change TimeValue to a dataclass

docs/guides/training_101/index.md

@@ -220,13 +220,14 @@ Example:
 
 ```python
 from refiners.training_utils import BaseConfig, TrainingConfig, OptimizerConfig, LRSchedulerConfig, Optimizers, LRSchedulers
+from refiners.training_utils.common import TimeUnit, TimeValue
 
 class AutoencoderConfig(BaseConfig):
     # Since we are using a synthetic dataset, we will use a arbitrary fixed epoch size.
     epoch_size: int = 2048
 
 training = TrainingConfig(
-    duration="1000:epoch",
+    duration=TimeValue(number=1000, unit=TimeUnit.EPOCH),
     batch_size=32,
     device="cuda" if torch.cuda.is_available() else "cpu",
     dtype="float32"
@@ -335,11 +336,11 @@ We can also evaluate the model using the `compute_evaluation` method.
 
 ```python
 training = TrainingConfig(
-    duration="1000:epoch",
+    duration=TimeValue(number=1000, unit=TimeUnit.EPOCH),
     batch_size=32,
     device="cuda" if torch.cuda.is_available() else "cpu",
     dtype="float32",
-    evaluation_interval="50:epoch" # We set the evaluation to be done every 10 epochs
+    evaluation_interval=TimeValue(number=50, unit=TimeUnit.EPOCH),
 )
 
 class AutoencoderTrainer(Trainer[AutoencoderConfig, Batch]):
@@ -459,6 +460,8 @@ You can train this toy model using the code below:
     import torch
     from loguru import logger
     from PIL import Image
+    from torch.nn import functional as F
+
     from refiners.fluxion import layers as fl
     from refiners.fluxion.utils import image_to_tensor, tensor_to_image
     from refiners.training_utils import (
@@ -476,7 +479,7 @@ You can train this toy model using the code below:
         register_callback,
         register_model,
     )
-    from torch.nn import functional as F
+    from refiners.training_utils.common import TimeUnit, TimeValue
 
 
     class ConvBlock(fl.Chain):
@@ -487,7 +490,7 @@ You can train this toy model using the code below:
                     out_channels=out_channels,
                     kernel_size=3,
                     padding=1,
-                    groups=min(in_channels, out_channels)
+                    groups=min(in_channels, out_channels),
                 ),
                 fl.LayerNorm2d(out_channels),
                 fl.SiLU(),
@@ -576,9 +579,7 @@ You can train this toy model using the code below:
         random.seed(seed)
 
         while True:
-            rectangle = Image.new(
-                "L", (random.randint(1, size), random.randint(1, size)), color=255
-            )
+            rectangle = Image.new("L", (random.randint(1, size), random.randint(1, size)), color=255)
             mask = Image.new("L", (size, size))
             mask.paste(
                 rectangle,
@@ -627,11 +628,11 @@ You can train this toy model using the code below:
     )
 
     training = TrainingConfig(
-        duration="1000:epoch",  # type: ignore
+        duration=TimeValue(number=1000, unit=TimeUnit.EPOCH),
         batch_size=32,
         device="cuda" if torch.cuda.is_available() else "cpu",
         dtype="float32",
-        evaluation_interval="50:epoch",  # type: ignore
+        evaluation_interval=TimeValue(number=50, unit=TimeUnit.EPOCH),
     )
 
     optimizer = OptimizerConfig(
@@ -639,9 +640,7 @@ You can train this toy model using the code below:
         learning_rate=1e-4,
     )
 
-    lr_scheduler = LRSchedulerConfig(
-        type=LRSchedulerType.CONSTANT_LR
-    )
+    lr_scheduler = LRSchedulerConfig(type=LRSchedulerType.CONSTANT_LR)
 
     config = AutoencoderConfig(
         training=training,
@@ -672,9 +671,7 @@ You can train this toy model using the code below:
             return Autoencoder()
 
         def compute_loss(self, batch: Batch) -> torch.Tensor:
-            x_reconstructed = self.autoencoder.decoder(
-                self.autoencoder.encoder(batch.image)
-            )
+            x_reconstructed = self.autoencoder.decoder(self.autoencoder.encoder(batch.image))
             return F.binary_cross_entropy(x_reconstructed, batch.image)
 
