wehub-resource-sync
238 lines
8.0 KiB
Python
238 lines
8.0 KiB
Python
# 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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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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from paddle import base
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class TestMultiplexOp(OpTest):
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def setUp(self):
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self.op_type = "multiplex"
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self.init_dtype()
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self.python_api = paddle.tensor.multiplex
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rows = 4
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index = np.arange(0, rows).astype('int32')
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np.random.shuffle(index)
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index = np.reshape(index, (rows, 1))
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ins1 = np.random.random((rows, 25)).astype(self.dtype)
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ins2 = np.random.random((rows, 25)).astype(self.dtype)
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ins3 = np.random.random((rows, 25)).astype(self.dtype)
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ins4 = np.random.random((rows, 25)).astype(self.dtype)
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if self.dtype == 'complex64' or self.dtype == 'complex128':
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ins1 = (
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np.random.random((rows, 25)) + 1j * np.random.random((rows, 25))
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).astype(self.dtype)
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ins2 = (
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np.random.random((rows, 25)) + 1j * np.random.random((rows, 25))
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).astype(self.dtype)
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ins3 = (
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np.random.random((rows, 25)) + 1j * np.random.random((rows, 25))
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).astype(self.dtype)
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ins4 = (
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np.random.random((rows, 25)) + 1j * np.random.random((rows, 25))
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).astype(self.dtype)
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self.inputs = {
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'Ids': index,
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'X': [('x1', ins1), ('x2', ins2), ('x3', ins3), ('x4', ins4)],
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}
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# multiplex output
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output = np.zeros_like(ins1)
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for i in range(0, rows):
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k = index[i][0]
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output[i] = self.inputs['X'][k][1][i]
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self.outputs = {'Out': output}
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def init_dtype(self):
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self.dtype = 'float64'
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(['x1', 'x2', 'x3', 'x4'], 'Out', check_pir=True)
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def test_check_grad_ignore_x1(self):
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self.check_grad(
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['x2', 'x3', 'x4'], 'Out', no_grad_set=set('x1'), check_pir=True
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)
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def test_check_grad_ignore_x1_x2(self):
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self.check_grad(
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['x3', 'x4'], 'Out', no_grad_set={'x1', 'x2'}, check_pir=True
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)
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def test_check_grad_ignore_x3(self):
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self.check_grad(
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['x1', 'x2', 'x4'], 'Out', no_grad_set=set('x3'), check_pir=True
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)
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class TestMultiplexOp_complex64(TestMultiplexOp):
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def init_dtype(self):
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self.dtype = "complex64"
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class TestMultiplexOp_complex128(TestMultiplexOp):
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def init_dtype(self):
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self.dtype = "complex128"
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class TestMultiplexOpError(unittest.TestCase):
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def test_errors(self):
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paddle.enable_static()
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with base.program_guard(base.Program(), base.Program()):
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x1 = paddle.static.data(name='x1', shape=[None, 2], dtype='int64')
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x2 = paddle.static.data(name='x2', shape=[None, 2], dtype='int64')
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index = paddle.static.data(
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name='index', shape=[None, 1], dtype='int32'
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)
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def test_list():
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# the inputs type must be list
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paddle.multiplex(inputs=x1, index=index)
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self.assertRaises(TypeError, test_list)
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def test_len():
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paddle.multiplex(inputs=[x1], index=index)
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self.assertRaises(ValueError, test_len)
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def test_type():
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y1 = paddle.static.data(
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name='y1', shape=[None, 2], dtype='int16'
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)
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y2 = paddle.static.data(
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name='y2', shape=[None, 2], dtype='int16'
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)
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paddle.multiplex(inputs=[y1, y2], index=index)
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self.assertRaises(TypeError, test_type)
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def test_type2():
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index2 = paddle.static.data(
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name='index2', shape=[None, 1], dtype='int16'
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)
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paddle.multiplex(inputs=[x1, x2], index=index2)
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self.assertRaises(TypeError, test_type2)
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class TestMultiplexODygrap(unittest.TestCase):
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def setUp(self):
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self.init_dtype()
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self.img1 = np.array([[1, 2], [3, 4]]).astype(self.dtype)
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self.img2 = np.array([[5, 6], [7, 8]]).astype(self.dtype)
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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self.img1 = (
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np.array([[1, 2], [3, 4]]) + 1j * np.array([[1, 2], [3, 4]])
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).astype(self.dtype)
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self.img2 = (
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np.array([[5, 6], [7, 8]]) + 1j * np.array([[1, 2], [3, 4]])
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).astype(self.dtype)
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def init_dtype(self):
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self.dtype = np.float32
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def test_multiplex_dygraph(self):
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paddle.disable_static()
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inputs = [paddle.to_tensor(self.img1), paddle.to_tensor(self.img2)]
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index = paddle.to_tensor(np.array([[1], [0]]).astype(np.int32))
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res = paddle.multiplex(inputs, index)
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paddle.enable_static()
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def test_dygraph_api(self):
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with base.dygraph.guard():
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inputs = [paddle.to_tensor(self.img1), paddle.to_tensor(self.img2)]
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index = paddle.to_tensor(np.array([[1], [0]]).astype(np.int32))
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inputs[0].stop_gradient = False
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inputs[1].stop_gradient = False
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res = paddle.multiplex(inputs, index)
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res.backward()
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inputs_eager = [
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paddle.to_tensor(self.img1),
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paddle.to_tensor(self.img2),
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]
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index_eager = paddle.to_tensor(
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np.array([[1], [0]]).astype(np.int32)
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)
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inputs_eager[0].stop_gradient = False
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inputs_eager[1].stop_gradient = False
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res_eager = paddle.multiplex(inputs_eager, index_eager)
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res_eager.backward()
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self.assertEqual((res.numpy() == res_eager.numpy()).all(), True)
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self.assertEqual(
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(inputs[0].grad.numpy() == inputs_eager[0].grad.numpy()).all(),
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True,
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)
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self.assertEqual(
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(inputs[1].grad.numpy() == inputs_eager[1].grad.numpy()).all(),
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True,
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)
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class TestMultiplexODygrap_complex64(TestMultiplexODygrap):
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def init_dtype(self):
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self.dtype = np.complex64
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class TestMultiplexODygrap_complex128(TestMultiplexODygrap):
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def init_dtype(self):
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self.dtype = np.complex128
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class TestMultiplexOp_ZeroSize(OpTest):
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def setUp(self):
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self.op_type = "multiplex"
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self.init_dtype()
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self.python_api = paddle.tensor.multiplex
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rows = 4
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index = np.array([0, 2, 2, 3]).astype('int32')
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np.random.shuffle(index)
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index = np.reshape(index, (rows, 1))
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ins1 = np.random.random((rows, 0)).astype(self.dtype)
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ins2 = np.random.random((rows, 0)).astype(self.dtype)
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ins3 = np.random.random((rows, 0)).astype(self.dtype)
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ins4 = np.random.random((rows, 0)).astype(self.dtype)
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self.inputs = {
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'Ids': index,
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'X': [('x1', ins1), ('x2', ins2), ('x3', ins3), ('x4', ins4)],
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}
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# multiplex output
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output = np.zeros_like(ins1)
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for i in range(0, rows):
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k = index[i][0]
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if self.inputs['X'][k][1][i].size != 0:
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output[i] = self.inputs['X'][k][1][i]
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self.outputs = {'Out': output}
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def init_dtype(self):
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self.dtype = 'float64'
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(['x1', 'x2', 'x3', 'x4'], 'Out', check_pir=True)
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if __name__ == '__main__':
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unittest.main()
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