055803902.pcd.bin 1532708055803902.0
154 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708055851933.pcd.bin 1532708055851933.0
155 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708055899118.pcd.bin 1532708055899118.0
156 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708055948641.pcd.bin 1532708055948641.0
157 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708055998401.pcd.bin 1532708055998401.0
158 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708056048736.pcd.bin 1532708056048736.0
159 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708056099968.pcd.bin 1532708056099968.0
160 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708056148253.pcd.bin 1532708056148253.0
161 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708056198586.pcd.bin 1532708056198586.0
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165 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708056398204.pcd.bin 1532708056398204.0
166 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708056448536.pcd.bin 1532708056448536.0
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170 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708056648138.pcd.bin 1532708056648138.0
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175 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708056898131.pcd.bin 1532708056898131.0
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178 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057049433.pcd.bin 1532708057049433.0
179 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057097693.pcd.bin 1532708057097693.0
180 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057147471.pcd.bin 1532708057147471.0
181 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057197772.pcd.bin 1532708057197772.0
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183 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057297377.pcd.bin 1532708057297377.0
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185 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057397398.pcd.bin 1532708057397398.0
186 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057447149.pcd.bin 1532708057447149.0
187 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057497463.pcd.bin 1532708057497463.0
188 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057547237.pcd.bin 1532708057547237.0
189 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057597557.pcd.bin 1532708057597557.0
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191 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057747409.pcd.bin 1532708057747409.0
192 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057797159.pcd.bin 1532708057797159.0
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195 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057947068.pcd.bin 1532708057947068.0
196 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708057996877.pcd.bin 1532708057996877.0
197 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708058047132.pcd.bin 1532708058047132.0
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200 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708058196987.pcd.bin 1532708058196987.0
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202 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708058296498.pcd.bin 1532708058296498.0
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216 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708059046858.pcd.bin 1532708059046858.0
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223 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708059396879.pcd.bin 1532708059396879.0
224 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708059447206.pcd.bin 1532708059447206.0
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226 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708059547287.pcd.bin 1532708059547287.0
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232 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708059846995.pcd.bin 1532708059846995.0
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253 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708060947942.pcd.bin 1532708060947942.0
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257 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061148153.pcd.bin 1532708061148153.0
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259 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061248202.pcd.bin 1532708061248202.0
260 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061297962.pcd.bin 1532708061297962.0
261 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061348287.pcd.bin 1532708061348287.0
262 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061398051.pcd.bin 1532708061398051.0
263 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061448376.pcd.bin 1532708061448376.0
264 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061498142.pcd.bin 1532708061498142.0
265 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061548451.pcd.bin 1532708061548451.0
266 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061598330.pcd.bin 1532708061598330.0
267 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061648534.pcd.bin 1532708061648534.0
268 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061698303.pcd.bin 1532708061698303.0
269 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061748618.pcd.bin 1532708061748618.0
270 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061798376.pcd.bin 1532708061798376.0
271 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061848705.pcd.bin 1532708061848705.0
272 | n008-2018-07-27-12-07-38-0400__LIDAR_TOP__1532708061898469.pcd.bin 1532708061898469.0
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/data/sceneflow_data.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import torch.nn as nn
3 | from torch.utils.data import Dataset
4 | import glob
5 | import numpy as np
6 | import os
7 | from tqdm import tqdm
8 |
9 | class Flythings3D(Dataset):
10 | def __init__(self, npoints=2048, root='data/data_processed_maxcut_35_20k_2k_8192', train=True, cache=None):
11 | self.npoints = npoints
12 | self.train = train
13 | self.root = root
14 | if self.train:
15 | self.datapath = glob.glob(os.path.join(self.root, 'TRAIN*.npz'))
16 | else:
17 | self.datapath = glob.glob(os.path.join(self.root, 'TEST*.npz'))
18 |
19 | if cache is None:
20 | self.cache = {}
21 | else:
22 | self.cache = cache
23 |
24 | self.cache_size = 30000
25 |
26 | ###### deal with one bad datapoint with nan value
27 | self.datapath = [d for d in self.datapath if 'TRAIN_C_0140_left_0006-0' not in d]
28 | ######
29 |
30 | def __getitem__(self, index):
31 | if index in self.cache:
32 | pos1, pos2, color1, color2, flow, mask1 = self.cache[index]
33 | else:
34 | fn = self.datapath[index]
35 | with open(fn, 'rb') as fp:
36 | data = np.load(fp)
37 | pos1 = data['points1'].astype('float32')
38 | pos2 = data['points2'].astype('float32')
39 | color1 = data['color1'].astype('float32') / 255
40 | color2 = data['color2'].astype('float32') / 255
41 | flow = data['flow'].astype('float32')
42 | mask1 = data['valid_mask1']
43 |
44 | if len(self.cache) < self.cache_size:
45 | self.cache[index] = (pos1, pos2, color1, color2, flow, mask1)
46 |
47 | if self.train:
