Sami's Developpement

bash/deploy_win64.sh

@@ -0,0 +1,7 @@
+
+# create a venv :
+python -m venv NAME_OF_YOUR_VIRTUAL_ENV
+# then install :
+pip install pyinstaller python-tk numpy SimpleITK scikit-image tqdm paramiko pyyaml matplotlib 
+# run the pyinstaller (with console) and exclude modules
+pyinstaller -F --clean --upx-dir PATH_TO_UPX   PATH_TO_GUI\gui.py --exclude=biom3d --exclude=torch --hidden-import='PIL._tkinter_finder'

bash/run_eval.sh

@@ -11,16 +11,21 @@
 #  --dir_lab data/msd/Task07_Pancreas/labelsTr_test\
 #  --num_classes 2
 
-# python -m biom3d.eval\
-#  --dir_pred data/btcv/Testing_small/preds/20230522-182916-unet_default\
-#  --dir_lab data/btcv/Testing_small/label\
-#  --num_classes 13
-
 python -m biom3d.eval\
- --dir_pred data/nucleus/official/test/preds/20230908-202124-nucleus_official_fold4\
- --dir_lab data/nucleus/official/test/msk\
+ --dir_pred data/msd/Task06_Lung/preds/20230531-092023-unet_lung\
+ --dir_lab data/msd/Task06_Lung/labelsTr_test\
  --num_classes 1
 
+# python -m biom3d.eval\
+#  --dir_pred data/msd/Task07_Pancreas/preds/20230523-105736-unet_default\
+#  --dir_lab data/msd/Task07_Pancreas/labelsTr_test\
+#  --num_classes 2
+
+# python -m biom3d.eval\
+#  --dir_pred data/nucleus/official/test/preds/20230908-202124-nucleus_official_fold4\
+#  --dir_lab data/nucleus/official/test/msk\
+#  --num_classes 1
+
 # python -m biom3d.eval\
 #  --dir_pred data/mito/test/pred/20230203-091249-unet_mito\
 #  --dir_lab data/mito/test/msk\

bash/run_pred.sh

@@ -112,18 +112,25 @@
 #  --dir_in "data/nucleus/aline_bug/img"\
 #  --dir_out "data/nucleus/aline_bug/preds"
 
+# python -m biom3d.pred\
+#     --name seg\
+#     --log logs/20240219-100225-reims_full_fold0\
+#     --dir_in data/reims/big_stack/img\
+#     --dir_out data/reims/big_stack/preds\
+
 python -m biom3d.pred\
-    --name seg\
-    --log logs/20240219-100225-reims_full_fold0\
-    --dir_in data/reims/big_stack/img\
-    --dir_out data/reims/big_stack/preds\
+    --name seg_eval\
+    --log logs/20240319-094430-reims_large_full_no47_fold0\
+    --dir_in data/reims/large/test/img\
+    --dir_out data/reims/large/test/preds\
+    --dir_lab data/reims/large/test/msk
 
 # python -m biom3d.pred\
 #     --name seg_eval\
-#     --log logs/20240218-072550-reims_fold0\
-#     --dir_in data/reims/test_match/img\
-#     --dir_out data/reims/test_match/preds\
-#     --dir_lab data/reims/test_match/msk
+#     --log logs/20230501-153638-unet_default\
+#     --dir_in data/mito/test/img\
+#     --dir_out data/mito/test/pred\
+#     --dir_lab data/mito/test/msk
 
