documentation/celltrackingchallenge/MIC-DKFZ.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"D0uBJLr23WpA"},"source":["This notebook provides an example for training a nnUnet on the Cell Tracking Challenge dataset Fluo-N2DH-SIM"]},{"cell_type":"markdown","metadata":{"id":"74xHUq4v3pNE"},"source":["First of all, clone the repository, install and import all necessary packages"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":836,"status":"ok","timestamp":1659343632884,"user":{"displayName":"PABLO DELGADO RODRIGUEZ","userId":"11999848093282927146"},"user_tz":-120},"id":"94H69Of68ux2","outputId":"67ad2c6a-0c71-4f43-878d-de92c5e9d344"},"outputs":[{"name":"stdout","output_type":"stream","text":["Cloning into 'nnUNet'...\n","remote: Enumerating objects: 5963, done.\u001b[K\n","remote: Counting objects: 100% (77/77), done.\u001b[K\n","remote: Compressing objects: 100% (36/36), done.\u001b[K\n","remote: Total 5963 (delta 40), reused 62 (delta 34), pack-reused 5886\u001b[K\n","Receiving objects: 100% (5963/5963), 1.46 MiB | 16.38 MiB/s, done.\n","Resolving deltas: 100% (4703/4703), done.\n"]}],"source":["!git clone https://github.com/MIC-DKFZ/nnUNet.git\n","!python -c 'import torch;print(torch.backends.cudnn.version())'\n","!python -c 'import torch;print(torch.__version__)'\n","\n","!pip install nnunet\n","!pip install --upgrade git+https://github.com/FabianIsensee/hiddenlayer.git@more_plotted_details#egg=hiddenlayer\n","!pip install imagecodecs"]},{"cell_type":"markdown","metadata":{"id":"HFOKut8c4AWq"},"source":["Now connect to your Google Drive "]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":91562,"status":"ok","timestamp":1659084038428,"user":{"displayName":"PABLO DELGADO RODRIGUEZ","userId":"11999848093282927146"},"user_tz":-120},"id":"dMyE53_y-lNl","outputId":"5023d7ef-18df-481b-dc57-009ab72e17d8"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"KUXBf0MRBYQx"},"source":["Start by creating the following paths in your googledrive:\n","1. ```CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_raw_data_base```\n","2. ```CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed```\n","3. ```CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_trained_models```\n","\n","Data preparation:\n","1. Obtain the data *Fluo-N2DH-SIM* from the Cell Tracking Cahllenge webpage: http://celltrackingchallenge.net/2d-datasets/\n","2. Place the data for training in the folder ```CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/``` and rename it to ```Fluo-N2DH-SIM+_train```\n","3. Place the data for the challenge in the folder ```CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/``` and rename it to ```Fluo-N2DH-SIM+_test```\n","4. Download the corresponding [Task089_Fluo-N2DH-SIM.py](https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/dataset_conversion/Task089_Fluo-N2DH-SIM.py) file from the nnUnet repository to convert the data: https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/celltrackingchallenge/instructions.md. \n","5. Place the ```Task089_Fluo-N2DH-SIM.py``` into the folder ```CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/```\n","\n","The conversion of the data to a fromat that is readable by nnU-Net will be done in the next cell.\n","\n","nnUNet_raw_data_base, nnUNet_preprocessed and RESULTS_FOLDER must also be defined in user specified previously created folders."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":634996,"status":"ok","timestamp":1659349926648,"user":{"displayName":"PABLO DELGADO RODRIGUEZ","userId":"11999848093282927146"},"user_tz":-120},"id":"hGjEXSwv9pM8","outputId":"bbcaf71d-df27-4f97-e3c5-190054db067e"},"outputs":[{"ename":"","evalue":"","output_type":"error","traceback":["\u001b[1;31mRunning cells with 'Python 3.8.10 64-bit' requires ipykernel package.\n","Run the following command to install 'ipykernel' into the Python environment. \n","Command: '/usr/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"]}],"source":["%%bash\n","cd \"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ\"\n","export nnUNet_raw_data_base=\"nnUNet_raw_data_base\"\n","export nnUNet_preprocessed=\"nnUNet_preprocessed\"\n","export RESULTS_FOLDER=\"nnUNet_trained_models\"\n","\n","python Task089_Fluo-N2DH-SIM.py  --source_train \"Fluo-N2DH-SIM+_train\" --source_test \"Fluo-N2DH-SIM+_test\""]},{"cell_type":"markdown","metadata":{"id":"2ceSppf_4nRx"},"source":["Verify the dataset integrity (update the paths)"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":638983,"status":"ok","timestamp":1659352961230,"user":{"displayName":"PABLO DELGADO RODRIGUEZ","userId":"11999848093282927146"},"user_tz":-120},"id":"ppg_v3s9-RNl","outputId":"0949ef62-9b5a-4a0f-fe2a-cbc92269e8c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","Verifying training set\n","checking case 01_t000\n","checking case 01_t001\n","checking case 01_t002\n","checking case 01_t003\n","checking case 01_t004\n","checking case 01_t005\n","checking case 01_t006\n","checking case 01_t007\n","checking case 01_t008\n","checking case 01_t009\n","checking case 01_t010\n","checking case 01_t011\n","checking case 01_t012\n","checking case 01_t013\n","checking case 01_t014\n","checking case 01_t015\n","checking case 01_t016\n","checking case 01_t017\n","checking case 01_t018\n","checking case 01_t019\n","checking case 01_t020\n","checking case 01_t021\n","checking case 01_t022\n","checking case 01_t023\n","checking case 01_t024\n","checking case 01_t025\n","checking case 01_t026\n","checking case 01_t027\n","checking case 01_t028\n","checking case 01_t029\n","checking case 01_t030\n","checking case 01_t031\n","checking case 01_t032\n","checking case 01_t033\n","checking case 01_t034\n","checking case 01_t035\n","checking case 01_t036\n","checking case 01_t037\n","checking case 01_t038\n","checking case 01_t039\n","checking case 01_t040\n","checking case 01_t041\n","checking case 01_t042\n","checking case 01_t043\n","checking case 01_t044\n","checking case 01_t045\n","checking case 01_t046\n","checking case 01_t047\n","checking case 01_t048\n","checking case 01_t049\n","checking case 01_t050\n","checking case 01_t051\n","checking case 01_t052\n","checking case 01_t053\n","checking case 