- Added pyproject.toml to set max line size to 120

- Reformatted with black to 120
- Made black running on dist-tests with pnpm dist_lint

Author: vfdev-5

CONTRIBUTING.md

@@ -122,6 +122,12 @@ To add a new template,
   bash scripts/run_code_style.sh source_lint
   ```
 
+- To check if generated code complies with a style guide:
+
+  ```sh
+  pnpm test && sh ./scripts/run_tests.sh unzip && pnpm dist_lint
+  ```
+
 _NOTE: Even if you have a half-completed/working PR, sending a PR is still a valid contribution and we can help you finish the PR._
 
 **NOTE : When sending a PR, please kindly check if the changes are required to run in the CI.**

pyproject.toml

@@ -0,0 +1,23 @@
+[tool.black]
+line-length = 120
+target-version = ['py38', 'py39']
+include = '\.pyi?$'
+exclude = '''
+
+(
+  /(
+      \.eggs         # exclude a few common directories in the
+    | \.git          # root of the project
+    | \.hg
+    | \.mypy_cache
+    | \.tox
+    | \.venv
+    | _build
+    | buck-out
+    | build
+    | dist
+  )/
+  | foo.py           # also separately exclude a file named foo.py in
+                     # the root of the project
+)
+'''

scripts/run_code_style.sh

@@ -6,11 +6,13 @@ if [ $1 == "dist_lint" ]; then
     # Check that ./dist-tests/ exists and code is unzipped
     ls ./dist-tests/vision-classification-all/main.py
     # Comment dist-tests in .gitignore to make black running on ./dist-tests folder
-    # TODO:
+    sed -i "s/dist-tests/# dist-tests/g" .gitignore
+
     ufmt diff .
     flake8 --select F401,F821 ./dist-tests  # find unused imports and non imported objects
+
     # Restore .gitignore
-    # TODO:
+    sed -i "s/\([# ]\+\)dist-tests/dist-tests/g" .gitignore
 elif [ $1 == "source_lint" ]; then
     ufmt diff .
 elif [ $1 == "fmt" ]; then

src/templates/template-common/utils.py

@@ -16,11 +16,7 @@
 
 #::: } :::#
 #::: if (it.save_training || it.save_evaluation) { :::#
-from ignite.handlers import (
-    Checkpoint,
-    DiskSaver,
-    global_step_from_engine,
-)  # usort: skip
+from ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine  # usort: skip
 
 #::: } else { :::#
 from ignite.handlers import Checkpoint
@@ -84,9 +80,7 @@ def log_metrics(engine: Engine, tag: str) -> None:
     tag
         a string to add at the start of output.
     """
-    metrics_format = "{0} [{1}/{2}]: {3}".format(
-        tag, engine.state.epoch, engine.state.iteration, engine.state.metrics
-    )
+    metrics_format = "{0} [{1}/{2}]: {3}".format(tag, engine.state.epoch, engine.state.iteration, engine.state.metrics)
     engine.logger.info(metrics_format)
 
 
@@ -175,21 +169,13 @@ def setup_exp_logging(config, trainer, optimizers, evaluators):
     """Setup Experiment Tracking logger from Ignite."""
 
     #::: if (it.logger === 'clearml') { :::#
-    logger = common.setup_clearml_logging(
-        trainer, optimizers, evaluators, config.log_every_iters
-    )
+    logger = common.setup_clearml_logging(trainer, optimizers, evaluators, config.log_every_iters)
     #::: } else if (it.logger === 'mlflow') { :::#
-    logger = common.setup_mlflow_logging(
-        trainer, optimizers, evaluators, config.log_every_iters
-    )
+    logger = common.setup_mlflow_logging(trainer, optimizers, evaluators, config.log_every_iters)
     #::: } else if (it.logger === 'neptune') { :::#
-    logger = common.setup_neptune_logging(
-        trainer, optimizers, evaluators, config.log_every_iters
-    )
+    logger = common.setup_neptune_logging(trainer, optimizers, evaluators, config.log_every_iters)
     #::: } else if (it.logger === 'polyaxon') { :::#
-    logger = common.setup_plx_logging(
-        trainer, optimizers, evaluators, config.log_every_iters
-    )
+    logger = common.setup_plx_logging(trainer, optimizers, evaluators, config.log_every_iters)
     #::: } else if (it.logger === 'tensorboard') { :::#
     logger = common.setup_tb_logging(
         config.output_dir,
@@ -199,13 +185,9 @@ def setup_exp_logging(config, trainer, optimizers, evaluators):
         config.log_every_iters,
     )
     #::: } else if (it.logger === 'visdom') { :::#
-    logger = common.setup_visdom_logging(
-        trainer, optimizers, evaluators, config.log_every_iters
-    )
+    logger = common.setup_visdom_logging(trainer, optimizers, evaluators, config.log_every_iters)