 # python -m biom3d.pred\
 #     --name seg_single\

docs/biom3d_colab.ipynb

@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"P8jnH3QZH9L4"},"outputs":[],"source":["#@markdown ## First check that your Runtime is in GPU mode (you can run this cell to do so)\n","\n","#@markdown If not, go to `Runtime` > `Change runtime type` > `Hardware accelerator` > `GPU`\n","\n","!nvidia-smi"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"NhwL5qt9fdg5"},"outputs":[],"source":["#@markdown ##Install Biom3d and import the necessary Python library\n","!pip3 install biom3d==0.0.31\n","\n","import os\n","import biom3d\n","from biom3d.train import train\n","from biom3d.pred import pred\n","from biom3d.eval import eval"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"Relk7WNAkTzM"},"outputs":[],"source":["#@markdown ##Mount your Google drive\n","\n","from google.colab import drive\n","drive.mount('/content/gdrive')"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"3RPq3yPxlNmj"},"outputs":[],"source":["#@markdown ## Define input and output folders\n","\n","#@markdown **Where training data will be saved?** This will include two folders: the config folder (containing configuration files) and the logs folder (containing the model folders)\n","\n","biom3d_dir = '/content/gdrive/MyDrive/biom3d'#@param {type:\"string\"}\n","model_name = \"unet_default\" #@param {type:\"string\"}\n","\n","#@markdown **Where are your images and masks?**\n","img_dir = \"\" #@param {type:\"string\"}\n","msk_dir = \"\" #@param {type:\"string\"}\n","\n","#@markdown **How many classes are there in your annotations?**\n","\n","num_classes = 1 #@param {type:\"number\"}\n","\n","#@markdown **How long would you like to train your model?**\n","num_epochs = 2 #@param {type:\"number\"}"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"EkLs_MoFtnLv"},"outputs":[],"source":["#@markdown ## Preprocess your data before training\n","config_path = biom3d.preprocess.auto_config_preprocess(\n","    img_dir=img_dir,\n","    msk_dir=msk_dir,\n","    num_classes=num_classes,\n","    desc=model_name,\n","    config_dir=os.path.join(biom3d_dir, \"configs/\"),\n","    base_config=None,\n","    ct_norm=False,\n","    num_epochs=num_epochs,\n","    logs_dir=os.path.join(biom3d_dir, \"logs/\"),\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"fn639Efzrfwe"},"outputs":[],"source":["#@markdown ## Start the training\n","\n","#@markdown If you want to use of different configuration file or use an existing one, complete the following field. **Leave this field empty if you want to use the config file obtained during the preprocessing.**\n","\n","custom_config_path = \"\" #@param {type:\"string\"}\n","\n","if len(custom_config_path) > 0:\n","  config_path = custom_config_path\n","\n","print(\"We will the following configuration file for training:\", config_path)\n","builder = train(config=config_path)"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"4eKiLPQnsokH"},"outputs":[],"source":["#@markdown ## Make predictions with your trained model\n","\n","#@markdown **Path to the model folder: leave it empty if you want to use the model obtained during the previous training step.** This should look like `/content/gdrive/MyDrive/biom3d/logs/20230602-162331-unet_default_fold0`.\n","\n","custom_log_path = \"\" #@param {type:\"string\"}\n","\n","if len(custom_log_path) > 0:\n","  log=custom_log_path\n","else:\n","  assert 'builder' in locals().keys(), \"No existing model folder found. Please complete the `custom_log_path` field or train a model.\"\n","  log = builder.base_dir\n","\n","#@markdown **Prediction input directory:**\n","pred_dir_in = \"\" #@param {type:\"string\"}\n","\n","#@markdown **Prediction output directory:** (where the prediction masks will be stored)\n","pred_dir_out = \"\"#@param {type:\"string\"}\n","\n","dir_out = pred(\n","    log=log,\n","    dir_in=pred_dir_in,\n","    dir_out=pred_dir_out\n","    )\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"8l8FPDb_13iI"},"outputs":[],"source":["#@markdown ## Evaluate your model on a test set\n","\n","#@markdown **Prediction output directory:** where the previous prediction masks have been stored and the number of classes in your images. Leave the default values if you want to use the previous prediction path.\n","new_pred_dir_out = \"\"#@param {type:\"string\"}\n","new_num_classes = 0 #@param {type:\"number\"}\n","\n","if new_num_classes == 0:\n","  assert 'num_classes' in locals().keys(), \"Number of classes equal to zero and the previous number of classes does not exist. Please provide one.\"\n","  new_num_classes = num_classes\n","\n","if len(new_pred_dir_out) == 0:\n","  assert len(dir_out) > 0, \"Prediction path seems to be empty and no previous path detected.\"\n","  new_pred_dir_out = dir_out\n","\n","#@markdown **Path to test masks** The test masks must correspond to the predictions.\n","test_msk_dir = \"\" #@param {type:\"string\"}\n","\n","eval(\n","    dir_lab=test_msk_dir,\n","    dir_out=new_pred_dir_out,\n","    num_classes=new_num_classes\n",")"]}],"metadata":{"accelerator":"GPU","colab":{"authorship_tag":"ABX9TyPrfgBkQIScxLMN00pfvqBQ","gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"P8jnH3QZH9L4"},"outputs":[],"source":["#@markdown ## First check that your Runtime is in GPU mode (you can run this cell to do so)\n","\n","#@markdown If not, go to `Runtime` > `Change runtime type` > `Hardware accelerator` > `GPU`\n","\n","!nvidia-smi"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"NhwL5qt9fdg5"},"outputs":[],"source":["#@markdown ##Install Biom3d and import the necessary Python library\n","!pip3 install biom3d==0.0.40 torchio deprecated --no-deps\n","!pip3 install SimpleITK paramiko netcat appdirs\n","\n","import os\n","import biom3d\n","from biom3d.train import train\n","from biom3d.pred import pred\n","from biom3d.eval import eval"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"Relk7WNAkTzM"},"outputs":[],"source":["#@markdown ##Mount your Google drive\n","\n","from google.colab import drive\n","drive.mount('/content/gdrive')"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"3RPq3yPxlNmj"},"outputs":[],"source":["#@markdown ## Define input and output folders\n","\n","#@markdown **Where training data will be saved?** This will include two folders: the config folder (containing configuration files) and the logs folder (containing the model folders)\n","\n","biom3d_dir = '/content/gdrive/MyDrive/biom3d'#@param {type:\"string\"}\n","model_name = \"unet_default\" #@param {type:\"string\"}\n","\n","#@markdown **Where are your images and masks?**\n","img_dir = \"\" #@param {type:\"string\"}\n","msk_dir = \"\" #@param {type:\"string\"}\n","\n","#@markdown **How many classes are there in your annotations?**\n","\n","num_classes = 1 #@param {type:\"number\"}\n","\n","#@markdown **How long would you like to train your model?**\n","num_epochs = 2 #@param {type:\"number\"}"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"EkLs_MoFtnLv"},"outputs":[],"source":["#@markdown ## Preprocess your data before training\n","config_path = biom3d.preprocess.auto_config_preprocess(\n","    img_dir=img_dir,\n","    msk_dir=msk_dir,\n","    num_classes=num_classes,\n","    desc=model_name,\n","    config_dir=os.path.join(biom3d_dir, \"configs/\"),\n","    base_config=None,\n","    ct_norm=False,\n","    num_epochs=num_epochs,\n","    logs_dir=os.path.join(biom3d_dir, \"logs/\"),\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"fn639Efzrfwe"},"outputs":[],"source":["#@markdown ## Start the training\n","\n","#@markdown If you want to use of different configuration file or use an existing one, complete the following field. **Leave this field empty if you want to use the config file obtained during the preprocessing.**\n","\n","custom_config_path = \"\" #@param {type:\"string\"}\n","\n","if len(custom_config_path) > 0:\n","  config_path = custom_config_path\n","\n","print(\"We will the following configuration file for training:\", config_path)\n","builder = train(config=config_path)"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"4eKiLPQnsokH"},"outputs":[],"source":["#@markdown ## Make predictions with your trained model\n","\n","#@markdown **Path to the model folder: leave it empty if you want to use the model obtained during the previous training step.** This should look like `/content/gdrive/MyDrive/biom3d/logs/20230602-162331-unet_default_fold0`.\n","\n","custom_log_path = \"\" #@param {type:\"string\"}\n","\n","if len(custom_log_path) > 0:\n","  log=custom_log_path\n","else:\n","  assert 'builder' in locals().keys(), \"No existing model folder found. Please complete the `custom_log_path` field or train a model.\"\n","  log = builder.base_dir\n","\n","#@markdown **Prediction input directory:**\n","pred_dir_in = \"\" #@param {type:\"string\"}\n","\n","#@markdown **Prediction output directory:** (where the prediction masks will be stored)\n","pred_dir_out = \"\"#@param {type:\"string\"}\n","\n","dir_out = pred(\n","    log=log,\n","    dir_in=pred_dir_in,\n","    dir_out=pred_dir_out\n","    )\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"8l8FPDb_13iI"},"outputs":[],"source":["#@markdown ## Evaluate your model on a test set\n","\n","#@markdown **Prediction output directory:** where the previous prediction masks have been stored and the number of classes in your images. Leave the default values if you want to use the previous prediction path.\n","new_pred_dir_out = \"\"#@param {type:\"string\"}\n","new_num_classes = 0 #@param {type:\"number\"}\n","\n","if new_num_classes == 0:\n","  assert 'num_classes' in locals().keys(), \"Number of classes equal to zero and the previous number of classes does not exist. Please provide one.\"\n","  new_num_classes = num_classes\n","\n","if len(new_pred_dir_out) == 0:\n","  assert len(dir_out) > 0, \"Prediction path seems to be empty and no previous path detected.\"\n","  new_pred_dir_out = dir_out\n","\n","#@markdown **Path to test masks** The test masks must correspond to the predictions.\n","test_msk_dir = \"\" #@param {type:\"string\"}\n","\n","eval(\n","    dir_lab=test_msk_dir,\n","    dir_out=new_pred_dir_out,\n","    num_classes=new_num_classes\n",")"]}],"metadata":{"accelerator":"GPU","colab":{"authorship_tag":"ABX9TyPrfgBkQIScxLMN00pfvqBQ","gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

