add tests for reduce ops (onnx#675)

* add tests for reduce ops

* add tests for reduce ops, fix formatting

* add tests for reduce ops, fix formatting

* update reduce_l2 for comments

* append docs and a few fixes

* fixe formatting

* more fixes

* more update

* fix build

* remove extra white spaces

* fix reduce_sum

* fix reduce_sum test failure

* remove test pb files as they need to be updated

* verifying tests

* formatting

* formatting

* formatting

* update according to reviewer's comments

* update changelog and operators doc after merging from master

* remove white spaces

* remove unneeded test data

* remove spaces that causing build failure

* extra spaces

* generate doc

* regenerate doc

* regenerate docs

Author: liqunfu

docs/Operators.md

@@ -0,0 +1,108 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceL1(Base):
+
+    @staticmethod
+    def export_do_not_keepdims():
+        shape = [3, 2, 2]
+        axes = [2]
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceL1',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[3., 7.], [11., 15.], [19., 23.]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims():
+        shape = [3, 2, 2]
+        axes = [2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL1',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_keep_dims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_keep_dims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims():
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL1',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
+        #print(reduced)
+        #[[[78.]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l1_default_axes_keepdims_random')

onnx/backend/test/case/node/reducel1.py

@@ -0,0 +1,117 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceL2(Base):
+
+    @staticmethod
+    def export_do_not_keepdims():
+        shape = [3, 2, 2]
+        axes = [2]
+        keepdims = 0
+
+        node = onnx.helper.make_node(
+            'ReduceL2',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+        #print(reduced)
+        #[[2.23606798, 5.],
+        # [7.81024968, 10.63014581],
+        # [13.45362405, 16.2788206]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims():
+        shape = [3, 2, 2]
+        axes = [2]
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL2',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+        #print(reduced)
+        #[[[2.23606798], [5.]]
+        # [[7.81024968], [10.63014581]]
+        # [[13.45362405], [16.2788206 ]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_keep_dims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced], name='test_reduce_l2_keep_dims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims():
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceL2',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims
+        )
+
+        data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
+        #print(data)
+        #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
+
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=axes, keepdims=keepdims == 1))
+        #print(reduced)
+        #[[[25.49509757]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.sqrt(np.sum(
+            a=np.square(data), axis=axes, keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_l2_default_axes_keepdims_random')

onnx/backend/test/case/node/reducel2.py

@@ -0,0 +1,116 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class ReduceLogSumExp(Base):
+
+    @staticmethod
+    def export_do_not_keepdims():
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 0
+        node = onnx.helper.make_node(
+            'ReduceLogSumExp',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+            dtype=np.float32)
+        reduced = np.log(np.sum(
+            np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+        # print(reduced)
+        #[[20., 2.31326175]
+        # [40.00004578, 2.31326175]
+        # [60.00671387, 2.31326175]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_do_not_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.log(np.sum(
+            np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+            name='test_reduce_log_sum_exp_do_not_keepdims_random')
+
+    @staticmethod
+    def export_keepdims():
+        shape = [3, 2, 2]
+        axes = [1]
+        keepdims = 1
+        node = onnx.helper.make_node(
+            'ReduceLogSumExp',
+            inputs=['data'],
+            outputs=['reduced'],
+            axes=axes,
+            keepdims=keepdims
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+            dtype=np.float32)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=tuple(axes),
+                                keepdims=keepdims == 1))
+        # print(reduced)
+        # [[[20., 2.31326175]]
+        # [[40.00004578, 2.31326175]]
+        # [[60.00671387, 2.31326175]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=tuple(axes),
+                                keepdims=keepdims == 1))
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_keepdims_random')
+
+    @staticmethod
+    def export_default_axes_keepdims():
+        shape = [3, 2, 2]
+        axes = None
+        keepdims = 1
+
+        node = onnx.helper.make_node(
+            'ReduceLogSumExp',
+            inputs=['data'],
+            outputs=['reduced'],
+            keepdims=keepdims
+        )
+
+        data = np.array(
+            [[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
+            dtype=np.float32)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=axes,
+                                keepdims=keepdims == 1))
+        # print(reduced)
+        # [[[60.00671387]]]
+
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_default_axes_keepdims_example')
+
+        np.random.seed(0)
+        data = np.random.uniform(-10, 10, shape).astype(np.float32)
+        reduced = np.log(np.sum(np.exp(data),
+                                axis=axes,
+                                keepdims=keepdims == 1))
+        expect(node, inputs=[data], outputs=[reduced],
+              name='test_reduce_log_sum_exp_default_axes_keepdims_random')