Merge pull request #13 from idiap/ruff

Ruff lint fixes

examples/train_mnist.py

@@ -1,9 +1,7 @@
-"""
-This example shows training of a simple Conv model with MNIST dataset using Auto Training mode of 👟.
-"""
+"""This example shows training of a simple Conv model with MNIST dataset using Auto Training mode of 👟."""
 
-import os
 from dataclasses import dataclass
+from pathlib import Path
 
 import torch
 from torch import nn
@@ -27,7 +25,7 @@ class MnistModelConfig(TrainerConfig):
 
 
 class MnistModel(TrainerModel):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
 
         # mnist images are (1, 28, 28) (channels, height, width)
@@ -46,8 +44,7 @@ def forward(self, x):
         x = F.relu(x)
         x = self.layer_3(x)
 
-        x = F.log_softmax(x, dim=1)
-        return x
+        return F.log_softmax(x, dim=1)
 
     def train_step(self, batch, criterion):
         x, y = batch
@@ -65,13 +62,12 @@ def eval_step(self, batch, criterion):
     def get_criterion():
         return torch.nn.NLLLoss()
 
-    def get_data_loader(self, config, assets, is_eval, samples, verbose, num_gpus, rank=0):  # pylint: disable=unused-argument
+    def get_data_loader(self, config, assets, *, is_eval, samples=None, verbose=False, num_gpus=1, rank=0):  # pylint: disable=unused-argument
         transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
-        dataset = MNIST(os.getcwd(), train=not is_eval, download=True, transform=transform)
+        dataset = MNIST(Path.cwd(), train=not is_eval, download=True, transform=transform)
         dataset.data = dataset.data[:256]
         dataset.targets = dataset.targets[:256]
-        dataloader = DataLoader(dataset, batch_size=config.batch_size)
-        return dataloader
+        return DataLoader(dataset, batch_size=config.batch_size)
 
 
 def main():
@@ -88,8 +84,8 @@ def main():
         train_args,
         config,
         model=model,
-        train_samples=model.get_data_loader(config, None, False, None, None, None),
-        eval_samples=model.get_data_loader(config, None, True, None, None, None),
+        train_samples=model.get_data_loader(config, None, is_eval=False),
+        eval_samples=model.get_data_loader(config, None, is_eval=True),
         parse_command_line_args=True,
     )
     trainer.fit()

examples/train_simple_gan.py

@@ -1,10 +1,10 @@
-"""
-This example shows training of a simple GAN model with MNIST dataset using Gradient Accumulation and Advanced
-Optimization where you call optimizer steps manually.
+"""This example shows training of a simple GAN model on the MNIST dataset.
+
+Using Gradient Accumulation and Advanced Optimization where you call optimizer steps manually.
 """
 
-import os
 from dataclasses import dataclass
+from pathlib import Path
 
 import numpy as np
 import torch
@@ -22,11 +22,11 @@
 
 
 class Generator(nn.Module):
-    def __init__(self, latent_dim, img_shape):
+    def __init__(self, latent_dim, img_shape) -> None:
         super().__init__()
         self.img_shape = img_shape
 
-        def block(in_feat, out_feat, normalize=True):
+        def block(in_feat, out_feat, *, normalize=True):
             layers = [nn.Linear(in_feat, out_feat)]
             if normalize:
                 layers.append(nn.BatchNorm1d(out_feat, 0.8))
@@ -44,12 +44,11 @@ def block(in_feat, out_feat, normalize=True):
 
     def forward(self, z):
         img = self.model(z)
-        img = img.view(img.size(0), *self.img_shape)
-        return img
+        return img.view(img.size(0), *self.img_shape)
 
 
 class Discriminator(nn.Module):
-    def __init__(self, img_shape):
+    def __init__(self, img_shape) -> None:
         super().__init__()
 
         self.model = nn.Sequential(
@@ -63,9 +62,7 @@ def __init__(self, img_shape):
 
     def forward(self, img):
         img_flat = img.view(img.size(0), -1)
-        validity = self.model(img_flat)
-
-        return validity
+        return self.model(img_flat)
 
