f75c37faf3
- Also, removed print statements from relu_op_test.py PiperOrigin-RevId: 287911742 Change-Id: Ib1763a5a010e5738e4d93e348391839e1e164108
731 lines
27 KiB
Python
731 lines
27 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for XLA matrix diag ops."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.ops import array_ops
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from tensorflow.python.platform import googletest
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default_v2_alignment = "LEFT_LEFT"
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alignment_list = ["RIGHT_LEFT", "LEFT_RIGHT"]
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def zip_to_first_list_length(a, b):
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if len(b) > len(a):
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return zip(a, b[:len(a)])
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return zip(a, b + [None] * (len(a) - len(b)))
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# Routines to convert test cases to have diagonals in a specified alignment.
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# Copied from //third_party/tensorflow/python/kernel_tests/diag_op_test.py
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def repack_diagonals(packed_diagonals,
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diag_index,
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num_rows,
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num_cols,
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align=None):
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# The original test cases are LEFT_LEFT aligned.
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if align == default_v2_alignment or align is None:
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return packed_diagonals
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align = align.split("_")
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d_lower, d_upper = diag_index
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batch_dims = packed_diagonals.ndim - (2 if d_lower < d_upper else 1)
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max_diag_len = packed_diagonals.shape[-1]
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index = (slice(None),) * batch_dims
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repacked_diagonals = np.zeros_like(packed_diagonals)
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# Aligns each diagonal row-by-row.
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for diag_index in range(d_lower, d_upper + 1):
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diag_len = min(num_rows + min(0, diag_index), num_cols - max(0, diag_index))
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row_index = d_upper - diag_index
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padding_len = max_diag_len - diag_len
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left_align = (diag_index >= 0 and
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align[0] == "LEFT") or (diag_index <= 0 and
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align[1] == "LEFT")
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# Prepares index tuples.
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extra_dim = tuple() if d_lower == d_upper else (row_index,)
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packed_last_dim = (slice(None),) if left_align else (slice(0, diag_len, 1),)
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repacked_last_dim = (slice(None),) if left_align else (slice(
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padding_len, max_diag_len, 1),)
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packed_index = index + extra_dim + packed_last_dim
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repacked_index = index + extra_dim + repacked_last_dim
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# Repacks the diagonal.
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repacked_diagonals[repacked_index] = packed_diagonals[packed_index]
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return repacked_diagonals
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def repack_diagonals_in_tests(tests, align=None):
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# The original test cases are LEFT_LEFT aligned.
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if align == default_v2_alignment or align is None:
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return tests
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new_tests = dict()
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# Loops through each case.
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for diag_index, (packed_diagonals, padded_diagonals) in tests.items():
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num_rows, num_cols = padded_diagonals.shape[-2:]
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repacked_diagonals = repack_diagonals(
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packed_diagonals, diag_index, num_rows, num_cols, align=align)
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new_tests[diag_index] = (repacked_diagonals, padded_diagonals)
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return new_tests
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# Test cases shared by MatrixDiagV2, MatrixDiagPartV2, and MatrixSetDiagV2.
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# Copied from //third_party/tensorflow/python/kernel_tests/diag_op_test.py
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def square_cases(align=None):
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# pyformat: disable
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mat = np.array([[[1, 2, 3, 4, 5],
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[6, 7, 8, 9, 1],
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[3, 4, 5, 6, 7],
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[8, 9, 1, 2, 3],
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[4, 5, 6, 7, 8]],
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[[9, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[1, 2, 3, 4, 5],
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[6, 7, 8, 9, 1],
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[2, 3, 4, 5, 6]]])
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tests = dict()
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# tests[d_lower, d_upper] = (compact_diagonals, padded_diagonals)
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tests[-1, -1] = (np.array([[6, 4, 1, 7],
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[5, 2, 8, 5]]),
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np.array([[[0, 0, 0, 0, 0],
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[6, 0, 0, 0, 0],
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[0, 4, 0, 0, 0],
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[0, 0, 1, 0, 0],
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[0, 0, 0, 7, 0]],
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[[0, 0, 0, 0, 0],
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[5, 0, 0, 0, 0],
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[0, 2, 0, 0, 0],
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[0, 0, 8, 0, 0],
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[0, 0, 0, 5, 0]]]))
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tests[-4, -3] = (np.array([[[8, 5],
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[4, 0]],
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[[6, 3],
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[2, 0]]]),
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np.array([[[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[8, 0, 0, 0, 0],
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[4, 5, 0, 0, 0]],
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[[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[6, 0, 0, 0, 0],
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[2, 3, 0, 0, 0]]]))
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tests[-2, 1] = (np.array([[[2, 8, 6, 3, 0],
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[1, 7, 5, 2, 8],
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[6, 4, 1, 7, 0],
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[3, 9, 6, 0, 0]],
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[[1, 7, 4, 1, 0],