     #::: } else if (it.logger === 'wandb') { :::#
-    logger = common.setup_wandb_logging(
-        trainer, optimizers, evaluators, config.log_every_iters
-    )
+    logger = common.setup_wandb_logging(trainer, optimizers, evaluators, config.log_every_iters)
     #::: } :::#
     return logger
 

src/templates/template-text-classification/data.py

@@ -48,21 +48,12 @@ def setup_data(config):
     if local_rank > 0:
         idist.barrier()
     #::: } :::#
-
-    dataset_train, dataset_eval = load_dataset(
-        "imdb", split=["train", "test"], cache_dir=config.data_path
-    )
-    tokenizer = AutoTokenizer.from_pretrained(
-        config.model, cache_dir=config.tokenizer_dir, do_lower_case=True
-    )
+    dataset_train, dataset_eval = load_dataset("imdb", split=["train", "test"], cache_dir=config.data_path)
+    tokenizer = AutoTokenizer.from_pretrained(config.model, cache_dir=config.tokenizer_dir, do_lower_case=True)
     train_texts, train_labels = dataset_train["text"], dataset_train["label"]
     test_texts, test_labels = dataset_eval["text"], dataset_eval["label"]
-    dataset_train = TransformerDataset(
-        train_texts, train_labels, tokenizer, config.max_length
-    )
-    dataset_eval = TransformerDataset(
-        test_texts, test_labels, tokenizer, config.max_length
-    )
+    dataset_train = TransformerDataset(train_texts, train_labels, tokenizer, config.max_length)
+    dataset_eval = TransformerDataset(test_texts, test_labels, tokenizer, config.max_length)
     #::: if (it.use_dist) { :::#
     if local_rank == 0:
         idist.barrier()

src/templates/template-text-classification/main.py

@@ -45,9 +45,7 @@ def run(local_rank: int, config: Any):
     )
 
     config.lr *= idist.get_world_size()
-    optimizer = idist.auto_optim(
-        optim.AdamW(model.parameters(), lr=config.lr, weight_decay=config.weight_decay)
-    )
+    optimizer = idist.auto_optim(optim.AdamW(model.parameters(), lr=config.lr, weight_decay=config.weight_decay))
     loss_fn = nn.BCEWithLogitsLoss().to(device=device)
 
     le = config.num_iters_per_epoch
@@ -56,9 +54,7 @@ def run(local_rank: int, config: Any):
         (le * config.num_warmup_epochs, config.lr),
         (le * config.max_epochs, 0.0),
     ]
-    lr_scheduler = PiecewiseLinear(
-        optimizer, param_name="lr", milestones_values=milestones_values
-    )
+    lr_scheduler = PiecewiseLinear(optimizer, param_name="lr", milestones_values=milestones_values)
 
     # setup metrics to attach to evaluator
     metrics = {
@@ -67,9 +63,7 @@ def run(local_rank: int, config: Any):
     }
 
     # trainer and evaluator
-    trainer = setup_trainer(
-        config, model, optimizer, loss_fn, device, dataloader_train.sampler
-    )
+    trainer = setup_trainer(config, model, optimizer, loss_fn, device, dataloader_train.sampler)
     evaluator = setup_evaluator(config, model, metrics, device)
 
     # setup engines logger with python logging
@@ -98,9 +92,7 @@ def run(local_rank: int, config: Any):
     #::: } else { :::#
     to_save_eval = None
     #::: } :::#
-    ckpt_handler_train, ckpt_handler_eval = setup_handlers(
-        trainer, evaluator, config, to_save_train, to_save_eval
-    )
+    ckpt_handler_train, ckpt_handler_eval = setup_handlers(trainer, evaluator, config, to_save_train, to_save_eval)
     #::: } else if (it.patience || it.terminate_on_nan || it.limit_sec) { :::#
     setup_handlers(trainer, evaluator, config)
     #::: } :::#

src/templates/template-text-classification/trainers.py

@@ -22,15 +22,9 @@ def setup_trainer(
 
     def train_function(engine: Union[Engine, DeterministicEngine], batch: Any):
         input_ids = batch["input_ids"].to(device, non_blocking=True, dtype=torch.long)
-        attention_mask = batch["attention_mask"].to(
-            device, non_blocking=True, dtype=torch.long
-        )
-        token_type_ids = batch["token_type_ids"].to(
-            device, non_blocking=True, dtype=torch.long
-        )
-        labels = (
-            batch["label"].view(-1, 1).to(device, non_blocking=True, dtype=torch.float)
-        )
+        attention_mask = batch["attention_mask"].to(device, non_blocking=True, dtype=torch.long)
+        token_type_ids = batch["token_type_ids"].to(device, non_blocking=True, dtype=torch.long)