01_t054\n","checking case 01_t055\n","checking case 01_t056\n","checking case 01_t057\n","checking case 01_t058\n","checking case 01_t059\n","checking case 01_t060\n","checking case 01_t061\n","checking case 01_t062\n","checking case 01_t063\n","checking case 01_t064\n","checking case 02_t000\n","checking case 02_t001\n","checking case 02_t002\n","checking case 02_t003\n","checking case 02_t004\n","checking case 02_t005\n","checking case 02_t006\n","checking case 02_t007\n","checking case 02_t008\n","checking case 02_t009\n","checking case 02_t010\n","checking case 02_t011\n","checking case 02_t012\n","checking case 02_t013\n","checking case 02_t014\n","checking case 02_t015\n","checking case 02_t016\n","checking case 02_t017\n","checking case 02_t018\n","checking case 02_t019\n","checking case 02_t020\n","checking case 02_t021\n","checking case 02_t022\n","checking case 02_t023\n","checking case 02_t024\n","checking case 02_t025\n","checking case 02_t026\n","checking case 02_t027\n","checking case 02_t028\n","checking case 02_t029\n","checking case 02_t030\n","checking case 02_t031\n","checking case 02_t032\n","checking case 02_t033\n","checking case 02_t034\n","checking case 02_t035\n","checking case 02_t036\n","checking case 02_t037\n","checking case 02_t038\n","checking case 02_t039\n","checking case 02_t040\n","checking case 02_t041\n","checking case 02_t042\n","checking case 02_t043\n","checking case 02_t044\n","checking case 02_t045\n","checking case 02_t046\n","checking case 02_t047\n","checking case 02_t048\n","checking case 02_t049\n","checking case 02_t050\n","checking case 02_t051\n","checking case 02_t052\n","checking case 02_t053\n","checking case 02_t054\n","checking case 02_t055\n","checking case 02_t056\n","checking case 02_t057\n","checking case 02_t058\n","checking case 02_t059\n","checking case 02_t060\n","checking case 02_t061\n","checking case 02_t062\n","checking case 02_t063\n","checking case 02_t064\n","checking case 02_t065\n","checking case 02_t066\n","checking case 02_t067\n","checking case 02_t068\n","checking case 02_t069\n","checking case 02_t070\n","checking case 02_t071\n","checking case 02_t072\n","checking case 02_t073\n","checking case 02_t074\n","checking case 02_t075\n","checking case 02_t076\n","checking case 02_t077\n","checking case 02_t078\n","checking case 02_t079\n","checking case 02_t080\n","checking case 02_t081\n","checking case 02_t082\n","checking case 02_t083\n","checking case 02_t084\n","checking case 02_t085\n","checking case 02_t086\n","checking case 02_t087\n","checking case 02_t088\n","checking case 02_t089\n","checking case 02_t090\n","checking case 02_t091\n","checking case 02_t092\n","checking case 02_t093\n","checking case 02_t094\n","checking case 02_t095\n","checking case 02_t096\n","checking case 02_t097\n","checking case 02_t098\n","checking case 02_t099\n","checking case 02_t100\n","checking case 02_t101\n","checking case 02_t102\n","checking case 02_t103\n","checking case 02_t104\n","checking case 02_t105\n","checking case 02_t106\n","checking case 02_t107\n","checking case 02_t108\n","checking case 02_t109\n","checking case 02_t110\n","checking case 02_t111\n","checking case 02_t112\n","checking case 02_t113\n","checking case 02_t114\n","checking case 02_t115\n","checking case 02_t116\n","checking case 02_t117\n","checking case 02_t118\n","checking case 02_t119\n","checking case 02_t120\n","checking case 02_t121\n","checking case 02_t122\n","checking case 02_t123\n","checking case 02_t124\n","checking case 02_t125\n","checking case 02_t126\n","checking case 02_t127\n","checking case 02_t128\n","checking case 02_t129\n","checking case 02_t130\n","checking case 02_t131\n","checking case 02_t132\n","checking case 02_t133\n","checking case 02_t134\n","checking case 02_t135\n","checking case 02_t136\n","checking case 02_t137\n","checking case 02_t138\n","checking case 02_t139\n","checking case 02_t140\n","checking case 02_t141\n","checking case 02_t142\n","checking case 02_t143\n","checking case 02_t144\n","checking case 02_t145\n","checking case 02_t146\n","checking case 02_t147\n","checking case 02_t148\n","checking case 02_t149\n","Verifying label values\n","Expected label values are [0, 1, 2]\n","Labels OK\n","Verifying test set\n","Dataset OK\n","01_t014\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t015\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t016\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t017\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t018\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t019\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t020\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t040\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t041\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t042\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t043\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t044\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t045\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t046\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t096\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t097\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t098\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t099\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t100\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t101\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t102\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t000\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t001\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t002\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t003\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t004\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t005\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t006\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t056\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t057\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t058\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t059\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t060\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t061\