# Copyright (c) 2023 PaddlePaddle 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 sys import unittest import numpy as np from eager_op_test import OpTest import paddle from paddle import fluid from paddle.fluid import core def cummax_dim2(arr, axis=None): if axis is None: arr = arr.flatten() cummax = np.maximum.accumulate(arr) shape = arr.shape indices = np.zeros(shape).astype('int32') max_val = -sys.maxsize max_ind = 0 for i in range(shape[0]): if arr[i] >= max_val: max_val = max(arr[i], max_val) max_ind = i indices[i] = i else: indices[i] = max_ind else: cummax = np.maximum.accumulate(arr, axis) shape = arr.shape indices = np.zeros(shape).astype('int32') if axis < 0: axis = axis + len(shape) if axis == 0: for j in range(shape[1]): max_ind = 0 max_val = -sys.maxsize for i in range(shape[0]): if arr[i][j] >= max_val: max_val = arr[i][j] max_ind = i indices[i][j] = i else: indices[i][j] = max_ind elif axis == 1: for i in range(shape[0]): max_ind = 0 max_val = -sys.maxsize for j in range(shape[1]): if arr[i][j] >= max_val: max_val = arr[i][j] max_ind = j indices[i][j] = j else: indices[i][j] = max_ind else: raise Exception("unfeasible axis") return cummax, indices class TestCummaxOp(OpTest): def setUp(self): self.op_type = "cummax" self.python_api = paddle.cummax self.dtype = np.float64 self.axis = -1 self.indices_type = 3 self.input_data = np.random.random((10, 10)).astype(self.dtype) self.set_attrs() self.inputs = {'x': self.input_data} self.attrs = {'axis': self.axis, 'dtype': self.indices_type} self.np_res, self.np_ind = cummax_dim2(self.input_data, axis=self.axis) self.outputs = {'out': self.np_res, 'indices': self.np_ind} def set_attrs(self): pass def test_check_output(self): paddle.enable_static() self.check_output() def test_check_grad(self): paddle.enable_static() self.check_grad(['x'], 'out') class TestCummaxOpAxis1(TestCummaxOp): def set_attrs(self): self.axis = 0 class TestCummaxOpAxis2(TestCummaxOp): def set_attrs(self): self.axis = -2 class TestCummaxOpIndexType(TestCummaxOp): def set_attrs(self): self.indices_type = 2 class TestCummaxAPI(unittest.TestCase): def run_cases(self): data_np = np.random.random((100, 100)).astype(np.float32) data = paddle.to_tensor(data_np) y, indices = paddle.cummax(data) z, ind = cummax_dim2(data_np) np.testing.assert_array_equal(z, y.numpy()) np.testing.assert_array_equal(ind, indices.numpy()) y, indices = paddle.cummax(data, axis=0) z, ind = cummax_dim2(data_np, axis=0) np.testing.assert_array_equal(z, y.numpy()) np.testing.assert_array_equal(ind, indices.numpy()) y, indices = paddle.cummax(data, axis=-1) z, ind = cummax_dim2(data_np, axis=-1) np.testing.assert_array_equal(z, y.numpy()) np.testing.assert_array_equal(ind, indices.numpy()) y, indices = paddle.cummax(data, axis=-2) z, ind = cummax_dim2(data_np, axis=-2) np.testing.assert_array_equal(z, y.numpy()) np.testing.assert_array_equal(ind, indices.numpy()) y, indices = paddle.cummax(data, axis=-2, dtype='int32') z, ind = cummax_dim2(data_np, axis=-2) np.testing.assert_array_equal(z, y.numpy()) np.testing.assert_array_equal(ind, indices.numpy()) self.assertTrue(indices.dtype == core.VarDesc.VarType.INT32) data_np = np.random.randint(0, 10, size=(100, 100)).astype(np.int32) data = paddle.to_tensor(data_np) y, indices = paddle.cummax(data, axis=0) z, ind = cummax_dim2(data_np, axis=0) np.testing.assert_array_equal(z, y.numpy()) np.testing.assert_array_equal(ind, indices.numpy()) def run_static(self, use_gpu=False): with fluid.program_guard(fluid.Program()): data_np = np.random.random((100, 100)).astype(np.float32) x = paddle.static.data('x', [100, 100]) y1, indices1 = paddle.cummax(x) y2, indices2 = paddle.cummax(x, axis=0) y3, indices3 = paddle.cummax(x, axis=-1) y4, indices4 = paddle.cummax(x, axis=-2) y5, indices5 = paddle.cummax(x, axis=-2, dtype=np.int32) place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) out = exe.run( feed={'x': data_np}, fetch_list=[ y1.name, indices1.name, y2.name, indices2.name, y3.name, indices3.name, y4.name, indices4.name, y5.name, indices5.name, ], ) z, ind = cummax_dim2(data_np) np.testing.assert_allclose(z, out[0], rtol=1e-05) np.testing.assert_allclose(ind, out[1], rtol=1e-05) z, ind = cummax_dim2(data_np, axis=0) np.testing.assert_allclose(z, out[2], rtol=1e-05) np.testing.assert_allclose(ind, out[3], rtol=1e-05) z, ind = cummax_dim2(data_np, axis=-1) np.testing.assert_allclose(z, out[4], rtol=1e-05) np.testing.assert_allclose(ind, out[5], rtol=1e-05) z, ind = cummax_dim2(data_np, axis=-2) np.testing.assert_allclose(z, out[6], rtol=1e-05) np.testing.assert_allclose(ind, out[7], rtol=1e-05) z, ind = cummax_dim2(data_np, axis=-2) np.testing.assert_allclose(z, out[8], rtol=1e-05) np.testing.assert_allclose(ind, out[9], rtol=1e-05) def test_cpu(self): paddle.disable_static(paddle.fluid.CPUPlace()) self.run_cases() paddle.enable_static() self.run_static() def test_gpu(self): if not fluid.core.is_compiled_with_cuda(): return paddle.disable_static(paddle.fluid.CUDAPlace(0)) self.run_cases() paddle.enable_static() self.run_static(use_gpu=True) def test_errors(self): paddle.enable_static() with fluid.program_guard(fluid.Program()): def test_x_type(): data = [1, 2, 3] y, indices = paddle.cummax(data, axis=0) self.assertRaises(TypeError, test_x_type) paddle.disable_static() def test_indices_type(): data_np = np.random.random((10, 10)).astype(np.float32) data = paddle.to_tensor(data_np) y, indices = paddle.cummax(data, dtype='float32') self.assertRaises(ValueError, test_indices_type) def test_axis_outrange(): data_np = np.random.random(100).astype(np.float32) data = paddle.to_tensor(data_np) y, indices = paddle.cummax(data, axis=-2) self.assertRaises(IndexError, test_axis_outrange) if __name__ == '__main__': unittest.main()