+        labels = batch["label"].view(-1, 1).to(device, non_blocking=True, dtype=torch.float)
 
         model.train()
 
@@ -75,15 +69,9 @@ def evalutate_function(engine: Engine, batch: Any):
         model.eval()
 
         input_ids = batch["input_ids"].to(device, non_blocking=True, dtype=torch.long)
-        attention_mask = batch["attention_mask"].to(
-            device, non_blocking=True, dtype=torch.long
-        )
-        token_type_ids = batch["token_type_ids"].to(
-            device, non_blocking=True, dtype=torch.long
-        )
-        labels = (
-            batch["label"].view(-1, 1).to(device, non_blocking=True, dtype=torch.float)
-        )
+        attention_mask = batch["attention_mask"].to(device, non_blocking=True, dtype=torch.long)
+        token_type_ids = batch["token_type_ids"].to(device, non_blocking=True, dtype=torch.long)
+        labels = batch["label"].view(-1, 1).to(device, non_blocking=True, dtype=torch.float)
 
         with autocast(enabled=config.use_amp):
             output = model(input_ids, attention_mask, token_type_ids)

src/templates/template-text-classification/utils.py

@@ -46,6 +46,7 @@ def setup_handlers(
     #::: } :::#
 
     #::: if (it.patience) { :::#
+
     # early stopping
     def score_fn(engine: Engine):
         return engine.state.metrics["Accuracy"]

src/templates/template-vision-classification/main.py

@@ -43,9 +43,7 @@ def run(local_rank: int, config: Any):
     lr_scheduler = PiecewiseLinear(optimizer, "lr", milestones_values=milestones_values)
 
     # trainer and evaluator
-    trainer = setup_trainer(
-        config, model, optimizer, loss_fn, device, dataloader_train.sampler
-    )
+    trainer = setup_trainer(config, model, optimizer, loss_fn, device, dataloader_train.sampler)
     evaluator = setup_evaluator(config, model, device)
 
     # attach metrics to evaluator
@@ -84,9 +82,7 @@ def run(local_rank: int, config: Any):
     #::: } else { :::#
     to_save_eval = None
     #::: } :::#
-    ckpt_handler_train, ckpt_handler_eval = setup_handlers(
-        trainer, evaluator, config, to_save_train, to_save_eval
-    )
+    ckpt_handler_train, ckpt_handler_eval = setup_handlers(trainer, evaluator, config, to_save_train, to_save_eval)
     #::: } else if (it.patience || it.terminate_on_nan || it.limit_sec) { :::#
     setup_handlers(trainer, evaluator, config)
     #::: } :::#

src/templates/template-vision-classification/test_all.py

@@ -23,9 +23,7 @@ def set_up():
 
 @pytest.mark.skipif(os.getenv("RUN_SLOW_TESTS", 0) == 0, reason="Skip slow tests")
 def test_setup_data():
-    config = Namespace(
-        data_path="~/data", train_batch_size=1, eval_batch_size=1, num_workers=0
-    )
+    config = Namespace(data_path="~/data", train_batch_size=1, eval_batch_size=1, num_workers=0)
     dataloader_train, dataloader_eval = setup_data(config)
 
     assert isinstance(dataloader_train, DataLoader)

src/templates/template-vision-classification/utils.py

@@ -46,6 +46,7 @@ def setup_handlers(
     #::: } :::#
 
     #::: if (it.patience) { :::#
+
     # early stopping
     def score_fn(engine: Engine):
         return -engine.state.metrics["eval_loss"]

src/templates/template-vision-dcgan/main.py

@@ -49,12 +49,8 @@ def run(local_rank: int, config: Any):
     loss_fn = nn.BCELoss().to(device=device)
 
     # optimizers
-    optimizer_d = idist.auto_optim(
-        optim.Adam(model_d.parameters(), lr=config.lr, betas=(0.5, 0.999))
-    )
-    optimizer_g = idist.auto_optim(
-        optim.Adam(model_g.parameters(), lr=config.lr, betas=(0.5, 0.999))
-    )
+    optimizer_d = idist.auto_optim(optim.Adam(model_d.parameters(), lr=config.lr, betas=(0.5, 0.999)))
+    optimizer_g = idist.auto_optim(optim.Adam(model_g.parameters(), lr=config.lr, betas=(0.5, 0.999)))
 
     # trainer and evaluator
     trainer = setup_trainer(
@@ -100,9 +96,7 @@ def run(local_rank: int, config: Any):
     #::: } else { :::#
     to_save_train = None
     #::: } :::#
-    ckpt_handler_train, ckpt_handler_eval = setup_handlers(
-        trainer, evaluator, config, to_save_train, to_save_eval
-    )
+    ckpt_handler_train, ckpt_handler_eval = setup_handlers(trainer, evaluator, config, to_save_train, to_save_eval)
