Merge pull request #657 from zimmerrol/dependencies

Bump up versions of test dependencies

.github/workflows/tests.yml

@@ -89,6 +89,7 @@ jobs:
         retry-with-backoff pip install -r tests/requirements.txt
     - name: mypy (package)
       run: |
+        mypy --install-types --non-interactive foolbox/
         mypy -p foolbox
     - name: mypy (tests)
       run: |

examples/zoo/mnist/foolbox_model.py

@@ -14,7 +14,7 @@ def create() -> PyTorchModel:
         nn.ReLU(),
         nn.MaxPool2d(2),
         nn.Dropout2d(0.25),
-        nn.Flatten(),  # type: ignore
+        nn.Flatten(),
         nn.Linear(9216, 128),
         nn.ReLU(),
         nn.Dropout2d(0.5),

foolbox/attacks/base.py

@@ -404,7 +404,7 @@ def __call__(  # type: ignore
         if any(eps is None for eps in epsilons):
             early_stop = None
         else:
-            early_stop = min(epsilons)
+            early_stop = min(epsilons)  # type: ignore
 
         # run the actual attack
         xp = self.run(model, x, criterion, early_stop=early_stop, **kwargs)

foolbox/attacks/gen_attack_utils.py

@@ -12,7 +12,7 @@ def rescale_jax(x: ep.JAXTensor, target_shape: List[int]) -> ep.JAXTensor:
 
     resize_rates = (target_shape[1] / x.shape[1], target_shape[2] / x.shape[2])
 
-    def interpolate_bilinear(  # type: ignore
+    def interpolate_bilinear(
         im: np.ndarray, rows: np.ndarray, cols: np.ndarray
     ) -> np.ndarray:
         # based on http://stackoverflow.com/a/12729229
@@ -21,11 +21,11 @@ def interpolate_bilinear(  # type: ignore
         row_lo = np.floor(rows).astype(int)
         row_hi = row_lo + 1
 
-        def cclip(cols: np.ndarray) -> np.ndarray:  # type: ignore
-            return np.clip(cols, 0, ncols - 1)
+        def cclip(cols: np.ndarray) -> np.ndarray:
+            return np.clip(cols, 0, ncols - 1)  # type: ignore
 
-        def rclip(rows: np.ndarray) -> np.ndarray:  # type: ignore
-            return np.clip(rows, 0, nrows - 1)
+        def rclip(rows: np.ndarray) -> np.ndarray:
+            return np.clip(rows, 0, nrows - 1)  # type: ignore
 
         nrows, ncols = im.shape[-3:-1]
 
@@ -39,7 +39,7 @@ def rclip(rows: np.ndarray) -> np.ndarray:  # type: ignore
         wc = np.expand_dims((cols - col_lo) * (row_hi - rows), -1)
         wd = np.expand_dims((cols - col_lo) * (rows - row_lo), -1)
 
-        return wa * Ia + wb * Ib + wc * Ic + wd * Id
+        return wa * Ia + wb * Ib + wc * Ic + wd * Id  # type: ignore
 
     nrows, ncols = img.shape[-3:-1]
     deltas = (0.5 / resize_rates[0], 0.5 / resize_rates[1])
@@ -49,8 +49,8 @@ def rclip(rows: np.ndarray) -> np.ndarray:  # type: ignore
     rows_grid, cols_grid = np.meshgrid(rows - 0.5, cols - 0.5, indexing="ij")
 
     img_resize_vec = interpolate_bilinear(img, rows_grid.flatten(), cols_grid.flatten())
-    img_resize = img_resize_vec.reshape(
-        img.shape[:-3] + (len(rows), len(cols)) + img.shape[-1:]
+    img_resize = np.reshape(
+        img_resize_vec, img.shape[:-3] + (len(rows), len(cols)) + img.shape[-1:]
     )
 
     return ep.JAXTensor(img_resize)