48 | n1 = pos1.shape[0]
49 | sample_idx1 = np.random.choice(n1, self.npoints, replace=False)
50 | n2 = pos2.shape[0]
51 | sample_idx2 = np.random.choice(n2, self.npoints, replace=False)
52 |
53 | pos1 = pos1[sample_idx1, :]
54 | pos2 = pos2[sample_idx2, :]
55 | color1 = color1[sample_idx1, :]
56 | color2 = color2[sample_idx2, :]
57 | flow = flow[sample_idx1, :]
58 | mask1 = mask1[sample_idx1]
59 | else:
60 | pos1 = pos1[:self.npoints, :]
61 | pos2 = pos2[:self.npoints, :]
62 | color1 = color1[:self.npoints, :]
63 | color2 = color2[:self.npoints, :]
64 | flow = flow[:self.npoints, :]
65 | mask1 = mask1[:self.npoints]
66 |
67 | pos1_center = np.mean(pos1, 0)
68 | pos1 -= pos1_center
69 | pos2 -= pos1_center
70 |
71 | pos1 = torch.from_numpy(pos1).t()
72 | pos2 = torch.from_numpy(pos2).t()
73 | color1 = torch.from_numpy(color1).t()
74 | color2 = torch.from_numpy(color2).t()
75 | flow = torch.from_numpy(flow).t()
76 | mask1 = torch.from_numpy(mask1)
77 |
78 | return pos1, pos2, color1, color2, flow, mask1
79 |
80 | def __len__(self):
81 | return len(self.datapath)
82 |
83 | class KittiSceneFlowDataset(Dataset):
84 | def __init__(self, root, npoints, train=True):
85 | self.npoints = npoints
86 | self.root = root
87 | self.datapath = glob.glob(os.path.join(self.root, '*.npz'))
88 |
89 | def __getitem__(self, index):
90 | fn = self.datapath[index]
91 | with open(fn, 'rb') as fp:
92 | data = np.load(fp)
93 | pos1 = data['pos1'].astype('float32')
94 | pos2 = data['pos2'].astype('float32')
95 | flow = data['gt'].astype('float32')
96 |
97 | n1 = pos1.shape[0]
98 | n2 = pos2.shape[0]
99 |
100 | if n1 >= self.npoints:
101 | sample_idx1 = np.random.choice(n1, self.npoints, replace=False)
102 | else:
103 | sample_idx1 = np.concatenate((np.arange(n1), np.random.choice(n1, self.npoints - n1, replace=True)), axis=-1)
104 | if n2 >= self.npoints:
105 | sample_idx2 = np.random.choice(n2, self.npoints, replace=False)
106 | else:
107 | sample_idx2 = np.concatenate((np.arange(n2), np.random.choice(n2, self.npoints - n2, replace=True)), axis=-1)
108 |
109 | pos1 = pos1[sample_idx1, :]
110 | pos2 = pos2[sample_idx2, :]
111 | flow = flow[sample_idx1, :]
112 |
113 | color1 = np.zeros([self.npoints,3], dtype=np.float32)
114 | color2 = np.zeros([self.npoints,3], dtype=np.float32)
115 | mask1 = np.ones([self.npoints])
116 |
117 | pos1 = torch.from_numpy(pos1).t()
118 | pos2 = torch.from_numpy(pos2).t()
119 | color1 = torch.from_numpy(color1).t()
120 | color2 = torch.from_numpy(color2).t()
121 | flow = torch.from_numpy(flow).t()
122 | mask1 = torch.from_numpy(mask1)
123 |
124 | return pos1, pos2, color1, color2, flow, mask1
125 |
126 | def __len__(self):
127 | return len(self.datapath)
128 |
129 | class KittiOdometrySceneflow(Dataset):
130 | def __init__(self, root, npoints, max_bias, train=True):
131 | self.npoints = npoints
132 | self.root = root
133 | self.train = train
134 | self.max_bias = max_bias
135 | self.datapath = glob.glob(os.path.join(self.root, '*.bin'))
136 | self.datapath = sorted(self.datapath)
137 |
138 | def get_cloud(self, fn):
139 | pc = np.fromfile(fn, dtype=np.float32, count=-1).reshape([-1,4])
140 | return pc
141 |
142 | def __getitem__(self, index):
143 | pc1_fn = os.path.join(self.root, self.datapath[index])
144 | max_ind = len(self.datapath)
145 | if index <= self.max_bias:
146 | bias = np.random.randint(1, self.max_bias+1)
147 | elif index >= max_ind - self.max_bias:
148 | bias = np.random.randint(-self.max_bias,0)
149 | else:
150 | bias = np.random.randint(-self.max_bias,self.max_bias+1)
151 | if bias == 0:
152 | bias = bias + 1
153 |
154 | pc2_fn = os.path.join(self.root, self.datapath[index+bias])
155 |
156 | points1 = self.get_cloud(pc1_fn)
157 | points2 = self.get_cloud(pc2_fn)
158 |
159 | n1 = points1.shape[0]
160 | n2 = points2.shape[0]
161 |
162 | if n1 >= self.npoints:
163 | sample_idx1 = np.random.choice(n1, self.npoints, replace=False)
164 | else:
165 | sample_idx1 = np.concatenate((np.arange(n1), np.random.choice(n1, self.npoints - n1, replace=True)), axis=-1)
166 | if n2 >= self.npoints:
167 | sample_idx2 = np.random.choice(n2, self.npoints, replace=False)
168 | else:
169 | sample_idx2 = np.concatenate((np.arange(n2), np.random.choice(n2, self.npoints - n2, replace=True)), axis=-1)
170 |
171 | points1 = points1[sample_idx1, :3].astype('float32')
172 | points2 = points2[sample_idx2, :3].astype('float32')
173 | color1 = np.zeros([self.npoints,3]).astype('float32')
174 | color2 = np.zeros([self.npoints,3]).astype('float32')
175 |
176 | points1 = torch.from_numpy(points1).t()
177 | points2 = torch.from_numpy(points2).t()
178 | color1 = torch.from_numpy(color1).t()
179 | color2 = torch.from_numpy(color2).t()
180 |
181 | return points1, points2, color1, color2
182 |
183 | def __len__(self):
184 | return len(self.datapath)
185 |
186 | class NuScenesFlow(Dataset):
187 | def __init__(self, root, npoints, scenes_list, max_bias):
188 | self.npoints = npoints
189 | self.root = root
190 | self.scenes = self.read_scene_list(scenes_list)
191 | self.times_list, self.fns_list = self.load_scene(self.scenes)
192 | self.max_bias = max_bias
193 | self.dataset_fns, self.dataset_times = self.make_dataset()
194 |
195 | def read_scene_list(self, scenes_list):
196 | scenes = []
197 | with open(scenes_list, 'r') as f:
198 | for line in f.readlines():
199 | line = line.strip('\n')
200 | scenes.append(line)
201 | return scenes
202 |
203 | def load_scene(self, scenes):
204 | times_list = []
205 | fns_list = []
206 |
207 | for scene in scenes:
208 | scene_file = os.path.join('./data/scene-split', scene+'.txt')
209 | times = []
210 | fns = []
211 | with open(scene_file) as f:
212 | for line in f.readlines():
213 | line = line.strip('\n').split(' ')
214 | fn = line[0]
215 | timestamp = float(line[1])
216 | fns.append(fn)
217 | times.append(timestamp)
218 | times_list.append(times)
219 | fns_list.append(fns)
220 | return times_list, fns_list
221 |
222 | def make_dataset(self):
223 | fns_lists = []
224 | times_lists = []
225 | for i in tqdm(range(len(self.times_list))):
226 | times = self.times_list[i]
227 | fns = self.fns_list[i]
228 | max_ind = len(times)
229 | ini_index = 0
230 | while (ini_index < max_ind - self.max_bias):
231 | if ini_index <= self.max_bias:
232 | bias = np.random.randint(1, self.max_bias+1)
233 | else:
234 | bias = np.random.randint(-self.max_bias,self.max_bias+1)
235 | if bias == 0:
236 | bias = bias + 1
237 | end_index = ini_index + bias
238 | fns_lists.append([fns[ini_index], fns[end_index]])
239 | times_lists.append([times[ini_index], times[end_index]])
240 | ini_index += 1
241 | return fns_lists, times_lists
242 |
243 | def get_lidar(self, fn):
244 | scan_in = np.fromfile(fn, np.float32).reshape(-1,5)
245 | scan = np.zeros((scan_in.shape[0],4),np.float32)
246 | scan = scan_in[:,:4]
247 | return scan
248 |
249 | def __getitem__(self, index):
250 | pc1_fn = os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][0])
251 | pc2_fn = os.path.join(self.root, 'sweeps', 'LIDAR_TOP', self.dataset_fns[index][1])
252 | points1 = self.get_lidar(pc1_fn)
253 | points2 = self.get_lidar(pc2_fn)
254 |
255 | n1 = points1.shape[0]
256 | n2 = points2.shape[0]
257 |
258 | if n1 >= self.npoints:
259 | sample_idx1 = np.random.choice(n1, self.npoints, replace=False)
260 | else:
261 | sample_idx1 = np.concatenate((np.arange(n1), np.random.choice(n1, self.npoints - n1, replace=True)), axis=-1)
262 | if n2 >= self.npoints:
263 | sample_idx2 = np.random.choice(n2, self.npoints, replace=False)
264 | else:
265 | sample_idx2 = np.concatenate((np.arange(n2), np.random.choice(n2, self.npoints - n2, replace=True)), axis=-1)
266 |
267 | points1 = points1[sample_idx1, :3].astype('float32')
268 | points2 = points2[sample_idx2, :3].astype('float32')
269 | color1 = np.zeros([self.npoints,3]).astype('float32')
270 | color2 = np.zeros([self.npoints,3]).astype('float32')
271 |
272 | points1 = torch.from_numpy(points1).t()
273 | points2 = torch.from_numpy(points2).t()
274 | color1 = torch.from_numpy(color1).t()
275 | color2 = torch.from_numpy(color2).t()
276 |
277 | return points1, points2, color1, color2
278 |
279 | def __len__(self):
280 | return len(self.dataset_fns)
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/demo.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import torch.nn as nn
3 | import numpy as np
4 |
5 | from models.models import PointINet
6 |
7 | import mayavi.mlab as mlab
8 |
9 | import argparse
10 | from tqdm import tqdm
11 | import os
12 |
13 | def parse_args():
14 | parser = argparse.ArgumentParser(description='demo')
15 | parser.add_argument('--gpu', type=str, default='0')
16 | parser.add_argument('--npoints', type=int, default=32768)
17 | parser.add_argument('--pretrain_model', type=str, default='./pretrain_model/interp_kitti.pth')
18 | parser.add_argument('--pretrain_flow_model', type=str, default='./pretrain_model/flownet3d_kitti_odometry_maxbias1.pth')
19 | parser.add_argument('--is_save', type=int, default=1)
20 | parser.add_argument('--visualize', type=int, default=1)
21 |
22 | return parser.parse_args()
23 |
24 | def get_lidar(fn, npoints):
25 | points = np.fromfile(fn, dtype=np.float32, count=-1).reshape([-1,4])
26 | raw_num = points.shape[0]
27 | if raw_num >= npoints:
28 | sample_idx = np.random.choice(raw_num, npoints, replace=False)
29 | else:
30 | sample_idx = np.concatenate((np.arange(raw_num), np.random.choice(raw_num, npoints - raw_num, replace=True)), axis=-1)
31 |
32 | pc = points[sample_idx, :]
33 | pc = torch.from_numpy(pc).t()
34 | color = np.zeros([npoints,3]).astype('float32')
35 | color = torch.from_numpy(color).t()
36 |
37 | pc = pc.unsqueeze(0).cuda()
38 | color = color.unsqueeze(0).cuda()
39 |
40 | return pc, color
41 |
42 | def demo(args):
43 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
44 |
45 | fn1 = './data/demo_data/original/000000.bin'
46 | fn2 = './data/demo_data/original/000001.bin'
47 |
48 | net = PointINet()
49 | net.load_state_dict(torch.load(args.pretrain_model))
50 | net.flow.load_state_dict(torch.load(args.pretrain_flow_model))
51 | net.eval()
52 | net.cuda()
53 |
54 | interp_scale = 5
55 | t_array = np.arange(1.0/interp_scale, 1.0, 1.0/interp_scale, dtype=np.float32)
56 |
57 | with torch.no_grad():
58 | pc1, color1 = get_lidar(fn1, args.npoints)
59 | pc2, color2 = get_lidar(fn2, args.npoints)
60 |
61 | for i in range(interp_scale-1):
62 | t = t_array[i]
63 | t = torch.tensor([t])
64 | t = t.cuda().float()
65 |
66 | pred_mid_pc = net(pc1, pc2, color1, color2, t)
67 |
68 | ini_pc = pc1.squeeze(0).permute(1,0).cpu().numpy()
69 | end_pc = pc2.squeeze(0).permute(1,0).cpu().numpy()
70 |
71 |
72 | pred_mid_pc = pred_mid_pc.squeeze(0).permute(1,0).cpu().numpy()
73 |
74 | if args.visualize == 1:
75 | fig = mlab.figure(figure=None, bgcolor=(1,1,1), fgcolor=(0,0,0), engine=None, size=(1600, 1000))
76 | mlab.points3d(ini_pc[:,0],ini_pc[:,1],ini_pc[:,2],color=(0,0,1),scale_factor=0.2,figure=fig, mode='sphere')
77 | mlab.points3d(end_pc[:,0],end_pc[:,1],end_pc[:,2],color=(0,1,0),scale_factor=0.2,figure=fig, mode='sphere')
78 | mlab.points3d(pred_mid_pc[:,0],pred_mid_pc[:,1],pred_mid_pc[:,2],color=(1,0,0),scale_factor=0.2,figure=fig, mode='sphere')
79 | mlab.show()
80 |
81 | if args.is_save == 1:
82 | save_dir = './data/demo_data/interpolated'
83 | if not os.path.exists(save_dir):
84 | os.makedirs(save_dir)
85 | save_name = os.path.join(save_dir, str(t.squeeze().cpu().numpy())+'.bin')
86 | pred_mid_pc.tofile(save_name)
87 | print("save interpolated point clouds to:", save_name)
88 |
89 | if __name__ == '__main__':
90 | args = parse_args()
91 | demo(args)
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/figs/interpolation.png:
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https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/figs/interpolation.png
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/models/layers.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import torch.nn as nn
3 | import torch.nn.functional as F
4 | import numpy as np
5 |
6 | # We utilize kaolin to implement layers for FlowNet3D
7 | # website: https://github.com/NVIDIAGameWorks/kaolin
8 | import kaolin as kal
9 | from kaolin.models.PointNet2 import furthest_point_sampling
10 | from kaolin.models.PointNet2 import fps_gather_by_index
11 | from kaolin.models.PointNet2 import ball_query
12 | from kaolin.models.PointNet2 import group_gather_by_index
13 | from kaolin.models.PointNet2 import three_nn
14 | from kaolin.models.PointNet2 import three_interpolate
15 |
16 | # We utilize pytorch3d to implement k-nearest-neighbor search
17 | # website: https://github.com/facebookresearch/pytorch3d
18 | from pytorch3d.ops import knn_points, knn_gather
19 |
20 | from .utils import pdist2squared
21 |
22 | '''
23 | Layers for FlowNet3D
24 | '''
25 | class Sample(nn.Module):
26 | '''
27 | Furthest point sample
28 | '''
29 | def __init__(self, num_points):
30 | super(Sample, self).__init__()
31 |
32 | self.num_points = num_points
33 |
34 | def forward(self, points):
35 | new_points_ind = furthest_point_sampling(points.permute(0, 2, 1).contiguous(), self.num_points)
36 | new_points = fps_gather_by_index(points, new_points_ind)
37 | return new_points
38 |
39 | class Group(nn.Module):
40 | '''
41 | kNN group for FlowNet3D
42 | '''
43 | def __init__(self, radius, num_samples, knn=False):
44 | super(Group, self).__init__()
45 |
46 | self.radius = radius
47 | self.num_samples = num_samples
48 | self.knn = knn
49 |
50 | def forward(self, points, new_points, features):
51 | if self.knn:
52 | dist = pdist2squared(points, new_points)
53 | ind = dist.topk(self.num_samples, dim=1, largest=False)[1].int().permute(0, 2, 1).contiguous()
54 | else:
55 | ind = ball_query(self.radius, self.num_samples, points.permute(0, 2, 1).contiguous(),
56 | new_points.permute(0, 2, 1).contiguous(), False)
57 | grouped_points = group_gather_by_index(points, ind)
58 | grouped_points_new = grouped_points - new_points.unsqueeze(3)
59 | grouped_features = group_gather_by_index(features, ind)
60 | new_features = torch.cat([grouped_points_new, grouped_features], dim=1)
61 | return new_features
62 |
63 | class SetConv(nn.Module):
64 | def __init__(self, num_points, radius, num_samples, in_channels, out_channels):
65 | super(SetConv, self).__init__()
66 |
67 | self.sample = Sample(num_points)
68 | self.group = Group(radius, num_samples)
69 |
70 | layers = []
71 | out_channels = [in_channels+3, *out_channels]
72 | for i in range(1, len(out_channels)):
73 | layers += [nn.Conv2d(out_channels[i - 1], out_channels[i], 1, bias=True), nn.BatchNorm2d(out_channels[i], eps=0.001), nn.ReLU()]
