├── .gitignore ├── README.md ├── Semantic_Scene_Completion ├── NYU │ ├── IOU.py │ ├── SATNet_Depth.py │ ├── SATNet_RGB.py │ ├── SATNet_SeeNetFuse.py │ ├── SATNet_ThinkNetFuse.py │ ├── engine.py │ ├── engine_depth.py │ ├── engine_fuse.py │ ├── engine_fuse1.py │ ├── gen_result_Depth.py │ ├── gen_result_RGB.py │ ├── gen_result_SeeNetFuse.py │ ├── gen_result_ThinkNetFuse.py │ ├── logs │ │ └── .gitkeep │ ├── pretrained_models │ │ └── .gitkeep │ ├── results │ │ └── .gitkeep │ ├── save_models │ │ ├── SATNet_Depth │ │ │ └── .gitkeep │ │ ├── SATNet_RGB │ │ │ └── .gitkeep │ │ ├── SATNet_SeeNetFuse │ │ │ └── .gitkeep │ │ └── SATNet_ThinkNetFuse │ │ │ └── .gitkeep │ ├── train_Depth.sh │ ├── train_RGB.sh │ ├── train_SeeNetFuse.sh │ └── train_ThinkNetFuse.sh ├── SUNCG_D │ ├── IOU.py │ ├── SATNet_Depth.py │ ├── engine_depth.py │ ├── gen_result_depth.py │ ├── logs │ │ └── .gitkeep │ ├── results │ │ └── .gitkeep │ ├── save_models │ │ └── SATNet_Depth │ │ │ └── .gitkeep │ └── train_Depth.sh └── SUNCG_RGBD │ ├── IOU.py │ ├── SATNet_Depth.py │ ├── SATNet_RGB.py │ ├── SATNet_SeeNetFuse.py │ ├── SATNet_ThinkNetFuse.py │ ├── engine.py │ ├── engine_depth.py │ ├── engine_fuse.py │ ├── engine_fuse1.py │ ├── eval_results.py │ ├── gen_result_Depth.py │ ├── gen_result_RGB.py │ ├── gen_result_SeeNetFuse.py │ ├── gen_result_ThinkNetFuse.py │ ├── labels │ ├── 000000.png │ ├── 000001.png │ ├── 000002.png │ ├── 000003.png │ ├── 000004.png │ ├── 000005.png │ ├── 000006.png │ ├── 000007.png │ ├── 000008.png │ ├── 000010.png │ ├── 000011.png │ ├── 000012.png │ ├── 000013.png │ ├── 000014.png │ ├── 000015.png │ ├── 000016.png │ ├── 000017.png │ ├── 000018.png │ ├── 000019.png │ ├── 000020.png │ ├── 000021.png │ ├── 000022.png │ ├── 000023.png │ ├── 000024.png │ ├── 000025.png │ ├── 000026.png │ ├── 000027.png │ ├── 000028.png │ ├── 000029.png │ ├── 000030.png │ ├── 000031.png │ ├── 000032.png │ ├── 000033.png │ ├── 000034.png │ ├── 000035.png │ ├── 000036.png │ ├── 000037.png │ ├── 000038.png │ ├── 000039.png │ ├── 000040.png │ ├── 000041.png │ ├── 000042.png │ ├── 000043.png │ ├── 000044.png │ ├── 000045.png │ ├── 000046.png │ ├── 000047.png │ ├── 000048.png │ ├── 000049.png │ ├── 000050.png │ ├── 000051.png │ ├── 000052.png │ ├── 000053.png │ ├── 000054.png │ ├── 000055.png │ ├── 000056.png │ ├── 000057.png │ ├── 000058.png │ ├── 000059.png │ ├── 000060.png │ ├── 000061.png │ ├── 000062.png │ ├── 000063.png │ ├── 000064.png │ ├── 000065.png │ ├── 000066.png │ ├── 000067.png │ ├── 000068.png │ ├── 000069.png │ ├── 000070.png │ ├── 000071.png │ ├── 000072.png │ ├── 000073.png │ ├── 000074.png │ ├── 000075.png │ ├── 000076.png │ ├── 000077.png │ ├── 000078.png │ ├── 000079.png │ ├── 000080.png │ ├── 000081.png │ ├── 000082.png │ ├── 000083.png │ ├── 000084.png │ ├── 000085.png │ ├── 000086.png │ ├── 000087.png │ ├── 000088.png │ ├── 000089.png │ ├── 000090.png │ ├── 000091.png │ ├── 000092.png │ ├── 000093.png │ ├── 000094.png │ ├── 000095.png │ ├── 000096.png │ ├── 000097.png │ ├── 000098.png │ ├── 000099.png │ ├── 000100.png │ ├── 000101.png │ ├── 000102.png │ ├── 000103.png │ ├── 000104.png │ ├── 000105.png │ ├── 000106.png │ ├── 000107.png │ ├── 000108.png │ ├── 000109.png │ ├── 000110.png │ ├── 000111.png │ ├── 000112.png │ ├── 000113.png │ ├── 000114.png │ ├── 000115.png │ ├── 000116.png │ ├── 000117.png │ ├── 000118.png │ ├── 000119.png │ ├── 000120.png │ ├── 000121.png │ ├── 000122.png │ ├── 000123.png │ ├── 000124.png │ ├── 000125.png │ ├── 000126.png │ ├── 000127.png │ ├── 000128.png │ ├── 000129.png │ ├── 000130.png │ ├── 000131.png │ ├── 000132.png │ ├── 000133.png │ ├── 000134.png │ ├── 000135.png │ ├── 000136.png │ ├── 000137.png │ ├── 000138.png │ ├── 000139.png │ ├── 000140.png │ ├── 000141.png │ ├── 000142.png │ ├── 000143.png │ ├── 000144.png │ ├── 000145.png │ ├── 000146.png │ ├── 000147.png │ ├── 000148.png │ ├── 000149.png │ ├── 000150.png │ ├── 000151.png │ ├── 000152.png │ ├── 000153.png │ ├── 000154.png │ ├── 000155.png │ ├── 000156.png │ ├── 000157.png │ ├── 000158.png │ ├── 000159.png │ ├── 000160.png │ ├── 000161.png │ ├── 000162.png │ ├── 000163.png │ ├── 000164.png │ ├── 000165.png │ ├── 000166.png │ ├── 000167.png │ ├── 000168.png │ ├── 000169.png │ ├── 000170.png │ ├── 000171.png │ ├── 000172.png │ ├── 000173.png │ ├── 000174.png │ ├── 000175.png │ ├── 000176.png │ ├── 000177.png │ ├── 000178.png │ ├── 000179.png │ ├── 000180.png │ ├── 000181.png │ ├── 000182.png │ ├── 000183.png │ ├── 000184.png │ ├── 000185.png │ ├── 000186.png │ ├── 000187.png │ ├── 000188.png │ ├── 000189.png │ ├── 000190.png │ ├── 000191.png │ ├── 000192.png │ ├── 000193.png │ ├── 000194.png │ ├── 000195.png │ ├── 000196.png │ ├── 000197.png │ ├── 000198.png │ ├── 000199.png │ ├── 000200.png │ ├── 000201.png │ ├── 000202.png │ ├── 000203.png │ ├── 000204.png │ ├── 000205.png │ ├── 000206.png │ ├── 000207.png │ ├── 000208.png │ ├── 000209.png │ ├── 000210.png │ ├── 000211.png │ ├── 000212.png │ ├── 000213.png │ ├── 000214.png │ ├── 000215.png │ ├── 000216.png │ ├── 000217.png │ ├── 000218.png │ ├── 000219.png │ ├── 000220.png │ ├── 000221.png │ ├── 000222.png │ ├── 000223.png │ ├── 000224.png │ ├── 000225.png │ ├── 000226.png │ ├── 000227.png │ ├── 000228.png │ ├── 000229.png │ ├── 000230.png │ ├── 000231.png │ ├── 000232.png │ ├── 000233.png │ ├── 000234.png │ ├── 000235.png │ ├── 000236.png │ ├── 000237.png │ ├── 000238.png │ ├── 000239.png │ ├── 000240.png │ ├── 000241.png │ ├── 000242.png │ ├── 000243.png │ ├── 000244.png │ ├── 000245.png │ ├── 000246.png │ ├── 000247.png │ ├── 000248.png │ ├── 000249.png │ ├── 000250.png │ ├── 000251.png │ ├── 000252.png │ ├── 000253.png │ ├── 000254.png │ ├── 000255.png │ ├── 000256.png │ ├── 000257.png │ ├── 000258.png │ ├── 000259.png │ ├── 000260.png │ ├── 000261.png │ ├── 000262.png │ ├── 000263.png │ ├── 000264.png │ ├── 000265.png │ ├── 000266.png │ ├── 000267.png │ ├── 000268.png │ ├── 000269.png │ ├── 000270.png │ ├── 000271.png │ ├── 000272.png │ ├── 000273.png │ ├── 000274.png │ ├── 000275.png │ ├── 000276.png │ ├── 000277.png │ ├── 000278.png │ ├── 000279.png │ ├── 000280.png │ ├── 000281.png │ ├── 000282.png │ ├── 000283.png │ ├── 000284.png │ ├── 000285.png │ ├── 000286.png │ ├── 000287.png │ ├── 000288.png │ ├── 000289.png │ ├── 000290.png │ ├── 000291.png │ ├── 000292.png │ ├── 000293.png │ ├── 000294.png │ ├── 000295.png │ ├── 000296.png │ ├── 000297.png │ ├── 000298.png │ ├── 000299.png │ ├── 000300.png │ ├── 000301.png │ ├── 000302.png │ ├── 000303.png │ ├── 000304.png │ ├── 000305.png │ ├── 000306.png │ ├── 000307.png │ ├── 000308.png │ ├── 000309.png │ ├── 000310.png │ ├── 000311.png │ ├── 000312.png │ ├── 000313.png │ ├── 000314.png │ ├── 000315.png │ ├── 000316.png │ ├── 000317.png │ ├── 000318.png │ ├── 000319.png │ ├── 000320.png │ ├── 000321.png │ ├── 000322.png │ ├── 000323.png │ ├── 000324.png │ ├── 000325.png │ ├── 000326.png │ ├── 000327.png │ ├── 000328.png │ ├── 000329.png │ ├── 000330.png │ ├── 000331.png │ ├── 000332.png │ ├── 000333.png │ ├── 000334.png │ ├── 000335.png │ ├── 000336.png │ ├── 000337.png │ ├── 000338.png │ ├── 000339.png │ ├── 000340.png │ ├── 000341.png │ ├── 000342.png │ ├── 000343.png │ ├── 000344.png │ ├── 000345.png │ ├── 000346.png │ ├── 000347.png │ ├── 000348.png │ ├── 000349.png │ ├── 000350.png │ ├── 000351.png │ ├── 000352.png │ ├── 000353.png │ ├── 000354.png │ ├── 000355.png │ ├── 000356.png │ ├── 000357.png │ ├── 000358.png │ ├── 000359.png │ ├── 000360.png │ ├── 000361.png │ ├── 000362.png │ ├── 000363.png │ ├── 000364.png │ ├── 000365.png │ ├── 000366.png │ ├── 000367.png │ ├── 000368.png │ ├── 000369.png │ ├── 000370.png │ ├── 000371.png │ ├── 000372.png │ ├── 000373.png │ ├── 000374.png │ ├── 000375.png │ ├── 000376.png │ ├── 000377.png │ ├── 000378.png │ ├── 000379.png │ ├── 000380.png │ ├── 000381.png │ ├── 000382.png │ ├── 000383.png │ ├── 000384.png │ ├── 000385.png │ ├── 000386.png │ ├── 000387.png │ ├── 000388.png │ ├── 000389.png │ ├── 000390.png │ ├── 000391.png │ ├── 000392.png │ ├── 000393.png │ ├── 000394.png │ ├── 000395.png │ ├── 000396.png │ ├── 000397.png │ ├── 000398.png │ ├── 000399.png │ ├── 000400.png │ ├── 000401.png │ ├── 000402.png │ ├── 000403.png │ ├── 000404.png │ ├── 000405.png │ ├── 000406.png │ ├── 000407.png │ ├── 000408.png │ ├── 000409.png │ ├── 000410.png │ ├── 000411.png │ ├── 000412.png │ ├── 000413.png │ ├── 000414.png │ ├── 000415.png │ ├── 000416.png │ ├── 000417.png │ ├── 000418.png │ ├── 000419.png │ ├── 000420.png │ ├── 000421.png │ ├── 000422.png │ ├── 000423.png │ ├── 000424.png │ ├── 000425.png │ ├── 000426.png │ ├── 000427.png │ ├── 000428.png │ ├── 000429.png │ ├── 000430.png │ ├── 000431.png │ ├── 000432.png │ ├── 000433.png │ ├── 000434.png │ ├── 000435.png │ ├── 000436.png │ ├── 000437.png │ ├── 000438.png │ ├── 000439.png │ ├── 000440.png │ ├── 000441.png │ ├── 000442.png │ ├── 000443.png │ ├── 000444.png │ ├── 000445.png │ ├── 000446.png │ ├── 000447.png │ ├── 000448.png │ ├── 000449.png │ ├── 000450.png │ ├── 000451.png │ ├── 000452.png │ ├── 000453.png │ ├── 000454.png │ ├── 000455.png │ ├── 000456.png │ ├── 000457.png │ ├── 000458.png │ ├── 000459.png │ ├── 000460.png │ ├── 000461.png │ ├── 000462.png │ ├── 000463.png │ ├── 000464.png │ ├── 000465.png │ ├── 000466.png │ ├── 000467.png │ ├── 000468.png │ ├── 000469.png │ ├── 000470.png │ ├── 000471.png │ ├── 000472.png │ ├── 000473.png │ ├── 000474.png │ ├── 000475.png │ ├── 000476.png │ ├── 000477.png │ ├── 000478.png │ ├── 000479.png │ ├── 000480.png │ ├── 000481.png │ ├── 000482.png │ ├── 000483.png │ ├── 000484.png │ ├── 000485.png │ ├── 000486.png │ ├── 000487.png │ ├── 000488.png │ ├── 000489.png │ ├── 000490.png │ ├── 000491.png │ ├── 000492.png │ ├── 000493.png │ ├── 000494.png │ ├── 000495.png │ ├── 000496.png │ ├── 000497.png │ ├── 000498.png │ └── 000499.png │ ├── logs │ └── .gitkeep │ ├── results │ └── .gitkeep │ ├── save_models │ ├── SATNet_Depth │ │ └── .gitkeep │ ├── SATNet_RGB │ │ └── .gitkeep │ ├── SATNet_SeeNetFuse │ │ └── .gitkeep │ └── SATNet_ThinkNetFuse │ │ └── .gitkeep │ ├── train_Depth.sh │ ├── train_RGB.sh │ ├── train_SeeNetFuse.sh │ └── train_ThinkNetFuse.sh ├── Semantic_Segmentation ├── IOU.py ├── engine.py ├── engine_fuse.py ├── logs │ └── .gitkeep ├── save_models │ ├── seg_RGB_nyu │ │ └── .gitkeep │ ├── seg_RGB_suncg │ │ └── .gitkeep │ ├── seg_depth_nyu │ │ └── .gitkeep │ ├── seg_depth_suncg │ │ └── .gitkeep │ ├── seg_fuse_nyu │ │ └── .gitkeep │ └── seg_fuse_suncg │ │ └── .gitkeep ├── seg_RGB_nyu.py ├── seg_RGB_suncg.py ├── seg_depth_nyu.py ├── seg_depth_suncg.py ├── seg_fuse_nyu.py ├── seg_fuse_suncg.py ├── train_seg_RGB_nyu.sh ├── train_seg_RGB_suncg.sh ├── train_seg_depth_nyu.sh ├── train_seg_depth_suncg.sh ├── train_seg_fuse_nyu.sh └── train_seg_fuse_suncg.sh ├── configs.py ├── image └── framework.png ├── poster └── SATNet_poster.pdf └── util ├── .gitkeep └── pre-get_training_data ├── CMakeLists.txt ├── build ├── CMakeCache.txt ├── CMakeFiles │ ├── 3.5.1 │ │ ├── CMakeCCompiler.cmake │ │ ├── CMakeCXXCompiler.cmake │ │ ├── CMakeDetermineCompilerABI_C.bin │ │ ├── CMakeDetermineCompilerABI_CXX.bin │ │ ├── CMakeSystem.cmake │ │ ├── CompilerIdC │ │ │ ├── CMakeCCompilerId.c │ │ │ └── a.out │ │ └── CompilerIdCXX │ │ │ ├── CMakeCXXCompilerId.cpp │ │ │ └── a.out │ ├── CMakeDirectoryInformation.cmake │ ├── CMakeError.log │ ├── CMakeOutput.log │ ├── CMakeRuleHashes.txt │ ├── Makefile.cmake │ ├── Makefile2 │ ├── TargetDirectories.txt │ ├── cmake.check_cache │ ├── datautil.dir │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ ├── datautil_generated_datautil.cu.o │ │ ├── datautil_generated_datautil.cu.o.cmake │ │ ├── datautil_generated_datautil.cu.o.depend │ │ ├── depend.internal │ │ ├── depend.make │ │ ├── flags.make │ │ ├── link.txt │ │ └── progress.make │ ├── feature_tests.bin │ ├── feature_tests.c │ ├── feature_tests.cxx │ └── progress.marks ├── Makefile ├── cmake_install.cmake ├── lib.linux-x86_64-2.7 │ └── DataProcess.so ├── libdatautil.so └── temp.linux-x86_64-2.7 │ └── datautil.o ├── datautil.cpp ├── datautil.cu ├── nyu.py ├── preget.py ├── preget_for_myselect_multiprocess.py ├── preget_for_nyu.py ├── preget_for_nyu_multiprocess.py ├── preget_for_shurans.py ├── preget_for_shurans_multiprocess.py ├── preget_for_shurans_multiprocess.py~ ├── reprojection.py ├── reprojection.pyc ├── setup_datautil.py └── test.sh /.gitignore: -------------------------------------------------------------------------------- 1 | *.pth.tar 2 | *.log 3 | -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/IOU.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | 4 | ### This version is same as Shurans'. 5 | 6 | # tensor size is (batchsize, ...) 7 | # channel0 is void and isn't counted. 8 | def computeIOU(output, label, num_classes = 12): 9 | output1, output2 = torch.max(output, 1) 10 | 11 | batchsize = output2.size(0) 12 | output2 = output2.view(batchsize, -1) 13 | label2 = label.view(batchsize, -1) 14 | 15 | if output2.size(1) != label2.size(1): 16 | print "Tensors' sizes don't equal. Tensor1 is (%d,%d), but tensor2 is (%d,%d)."%( 17 | batchsize, output2.size(1), batchsize, label2.size(1)) 18 | return -1 19 | 20 | # tp fp fn 21 | result = np.zeros((3, num_classes-1), dtype = np.float32) 22 | for i in range(1, num_classes): 23 | a = torch.eq(output2, i) 24 | b = torch.ne(output2, i) 25 | c = torch.eq(label2, i) 26 | d = torch.ne(label2, i) 27 | tp = torch.mul(a, c) 28 | fp = torch.mul(a, d) 29 | fn = torch.mul(b, c) 30 | result[0, i-1] += torch.nonzero(tp).nelement() 31 | result[1, i-1] += torch.nonzero(fp).nelement() 32 | result[2, i-1] += torch.nonzero(fn).nelement() 33 | 34 | return result -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/gen_result_Depth.py: -------------------------------------------------------------------------------- 1 | from SATNet_Depth import ImageGen3DNet, TrainDataLoader # HERE 2 | import sys, resource, os 3 | import numpy as np 4 | import cv2 5 | import torch 6 | from torchvision.transforms import Compose, Normalize, ToTensor 7 | import torch.backends.cudnn as cudnn 8 | import h5py 9 | sys.path.append('../../') 10 | import configs 11 | 12 | 13 | # CUDA_VISIBLE_DEVICES=0 python gen_result_Depth.py 14 | def main(): 15 | cudnn.benchmark = True 16 | resume_path = './save_models/SATNet_Depth/checkpoint.pth.tar' # HERE 17 | output_path = './results/nyu_pred.hdf5' 18 | 19 | input_transform = Compose([ 20 | ToTensor(), 21 | Normalize([.5282, .3914, .4266], [.1945, .2480, .1506]) 22 | ]) 23 | dataset = TrainDataLoader(NYU_SAMPLE_TXT_TEST, NYU_NPZ_PATH_TEST, 'test', img_transform = input_transform) 24 | data_loader = torch.utils.data.DataLoader(dataset, batch_size = 4, 25 | shuffle = False, num_workers = 1) 26 | data_loader.pin_memory = True 27 | 28 | # model = SceneCompletionRGBD() 29 | model = ImageGen3DNet((384, 288)) # HERE 30 | if not os.path.isfile(resume_path): 31 | print "=> no checkpoint found at '{}'".format(resume_path) 32 | exit() 33 | checkpoint = torch.load(resume_path) 34 | model.load_state_dict(checkpoint['state_dict']) 35 | model = torch.nn.DataParallel(model).cuda(0) 36 | 37 | softmax_layer = torch.nn.Softmax(dim = 1).cuda(0) 38 | 39 | predictions = [] 40 | with torch.no_grad(): 41 | for i, (color, label, label_weight, depth_mapping_3d) in enumerate(data_loader): 42 | print '{0}/{1}'.format(i, len(data_loader)) 43 | 44 | input_var = torch.autograd.Variable(color.cuda(async=True)) 45 | depth_mapping_3d_var = torch.autograd.Variable(depth_mapping_3d.cuda(async=True)) 46 | 47 | output = model(input_var, depth_mapping_3d_var) 48 | output = softmax_layer(output) # HERE 49 | predictions.append(output.cpu().data.numpy()) 50 | predictions = np.vstack(predictions) 51 | 52 | fp = h5py.File(output_path, 'w') 53 | result = fp.create_dataset('result', predictions.shape, dtype='f') 54 | result[...] = predictions 55 | fp.close() 56 | 57 | if __name__ == '__main__': 58 | 59 | resource.setrlimit(resource.RLIMIT_STACK, (-1,-1)) 60 | sys.setrecursionlimit(100000) 61 | 62 | main() -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/gen_result_RGB.py: -------------------------------------------------------------------------------- 1 | from SATNet_RGB import ImageGen3DNet, TrainDataLoader # HERE 2 | import sys, resource, os 3 | import numpy as np 4 | import cv2 5 | import torch 6 | from torchvision.transforms import Compose, Normalize, ToTensor 7 | import torch.backends.cudnn as cudnn 8 | import h5py 9 | sys.path.append('../../') 10 | import configs 11 | 12 | 13 | # CUDA_VISIBLE_DEVICES=0 python gen_result_RGB.py 14 | def main(): 15 | cudnn.benchmark = True 16 | resume_path = './save_models/SATNet_RGB/checkpoint.pth.tar' # HERE 17 | output_path = './results/nyu_pred.hdf5' 18 | 19 | input_transform = Compose([ 20 | ToTensor(), 21 | Normalize([.485, .456, .406], [.229, .224, .225]) 22 | ]) # HERE 23 | dataset = TrainDataLoader(NYU_SAMPLE_TXT_TEST, NYU_NPZ_PATH_TEST, 'test', img_transform = input_transform) 24 | data_loader = torch.utils.data.DataLoader(dataset, batch_size = 4, 25 | shuffle = False, num_workers = 1) 26 | data_loader.pin_memory = True 27 | 28 | # model = SceneCompletionRGBD() 29 | model = ImageGen3DNet((384, 288)) # HERE 30 | if not