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341 lines
11 KiB
341 lines
11 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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import paddle.fluid.core as core
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from op_test import OpTest, skip_check_grad_ci
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class ElementwiseDivOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.dtype = np.float64
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self.init_dtype()
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""" Warning
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CPU gradient check error!
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'X': np.random.random((32,84)).astype("float32"),
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'Y': np.random.random((32,84)).astype("float32")
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"""
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype),
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'Y': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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}
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self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['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_normal(self):
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self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05)
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def test_check_grad_ingore_x(self):
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self.check_grad(
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['Y'], 'Out', max_relative_error=0.05, no_grad_set=set("X"))
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def test_check_grad_ingore_y(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
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def init_dtype(self):
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pass
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@skip_check_grad_ci(
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reason="[skip shape check] Use y_shape(1) to test broadcast.")
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class TestElementwiseDivOp_scalar(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [20, 3, 4]).astype(np.float64),
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'Y': np.random.uniform(0.1, 1, [1]).astype(np.float64)
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}
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self.outputs = {'Out': self.inputs['X'] / self.inputs['Y']}
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class TestElementwiseDivOp_Vector(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [100]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
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}
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self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseDivOp_broadcast_0(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [100, 3, 4]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
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}
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self.attrs = {'axis': 0}
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self.outputs = {
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'Out':
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np.divide(self.inputs['X'], self.inputs['Y'].reshape(100, 1, 1))
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}
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class TestElementwiseDivOp_broadcast_1(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 100, 4]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
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}
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self.attrs = {'axis': 1}
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self.outputs = {
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'Out':
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np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 100, 1))
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}
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class TestElementwiseDivOp_broadcast_2(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 3, 100]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
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}
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self.outputs = {
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'Out':
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np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 100))
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}
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class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 10, 12, 5]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [10, 12]).astype("float64")
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}
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self.attrs = {'axis': 1}
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self.outputs = {
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'Out':
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np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 10, 12, 1))
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}
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class TestElementwiseDivOp_broadcast_4(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 3, 50]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [2, 1, 50]).astype("float64")
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}
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self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseDivOp_broadcast_5(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 3, 4, 20]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [2, 3, 1, 20]).astype("float64")
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}
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self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseDivOp_commonuse_1(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [2, 3, 100]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [1, 1, 100]).astype("float64"),
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}
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self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseDivOp_commonuse_2(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [30, 3, 1, 5]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [30, 1, 4, 1]).astype("float64"),
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}
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self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseDivOp_xsize_lessthan_ysize(ElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [10, 12]).astype("float64"),
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'Y': np.random.uniform(0.1, 1, [2, 3, 10, 12]).astype("float64"),
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}
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self.attrs = {'axis': 2}
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self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseDivOp_INT(OpTest):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.dtype = np.int32
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self.init_dtype()
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self.inputs = {
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'X': np.random.randint(
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1, 5, size=[13, 17]).astype(self.dtype),
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'Y': np.random.randint(
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1, 5, size=[13, 17]).astype(self.dtype)
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}
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self.outputs = {'Out': self.inputs['X'] // self.inputs['Y']}
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def test_check_output(self):
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self.check_output()
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def init_dtype(self):
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pass
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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 TestElementwiseDivOpFp16(ElementwiseDivOp):
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def init_dtype(self):
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self.dtype = np.float16
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', max_relative_error=1)
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def test_check_grad_ingore_x(self):
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self.check_grad(
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['Y'], 'Out', max_relative_error=1, no_grad_set=set("X"))
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def test_check_grad_ingore_y(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=1, no_grad_set=set('Y'))
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class TestElementwiseDivBroadcast(unittest.TestCase):
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def test_shape_with_batch_sizes(self):
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with fluid.program_guard(fluid.Program()):
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x_var = fluid.data(
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name='x', dtype='float32', shape=[None, 3, None, None])
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one = 2.
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out = one / x_var
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exe = fluid.Executor(fluid.CPUPlace())
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x = np.random.uniform(0.1, 0.6, (1, 3, 32, 32)).astype("float32")
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out_result, = exe.run(feed={'x': x}, fetch_list=[out])
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self.assertEqual((out_result == (2 / x)).all(), True)
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class TestDivideOp(unittest.TestCase):
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def test_name(self):
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with fluid.program_guard(fluid.Program()):
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x = fluid.data(name="x", shape=[2, 3], dtype="float32")
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y = fluid.data(name='y', shape=[2, 3], dtype='float32')
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y_1 = paddle.divide(x, y, name='div_res')
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self.assertEqual(('div_res' in y_1.name), True)
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def test_dygraph(self):
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with fluid.dygraph.guard():
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np_x = np.array([2, 3, 4]).astype('float64')
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np_y = np.array([1, 5, 2]).astype('float64')
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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z = paddle.divide(x, y)
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np_z = z.numpy()
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z_expected = np.array([2., 0.6, 2.])
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self.assertEqual((np_z == z_expected).all(), True)
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class TestComplexElementwiseDivOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.init_base_dtype()
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self.init_input_output()
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self.init_grad_input_output()
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self.inputs = {
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'X': OpTest.np_dtype_to_fluid_dtype(self.x),
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'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
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}
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self.attrs = {'axis': -1, 'use_mkldnn': False}
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self.outputs = {'Out': self.out}
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def init_base_dtype(self):
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self.dtype = np.float64
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def init_input_output(self):
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self.x = np.random.random(
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(2, 3, 4, 5)).astype(self.dtype) + 1J * np.random.random(
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(2, 3, 4, 5)).astype(self.dtype)
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self.y = np.random.random(
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(2, 3, 4, 5)).astype(self.dtype) + 1J * np.random.random(
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(2, 3, 4, 5)).astype(self.dtype)
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self.out = self.x / self.y
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def init_grad_input_output(self):
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self.grad_out = np.ones((2, 3, 4, 5), self.dtype) + 1J * np.ones(
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(2, 3, 4, 5), self.dtype)
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self.grad_x = self.grad_out / np.conj(self.y)
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self.grad_y = -self.grad_out * np.conj(self.x / self.y / 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_normal(self):
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self.check_grad(
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['X', 'Y'],
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'Out',
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user_defined_grads=[self.grad_x, self.grad_y],
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user_defined_grad_outputs=[self.grad_out])
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def test_check_grad_ingore_x(self):
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self.check_grad(
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['Y'],
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'Out',
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no_grad_set=set("X"),
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user_defined_grads=[self.grad_y],
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user_defined_grad_outputs=[self.grad_out])
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def test_check_grad_ingore_y(self):
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self.check_grad(
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['X'],
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'Out',
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no_grad_set=set('Y'),
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user_defined_grads=[self.grad_x],
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user_defined_grad_outputs=[self.grad_out])
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class TestRealComplexElementwiseDivOp(TestComplexElementwiseDivOp):
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def init_input_output(self):
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self.x = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.y = np.random.random(
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(2, 3, 4, 5)).astype(self.dtype) + 1J * np.random.random(
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(2, 3, 4, 5)).astype(self.dtype)
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self.out = self.x / self.y
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def init_grad_input_output(self):
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self.grad_out = np.ones((2, 3, 4, 5), self.dtype) + 1J * np.ones(
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(2, 3, 4, 5), self.dtype)
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self.grad_x = np.real(self.grad_out / np.conj(self.y))
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self.grad_y = -self.grad_out * np.conj(self.x / self.y / self.y)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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