74 | self.conv = nn.Sequential(*layers)
75 |
76 | def forward(self, points, features):
77 | new_points = self.sample(points)
78 | new_features = self.group(points, new_points, features)
79 | new_features = self.conv(new_features)
80 | new_features = new_features.max(dim=3)[0]
81 | return new_points, new_features
82 |
83 | class FlowEmbedding(nn.Module):
84 | def __init__(self, num_samples, in_channels, out_channels):
85 | super(FlowEmbedding, self).__init__()
86 |
87 | self.num_samples = num_samples
88 |
89 | self.group = Group(None, self.num_samples, knn=True)
90 |
91 | layers = []
92 | out_channels = [2*in_channels+3, *out_channels]
93 | for i in range(1, len(out_channels)):
94 | layers += [nn.Conv2d(out_channels[i - 1], out_channels[i], 1, bias=True), nn.BatchNorm2d(out_channels[i], eps=0.001), nn.ReLU()]
95 | self.conv = nn.Sequential(*layers)
96 |
97 | def forward(self, points1, points2, features1, features2):
98 | new_features = self.group(points2, points1, features2)
99 | new_features = torch.cat([new_features, features1.unsqueeze(3).expand(-1, -1, -1, self.num_samples)], dim=1)
100 | new_features = self.conv(new_features)
101 | new_features = new_features.max(dim=3)[0]
102 | return new_features
103 |
104 | class SetUpConv(nn.Module):
105 | def __init__(self, num_samples, in_channels1, in_channels2, out_channels1, out_channels2):
106 | super(SetUpConv, self).__init__()
107 |
108 | self.group = Group(None, num_samples, knn=True)
109 |
110 | layers = []
111 | out_channels1 = [in_channels1+3, *out_channels1]
112 | for i in range(1, len(out_channels1)):
113 | layers += [nn.Conv2d(out_channels1[i - 1], out_channels1[i], 1, bias=True), nn.BatchNorm2d(out_channels1[i], eps=0.001), nn.ReLU()]
114 | self.conv1 = nn.Sequential(*layers)
115 |
116 | layers = []
117 | if len(out_channels1) == 1:
118 | out_channels2 = [in_channels1+in_channels2+3, *out_channels2]
119 | else:
120 | out_channels2 = [out_channels1[-1]+in_channels2, *out_channels2]
121 | for i in range(1, len(out_channels2)):
122 | layers += [nn.Conv2d(out_channels2[i - 1], out_channels2[i], 1, bias=True), nn.BatchNorm2d(out_channels2[i], eps=0.001), nn.ReLU()]
123 | self.conv2 = nn.Sequential(*layers)
124 |
125 | def forward(self, points1, points2, features1, features2):
126 | new_features = self.group(points1, points2, features1)
127 | new_features = self.conv1(new_features)
128 | new_features = new_features.max(dim=3)[0]
129 | new_features = torch.cat([new_features, features2], dim=1)
130 | new_features = new_features.unsqueeze(3)
131 | new_features = self.conv2(new_features)
132 | new_features = new_features.squeeze(3)
133 | return new_features
134 |
135 | class FeaturePropagation(nn.Module):
136 | def __init__(self, in_channels1, in_channels2, out_channels):
137 | super(FeaturePropagation, self).__init__()
138 |
139 | layers = []
140 | out_channels = [in_channels1+in_channels2, *out_channels]
141 | for i in range(1, len(out_channels)):
142 | layers += [nn.Conv2d(out_channels[i - 1], out_channels[i], 1, bias=True), nn.BatchNorm2d(out_channels[i], eps=0.001), nn.ReLU()]
143 | self.conv = nn.Sequential(*layers)
144 |
145 | def forward(self, points1, points2, features1, features2):
146 | dist, ind = three_nn(points2.permute(0, 2, 1).contiguous(), points1.permute(0, 2, 1).contiguous())
147 | dist = dist * dist
148 | dist[dist < 1e-10] = 1e-10
149 | inverse_dist = 1.0 / dist
150 | norm = torch.sum(inverse_dist, dim=2, keepdim=True)
151 | weights = inverse_dist / norm
152 | new_features = torch.sum(group_gather_by_index(features1, ind) * weights.unsqueeze(1), dim = 3)
153 | new_features = torch.cat([new_features, features2], dim=1)
154 | new_features = self.conv(new_features.unsqueeze(3)).squeeze(3)
155 | return new_features
156 |
157 | class PointsFusion(nn.Module):
158 | def __init__(self, in_channels, out_channels):
159 | super(PointsFusion, self).__init__()
160 |
161 | layers = []
162 | out_channels = [in_channels, *out_channels]
163 | for i in range(1, len(out_channels)):
164 | layers += [nn.Conv2d(out_channels[i - 1], out_channels[i], 1, bias=True), nn.BatchNorm2d(out_channels[i], eps=0.001), nn.ReLU()]
165 |
166 | self.conv = nn.Sequential(*layers)
167 |
168 | def knn_group(self, points1, points2, features2, k):
169 | '''
170 | For each point in points1, query kNN points/features in points2/features2
171 | Input:
172 | points1: [B,3,N]
173 | points2: [B,3,N]
174 | features2: [B,C,N]
175 | Output:
176 | new_features: [B,4,N]
177 | nn: [B,3,N]
178 | grouped_features: [B,C,N]
179 | '''
180 | points1 = points1.permute(0,2,1).contiguous()
181 | points2 = points2.permute(0,2,1).contiguous()
182 | _, nn_idx, nn = knn_points(points1, points2, K=k, return_nn=True)
183 | points_resi = nn - points1.unsqueeze(2).repeat(1,1,k,1)
184 | grouped_dist = torch.norm(points_resi, dim=-1, keepdim=True)
185 | grouped_features = knn_gather(features2.permute(0,2,1), nn_idx)
186 | new_features = torch.cat([points_resi, grouped_dist], dim=-1)
187 |
188 | return new_features.permute(0,3,1,2).contiguous(),\
189 | nn.permute(0,3,1,2).contiguous(),\
190 | grouped_features.permute(0,3,1,2).contiguous()
191 |
192 | def forward(self, points1, points2, features1, features2, k, t):
193 | '''
194 | Input:
195 | points1: [B,3,N]
196 | points2: [B,3,N]
197 | features1: [B,C,N] (only for inference of additional features)
198 | features2: [B,C,N] (only for inference of additional features)
199 | k: int, number of kNN cluster
200 | t: [B], time step in (0,1)
201 | Output:
202 | fused_points: [B,3+C,N]
203 | '''
204 | N = points1.shape[-1]
205 | B = points1.shape[0] # batch size
206 |
207 | new_features_list = []
208 | new_grouped_points_list = []
209 | new_grouped_features_list = []
210 |
211 | for i in range(B):
212 | t1 = t[i]
213 | new_points1 = points1[i:i+1,:,:]
214 | new_points2 = points2[i:i+1,:,:]
215 | new_features1 = features1[i:i+1,:,:]
216 | new_features2 = features2[i:i+1,:,:]
217 |
218 | N2 = int(N*t1)
219 | N1 = N - N2
220 |
221 | k2 = int(k*t1)
222 | k1 = k - k2
223 |
224 | randidx1 = torch.randperm(N)[:N1]
225 | randidx2 = torch.randperm(N)[:N2]
226 | new_points = torch.cat((new_points1[:,:,randidx1], new_points2[:,:,randidx2]), dim=-1)
227 |
228 | new_features1, grouped_points1, grouped_features1 = self.knn_group(new_points, new_points1, new_features1, k1)
229 | new_features2, grouped_points2, grouped_features2 = self.knn_group(new_points, new_points2, new_features2, k2)
230 |
231 | new_features = torch.cat((new_features1, new_features2), dim=-1)
232 | new_grouped_points = torch.cat((grouped_points1, grouped_points2), dim=-1)
233 | new_grouped_features = torch.cat((grouped_features1, grouped_features2), dim=-1)
234 |
235 | new_features_list.append(new_features)
236 | new_grouped_points_list.append(new_grouped_points)
237 | new_grouped_features_list.append(new_grouped_features)
238 |
239 | new_features = torch.cat(new_features_list, dim=0)
240 | new_grouped_points = torch.cat(new_grouped_points_list, dim=0)
241 | new_grouped_features = torch.cat(new_grouped_features_list, dim=0)
242 |
243 | new_features = self.conv(new_features)
244 | new_features = torch.max(new_features, dim=1, keepdim=False)[0]
245 | weights = F.softmax(new_features, dim=-1)
246 |
247 | C = features1.shape[1]