 
 @dataclass
@@ -76,7 +73,7 @@ class GANModelConfig(TrainerConfig):
 
 
 class GANModel(TrainerModel):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
         data_shape = (1, 28, 28)
         self.generator = Generator(latent_dim=100, img_shape=data_shape)
@@ -153,11 +150,10 @@ def get_criterion(self):
 
     def get_data_loader(self, config, assets, is_eval, samples, verbose, num_gpus, rank=0):  # pylint: disable=unused-argument
         transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
-        dataset = MNIST(os.getcwd(), train=not is_eval, download=True, transform=transform)
+        dataset = MNIST(Path.cwd(), train=not is_eval, download=True, transform=transform)
         dataset.data = dataset.data[:64]
         dataset.targets = dataset.targets[:64]
-        dataloader = DataLoader(dataset, batch_size=config.batch_size, drop_last=True, shuffle=True)
-        return dataloader
+        return DataLoader(dataset, batch_size=config.batch_size, drop_last=True, shuffle=True)
 
 
 if __name__ == "__main__":
@@ -166,6 +162,6 @@ def get_data_loader(self, config, assets, is_eval, samples, verbose, num_gpus, r
     config.grad_clip = None
 
     model = GANModel()
-    trainer = Trainer(TrainerArgs(), config, model=model, gpu=0 if is_cuda else None)
+    trainer = Trainer(TrainerArgs(), config, model=model, output_path=Path.cwd(), gpu=0 if is_cuda else None)
     trainer.config.epochs = 10
     trainer.fit()

pyproject.toml

@@ -1,9 +1,28 @@
-[build-system]
-requires = ["setuptools", "setuptools-scm"]
-build-backend = "setuptools.build_meta"
+#
+#                   ,*++++++*,                ,*++++++*,
+#                *++.        .+++          *++.        .++*
+#              *+*     ,++++*   *+*      *+*   ,++++,     *+*
+#             ,+,   .++++++++++* ,++,,,,*+, ,++++++++++.   *+,
+#             *+.  .++++++++++++..++    *+.,++++++++++++.  .+*
+#             .+*   ++++++++++++.*+,    .+*.++++++++++++   *+,
+#              .++   *++++++++* ++,      .++.*++++++++*   ++,
+#               ,+++*.    . .*++,          ,++*.      .*+++*
+#              *+,   .,*++**.                  .**++**.   ,+*
+#             .+*                                          *+,
+#             *+.                   Coqui                  .+*
+#             *+*              +++ Trainer +++             *+*
+#             .+++*.            .          .             *+++.
+#              ,+* *+++*...                       ...*+++* *+,
+#               .++.    .""""+++++++****+++++++"""".     ++.
+#                 ,++.              ****              .++,
+#                   .++*                            *++.
+#                       *+++,                  ,+++*
+#                           .,*++++::::::++++*,.
+#
 
-[tool.setuptools.packages.find]
-include = ["trainer*"]
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
 
 [project]
 name = "coqui-tts-trainer"
@@ -76,32 +95,60 @@ Homepage = "https://github.com/idiap/coqui-ai-Trainer"
 Repository = "https://github.com/idiap/coqui-ai-Trainer"
 Issues = "https://github.com/idiap/coqui-ai-Trainer/issues"
 
+[tool.hatch.build]
+exclude = [
+    "/.github",
+    "/.gitignore",
+    "/.pre-commit-config.yaml",
+    "/CODE_OF_CONDUCT.md",
+    "/CONTRIBUTING.md",
+    "/Makefile",
+    "/tests",
+    "/trainer/TODO.txt",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["trainer"]
+
 [tool.ruff]
 line-length = 120
 target-version = "py39"
 lint.extend-select = [
-    "B",  # bugbear
-    "I",  # import sorting
+    "ANN204", # type hints
+    "B",      # bugbear
+    "D2",     # docs
+    "D412",
+    "D415",
+    "EM",     # error messages
+    "FBT",    # boolean arguments
+    "FLY",
+    "I",      # import sorting
     "PIE",
-    "PLC",
-    "PLE",
-    "PLW",
-    "RUF",
-    "UP",  # pyupgrade
+    "PL",     # pylint
+    "RET",    # return statements
+    "RUF",    # ruff-specific
+    "UP",     # pyupgrade
+    "SIM",    # simplify
 ]
 
 lint.ignore = [
     "F821",  # TODO: enable
     "PLW2901",  # TODO: enable
-    "UP032",  # TODO: enable
+    "PLR09",
 ]
 
 [tool.ruff.lint.per-file-ignores]
 "**/__init__.py" = [
-    "F401", # init files may have "unused" imports for now
-    "F403", # init files may have star imports for now
+    "F401",  # init files may have "unused" imports for now
+    "F403",  # init files may have star imports for now
+]
+"tests/**" = [
+    "PLR2004",  # magic values are ok in tests
 ]
 
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
 [tool.coverage.report]
 show_missing = true
 skip_empty = true