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180 lines
7.4 KiB
180 lines
7.4 KiB
#Copyright (c) 2020 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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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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from op_test import OpTest
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from paddle.fluid import compiler, Program, program_guard
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from paddle.fluid.op import Operator
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from paddle.fluid.backward import append_backward
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class TestWhereOp(OpTest):
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def setUp(self):
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self.op_type = "where"
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self.init_config()
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self.inputs = {'Condition': self.cond, 'X': self.x, 'Y': self.y}
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self.outputs = {'Out': np.where(self.cond, self.x, self.y)}
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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', 'Y'], 'Out')
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def init_config(self):
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self.x = np.random.uniform(-3, 5, (100)).astype("float64")
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self.y = np.random.uniform(-3, 5, (100)).astype("float64")
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self.cond = np.zeros((100)).astype("bool")
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class TestWhereOp2(TestWhereOp):
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def init_config(self):
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self.x = np.random.uniform(-5, 5, (60, 2)).astype("float64")
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self.y = np.random.uniform(-5, 5, (60, 2)).astype("float64")
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self.cond = np.ones((60, 2)).astype("bool")
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class TestWhereOp3(TestWhereOp):
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def init_config(self):
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self.x = np.random.uniform(-3, 5, (20, 2, 4)).astype("float64")
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self.y = np.random.uniform(-3, 5, (20, 2, 4)).astype("float64")
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self.cond = np.array(np.random.randint(2, size=(20, 2, 4)), dtype=bool)
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class TestWhereAPI(unittest.TestCase):
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def setUp(self):
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self.init_data()
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def init_data(self):
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self.shape = [10, 15]
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self.cond = np.array(np.random.randint(2, size=self.shape), dtype=bool)
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self.x = np.random.uniform(-2, 3, self.shape).astype(np.float32)
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self.y = np.random.uniform(-2, 3, self.shape).astype(np.float32)
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self.out = np.where(self.cond, self.x, self.y)
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def ref_x_backward(self, dout):
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return np.where(self.cond == True, dout, 0)
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def ref_y_backward(self, dout):
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return np.where(self.cond == False, dout, 0)
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def test_api(self, use_cuda=False):
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for x_stop_gradient in [False, True]:
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for y_stop_gradient in [False, True]:
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with fluid.program_guard(Program(), Program()):
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cond = fluid.layers.data(
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name='cond', shape=self.shape, dtype='bool')
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x = fluid.layers.data(
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name='x', shape=self.shape, dtype='float32')
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y = fluid.layers.data(
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name='y', shape=self.shape, dtype='float32')
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x.stop_gradient = x_stop_gradient
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y.stop_gradient = y_stop_gradient
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result = paddle.where(cond, x, y)
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append_backward(layers.mean(result))
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for use_cuda in [False, True]:
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if use_cuda and not fluid.core.is_compiled_with_cuda():
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break
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place = fluid.CUDAPlace(
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0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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fetch_list = [result, result.grad_name]
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if x_stop_gradient is False:
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fetch_list.append(x.grad_name)
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if y_stop_gradient is False:
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fetch_list.append(y.grad_name)
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out = exe.run(
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fluid.default_main_program(),
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feed={'cond': self.cond,
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'x': self.x,
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'y': self.y},
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fetch_list=fetch_list)
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assert np.array_equal(out[0], self.out)
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if x_stop_gradient is False:
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assert np.array_equal(out[2],
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self.ref_x_backward(out[1]))
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if y.stop_gradient is False:
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assert np.array_equal(
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out[3], self.ref_y_backward(out[1]))
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elif y.stop_gradient is False:
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assert np.array_equal(out[2],
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self.ref_y_backward(out[1]))
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def test_api_broadcast(self, use_cuda=False):
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main_program = Program()
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with fluid.program_guard(main_program):
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x = fluid.layers.data(name='x', shape=[4, 1], dtype='float32')
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y = fluid.layers.data(name='y', shape=[4, 2], dtype='float32')
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x_i = np.array([[0.9383, 0.1983, 3.2, 1.2]]).astype("float32")
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y_i = np.array([[1.0, 1.0, 1.0, 1.0],
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[1.0, 1.0, 1.0, 1.0]]).astype("float32")
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result = paddle.where(x > 1, x=x, y=y)
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for use_cuda in [False, True]:
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if use_cuda and not fluid.core.is_compiled_with_cuda():
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return
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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out = exe.run(fluid.default_main_program(),
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feed={'x': x_i,
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'y': y_i},
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fetch_list=[result])
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assert np.array_equal(out[0], np.where(x_i > 1, x_i, y_i))
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class TestWhereDygraphAPI(unittest.TestCase):
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def test_api(self):
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with fluid.dygraph.guard():
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x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float64")
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y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float64")
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cond_i = np.array([False, False, True, True]).astype("bool")
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x = fluid.dygraph.to_variable(x_i)
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y = fluid.dygraph.to_variable(y_i)
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cond = fluid.dygraph.to_variable(cond_i)
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out = paddle.where(cond, x, y)
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assert np.array_equal(out.numpy(), np.where(cond_i, x_i, y_i))
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class TestWhereOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float64")
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y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float64")
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cond_i = np.array([False, False, True, True]).astype("bool")
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def test_Variable():
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paddle.where(cond_i, x_i, y_i)
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self.assertRaises(TypeError, test_Variable)
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def test_type():
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x = fluid.layers.data(name='x', shape=[4], dtype='bool')
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y = fluid.layers.data(name='y', shape=[4], dtype='float16')
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cond = fluid.layers.data(name='cond', shape=[4], dtype='int32')
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paddle.where(cond, x, y)
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self.assertRaises(TypeError, test_type)
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if __name__ == '__main__':
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unittest.main()
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