248 | weights = weights.unsqueeze(1).repeat(1,3+C,1,1)
249 | fused_points = torch.cat([new_grouped_points, new_grouped_features], dim=1)
250 | fused_points = torch.sum(torch.mul(weights, fused_points), dim=-1, keepdim=False)
251 |
252 | return fused_points
253 |
254 | def knn_group_withI(points1, points2, intensity2, k):
255 | '''
256 | Input:
257 | points1: [B,3,N]
258 | points2: [B,3,N]
259 | intensity2: [B,1,N]
260 | '''
261 | points1 = points1.permute(0,2,1).contiguous()
262 | points2 = points2.permute(0,2,1).contiguous()
263 | _, nn_idx, nn = knn_points(points1, points2, K=k, return_nn=True)
264 | points_resi = nn - points1.unsqueeze(2).repeat(1,1,k,1) # [B,M,k,3]
265 | grouped_dist = torch.norm(points_resi, dim=-1, keepdim=True)
266 | grouped_features = knn_gather(intensity2.permute(0,2,1), nn_idx) # [B,M,k,1]
267 | new_features = torch.cat([points_resi, grouped_dist], dim=-1)
268 |
269 | # [B,5,M,k], [B,3,M,k], [B,1,M,k]
270 | return new_features.permute(0,3,1,2).contiguous(), \
271 | nn.permute(0,3,1,2).contiguous(), \
272 | grouped_features.permute(0,3,1,2).contiguous()
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/models/models.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import torch.nn as nn
3 | import datetime
4 | import numpy as np
5 |
6 | from .layers import SetConv, FlowEmbedding, SetUpConv, FeaturePropagation, PointsFusion
7 |
8 | class FlowNet3D(nn.Module):
9 | '''
10 | Implementation of FlowNet3D (CVPR 2019) in PyTorch
11 | We refer to original Tensorflow implementation (https://github.com/xingyul/flownet3d)
12 | and open source PyTorch implementation (https://github.com/multimodallearning/flownet3d.pytorch)
13 | to implement the code for FlowNet3D.
14 | '''
15 | def __init__(self):
16 | super(FlowNet3D, self).__init__()
17 |
18 | self.set_conv1 = SetConv(1024, 0.5, 16, 3, [32, 32, 64])
19 | self.set_conv2 = SetConv(256, 1.0, 16, 64, [64, 64, 128])
20 | self.flow_embedding = FlowEmbedding(64, 128, [128, 128, 128])
21 | self.set_conv3 = SetConv(64, 2.0, 8, 128, [128, 128, 256])
22 | self.set_conv4 = SetConv(16, 4.0, 8, 256, [256, 256, 512])
23 | self.set_upconv1 = SetUpConv(8, 512, 256, [], [256, 256])
24 | self.set_upconv2 = SetUpConv(8, 256, 256, [128, 128, 256], [256])
25 | self.set_upconv3 = SetUpConv(8, 256, 64, [128, 128, 256], [256])
26 | self.fp = FeaturePropagation(256, 3, [256, 256])
27 | self.classifier = nn.Sequential(
28 | nn.Conv1d(256, 128, 1, bias=True),
29 | nn.BatchNorm1d(128, eps=0.001),
30 | nn.ReLU(),
31 | nn.Conv1d(128, 3, 1, bias=True)
32 | )
33 |
34 | def forward(self, points1, points2, features1, features2):
35 | '''
36 | Input:
37 | points1: [B,3,N]
38 | points2: [B,3,N]
39 | features1: [B,3,N] (colors for Flythings3D and zero for LiDAR)
40 | features2: [B,3,N] (colors for Flythings3D and zero for LiDAR)
41 | Output:
42 | flow: [B,3,N]
43 | '''
44 | points1_1, features1_1 = self.set_conv1(points1, features1)
45 | points1_2, features1_2 = self.set_conv2(points1_1, features1_1)
46 |
47 | points2_1, features2_1 = self.set_conv1(points2, features2)
48 | points2_2, features2_2 = self.set_conv2(points2_1, features2_1)
49 |
50 | embedding = self.flow_embedding(points1_2, points2_2, features1_2, features2_2)
51 |
52 | points1_3, features1_3 = self.set_conv3(points1_2, embedding)
53 | points1_4, features1_4 = self.set_conv4(points1_3, features1_3)
54 |
55 | new_features1_3 = self.set_upconv1(points1_4, points1_3, features1_4, features1_3)
56 | new_features1_2 = self.set_upconv2(points1_3, points1_2, new_features1_3, torch.cat([features1_2, embedding], dim=1))
57 | new_features1_1 = self.set_upconv3(points1_2, points1_1, new_features1_2, features1_1)
58 | new_features1 = self.fp(points1_1, points1, new_features1_1, features1)
59 |
60 | flow = self.classifier(new_features1)
61 |
62 | return flow
63 |
64 | class PointINet(nn.Module):
65 | def __init__(self, freeze=1):
66 | super(PointINet, self).__init__()
67 | self.flow = FlowNet3D()
68 | if freeze == 1:
69 | for p in self.parameters():
70 | p.requires_grad = False
71 |
72 | self.fusion = PointsFusion(4, [64, 64, 128])
73 |
74 | def forward(self, points1, points2, features1, features2, t):
75 | '''
76 | Input:
77 | points1: [B,3+C,N]
78 | points2: [B,3+C,N]
79 | features1: [B,3,N] (zeros)
80 | features2: [B,3,N]
81 | '''
82 |
83 | points1_feature = points1[:,3:,:].contiguous()
84 | points2_feature = points2[:,3:,:].contiguous()
85 | points1 = points1[:,:3,:].contiguous()
86 | points2 = points2[:,:3,:].contiguous()
87 |
88 |
89 | # Estimate 3D scene flow
90 | with torch.no_grad():
91 | flow_forward = self.flow(points1, points2, features1, features2)
92 | flow_backward = self.flow(points2, points1, features2, features1)
93 |
94 | t = t.unsqueeze(1).unsqueeze(1)
95 |
96 | # Warp two input point clouds
97 | warped_points1_xyz = points1 + flow_forward * t
98 | warped_points2_xyz = points2 + flow_backward * (1-t)
99 |
100 | k = 32
101 |
102 | # Points fusion
103 | fused_points = self.fusion(warped_points1_xyz, warped_points2_xyz, points1_feature, points2_feature, k, t)
104 |
105 | return fused_points
106 |
107 | if __name__ == '__main__':
108 | net = PointINet(freeze=1).cuda()
109 |
110 | points1 = torch.rand(1,4,16384).cuda()
111 | points2 = torch.rand(1,4,16384).cuda()
112 | features1 = torch.rand(1,3,16384).cuda()
113 | features2 = torch.rand(1,3,16384).cuda()
114 |
115 | t = np.array([0.2]).astype('float32')
116 | t = torch.from_numpy(t).cuda()
117 |
118 | fused_points = net(points1, points2, features1, features2, t)
119 |
120 | print(fused_points.shape)
--------------------------------------------------------------------------------
/models/utils.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import torch.nn as nn
3 | from torch.autograd import Function
4 | import numpy as np
5 | import random
6 | import torch.optim as optim
7 |
8 | from pytorch3d.loss import chamfer_distance
9 | import emd_cuda
10 |
11 | def pdist2squared(x, y):
12 | xx = (x**2).sum(dim=1).unsqueeze(2)
13 | yy = (y**2).sum(dim=1).unsqueeze(1)
14 | dist = xx + yy - 2.0 * torch.bmm(x.permute(0, 2, 1), y)
15 | dist[dist != dist] = 0
16 | dist = torch.clamp(dist, 0.0, np.inf)
17 | return dist
18 |
19 | class ClippedStepLR(optim.lr_scheduler._LRScheduler):
20 | def __init__(self, optimizer, step_size, min_lr, gamma=0.1, last_epoch=-1):
21 | self.step_size = step_size
22 | self.min_lr = min_lr
23 | self.gamma = gamma
24 | super(ClippedStepLR, self).__init__(optimizer, last_epoch)
25 |
26 | def get_lr(self):
27 | return [max(base_lr * self.gamma ** (self.last_epoch // self.step_size), self.min_lr)
28 | for base_lr in self.base_lrs]
29 |
30 | def flow_criterion(pred_flow, flow, mask):
31 | loss = torch.mean(mask * torch.sum((pred_flow - flow) * (pred_flow - flow), dim=1) / 2.0)
32 | return loss
33 |
34 | def chamfer_loss(pc1, pc2):
35 | '''
36 | Input:
37 | pc1: [B,3,N]
38 | pc2: [B,3,N]
39 | '''
40 | pc1 = pc1.permute(0,2,1)
41 | pc2 = pc2.permute(0,2,1)
42 | chamfer_dist, _ = chamfer_distance(pc1, pc2)
43 | return chamfer_dist
44 |
45 | class EarthMoverDistanceFunction(torch.autograd.Function):
46 | @staticmethod
47 | def forward(ctx, xyz1, xyz2):
48 | xyz1 = xyz1.contiguous()
49 | xyz2 = xyz2.contiguous()
50 | assert xyz1.is_cuda and xyz2.is_cuda, "Only support cuda currently."
