Fix documentation for CLI changes

README.md

@@ -66,11 +66,20 @@ We can test each opereations of drore_run.sh by using shell script.
 - `expect` >= version 5.45
 
 ```
-$ ./blueoil_test.sh
+$ make test
+```
 
-Usage: ./blueoil_test.sh <YML_CONFIG_FILE(optional)>
+You can test specific task.
 
-Arguments:
-  YML_CONFIG_FILE       config file path for this test (optional)
 ```
+$ CUDA_VISIBLE_DEVICES={YOUR_GPU_ID} make test-classification
+$ CUDA_VISIBLE_DEVICES={YOUR_GPU_ID} make test-object-detection
+$ CUDA_VISIBLE_DEVICES={YOUR_GPU_ID} make test-semantic-segmentation
+```
+
+You can also test the modules used in Blueoil.
 
+```
+$ make test-lmnet
+$ make test-dlk
+```

docs/tutorial/image_cls.md

@@ -48,9 +48,9 @@ The CIFAR-10 dataset consists of 60,000 32x32 color images split into 10 classe
 
 ## Generate a configuration file
 
-Generate your model configuration file interactively by running the `blueoil init` command.
+Generate your model configuration file interactively by running the `python blueoil/cmd/main.py init` command.
 
-    $ ./blueoil.sh init
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py init
 
 Below is an example configuration.
 
@@ -79,19 +79,23 @@ Below is an example configuration.
 - Image size: 32x32
 - Number of epoch: (Any number)
 
-If configuration finishes, the configuration file is generated in the `{Model name}.yml` under `./config` directory.
+If configuration finishes, the configuration file is generated in the `{Model name}.yml` under current directory.
+
+When you want to create config yaml in specific filename or directory, you can use `-o` option.
+
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py init -o ./configs/my_config.yml
 
 ## Train a neural network
 
-Train your model by running `blueoil train` with model configuration.
+Train your model by running `python blueoil/cmd/main.py train` with model configuration.
 
-    $ ./blueoil.sh train config/{Model name}.yml
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py train -c {PATH_TO_CONFIG.yml}
 
 When training has started, the training log and checkpoints are generated under `./saved/{Mode name}_{TIMESTAMP}`.
 
 Training is running on TensorFlow backend. So you can use TensorBoard to visualize your training process.
 
-    $ ./blueoil.sh tensorboard saved/{Model name}_{TIMESTAMP} {Port}
+    $ tensorboard --logdir=saved/{Model name}_{TIMESTAMP} --port {Port}
 
 - Loss / Cross Entropy, Loss, Weight Decay
 <img src="../_static/train_loss.png">
@@ -105,9 +109,9 @@ Training is running on TensorFlow backend. So you can use TensorBoard to visuali
 Convert trained model to executable binary files for x86, ARM, and FPGA.
 Currently, conversion for FPGA only supports Intel Cyclone® V SoC FPGA.
 
-    $ ./blueoil.sh convert config/[Model name].yml saved/{Mode name}_{TIMESTAMP}
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py convert -e {Model name}
 
-`Blueoil convert` automatically executes some conversion processes.
+`python blueoil/cmd/main.py convert` automatically executes some conversion processes.
 - Converts Tensorflow checkpoint to protocol buffer graph.
 - Optimizes graph.
 - Generates source code for executable binary.

docs/tutorial/image_det.md

@@ -27,9 +27,9 @@ This dataset consists of 2866 Human Face images and 5170 annotation boxes.
 
 ## Generate a configuration file
 
-Generate your model configuration file interactively by running `blueoil.sh init`.
+Generate your model configuration file interactively by running the `python blueoil/cmd/main.py init` command.
 
-    $ ./blueoil.sh init
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py init
 
 Below is an example of initialization.
 
