kaist pd 실습 thermal.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"kaist pd 실습 thermal.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm","authorship_tag":"ABX9TyMOSs8Mzqhqm0sPS1uptNSN"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"daqz3Xi3bkAq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1658802027260,"user_tz":-540,"elapsed":27663,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}},"outputId":"423041ca-db32-4a5f-fce2-0887bb989790"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["# 현재경로 설정\n","!cd \"/content/drive/My Drive/Colab Notebooks/URP/kaist_pd_urp\""],"metadata":{"id":"jd-4LFlF2mFe","executionInfo":{"status":"ok","timestamp":1658802027978,"user_tz":-540,"elapsed":721,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks/URP/kaist_pd_urp')"],"metadata":{"id":"qx1JvZDf3Npe","executionInfo":{"status":"ok","timestamp":1658802027978,"user_tz":-540,"elapsed":1,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["# !git clone https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection.git"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-eirjoaU2ymu","executionInfo":{"status":"ok","timestamp":1658123416190,"user_tz":-540,"elapsed":3825,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}},"outputId":"c00cdeff-00c2-480b-8bcc-9efb0faa82af"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'a-PyTorch-Tutorial-to-Object-Detection'...\n","remote: Enumerating objects: 249, done.\u001b[K\n","remote: Counting objects: 100% (68/68), done.\u001b[K\n","remote: Compressing objects: 100% (11/11), done.\u001b[K\n","remote: Total 249 (delta 60), reused 57 (delta 57), pack-reused 181\u001b[K\n","Receiving objects: 100% (249/249), 175.95 MiB | 71.16 MiB/s, done.\n","Resolving deltas: 100% (118/118), done.\n"]}]},{"cell_type":"code","source":["!python utils.py"],"metadata":{"id":"k1Shvmbv215N"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python create_data_lists.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HpPVGkUbNqYn","executionInfo":{"status":"ok","timestamp":1658674520107,"user_tz":-540,"elapsed":3016948,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}},"outputId":"dad8bcf8-c95b-45da-c64f-1340a4eb4621"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["시작2\n","시작8\n","100% 8730/8730 [50:14<00:00,  2.90it/s]\n","\n","TRAIN DATA) Files have been saved to /content/drive/MyDrive/kaist_output.\n","\n","\n","There are 8730 training images containing a total of 26384 objects. Files have been saved to /content/drive/MyDrive/kaist_output.\n","\n","TEST DATA) START2\n","\n","TEST DATA) Files have been saved to /content/drive/MyDrive/kaist_output.\n","\n"]}]},{"cell_type":"code","source":["!python datasets.py"],"metadata":{"id":"NSQsEXoUVD-k"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python model.py"],"metadata":{"id":"F9k8uWWmKZqO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python train.