Guarin lig 5512 fix local type check

lightly/models/batchnorm.py

@@ -56,29 +56,30 @@ def forward(self, input: Tensor) -> Tensor:
 
         # during training, use different stats for each split and otherwise
         # use the stats from the first split
+        momentum = 0.0 if self.momentum is None else self.momentum
         if self.training or not self.track_running_stats:
             result = nn.functional.batch_norm(
-                input.view(-1, C * self.num_splits, H, W),
-                self.running_mean,
-                self.running_var,
-                self.weight.repeat(self.num_splits),
-                self.bias.repeat(self.num_splits),
-                True,
-                self.momentum,
-                self.eps,
+                input=input.view(-1, C * self.num_splits, H, W),
+                running_mean=self.running_mean,
+                running_var=self.running_var,
+                weight=self.weight.repeat(self.num_splits),
+                bias=self.bias.repeat(self.num_splits),
+                training=True,
+                momentum=momentum,
+                eps=self.eps,
             ).view(N, C, H, W)
         else:
             # We have to ignore the type errors here, because we know that running_mean
             # and running_var are not None, but the type checker does not.
             result = nn.functional.batch_norm(
-                input,
-                self.running_mean[: self.num_features],  # type: ignore[index]
-                self.running_var[: self.num_features],  # type: ignore[index]
-                self.weight,
-                self.bias,
-                False,
-                self.momentum,
-                self.eps,
+                input=input,
+                running_mean=self.running_mean[: self.num_features],  # type: ignore[index]
+                running_var=self.running_var[: self.num_features],  # type: ignore[index]
+                weight=self.weight,
+                bias=self.bias,
+                training=False,
+                momentum=momentum,
+                eps=self.eps,
             )
 
         return result

lightly/transforms/dino_transform.py

@@ -219,7 +219,8 @@ def __init__(
             T.RandomResizedCrop(
                 size=crop_size,
                 scale=crop_scale,
-                interpolation=PIL.Image.BICUBIC,
+                # Type ignore needed because BICUBIC is not recognized as an attribute.
+                interpolation=PIL.Image.BICUBIC,  # type: ignore[attr-defined]
             ),
             T.RandomHorizontalFlip(p=hf_prob),
             T.RandomVerticalFlip(p=vf_prob),

lightly/transforms/jigsaw.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2021. Lightly AG and its affiliates.
 # All Rights Reserved
 
-from typing import List
+from typing import TYPE_CHECKING, Callable, List
 
 import numpy as np
 import torch
@@ -10,6 +10,9 @@
 from torch import Tensor
 from torchvision import transforms as T
 
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
 
 class Jigsaw(object):
     """Implementation of Jigsaw image augmentation, inspired from PyContrast library.
@@ -49,7 +52,7 @@ def __init__(
         self.crop_size = crop_size
         self.grid_size = int(img_size / self.n_grid)
         self.side = self.grid_size - self.crop_size
-        self.transform = transform
+        self.transform: Callable[[PILImage], Tensor] = transform
 
         yy, xx = np.meshgrid(np.arange(n_grid), np.arange(n_grid))
         self.yy = np.reshape(yy * self.grid_size, (n_grid * n_grid,))
@@ -66,11 +69,11 @@ def __call__(self, img: PILImage) -> Tensor:
         """
         r_x = np.random.randint(0, self.side + 1, self.n_grid * self.n_grid)
         r_y = np.random.randint(0, self.side + 1, self.n_grid * self.n_grid)
-        img = np.asarray(img, np.uint8)
-        crops: List[PILImage] = []
+        img_arr = np.asarray(img, np.uint8)
+        crops: List[NDArray[np.uint8]] = []
         for i in range(self.n_grid * self.n_grid):
             crops.append(
-                img[
+                img_arr[
                     self.xx[i] + r_x[i] : self.xx[i] + r_x[i] + self.crop_size,
                     self.yy[i] + r_y[i] : self.yy[i] + r_y[i] + self.crop_size,
                     :,

lightly/transforms/rotation.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2020. Lightly AG and its affiliates.
 # All Rights Reserved
 
-from typing import Tuple, Union
+from typing import Callable, Tuple, Union
 
 import numpy as np
 import torchvision.transforms as T
@@ -65,7 +65,9 @@ class RandomRotateDegrees:
     """
 
     def __init__(self, prob: float, degrees: Union[float, Tuple[float, float]]):
-        self.transform = T.RandomApply([T.RandomRotation(degrees=degrees)], p=prob)
+        self.transform: Callable[
+            [Union[Image, Tensor]], Union[Image, Tensor]
+        ] = T.RandomApply([T.RandomRotation(degrees=degrees)], p=prob)
 
     def __call__(self, image: Union[Image, Tensor]) -> Union[Image, Tensor]:
         """Rotates the images with a given probability.