@@ -52,17 +52,23 @@ Please choose augmentors:  done (5 selections)
 apply quantization at the first layer?  no
 ```
 
+If configuration finishes, the configuration file is generated in the `{Model name}.yml` under current directory.
+
+When you wnat to create config yaml in specific filename or directory, you can use `-o` option.
+
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py init -o ./configs/my_config.yml
+
 ## Train a network model
 
-Train your model by running `blueoil.sh train` with a model configuration.
+Train your model by running `python blueoil/cmd/main.py train` with a model configuration.
 
-    $ ./blueoil.sh train config/{Model name}.yml
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py train -c {PATH_TO_CONFIG.yml}
 
 When training has started, the training log and checkpoints are generated under `./saved/{Mode name}_{TIMESTAMP}`.
 
 Training runs on the TensorFlow backend. So you can use TensorBoard to visualize your training process.
 
-    $ ./blueoil.sh tensorboard saved/{Model name}_{TIMESTAMP} {Port}
+    $ tensorboard --logdir=saved/{Model name}_{TIMESTAMP} --port {Port}
 
 - Metrics / Accuracy
 <img src="../_static/object_detection_train_metrics.png">
@@ -79,9 +85,9 @@ Training runs on the TensorFlow backend. So you can use TensorBoard to visualize
 Convert trained model to executable binary files for x86, ARM, and FPGA.
 Currently, conversion for FPGA only supports Intel Cyclone® V SoC FPGA.
 
-    $ ./blueoil.sh convert config/{Model name}.yml saved/{Mode name}_{TIMESTAMP}
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py convert -e {Model name}
 
-`blueoil.sh convert` automatically executes some conversion processes.
+`python blueoil/cmd/main.py convert` automatically executes some conversion processes.
 - Converts Tensorflow checkpoint to protocol buffer graph.
 - Optimizes graph.
 - Generates source code for executable binary.

docs/tutorial/image_seg.md

@@ -54,9 +54,9 @@ CamVid dataset consists of 360x480 color images in 12 classes. There are 367 tr
 
 ## Generate a configuration file
 
-Generate your model configuration file interactively by running `blueoil.sh init` command.
+Generate your model configuration file interactively by running the `python blueoil/cmd/main.py init` command.
 
-    $ ./blueoil.sh init
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py init
 
 This is an example of the initialization procedure.
 
@@ -81,17 +81,23 @@ Please choose augmentors:  done (5 selections)
 apply quantization at the first layer?  no
 ```
 
+If configuration finishes, the configuration file is generated in the `{Model name}.yml` under current directory.
+
+When you wnat to create config yaml in specific filename or directory, you can use `-o` option.
+
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py init -o ./configs/my_config.yml
+
 ## Train a network model
 
-Train your model by running `blueoil.sh train` command with model configuration.
+Train your model by running `python blueoil/cmd/main.py train` command with model configuration.
 
-    $ ./blueoil.sh train config/{Model name}.yml
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py train -c {PATH_TO_CONFIG.yml}
 
 When training has started, the training log and checkpoints will be generated under `./saved/{Mode name}_{TIMESTAMP}`.
 
 Training is running on the TensorFlow backend. So you can use TensorBoard to visualize your training progress.
 
-    $ ./blueoil.sh tensorboard saved/{Model name}_{TIMESTAMP} {Port}
+    $ tensorboard --logdir=saved/{Model name}_{TIMESTAMP} --port {Port}
 
 - Learning Rate / Loss
 <img src="../_static/semantic_segmentation_loss.png">
@@ -107,9 +113,9 @@ Training is running on the TensorFlow backend. So you can use TensorBoard to vis
 Convert trained model to executable binary files for x86, ARM, and FPGA.
 Currently, conversion for FPGA only supports Intel Cyclone® V SoC FPGA.
 
