wehub-resource-sync
318 lines
10 KiB
Python
318 lines
10 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import OpTest, get_places
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import paddle
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from paddle import base, static
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numpy_apis = {
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"real": np.real,
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"imag": np.imag,
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}
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paddle_apis = {
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"real": paddle.real,
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"imag": paddle.imag,
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}
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class TestRealOp(OpTest):
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def setUp(self):
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# switch to static
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paddle.enable_static()
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# op test attrs
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self.op_type = "real"
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self.python_api = paddle.real
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self.dtype = np.float64
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self.init_input_output()
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# backward attrs
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self.init_grad_input_output()
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def init_input_output(self):
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self.inputs = {
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'X': np.random.random((20, 5)).astype(self.dtype)
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+ 1j * np.random.random((20, 5)).astype(self.dtype)
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}
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self.outputs = {'Out': numpy_apis[self.op_type](self.inputs['X'])}
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def init_grad_input_output(self):
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self.grad_out = np.ones((20, 5), self.dtype)
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self.grad_x = np.real(self.grad_out) + 1j * np.zeros(
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self.grad_out.shape
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)
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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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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check_pir=True,
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)
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class TestRealOpZeroSize1(TestRealOp):
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def init_input_output(self):
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self.inputs = {
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'X': np.random.random(0).astype(self.dtype)
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+ 1j * np.random.random(0).astype(self.dtype)
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}
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self.outputs = {'Out': numpy_apis[self.op_type](self.inputs['X'])}
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def init_grad_input_output(self):
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self.grad_out = np.ones((0), self.dtype)
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self.grad_x = np.real(self.grad_out) + 1j * np.zeros(
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self.grad_out.shape
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)
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class TestRealOpZeroSize2(TestRealOp):
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def init_input_output(self):
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self.inputs = {
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'X': np.random.random((0, 5)).astype(self.dtype)
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+ 1j * np.random.random((0, 5)).astype(self.dtype)
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}
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self.outputs = {'Out': numpy_apis[self.op_type](self.inputs['X'])}
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def init_grad_input_output(self):
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self.grad_out = np.ones((0, 5), self.dtype)
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self.grad_x = np.real(self.grad_out) + 1j * np.zeros(
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self.grad_out.shape
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)
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class TestRealOpZeroSize3(TestRealOp):
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def init_input_output(self):
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self.inputs = {
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'X': np.random.random((2, 0, 5)).astype(self.dtype)
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+ 1j * np.random.random((2, 0, 5)).astype(self.dtype)
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}
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self.outputs = {'Out': numpy_apis[self.op_type](self.inputs['X'])}
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def init_grad_input_output(self):
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self.grad_out = np.ones((2, 0, 5), self.dtype)
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self.grad_x = np.real(self.grad_out) + 1j * np.zeros(
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self.grad_out.shape
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)
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class TestImagOp(TestRealOp):
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def setUp(self):
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# switch to static
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paddle.enable_static()
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# op test attrs
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self.op_type = "imag"
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self.python_api = paddle.imag
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self.dtype = np.float64
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self.init_input_output()
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# backward attrs
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self.init_grad_input_output()
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def init_grad_input_output(self):
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self.grad_out = np.ones((20, 5), self.dtype)
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self.grad_x = np.zeros(self.grad_out.shape) + 1j * np.real(
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self.grad_out
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)
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class TestImagOpZeroSize1(TestImagOp):
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def init_input_output(self):
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self.inputs = {
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'X': np.random.random(0).astype(self.dtype)
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+ 1j * np.random.random(0).astype(self.dtype)
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}
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self.outputs = {'Out': numpy_apis[self.op_type](self.inputs['X'])}
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def init_grad_input_output(self):
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self.grad_out = np.ones((0), self.dtype)
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self.grad_x = np.zeros(self.grad_out.shape) + 1j * np.real(
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self.grad_out
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)
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class TestImagOpZeroSize2(TestImagOp):
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def init_input_output(self):
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self.inputs = {
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'X': np.random.random((0, 5)).astype(self.dtype)
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+ 1j * np.random.random((0, 5)).astype(self.dtype)
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}
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self.outputs = {'Out': numpy_apis[self.op_type](self.inputs['X'])}
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def init_grad_input_output(self):
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self.grad_out = np.ones((0, 5), self.dtype)
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self.grad_x = np.zeros(self.grad_out.shape) + 1j * np.real(
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self.grad_out
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)
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class TestImagOpZeroSize3(TestImagOp):
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def init_input_output(self):
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self.inputs = {
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'X': np.random.random((20, 0, 5)).astype(self.dtype)
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+ 1j * np.random.random((20, 0, 5)).astype(self.dtype)
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}
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self.outputs = {'Out': numpy_apis[self.op_type](self.inputs['X'])}