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t062\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t047\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t048\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t049\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t050\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t051\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t052\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t053\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t103\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t104\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t105\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t106\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t107\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t108\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t109\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t007\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t008\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t009\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t010\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t011\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t012\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t013\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t063\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t064\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t000\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t001\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t002\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t003\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t004\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t054\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t055\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t056\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t057\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t058\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t059\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t060\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t110\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t111\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t112\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t113\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t114\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t115\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t116\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t028\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t029\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t030\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t031\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t032\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t033\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t034\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t026\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t027\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t028\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t029\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t030\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t031\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t032\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t082\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t083\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t084\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t085\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t086\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t087\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t088\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t145\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t146\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t147\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t148\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t149\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t035\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t036\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t037\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t038\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t039\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t040\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t041\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t012\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t013\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t014\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t015\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t016\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t017\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t018\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t061\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t062\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t063\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t064\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t065\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t066\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t067\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t117\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t118\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t119\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t120\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t121\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t122\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t123\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t021\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t022\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t023\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t024\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t025\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t026\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t027\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t005\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t006\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t007\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t008\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t009\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t010\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t011\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t075\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t076\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t077\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t078\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t079\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t080\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t081\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t124\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t125\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t126\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t127\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t128\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t129\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t130\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t042\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t043\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t044\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t045\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t046\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t047\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t048\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t033\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t034\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t035\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t036\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t037\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t038\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t039\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t089\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t090\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t091\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t092\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t093\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t094\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t095\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t131\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t132\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t133\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t134\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t135\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t136\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t137\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t049\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t050\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t051\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t052\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t053\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t054\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","01_t055\n","before crop: (5, 1, 690, 628) after crop: (5, 1, 690, 628) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t019\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t020\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t021\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t022\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t023\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t024\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t025\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t068\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t069\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t070\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t071\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t072\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t073\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t074\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t138\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t139\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t140\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t141\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t142\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t143\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","02_t144\n","before crop: (5, 1, 773, 739) after crop: (5, 1, 773, 739) spacing: [9.99e+02 1.25e-01 1.25e-01] \n","\n","\n","\n","\n"," Task089_Fluo-N2DH-SIM_thickborder_time\n","number of threads:  (8, 8) \n","\n","not using nonzero mask for normalization\n","not using nonzero mask for normalization\n","not using nonzero mask for normalization\n","not using nonzero mask for normalization\n","not using nonzero mask for normalization\n","Are we using the nonzero mask for normalizaion? OrderedDict([(0, False), (1, False), (2, False), (3, False), (4, False)])\n","the median shape of the dataset is  [  1. 773. 739.]\n","the max shape in the dataset is  [  1. 773. 739.]\n","the min shape in the dataset is  [  1. 690. 628.]\n","we don't want feature maps smaller than  4  in the bottleneck\n","the transposed median shape of the dataset is  [  1. 773. 739.]\n","generating configuration for 3d_fullres\n","{0: {'batch_size': 5, 'num_pool_per_axis': [0, 7, 7], 'patch_size': array([  1, 896, 768]), 'median_patient_size_in_voxels': array([  1, 773, 739]), 'current_spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'original_spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'do_dummy_2D_data_aug': True, 'pool_op_kernel_sizes': [[1, 2, 2], [1, 2, 2], [1, 2, 2], [1, 2, 2], [1, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [1, 3, 3], [1, 3, 3], [1, 3, 3], [1, 3, 3], [1, 3, 3], [3, 3, 3]]}}\n","transpose forward [0, 1, 2]\n","transpose backward [0, 1, 2]\n","Initializing to run preprocessing\n","npz folder: /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_raw_data_base/nnUNet_cropped_data/Task089_Fluo-N2DH-SIM_thickborder_time\n","output_folder: /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t000.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t001.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t002.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t003.