Reformatted, added header

setup.cfg

@@ -1,2 +1,2 @@
 [metadata]
-license_file = LICENSE
+license_file = LICENSE

setup.py

@@ -8,7 +8,7 @@
     author="Sidney Radcliffe",
     author_email="sidneyradcliffe@gmail.com",
     description="Minimal automatic differentiation implementation in Python, NumPy.",
-    long_description=pathlib.Path('README.md').read_text(),
+    long_description=pathlib.Path("README.md").read_text(),
     long_description_content_type="text/markdown",
     url="https://github.com/sradc/smallpebble",
     license="Apache License 2.0",

smallpebble/__init__.py

@@ -1,3 +1,17 @@
+# Copyright 2021 The SmallPebble authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from smallpebble.array_library import use
 from smallpebble.core import *
 from smallpebble.nn import *

smallpebble/array_library.py

@@ -1,3 +1,17 @@
+# Copyright 2021 The SmallPebble authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 """This module acts as a proxy, allowing NumPy/CuPy to be switched dynamically.
 The default library is NumPy.
 

smallpebble/core/__init__.py

@@ -1 +1,15 @@
-from smallpebble.core.ops import *
+# Copyright 2021 The SmallPebble authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from smallpebble.core.ops import *

smallpebble/core/ops.py

@@ -1,3 +1,17 @@
+# Copyright 2021 The SmallPebble authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 # ----------------
 # ---------------- AUTOMATIC DIFFERENTIATION
 # ----------------
@@ -8,6 +22,7 @@
 import numpy
 import smallpebble.array_library as np
 
+
 class Variable:
     "To be used in calculations to be differentiated."
 
@@ -59,8 +74,7 @@ def __call__(self, *args):
     def run(self):
         "Compute the value of this node."
         if not self.arguments:
-            raise AssignmentError(
-                f"No arguments have been assigned to {self}.")
+            raise AssignmentError(f"No arguments have been assigned to {self}.")
         argvals = (a.run() if hasattr(a, "run") else a for a in self.arguments)
         return self.function(*argvals)
 
@@ -75,6 +89,7 @@ def assign_value(self, variable):
         "Assign a Variable instance to this placeholder."
         self.arguments = [variable]
 
+
 def get_gradients(variable):
     "Compute the first derivatives of `variable` with respect to child variables."
     gradients = defaultdict(lambda: 0)
@@ -93,10 +108,10 @@ def compute_gradients(variable, path_value):
 def reshape(a, shape):
     "Reshape `a` into shape `shape`."
     value = np.reshape(a.array, shape)
-    local_gradients = [
-        (a, lambda path_value: path_value.reshape(a.array.shape))]
+    local_gradients = [(a, lambda path_value: path_value.reshape(a.array.shape))]
     return Variable(value, local_gradients)
 
+
 def mul(a, b):
     "Elementwise multiplication."
     value = a.array * b.array
@@ -115,7 +130,6 @@ def neg(a):
     return Variable(value, local_gradients)
 
 
-
 def add(a, b):
     "Elementwise addition."
     value = a.array + b.array
@@ -140,6 +154,7 @@ def add_at(a, indices, b):
     ]
     return Variable(value, local_gradients)
 
+
 def div(a, b):
     "Elementwise division."
     value = a.array / b.array
@@ -158,13 +173,11 @@ def enable_broadcast(a, b, matmul=False):
     a_repeatdims, b_repeatdims = broadcastinfo(a_shape, b_shape)
 
     def multiply_by_locgrad_a(path_value):
-        path_value = np.sum(
-            path_value, axis=a_repeatdims).reshape(a.array.shape)
+        path_value = np.sum(path_value, axis=a_repeatdims).reshape(a.array.shape)
         return np.zeros(a.array.shape, a.array.dtype) + path_value
 
     def multiply_by_locgrad_b(path_value):
-        path_value = np.sum(
-            path_value, axis=b_repeatdims).reshape(b.array.shape)
+        path_value = np.sum(path_value, axis=b_repeatdims).reshape(b.array.shape)
         return np.zeros(b.array.shape, b.array.dtype) + path_value
 
     a_ = Variable(a.array, local_gradients=[(a, multiply_by_locgrad_a)])
@@ -250,8 +263,7 @@ def exp(a):
 def expand_dims(a, axis):
     "Add new axes with size of 1, indices specified by `axis`."
     value = np.expand_dims(a.array, axis)
-    local_gradients = [
-        (a, lambda path_value: path_value.reshape(a.array.shape))]
+    local_gradients = [(a, lambda path_value: path_value.reshape(a.array.shape))]
     return Variable(value, local_gradients)
 