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def init_grad_input_output(self):
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self.grad_out = np.ones((20, 0, 5), self.dtype)
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self.grad_x = np.zeros(self.grad_out.shape) + 1j * np.real(
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self.grad_out
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)
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class TestRealAPI(unittest.TestCase):
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def setUp(self):
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# switch to static
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paddle.enable_static()
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# prepare test attrs
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self.api = "real"
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self.dtypes = ["complex64", "complex128"]
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self.places = get_places()
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self._shape = [2, 20, 2, 3]
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def test_in_static_mode(self):
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def init_input_output(dtype):
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input = np.random.random(self._shape).astype(
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dtype
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) + 1j * np.random.random(self._shape).astype(dtype)
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return {'x': input}, numpy_apis[self.api](input)
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for dtype in self.dtypes:
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input_dict, np_res = init_input_output(dtype)
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for place in self.places:
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with static.program_guard(static.Program()):
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x = static.data(name="x", shape=self._shape, dtype=dtype)
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out = paddle_apis[self.api](x)
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exe = static.Executor(place)
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out_value = exe.run(feed=input_dict, fetch_list=[out])
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np.testing.assert_array_equal(np_res, out_value[0])
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def test_in_dynamic_mode(self):
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for dtype in self.dtypes:
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input = np.random.random(self._shape).astype(
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dtype
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) + 1j * np.random.random(self._shape).astype(dtype)
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np_res = numpy_apis[self.api](input)
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for place in self.places:
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# it is more convenient to use `guard` than `enable/disable_**` here
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with base.dygraph.guard(place):
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input_t = paddle.to_tensor(input)
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res = paddle_apis[self.api](input_t).numpy()
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np.testing.assert_array_equal(np_res, res)
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res_t = (
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input_t.real().numpy()
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if self.api == "real"
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else input_t.imag().numpy()
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)
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np.testing.assert_array_equal(np_res, res_t)
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def test_dtype_static_error(self):
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# in static graph mode
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with (
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self.assertRaises(TypeError),
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static.program_guard(static.Program()),
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):
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x = static.data(name="x", shape=self._shape, dtype="float32")
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out = paddle_apis[self.api](x, name="real_res")
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def test_dtype_dygraph_error(self):
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# in dynamic mode
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with (
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self.assertRaises(RuntimeError),
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base.dygraph.guard(),
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):
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input = np.random.random(self._shape).astype("float32")
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input_t = paddle.to_tensor(input)
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res = paddle_apis[self.api](input_t)
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class TestImagAPI(TestRealAPI):
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def setUp(self):
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# switch to static
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paddle.enable_static()
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# prepare test attrs
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self.api = "imag"
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self.dtypes = ["complex64", "complex128"]
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self.places = get_places()
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self.init_shape()
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def init_shape(self):
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self._shape = [2, 20, 2, 3]
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class TestImagAPIZeroSize(TestImagAPI):
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def init_shape(self):
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self._shape = [2, 0, 2, 3]
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class TestImagAPIZeroSize1(TestImagAPI):
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def init_shape(self):
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self._shape = [2, 0, 0, 3]
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class TestImagAPIZeroSize2(TestImagAPI):
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def init_shape(self):
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self._shape = [0, 0, 0, 0]
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class TestImagAPIZeroDtype(unittest.TestCase):
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def init_data(self):
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self.shape = [8, 0, 8]
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self.dtype = 'float32'
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self.expact_dtype = paddle.float32
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def test_dtype(self):
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with paddle.base.dygraph.guard():
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self.init_data()
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real_part = paddle.rand(self.shape, dtype=self.dtype)
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imag_part = paddle.rand(self.shape, dtype=self.dtype)
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complex_matrix = paddle.complex(real_part, imag_part)
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imag = paddle.imag(complex_matrix)
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self.assertTrue(imag.dtype == self.expact_dtype)
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class TestImagAPIZeroDtype1(TestImagAPIZeroDtype):
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def init_shape(self):
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self.shape = [8, 0, 8]
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self.dtype = 'float64'
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self.expact_dtype = paddle.float64
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class TestRealAPIZeroDtype(unittest.TestCase):
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def init_data(self):
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self.shape = [8, 0, 8]
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self.dtype = 'float32'
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self.expact_dtype = paddle.float32
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def test_dtype(self):
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with paddle.base.dygraph.guard():
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self.init_data()
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real_part = paddle.rand(self.shape, dtype=self.dtype)
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imag_part = paddle.rand(self.shape, dtype=self.dtype)
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complex_matrix = paddle.complex(real_part, imag_part)
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real = paddle.real(complex_matrix)
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self.assertTrue(real.dtype == self.expact_dtype)
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if __name__ == "__main__":
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unittest.main()
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