docs/quick_run_gui.md

@@ -18,7 +18,11 @@ Biom3d comes with 2 modes: local or remote. 'Local' means that the computation w
 
 If you have installed biom3d with the local version simply click on the 'Start locally' button to start, you can choose a path to store your files in the field over the button, by default, the files are stored in the directory where biom3d have been launched.
 
+<<<<<<< HEAD
+If you have installed biom3d with the remote version, you must then complete the required fields. The first one is the IP address of your remote computer (where the API of biom3d is installed). The second and third one is your user name and password to connect to the remote computer, the forth one is the path to your virtual environment (if you don't have a virtual environment leave it empty ).
+=======
 If you have installed biom3d with the remote version, you must then complete the required fields. The first one is the IP address of your remote computer (where the API of biom3d is installed). The second and third one is your user name and password to connect to the remote computer, the forth one is the path to your virtual environment (if you don't have a virtual environment leave it empty).
+>>>>>>> f562750641a74b960ed698e427ed811313552d0e
 
 
 ## Preprocess & Train

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "biom3d"
-version = "0.0.37"
+version = "0.0.40"
 authors = [
   {name="Guillaume Mougeot", email="guillaume.mougeot@laposte.net"},
 ]
@@ -17,7 +17,7 @@ classifiers = [
     "Operating System :: OS Independent",
 ]
 dependencies = [
-    "torch>1.10.0",
+    "torch",
     "tqdm>=4.62.3",
     "scikit-image>=0.14",
     "scipy>=1.9.1",