 
@@ -303,8 +315,7 @@ def maxax(a, axis):
     flatshape = value.shape
     idx = np.argmax(value, axis=-1)
     value = np.take_along_axis(value, idx[..., np.newaxis], -1)
-    value = value.reshape(
-        tuple(1 if i == axis else v for i, v in enumerate(a.shape)))
+    value = value.reshape(tuple(1 if i == axis else v for i, v in enumerate(a.shape)))
 
     def multiply_by_locgrad(path_value):
         result = np.zeros(flatshape)
@@ -330,7 +341,6 @@ def multiply_by_locgrad(path_value):
     return Variable(value, local_gradients)
 
 
-
 def broadcastinfo(a_shape, b_shape):
     "Get which dimensions are added or repeated when `a` and `b` are broadcast."
     ndim = max(len(a_shape), len(b_shape))
@@ -346,8 +356,7 @@ def broadcastinfo(a_shape, b_shape):
 
     a_repeatdims = (a_shape_ == 1) & (b_shape_ > 1)  # the repeated dims
     a_repeatdims[:add_ndims_to_a] = True  # the added dims
-    a_repeatdims = np.where(a_repeatdims == True)[
-        0]  # indices of axes where True
+    a_repeatdims = np.where(a_repeatdims == True)[0]  # indices of axes where True
     a_repeatdims = [int(i) for i in a_repeatdims]
 
     b_repeatdims = (b_shape_ == 1) & (a_shape_ > 1)
@@ -365,8 +374,7 @@ def np_add_at(a, indices, b):
     elif np.library.__name__ == "cupy":
         np.scatter_add(a, indices, b)
     else:
-        raise ValueError(
-            "Expected np.library.__name__ to be `numpy` or `cupy`.")
+        raise ValueError("Expected np.library.__name__ to be `numpy` or `cupy`.")
 
 
 def np_strided_sliding_view(x, window_shape: tuple, strides: tuple):
@@ -385,18 +393,15 @@ def np_strided_sliding_view(x, window_shape: tuple, strides: tuple):
     """
     # Need the checks, because as_strided is not memory safe.
     if not len(window_shape) == x.ndim:
-        raise ValueError(
-            f"Must provide one window size for each dimension of x.")
+        raise ValueError(f"Must provide one window size for each dimension of x.")
     if not len(strides) == x.ndim:
-        raise ValueError(
-            f"Must provide one stride size for each dimension of x.")
+        raise ValueError(f"Must provide one stride size for each dimension of x.")
     if any(size < 0 for size in window_shape):
         raise ValueError("`window_shape` cannot contain negative values")
     if any(stride < 0 for stride in strides):
         raise ValueError("`strides` cannot contain negative values")
     if any(x_size < w_size for x_size, w_size in zip(x.shape, window_shape)):
-        raise ValueError(
-            "window shape cannot be larger than input array shape")
+        raise ValueError("window shape cannot be larger than input array shape")
     reduced_shape = tuple(
         math.ceil((x - w + 1) / s) for x, s, w in zip(x.shape, strides, window_shape)
     )
@@ -408,9 +413,6 @@ def np_strided_sliding_view(x, window_shape: tuple, strides: tuple):
     return np.lib.stride_tricks.as_strided(x, strides=out_strides, shape=out_shape)
 
 
-
-
-
 # ---------------- AUGMENTING `VARIABLE`
 # Add methods/properties to the Variable class for convenience/user-experience.
 # This isn't essential to autodiff.