         def compute_evaluation(self) -> None:
@@ -687,7 +684,7 @@ You can train this toy model using the code below:
                 x_reconstructed = self.autoencoder.decoder(self.autoencoder.encoder(mask))
                 loss = F.mse_loss(x_reconstructed, mask)
                 validation_losses.append(loss.detach().cpu().item())
-                grid.append((tensor_to_image(mask), tensor_to_image((x_reconstructed>0.5).float())))
+                grid.append((tensor_to_image(mask), tensor_to_image((x_reconstructed > 0.5).float())))
 
             mean_loss = sum(validation_losses) / len(validation_losses)
             logger.info(f"Mean validation loss: {mean_loss}, epoch: {self.clock.epoch}")

src/refiners/training_utils/clock.py

@@ -48,11 +48,11 @@ class TrainingClock(Callback["Trainer[BaseConfig, Any]"]):
 
     @cached_property
     def unit_to_steps(self) -> dict[TimeUnit, int]:
-        iteration_factor = self.num_batches_per_epoch if self.gradient_accumulation["unit"] == TimeUnit.EPOCH else 1
+        iteration_factor = self.num_batches_per_epoch if self.gradient_accumulation.unit == TimeUnit.EPOCH else 1
         return {
             TimeUnit.STEP: 1,
             TimeUnit.EPOCH: self.num_batches_per_epoch,
-            TimeUnit.ITERATION: self.gradient_accumulation["number"] * iteration_factor,
+            TimeUnit.ITERATION: self.gradient_accumulation.number * iteration_factor,
         }
 
     def convert_time_unit_to_steps(self, number: int, unit: TimeUnit) -> int:
@@ -62,7 +62,7 @@ class TrainingClock(Callback["Trainer[BaseConfig, Any]"]):
         return steps // self.unit_to_steps[unit]
 
     def convert_time_value(self, time_value: TimeValue, target_unit: TimeUnit) -> int:
-        number, unit = time_value["number"], time_value["unit"]
+        number, unit = time_value.number, time_value.unit
         steps = self.convert_time_unit_to_steps(number=number, unit=unit)
         return self.convert_steps_to_time_unit(steps=steps, unit=target_unit)
 
@@ -81,13 +81,13 @@ class TrainingClock(Callback["Trainer[BaseConfig, Any]"]):
     @cached_property
     def num_step_per_iteration(self) -> int:
         return self.convert_time_unit_to_steps(
-            number=self.gradient_accumulation["number"], unit=self.gradient_accumulation["unit"]
+            number=self.gradient_accumulation.number, unit=self.gradient_accumulation.unit
         )
 
     @cached_property
     def num_step_per_evaluation(self) -> int:
         return self.convert_time_unit_to_steps(
-            number=self.evaluation_interval["number"], unit=self.evaluation_interval["unit"]
+            number=self.evaluation_interval.number, unit=self.evaluation_interval.unit
         )
 
     def reset(self) -> None:
@@ -113,13 +113,13 @@ class TrainingClock(Callback["Trainer[BaseConfig, Any]"]):
     @cached_property
     def evaluation_interval_steps(self) -> int:
         return self.convert_time_unit_to_steps(
-            number=self.evaluation_interval["number"], unit=self.evaluation_interval["unit"]
+            number=self.evaluation_interval.number, unit=self.evaluation_interval.unit
         )
 
     @cached_property
     def lr_scheduler_interval_steps(self) -> int:
         return self.convert_time_unit_to_steps(
-            number=self.lr_scheduler_interval["number"], unit=self.lr_scheduler_interval["unit"]
+            number=self.lr_scheduler_interval.number, unit=self.lr_scheduler_interval.unit
         )
 
     @property

src/refiners/training_utils/common.py

@@ -1,4 +1,5 @@
 import random
+from dataclasses import dataclass
 from enum import Enum
 from functools import wraps
 from typing import Any, Callable, Iterable
@@ -7,7 +8,6 @@ import numpy as np
 import torch
 from loguru import logger
 from torch import Tensor, cuda, nn
-from typing_extensions import TypedDict
 
 from refiners.fluxion.utils import manual_seed
 
@@ -79,26 +79,32 @@ def scoped_seed(seed: int | Callable[..., int] | None = None) -> Callable[..., C
     return decorator
 
 
-class TimeUnit(Enum):
+class TimeUnit(str, Enum):
     STEP = "step"
     EPOCH = "epoch"
     ITERATION = "iteration"
     DEFAULT = "step"
 