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[9, 6, 3, 9, 6],
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[5, 2, 8, 5, 0],
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[1, 7, 4, 0, 0]]]),
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np.array([[[1, 2, 0, 0, 0],
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[6, 7, 8, 0, 0],
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[3, 4, 5, 6, 0],
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[0, 9, 1, 2, 3],
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[0, 0, 6, 7, 8]],
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[[9, 1, 0, 0, 0],
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[5, 6, 7, 0, 0],
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[1, 2, 3, 4, 0],
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[0, 7, 8, 9, 1],
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[0, 0, 4, 5, 6]]]))
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tests[2, 4] = (np.array([[[5, 0, 0],
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[4, 1, 0],
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[3, 9, 7]],
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[[4, 0, 0],
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[3, 9, 0],
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[2, 8, 5]]]),
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np.array([[[0, 0, 3, 4, 5],
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[0, 0, 0, 9, 1],
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[0, 0, 0, 0, 7],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]],
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[[0, 0, 2, 3, 4],
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[0, 0, 0, 8, 9],
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[0, 0, 0, 0, 5],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]]]))
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# pyformat: enable
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return (mat, repack_diagonals_in_tests(tests, align))
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def tall_cases(align=None):
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# pyformat: disable
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mat = np.array([[[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9],
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[9, 8, 7],
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[6, 5, 4]],
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[[3, 2, 1],
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[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9],
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[9, 8, 7]]])
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tests = dict()
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# tests[d_lower, d_upper] = (compact_diagonals, padded_diagonals)
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tests[0, 0] = (np.array([[1, 5, 9],
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[3, 2, 6]]),
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np.array([[[1, 0, 0],
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[0, 5, 0],
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[0, 0, 9],
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[0, 0, 0]],
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[[3, 0, 0],
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[0, 2, 0],
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[0, 0, 6],
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[0, 0, 0]]]))
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tests[-4, -3] = (np.array([[[9, 5],
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[6, 0]],
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[[7, 8],
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[9, 0]]]),
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np.array([[[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[9, 0, 0],
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[6, 5, 0]],
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[[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[7, 0, 0],
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[9, 8, 0]]]))
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tests[-2, -1] = (np.array([[[4, 8, 7],
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[7, 8, 4]],
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[[1, 5, 9],
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[4, 8, 7]]]),
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np.array([[[0, 0, 0],
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[4, 0, 0],
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[7, 8, 0],
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[0, 8, 7],
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[0, 0, 4]],
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[[0, 0, 0],
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[1, 0, 0],
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[4, 5, 0],
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[0, 8, 9],
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[0, 0, 7]]]))
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tests[-2, 1] = (np.array([[[2, 6, 0],
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[1, 5, 9],
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[4, 8, 7],
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[7, 8, 4]],
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[[2, 3, 0],
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[3, 2, 6],
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[1, 5, 9],
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[4, 8, 7]]]),
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np.array([[[1, 2, 0],
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[4, 5, 6],
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[7, 8, 9],
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[0, 8, 7],
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[0, 0, 4]],
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[[3, 2, 0],
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[1, 2, 3],
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[4, 5, 6],
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[0, 8, 9],
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[0, 0, 7]]]))
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tests[1, 2] = (np.array([[[3, 0],
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[2, 6]],
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[[1, 0],
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[2, 3]]]),
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np.array([[[0, 2, 3],
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[0, 0, 6],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0]],
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[[0, 2, 1],
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[0, 0, 3],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0]]]))
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# pyformat: enable
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return (mat, repack_diagonals_in_tests(tests, align))
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def fat_cases(align=None):
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# pyformat: disable
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mat = np.array([[[1, 2, 3, 4],
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[5, 6, 7, 8],
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[9, 1, 2, 3]],
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[[4, 5, 6, 7],
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[8, 9, 1, 2],
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[3, 4, 5, 6]]])
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tests = dict()
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# tests[d_lower, d_upper] = (compact_diagonals, padded_diagonals)
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tests[0, 0] = (np.array([[1, 6, 2],
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[4, 9, 5]]),
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np.array([[[1, 0, 0, 0],
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[0, 6, 0, 0],
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[0, 0, 2, 0]],
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[[4, 0, 0, 0],
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[0, 9, 0, 0],
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[0, 0, 5, 0]]]))
