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239 lines
8.6 KiB
239 lines
8.6 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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import paddle
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import paddle.fluid as fluid
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from op_test import OpTest
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import paddle.fluid.core as core
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class TestScatterOp(OpTest):
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def setUp(self):
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self.op_type = "scatter"
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ref_np = np.ones((3, 50)).astype("float32")
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index_np = np.array([1, 2]).astype("int32")
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updates_np = np.random.random((2, 50)).astype("float32")
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output_np = np.copy(ref_np)
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output_np[index_np] = updates_np
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self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
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self.outputs = {'Out': output_np}
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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", "Updates"], "Out")
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class TestScatterOp0(OpTest):
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def setUp(self):
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self.op_type = "scatter"
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ref_np = np.ones((3, 3)).astype("float32")
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index_np = np.array([1, 2]).astype("int32")
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updates_np = np.random.random((2, 3)).astype("float32")
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output_np = np.copy(ref_np)
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output_np[index_np] = updates_np
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self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
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self.attrs = {'overwrite': True}
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self.outputs = {'Out': output_np}
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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", "Updates"], "Out")
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class TestScatterOp1(OpTest):
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def setUp(self):
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self.op_type = "scatter"
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ref_np = np.ones((3, 3)).astype("float32")
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zeros_np = np.zeros([2, 3]).astype('float32')
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index_np = np.array([1, 1]).astype("int32")
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updates_np = np.random.random((2, 3)).astype("float32")
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output_np = np.copy(ref_np)
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output_np[index_np] = zeros_np
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for i in range(0, len(index_np)):
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output_np[index_np[i]] += updates_np[i]
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self.attrs = {'overwrite': False}
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self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
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self.outputs = {'Out': output_np}
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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", "Updates"], "Out")
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestScatterOp2(OpTest):
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def setUp(self):
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self.op_type = "scatter"
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ref_np = np.ones((3, 3)).astype("float32")
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index_np = np.array([1, 2]).astype("int32")
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updates_np = np.random.random((2, 3)).astype("float32")
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output_np = np.copy(ref_np)
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output_np[index_np] = updates_np
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self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
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self.outputs = {'Out': output_np}
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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self.check_output_with_place(place, atol=1e-3)
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def test_check_grad(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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self.check_grad_with_place(place, ['X', 'Updates'], 'Out')
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestScatterOp3(OpTest):
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def setUp(self):
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self.op_type = "scatter"
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ref_np = np.ones((3, 3)).astype("float32")
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zeros_np = np.zeros([2, 3]).astype('float32')
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index_np = np.array([1, 1]).astype("int32")
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updates_np = np.random.random((2, 3)).astype("float32")
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output_np = np.copy(ref_np)
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output_np[index_np] = zeros_np
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for i in range(0, len(index_np)):
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output_np[index_np[i]] += updates_np[i]
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self.attrs = {'overwrite': False}
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self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
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self.outputs = {'Out': output_np}
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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self.check_output_with_place(place, atol=1e-3)
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def test_check_grad(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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self.check_grad_with_place(place, ['X', 'Updates'], 'Out')
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class TestScatterOp4(OpTest):
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def setUp(self):
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self.op_type = "scatter"
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ref_np = np.ones((3, 3)).astype("float32")
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index_np = np.array([1, 2]).astype("int64")
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updates_np = np.random.random((2, 3)).astype("float32")
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output_np = np.copy(ref_np)
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output_np[index_np] = updates_np
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self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
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self.outputs = {'Out': output_np}
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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', 'Updates'], 'Out')
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestScatterOp5(OpTest):
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def setUp(self):
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self.op_type = "scatter"
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ref_np = np.ones((3, 3)).astype("float32")
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index_np = np.array([1, 2]).astype("int64")
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updates_np = np.random.random((2, 3)).astype("float32")
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output_np = np.copy(ref_np)
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output_np[index_np] = updates_np
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self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
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self.outputs = {'Out': output_np}
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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self.check_output_with_place(place, atol=1e-3)
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def test_check_grad(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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self.check_grad_with_place(place, ['X', 'Updates'], 'Out')
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class TestScatterAPI(unittest.TestCase):
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def setUp(self):
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self.places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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self.places.append(fluid.CUDAPlace(0))
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self.executed_api()
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def executed_api(self):
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self.scatter = paddle.scatter
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def check_static_result(self, place):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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input = fluid.data(name="input", shape=[3, 2], dtype="float64")
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index = fluid.data(name="index", shape=[4], dtype="int64")
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updates = fluid.data(name="updates", shape=[4, 2], dtype="float64")
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result = self.scatter(input, index, updates, False)
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input_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float64)
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index_data = np.array([2, 1, 0, 1]).astype(np.int64)
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updates_data = np.array(
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[[1, 1], [2, 2], [3, 3], [4, 4]]).astype(np.float64)
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exe = fluid.Executor(place)
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fetches = exe.run(fluid.default_main_program(),
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feed={
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"input": input_data,
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"index": index_data,
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"updates": updates_data
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},
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fetch_list=[result])
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self.assertEqual((fetches[0] == \
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np.array([[3., 3.],[6., 6.],[1., 1.]])).all(), True)
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph(self):
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for place in self.places:
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with fluid.dygraph.guard(place):
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x_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float64)
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index_data = np.array([2, 1, 0, 1]).astype(np.int64)
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updates_data = np.array(
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[[1, 1], [2, 2], [3, 3], [4, 4]]).astype(np.float64)
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x = fluid.dygraph.to_variable(x_data)
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index = fluid.dygraph.to_variable(index_data)
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updates = fluid.dygraph.to_variable(updates_data)
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output1 = self.scatter(x, index, updates, overwrite=False)
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self.assertEqual((output1.numpy() == \
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np.array([[3., 3.],[6., 6.],[1., 1.]])).all(), True)
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class TestScatterInplaceAPI(TestScatterAPI):
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def executed_api(self):
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self.scatter = paddle.scatter_
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if __name__ == "__main__":
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unittest.main()
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