51 | match = emd_cuda.approxmatch_forward(xyz1, xyz2)
52 | cost = emd_cuda.matchcost_forward(xyz1, xyz2, match)
53 | ctx.save_for_backward(xyz1, xyz2, match)
54 | return cost
55 |
56 | @staticmethod
57 | def backward(ctx, grad_cost):
58 | xyz1, xyz2, match = ctx.saved_tensors
59 | grad_cost = grad_cost.contiguous()
60 | grad_xyz1, grad_xyz2 = emd_cuda.matchcost_backward(grad_cost, xyz1, xyz2, match)
61 | return grad_xyz1, grad_xyz2
62 |
63 |
64 | def earth_mover_distance(xyz1, xyz2, transpose=True):
65 | """Earth Mover Distance (Approx)
66 |
67 | Args:
68 | xyz1 (torch.Tensor): (b, 3, n1)
69 | xyz2 (torch.Tensor): (b, 3, n1)
70 | transpose (bool): whether to transpose inputs as it might be BCN format.
71 | Extensions only support BNC format.
72 |
73 | Returns:
74 | cost (torch.Tensor): (b)
75 |
76 | """
77 | if xyz1.dim() == 2:
78 | xyz1 = xyz1.unsqueeze(0)
79 | if xyz2.dim() == 2:
80 | xyz2 = xyz2.unsqueeze(0)
81 | if transpose:
82 | xyz1 = xyz1.transpose(1, 2)
83 | xyz2 = xyz2.transpose(1, 2)
84 | cost = EarthMoverDistanceFunction.apply(xyz1, xyz2)
85 | return cost
86 |
87 | def EMD(pc1, pc2):
88 | '''
89 | Input:
90 | pc1: [1,3,M]
91 | pc2: [1,3,M]
92 | Ret:
93 | d: torch.float32
94 | '''
95 | pc1 = pc1.permute(0,2,1).contiguous()
96 | pc2 = pc2.permute(0,2,1).contiguous()
97 | d = earth_mover_distance(pc1, pc2, transpose=False)
98 | d = torch.mean(d)/pc1.shape[1]
99 | return d
--------------------------------------------------------------------------------
/pretrain_model/flownet3d_kitti_odometry_maxbias1.pth:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/pretrain_model/flownet3d_kitti_odometry_maxbias1.pth
--------------------------------------------------------------------------------
/pretrain_model/interp_kitti.pth:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/ispc-lab/PointINet/9e7b85950f9331d7d326f4c8c81eceba1b76d93d/pretrain_model/interp_kitti.pth
--------------------------------------------------------------------------------
/test.py:
--------------------------------------------------------------------------------
1 | import os
2 | import torch
3 | import numpy as np
4 | from torch.utils.data import DataLoader
5 |
6 | from models.models import PointINet
7 | from models.utils import chamfer_loss, EMD
8 |
9 | from data.interpolation_data import NuscenesDataset, KittiInterpolationDataset
10 |
11 | import argparse
12 | from tqdm import tqdm
13 |
14 | def parse_args():
15 | parser = argparse.ArgumentParser(description='Test')
16 | parser.add_argument('--batch_size', type=int, default=1)
17 | parser.add_argument('--gpu', type=str, default='0')
18 | parser.add_argument('--root', type=str, default='')
19 | parser.add_argument('--npoints', type=int, default=16384)
20 | parser.add_argument('--pretrain_model', type=str, default='./pretrain_model/interp_kitti.pth')
21 | parser.add_argument('--pretrain_flow_model', type=str, default='./pretrain_model/flownet3d_kitti_odometry_maxbias5.pth')
22 | parser.add_argument('--dataset', type=str, default='kitti', help='kitti/nuscenes')
23 | parser.add_argument('--scenelist', type=str, default='')
24 |
25 | return parser.parse_args()
26 |
27 | def test(args):
28 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
29 | if args.dataset == 'nuscenes':
30 | scene_list = args.scenelist
31 | test_set = NuscenesDataset(args.npoints, args.root, scene_list, True, 5, False)
32 | elif args.dataset == 'kitti':
33 | test_set = KittiInterpolationDataset(args.root, args.npoints, 5, False, True)
34 |
35 | test_loader = DataLoader(test_set,
36 | batch_size=args.batch_size,
37 | num_workers=4,
38 | pin_memory=True,
39 | drop_last=True)
40 |
41 | net = PointINet(freeze=1)
42 | net.load_state_dict(torch.load(args.pretrain_model))
43 | net.flow.load_state_dict(torch.load(args.pretrain_flow_model))
44 | net.eval()
45 | net.cuda()
46 |
47 | with torch.no_grad():
48 |
49 | chamfer_loss_list = []
50 | emd_loss_list = []
51 |
52 | pbar = tqdm(test_loader)
53 | for data in pbar:
54 |
55 | ini_pc, mid_pc, end_pc, ini_color, mid_color, end_color, t = data
56 | ini_pc = ini_pc.cuda(non_blocking=True)
57 | mid_pc = mid_pc.cuda(non_blocking=True)
58 | end_pc = end_pc.cuda(non_blocking=True)
59 | ini_color = ini_color.cuda(non_blocking=True)
60 | mid_color = mid_color.cuda(non_blocking=True)
61 | end_color = end_color.cuda(non_blocking=True)
62 | t = t.cuda().float()
63 |
64 | pred_mid_pc = net(ini_pc, end_pc, ini_color, end_color, t)
65 |
66 | cd = chamfer_loss(pred_mid_pc[:,:3,:], mid_pc[:,:3,:])
67 | emd = EMD(pred_mid_pc[:,:3,:], mid_pc[:,:3,:])
68 |
69 | cd = cd.squeeze().cpu().numpy()
70 | emd = emd.squeeze().cpu().numpy()
71 |
72 | chamfer_loss_list.append(cd)
73 | emd_loss_list.append(emd)
74 |
75 | pbar.set_description('CD:{:.3} EMD:{:.3}'.format(cd, emd))
76 |
77 | chamfer_loss_array = np.array(chamfer_loss_list)
78 | emd_loss_array = np.array(emd_loss_list)
79 | mean_chamfer_loss = np.mean(chamfer_loss_array)
80 | mean_emd_loss = np.mean(emd_loss_array)
81 |
82 | print("Mean chamfer distance: ", mean_chamfer_loss)
83 | print("Mean earth mover's distance: ", mean_emd_loss)
84 |
85 | if __name__ == '__main__':
86 | args = parse_args()
87 | test(args)
--------------------------------------------------------------------------------
/train_interp.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import torch.nn as nn
3 | from torch.utils.data import DataLoader
4 | import torch.optim as optim
5 | import os
6 |
7 | from data.interpolation_data import KittiInterpolationDataset
8 | from models.models import PointINet
9 | from models.utils import ClippedStepLR, chamfer_loss
10 |
11 | from tqdm import tqdm
12 | import argparse
13 |
14 | def parse_args():
15 | parser = argparse.ArgumentParser(description='PointINet')
16 |
17 | parser.add_argument('--batch_size', type=int, default=2)
18 | parser.add_argument('--epochs', type=int, default=100)
19 | parser.add_argument('--init_lr', type=float, default=0.001)