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tests[2, 2] = (np.array([[3, 8],
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[6, 2]]),
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np.array([[[0, 0, 3, 0],
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[0, 0, 0, 8],
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[0, 0, 0, 0]],
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[[0, 0, 6, 0],
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[0, 0, 0, 2],
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[0, 0, 0, 0]]]))
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tests[-2, 0] = (np.array([[[1, 6, 2],
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[5, 1, 0],
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[9, 0, 0]],
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[[4, 9, 5],
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[8, 4, 0],
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[3, 0, 0]]]),
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np.array([[[1, 0, 0, 0],
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[5, 6, 0, 0],
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[9, 1, 2, 0]],
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[[4, 0, 0, 0],
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[8, 9, 0, 0],
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[3, 4, 5, 0]]]))
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tests[-1, 1] = (np.array([[[2, 7, 3],
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[1, 6, 2],
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[5, 1, 0]],
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[[5, 1, 6],
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[4, 9, 5],
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[8, 4, 0]]]),
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np.array([[[1, 2, 0, 0],
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[5, 6, 7, 0],
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[0, 1, 2, 3]],
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[[4, 5, 0, 0],
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[8, 9, 1, 0],
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[0, 4, 5, 6]]]))
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tests[0, 3] = (np.array([[[4, 0, 0],
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[3, 8, 0],
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[2, 7, 3],
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[1, 6, 2]],
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[[7, 0, 0],
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[6, 2, 0],
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[5, 1, 6],
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[4, 9, 5]]]),
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np.array([[[1, 2, 3, 4],
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[0, 6, 7, 8],
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[0, 0, 2, 3]],
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[[4, 5, 6, 7],
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[0, 9, 1, 2],
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[0, 0, 5, 6]]]))
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# pyformat: enable
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return (mat, repack_diagonals_in_tests(tests, align))
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def all_tests(align=None):
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return [square_cases(align), tall_cases(align), fat_cases(align)]
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class MatrixDiagTest(xla_test.XLATestCase):
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def _assertOpOutputMatchesExpected(self,
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params,
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solution,
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rtol=1e-3,
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atol=1e-5):
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"""Verifies that matrix_diag produces `solution` when fed `params`.
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Args:
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params: dictionary containing input parameters to matrix_diag.
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solution: numpy array representing the expected output of matrix_diag.
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rtol: relative tolerance for equality test.
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atol: absolute tolerance for equality test.
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"""
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diagonal = params["diagonal"]
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with self.session() as session:
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for dtype in self.numeric_types - {np.int8, np.uint8}:
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expected = solution.astype(dtype)
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with self.test_scope():
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params["diagonal"] = array_ops.placeholder(
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dtype, diagonal.shape, name="diagonal")
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output = array_ops.matrix_diag(**params)
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result = session.run(output,
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{params["diagonal"]: diagonal.astype(dtype)})
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self.assertEqual(output.dtype, expected.dtype)
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self.assertAllCloseAccordingToType(
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expected, result, rtol=rtol, atol=atol, bfloat16_rtol=0.03)
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# Generic tests applicable to both v1 and v2 ops.
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# Originally from unary_ops_tests.py.
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def testV1(self):
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# pyformat: disable
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vecs1 = np.array([[1, 2],
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[3, 4]])
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solution1 = np.array([[[1, 0], [0, 2]],
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[[3, 0], [0, 4]]])
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vecs2 = np.array([1, 2, 3, 4])
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solution2 = np.array([[1, 0, 0, 0],
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[0, 2, 0, 0],
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[0, 0, 3, 0],
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[0, 0, 0, 4]])
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vecs3 = np.array([[[1, 2, 3],
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[4, 5, 6]],
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[[7, 8, 9], # pylint: disable=bad-whitespace
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[10, 11, 12]]])
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solution3 = np.array([[[[1, 0, 0],
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[0, 2, 0],
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[0, 0, 3]],
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[[4, 0, 0],
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[0, 5, 0],
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[0, 0, 6]]],
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[[[7, 0, 0],
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[0, 8, 0],
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[0, 0, 9]],
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[[10, 0, 0],
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[0, 11, 0],
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[0, 0, 12]]]])
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# pyformat: enable
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self._assertOpOutputMatchesExpected({"diagonal": vecs1}, solution1)
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self._assertOpOutputMatchesExpected({"diagonal": vecs2}, solution2)
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self._assertOpOutputMatchesExpected({"diagonal": vecs3}, solution3)
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# From here onwards are v2-only tests.