smallpebble/misc/__init__.py

@@ -1 +1,15 @@
+# Copyright 2021 The SmallPebble authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from smallpebble.misc.data import load_data

smallpebble/misc/data.py

@@ -1,3 +1,17 @@
+# Copyright 2021 The SmallPebble authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 """Rough and ready... load (/download) MNIST/CIFAR data from openml.org."""
 from collections import namedtuple
 import hashlib

smallpebble/nn/__init__.py

@@ -1,2 +1,16 @@
+# Copyright 2021 The SmallPebble authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from smallpebble.nn.conv import *
 from smallpebble.nn.training import *

smallpebble/nn/conv.py

@@ -1,9 +1,24 @@
+# Copyright 2021 The SmallPebble authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import smallpebble.core as core
 from collections import defaultdict
 import math
 import numpy
 import smallpebble.array_library as np
 
+
 def conv2d(images, kernels, padding="SAME", strides=[1, 1]) -> core.Variable:
     """2D convolution, with same api as tf.nn.conv2d [1].
 
@@ -31,12 +46,10 @@ def conv2d(images, kernels, padding="SAME", strides=[1, 1]) -> core.Variable:
         images, padding, imheight, imwidth, stride_y, stride_x, kernheight, kernwidth
     )
     window_shape = (1, kernheight, kernwidth, channels_in)
-    image_patches = strided_sliding_view(
-        images, window_shape, (1, stride_y, stride_x, 1))
+    image_patches = strided_sliding_view(images, window_shape, (1, stride_y, stride_x, 1))
     outh, outw = image_patches.shape[1], image_patches.shape[2]
     patches_as_matrix = core.reshape(
-        image_patches, [n_images * outh * outw,
-                        kernheight * kernwidth * channels_in]
+        image_patches, [n_images * outh * outw, kernheight * kernwidth * channels_in]
     )
     kernels_as_matrix = core.reshape(
         kernels, [kernheight * kernwidth * channels_in, channels_out]
@@ -53,8 +66,7 @@ def maxpool2d(images, kernheight, kernwidth, padding="SAME", strides=[1, 1]):
         images, padding, imheight, imwidth, stride_y, stride_x, kernheight, kernwidth
     )
     window_shape = (1, kernheight, kernwidth, 1)
-    image_patches = strided_sliding_view(
-        images, window_shape, [1, stride_y, stride_x, 1])
+    image_patches = strided_sliding_view(images, window_shape, [1, stride_y, stride_x, 1])
     flat_patches_shape = image_patches.array.shape[:4] + (-1,)
     image_patches = core.reshape(image_patches, shape=flat_patches_shape)
     result = core.maxax(image_patches, axis=-1)
@@ -81,8 +93,9 @@ def strided_sliding_view(a, window_shape, strides):
 
     def multiply_by_locgrad(path_value):  # TODO: a faster method
         result = np.zeros(a.shape, a.dtype)
-        core.np_add_at(core.np_strided_sliding_view(
-            result, window_shape, strides), None, path_value)
+        core.np_add_at(
+            core.np_strided_sliding_view(result, window_shape, strides), None, path_value
+        )
         return result
 
     local_gradients = [(a, multiply_by_locgrad)]
@@ -117,14 +130,11 @@ def patches_index(imheight, imwidth, kernheight, kernwidth, stride_y, stride_x):
     "Index to get image patches, e.g. for 2d convolution."
     max_y_idx = imheight - kernheight + 1
     max_x_idx = imwidth - kernwidth + 1
-    row_major_index = np.arange(
-        imheight * imwidth).reshape([imheight, imwidth])
+    row_major_index = np.arange(imheight * imwidth).reshape([imheight, imwidth])
     patch_corners = row_major_index[0:max_y_idx:stride_y, 0:max_x_idx:stride_x]
     elements_relative = row_major_index[0:kernheight, 0:kernwidth]
-    index_of_patches = patch_corners.reshape(
-        [-1, 1]) + elements_relative.reshape([1, -1])
-    index_of_patches = np.unravel_index(
-        index_of_patches, shape=[imheight, imwidth])
+    index_of_patches = patch_corners.reshape([-1, 1]) + elements_relative.reshape([1, -1])
+    index_of_patches = np.unravel_index(index_of_patches, shape=[imheight, imwidth])
     outheight, outwidth = patch_corners.shape
     n_patches = outheight * outwidth
     return index_of_patches, outheight, outwidth, n_patches