20 | parser.add_argument('--min_lr', type=float, default=0.00001)
21 | parser.add_argument('--step_size_lr', type=int, default=10)
22 | parser.add_argument('--gamma_lr', type=float, default=0.7)
23 | parser.add_argument('--init_bn_momentum', type=float, default=0.5)
24 | parser.add_argument('--min_bn_momentum', type=float, default=0.01)
25 | parser.add_argument('--step_size_bn_momentum', type=int, default=10)
26 | parser.add_argument('--gamma_bn_momentum', type=float, default=0.5)
27 | parser.add_argument('--gpu', type=str, default='0')
28 | parser.add_argument('--root', type=str, default='')
29 | parser.add_argument('--save_dir', type=str, default='')
30 | parser.add_argument('--npoints', type=int, default=16384)
31 | parser.add_argument('--dataset', type=str, default='kitti', help='kitti/nuscenes')
32 | parser.add_argument('--pretrain_model', type=str, default='')
33 | parser.add_argument('--freeze', type=int, default=1)
34 | parser.add_argument('--use_wandb', type=int, default=1)
35 |
36 | return parser.parse_args()
37 |
38 | def init_weights(m):
39 | if isinstance(m, nn.Conv1d) or isinstance(m, nn.Conv2d):
40 | torch.nn.init.xavier_uniform_(m.weight)
41 | m.bias.data.fill_(0.0)
42 | elif isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d):
43 | m.weight.data.fill_(1.0)
44 | m.bias.data.fill_(0.0)
45 |
46 | def train(args):
47 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
48 | train_dataset = KittiInterpolationDataset(args.root, args.npoints, 5, True, True)
49 | train_loader = DataLoader(train_dataset,
50 | batch_size=args.batch_size,
51 | num_workers=4,
52 | shuffle=True,
53 | pin_memory=True,
54 | drop_last=True)
55 |
56 | net = PointINet(args.freeze).cuda()
57 | if args.use_wandb:
58 | wandb.watch(net)
59 |
60 | net.flow.load_state_dict(torch.load(args.pretrain_model))
61 | optimizer = optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=args.init_lr)
62 | lr_scheduler = ClippedStepLR(optimizer, args.step_size_lr, args.min_lr, args.gamma_lr)
63 | def update_bn_momentum(epoch):
64 | for m in net.modules():
65 | if isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d):
66 | m.momentum = max(args.init_bn_momentum * args.gamma_bn_momentum ** (epoch // args.step_size_bn_momentum), args.min_bn_momentum)
67 |
68 | best_train_loss = float('inf')
69 |
70 | for epoch in range(args.epochs):
71 | update_bn_momentum(epoch)
72 | net.train()
73 | count = 0
74 | total_loss = 0
75 |
76 | pbar = tqdm(enumerate(train_loader))
77 |
78 | for i, data in pbar:
79 | ini_pc, mid_pc, end_pc, ini_color, mid_color, end_color, t = data
80 | ini_pc = ini_pc.cuda(non_blocking=True)
81 | mid_pc = mid_pc.cuda(non_blocking=True)
82 | end_pc = end_pc.cuda(non_blocking=True)
83 | ini_color = ini_color.cuda(non_blocking=True)
84 | mid_color = mid_color.cuda(non_blocking=True)
85 | end_color = end_color.cuda(non_blocking=True)
86 | t = t.cuda().float()
87 |
88 | optimizer.zero_grad()
89 | pred_mid_pc = net(ini_pc, end_pc, ini_color, end_color, t)
90 |
91 | loss = chamfer_loss(pred_mid_pc[:,:3,:], mid_pc[:,:3,:])
92 |
93 | loss.backward()
94 | optimizer.step()
95 |
96 | count += 1
97 | total_loss += loss.item()
98 | if i % 10 == 0:
99 | pbar.set_description('Train Epoch:{}[{}/{}({:.0f}%)]\tLoss: {:.6f}'.format(
100 | epoch+1, i, len(train_loader), 100. * i/len(train_loader), loss.item()
101 | ))
102 |
103 | lr_scheduler.step()
104 | total_loss = total_loss/count
105 | if args.use_wandb == 1:
106 | wandb.log({"loss":total_loss})
107 |
108 | print('Epoch ', epoch+1, 'finished ', 'loss = ', total_loss)
109 |
110 | if total_loss < best_train_loss:
111 | torch.save(net.state_dict(), args.save_dir+'best_train.pth')
112 | best_train_loss = total_loss
113 |
114 | print('Best train loss: {:.4f}'.format(best_train_loss))
115 |
116 | if __name__ == '__main__':
117 | args = parse_args()
118 | if args.use_wandb == 1:
119 | import wandb
120 | wandb.init(config=args, project='PointINet')
121 |
122 | train(args)
--------------------------------------------------------------------------------
/train_sceneflow.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import torch.nn as nn
3 | from torch.utils.data import DataLoader
4 | import torch.optim as optim
5 | import os
6 |
7 | from data.sceneflow_data import Flythings3D, KittiSceneFlowDataset, KittiOdometrySceneflow, NuScenesFlow
8 |
9 | from models.models import FlowNet3D
10 | from models.utils import ClippedStepLR, flow_criterion, chamfer_loss
11 |
12 | from tqdm import tqdm
13 | import argparse
14 |
15 | def parse_args():
16 | parser = argparse.ArgumentParser(description='FlowNet3d')
17 |
18 | parser.add_argument('--batch_size', type=int, default=2)
19 | parser.add_argument('--epochs', type=int, default=100)
20 | parser.add_argument('--init_lr', type=float, default=0.001)
21 | parser.add_argument('--min_lr', type=float, default=0.00001)
22 | parser.add_argument('--step_size_lr', type=int, default=10)
23 | parser.add_argument('--gamma_lr', type=float, default=0.7)
24 | parser.add_argument('--init_bn_momentum', type=float, default=0.5)
25 | parser.add_argument('--min_bn_momentum', type=float, default=0.01)
26 | parser.add_argument('--step_size_bn_momentum', type=int, default=10)
27 | parser.add_argument('--gamma_bn_momentum', type=float, default=0.5)
28 | parser.add_argument('--gpu', type=str, default='0')
29 | parser.add_argument('--root', type=str, default='')
30 | parser.add_argument('--save_dir', type=str, default='')
31 | parser.add_argument('--npoints', type=int, default=8192)
32 | parser.add_argument('--dataset', type=str, default='Flythings3D', help='Flythings3D/Kitti/nuscenes')
33 | parser.add_argument('--pretrain_model', type=str, default='')
34 | parser.add_argument('--max_bias', type=int, default=2)
35 | parser.add_argument('--freeze', type=int, default=1)
36 | parser.add_argument('--use_wandb', action='store_true')
37 | parser.add_argument('--train_type', type=str, default='init', help='init/refine')
38 |
39 | return parser.parse_args()
40 |
41 | def init_weights(m):