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def testSquare(self):
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for align in alignment_list:
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for _, tests in [square_cases(align)]:
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for diag_index, (vecs, solution) in tests.items():
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params = {"diagonal": vecs[0], "k": diag_index, "align": align}
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self._assertOpOutputMatchesExpected(params, solution[0])
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def testSquareBatch(self):
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for align in alignment_list:
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for _, tests in [square_cases(align)]:
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for diag_index, (vecs, solution) in tests.items():
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params = {"diagonal": vecs, "k": diag_index, "align": align}
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self._assertOpOutputMatchesExpected(params, solution)
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def testRectangularBatch(self):
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# Stores expected num_rows and num_cols (when the other is given).
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# expected[(d_lower, d_upper)] = (expected_num_rows, expected_num_cols)
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test_list = list()
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# Do not align the test cases here. Re-alignment needs to happen after the
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# solution shape is updated.
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# Square cases:
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expected = {
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(-1, -1): (5, 4),
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(-4, -3): (5, 2),
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(-2, 1): (5, 5),
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(2, 4): (3, 5),
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}
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test_list.append((expected, square_cases()))
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# Tall cases
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expected = {
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(0, 0): (3, 3),
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(-4, -3): (5, 2),
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(-2, -1): (4, 3),
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(-2, 1): (3, 3),
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(1, 2): (2, 3)
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}
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test_list.append((expected, tall_cases()))
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# Fat cases
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expected = {
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(2, 2): (2, 4),
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(-2, 0): (3, 3),
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(-1, 1): (3, 3),
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(0, 3): (3, 3)
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}
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test_list.append((expected, fat_cases()))
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# Giving both num_rows and num_cols
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align = alignment_list[0]
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for _, tests in [tall_cases(align), fat_cases(align)]:
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for diag_index, (vecs, solution) in tests.items():
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self._assertOpOutputMatchesExpected(
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{
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"diagonal": vecs,
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"k": diag_index,
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"num_rows": solution.shape[-2],
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"num_cols": solution.shape[-1],
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"align": align
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}, solution)
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# We go through each alignment in a round-robin manner.
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align_index = 0
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|
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# Giving just num_rows or num_cols.
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for expected, (_, tests) in test_list:
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for diag_index, (new_num_rows, new_num_cols) in expected.items():
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|
align = alignment_list[align_index]
|
|
align_index = (align_index + 1) % len(alignment_list)
|
|
vecs, solution = tests[diag_index]
|
|
solution_given_num_rows = solution.take(
|
|
indices=range(new_num_cols), axis=-1)
|
|
# Repacks the diagonal input according to the new solution shape.
|
|
vecs_given_num_rows = repack_diagonals(
|
|
vecs,
|
|
diag_index,
|
|
solution_given_num_rows.shape[-2],
|
|
new_num_cols,
|
|
align=align)
|
|
self._assertOpOutputMatchesExpected(
|
|
{
|
|
"diagonal": vecs_given_num_rows,
|
|
"k": diag_index,
|
|
"num_rows": solution_given_num_rows.shape[-2],
|
|
"align": align
|
|
}, solution_given_num_rows)
|
|
solution_given_num_cols = solution.take(
|
|
indices=range(new_num_rows), axis=-2)
|
|
# Repacks the diagonal input according to the new solution shape.
|
|
vecs_given_num_cols = repack_diagonals(
|
|
vecs,
|
|
diag_index,
|
|
new_num_rows,
|
|
solution_given_num_cols.shape[-1],
|
|
align=align)
|
|
self._assertOpOutputMatchesExpected(
|
|
{
|
|
"diagonal": vecs_given_num_cols,
|
|
"k": diag_index,
|
|
"num_cols": solution_given_num_cols.shape[-1],
|
|
"align": align
|
|
}, solution_given_num_cols)
|
|
|
|
def testPadding(self):
|
|
for padding_value, align in zip_to_first_list_length([555, -11],
|
|
alignment_list):
|
|
for _, tests in all_tests(align):
|
|
for diag_index, (vecs, solution) in tests.items():
|
|
mask = (solution == 0)
|
|
solution = solution + (mask * padding_value)
|
|
self._assertOpOutputMatchesExpected(
|
|
{
|
|
"diagonal": vecs,
|
|
"k": diag_index,
|
|
"num_rows": solution.shape[-2],
|
|
"num_cols": solution.shape[-1],
|
|
"padding_value": padding_value,
|
|
"align": align
|
|
}, solution)
|
|
|
|
|
|
class MatrixSetDiagTest(xla_test.XLATestCase):
|
|
|
|
def _assertOpOutputMatchesExpected(self,
|
|
params,
|
|
solution,
|
|
rtol=1e-3,
|
|
atol=1e-5):
|
|
"""Verifies that matrix_set_diag produces `solution` when fed `params`.