os.path.isfile(resume_path): 31 | print "=> no checkpoint found at '{}'".format(resume_path) 32 | exit() 33 | checkpoint = torch.load(resume_path) 34 | model.load_state_dict(checkpoint['state_dict']) 35 | model = torch.nn.DataParallel(model).cuda(0) 36 | 37 | softmax_layer = torch.nn.Softmax(dim = 1).cuda(0) 38 | 39 | predictions = [] 40 | with torch.no_grad(): 41 | for i, (color, label, label_weight, depth_mapping_3d) in enumerate(data_loader): 42 | print '{0}/{1}'.format(i, len(data_loader)) 43 | 44 | input_var = torch.autograd.Variable(color.cuda(async=True)) 45 | depth_mapping_3d_var = torch.autograd.Variable(depth_mapping_3d.cuda(async=True)) 46 | 47 | output = model(input_var, depth_mapping_3d_var) 48 | output = softmax_layer(output) # HERE 49 | predictions.append(output.cpu().data.numpy()) 50 | predictions = np.vstack(predictions) 51 | 52 | fp = h5py.File(output_path, 'w') 53 | result = fp.create_dataset('result', predictions.shape, dtype='f') 54 | result[...] = predictions 55 | fp.close() 56 | 57 | if __name__ == '__main__': 58 | 59 | resource.setrlimit(resource.RLIMIT_STACK, (-1,-1)) 60 | sys.setrecursionlimit(100000) 61 | 62 | main() -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/gen_result_SeeNetFuse.py: -------------------------------------------------------------------------------- 1 | from SATNet_SeeNetFuse import ImageGen3DNet, TrainDataLoader # HERE 2 | import sys, resource, os 3 | import numpy as np 4 | import cv2 5 | import torch 6 | import torch.backends.cudnn as cudnn 7 | import h5py 8 | sys.path.append('../../') 9 | import configs 10 | 11 | 12 | # CUDA_VISIBLE_DEVICES=0 python gen_result_SeeNetFuse.py 13 | def main(): 14 | cudnn.benchmark = True 15 | resume_path = './save_models/SATNet_SeeNetFuse/checkpoint.pth.tar' # HERE 16 | output_path = './results/nyu_pred.hdf5' 17 | 18 | dataset = TrainDataLoader(NYU_SAMPLE_TXT_TEST, NYU_NPZ_PATH_TEST, 'test') 19 | data_loader = torch.utils.data.DataLoader(dataset, batch_size = 4, 20 | shuffle = False, num_workers = 1) 21 | data_loader.pin_memory = True 22 | 23 | # model = SceneCompletionRGBD() 24 | model = ImageGen3DNet((384, 288)) 25 | if not os.path.isfile(resume_path): 26 | print "=> no checkpoint found at '{}'".format(resume_path) 27 | exit() 28 | checkpoint = torch.load(resume_path) 29 | model.load_state_dict(checkpoint['state_dict']) 30 | model = torch.nn.DataParallel(model).cuda(0) 31 | 32 | softmax_layer = torch.nn.Softmax(dim = 1).cuda(0) 33 | 34 | predictions = [] 35 | with torch.no_grad(): 36 | for i, (color, depth, label, label_weight, depth_mapping_3d) in enumerate(data_loader): 37 | print '{0}/{1}'.format(i, len(data_loader)) 38 | 39 | input_var = torch.autograd.Variable(color.cuda(async=True)) 40 | depth_var = torch.autograd.Variable(depth.cuda(async=True)) 41 | depth_mapping_3d_var = torch.autograd.Variable(depth_mapping_3d.cuda(async=True)) 42 | 43 | output = model(input_var, depth_var, depth_mapping_3d_var) 44 | output = softmax_layer(output) # HERE 45 | predictions.append(output.cpu().data.numpy()) 46 | predictions = np.vstack(predictions) 47 | 48 | fp = h5py.File(output_path, 'w') 49 | result = fp.create_dataset('result', predictions.shape, dtype='f') 50 | result[...] = predictions 51 | fp.close() 52 | 53 | if __name__ == '__main__': 54 | 55 | resource.setrlimit(resource.RLIMIT_STACK, (-1,-1)) 56 | sys.setrecursionlimit(100000) 57 | 58 | main() -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/gen_result_ThinkNetFuse.py: -------------------------------------------------------------------------------- 1 | from SATNet_ThinkNetFuse import ImageGen3DNet, TrainDataLoader # HERE 2 | import sys, resource, os 3 | import numpy as np 4 | import cv2 5 | import torch 6 | from torchvision.transforms import Compose, Normalize, ToTensor 7 | import torch.backends.cudnn as cudnn 8 | import h5py 9 | sys.path.append('../../') 10 | import configs 11 | 12 | 13 | # CUDA_VISIBLE_DEVICES=0,1 python gen_result_ThinkNetFuse.py 14 | def main(): 15 | cudnn.benchmark = True 16 | resume_path = './save_models/SATNet_ThinkNetFuse/checkpoint.pth.tar' # HERE 17 | output_path = './results/nyu_pred.hdf5' 18 | 19 | dataset = TrainDataLoader(NYU_SAMPLE_TXT_TEST, NYU_NPZ_PATH_TEST, 'test') 20 | data_loader = torch.utils.data.DataLoader(dataset, batch_size = 4, 21 | shuffle = False, num_workers = 1) 22 | data_loader.pin_memory = True 23 | 24 | # model = SceneCompletionRGBD() 25 | model = ImageGen3DNet() 26 | if not os.path.isfile(resume_path): 27 | print "=> no checkpoint found at '{}'".format(resume_path) 28 | exit() 29 | checkpoint = torch.load(resume_path) 30 | model.load_state_dict(checkpoint['state_dict']) 31 | 32 | softmax_layer = torch.nn.Softmax(dim = 1).cuda() 33 | 34 | predictions = [] 35 | with torch.no_grad(): 36 | for i, (color, depth, label, label_weight, depth_mapping_3d) in enumerate(data_loader): 37 | print '{0}/{1}'.format(i, len(data_loader)) 38 | 39 | input_var = torch.autograd.Variable(color.cuda(0, async=True)) 40 | depth_var = torch.autograd.Variable(depth.cuda(1, async=True)) 41 | depth_mapping_3d_var0 = torch.autograd.Variable(depth_mapping_3d.cuda(0, async=True)) 42 | depth_mapping_3d_var1 = torch.autograd.Variable(depth_mapping_3d.cuda(1, async=True)) 43 | 44 | output = model(input_var, depth_var, depth_mapping_3d_var0, depth_mapping_3d_var1) 45 | output = softmax_layer(output) 46 | predictions.append(output.cpu().data.numpy()) 47 | predictions = np.vstack(predictions) 48 | 49 | fp = h5py.File(output_path, 'w') 50 | result = fp.create_dataset('result', predictions.shape, dtype='f') 51 | result[...] = predictions 52 | fp.close() 53 | 54 | if __name__ == '__main__': 55 | 56 | resource.setrlimit(resource.RLIMIT_STACK, (-1,-1)) 57 | sys.setrecursionlimit(100000) 58 | 59 | main() -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/logs/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiceLiu/SATNet/394068e44c68c29ab6115ed96949cd0a56587c3a/Semantic_Scene_Completion/NYU/logs/.gitkeep -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/pretrained_models/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiceLiu/SATNet/394068e44c68c29ab6115ed96949cd0a56587c3a/Semantic_Scene_Completion/NYU/pretrained_models/.gitkeep -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/train_Depth.