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t004.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t005.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t006.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t056.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t057.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t058.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t059.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t060.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t061.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/01_t062.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 9285\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t047.npz\n","no resampling necessary\n","no 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resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t130.npz\n"," \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t069.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t070.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t071.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t072.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t073.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t074.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  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1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t133.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t134.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t135.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t136.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t137.npz\n"," \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t097.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t098.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t099.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t100.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t101.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t102.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t138.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t139.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t140.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t141.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t142.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t143.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)} \n","\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_stage0/02_t144.npz\n","not using nonzero mask for normalization\n","not using nonzero mask for normalization\n","not using nonzero mask for normalization\n","not using nonzero mask for normalization\n","not using nonzero mask for normalization\n","Are we using the nonzero maks for normalizaion? OrderedDict([(0, False), (1, False), (2, False), (3, False), (4, False)])\n","the median shape of the dataset is  [  1. 773. 739.]\n","the max shape in the dataset is  [  1. 773. 739.]\n","the min shape in the dataset is  [  1. 690. 628.]\n","we don't want feature maps smaller than  4  in the bottleneck\n","the transposed median shape of the dataset is  [  1. 773. 739.]\n","[{'batch_size': 4, 'num_pool_per_axis': [7, 7], 'patch_size': array([896, 768]), 'median_patient_size_in_voxels': array([  1, 773, 739]), 'current_spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'original_spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'pool_op_kernel_sizes': [[2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'do_dummy_2D_data_aug': False}]\n","Initializing to run preprocessing\n","npz folder: /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_raw_data_base/nnUNet_cropped_data/Task089_Fluo-N2DH-SIM_thickborder_time\n","output_folder: /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t000.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t001.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t002.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t003.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t004.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t005.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t006.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t056.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t057.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t058.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t059.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t060.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t061.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t062.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 773, 739)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 773, 739)}no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t021.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t022.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t023.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t024.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t025.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t026.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t027.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t063.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is transposed)': (5, 1, 690, 628)} \n","after:  {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'data.shape (data is resampled)': (5, 1, 690, 628)} \n","\n","normalization...