|
|
|
|
Args:
|
|
params: dictionary containing input parameters to matrix_set_diag.
|
|
solution: numpy array representing the expected output of matrix_set_diag.
|
|
rtol: relative tolerance for equality test.
|
|
atol: absolute tolerance for equality test.
|
|
"""
|
|
input = params["input"] # pylint: disable=redefined-builtin
|
|
diagonal = params["diagonal"]
|
|
with self.session() as session:
|
|
for dtype in self.numeric_types - {np.int8, np.uint8}:
|
|
expected = solution.astype(dtype)
|
|
with self.test_scope():
|
|
params["input"] = array_ops.placeholder(
|
|
dtype, input.shape, name="input")
|
|
params["diagonal"] = array_ops.placeholder(
|
|
dtype, diagonal.shape, name="diagonal")
|
|
output = array_ops.matrix_set_diag(**params)
|
|
result = session.run(
|
|
output, {
|
|
params["input"]: input.astype(dtype),
|
|
params["diagonal"]: diagonal.astype(dtype)
|
|
})
|
|
self.assertEqual(output.dtype, expected.dtype)
|
|
self.assertAllCloseAccordingToType(
|
|
expected, result, rtol=rtol, atol=atol, bfloat16_rtol=0.03)
|
|
|
|
# Generic tests applicable to both v1 and v2 ops.
|
|
# Originally from binary_ops_tests.py.
|
|
def testV1(self):
|
|
test_cases = list()
|
|
|
|
# pyformat: disable
|
|
# pylint: disable=bad-whitespace
|
|
# Square cases.
|
|
input = np.array([[0, 1, 0], # pylint: disable=redefined-builtin
|
|
[1, 0, 1],
|
|
[1, 1, 1]])
|
|
diag = np.array([1, 2, 3])
|
|
solution = np.array([[1, 1, 0],
|
|
[1, 2, 1],
|
|
[1, 1, 3]])
|
|
test_cases.append(({"input": input, "diagonal": diag}, solution))
|
|
|
|
input = np.array([[[1, 0, 3],
|
|
[0, 2, 0],
|
|
[1, 0, 3]],
|
|
[[4, 0, 4],
|
|
[0, 5, 0],
|
|
[2, 0, 6]]])
|
|
diag = np.array([[-1, 0, -3],
|
|
[-4, -5, -6]])
|
|
solution = np.array([[[-1, 0, 3],
|
|
[ 0, 0, 0],
|
|
[ 1, 0, -3]],
|
|
[[-4, 0, 4],
|
|
[ 0, -5, 0],
|
|
[ 2, 0, -6]]])
|
|
test_cases.append(({"input": input, "diagonal": diag}, solution))
|
|
|
|
# Rectangular cases.
|
|
input = np.array([[0, 1, 0],
|
|
[1, 0, 1]])
|
|
diag = np.array([3, 4])
|
|
solution = np.array([[3, 1, 0],
|
|
[1, 4, 1]])
|
|
test_cases.append(({"input": input, "diagonal": diag}, solution))
|
|
|
|
input = np.array([[0, 1],
|
|
[1, 0],
|
|
[1, 1]])
|
|
diag = np.array([3, 4])
|
|
solution = np.array([[3, 1],
|
|
[1, 4],
|
|
[1, 1]])
|
|
test_cases.append(({"input": input, "diagonal": diag}, solution))
|
|
|
|
input = np.array([[[1, 0, 3],
|
|
[0, 2, 0]],
|
|
[[4, 0, 4],
|
|
[0, 5, 0]]])
|
|
diag = np.array([[-1, -2], [-4, -5]])
|
|
solution = np.array([[[-1, 0, 3],
|
|
[ 0, -2, 0]],
|
|
[[-4, 0, 4],
|
|
[ 0, -5, 0]]])
|
|
test_cases.append(({"input": input, "diagonal": diag}, solution))
|
|
# pylint: enable=bad-whitespace
|
|
# pyformat: enable
|
|
|
|
for test in test_cases:
|
|
self._assertOpOutputMatchesExpected(test[0], test[1])
