feat: configurations in separate config file in single template

app/streamlit_app.py

@@ -15,12 +15,12 @@
 }
 
 TIP = """
-> **💡 TIP**
->
-> To quickly adapt to the generated code structure, there are TODOs in the files that are needed to be edited.
-> [PyCharm TODO comments](https://www.jetbrains.com/help/pycharm/using-todo.html) or
-> [VSCode Todo Tree](https://marketplace.visualstudio.com/items?itemName=Gruntfuggly.todo-tree)
-> can help you find them easily.
+**💡 TIP**
+
+To quickly adapt to the generated code structure, there are TODOs in the files that are needed to be edited.
+[PyCharm TODO comments](https://www.jetbrains.com/help/pycharm/using-todo.html) or
+[VSCode Todo Tree](https://marketplace.visualstudio.com/items?itemName=Gruntfuggly.todo-tree)
+can help you find them easily.
 """
 
 

templates/single/README.md

@@ -18,8 +18,6 @@ Table of Contents
 
 ## Getting Started
 
-- After downloaded and extracted an archive, there will be a folder named `single` (directory name). Inside that there will be an another folder named `single_cg` (package name).
-
 <details>
 <summary>
 Detailed Directory List
@@ -51,15 +49,7 @@ single
 
 </details>
 
-- Folder names must be renamed by using `mv` command in Unix/macOS and Linux and `move` in Windows. Or simply rename them.
-
-- Since the generated code are using absolute imports, the package name must be renamed to the name you have changed in the above step. There is a `find_and_replace.sh` script (Unix and Linux only) to easily find and replace the package name in the generated code. Usage is:
-
-  ```sh
-  bash find_and_replace.sh old_pkg_name new_pkg_name
-  ```
-
-- Install the dependencies with `pip` and install the package in `editable` mode:
+- Install the dependencies with `pip` and install the project in `editable` mode:
 
   ```sh
   pip install -r requirements.txt --progress-bar off -U
@@ -75,8 +65,8 @@ single
 
 - Edit `datasets.py` for your custom datasets and dataloaders.
 - Edit `models.py` for your custom models.
-- Extend `utils.py` for additional command line arguments.
-- Extend `engines.py` for your custom models' forward pass, backward pass, and evaluation.
+- Extend `config.py` for additional command line arguments.
+- Extend `trainers.py` for your custom models' forward pass, backward pass, and evaluation.
 - Extend `handlers.py` for your custom handlers. _(**OPTIONAL**)_
 
 ## Training

templates/single/requirements.txt

@@ -1,3 +1,4 @@
+setuptools
 torch>=1.7.0
 pytorch-ignite>=0.4.4
 {{ handler_deps }}

templates/single/single/config.pyi

@@ -0,0 +1 @@
+{% include "_argparse.pyi" %}

templates/single/single/main.pyi

@@ -10,10 +10,10 @@ import ignite.distributed as idist
 from ignite.engine.events import Events
 from ignite.utils import manual_seed
 
-from {{project_name}}.engines import create_engines
-from {{project_name}}.events import TrainEvents
+from {{project_name}}.trainers import create_trainers, TrainEvents
 from {{project_name}}.handlers import get_handlers, get_logger
-from {{project_name}}.utils import get_default_parser, setup_logging, log_metrics, log_basic_info, initialize, resume_from
+from {{project_name}}.utils import setup_logging, log_metrics, log_basic_info, initialize, resume_from
+from {{project_name}}.config import get_default_parser
 
 
 def run(local_rank: int, config: Any, *args: Any, **kwargs: Any):
@@ -43,7 +43,7 @@ def run(local_rank: int, config: Any, *args: Any, **kwargs: Any):
     # train_engine and eval_engine
     # -----------------------------
 
-    train_engine, eval_engine = create_engines(
+    train_engine, eval_engine = create_trainers(
         config=config,
         model=model,
         optimizer=optimizer,

templates/single/single/engines.pyi → templates/single/single/trainers.pyi

@@ -8,7 +8,8 @@ from ignite.engine import Engine
 from torch.cuda.amp import autocast
 from torch.optim.optimizer import Optimizer
 
-from {{project_name}}.events import TrainEvents, train_events_to_attr
+
+{% include "_events.pyi" %}
 
 
 # Edit below functions the way how the model will be training
@@ -116,7 +117,7 @@ def evaluate_function(
 
 # function for creating engines which will be used in main.py
 # any necessary arguments can be provided.
-def create_engines(**kwargs) -> Tuple[Engine, Engine]:
+def create_trainers(**kwargs) -> Tuple[Engine, Engine]:
     """Create Engines for training and evaluation.
 
     Parameters

templates/single/single/utils.pyi

@@ -19,8 +19,6 @@ from torch.nn import Module
 from torch.optim.lr_scheduler import _LRScheduler
 from torch.optim.optimizer import Optimizer
 
-{% include "_argparse.pyi" %}
-
 
 # we can use `idist.auto_model` to handle distributed configurations
 # for your model : https://pytorch.org/ignite/distributed.html#ignite.distributed.auto.auto_model

templates/single/tests/test_engines.pyi → templates/single/tests/test_trainers.pyi

@@ -6,8 +6,7 @@ from unittest.mock import MagicMock
 import ignite.distributed as idist
 import torch
 from ignite.engine.engine import Engine
-from {{project_name}}.engines import create_engines, evaluate_function, train_function
-from {{project_name}}.events import TrainEvents, train_events_to_attr
+from {{project_name}}.trainers import create_trainers, evaluate_function, train_function, TrainEvents, train_events_to_attr
 from torch import nn, optim
 
 
@@ -110,8 +109,8 @@ class TestEngines(unittest.TestCase):
         output = evaluate_function(config, engine, self.batch, self.model, self.loss_fn, self.device)
         self.assertIsInstance(output, Number)
 
-    def test_create_engines(self):
-        train_engine, eval_engine = create_engines(
+    def test_create_trainers(self):
+        train_engine, eval_engine = create_trainers(
             config=Namespace(use_amp=True),
             model=self.model,
             loss_fn=self.loss_fn,

templates/single/tests/test_utils.pyi

@@ -7,8 +7,8 @@ from tempfile import TemporaryDirectory
 import torch
 from ignite.engine import Engine
 from ignite.utils import setup_logger
+from {{project_name}}.config import get_default_parser
 from {{project_name}}.utils import (
-    get_default_parser,
     hash_checkpoint,
     log_metrics,
     resume_from,