foolbox/attacks/spatial_attack_transformations.py

@@ -50,7 +50,7 @@ def transform_pt(
     def create_meshgrid(x: torch.Tensor) -> torch.Tensor:
         space_x = torch.linspace(-1, 1, n_x, device=x.device)
         space_y = torch.linspace(-1, 1, n_y, device=x.device)
-        meshgrid = torch.meshgrid([space_x, space_y])  # type: ignore
+        meshgrid = torch.meshgrid([space_x, space_y])
         ones = torch.ones(meshgrid[0].shape, device=x.device)
         gridder = torch.stack([meshgrid[1], meshgrid[0], ones], dim=2)
         grid = gridder[None, ...].repeat(bs, 1, 1, 1)[..., None]
@@ -62,7 +62,7 @@ def create_meshgrid(x: torch.Tensor) -> torch.Tensor:
     new_coords = new_coords.squeeze_(-1)
 
     # align_corners=True to match tf implementation
-    transformed_images = torch.nn.functional.grid_sample(  # type: ignore
+    transformed_images = torch.nn.functional.grid_sample(
         x, new_coords, mode="bilinear", padding_mode="zeros", align_corners=True
     )
     return astensor(transformed_images)

foolbox/zoo/model_loader.py

@@ -45,5 +45,5 @@ def load(
         self, path: str, module_name: str = "foolbox_model", **kwargs: Any
     ) -> Model:
         module = super()._import_module(path, module_name=module_name)
-        model = module.create(**kwargs)  # type: ignore
+        model = module.create(**kwargs)
         return cast(Model, model)

requirements.txt

@@ -5,5 +5,5 @@ pytest-cov==2.8.1
 coverage==5.1
 codecov==2.0.15
 coveralls==1.10.0
-mypy==0.770
+mypy==0.931
 pre-commit==2.3.0

setup.py

@@ -1,4 +1,4 @@
-from setuptools import setup  # type: ignore
+from setuptools import setup
 from setuptools import find_packages
 from os.path import join, dirname
 
@@ -20,7 +20,7 @@
     "numpy",
     "scipy",
     "setuptools",
-    "eagerpy==0.29.0",
+    "eagerpy>=0.30.0",
     "GitPython>=3.0.7",
     "typing-extensions>=3.7.4.1",
     "requests>=2.24.0",

tests/conftest.py

@@ -60,7 +60,7 @@ def pytorch_simple_model(
     import torch
 
     class Model(torch.nn.Module):
-        def forward(self, x: torch.Tensor) -> torch.Tensor:  # type: ignore
+        def forward(self, x: torch.Tensor) -> torch.Tensor:
             x = torch.mean(x, 3)
             x = torch.mean(x, 2)
             return x

tests/requirements.txt

@@ -1,8 +1,7 @@
-torch==1.4.0
-torchvision==0.5.0
-jaxlib==0.1.46
-jax==0.1.66
-tensorflow==2.5.0
+torch==1.10.1
+torchvision==0.11.2
+jax[cpu]==0.2.17
+tensorflow==2.6.2
 numba==0.50.1
 matplotlib==3.2.1
 pillow==8.1.1

tests/test_gen_attack_utils.py

@@ -21,7 +21,7 @@ def test_rescale_axis(request: Any, dummy: ep.Tensor) -> None:
     x_up_ep = rescale_images(x_ep, (16, 3, 128, 128), 1)
     x_up = x_up_ep.numpy()
 
-    assert np.allclose(x_up_np, x_up)
+    assert np.allclose(x_up_np, x_up, atol=1e-5)
 
 
 def test_rescale_axis_nhwc(request: Any, dummy: ep.Tensor) -> None:
@@ -39,4 +39,4 @@ def test_rescale_axis_nhwc(request: Any, dummy: ep.Tensor) -> None:
     x_up_ep = rescale_images(x_ep, (16, 128, 128, 3), -1)
     x_up = x_up_ep.numpy()
 
-    assert np.allclose(x_up_np, x_up)
+    assert np.allclose(x_up_np, x_up, atol=1e-5)

tests/test_models.py

@@ -51,7 +51,7 @@ def test_pytorch_training_warning(request: Any) -> None:
     import torch
 
     class Model(torch.nn.Module):
-        def forward(self, x: torch.Tensor) -> torch.Tensor:  # type: ignore
+        def forward(self, x: torch.Tensor) -> torch.Tensor:
             return x
 
     model = Model().train()