|
|
|
|
# From here onwards are v2-only tests.
|
|
def testSingleMatrix(self):
|
|
for align in alignment_list:
|
|
for _, tests in all_tests(align):
|
|
for diag_index, (vecs, banded_mat) in tests.items():
|
|
mask = (banded_mat[0] == 0)
|
|
input_mat = np.random.randint(10, size=mask.shape)
|
|
solution = input_mat * mask + banded_mat[0]
|
|
self._assertOpOutputMatchesExpected(
|
|
{
|
|
"input": input_mat,
|
|
"diagonal": vecs[0],
|
|
"k": diag_index,
|
|
"align": align
|
|
}, solution)
|
|
|
|
def testBatch(self):
|
|
for align in alignment_list:
|
|
for _, tests in all_tests(align):
|
|
for diag_index, (vecs, banded_mat) in tests.items():
|
|
mask = (banded_mat == 0)
|
|
input_mat = np.random.randint(10, size=mask.shape)
|
|
solution = input_mat * mask + banded_mat
|
|
self._assertOpOutputMatchesExpected(
|
|
{
|
|
"input": input_mat,
|
|
"diagonal": vecs,
|
|
"k": diag_index,
|
|
"align": align
|
|
}, solution)
|
|
|
|
|
|
class MatrixDiagPartTest(xla_test.XLATestCase):
|
|
|
|
def _assertOpOutputMatchesExpected(self,
|
|
params,
|
|
solution,
|
|
rtol=1e-3,
|
|
atol=1e-5):
|
|
"""Verifies that matrix_diag_part produces `solution` when fed `params`.
|
|
|
|
Args:
|
|
params: dictionary containing input parameters to matrix_diag_part.
|
|
solution: numpy array representing the expected output.
|
|
rtol: relative tolerance for equality test.
|
|
atol: absolute tolerance for equality test.
|
|
"""
|
|
input = params["input"] # pylint: disable=redefined-builtin
|
|
with self.session() as session:
|
|
for dtype in self.numeric_types - {np.int8, np.uint8}:
|
|
expected = solution.astype(dtype)
|
|
with self.test_scope():
|
|
params["input"] = array_ops.placeholder(
|
|
dtype, input.shape, name="input")
|
|
output = array_ops.matrix_diag_part(**params)
|
|
result = session.run(output, {
|
|
params["input"]: input.astype(dtype),
|
|
})
|
|
self.assertEqual(output.dtype, expected.dtype)
|
|
self.assertAllCloseAccordingToType(
|
|
expected, result, rtol=rtol, atol=atol, bfloat16_rtol=0.03)
|
|
|
|
# Generic tests applicable to both v1 and v2 ops.
|
|
# Originally from unary_ops_tests.py.
|
|
def testV1(self):
|
|
matrices = np.arange(3 * 2 * 4).reshape([3, 2, 4])
|
|
solution = np.array([[0, 5], [8, 13], [16, 21]])
|
|
self._assertOpOutputMatchesExpected({"input": matrices}, solution)
|
|
|
|
# From here onwards are v2-only tests.
|
|
def testSingleMatrix(self):
|
|
for align in alignment_list:
|
|
test_list = [square_cases(align), tall_cases(align), fat_cases(align)]
|
|
for mat, tests in test_list:
|
|
for diag_index, (solution, _) in tests.items():
|
|
self._assertOpOutputMatchesExpected(
|
|
{
|
|
"input": mat[0],
|
|
"k": diag_index,
|
|
"align": align
|
|
}, solution[0])
|
|
|
|
def testBatch(self):
|
|
for align in alignment_list:
|
|
for mat, tests in all_tests(align):
|
|
for diag_index, (solution, _) in tests.items():
|
|
self._assertOpOutputMatchesExpected(
|
|
{
|
|
"input": mat,
|
|
"k": diag_index,
|
|
"align": align
|
|
}, solution)
|
|
|
|
def testPadding(self):
|
|
for padding_value, align in zip_to_first_list_length([555, -11],
|
|
alignment_list):
|
|
for mat, tests in all_tests(align):
|
|
for diag_index, (solution, _) in tests.items():
|
|
mask = (solution == 0)
|
|
solution = solution + (mask * padding_value)
|
|
self._assertOpOutputMatchesExpected(
|
|
{
|
|
"input": mat,
|
|
"k": diag_index,
|
|
"padding_value": padding_value,
|
|
"align": align
|
|
}, solution)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
googletest.main()
|