You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
246 lines
8.1 KiB
246 lines
8.1 KiB
# Copyright (c) 2018 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.
|
|
|
|
from __future__ import print_function
|
|
import unittest
|
|
import numpy as np
|
|
from op_test import OpTest, skip_check_grad_ci
|
|
import paddle.fluid.core as core
|
|
from paddle.fluid.op import Operator
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid import compiler, Program, program_guard
|
|
|
|
|
|
class ElementwiseMulOp(OpTest):
|
|
def init_kernel_type(self):
|
|
self.use_mkldnn = False
|
|
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.dtype = np.float64
|
|
self.axis = -1
|
|
self.init_dtype()
|
|
self.init_input_output()
|
|
self.init_kernel_type()
|
|
self.init_axis()
|
|
|
|
self.inputs = {
|
|
'X': OpTest.np_dtype_to_fluid_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
|
|
}
|
|
self.outputs = {'Out': self.out}
|
|
self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
|
|
|
|
def test_check_output(self):
|
|
# TODO(wangzhongpu): support mkldnn op in dygraph mode
|
|
self.check_output(check_dygraph=(self.use_mkldnn == False))
|
|
|
|
def test_check_grad_normal(self):
|
|
# TODO(wangzhongpu): support mkldnn op in dygraph mode
|
|
self.check_grad(
|
|
['X', 'Y'], 'Out', check_dygraph=(self.use_mkldnn == False))
|
|
|
|
def test_check_grad_ingore_x(self):
|
|
# TODO(wangzhongpu): support mkldnn op in dygraph mode
|
|
self.check_grad(
|
|
['Y'],
|
|
'Out',
|
|
no_grad_set=set("X"),
|
|
check_dygraph=(self.use_mkldnn == False))
|
|
|
|
def test_check_grad_ingore_y(self):
|
|
# TODO(wangzhongpu): support mkldnn op in dygraph mode
|
|
self.check_grad(
|
|
['X'],
|
|
'Out',
|
|
no_grad_set=set('Y'),
|
|
check_dygraph=(self.use_mkldnn == False))
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.out = np.multiply(self.x, self.y)
|
|
|
|
def init_dtype(self):
|
|
pass
|
|
|
|
def init_axis(self):
|
|
pass
|
|
|
|
|
|
@skip_check_grad_ci(
|
|
reason="[skip shape check] Use y_shape(1) to test broadcast.")
|
|
class TestElementwiseMulOp_scalar(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.rand(10, 3, 4).astype(np.float64),
|
|
'Y': np.random.rand(1).astype(np.float64)
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_Vector(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.random((100, )).astype("float64"),
|
|
'Y': np.random.random((100, )).astype("float64")
|
|
}
|
|
self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_broadcast_0(ElementwiseMulOp):
|
|
def init_input_output(self):
|
|
self.x = np.random.rand(100, 2, 3).astype(self.dtype)
|
|
self.y = np.random.rand(100).astype(self.dtype)
|
|
self.out = self.x * self.y.reshape(100, 1, 1)
|
|
|
|
def init_axis(self):
|
|
self.axis = 0
|
|
|
|
|
|
class TestElementwiseMulOp_broadcast_1(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 100, 3).astype(np.float64),
|
|
'Y': np.random.rand(100).astype(np.float64)
|
|
}
|
|
|
|
self.attrs = {'axis': 1}
|
|
self.outputs = {
|
|
'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 100, 1)
|
|
}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_broadcast_2(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 3, 100).astype(np.float64),
|
|
'Y': np.random.rand(100).astype(np.float64)
|
|
}
|
|
|
|
self.outputs = {
|
|
'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 1, 100)
|
|
}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 10, 12, 3).astype(np.float64),
|
|
'Y': np.random.rand(10, 12).astype(np.float64)
|
|
}
|
|
|
|
self.attrs = {'axis': 1}
|
|
self.outputs = {
|
|
'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 10, 12, 1)
|
|
}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_broadcast_4(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.rand(10, 2, 11).astype(np.float64),
|
|
'Y': np.random.rand(10, 1, 11).astype(np.float64)
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_broadcast_5(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.rand(10, 4, 2, 3).astype(np.float64),
|
|
'Y': np.random.rand(10, 4, 1, 3).astype(np.float64)
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
|
|
self.init_kernel_type()
|
|
|
|
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestElementwiseMulOpFp16(ElementwiseMulOp):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
|
|
class TestElementwiseMulOp_commonuse_1(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 3, 100).astype(np.float64),
|
|
'Y': np.random.rand(1, 1, 100).astype(np.float64)
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_commonuse_2(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.rand(30, 3, 1, 5).astype(np.float64),
|
|
'Y': np.random.rand(30, 1, 4, 1).astype(np.float64)
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_xsize_lessthan_ysize(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.inputs = {
|
|
'X': np.random.rand(10, 10).astype(np.float64),
|
|
'Y': np.random.rand(2, 2, 10, 10).astype(np.float64)
|
|
}
|
|
|
|
self.attrs = {'axis': 2}
|
|
|
|
self.outputs = {
|
|
'Out': self.inputs['X'].reshape(1, 1, 10, 10) * self.inputs['Y']
|
|
}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
# the input of elementwise_mul must be Variable.
|
|
x1 = fluid.create_lod_tensor(
|
|
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
|
|
y1 = fluid.create_lod_tensor(
|
|
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
|
|
self.assertRaises(TypeError, fluid.layers.elementwise_mul, x1, y1)
|
|
|
|
# the input dtype of elementwise_mul must be float16 or float32 or float64 or int32 or int64
|
|
# float16 only can be set on GPU place
|
|
x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8")
|
|
y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8")
|
|
self.assertRaises(TypeError, fluid.layers.elementwise_mul, x2, y2)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|