Tests and deps fixed to support the newest packages

.pre-commit-config.yaml

@@ -1,16 +1,19 @@
 repos:
 - repo: https://github.com/ambv/black
-  rev: 19.10b0
+  rev: 22.6.0
   hooks:
   - id: black
     args: [--config=black.toml]
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v2.3.0
+- repo: https://github.com/pycqa/flake8
+  rev: 4.0.1
   hooks:
   - id: flake8
     args: [--max-line-length=120]
     exclude: ^tests/
-  - id: check-case-conflict
-  - id: check-symlinks
-  - id: end-of-file-fixer
-  - id: requirements-txt-fixer
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.3.0
+  hooks:
+    - id: check-case-conflict
+    - id: check-symlinks
+    - id: end-of-file-fixer
+    - id: requirements-txt-fixer

ci/requirements.txt

@@ -1,13 +1,14 @@
-black==19.10b0
-codecov==2.0.15
+black-22.6.0
+codecov-2.1.12
 coverage==5.0.3
 ipykernel==4.8.2
 msmb-theme==1.2.0
 nbsphinx-link==1.2
-opencv-python-headless==4.1.2.30
+#opencv-python-headless==4.1.2.30
+opencv-python-headless==4.6.0.66
 pytest==3.6.4
 pytest-cov==2.6.0
-pytest-flake8==1.0.2
+pytest-flake8-1.1.1
 pytest-pep8==1.0.6
 sphinx==1.8.4
 sphinx-rtd-theme==0.4.1

doc/source/conf.py

@@ -158,7 +158,15 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, "solt", "solt Documentation", author, "solt", "One line description of project.", "Miscellaneous",),
+    (
+        master_doc,
+        "solt",
+        "solt Documentation",
+        author,
+        "solt",
+        "One line description of project.",
+        "Miscellaneous",
+    ),
 ]
 
 

solt/core/_base_transforms.py

@@ -139,7 +139,14 @@ def wrap_data(data):
         return data
 
     def __call__(
-        self, data, return_torch=False, as_dict=True, scale_keypoints=True, normalize=True, mean=None, std=None,
+        self,
+        data,
+        return_torch=False,
+        as_dict=True,
+        scale_keypoints=True,
+        normalize=True,
+        mean=None,
+        std=None,
     ):
         """
         Applies the transform to a DataContainer
@@ -184,7 +191,11 @@ def __call__(
 
         if return_torch:
             return res.to_torch(
-                as_dict=as_dict, scale_keypoints=scale_keypoints, normalize=normalize, mean=mean, std=std,
+                as_dict=as_dict,
+                scale_keypoints=scale_keypoints,
+                normalize=normalize,
+                mean=mean,
+                std=std,
             )
         return res
 
@@ -349,7 +360,13 @@ class MatrixTransform(BaseTransform, InterpolationPropertyHolder, PaddingPropert
     """
 
     def __init__(
-        self, interpolation="bilinear", padding="z", p=0.5, ignore_state=True, affine=True, ignore_fast_mode=False,
+        self,
+        interpolation="bilinear",
+        padding="z",
+        p=0.5,
+        ignore_state=True,
+        affine=True,
+        ignore_fast_mode=False,
     ):
         BaseTransform.__init__(self, p=p, data_indices=None)
         InterpolationPropertyHolder.__init__(self, interpolation=interpolation)

solt/core/_core.py

@@ -40,7 +40,12 @@ class Stream(Serializable):
     How the class should be stored in the registry"""
 
     def __init__(
-        self, transforms=None, interpolation=None, padding=None, optimize_stack=False, ignore_fast_mode=False,
+        self,
+        transforms=None,
+        interpolation=None,
+        padding=None,
+        optimize_stack=False,
+        ignore_fast_mode=False,
     ):
         super(Stream, self).__init__()
 
@@ -118,7 +123,14 @@ def reset_padding(self, value):
                     trf.reset_padding(self.padding)
 
     def __call__(
-        self, data, return_torch=True, as_dict=True, scale_keypoints=True, normalize=True, mean=None, std=None,
+        self,
+        data,
+        return_torch=True,
+        as_dict=True,
+        scale_keypoints=True,
+        normalize=True,
+        mean=None,
+        std=None,
     ):
         """
         Executes the list of the pre-defined transformations for a given data container.
@@ -153,7 +165,11 @@ def __call__(
 
         if return_torch:
             return res.to_torch(
-                as_dict=as_dict, scale_keypoints=scale_keypoints, normalize=normalize, mean=mean, std=std,
+                as_dict=as_dict,
+                scale_keypoints=scale_keypoints,
+                normalize=normalize,
+                mean=mean,
+                std=std,
             )
         return res
 
@@ -242,7 +258,12 @@ class SelectiveStream(Stream):
     """How the class should be stored in the registry"""
 
     def __init__(
-        self, transforms=None, n=1, probs=None, optimize_stack=False, ignore_fast_mode=False,
+        self,
+        transforms=None,
+        n=1,
+        probs=None,
+        optimize_stack=False,
+        ignore_fast_mode=False,
     ):
         """
         Constructor.
@@ -257,7 +278,9 @@ def __init__(
             Whether to execute stack optimization for augmentations.
         """
         super(SelectiveStream, self).__init__(
-            transforms=transforms, optimize_stack=optimize_stack, ignore_fast_mode=ignore_fast_mode,
+            transforms=transforms,
+            optimize_stack=optimize_stack,
+            ignore_fast_mode=ignore_fast_mode,
         )
         if transforms is None:
             transforms = []
@@ -270,7 +293,14 @@ def __init__(
         self.probs = probs
 