42 | if isinstance(m, nn.Conv1d) or isinstance(m, nn.Conv2d):
43 | torch.nn.init.xavier_uniform_(m.weight)
44 | m.bias.data.fill_(0.0)
45 | elif isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d):
46 | m.weight.data.fill_(1.0)
47 | m.bias.data.fill_(0.0)
48 |
49 | def train(args):
50 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
51 | if args.dataset == 'Flythings3D':
52 | train_dataset = Flythings3D(npoints=args.npoints, root=args.root, train=True)
53 | elif args.dataset == 'Kitti':
54 | train_dataset = KittiSceneFlowDataset(args.root, args.npoints, True)
55 | else:
56 | raise('Invalid dataset')
57 |
58 | train_loader = DataLoader(train_dataset,
59 | batch_size=args.batch_size,
60 | num_workers=4,
61 | shuffle=True,
62 | pin_memory=True,
63 | drop_last=True)
64 |
65 | net = FlowNet3D().cuda()
66 |
67 | if args.use_wandb:
68 | wandb.watch(net)
69 |
70 | if args.dataset == 'Flythings3D':
71 | net.apply(init_weights)
72 | elif args.dataset == 'Kitti':
73 | net.load_state_dict(torch.load(args.pretrain_model))
74 | else:
75 | raise('Invalid dataset')
76 |
77 | optimizer = optim.Adam(net.parameters(), lr=args.init_lr)
78 | lr_scheduler = ClippedStepLR(optimizer, args.step_size_lr, args.min_lr, args.gamma_lr)
79 |
80 | def update_bn_momentum(epoch):
81 | for m in net.modules():
82 | if isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d):
83 | m.momentum = max(args.init_bn_momentum * args.gamma_bn_momentum ** (epoch // args.step_size_bn_momentum), args.min_bn_momentum)
84 |
85 | best_train_loss = float('inf')
86 |
87 | for epoch in range(args.epochs):
88 | update_bn_momentum(epoch)
89 |
90 | net.train()
91 |
92 | count = 0
93 | total_loss = 0
94 | pbar = tqdm(enumerate(train_loader))
95 |
96 | for i, data in pbar:
97 | points1, points2, features1, features2, flow, mask1 = data
98 | points1 = points1.cuda(non_blocking=True)
99 | points2 = points2.cuda(non_blocking=True)
100 | features1 = features1.cuda(non_blocking=True)
101 | features2 = features2.cuda(non_blocking=True)
102 | flow = flow.cuda(non_blocking=True)
103 | mask1 = mask1.cuda(non_blocking=True).float()
104 |
105 | optimizer.zero_grad()
106 | pred_flow = net(points1, points2, features1, features2)
107 |
108 | loss = flow_criterion(pred_flow, flow, mask1)
109 | loss.backward()
110 | optimizer.step()
111 |
112 | count += 1
113 | total_loss += loss.item()
114 |
115 | if i % 10 == 0:
116 | pbar.set_description('Train Epoch:{}[{}/{}({:.0f}%)]\tLoss: {:.6f}'.format(
117 | epoch+1, i, len(train_loader), 100. * i/len(train_loader), loss.item()
118 | ))
119 |
120 | lr_scheduler.step()
121 | total_loss = total_loss/count
122 | if args.use_wandb:
123 | wandb.log({"loss":total_loss})
124 |
125 | print('Epoch ', epoch+1, 'finished ', 'loss = ', total_loss)
126 | if total_loss < best_train_loss:
127 | torch.save(net.state_dict(), args.save_dir+'best_train.pth')
128 | best_train_loss = total_loss
129 |
130 | print('Best train loss: {:.4f}'.format(best_train_loss))
131 |
132 | def train_unsupervised(args):
133 | os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
134 | if args.dataset == 'Kitti':
135 | train_dataset = KittiOdometrySceneflow(root=args.root, npoints=args.npoints, max_bias=args.max_bias)
136 | elif args.dataset == 'nuscenes':
137 | scenes_list = './data/nuscenes_trainlist.txt'
138 | train_dataset = NuScenesFlow(root=args.root, npoints=args.npoints, scenes_list=scenes_list, max_bias=args.max_bias)
139 |
140 | train_loader = DataLoader(train_dataset,
141 | batch_size=args.batch_size,
142 | num_workers=4,
143 | shuffle=True,
144 | pin_memory=True,
145 | drop_last=True)
146 |
147 | net = FlowNet3D().cuda()
148 |
149 | if args.use_wandb:
150 | wandb.watch(net)
151 |
152 | net.load_state_dict(torch.load(args.pretrain_model))
153 |
154 | optimizer = optim.Adam(net.parameters(), lr=args.init_lr)
155 | lr_scheduler = ClippedStepLR(optimizer, args.step_size_lr, args.min_lr, args.gamma_lr)
156 |
157 | def update_bn_momentum(epoch):
158 | for m in net.modules():
159 | if isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d):
160 | m.momentum = max(args.init_bn_momentum * args.gamma_bn_momentum ** (epoch // args.step_size_bn_momentum), args.min_bn_momentum)
161 |
162 | best_train_loss = float('inf')
163 |
164 | for epoch in range(args.epochs):
165 | net.train()
166 | count = 0
167 |
168 | total_loss = 0
169 |
170 | pbar = tqdm(enumerate(train_loader))
171 |
172 | for i, data in pbar:
173 | points1, points2, features1, features2 = data
174 | points1 = points1.cuda(non_blocking=True)
175 | points2 = points2.cuda(non_blocking=True)
176 | features1 = features1.cuda(non_blocking=True)
177 | features2 = features2.cuda(non_blocking=True)
178 |
179 | optimizer.zero_grad()
180 | pred_flow = net(points1, points2, features1, features2)
181 | trans_points1 = points1 + pred_flow
182 |
183 | loss = chamfer_loss(trans_points1, points2)
184 |
185 | loss.backward()
186 | optimizer.step()
187 |
188 | count += 1
189 | total_loss += loss.item()
190 |
191 | if i % 10 == 0:
192 | pbar.set_description('Train Epoch:{}[{}/{}({:.0f}%)]\tLoss: {:.6f}'.format(
193 | epoch+1, i, len(train_loader), 100. * i/len(train_loader), loss.item()
194 | ))
195 |
196 | lr_scheduler.step()
197 | total_loss = total_loss/count
198 |
199 | if args.use_wandb == 1:
200 | wandb.log({"loss":total_loss})
201 |
202 | print('Epoch ', epoch+1, 'finished ', 'loss = ', total_loss)
203 |
204 | if total_loss < best_train_loss:
205 | torch.save(net.state_dict(), args.save_dir+'best_train.pth')
206 | best_train_loss = total_loss
207 |
208 | print('Best train loss: {:.4f}'.format(best_train_loss))
209 |
210 |
211 | if __name__ == '__main__':
212 | args = parse_args()
213 | if args.use_wandb == 1:
214 | import wandb
215 | wandb.init(config=args, project='PointINet')
216 |
217 | if args.train_type == 'init':
218 | train(args)
219 | elif args.train_type == 'refine':
220 | train_unsupervised(args)
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