lightly/utils/benchmarking/linear_classifier.py

@@ -129,7 +129,9 @@ def validation_step(self, batch: Tuple[Tensor, ...], batch_idx: int) -> Tensor:
         self.log_dict(log_dict, prog_bar=True, sync_dist=True, batch_size=batch_size)
         return loss
 
-    def configure_optimizers(
+    # Type ignore is needed because return type of LightningModule.configure_optimizers
+    # is complicated and typing changes between versions.
+    def configure_optimizers(  # type: ignore[override]
         self,
     ) -> Tuple[List[Optimizer], List[Dict[str, Union[Any, str]]]]:
         parameters = list(self.classification_head.parameters())

lightly/utils/lars.py

@@ -1,8 +1,7 @@
-from typing import Any, Callable, Dict, Optional, Union
+from typing import Any, Callable, Dict, Optional, overload
 
 import torch
-from torch import Tensor
-from torch.optim.optimizer import Optimizer, required  # type: ignore[attr-defined]
+from torch.optim.optimizer import Optimizer
 
 
 class LARS(Optimizer):
@@ -69,15 +68,15 @@ class LARS(Optimizer):
     def __init__(
         self,
         params: Any,
-        lr: float = required,
+        lr: float,
         momentum: float = 0,
         dampening: float = 0,
         weight_decay: float = 0,
         nesterov: bool = False,
         trust_coefficient: float = 0.001,
         eps: float = 1e-8,
     ):
-        if lr is not required and lr < 0.0:
+        if lr < 0.0:
             raise ValueError(f"Invalid learning rate: {lr}")
         if momentum < 0.0:
             raise ValueError(f"Invalid momentum value: {momentum}")
@@ -104,6 +103,15 @@ def __setstate__(self, state: Dict[str, Any]) -> None:
         for group in self.param_groups:
             group.setdefault("nesterov", False)
 
+    # Type ignore for overloads is required for Python 3.7
+    @overload  # type: ignore[override]
+    def step(self, closure: None = None) -> None:
+        ...
+
+    @overload  # type: ignore[override]
+    def step(self, closure: Callable[[], float]) -> float:
+        ...
+
     @torch.no_grad()
     def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
         """Performs a single optimization step.

pyproject.toml

@@ -145,7 +145,6 @@ omit = ["lightly/openapi_generated/*"]
 
 [tool.mypy]
 ignore_missing_imports = true
-python_version = "3.10"
 warn_unused_configs = true
 strict_equality = true
 
@@ -167,7 +166,7 @@ no_implicit_optional = true
 strict_optional = true
 
 # Configuring warnings
-warn_unused_ignores = true
+warn_unused_ignores = false   # Different ignores are required for different Python versions
 warn_no_return = true
 warn_return_any = true
 warn_redundant_casts = true

tests/helpers.py

@@ -0,0 +1,19 @@
+from typing import Any, List
+
+from torch import Tensor
+
+
+def assert_list_tensor(items: Any) -> List[Tensor]:
+    """Makes sure that the input is a list of tensors.
+
+    Should be used in tests where functions return Union[List[Tensor], List[Image]] and
+    we want to make sure that the output is a list of tensors.
+
+    Example:
+        >>> output: Union[List[Tensor], List[Image]] = transform(images)
+        >>> tensors: List[Tensor] = assert_list_tensor(output)
+
+    """
+    assert isinstance(items, list)
+    assert all(isinstance(item, Tensor) for item in items)
+    return items

tests/transforms/test_byol_transform.py

@@ -6,6 +6,8 @@
     BYOLView2Transform,
 )
 
+from .. import helpers
+
 
 def test_view_on_pil_image() -> None:
     single_view_transform = BYOLView1Transform(input_size=32)
@@ -20,7 +22,7 @@ def test_multi_view_on_pil_image() -> None:
         view_2_transform=BYOLView2Transform(input_size=32),
     )
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 2
     assert output[0].shape == (3, 32, 32)
     assert output[1].shape == (3, 32, 32)

tests/transforms/test_densecl_transform.py

@@ -2,11 +2,13 @@
 
 from lightly.transforms import DenseCLTransform
 
+from .. import helpers
+
 
 def test_multi_view_on_pil_image() -> None:
     multi_view_transform = DenseCLTransform(input_size=32)
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 2
     assert output[0].shape == (3, 32, 32)
     assert output[1].shape == (3, 32, 32)

tests/transforms/test_dino_transform.py

@@ -2,6 +2,8 @@
 
 from lightly.transforms.dino_transform import DINOTransform, DINOViewTransform
 
+from .. import helpers
+
 
 def test_view_on_pil_image() -> None:
     single_view_transform = DINOViewTransform(crop_size=32)
@@ -13,7 +15,7 @@ def test_view_on_pil_image() -> None:
 def test_multi_view_on_pil_image() -> None:
     multi_view_transform = DINOTransform(global_crop_size=32, local_crop_size=8)
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 8
     # global views
     assert all(out.shape == (3, 32, 32) for out in output[:2])