\n","normalization done\n","1 10000\n","2 10000\n","saving:  /content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed/Task089_Fluo-N2DH-SIM_thickborder_time/nnUNetData_plans_v2.1_2D_stage0/01_t064.npz\n","no resampling necessary\n","no resampling necessary\n","before: {'spacing': array([9.99e+02, 1.25e-01, 1.25e-01]), 'spacing_transposed': array([9.99e+02, 1.25e-01, 1.25e-01]), 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nnUNet_raw_data_base=\"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_raw_data_base\"\n","export nnUNet_preprocessed=\"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed\"\n","export RESULTS_FOLDER=\"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_trained_models\"\n","\n","nnUNet_train 2d nnUNetTrainerV2 Task089_Fluo-N2DH-SIM_thickborder_time 1"]},{"cell_type":"markdown","metadata":{"id":"R1nNm_ozzo3W"},"source":["Since this training is very time-consuming in Colab  and it is possible that the session gets closed, this is an alternative to use one of the pretrained models for inference (instead of the model that was just trained here). \n","\n","List all the available pretrained models and download one of them\n","\n","(update the paths)"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":28554,"status":"ok","timestamp":1659355616661,"user":{"displayName":"PABLO DELGADO RODRIGUEZ","userId":"11999848093282927146"},"user_tz":-120},"id":"l6hM4EJRzOGl","outputId":"32c6698a-d54c-47a3-c090-d29aeeb9a612"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","The following pretrained models are available:\n","\n","\n","Task001_BrainTumour\n","Brain Tumor Segmentation. \n","Segmentation targets are edema, enhancing tumor and necrosis, \n","Input modalities are 0: FLAIR, 1: T1, 2: T1 with contrast agent, 3: T2. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task002_Heart\n","Left Atrium Segmentation. \n","Segmentation target is the left atrium, \n","Input modalities are 0: MRI. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task003_Liver\n","Liver and Liver Tumor Segmentation. \n","Segmentation targets are liver and tumors, \n","Input modalities are 0: abdominal CT scan. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task004_Hippocampus\n","Hippocampus Segmentation. \n","Segmentation targets posterior and anterior parts of the hippocampus, \n","Input modalities are 0: MRI. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task005_Prostate\n","Prostate Segmentation. \n","Segmentation targets are peripheral and central zone, \n","Input modalities are 0: T2, 1: ADC. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task006_Lung\n","Lung Nodule Segmentation. \n","Segmentation target are lung nodules, \n","Input modalities are 0: abdominal CT scan. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task007_Pancreas\n","Pancreas Segmentation. \n","Segmentation targets are pancras and pancreas tumor, \n","Input modalities are 0: abdominal CT scan. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task008_HepaticVessel\n","Hepatic Vessel Segmentation. \n","Segmentation targets are hepatic vesels and liver tumors, \n","Input modalities are 0: abdominal CT scan. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task009_Spleen\n","Spleen Segmentation. \n","Segmentation target is the spleen, \n","Input modalities are 0: abdominal CT scan. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task010_Colon\n","Colon Cancer Segmentation. \n","Segmentation target are colon caner primaries, \n","Input modalities are 0: CT scan. \n","Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/\n","\n","Task017_AbdominalOrganSegmentation\n","Multi-Atlas Labeling Beyond the Cranial Vault - Abdomen. \n","Segmentation targets are thirteen different abdominal organs, \n","Input modalities are 0: abdominal CT scan. \n","Also see https://www.synapse.org/#!Synapse:syn3193805/wiki/217754\n","\n","Task024_Promise\n","Prostate MR Image Segmentation 2012. \n","Segmentation target is the prostate, \n","Input modalities are 0: T2. \n","Also see https://promise12.grand-challenge.org/\n","\n","Task027_ACDC\n","Automatic Cardiac Diagnosis Challenge. \n","Segmentation targets are right ventricle, left ventricular cavity and left myocardium, \n","Input modalities are 0: cine MRI. \n","Also see https://acdc.creatis.insa-lyon.fr/\n","\n","Task029_LiTS\n","Liver and Liver Tumor Segmentation Challenge. \n","Segmentation targets are liver and liver tumors, \n","Input modalities are 0: abdominal CT scan. \n","Also see https://competitions.codalab.org/competitions/17094\n","\n","Task035_ISBILesionSegmentation\n","Longitudinal multiple sclerosis lesion segmentation Challenge. \n","Segmentation target is MS lesions, \n","input modalities are 0: FLAIR, 1: MPRAGE, 2: proton density, 3: T2. \n","Also see https://smart-stats-tools.org/lesion-challenge\n","\n","Task038_CHAOS_Task_3_5_Variant2\n","CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge (Task 3 & 5). \n","Segmentation targets are left and right kidney, liver, spleen, \n","Input modalities are 0: T1 in-phase, T1 out-phase, T2 (can be any of those)\n","Also see https://chaos.grand-challenge.org/\n","\n","Task048_KiTS_clean\n","Kidney and Kidney Tumor Segmentation Challenge. Segmentation targets kidney and kidney tumors, Input modalities are 0: abdominal CT scan. Also see https://kits19.grand-challenge.org/\n","\n","Task055_SegTHOR\n","SegTHOR: Segmentation of THoracic Organs at Risk in CT images. \n","Segmentation targets are aorta, esophagus, heart and trachea, \n","Input modalities are 0: CT scan. \n","Also see https://competitions.codalab.org/competitions/21145\n","\n","Task061_CREMI\n","MICCAI Challenge on Circuit Reconstruction from Electron Microscopy Images (Synaptic Cleft segmentation task). \n","Segmentation target is synaptic clefts, \n","Input modalities are 0: serial section transmission electron microscopy of neural tissue. \n","Also see https://cremi.org/\n","\n","Task075_Fluo_C3DH_A549_ManAndSim\n","Fluo-C3DH-A549-SIM and Fluo-C3DH-A549 datasets of the cell tracking challenge. Segmentation target are C3DH cells in fluorescence microscopy images.\n","Input modalities are 0: fluorescence_microscopy\n","Also see http://celltrackingchallenge.net/\n","\n","Task076_Fluo_N3DH_SIM\n","Fluo-N3DH-SIM dataset of the cell tracking challenge. Segmentation target are N3DH cells and cell borders in fluorescence microscopy images.\n","Input modalities are 0: fluorescence_microscopy\n","Also see http://celltrackingchallenge.net/\n","Note that the segmentation output of the models are cell center and cell border. These outputs mus tbe converted to an instance segmentation for the challenge. \n","See https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/dataset_conversion/Task076_Fluo_N3DH_SIM.py\n","\n","Task082_BraTS2020\n","Brain tumor segmentation challenge 2020 (BraTS)\n","Segmentation targets are 0: background, 1: edema, 2: enhancing tumor, 3: necrosis\n","Input modalities are 0: T1, 1: T1ce, 2: T2, 3: FLAIR (MRI images)\n","Also see https://www.med.upenn.edu/cbica/brats2020/\n","\n","Task089_Fluo-N2DH-SIM_thickborder_time\n","Fluo-N2DH-SIM dataset of the cell tracking challenge. Segmentation target are nuclei of N2DH cells and cell borders in fluorescence microscopy images.