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bb5tP3lyKluY","executionInfo":{"status":"ok","timestamp":1658819823106,"user_tz":-540,"elapsed":17648856,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}},"outputId":"17630643-4aae-45cf-82c4-41b6e3e7be4d"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Loaded checkpoint from epoch 25.\n","\n","/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.\n","  warnings.warn(warning.format(ret))\n","Epoch: [25][0/273]\tBatch Time 55.081 (55.081)\tData Time 42.685 (42.685)\tLoss 2.4798 (2.4798)\t\n","Epoch: [25][200/273]\tBatch Time 8.818 (5.199)\tData Time 8.295 (4.622)\tLoss 2.5119 (2.5741)\t\n","Epoch: [26][0/273]\tBatch Time 5.106 (5.106)\tData Time 4.519 (4.519)\tLoss 2.6204 (2.6204)\t\n","Epoch: [26][200/273]\tBatch Time 0.580 (1.111)\tData Time 0.010 (0.560)\tLoss 2.1657 (2.5539)\t\n","Epoch: [27][0/273]\tBatch Time 4.538 (4.538)\tData Time 3.995 (3.995)\tLoss 2.4822 (2.4822)\t\n","Epoch: [27][200/273]\tBatch Time 0.558 (1.096)\tData Time 0.001 (0.549)\tLoss 2.3817 (2.5262)\t\n","Epoch: [28][0/273]\tBatch Time 4.341 (4.341)\tData Time 3.764 (3.764)\tLoss 2.4231 (2.4231)\t\n","Epoch: [28][200/273]\tBatch Time 2.790 (1.120)\tData Time 2.251 (0.571)\tLoss 2.6036 (2.5481)\t\n","Epoch: [29][0/273]\tBatch Time 4.864 (4.864)\tData Time 4.273 (4.273)\tLoss 2.3425 (2.3425)\t\n","Epoch: [29][200/273]\tBatch Time 0.521 (1.091)\tData Time 0.000 (0.540)\tLoss 2.4219 (2.5376)\t\n","Epoch: [30][0/273]\tBatch Time 5.127 (5.127)\tData Time 4.578 (4.578)\tLoss 2.7359 (2.7359)\t\n","Epoch: [30][200/273]\tBatch Time 1.959 (1.101)\tData Time 1.382 (0.549)\tLoss 2.4593 (2.5121)\t\n","Epoch: [31][0/273]\tBatch Time 4.610 (4.610)\tData Time 4.008 (4.008)\tLoss 2.7137 (2.7137)\t\n","Epoch: [31][200/273]\tBatch Time 0.523 (1.109)\tData Time 0.001 (0.560)\tLoss 2.5415 (2.5212)\t\n","Epoch: [32][0/273]\tBatch Time 4.871 (4.871)\tData Time 4.271 (4.271)\tLoss 2.3417 (2.3417)\t\n","Epoch: [32][200/273]\tBatch Time 0.560 (1.108)\tData Time 0.001 (0.562)\tLoss 2.5436 (2.4555)\t\n","Epoch: [33][0/273]\tBatch Time 5.022 (5.022)\tData Time 4.493 (4.493)\tLoss 2.5036 (2.5036)\t\n","Epoch: [33][200/273]\tBatch Time 2.257 (1.120)\tData Time 1.670 (0.571)\tLoss 2.4731 (2.4876)\t\n","Epoch: [34][0/273]\tBatch Time 4.310 (4.310)\tData Time 3.750 (3.750)\tLoss 2.8946 (2.8946)\t\n","Epoch: [34][200/273]\tBatch Time 0.530 (1.107)\tData Time 0.001 (0.555)\tLoss 2.3616 (2.4955)\t\n","Epoch: [35][0/273]\tBatch Time 5.198 (5.198)\tData Time 4.623 (4.623)\tLoss 2.4334 (2.4334)\t\n","Epoch: [35][200/273]\tBatch Time 0.963 (1.105)\tData Time 0.413 (0.555)\tLoss 2.3568 (2.4441)\t\n","Epoch: [36][0/273]\tBatch Time 4.243 (4.243)\tData Time 3.681 (3.681)\tLoss 2.5654 (2.5654)\t\n","Epoch: [36][200/273]\tBatch Time 0.748 (1.101)\tData Time 0.187 (0.553)\tLoss 2.5055 (2.4606)\t\n","Epoch: [37][0/273]\tBatch Time 6.189 (6.189)\tData Time 5.636 (5.636)\tLoss 2.3947 (2.3947)\t\n","Epoch: [37][200/273]\tBatch Time 0.580 (1.110)\tData Time 0.000 (0.561)\tLoss 2.1066 (2.4569)\t\n","Epoch: [38][0/273]\tBatch Time 4.646 (4.646)\tData Time 4.094 (4.094)\tLoss 2.9062 (2.9062)\t\n","Epoch: [38][200/273]\tBatch Time 1.437 (1.085)\tData Time 0.880 (0.539)\tLoss 2.2688 (2.4292)\t\n","Epoch: [39][0/273]\tBatch Time 4.780 (4.780)\tData Time 4.204 (4.204)\tLoss 2.2709 (2.2709)\t\n","Epoch: [39][200/273]\tBatch Time 1.646 (1.112)\tData Time 1.074 (0.565)\tLoss 2.4030 (2.4425)\t\n","Epoch: [40][0/273]\tBatch Time 4.466 (4.466)\tData Time 3.893 (3.893)\tLoss 1.7812 (1.7812)\t\n","Epoch: [40][200/273]\tBatch Time 0.521 (1.100)\tData Time 0.001 (0.553)\tLoss 2.5347 (2.4252)\t\n","Epoch: [41][0/273]\tBatch Time 5.130 (5.130)\tData Time 4.589 (4.589)\tLoss 2.4404 (2.4404)\t\n","Epoch: [41][200/273]\tBatch Time 3.514 (1.105)\tData Time 2.980 (0.556)\tLoss 2.3247 (2.4212)\t\n","Epoch: [42][0/273]\tBatch Time 4.618 (4.618)\tData Time 4.047 (4.047)\tLoss 2.4777 (2.4777)\t\n","Epoch: [42][200/273]\tBatch Time 0.527 (1.095)\tData Time 0.001 (0.545)\tLoss 2.3018 (2.4139)\t\n","Epoch: [43][0/273]\tBatch Time 4.938 (4.938)\tData Time 4.365 (4.365)\tLoss 2.2499 (2.2499)\t\n","Epoch: [43][200/273]\tBatch Time 0.562 (1.109)\tData Time 0.001 (0.561)\tLoss 2.3483 (2.3975)\t\n","Epoch: [44][0/273]\tBatch Time 4.868 (4.868)\tData Time 4.333 (4.333)\tLoss 2.3833 (2.3833)\t\n","Epoch: [44][200/273]\tBatch Time 2.754 (1.116)\tData Time 2.173 (0.567)\tLoss 2.7664 (2.4159)\t\n","Epoch: [45][0/273]\tBatch Time 4.971 (4.971)\tData Time 4.417 (4.417)\tLoss 2.8234 (2.8234)\t\n","Epoch: [45][200/273]\tBatch Time 0.573 (1.117)\tData Time 0.001 (0.566)\tLoss 2.4138 (2.4043)\t\n","Epoch: [46][0/273]\tBatch Time 4.931 (4.931)\tData Time 4.403 (4.403)\tLoss 2.2978 (2.2978)\t\n","Epoch: [46][200/273]\tBatch Time 0.553 (1.126)\tData Time 0.000 (0.577)\tLoss 2.8159 (2.3760)\t\n","Epoch: [47][0/273]\tBatch Time 4.079 (4.079)\tData Time 3.487 (3.487)\tLoss 2.5965 (2.5965)\t\n","Epoch: [47][200/273]\tBatch Time 0.564 (1.109)\tData Time 0.001 (0.558)\tLoss 2.4234 (2.3932)\t\n","Epoch: [48][0/273]\tBatch Time 4.969 (4.969)\tData Time 4.381 (4.381)\tLoss 2.1317 (2.1317)\t\n","Epoch: [48][200/273]\tBatch Time 1.338 (1.107)\tData Time 0.777 (0.559)\tLoss 2.2702 (2.3628)\t\n","Epoch: [49][0/273]\tBatch Time 4.192 (4.192)\tData Time 3.644 (3.644)\tLoss 2.5328 (2.5328)\t\n","Epoch: [49][200/273]\tBatch Time 1.971 (1.104)\tData Time 1.407 (0.555)\tLoss 2.2480 (2.3489)\t\n","Epoch: [50][0/273]\tBatch Time 4.975 (4.975)\tData Time 4.379 (4.379)\tLoss 1.9436 (1.9436)\t\n","Epoch: [50][200/273]\tBatch Time 2.810 (1.134)\tData Time 2.239 (0.588)\tLoss 2.1202 (2.4031)\t\n","Epoch: [51][0/273]\tBatch Time 4.640 (4.640)\tData Time 4.103 (4.103)\tLoss 2.5539 (2.5539)\t\n","Epoch: [51][200/273]\tBatch Time 3.276 (1.117)\tData Time 2.730 (0.569)\tLoss 2.1583 (2.3650)\t\n","Epoch: [52][0/273]\tBatch Time 4.650 (4.650)\tData Time 4.054 (4.054)\tLoss 2.4883 (2.4883)\t\n","Epoch: [52][200/273]\tBatch Time 0.533 (1.121)\tData Time 0.000 (0.575)\tLoss 2.3575 (2.3595)\t\n","Epoch: [53][0/273]\tBatch Time 5.195 (5.195)\tData Time 4.669 (4.669)\tLoss 2.2538 (2.2538)\t\n","Epoch: [53][200/273]\tBatch Time 2.799 (1.111)\tData Time 2.213 (0.564)\tLoss 2.3340 (2.3313)\t\n","Epoch: [54][0/273]\tBatch Time 4.741 (4.741)\tData Time 4.158 (4.158)\tLoss 2.3221 (2.3221)\t\n","Epoch: [54][200/273]\tBatch Time 3.611 (1.116)\tData Time 3.066 (0.568)\tLoss 2.6156 (2.3544)\t\n","Epoch: [55][0/273]\tBatch Time 5.145 (5.145)\tData Time 4.568 (4.568)\tLoss 2.3262 (2.3262)\t\n","Epoch: [55][200/273]\tBatch Time 3.982 (1.109)\tData Time 3.392 (0.562)\tLoss 2.0064 (2.3208)\t\n","Epoch: [56][0/273]\tBatch Time 4.863 (4.863)\tData Time 4.274 (4.274)\tLoss 2.2289 (2.2289)\t\n","Epoch: [56][200/273]\tBatch Time 2.259 (1.103)\tData Time 1.714 (0.556)\tLoss 2.3720 (2.3084)\t\n","Epoch: [57][0/273]\tBatch Time 4.570 (4.570)\tData Time 3.955 (3.955)\tLoss 2.2751 (2.2751)\t\n","Epoch: [57][200/273]\tBatch Time 1.708 (1.087)\tData Time 1.180 (0.535)\tLoss 2.4442 (2.2962)\t\n","Epoch: [58][0/273]\tBatch Time 4.952 (4.952)\tData Time 4.360 (4.360)\tLoss 2.3328 (2.3328)\t\n","Epoch: [58][200/273]\tBatch Time 2.612 (1.103)\tData Time 2.056 (0.556)\tLoss 2.4570 (2.3163)\t\n","Epoch: [59][0/273]\tBatch Time 4.558 (4.558)\tData Time 4.006 (4.006)\tLoss 2.3284 (2.3284)\t\n","Epoch: [59][200/273]\tBatch Time 0.527 (1.106)\tData Time 0.000 (0.559)\tLoss 2.0662 (2.3241)\t\n","Epoch: [60][0/273]\tBatch Time 4.087 (4.087)\tData Time 3.512 (3.512)\tLoss 2.1504 (2.1504)\t\n","Epoch: [60][200/273]\tBatch Time 2.407 (1.117)\tData Time 1.885 (0.570)\tLoss 2.0436 (2.3409)\t\n","Epoch: [61][0/273]\tBatch Time 4.122 (4.122)\tData Time 3.498 (3.498)\tLoss 2.2468 (2.2468)\t\n","Epoch: [61][200/273]\tBatch Time 1.719 (1.105)\tData Time 1.176 (0.559)\tLoss 1.8234 (2.3022)\t\n","Epoch: [62][0/273]\tBatch Time 4.758 (4.758)\tData Time 4.211 (4.211)\tLoss 2.3771 (2.3771)\t\n","Epoch: [62][200/273]\tBatch Time 0.533 (1.112)\tData Time 0.001 (0.563)\tLoss 2.1815 (2.2881)\t\n","Epoch: [63][0/273]\tBatch Time 4.673 (4.673)\tData Time 4.052 (4.052)\tLoss 2.1201 (2.1201)\t\n","Epoch: [63][200/273]\tBatch Time 0.561 (1.085)\tData Time 0.000 (0.536)\tLoss 2.5533 (2.2998)\t\n","Epoch: [64][0/273]\tBatch Time 5.196 (5.196)\tData Time 4.664 (4.664)\tLoss 2.2496 (2.2496)\t\n","Epoch: [64][200/273]\tBatch Time 2.524 (1.126)\tData Time 1.977 (0.580)\tLoss 2.3031 (2.2693)\t\n","Epoch: [65][0/273]\tBatch Time 5.251 (5.251)\tData Time 4.675 (4.675)\tLoss 2.4540 (2.4540)\t\n","Epoch: [65][200/273]\tBatch Time 0.521 (1.115)\tData Time 0.000 (0.565)\tLoss 2.0739 (2.3168)\t\n","Epoch: [66][0/273]\tBatch Time 4.510 (4.510)\tData Time 3.930 (3.930)\tLoss 2.7273 (2.7273)\t\n","Epoch: [66][200/273]\tBatch Time 0.532 (1.119)\tData Time 0.001 (0.571)\tLoss 2.2851 (2.2721)\t\n","Epoch: [67][0/273]\tBatch Time 5.030 (5.030)\tData Time 4.450 (4.450)\tLoss 2.2584 (2.2584)\t\n","Epoch: [67][200/273]\tBatch Time 1.490 (1.123)\tData Time 0.916 (0.574)\tLoss 2.4397 (2.2672)\t\n","Epoch: [68][0/273]\tBatch Time 4.959 (4.959)\tData Time 4.419 (4.419)\tLoss 2.2471 (2.2471)\t\n","Epoch: [68][200/273]\tBatch Time 3.178 (1.128)\tData Time 2.658 (0.581)\tLoss 2.6536 (2.2647)\t\n","Epoch: [69][0/273]\tBatch Time 5.554 (5.554)\tData Time 5.007 (5.007)\tLoss 2.4516 (2.4516)\t\n","Epoch: [69][200/273]\tBatch Time 0.527 (1.124)\tData Time 0.001 (0.576)\tLoss 2.4116 (2.2448)\t\n","adjust_learning_rate\n","DECAYING learning rate.