update readme

replace pymic_run with pymic_train or pymic_test

Author: taigw

classification/AntBee/README.md

@@ -26,7 +26,7 @@ update_mode = all
 Then start to train by running:
 
 ```bash
-pymic_run train config/train_test_ce1.cfg
+pymic_train config/train_test_ce1.cfg
 ```
 
 2. During training or after training, run `tensorboard --logdir model/resnet18_ce1` and you will see a link in the output, such as `http://your-computer:6006`. Open the link in the browser and you can observe the average loss and accuracy during the training stage, such as shown in the following images, where blue and red curves are for training set and validation set respectively. The iteration number obtained the highest accuracy on the validation set was 400, and may be different based on the hardware environment. After training, you can find the trained models in `./model/resnet18_ce1`. 
@@ -39,7 +39,7 @@ pymic_run train config/train_test_ce1.cfg
 
 ```bash
 mkdir result
-pymic_run test config/train_test_ce1.cfg
+pymic_test config/train_test_ce1.cfg
 ```
 
 2. Then run the following command to obtain quantitative evaluation results in terms of accuracy. 

classification/AntBee/config/train_test_ce1.cfg

@@ -77,5 +77,6 @@ gpus        = [0]
 
 # checkpoint mode can be [0-latest, 1-best, 2-specified]
 ckpt_mode        = 1
-output_csv       = result/resnet18_ce1.csv
+output_dir       = result
+output_csv       = resnet18_ce1.csv
 save_probability = True

classification/AntBee/config/train_test_ce2.cfg

@@ -77,5 +77,6 @@ gpus        = [0]
 
 # checkpoint mode can be [0-latest, 1-best, 2-specified]
 ckpt_mode        = 1
-output_csv       = result/resnet18_ce2.csv
+output_dir       = result
+output_csv       = restnet18_ce2.csv    
 save_probability = True

classification/CHNCXR/README.md

@@ -26,7 +26,7 @@ update_mode = all
 Start to train by running:
 
 ```bash
-pymic_run train config/net_resnet18.cfg
+pymic_train config/net_resnet18.cfg
 ```
 
 2. During training or after training, run `tensorboard --logdir model/resnet18` and you will see a link in the output, such as `http://your-computer:6006`. Open the link in the browser and you can observe the average loss and accuracy during the training stage, such as shown in the following images, where blue and red curves are for training set and validation set respectively. The iteration number obtained the highest accuracy on the validation set was 1800, and may be different based on the hardware environment. After training, you can find the trained models in `./model/resnet18`. 
@@ -39,7 +39,7 @@ pymic_run train config/net_resnet18.cfg
 
 ```bash
 mkdir result
-pymic_run test config/net_resnet18.cfg
+pymic_test config/net_resnet18.cfg
 ```
 
 2. Then run the following command to obtain quantitative evaluation results in terms of accuracy. 

classification/CHNCXR/config/net_resnet18.cfg

@@ -75,5 +75,6 @@ gpus        = [0]
 
 # checkpoint mode can be [0-latest, 1-best, 2-specified]
 ckpt_mode        = 1
-output_csv       = result/resnet18.csv
+output_dir       = result
+output_csv       = resnet18.csv
 save_probability = True

classification/CHNCXR/config/net_vgg16.cfg

@@ -75,5 +75,6 @@ gpus        = [0]
 
 # checkpoint mode can be [0-latest, 1-best, 2-specified]
 ckpt_mode        = 1
-output_csv       = result/vgg16.csv
+output_dir       = result
+output_csv       = vgg16.csv
 save_probability = True

segmentation/JSRT/README.md

@@ -16,7 +16,7 @@ In this example, we use 2D U-Net to segment the lung from X-Ray images. First we
 1. Start to train by running:
 
 ```bash
-pymic_run  train config/unet.cfg
+pymic_train config/unet.cfg
 ```
 
 2. During training or after training, run `tensorboard --logdir model/unet` and you will see a link in the output, such as `http://your-computer:6006`. Open the link in the browser and you can observe the average Dice score and loss during the training stage, such as shown in the following images, where red and blue curves are for training set and validation set respectively. We can observe some over-fitting on the training set. 
@@ -28,7 +28,7 @@ pymic_run  train config/unet.cfg
 1. Run the following command to obtain segmentation results of testing images. By default we use the latest checkpoint. You can set `ckpt_mode` to 1 in `config/unet.cfg` to use the best performing checkpoint based on the validation set.
 
 ```bash
-pymic_run test config/unet.cfg
+pymic_test config/unet.cfg
 ```
 
 2. Then edit `config/evaluation.cfg` by setting `ground_truth_folder_root` as your `JSRT_root`, and run the following command to obtain quantitative evaluation results in terms of dice. 

segmentation/fetal_hc/README.md

@@ -15,7 +15,7 @@ In this example, we use 2D U-Net to segment the fetal brain from ultrasound imag
 1. Start to train by running:
 
 ```bash
-pymic_run train config/unet.cfg
+pymic_train config/unet.cfg
 ```
 
 2. During training or after training, run `tensorboard --logdir model/unet` and you will see a link in the output, such as `http://your-computer:6006`. Open the link in the browser and you can observe the average Dice score and loss during the training stage, such as shown in the following images, where red and blue curves are for training set and validation set respectively. 
@@ -27,7 +27,7 @@ pymic_run train config/unet.cfg
 1. Run the following command to obtain segmentation results of testing images based on the best-performing checkpoint on the validation set. By default we use sliding window inference to get better results. You can also edit the `testing` section of `config/unet.cfg` to use other inference strategies.
 
 ```bash
-pymic_run test config/unet.cfg
+pymic_test config/unet.cfg
 ```
 
 2. Use the following command to obtain quantitative evaluation results in terms of Dice. 

segmentation/prostate/README.md

@@ -13,7 +13,7 @@ In this example, we use 3D U-Net with deep supervision to segment the prostate f
 1. Start to train by running:
 
 ```bash
-pymic_run train config/unet3d.cfg
+pymic_train config/unet3d.cfg
 ```
 
 Note that we set `multiscale_pred = True`, `deep_supervise = True` and `loss_type = [DiceLoss, CrossEntropyLoss]` in the configure file. We also use Mixup for data
@@ -28,7 +28,7 @@ augmentation by setting `mixup_probability=0.5`.
 1. Run the following command to obtain segmentation results of testing images. By default we set `ckpt_mode` to 1, which means using the best performing checkpoint based on the validation set.
 
 ```bash
-pymic_run test config/unet3d.cfg
+pymic_test config/unet3d.cfg
 ```
 
 2. Run the following command to obtain quantitative evaluation results in terms of Dice.