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310 lines
9.3 KiB
310 lines
9.3 KiB
# Copyright (c) 2018 PaddlePaddle 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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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class TestCumsumOp(unittest.TestCase):
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def run_cases(self):
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data_np = np.arange(12).reshape(3, 4)
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data = paddle.to_tensor(data_np)
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y = paddle.cumsum(data)
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z = np.cumsum(data_np)
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self.assertTrue(np.array_equal(z, y.numpy()))
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y = paddle.cumsum(data, axis=0)
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z = np.cumsum(data_np, axis=0)
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self.assertTrue(np.array_equal(z, y.numpy()))
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y = paddle.cumsum(data, axis=-1)
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z = np.cumsum(data_np, axis=-1)
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self.assertTrue(np.array_equal(z, y.numpy()))
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y = paddle.cumsum(data, dtype='float64')
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self.assertTrue(y.dtype == core.VarDesc.VarType.FP64)
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y = paddle.cumsum(data, dtype=np.int32)
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self.assertTrue(y.dtype == core.VarDesc.VarType.INT32)
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y = paddle.cumsum(data, axis=-2)
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z = np.cumsum(data_np, axis=-2)
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self.assertTrue(np.array_equal(z, y.numpy()))
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def run_static(self, use_gpu=False):
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with fluid.program_guard(fluid.Program()):
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data_np = np.random.random((100, 100)).astype(np.float32)
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x = paddle.static.data('X', [100, 100])
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y = paddle.cumsum(x)
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y2 = paddle.cumsum(x, axis=0)
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y3 = paddle.cumsum(x, axis=-1)
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y4 = paddle.cumsum(x, dtype='float64')
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y5 = paddle.cumsum(x, dtype=np.int32)
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y6 = paddle.cumsum(x, axis=-2)
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place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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out = exe.run(feed={'X': data_np},
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fetch_list=[
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y.name, y2.name, y3.name, y4.name, y5.name,
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y6.name
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])
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z = np.cumsum(data_np)
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self.assertTrue(np.allclose(z, out[0]))
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z = np.cumsum(data_np, axis=0)
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self.assertTrue(np.allclose(z, out[1]))
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z = np.cumsum(data_np, axis=-1)
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self.assertTrue(np.allclose(z, out[2]))
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self.assertTrue(out[3].dtype == np.float64)
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self.assertTrue(out[4].dtype == np.int32)
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z = np.cumsum(data_np, axis=-2)
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self.assertTrue(np.allclose(z, out[5]))
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def test_cpu(self):
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paddle.disable_static(paddle.fluid.CPUPlace())
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self.run_cases()
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paddle.enable_static()
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self.run_static()
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def test_gpu(self):
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if not fluid.core.is_compiled_with_cuda():
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return
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paddle.disable_static(paddle.fluid.CUDAPlace(0))
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self.run_cases()
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paddle.enable_static()
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self.run_static(use_gpu=True)
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def test_name(self):
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with fluid.program_guard(fluid.Program()):
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x = paddle.static.data('x', [3, 4])
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y = paddle.cumsum(x, name='out')
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self.assertTrue('out' in y.name)
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class TestSumOp1(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': 2}
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].cumsum(axis=2)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestSumOp2(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': -1, 'reverse': True}
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.outputs = {
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'Out': np.flip(
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np.flip(
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self.inputs['X'], axis=2).cumsum(axis=2), axis=2)
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestSumOp3(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': 1}
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestSumOp4(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': 0}
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self.inputs = {'X': np.random.random((5, 6, 10)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestSumOp5(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.inputs = {'X': np.random.random((5, 20)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].cumsum(axis=1)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestSumOp7(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.inputs = {'X': np.random.random((100)).astype("float64")}
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self.outputs = {'Out': self.inputs['X'].cumsum(axis=0)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestSumOpExclusive1(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': 2, "exclusive": True}
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a = np.random.random((4, 5, 65)).astype("float64")
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self.inputs = {'X': a}
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self.outputs = {
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'Out': np.concatenate(
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(np.zeros(
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(4, 5, 1), dtype=np.float64), a[:, :, :-1].cumsum(axis=2)),
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axis=2)
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}
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def test_check_output(self):
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self.check_output()
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class TestSumOpExclusive2(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': 2, "exclusive": True}
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a = np.random.random((1, 1, 888)).astype("float64")
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self.inputs = {'X': a}
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self.outputs = {
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'Out': np.concatenate(
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(np.zeros(
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(1, 1, 1), dtype=np.float64), a[:, :, :-1].cumsum(axis=2)),
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axis=2)
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}
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def test_check_output(self):
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self.check_output()
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class TestSumOpExclusive3(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': 2, "exclusive": True}
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a = np.random.random((4, 5, 888)).astype("float32")
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self.inputs = {'X': a}
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self.outputs = {
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'Out': np.concatenate(
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(np.zeros(
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(4, 5, 1), dtype=np.float64), a[:, :, :-1].cumsum(axis=2)),
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axis=2)
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}
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def test_check_output(self):
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self.check_output()
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class TestSumOpExclusive4(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': 2, "exclusive": True}
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a = np.random.random((1, 1, 3049)).astype("float64")
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self.inputs = {'X': a}
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self.outputs = {
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'Out': np.concatenate(
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(np.zeros(
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(1, 1, 1), dtype=np.float64), a[:, :, :-1].cumsum(axis=2)),
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axis=2)
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}
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def test_check_output(self):
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self.check_output()
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class TestSumOpExclusive5(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': 2, "exclusive": True}
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a = np.random.random((4, 5, 3096)).astype("float64")
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self.inputs = {'X': a}
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self.outputs = {
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'Out': np.concatenate(
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(np.zeros(
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(4, 5, 1), dtype=np.float64), a[:, :, :-1].cumsum(axis=2)),
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axis=2)
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}
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def test_check_output(self):
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self.check_output()
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class TestSumOpReverseExclusive(OpTest):
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def setUp(self):
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self.op_type = "cumsum"
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self.attrs = {'axis': 2, 'reverse': True, "exclusive": True}
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a = np.random.random((4, 5, 6)).astype("float64")
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self.inputs = {'X': a}
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a = np.flip(a, axis=2)
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self.outputs = {
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'Out': np.concatenate(
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(np.flip(
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a[:, :, :-1].cumsum(axis=2), axis=2), np.zeros(
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(4, 5, 1), dtype=np.float64)),
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axis=2)
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}
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def test_check_output(self):
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self.check_output()
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class BadInputTest(unittest.TestCase):
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def test_error(self):
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with fluid.program_guard(fluid.Program()):
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def test_bad_x():
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data = [1, 2, 4]
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result = fluid.layers.cumsum(data, axis=0)
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self.assertRaises(TypeError, test_bad_x)
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if __name__ == '__main__':
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unittest.main()
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