\n"," The new LR is 0.000050\n","\n","Epoch: [70][0/273]\tBatch Time 3.992 (3.992)\tData Time 3.410 (3.410)\tLoss 1.9806 (1.9806)\t\n","Epoch: [70][200/273]\tBatch Time 2.532 (1.132)\tData Time 1.956 (0.584)\tLoss 2.5770 (2.2065)\t\n","Epoch: [71][0/273]\tBatch Time 4.442 (4.442)\tData Time 3.892 (3.892)\tLoss 2.1358 (2.1358)\t\n","Epoch: [71][200/273]\tBatch Time 0.579 (1.117)\tData Time 0.000 (0.571)\tLoss 2.0225 (2.1586)\t\n","Epoch: [72][0/273]\tBatch Time 5.519 (5.519)\tData Time 4.973 (4.973)\tLoss 2.0596 (2.0596)\t\n","Epoch: [72][200/273]\tBatch Time 2.492 (1.124)\tData Time 1.926 (0.574)\tLoss 2.2329 (2.1747)\t\n","Epoch: [73][0/273]\tBatch Time 4.564 (4.564)\tData Time 4.023 (4.023)\tLoss 2.2479 (2.2479)\t\n","Epoch: [73][200/273]\tBatch Time 2.379 (1.118)\tData Time 1.790 (0.568)\tLoss 2.0830 (2.1552)\t\n","Epoch: [74][0/273]\tBatch Time 4.500 (4.500)\tData Time 3.941 (3.941)\tLoss 2.1541 (2.1541)\t\n","Epoch: [74][200/273]\tBatch Time 1.142 (1.088)\tData Time 0.605 (0.541)\tLoss 2.2409 (2.1496)\t\n","Epoch: [75][0/273]\tBatch Time 5.036 (5.036)\tData Time 4.435 (4.435)\tLoss 2.0619 (2.0619)\t\n","Epoch: [75][200/273]\tBatch Time 0.585 (1.104)\tData Time 0.011 (0.553)\tLoss 2.1069 (2.1679)\t\n","Epoch: [76][0/273]\tBatch Time 4.947 (4.947)\tData Time 4.389 (4.389)\tLoss 2.3312 (2.3312)\t\n","Epoch: [76][200/273]\tBatch Time 1.780 (1.105)\tData Time 1.215 (0.555)\tLoss 2.6063 (2.1541)\t\n","Epoch: [77][0/273]\tBatch Time 5.155 (5.155)\tData Time 4.612 (4.612)\tLoss 2.1376 (2.1376)\t\n","Epoch: [77][200/273]\tBatch Time 0.579 (1.098)\tData Time 0.001 (0.550)\tLoss 1.9513 (2.1587)\t\n","Epoch: [78][0/273]\tBatch Time 5.412 (5.412)\tData Time 4.849 (4.849)\tLoss 2.0995 (2.0995)\t\n","Epoch: [78][200/273]\tBatch Time 0.572 (1.101)\tData Time 0.041 (0.556)\tLoss 2.2127 (2.1697)\t\n","Epoch: [79][0/273]\tBatch Time 5.902 (5.902)\tData Time 5.328 (5.328)\tLoss 2.6129 (2.6129)\t\n","Epoch: [79][200/273]\tBatch Time 1.661 (1.107)\tData Time 1.104 (0.559)\tLoss 2.0341 (2.1492)\t\n"]}]},{"cell_type":"code","source":["!python detect.py"],"metadata":{"id":"KW4tm_7RK253","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1658821279730,"user_tz":-540,"elapsed":1456628,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}},"outputId":"38e1d9e7-776e-408e-8576-967bceb148dc"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Loaded checkpoint from epoch 80.\n","\n","1it [00:04,  4.34s/it]/content/drive/MyDrive/Colab Notebooks/URP/kaist_pd_urp/model.py:503: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (Triggered internally at  ../aten/src/ATen/native/IndexingUtils.h:27.)\n","  image_boxes.append(class_decoded_locs[1 - suppress])\n","/content/drive/MyDrive/Colab Notebooks/URP/kaist_pd_urp/model.py:505: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (Triggered internally at  ../aten/src/ATen/native/IndexingUtils.h:27.)