-    $ ./blueoil.sh convert config/{Model name}.yml saved/{Mode name}_{TIMESTAMP}
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py convert -e {Model name}
 
-`blueoil.sh convert` automatically executes some conversion processes.
+`python blueoil/cmd/main.py convert` automatically executes some conversion processes.
 - Convert Tensorflow checkpoint to protocol buffer graph.
 - Optimize graph
 - Generate source code for executable binary

docs/usage/convert.md

@@ -1,19 +1,20 @@
 # Convert your training result to FPGA ready format
 
 ```
-$ ./blueoil.sh convert config/test.yml ./saved/test_20180101000000
+$ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py convert -e test_20180101000000
 
 Usage:
-  ./blueoil.sh convert <YML_CONFIG_FILE> <EXPERIMENT_DIRECTORY> <CHECKPOINT_NO(optional)>
+  main.py convert [OPTIONS]
 
   Arguments:
-    YML_CONFIG_FILE       config file path for this training  [required]
-    EXPERIMENT_DIRECTORY  experiment directory path for input [required]
-                          this is same as {OUTPUT_DIRECTORY}/{EXPERIMENT_ID} in training options.
-    CHECKPOINT_NO         checkpoint number [optional] (default is latest checkpoint)
-                          if you want to use save.ckpt-1000, you can set CHECKPOINT_NO as 1000.
+    -e, --experiment_id TEXT        ID of this experiment.  [required]
+    -p, --checkpoint TEXT           Checkpoint name. e.g. save.ckpt-10001
+    -t, --template TEXT             Path of output template directory.
+    --image_size <INTEGER INTEGER>  input image size height and width. if these are not provided, it restores from saved experiment config.e.g --image_size 320 320
+    --project_name TEXT             project name which generated by convert
+    --help                          Show this message and exit.
 ```
 
-`blueoil convert` command converts trained models to executable binary files for x86, ARM Cortex-A9, and FPGA.
+`python blueoil/cmd/main.py convert` command converts trained models to executable binary files for x86, ARM Cortex-A9, and FPGA.
 
 

docs/usage/init.md

@@ -1,10 +1,10 @@
 # Generate a configuration file
 
-You can generate your configuration file interactively by running `blueoil init`.
+You can generate your configuration file interactively by running `python blueoil/cmd/main.py init`.
 
-    $ ./blueoil.sh init
+    $ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py init
 
-`blueoil init` generates a configuration file used to train your new model.
+`python blueoil/cmd/main.py init` generates a configuration file used to train your new model.
 
 Below is an example.
 ```

docs/usage/train.md

@@ -2,22 +2,22 @@
 
 
 ```
-$ ./blueoil.sh train config/test.yml
+$ PYTHONPATH=.:lmnet:dlk/python/dlk python blueoil/cmd/main.py train -c config/test.yml
 
 Usage:
-  ./blueoil.sh train <YML_CONFIG_FILE> <OUTPUT_DIRECTORY(optional)> <EXPERIMENT_ID(optional)>
+  main.py train [OPTIONS]
 
 Arguments:
-   YML_CONFIG_FILE       config file path for this training  [required]
-   OUTPUT_DIRECTORY      output directory path for saving models [optional] (defalt is ./saved)
-   EXPERIMENT_ID         id of this training [optional] (default is {CONFIG_NAME}_{TIMESTAMP})
+  -c, --config TEXT         Path of config file.  [required]
+  -e, --experiment_id TEXT  ID of this training.
+  --help                    Show this message and exit.
 ```
 
-`blueoil train` command runs actual training.
+`python blueoil/cmd/main.py train` command runs actual training.
 
-Before running `blueoil train`, make sure you've already put training/test data in the proper location, as defined in the configuration file.
+Before running `python blueoil/cmd/main.py train`, make sure you've already put training/test data in the proper location, as defined in the configuration file.
 
-If you want to stop training, you should press `Ctrl + C` or kill the `blueoil.sh` processes. You can restart training from saved checkpoints by setting `EXPERIMENT_ID` to be the same as an existing id.
+If you want to stop training, you should press `Ctrl + C` or kill the `blueoil train` processes. You can restart training from saved checkpoints by setting `experiment_id` to be the same as an existing id.
 
 ## Training on GPUs