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | CUDA_VISIBLE_DEVICES=0 python SATNet_Depth.py 2>&1 | tee logs/SATNet_Depth.log -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/train_RGB.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | CUDA_VISIBLE_DEVICES=0 python SATNet_RGB.py 2>&1 | tee logs/SATNet_RGB.log -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/train_SeeNetFuse.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | CUDA_VISIBLE_DEVICES=1 python SATNet_SeeNetFuse.py 2>&1 | tee logs/SATNet_SeeNetFuse.log -------------------------------------------------------------------------------- /Semantic_Scene_Completion/NYU/train_ThinkNetFuse.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | CUDA_VISIBLE_DEVICES=0,1 python SATNet_ThinkNetFuse.py 2>&1 | tee logs/SATNet_ThinkNetFuse.log -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_D/IOU.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | 4 | ### This version is same as Shurans'. 5 | 6 | # tensor size is (batchsize, ...) 7 | # channel0 is void and isn't counted. 8 | def computeIOU(output, label, num_classes = 12): 9 | output1, output2 = torch.max(output, 1) 10 | 11 | batchsize = output2.size(0) 12 | output2 = output2.view(batchsize, -1) 13 | label2 = label.view(batchsize, -1) 14 | 15 | if output2.size(1) != label2.size(1): 16 | print "Tensors' sizes don't equal. Tensor1 is (%d,%d), but tensor2 is (%d,%d)."%( 17 | batchsize, output2.size(1), batchsize, label2.size(1)) 18 | return -1 19 | 20 | # tp fp fn 21 | result = np.zeros((3, num_classes-1), dtype = np.float32) 22 | for i in range(1, num_classes): 23 | a = torch.eq(output2, i) 24 | b = torch.ne(output2, i) 25 | c = torch.eq(label2, i) 26 | d = torch.ne(label2, i) 27 | tp = torch.mul(a, c) 28 | fp = torch.mul(a, d) 29 | fn = torch.mul(b, c) 30 | result[0, i-1] += torch.nonzero(tp).nelement() 31 | result[1, i-1] += torch.nonzero(fp).nelement() 32 | result[2, i-1] += torch.nonzero(fn).nelement() 33 | 34 | return result -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_D/gen_result_depth.py: -------------------------------------------------------------------------------- 1 | import sys, resource, os 2 | import numpy as np 3 | import cv2 4 | import torch 5 | from torchvision.transforms import Compose, Normalize, ToTensor 6 | import h5py 7 | 8 | from SATNet_Depth import ImageGen3DNet, TrainDataLoader 9 | sys.path.append('../../') 10 | import configs 11 | 12 | # CUDA_VISIBLE_DEVICES=0 python gen_result_depth.py 13 | def main(): 14 | resume_path = './save_models/SATNet_Depth/checkpoint.pth.tar' # HERE 15 | output_path = './results/result_suncg.hdf5' 16 | 17 | input_transform = Compose([ 18 | ToTensor(), 19 | Normalize([.5282, .3914, .4266], [.1945, .2480, .1506]) 20 | ]) # HERE 21 | dataset = TrainDataLoader(SUNCGD_HHA_PATH_TEST, SUNCGD_NPZ_PATH_TEST, 'test', img_transform = input_transform) 22 | data_loader = torch.utils.data.DataLoader(dataset, batch_size = 1, 23 | shuffle = False, num_workers = 1) 24 | data_loader.pin_memory = True 25 | 26 | # model = SceneCompletionRGBD() 27 | model = ImageGen3DNet('../../Semantic_Segmentation/save_models/seg_depth_suncg/checkpoint.pth.tar', (384, 288)) # HERE 28 | if not os.path.isfile(resume_path): 29 | print "=> no checkpoint found at '{}'".format(resume_path) 30 | exit() 31 | checkpoint = torch.load(resume_path) 32 | model.load_state_dict(checkpoint['state_dict']) 33 | model = torch.nn.DataParallel(model).cuda(0) 34 | model.eval() 35 | 36 | softmax_layer = torch.nn.Softmax(dim = 1).cuda(0) 37 | 38 | predictions = [] 39 | with torch.no_grad(): 40 | for i, (color, label, label_weight, depth_mapping_3d) in enumerate(data_loader): 41 | print '{0}/{1}'.format(i, len(data_loader)) 42 | 43 | input_var = torch.autograd.Variable(color.cuda(async=True)) 44 | depth_mapping_3d_var = torch.autograd.Variable(depth_mapping_3d.cuda(async=True)) 45 | 46 | output = model(input_var, depth_mapping_3d_var) 47 | output = softmax_layer(output) # HERE 48 | predictions.append(output.cpu().data.numpy()) 49 | predictions = np.vstack(predictions) 50 | 51 | fp = h5py.File(output_path, 'w') 52 | result = fp.create_dataset('result', predictions.shape, dtype='f') 53 | result[...] = predictions 54 | fp.close() 55 | 56 | if __name__ == '__main__': 57 | 58 | resource.setrlimit(resource.RLIMIT_STACK, (-1,-1)) 59 | sys.setrecursionlimit(100000) 60 | 61 | main() -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_D/logs/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiceLiu/SATNet/394068e44c68c29ab6115ed96949cd0a56587c3a/Semantic_Scene_Completion/SUNCG_D/logs/.gitkeep -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_D/results/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiceLiu/SATNet/394068e44c68c29ab6115ed96949cd0a56587c3a/Semantic_Scene_Completion/SUNCG_D/results/.gitkeep -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_D/save_models/SATNet_Depth/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiceLiu/SATNet/394068e44c68c29ab6115ed96949cd0a56587c3a/Semantic_Scene_Completion/SUNCG_D/save_models/SATNet_Depth/.gitkeep -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_D/train_Depth.