Automated Code Change

tensorflow_graphics/datasets/modelnet40/modelnet40.py

@@ -80,13 +80,13 @@ def _split_generators(self, download_manager):
     # Note: VALIDATION split was not provided by the authors
     return [
         tfds.core.SplitGenerator(
-            name=tfds.Split.TRAIN,
+            name=tfds.Split.TRAIN,  # pyrefly: ignore[missing-attribute]
             gen_kwargs=dict(
                 filename_list_path=os.path.join(
                     extracted_path,
                     'modelnet40_ply_hdf5_2048/train_files.txt'),)),
         tfds.core.SplitGenerator(
-            name=tfds.Split.TEST,
+            name=tfds.Split.TEST,  # pyrefly: ignore[missing-attribute]
             gen_kwargs=dict(
                 filename_list_path=os.path.join(
                     extracted_path,

tensorflow_graphics/datasets/pix3d/pix3d.py

@@ -148,14 +148,14 @@ def _split_generators(self, dl_manager):
 
     return [
         tfds.core.SplitGenerator(
-            name=tfds.Split.TRAIN,
+            name=tfds.Split.TRAIN,  # pyrefly: ignore[missing-attribute]
             gen_kwargs={
                 'samples_directory': pix3d_dir,
                 'split_file': self.TRAIN_SPLIT_IDX
             },
         ),
         tfds.core.SplitGenerator(
-            name=tfds.Split.TEST,
+            name=tfds.Split.TEST,  # pyrefly: ignore[missing-attribute]
             gen_kwargs={
                 'samples_directory': pix3d_dir,
                 'split_file': self.TEST_SPLIT_IDX

tensorflow_graphics/datasets/shapenet/shapenet.py

@@ -122,21 +122,21 @@ def split_generators(self, dl_manager, dataset_builder):
 
     return [
         tfds.core.SplitGenerator(
-            name=tfds.Split.TRAIN,
+            name=tfds.Split.TRAIN,  # pyrefly: ignore[missing-attribute]
             gen_kwargs={
                 'base_dir': dl_manager.manual_dir,
                 'models': model_items['train']
             },
         ),
         tfds.core.SplitGenerator(
-            name=tfds.Split.TEST,
+            name=tfds.Split.TEST,  # pyrefly: ignore[missing-attribute]
             gen_kwargs={
                 'base_dir': dl_manager.manual_dir,
                 'models': model_items['test']
             },
         ),
         tfds.core.SplitGenerator(
-            name=tfds.Split.VALIDATION,
+            name=tfds.Split.VALIDATION,  # pyrefly: ignore[missing-attribute]
             gen_kwargs={
                 'base_dir': dl_manager.manual_dir,
                 'models': model_items['val']

tensorflow_graphics/geometry/convolution/graph_convolution.py

@@ -18,7 +18,7 @@
 from __future__ import print_function
 
 from typing import Any, Callable, Dict
-from six.moves import zip
+from six.moves import zip  # pyrefly: ignore[missing-source-for-stubs]
 import tensorflow as tf
 
 from tensorflow_graphics.geometry.convolution import utils
@@ -96,7 +96,7 @@ def feature_steered_convolution(
     ValueError: if the input dimensions are invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     data = tf.convert_to_tensor(value=data)
     neighbors = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=neighbors)
     if sizes is not None:
@@ -157,7 +157,7 @@ def feature_steered_convolution(
       y_i_m.append(tf.matmul(p_sum, w_m))
     y_out = tf.add_n(inputs=y_i_m) + tf.reshape(var_b, [1, -1])
     if data_ndims > 2:
-      y_out = unflatten(y_out)
+      y_out = unflatten(y_out)  # pyrefly: ignore[unbound-name]
     return y_out
 
 
@@ -239,7 +239,7 @@ def edge_convolution_template(
     ValueError: if the input dimensions are invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     data = tf.convert_to_tensor(value=data)
     neighbors = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=neighbors)
     if sizes is not None:
@@ -286,7 +286,7 @@ def edge_convolution_template(
              tf.compat.dimension_value(edge_features.shape[-1]))))
 
     if data_ndims > 2:
-      features = unflatten(features)
+      features = unflatten(features)  # pyrefly: ignore[unbound-name]
     return features
 
 

tensorflow_graphics/geometry/convolution/graph_pooling.py

@@ -74,7 +74,7 @@ def pool(data: type_alias.TensorLike,
     ValueError: if `algorithm` is invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     data = tf.convert_to_tensor(value=data)
     pool_map = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=pool_map)
     if sizes is not None:
@@ -161,7 +161,7 @@ def unpool(data: type_alias.TensorLike,
     ValueError: if the input dimensions are invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     data = tf.convert_to_tensor(value=data)
     pool_map = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=pool_map)
     if sizes is not None:
@@ -253,7 +253,7 @@ def upsample_transposed_convolution(
     ValueError: if the input dimensions are invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     data = tf.convert_to_tensor(value=data)
     pool_map = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=pool_map)
     if sizes is not None:

tensorflow_graphics/geometry/convolution/utils.py

@@ -249,7 +249,7 @@ def flatten_batch_to_2d(data: type_alias.TensorLike,
   Raises:
     ValueError: if the input tensor dimensions are invalid.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     data = tf.convert_to_tensor(value=data)
     if sizes is not None:
       sizes = tf.convert_to_tensor(value=sizes)
@@ -272,7 +272,7 @@ def flatten_batch_to_2d(data: type_alias.TensorLike,
 
       def unflatten(flat, name="utils_unflatten"):
         """Invert flatten_batch_to_2d."""
-        with tf.name_scope(name):
+        with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
           flat = tf.convert_to_tensor(value=flat)
           output_shape = tf.concat((data_shape[:-1], tf.shape(input=flat)[-1:]),
                                    axis=0)
@@ -288,7 +288,7 @@ def unflatten(flat, name="utils_unflatten"):
 
       def unflatten(flat, name="utils_unflatten"):
         """Invert flatten_batch_to_2d."""
-        with tf.name_scope(name):
+        with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
           flat = tf.convert_to_tensor(value=flat)
           output_shape = tf.concat((data_shape[:-1], tf.shape(input=flat)[-1:]),
                                    axis=0)
@@ -362,7 +362,7 @@ def unflatten_2d_to_batch(data: type_alias.TensorLike,
   Returns:
     A tensor with shape `[A1, A2, ..., max_rows, D2]`.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     data = tf.convert_to_tensor(value=data)
     sizes = tf.convert_to_tensor(value=sizes)
     if max_rows is None:
@@ -418,7 +418,7 @@ def convert_to_block_diag_2d(data: tf.sparse.SparseTensor,
     TypeError: if the input types are invalid.
     ValueError: if the input dimensions are invalid.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     data = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=data)
     if sizes is not None:
       sizes = tf.convert_to_tensor(value=sizes)

tensorflow_graphics/geometry/deformation_energy/as_conformal_as_possible.py

@@ -95,7 +95,7 @@ def energy(vertices_rest_pose: type_alias.TensorLike,
     ValueError: if the shape of `vertices_rest_pose`, `vertices_deformed_pose`,
     `quaternions`, `edges`, `vertex_weight`, or `edge_weight` is not supported.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     vertices_rest_pose = tf.convert_to_tensor(value=vertices_rest_pose)
     vertices_deformed_pose = tf.convert_to_tensor(value=vertices_deformed_pose)
     quaternions = tf.convert_to_tensor(value=quaternions)

tensorflow_graphics/geometry/representation/mesh/normals.py

@@ -19,7 +19,7 @@
 
 import numpy as np
 
-from six.moves import range
+from six.moves import range  # pyrefly: ignore[missing-source-for-stubs]
 import tensorflow as tf
 
 from tensorflow_graphics.geometry.representation import triangle
@@ -54,7 +54,7 @@ def gather_faces(vertices: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `vertices` or `indices` is not supported.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     vertices = tf.convert_to_tensor(value=vertices)
     indices = tf.convert_to_tensor(value=indices)
 
@@ -109,7 +109,7 @@ def face_normals(faces: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `vertices`, `faces` is not supported.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     faces = tf.convert_to_tensor(value=faces)
 
     shape.check_static(
@@ -158,7 +158,7 @@ def vertex_normals(
   Raises:
     ValueError: If the shape of `vertices`, `indices` is not supported.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     vertices = tf.convert_to_tensor(value=vertices)
     indices = tf.convert_to_tensor(value=indices)
 

tensorflow_graphics/geometry/representation/mesh/sampler.py

@@ -75,7 +75,7 @@ def triangle_area(vertex0: type_alias.TensorLike,
     A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents
       the triangle areas.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     vertex0 = tf.convert_to_tensor(value=vertex0)
     vertex1 = tf.convert_to_tensor(value=vertex1)
     vertex2 = tf.convert_to_tensor(value=vertex2)
@@ -116,7 +116,7 @@ def _random_categorical_sample(
   Returns:
     A `sample_dtype` tensor of shape `[A1, ..., An, num_samples]`.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     asserts.assert_all_above(weights, 0)
     logits = tf.math.log(weights)
     num_faces = tf.shape(input=logits)[-1]
@@ -168,7 +168,7 @@ def generate_random_face_indices(
     An `int32` tensor of shape `[A1, ..., An, num_samples]` denoting sampled
       face indices.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     num_samples = tf.convert_to_tensor(value=num_samples)
     face_weights = tf.convert_to_tensor(value=face_weights)
     shape.check_static(
@@ -222,7 +222,7 @@ def generate_random_barycentric_coordinates(
 