 
-class TimeValue(TypedDict):
+@dataclass
+class TimeValue:
     number: int
     unit: TimeUnit
 
 
-def parse_number_unit_field(value: str | int | dict[str, str | int]) -> TimeValue:
+TimeValueInput = str | int | dict[str, str | int] | TimeValue
+
+
+def parse_number_unit_field(value: TimeValueInput) -> TimeValue:
     match value:
         case str(value_str):
             number, unit = value_str.split(sep=":")
-            return {"number": int(number.strip()), "unit": TimeUnit(value=unit.strip().lower())}
+            return TimeValue(number=int(number.strip()), unit=TimeUnit(value=unit.strip().lower()))
         case int(number):
-            return {"number": number, "unit": TimeUnit.DEFAULT}
+            return TimeValue(number=number, unit=TimeUnit.DEFAULT)
         case {"number": int(number), "unit": str(unit)}:
-            return {"number": number, "unit": TimeUnit(value=unit.lower())}
+            return TimeValue(number=number, unit=TimeUnit(value=unit.lower()))
+        case TimeValue(number, unit):
+            return TimeValue(number=number, unit=unit)
         case _:
             raise ValueError(f"Unsupported value format: {value}")

src/refiners/training_utils/config.py

@@ -23,11 +23,11 @@ ParamsT = Iterable[Tensor] | Iterable[dict[str, Any]]
 class TrainingConfig(BaseModel):
     device: str = "cpu"
     dtype: str = "float32"
-    duration: TimeValue = {"number": 1, "unit": TimeUnit.ITERATION}
+    duration: TimeValue = TimeValue(number=1, unit=TimeUnit.ITERATION)
     seed: int = 0
     batch_size: int = 1
-    gradient_accumulation: TimeValue = {"number": 1, "unit": TimeUnit.STEP}
-    evaluation_interval: TimeValue = {"number": 1, "unit": TimeUnit.ITERATION}
+    gradient_accumulation: TimeValue = TimeValue(number=1, unit=TimeUnit.STEP)
+    evaluation_interval: TimeValue = TimeValue(number=1, unit=TimeUnit.ITERATION)
     evaluation_seed: int = 0
 
     model_config = ConfigDict(extra="forbid")
@@ -63,8 +63,8 @@ class LRSchedulerType(str, Enum):
 
 class LRSchedulerConfig(BaseModel):
     type: LRSchedulerType = LRSchedulerType.DEFAULT
-    update_interval: TimeValue = {"number": 1, "unit": TimeUnit.ITERATION}
-    warmup: TimeValue = {"number": 0, "unit": TimeUnit.ITERATION}
+    update_interval: TimeValue = TimeValue(number=1, unit=TimeUnit.ITERATION)
+    warmup: TimeValue = TimeValue(number=0, unit=TimeUnit.ITERATION)
     gamma: float = 0.1
     lr_lambda: Callable[[int], float] | None = None
     mode: Literal["min", "max"] = "min"

src/refiners/training_utils/trainer.py

@@ -241,7 +241,7 @@ class Trainer(Generic[ConfigType, Batch], ABC):
     @cached_property
     def lr_scheduler(self) -> LRScheduler:
         config = self.config.lr_scheduler
-        scheduler_step_size = config.update_interval["number"]
+        scheduler_step_size = config.update_interval.number
 
         match config.type:
             case LRSchedulerType.CONSTANT_LR:
@@ -286,7 +286,7 @@ class Trainer(Generic[ConfigType, Batch], ABC):
             case _:
                 raise ValueError(f"Unknown scheduler type: {config.type}")
 
-        warmup_scheduler_steps = self.clock.convert_time_value(config.warmup, config.update_interval["unit"])
+        warmup_scheduler_steps = self.clock.convert_time_value(config.warmup, config.update_interval.unit)
         if warmup_scheduler_steps > 0:
             lr_scheduler = WarmupScheduler(
                 optimizer=self.optimizer,