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | CUDA_VISIBLE_DEVICES=1 python SATNet_Depth.py 2>&1 | tee logs/SATNet_Depth.log -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_RGBD/IOU.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | 4 | ### This version is same as Shurans'. 5 | 6 | # tensor size is (batchsize, ...) 7 | # channel0 is void and isn't counted. 8 | def computeIOU(output, label, num_classes = 12): 9 | output1, output2 = torch.max(output, 1) 10 | 11 | batchsize = output2.size(0) 12 | output2 = output2.view(batchsize, -1) 13 | label2 = label.view(batchsize, -1) 14 | 15 | if output2.size(1) != label2.size(1): 16 | print "Tensors' sizes don't equal. Tensor1 is (%d,%d), but tensor2 is (%d,%d)."%( 17 | batchsize, output2.size(1), batchsize, label2.size(1)) 18 | return -1 19 | 20 | # tp fp fn 21 | result = np.zeros((3, num_classes-1), dtype = np.float32) 22 | for i in range(1, num_classes): 23 | a = torch.eq(output2, i) 24 | b = torch.ne(output2, i) 25 | c = torch.eq(label2, i) 26 | d = torch.ne(label2, i) 27 | tp = torch.mul(a, c) 28 | fp = torch.mul(a, d) 29 | fn = torch.mul(b, c) 30 | result[0, i-1] += torch.nonzero(tp).nelement() 31 | result[1, i-1] += torch.nonzero(fp).nelement() 32 | result[2, i-1] += torch.nonzero(fn).nelement() 33 | 34 | return result -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_RGBD/gen_result_Depth.py: -------------------------------------------------------------------------------- 1 | import sys, resource, os 2 | import numpy as np 3 | import cv2 4 | import torch 5 | from torchvision.transforms import Compose, Normalize, ToTensor 6 | import h5py 7 | 8 | from SATNet_Depth import ImageGen3DNet, TrainDataLoader 9 | sys.path.append('../../') 10 | import configs 11 | 12 | # CUDA_VISIBLE_DEVICES=0 python gen_result_Depth.py 13 | def main(): 14 | resume_path = './save_models/SATNet_Depth/checkpoint.pth.tar' # HERE 15 | output_path = './results/result_suncg.hdf5' 16 | 17 | input_transform = Compose([ 18 | ToTensor(), 19 | Normalize([.485, .456, .406], [.229, .224, .225]) 20 | ]) # HERE 21 | dataset = TrainDataLoader(SUNCGRGBD_SAMPLE_TXT_TEST, SUNCGRGBD_NPZ_PATH_TEST, 'test', img_transform = input_transform) 22 | data_loader = torch.utils.data.DataLoader(dataset, batch_size = 4, 23 | shuffle = False, num_workers = 1) 24 | data_loader.pin_memory = True 25 | 26 | # model = SceneCompletionRGBD() 27 | model = ImageGen3DNet('../../Semantic_Segmentation/save_models/seg_depth_suncg/checkpoint.pth.tar', (384, 288)) # HERE 28 | if not os.path.isfile(resume_path): 29 | print "=> no checkpoint found at '{}'".format(resume_path) 30 | exit() 31 | checkpoint = torch.load(resume_path) 32 | model.load_state_dict(checkpoint['state_dict'], strict = False) 33 | model = torch.nn.DataParallel(model).cuda(0) 34 | model.eval() 35 | 36 | softmax_layer = torch.nn.Softmax(dim = 1).cuda(0) 37 | 38 | predictions = [] 39 | with torch.no_grad(): 40 | for i, (color, label, label_weight, depth_mapping_3d) in enumerate(data_loader): 41 | print '{0}/{1}'.format(i, len(data_loader)) 42 | 43 | input_var = torch.autograd.Variable(color.cuda(async=True)) 44 | depth_mapping_3d_var = torch.autograd.Variable(depth_mapping_3d.cuda(async=True)) 45 | 46 | output = model(input_var, depth_mapping_3d_var) 47 | output = softmax_layer(output) # HERE 48 | predictions.append(output.cpu().data.numpy()) 49 | predictions = np.vstack(predictions) 50 | 51 | fp = h5py.File(output_path, 'w') 52 | result = fp.create_dataset('result', predictions.shape, dtype='f') 53 | result[...] = predictions 54 | fp.close() 55 | 56 | if __name__ == '__main__': 57 | 58 | resource.setrlimit(resource.RLIMIT_STACK, (-1,-1)) 59 | sys.setrecursionlimit(100000) 60 | 61 | main() -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_RGBD/gen_result_RGB.py: -------------------------------------------------------------------------------- 1 | import sys, resource, os 2 | import numpy as np 3 | import cv2 4 | import torch 5 | from torchvision.transforms import Compose, Normalize, ToTensor 6 | import h5py 7 | 8 | from SATNet_RGB import ImageGen3DNet, TrainDataLoader 9 | sys.path.append('../../') 10 | import configs 11 | 12 | # CUDA_VISIBLE_DEVICES=0 python gen_result_RGB.py 13 | def main(): 14 | resume_path = './save_models/SATNet_RGB/checkpoint.pth.tar' # HERE 15 | output_path = './results/result_suncg.hdf5' 16 | 17 | input_transform = Compose([ 18 | ToTensor(), 19 | Normalize([.485, .456, .406], [.229, .224, .225]) 20 | ]) # HERE 21 | dataset = TrainDataLoader(SUNCGRGBD_SAMPLE_TXT_TEST, SUNCGRGBD_NPZ_PATH_TEST, 'test', img_transform = input_transform) 22 | data_loader = torch.utils.data.DataLoader(dataset, batch_size = 4, 23 | shuffle = False, num_workers = 1) 24 | data_loader.pin_memory = True 25 | 26 | # model = SceneCompletionRGBD() 27 | model = ImageGen3DNet('../../Semantic_Segmentation/save_models/seg_RGB_suncg/checkpoint.pth.tar', (384, 288)) # HERE 28 | if not os.path.isfile(resume_path): 29 | print "=> no checkpoint found at '{}'".format(resume_path) 30 | exit() 31 | checkpoint = torch.load(resume_path) 32 | model.load_state_dict(checkpoint['state_dict'], strict = False) 33 | model = torch.nn.DataParallel(model).cuda(0) 34 | model.eval() 35 | 36 | softmax_layer = torch.nn.Softmax(dim = 1).cuda(0) 37 | 38 | predictions = [] 39 | with torch.no_grad(): 40 | for i, (color, label, label_weight, depth_mapping_3d) in enumerate(data_loader): 41 | print '{0}/{1}'.format(i, len(data_loader)) 42 | 43 | input_var = torch.autograd.Variable(color.cuda(async=True)) 44 | depth_mapping_3d_var = torch.autograd.Variable(depth_mapping_3d.cuda(async=True)) 45 | 46 | output = model(input_var, depth_mapping_3d_var) 47 | output = softmax_layer(output) # HERE 48 | predictions.append(output.cpu().data.numpy()) 