\n","  image_scores.append(class_scores[1 - suppress])\n","2252it [24:11,  1.55it/s]\n"]}]},{"cell_type":"code","source":["!python Evaluation_official.py"],"metadata":{"id":"Hhs4F9ilnOKL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1658821286344,"user_tz":-540,"elapsed":6618,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}},"outputId":"fa7e72bd-04f5-4b36-bcc1-be632ea3cdd3"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["loading annotations into memory...\n","Done (t=1.02s)\n","creating index...\n","index created!\n","Loading and preparing results...\n","DONE (t=0.02s)\n","creating index...\n","index created!\n","Running per image evaluation...\n","Evaluate annotation type *bbox*\n","DONE (t=0.22s).\n","Accumulating evaluation results...\n","DONE (t=0.00s).\n"," Average Miss Rate  (MR) @ Reasonable         [ IoU=0.50      | height=[55:10000000000] | visibility=[none+partial_occ] ] = 29.34%\n","Recall: 0.8299367533380183\n"]}]},{"cell_type":"code","source":["!python moving_image.py"],"metadata":{"id":"x6NsoD6m7hp2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1658821358462,"user_tz":-540,"elapsed":72120,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}},"outputId":"465f1d59-c333-4983-c358-7a31109d383f"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Loaded checkpoint from epoch 80.\n","\n","출력4\n","1it [00:01,  1.03s/it]/content/drive/MyDrive/Colab Notebooks/URP/kaist_pd_urp/model.py:503: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (Triggered internally at  ../aten/src/ATen/native/IndexingUtils.h:27.)\n","  image_boxes.append(class_decoded_locs[1 - suppress])\n","/content/drive/MyDrive/Colab Notebooks/URP/kaist_pd_urp/model.py:505: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (Triggered internally at  ../aten/src/ATen/native/IndexingUtils.h:27.)\n","  image_scores.append(class_scores[1 - suppress])\n","2252it [00:58, 38.30it/s]\n","OpenCV: FFMPEG: tag 0x58564944/'DIVX' is not supported with codec id 12 and format 'mp4 / MP4 (MPEG-4 Part 14)'\n","OpenCV: FFMPEG: fallback to use tag 0x7634706d/'mp4v'\n"]}]},{"cell_type":"code","source":["# import os\n","# import json\n","\n","# with open(os.path.join('/content/drive/MyDrive/kaist_output/TRAIN_objects.json'), 'r') as j:\n","#       nobjects = json.load(j)\n","# with open(os.path.join('/content/drive/MyDrive/kaist_output/TRAIN_nobjects.json'), 'r') as j:\n","#       objects = json.load(j)\n","\n","# nobjects == objects"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xNpHgMH8HZw4","executionInfo":{"status":"ok","timestamp":1658748723123,"user_tz":-540,"elapsed":1646,"user":{"displayName":"Juyeon Kim","userId":"17747853872644742840"}},"outputId":"f6d4429a-4712-4a1b-8866-7e5b0a7ee28b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["!python "],"metadata":{"id":"VZyFRhAegJgx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[""],"metadata":{"id":"QgcgHtrUnA9w"},"execution_count":null,"outputs":[]}]}