 
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     sample_shape = tf.convert_to_tensor(value=sample_shape)
     shape.check_static(
         tensor=sample_shape, tensor_name="sample_shape", has_rank=1)
@@ -279,7 +279,7 @@ def weighted_random_sample_triangle_mesh(
       where D is dimensionality of each sampled point.
     sample_face_indices: A `int` tensor of shape `[A1, ..., An, num_samples]`.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     faces = tf.convert_to_tensor(value=faces)
     vertex_attributes = tf.convert_to_tensor(value=vertex_attributes)
     face_weights = tf.convert_to_tensor(value=face_weights)
@@ -369,7 +369,7 @@ def area_weighted_random_sample_triangle_mesh(
       where D is dimensionality of each sampled point.
     sample_face_indices: A `int` tensor of shape `[A1, ..., An, num_samples]`.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     faces = tf.convert_to_tensor(value=faces)
     vertex_attributes = tf.convert_to_tensor(value=vertex_attributes)
     num_samples = tf.convert_to_tensor(value=num_samples)

tensorflow_graphics/geometry/transformation/tests/test_helpers.py

@@ -22,7 +22,7 @@
 
 import numpy as np
 from scipy import stats
-from six.moves import range
+from six.moves import range  # pyrefly: ignore[missing-source-for-stubs]
 import tensorflow.compat.v2 as tf
 
 from tensorflow_graphics.geometry.transformation import axis_angle

tensorflow_graphics/image/color_space/linear_rgb.py

@@ -51,7 +51,7 @@ def from_srgb(srgb: type_alias.TensorLike,
     A tensor of shape `[A_1, ..., A_n, 3]`, where the last dimension represents
     RGB values in linear color space.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     srgb = tf.convert_to_tensor(value=srgb)
 
     shape.check_static(

tensorflow_graphics/image/color_space/srgb.py

@@ -58,7 +58,7 @@ def from_linear_rgb(linear_rgb: type_alias.TensorLike,
     A tensor of shape `[A_1, ..., A_n, 3]`, where the last dimension represents
     sRGB values.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     linear_rgb = tf.convert_to_tensor(value=linear_rgb)
 
     shape.check_static(

tensorflow_graphics/image/matting.py

@@ -131,7 +131,7 @@ def build_matrices(image: type_alias.TensorLike,
   Raises:
     ValueError: If `image` is not of rank 4.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     image = tf.convert_to_tensor(value=image)
     eps = tf.constant(value=eps, dtype=image.dtype)
 
@@ -192,7 +192,7 @@ def linear_coefficients(matte: type_alias.TensorLike,
     of rank 4. If `pseudo_inverse` is not of rank 5. If `B` is different
     between `matte` and `pseudo_inverse`.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     matte = tf.convert_to_tensor(value=matte)
     pseudo_inverse = tf.convert_to_tensor(value=pseudo_inverse)
 
@@ -216,7 +216,7 @@ def linear_coefficients(matte: type_alias.TensorLike,
     width = tf.shape(input=matte)[2] + size - 1
     coeffs = tf.image.resize_with_crop_or_pad(coeffs, height, width)
     ones = tf.image.resize_with_crop_or_pad(ones, height, width)
-    coeffs = _image_average(coeffs, size) / _image_average(ones, size)
+    coeffs = _image_average(coeffs, size) / _image_average(ones, size)  # pyrefly: ignore[unsupported-operation]
     return tf.split(coeffs, (-1, 1), axis=-1)
 
 
@@ -246,7 +246,7 @@ def loss(matte: type_alias.TensorLike,
     same size. If `laplacian` is not of rank 5. If `B` is different
     between `matte` and `laplacian`.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     matte = tf.convert_to_tensor(value=matte)
     laplacian = tf.convert_to_tensor(value=laplacian)
 
@@ -287,7 +287,7 @@ def reconstruct(image: type_alias.TensorLike,
     the last dimension of `coeff_add` is not 1. If the batch dimensions of
     `image`, `coeff_mul`, and `coeff_add` do not match.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     image = tf.convert_to_tensor(value=image)
     coeff_mul = tf.convert_to_tensor(value=coeff_mul)
     coeff_add = tf.convert_to_tensor(value=coeff_add)

tensorflow_graphics/image/pyramid.py

@@ -24,7 +24,7 @@
 from typing import Callable, List, Optional, Tuple
 
 import numpy as np
-from six.moves import range
+from six.moves import range  # pyrefly: ignore[missing-source-for-stubs]
 import tensorflow as tf
 
 from tensorflow_graphics.util import export_api
@@ -170,7 +170,7 @@ def downsample(image: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `image` is not supported.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     image = tf.convert_to_tensor(value=image)
 
     shape.check_static(tensor=image, tensor_name="image", has_rank=4)
@@ -196,7 +196,7 @@ def merge(levels: List[type_alias.TensorLike],
   Raises:
     ValueError: If the shape of the elements of `levels` is not supported.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     levels = [tf.convert_to_tensor(value=level) for level in levels]
 
     for index, level in enumerate(levels):
@@ -229,7 +229,7 @@ def split(image: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `image` is not supported.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     image = tf.convert_to_tensor(value=image)
 
     shape.check_static(tensor=image, tensor_name="image", has_rank=4)
@@ -264,7 +264,7 @@ def upsample(image: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `image` is not supported.
   """
-  with tf.name_scope(name):
+  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
     image = tf.convert_to_tensor(value=image)
 
     shape.check_static(tensor=image, tensor_name="image", has_rank=4)