49 | predictions = np.vstack(predictions) 50 | 51 | fp = h5py.File(output_path, 'w') 52 | result = fp.create_dataset('result', predictions.shape, dtype='f') 53 | result[...] = predictions 54 | fp.close() 55 | 56 | if __name__ == '__main__': 57 | 58 | resource.setrlimit(resource.RLIMIT_STACK, (-1,-1)) 59 | sys.setrecursionlimit(100000) 60 | 61 | main() -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_RGBD/gen_result_SeeNetFuse.py: -------------------------------------------------------------------------------- 1 | import sys, resource, os 2 | import numpy as np 3 | import cv2 4 | import torch 5 | from torchvision.transforms import Compose, Normalize, ToTensor 6 | import h5py 7 | 8 | from SATNet_SeeNetFuse import ImageGen3DNet, TrainDataLoader 9 | sys.path.append('../../') 10 | import configs 11 | 12 | # CUDA_VISIBLE_DEVICES=0 python gen_result_SeeNetFuse.py 13 | def main(): 14 | resume_path = './save_models/SATNet_SeeNetFuse/checkpoint.pth.tar' # HERE 15 | output_path = './results/result_suncg.hdf5' 16 | 17 | dataset = TrainDataLoader(SUNCGRGBD_SAMPLE_TXT_TEST, SUNCGRGBD_NPZ_PATH_TEST, "test") 18 | data_loader = torch.utils.data.DataLoader(dataset, batch_size = 4, 19 | shuffle = False, num_workers = 1) 20 | data_loader.pin_memory = True 21 | 22 | # model = SceneCompletionRGBD() 23 | model = ImageGen3DNet('../../Semantic_Segmentation/save_models/seg_fuse_suncg/checkpoint.pth.tar', (384, 288)) # HERE 24 | if not os.path.isfile(resume_path): 25 | print "=> no checkpoint found at '{}'".format(resume_path) 26 | exit() 27 | checkpoint = torch.load(resume_path) 28 | model.load_state_dict(checkpoint['state_dict'], strict = False) 29 | model = torch.nn.DataParallel(model).cuda(0) 30 | model.eval() 31 | 32 | softmax_layer = torch.nn.Softmax(dim = 1).cuda(0) 33 | 34 | predictions = [] 35 | with torch.no_grad(): 36 | for i, (color, depth, label, label_weight, depth_mapping_3d) in enumerate(data_loader): 37 | print '{0}/{1}'.format(i, len(data_loader)) 38 | 39 | input_var = torch.autograd.Variable(color.cuda(async=True)) 40 | depth_var = torch.autograd.Variable(depth.cuda(async=True)) 41 | depth_mapping_3d_var = torch.autograd.Variable(depth_mapping_3d.cuda(async=True)) 42 | 43 | output = model(input_var, depth_var, depth_mapping_3d_var) 44 | output = softmax_layer(output) # HERE 45 | predictions.append(output.cpu().data.numpy()) 46 | predictions = np.vstack(predictions) 47 | 48 | fp = h5py.File(output_path, 'w') 49 | result = fp.create_dataset('result', predictions.shape, dtype='f') 50 | result[...] = predictions 51 | fp.close() 52 | 53 | if __name__ == '__main__': 54 | 55 | resource.setrlimit(resource.RLIMIT_STACK, (-1,-1)) 56 | sys.setrecursionlimit(100000) 57 | 58 | main() -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_RGBD/gen_result_ThinkNetFuse.py: -------------------------------------------------------------------------------- 1 | import sys, resource, os 2 | import numpy as np 3 | import cv2 4 | import torch 5 | from torchvision.transforms import Compose, Normalize, ToTensor 6 | import h5py 7 | 8 | from SATNet_ThinkNetFuse import ImageGen3DNet, TrainDataLoader 9 | sys.path.append('../../') 10 | import configs 11 | 12 | # CUDA_VISIBLE_DEVICES=0 python gen_result_ThinkNetFuse.py 13 | def main(): 14 | resume_path = './save_models/SATNet_ThinkNetFuse/checkpoint.pth.tar' # HERE 15 | output_path = './results/result_suncg.hdf5' 16 | 17 | dataset = TrainDataLoader(SUNCGRGBD_SAMPLE_TXT_TEST, SUNCGRGBD_NPZ_PATH_TEST, "test") 18 | data_loader = torch.utils.data.DataLoader(dataset, batch_size = 4, 19 | shuffle = False, num_workers = 1) 20 | data_loader.pin_memory = True 21 | 22 | # model = SceneCompletionRGBD() 23 | model = ImageGen3DNet('./save_models/SATNet_RGB/checkpoint.pth.tar', 24 | './save_models/SATNet_Depth/checkpoint.pth.tar', (384, 288)) 25 | if not os.path.isfile(resume_path): 26 | print "=> no checkpoint found at '{}'".format(resume_path) 27 | exit() 28 | checkpoint = torch.load(resume_path) 29 | model.load_state_dict(checkpoint['state_dict'], strict = False) 30 | model.eval() 31 | 32 | softmax_layer = torch.nn.Softmax(dim = 1).cuda(0) 33 | 34 | predictions = [] 35 | with torch.no_grad(): 36 | for i, (color, depth, label, label_weight, depth_mapping_3d) in enumerate(data_loader): 37 | print '{0}/{1}'.format(i, len(data_loader)) 38 | 39 | input_var = torch.autograd.Variable(color.cuda(0, async=True)) 40 | depth_var = torch.autograd.Variable(depth.cuda(1, async=True)) 41 | depth_mapping_3d_var0 = torch.autograd.Variable(depth_mapping_3d.cuda(0, async=True)) 42 | depth_mapping_3d_var1 = torch.autograd.Variable(depth_mapping_3d.cuda(1, async=True)) 43 | 44 | output = model(input_var, depth_var, depth_mapping_3d_var0, depth_mapping_3d_var1) 45 | output = softmax_layer(output) # HERE 46 | predictions.append(output.cpu().data.numpy()) 47 | predictions = np.vstack(predictions) 48 | 49 | fp = h5py.File(output_path, 'w') 50 | result = fp.create_dataset('result', predictions.shape, dtype='f') 51 | result[...] = predictions 52 | fp.close() 53 | 54 | if __name__ == '__main__': 55 | 56 | resource.setrlimit(resource.RLIMIT_STACK, (-1,-1)) 57 | sys.setrecursionlimit(100000) 58 | 59 | main() -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_RGBD/labels/000000.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShiceLiu/SATNet/394068e44c68c29ab6115ed96949cd0a56587c3a/Semantic_Scene_Completion/SUNCG_RGBD/labels/000000.png 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/Semantic_Scene_Completion/SUNCG_RGBD/train_RGB.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | CUDA_VISIBLE_DEVICES=0 python SATNet_RGB.py 2>&1 | tee logs/SATNet_RGB.log -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_RGBD/train_SeeNetFuse.