\n","Input modalities are 0: t minus 4, 0: t minus 3, 0: t minus 2, 0: t minus 1, 0: frame of interest\n","Note that the input channels are different time steps from a time series acquisition\n","Note that the segmentation output of the models are cell center and cell border. These outputs mus tbe converted to an instance segmentation for the challenge. \n","See https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/dataset_conversion/Task089_Fluo-N2DH-SIM.py\n","Also see http://celltrackingchallenge.net/\n","\n","Task114_heart_MNMs\n","Cardiac MRI short axis images from the M&Ms challenge 2020.\n","Input modalities are 0: MRI \n","See also https://www.ub.edu/mnms/ \n","Note: Labels of the M&Ms Challenge are not in the same order as for the ACDC challenge. \n","See https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/dataset_conversion/Task114_heart_mnms.py\n","\n","Task115_COVIDSegChallenge\n","Covid lesion segmentation in CT images. Data originates from COVID-19-20 challenge.\n","Predicted labels are 0: background, 1: covid lesion\n","Input modalities are 0: CT \n","See also https://covid-segmentation.grand-challenge.org/\n","\n","Task135_KiTS2021\n","Kidney and kidney tumor segmentation in CT images. Data originates from KiTS2021 challenge.\n","Predicted labels are 0: background, 1: kidney, 2: tumor, 3: cyst \n","Input modalities are 0: CT \n","See also https://kits21.kits-challenge.org/\n","\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","\n","######################################################\n","!!!!!!!!!!!!!!!!!!!!!!!!WARNING!!!!!!!!!!!!!!!!!!!!!!!\n","######################################################\n","Using the pretrained model weights is subject to the license of the dataset they were trained on. Some allow commercial use, others don't. It is your responsibility to make sure you use them appropriately! Use nnUNet_print_pretrained_model_info(task_name) to see a summary of the dataset and where to find its license!\n","######################################################\n","\n","Downloading pretrained model from url: https://zenodo.org/record/4003545/files/Task089_Fluo-N2DH-SIM_thickborder_time.zip?download=1\n","Download finished. Extracting...\n","Done\n","\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n","Fluo-N2DH-SIM dataset of the cell tracking challenge. Segmentation target are nuclei of N2DH cells and cell borders in fluorescence microscopy images.\n","Input modalities are 0: t minus 4, 0: t minus 3, 0: t minus 2, 0: t minus 1, 0: frame of interest\n","Note that the input channels are different time steps from a time series acquisition\n","Note that the segmentation output of the models are cell center and cell border. These outputs mus tbe converted to an instance segmentation for the challenge. \n","See https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/dataset_conversion/Task089_Fluo-N2DH-SIM.py\n","Also see http://celltrackingchallenge.net/\n"]}],"source":["%%bash\n","export nnUNet_raw_data_base=\"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_raw_data_base\"\n","export nnUNet_preprocessed=\"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed\"\n","export RESULTS_FOLDER=\"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_trained_models\"\n","\n","nnUNet_print_available_pretrained_models\n","nnUNet_download_pretrained_model Task089_Fluo-N2DH-SIM_thickborder_time\n","nnUNet_print_pretrained_model_info Task089_Fluo-N2DH-SIM_thickborder_time"]},{"cell_type":"markdown","metadata":{"id":"Rpp2CQqkpHvQ"},"source":["Predict with the chosen model (update the paths)"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9560,"status":"ok","timestamp":1659365058041,"user":{"displayName":"PABLO DELGADO RODRIGUEZ","userId":"11999848093282927146"},"user_tz":-120},"id":"Ht-QXttsC3tA","outputId":"63d7a418-7739-4e2c-ad6d-2b192625b678"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n","Please cite the following paper when using nnUNet:\n","\n","Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n","\n","\n","If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n","\n"]},{"name":"stderr","output_type":"stream","text":["usage: nnUNet_predict [-h] -i INPUT_FOLDER -o OUTPUT_FOLDER -t TASK_NAME\n","                      [-tr TRAINER_CLASS_NAME]\n","                      [-ctr CASCADE_TRAINER_CLASS_NAME] [-m MODEL]\n","                      [-p PLANS_IDENTIFIER] [-f FOLDS [FOLDS ...]] [-z]\n","                      [-l LOWRES_SEGMENTATIONS] [--part_id PART_ID]\n","                      [--num_parts NUM_PARTS]\n","                      [--num_threads_preprocessing NUM_THREADS_PREPROCESSING]\n","                      [--num_threads_nifti_save NUM_THREADS_NIFTI_SAVE]\n","                      [--disable_tta] [--overwrite_existing] [--mode MODE]\n","                      [--all_in_gpu ALL_IN_GPU] [--step_size STEP_SIZE]\n","                      [-chk CHK] [--disable_mixed_precision]\n","nnUNet_predict: error: the following arguments are required: -t/--task_name\n"]}],"source":["%%bash\n","export nnUNet_raw_data_base=\"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_raw_data_base\"\n","export nnUNet_preprocessed=\"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_preprocessed\"\n","export RESULTS_FOLDER=\"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/nnUNet_trained_models\"\n","\n","nnUNet_predict -i $nnUNet_raw_data_base/nnUNet_raw_data/Task089_Fluo-N2DH-SIM_thickborder_time/imagesTs/ -o \"/content/drive/MyDrive/CTC_trainable_solutions/Data_Pablo/MIC-DKFZ/output_2D\" -t 89 --save_npz -m 2d -f all\n"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyPVySCYTaQDTB+X8AJpjDTk","collapsed_sections":[],"mount_file_id":"1yN6b_Oe2Niafn6SAQaKlcM14zXZiuOeS","name":"MIC-DKFZ.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3.8.10 64-bit","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.8.10"},"vscode":{"interpreter":{"hash":"31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"}}},"nbformat":4,"nbformat_minor":0}