     def __call__(
-        self, data, return_torch=True, as_dict=True, scale_keypoints=True, normalize=True, mean=None, std=None,
+        self,
+        data,
+        return_torch=True,
+        as_dict=True,
+        scale_keypoints=True,
+        normalize=True,
+        mean=None,
+        std=None,
     ):
         """
         Applies randomly selected n transforms to the given data item
@@ -302,7 +332,7 @@ def __call__(
         data = BaseTransform.wrap_data(data)
 
         if len(self.transforms) > 0:
-            random_state = np.random.RandomState(random.randint(0, 2 ** 32 - 1))
+            random_state = np.random.RandomState(random.randint(0, 2**32 - 1))
             trfs = random_state.choice(self.transforms, self.n, replace=False, p=self.probs)
             if self.optimize_stack:
                 trfs = [copy.deepcopy(x) for x in trfs]
@@ -311,7 +341,11 @@ def __call__(
 
         if return_torch:
             return data.to_torch(
-                as_dict=as_dict, scale_keypoints=scale_keypoints, normalize=normalize, mean=mean, std=std,
+                as_dict=as_dict,
+                scale_keypoints=scale_keypoints,
+                normalize=normalize,
+                mean=mean,
+                std=std,
             )
 
         return data

solt/core/_data.py

@@ -70,7 +70,11 @@ def __init__(self, data, fmt, transform_settings=None):
                     transform_settings[idx]["padding"] = validate_parameter(None, ALLOWED_PADDINGS, "z", str, True)
                 else:
                     transform_settings[idx]["padding"] = validate_parameter(
-                        (transform_settings[idx]["padding"], "strict"), ALLOWED_PADDINGS, "z", str, True,
+                        (transform_settings[idx]["padding"], "strict"),
+                        ALLOWED_PADDINGS,
+                        "z",
+                        str,
+                        True,
                     )
             else:
                 if "interpolation" in transform_settings[idx] or "padding" in transform_settings[idx]:

solt/transforms/_transforms.py

@@ -112,7 +112,13 @@ class Rotate(MatrixTransform):
     """How the class should be stored in the registry"""
 
     def __init__(
-        self, angle_range=None, interpolation="bilinear", padding="z", p=0.5, ignore_state=True, ignore_fast_mode=False,
+        self,
+        angle_range=None,
+        interpolation="bilinear",
+        padding="z",
+        p=0.5,
+        ignore_state=True,
+        ignore_fast_mode=False,
     ):
         super(Rotate, self).__init__(
             interpolation=interpolation,
@@ -614,7 +620,11 @@ def _apply_pts(self, pts: Keypoints, settings: dict):
         pts_data[:, 0] += pad_w_left
         pts_data[:, 1] += pad_h_top
 
-        return Keypoints(pts_data, pad_h_top + pts.height + pad_h_bottom, pad_w_left + pts.width + pad_w_right,)
+        return Keypoints(
+            pts_data,
+            pad_h_top + pts.height + pad_h_bottom,
+            pad_w_left + pts.width + pad_w_right,
+        )
 
 
 class Resize(BaseTransform, InterpolationPropertyHolder):
@@ -820,7 +830,7 @@ def sample_transform(self, data: DataContainer):
         if w is None or h is None or c is None:
             raise ValueError
 
-        random_state = np.random.RandomState(random.randint(0, 2 ** 32 - 1))
+        random_state = np.random.RandomState(random.randint(0, 2**32 - 1))
         noise_img = random_state.randn(h, w, c)
 
         noise_img -= noise_img.min()
@@ -833,7 +843,11 @@ def sample_transform(self, data: DataContainer):
     @img_shape_checker
     def _apply_img(self, img: np.ndarray, settings: dict):
         return cv2.addWeighted(
-            img, (1 - self.state_dict["gain"]), self.state_dict["noise"], self.state_dict["gain"], 0,
+            img,
+            (1 - self.state_dict["gain"]),
+            self.state_dict["noise"],
+            self.state_dict["gain"],
+            0,
         )
 
     def _apply_mask(self, mask: np.ndarray, settings: dict):
@@ -960,7 +974,7 @@ def sample_transform(self, data: DataContainer):
         gain = random.uniform(self.gain_range[0], self.gain_range[1])
         salt_p = random.uniform(self.salt_p[0], self.salt_p[1])
 
-        random_state = np.random.RandomState(random.randint(0, 2 ** 32 - 1))
+        random_state = np.random.RandomState(random.randint(0, 2**32 - 1))
         sp = random_state.rand(h, w) <= gain
         salt = sp.copy() * 1.0
         pepper = sp.copy() * 1.0
@@ -1140,7 +1154,9 @@ def sample_transform(self, data):
     def _apply_img(self, img: np.ndarray, settings: dict):
         if self.blur == "g":
             return cv2.GaussianBlur(
-                img, ksize=(self.state_dict["k_size"], self.state_dict["k_size"]), sigmaX=self.state_dict["sigma"],
+                img,
+                ksize=(self.state_dict["k_size"], self.state_dict["k_size"]),
+                sigmaX=self.state_dict["sigma"],
             )
         if self.blur == "m":
             return cv2.medianBlur(img, ksize=self.state_dict["k_size"])
@@ -1531,7 +1547,7 @@ def __init__(self, d_range=None, ratio=0.6, rotate=0, mode=None, data_indices=No
     def sample_transform(self, data: DataContainer):
         h, w = super(GridMask, self).sample_transform(data)
 
-        hh = int(np.ceil(np.sqrt(h ** 2 + w ** 2)))
+        hh = int(np.ceil(np.sqrt(h**2 + w**2)))
         d = random.randint(self.d_range[0], self.d_range[1])
 
         mask = np.ones((hh, hh), np.float32)