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | CUDA_VISIBLE_DEVICES=0 python SATNet_SeeNetFuse.py 2>&1 | tee logs/SATNet_SeeNetFuse.log -------------------------------------------------------------------------------- /Semantic_Scene_Completion/SUNCG_RGBD/train_ThinkNetFuse.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | CUDA_VISIBLE_DEVICES=0,1 python SATNet_ThinkNetFuse.py 2>&1 | tee logs/SATNet_ThinkNetFuse.log -------------------------------------------------------------------------------- /Semantic_Segmentation/IOU.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | 4 | # tensor size is (batchsize, ...) 5 | # channel0 is void and isn't counted. 6 | def computeIOU(output, label, num_classes = 12): 7 | output1, output2 = torch.max(output, 1) 8 | 9 | batchsize = output2.size(0) 10 | output2 = output2.view(batchsize, -1) 11 | label2 = label.view(batchsize, -1) 12 | 13 | if output2.size(1) != label2.size(1): 14 | print "Tensors' sizes don't equal. Tensor1 is (%d,%d), but tensor2 is (%d,%d)."%( 15 | batchsize, output2.size(1), batchsize, label2.size(1)) 16 | return -1 17 | 18 | result = np.ones((batchsize, num_classes-1), dtype = np.float32) 19 | for i in range(1, num_classes): 20 | a = torch.eq(output2, i) 21 | b = torch.eq(label2, i) 22 | c = torch.add(a, 1, b) 23 | d = torch.mul(a, b) 24 | for j in xrange(batchsize): 25 | union = torch.nonzero(c[j]).nelement() 26 | intersection = torch.nonzero(d[j]).nelement() 27 | if union == 0: 28 | result[j, i-1] = -1 29 | else: 30 | result[j, i-1] = float(intersection) / float(union) 31 | 32 | return result -------------------------------------------------------------------------------- /Semantic_Segmentation/logs/.gitkeep: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Semantic_Segmentation/save_models/seg_RGB_nyu/.gitkeep: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Semantic_Segmentation/save_models/seg_RGB_suncg/.gitkeep: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Semantic_Segmentation/save_models/seg_depth_nyu/.gitkeep: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Semantic_Segmentation/save_models/seg_depth_suncg/.gitkeep: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Semantic_Segmentation/save_models/seg_fuse_nyu/.gitkeep: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Semantic_Segmentation/save_models/seg_fuse_suncg/.gitkeep: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Semantic_Segmentation/train_seg_RGB_nyu.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | 4 | NYUIMAGE_PATH=/home/jason/NYUv2/NYU_images 5 | CUDA_VISIBLE_DEVICES=0 python seg_RGB_nyu.py $NYUIMAGE_PATH 2>&1 | tee logs/seg_RGB_nyu.log -------------------------------------------------------------------------------- /Semantic_Segmentation/train_seg_RGB_suncg.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | 4 | SELECTEDIMAGE_PATH=/home/jason/lscCcode/selectImageFromRawSUNCG/selectedImage 5 | CUDA_VISIBLE_DEVICES=1 python seg_RGB_suncg.py $SELECTEDIMAGE_PATH 2>&1 | tee ./logs/seg_RGB_suncg.log -------------------------------------------------------------------------------- /Semantic_Segmentation/train_seg_depth_nyu.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | 4 | NYUIMAGE_PATH=/home/jason/NYUv2/NYU_images 5 | CUDA_VISIBLE_DEVICES=0 python seg_depth_nyu.py $NYUIMAGE_PATH 2>&1 | tee logs/seg_depth_nyu.log -------------------------------------------------------------------------------- /Semantic_Segmentation/train_seg_depth_suncg.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Note that 'HHA_path' in the 'seg_depth_suncg.py' should be replaced to your local path. 4 | SELECTEDIMAGE_PATH=/home/jason/lscCcode/selectImageFromRawSUNCG/selectedImage 5 | CUDA_VISIBLE_DEVICES=0 python seg_depth_suncg.py $SELECTEDIMAGE_PATH 2>&1 | tee ./logs/seg_depth_suncg.log -------------------------------------------------------------------------------- /Semantic_Segmentation/train_seg_fuse_nyu.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | 4 | NYUIMAGE_PATH=/home/jason/NYUv2/NYU_images 5 | CUDA_VISIBLE_DEVICES=1 python seg_fuse_nyu.py $NYUIMAGE_PATH 2>&1 | tee logs/seg_fuse_nyu.log -------------------------------------------------------------------------------- /Semantic_Segmentation/train_seg_fuse_suncg.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | 4 | SELECTEDIMAGE_PATH=/home/jason/lscCcode/selectImageFromRawSUNCG/selectedImage 5 | CUDA_VISIBLE_DEVICES=1 python seg_fuse_suncg.py $SELECTEDIMAGE_PATH 2>&1 | tee logs/seg_fuse_suncg.log -------------------------------------------------------------------------------- /configs.py: -------------------------------------------------------------------------------- 1 | SUNCGD_HHA_PATH_TRAIN = '/home/jason/lscMATLABcode/HHA_image_gen-master/shurans_selected_HHA' 2 | SUNCGD_HHA_PATH_TEST = '/home/jason/lscMATLABcode/HHA_image_gen-master/shurans_selected_val_HHA' 3 | 4 | SUNCGD_NPZ_PATH_TRAIN = '/media/jason/JetsonSSD/shurans_selected' 5 | SUNCGD_NPZ_PATH_TEST = '/media/jason/JetsonSSD/shurans_selected_val' 6 | 7 | SUNCGRGBD_HHA_PATH_TRAIN = '/media/jason/JetsonSSD/myselect_suncg_HHA' 8 | SUNCGRGBD_HHA_PATH_TEST = '/media/jason/JetsonSSD/myselect_suncg_val_HHA' 9 | 10 | SUNCGRGBD_NPZ_PATH_TRAIN = '/media/jason/JetsonSSD/myselect_suncg' 11 | SUNCGRGBD_NPZ_PATH_TEST = '/media/jason/JetsonSSD/myselect_suncg_val' 12 | 13 | SUNCGRGBD_SAMPLE_TXT_TRAIN = '/home/jason/lscCcode/selectImageFromRawSUNCG/selectedImage/image_list_train.txt' 14 | SUNCGRGBD_SAMPLE_TXT_TEST = '/home/jason/lscCcode/selectImageFromRawSUNCG/selectedImage/image_list_val.txt' 15 | 16 | NYU_HHA_PATH_TRAIN = '/media/jason/JetsonSSD/nyu_selected_HHA' 17 | NYU_HHA_PATH_TEST = 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