     #::: } else if (it.patience || it.terminate_on_nan || it.limit_sec) { :::#
     setup_handlers(trainer, evaluator, config)
     #::: } :::#

src/templates/template-vision-dcgan/test_all.py

@@ -24,9 +24,7 @@ def set_up():
 
 @pytest.mark.skipif(os.getenv("RUN_SLOW_TESTS", 0) == 0, reason="Skip slow tests")
 def test_setup_data():
-    config = Namespace(
-        data_path="~/data", train_batch_size=1, eval_batch_size=1, num_workers=0
-    )
+    config = Namespace(data_path="~/data", train_batch_size=1, eval_batch_size=1, num_workers=0)
     dataloader_train, dataloader_eval, _ = setup_data(config)
 
     assert isinstance(dataloader_train, DataLoader)
@@ -61,8 +59,6 @@ def test_models():
 def test_setup_trainer():
     model, optimizer, device, loss_fn, batch = set_up()
     config = Namespace(use_amp=False, train_batch_size=2, z_dim=100)
-    trainer = setup_trainer(
-        config, model, model, optimizer, optimizer, loss_fn, device, None
-    )
+    trainer = setup_trainer(config, model, model, optimizer, optimizer, loss_fn, device, None)
     trainer.run([batch, batch])
     assert isinstance(trainer.state.output, dict)

src/templates/template-vision-dcgan/trainers.py

@@ -23,9 +23,7 @@ def setup_trainer(
 
     real_labels = torch.ones(config.train_batch_size // ws, device=device)
     fake_labels = torch.zeros(config.train_batch_size // ws, device=device)
-    noise = torch.randn(
-        config.train_batch_size // ws, config.z_dim, 1, 1, device=device
-    )
+    noise = torch.randn(config.train_batch_size // ws, config.z_dim, 1, 1, device=device)
 
     def train_function(engine: Union[Engine, DeterministicEngine], batch: Any):
         model_g.train()

src/templates/template-vision-dcgan/utils.py

@@ -46,6 +46,7 @@ def setup_handlers(
     #::: } :::#
 
     #::: if (it.patience) { :::#
+
     # early stopping
     def score_fn(engine: Engine):
         return -engine.state.metrics["errD"]

src/templates/template-vision-segmentation/data.py

@@ -88,9 +88,7 @@ def setup_data(config: Namespace):
             "Dataset not found. You can use `download_datasets` from data.py function to download it."
         ) from e
 
-    dataset_eval = VOCSegmentationPIL(
-        root=config.data_path, year="2012", image_set="val", download=False
-    )
+    dataset_eval = VOCSegmentationPIL(root=config.data_path, year="2012", image_set="val", download=False)
 
     val_img_size = 513
     train_img_size = 480
@@ -100,9 +98,7 @@ def setup_data(config: Namespace):
 
     transform_train = A.Compose(
         [
-            A.RandomScale(
-                scale_limit=(0.0, 1.5), interpolation=cv2.INTER_LINEAR, p=1.0
-            ),
+            A.RandomScale(scale_limit=(0.0, 1.5), interpolation=cv2.INTER_LINEAR, p=1.0),
             A.PadIfNeeded(val_img_size, val_img_size, border_mode=cv2.BORDER_CONSTANT),
             A.RandomCrop(train_img_size, train_img_size),
             A.HorizontalFlip(),
@@ -176,7 +172,6 @@ def download_datasets(data_path):
         # Ensure that only rank 0 download the dataset
         idist.barrier()
     #::: } :::#
-
     VOCSegmentation(data_path, image_set="train", download=True)
     VOCSegmentation(data_path, image_set="val", download=True)
     #::: if (it.use_dist) { :::#

src/templates/template-vision-segmentation/main.py

@@ -66,9 +66,7 @@ def run(local_rank: int, config: Any):
     metrics = {"IoU": IoU(cm_metric), "mIoU_bg": mIoU(cm_metric)}
 
     # trainer and evaluator
-    trainer = setup_trainer(
-        config, model, optimizer, loss_fn, device, dataloader_train.sampler
-    )
+    trainer = setup_trainer(config, model, optimizer, loss_fn, device, dataloader_train.sampler)
     evaluator = setup_evaluator(config, model, metrics, device)
 
     # setup engines logger with python logging
@@ -105,9 +103,7 @@ def run(local_rank: int, config: Any):
     #::: } else { :::#
     to_save_eval = None
     #::: } :::#
-    ckpt_handler_train, ckpt_handler_eval = setup_handlers(
-        trainer, evaluator, config, to_save_train, to_save_eval
-    )
+    ckpt_handler_train, ckpt_handler_eval = setup_handlers(trainer, evaluator, config, to_save_train, to_save_eval)
     #::: } else if (it.patience || it.terminate_on_nan || it.limit_sec) { :::#
     setup_handlers(trainer, evaluator, config)
     #::: } :::#