tests/training_utils/test_common.py

@@ -0,0 +1,33 @@
+import pytest
+
+from refiners.training_utils.common import TimeUnit, TimeValue, TimeValueInput, parse_number_unit_field
+
+
+@pytest.mark.parametrize(
+    "input_value, expected_output",
+    [
+        ("10: step", TimeValue(number=10, unit=TimeUnit.STEP)),
+        ("20 :epoch", TimeValue(number=20, unit=TimeUnit.EPOCH)),
+        ("30: Iteration", TimeValue(number=30, unit=TimeUnit.ITERATION)),
+        (50, TimeValue(number=50, unit=TimeUnit.DEFAULT)),
+        ({"number": 100, "unit": "STEP"}, TimeValue(number=100, unit=TimeUnit.STEP)),
+        (TimeValue(number=200, unit=TimeUnit.EPOCH), TimeValue(number=200, unit=TimeUnit.EPOCH)),
+    ],
+)
+def test_parse_number_unit_field(input_value: TimeValueInput, expected_output: TimeValue):
+    result = parse_number_unit_field(input_value)
+    assert result == expected_output
+
+
+@pytest.mark.parametrize(
+    "invalid_input",
+    [
+        "invalid:input",
+        {"number": "not_a_number", "unit": "step"},
+        {"invalid_key": 10},
+        None,
+    ],
+)
+def test_parse_number_unit_field_invalid_input(invalid_input: TimeValueInput):
+    with pytest.raises(ValueError):
+        parse_number_unit_field(invalid_input)

tests/training_utils/test_trainer.py

@@ -10,7 +10,7 @@ from torch.optim import SGD
 
 from refiners.fluxion import layers as fl
 from refiners.fluxion.utils import norm
-from refiners.training_utils.common import TimeUnit, count_learnable_parameters, human_readable_number
+from refiners.training_utils.common import TimeUnit, TimeValue, count_learnable_parameters, human_readable_number
 from refiners.training_utils.config import BaseConfig, ModelConfig
 from refiners.training_utils.trainer import (
     Trainer,
@@ -96,7 +96,7 @@ def mock_trainer(mock_config: MockConfig) -> MockTrainer:
 @pytest.fixture
 def mock_trainer_short(mock_config: MockConfig) -> MockTrainer:
     mock_config_short = mock_config.model_copy(deep=True)
-    mock_config_short.training.duration = {"number": 3, "unit": TimeUnit.STEP}
+    mock_config_short.training.duration = TimeValue(number=3, unit=TimeUnit.STEP)
     return MockTrainer(config=mock_config_short)
 
 
@@ -130,10 +130,10 @@ def training_clock() -> TrainingClock:
     return TrainingClock(
         dataset_length=100,
         batch_size=10,
-        training_duration={"number": 5, "unit": TimeUnit.EPOCH},
-        gradient_accumulation={"number": 1, "unit": TimeUnit.EPOCH},
-        evaluation_interval={"number": 1, "unit": TimeUnit.EPOCH},
-        lr_scheduler_interval={"number": 1, "unit": TimeUnit.EPOCH},
+        training_duration=TimeValue(number=5, unit=TimeUnit.EPOCH),
+        gradient_accumulation=TimeValue(number=1, unit=TimeUnit.EPOCH),
+        evaluation_interval=TimeValue(number=1, unit=TimeUnit.EPOCH),
+        lr_scheduler_interval=TimeValue(number=1, unit=TimeUnit.EPOCH),
     )
 
 
@@ -183,7 +183,7 @@ def test_training_cycle(mock_trainer: MockTrainer) -> None:
     clock = mock_trainer.clock
     config = mock_trainer.config
 
-    assert clock.num_step_per_iteration == config.training.gradient_accumulation["number"]
+    assert clock.num_step_per_iteration == config.training.gradient_accumulation.number
     assert clock.num_batches_per_epoch == mock_trainer.dataset_length // config.training.batch_size
 
     assert mock_trainer.step_counter == 0
@@ -191,8 +191,8 @@ def test_training_cycle(mock_trainer: MockTrainer) -> None:
 
     mock_trainer.train()
 
-    assert clock.epoch == config.training.duration["number"]
-    assert clock.step == config.training.duration["number"] * clock.num_batches_per_epoch
+    assert clock.epoch == config.training.duration.number
+    assert clock.step == config.training.duration.number * clock.num_batches_per_epoch
 
     assert mock_trainer.step_counter == mock_trainer.clock.step
 
@@ -206,7 +206,7 @@ def test_training_short_cycle(mock_trainer_short: MockTrainer) -> None:
 
     mock_trainer_short.train()
 
-    assert clock.step == config.training.duration["number"]
+    assert clock.step == config.training.duration.number
 
 
 @pytest.fixture