tests/transforms/test_fastsiam_transform.py

@@ -2,11 +2,13 @@
 
 from lightly.transforms.fast_siam_transform import FastSiamTransform
 
+from .. import helpers
+
 
 def test_multi_view_on_pil_image() -> None:
     multi_view_transform = FastSiamTransform(num_views=3, input_size=32)
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 3
     assert output[0].shape == (3, 32, 32)
     assert output[1].shape == (3, 32, 32)

tests/transforms/test_moco_transform.py

@@ -2,11 +2,13 @@
 
 from lightly.transforms.moco_transform import MoCoV1Transform, MoCoV2Transform
 
+from .. import helpers
+
 
 def test_moco_v1_multi_view_on_pil_image() -> None:
     multi_view_transform = MoCoV1Transform(input_size=32)
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 2
     assert output[0].shape == (3, 32, 32)
     assert output[1].shape == (3, 32, 32)
@@ -15,7 +17,7 @@ def test_moco_v1_multi_view_on_pil_image() -> None:
 def test_moco_v2_multi_view_on_pil_image() -> None:
     multi_view_transform = MoCoV2Transform(input_size=32)
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 2
     assert output[0].shape == (3, 32, 32)
     assert output[1].shape == (3, 32, 32)

tests/transforms/test_msn_transform.py

@@ -2,6 +2,8 @@
 
 from lightly.transforms.msn_transform import MSNTransform, MSNViewTransform
 
+from .. import helpers
+
 
 def test_view_on_pil_image() -> None:
     single_view_transform = MSNViewTransform(crop_size=32)
@@ -13,7 +15,7 @@ def test_view_on_pil_image() -> None:
 def test_multi_view_on_pil_image() -> None:
     multi_view_transform = MSNTransform(random_size=32, focal_size=8)
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 12
     # global views
     assert all(out.shape == (3, 32, 32) for out in output[:2])

tests/transforms/test_pirl_transform.py

@@ -2,11 +2,13 @@
 
 from lightly.transforms.pirl_transform import PIRLTransform
 
+from .. import helpers
+
 
 def test_multi_view_on_pil_image() -> None:
     multi_view_transform = PIRLTransform(input_size=32)
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 2
     assert output[0].shape == (3, 32, 32)
     assert output[1].shape == (9, 3, 10, 10)

tests/transforms/test_simclr_transform.py

@@ -2,6 +2,8 @@
 
 from lightly.transforms.simclr_transform import SimCLRTransform, SimCLRViewTransform
 
+from .. import helpers
+
 
 def test_view_on_pil_image() -> None:
     single_view_transform = SimCLRViewTransform(input_size=32)
@@ -13,7 +15,7 @@ def test_view_on_pil_image() -> None:
 def test_multi_view_on_pil_image() -> None:
     multi_view_transform = SimCLRTransform(input_size=32)
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 2
     assert output[0].shape == (3, 32, 32)
     assert output[1].shape == (3, 32, 32)

tests/transforms/test_simsiam_transform.py

@@ -2,6 +2,8 @@
 
 from lightly.transforms.simsiam_transform import SimSiamTransform, SimSiamViewTransform
 
+from .. import helpers
+
 
 def test_view_on_pil_image() -> None:
     single_view_transform = SimSiamViewTransform(input_size=32)
@@ -13,7 +15,7 @@ def test_view_on_pil_image() -> None:
 def test_multi_view_on_pil_image() -> None:
     multi_view_transform = SimSiamTransform(input_size=32)
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 2
     assert output[0].shape == (3, 32, 32)
     assert output[1].shape == (3, 32, 32)

tests/transforms/test_smog_transform.py

@@ -2,6 +2,8 @@
 
 from lightly.transforms.smog_transform import SMoGTransform, SmoGViewTransform
 
+from .. import helpers
+
 
 def test_view_on_pil_image() -> None:
     single_view_transform = SmoGViewTransform(crop_size=32)
@@ -13,7 +15,7 @@ def test_view_on_pil_image() -> None:
 def test_multi_view_on_pil_image() -> None:
     multi_view_transform = SMoGTransform(crop_sizes=(32, 8))
     sample = Image.new("RGB", (100, 100))
-    output = multi_view_transform(sample)
+    output = helpers.assert_list_tensor(multi_view_transform(sample))
     assert len(output) == 8
     assert all(out.shape == (3, 32, 32) for out in output[:4])
     assert all(out.shape == (3, 8, 8) for out in output[4:])