wehub-resource-sync
359 lines
13 KiB
Python
359 lines
13 KiB
Python
# Copyright (c) 2021 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 get_device_place, is_custom_device
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import paddle
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from paddle import base
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class TestRot90_API(unittest.TestCase):
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"""Test rot90 api."""
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def test_static_graph(self):
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paddle.enable_static()
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(train_program, startup_program):
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=1, axes=[0, 1])
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output = paddle.rot90(output, k=1, axes=[0, 1])
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output = output.rot90(k=1, axes=[0, 1])
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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exe.run(startup_program)
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img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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res = exe.run(
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train_program, feed={'input': img}, fetch_list=[output]
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)
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out_np = np.array(res[0])
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out_ref = np.array([[4, 1], [5, 2], [6, 3]]).astype(np.float32)
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self.assertTrue(
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(out_np == out_ref).all(),
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msg='rot90 output is wrong, out =' + str(out_np),
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)
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def test_static_k_0(self):
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paddle.enable_static()
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input = paddle.static.data(name='input', dtype='float32', shape=[2, 3])
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(train_program, startup_program):
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=0, axes=[0, 1])
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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exe.run(startup_program)
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img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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res = exe.run(
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train_program, feed={'input': img}, fetch_list=[output]
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)
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out_np = np.array(res[0])
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out_ref = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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self.assertTrue(
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(out_np == out_ref).all(),
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msg='rot90 output is wrong, out =' + str(out_np),
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)
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def test_static_k_2(self):
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paddle.enable_static()
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input = paddle.static.data(name='input', dtype='float32', shape=[2, 3])
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(train_program, startup_program):
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=2, axes=[0, 1])
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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exe.run(startup_program)
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img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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res = exe.run(
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train_program, feed={'input': img}, fetch_list=[output]
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)
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out_np = np.array(res[0])
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out_ref = np.array([[6, 5, 4], [3, 2, 1]]).astype(np.float32)
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self.assertTrue(
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(out_np == out_ref).all(),
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msg='rot90 output is wrong, out =' + str(out_np),
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)
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def test_static_k_3(self):
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paddle.enable_static()
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input = paddle.static.data(name='input', dtype='float32', shape=[2, 3])
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(train_program, startup_program):
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=3, axes=[0, 1])
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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exe.run(startup_program)
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img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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res = exe.run(
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train_program, feed={'input': img}, fetch_list=[output]
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)
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out_np = np.array(res[0])
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out_ref = np.array([[4, 1], [5, 2], [6, 3]]).astype(np.float32)
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self.assertTrue(
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(out_np == out_ref).all(),
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msg='rot90 output is wrong, out =' + str(out_np),
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)
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def test_static_neg_k_1(self):
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paddle.enable_static()
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input = paddle.static.data(name='input', dtype='float32', shape=[2, 3])
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(train_program, startup_program):
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=-1, axes=[0, 1])
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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exe.run(startup_program)
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img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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res = exe.run(
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train_program, feed={'input': img}, fetch_list=[output]
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)
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out_np = np.array(res[0])
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out_ref = np.array([[4, 1], [5, 2], [6, 3]]).astype(np.float32)
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self.assertTrue(
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(out_np == out_ref).all(),
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msg='rot90 output is wrong, out =' + str(out_np),
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)
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def test_static_neg_k_2(self):
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paddle.enable_static()
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input = paddle.static.data(name='input', dtype='float32', shape=[2, 3])
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(train_program, startup_program):
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=-2, axes=[0, 1])
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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exe.run(startup_program)
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img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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res = exe.run(
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train_program, feed={'input': img}, fetch_list=[output]
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)
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out_np = np.array(res[0])
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out_ref = np.array([[6, 5, 4], [3, 2, 1]]).astype(np.float32)
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self.assertTrue(
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(out_np == out_ref).all(),
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msg='rot90 output is wrong, out =' + str(out_np),
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)
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def test_static_neg_k_3(self):
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paddle.enable_static()
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input = paddle.static.data(name='input', dtype='float32', shape=[2, 3])
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(train_program, startup_program):
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=-3, axes=[0, 1])
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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exe.run(startup_program)
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img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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res = exe.run(
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train_program, feed={'input': img}, fetch_list=[output]
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)
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out_np = np.array(res[0])
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out_ref = np.array([[3, 6], [2, 5], [1, 4]]).astype(np.float32)
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self.assertTrue(
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(out_np == out_ref).all(),
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msg='rot90 output is wrong, out =' + str(out_np),
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)
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def test_static_neg_k_4(self):
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paddle.enable_static()
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input = paddle.static.data(name='input', dtype='float32', shape=[2, 3])
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(train_program, startup_program):
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=-4, axes=[0, 1])
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place = base.CPUPlace()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = base.Executor(place)
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exe.run(startup_program)
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img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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res = exe.run(
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train_program, feed={'input': img}, fetch_list=[output]
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)
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out_np = np.array(res[0])
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out_ref = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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self.assertTrue(
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(out_np == out_ref).all(),
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msg='rot90 output is wrong, out =' + str(out_np),
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)
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def test_error_api(self):
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paddle.enable_static()
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# dims error
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def run1():
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=1, axes=[0])
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self.assertRaises(ValueError, run1)
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# input dims error
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def run2():
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input = paddle.static.data(name='input', dtype='float32', shape=[2])
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output = paddle.rot90(input, k=1, axes=[0, 1])
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self.assertRaises(ValueError, run2)
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def run3():
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=1, axes=[0, 0])
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self.assertRaises(ValueError, run3)
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def run4():
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=1, axes=[3, 1])
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self.assertRaises(ValueError, run4)
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def run5():
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input = paddle.static.data(
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name='input', dtype='float32', shape=[2, 3]
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)
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output = paddle.rot90(input, k=1, axes=[0, 3])
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self.assertRaises(ValueError, run5)
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def test_dygraph(self):
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img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
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with base.dygraph.guard():
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inputs = paddle.to_tensor(img)
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ret = paddle.rot90(inputs, k=1, axes=[0, 1])
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ret = ret.rot90(1, axes=[0, 1])
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ret = paddle.rot90(ret, k=1, axes=[0, 1])
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out_ref = np.array([[4, 1], [5, 2], [6, 3]]).astype(np.float32)
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self.assertTrue(
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(ret.numpy() == out_ref).all(),
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msg='rot90 output is wrong, out =' + str(ret.numpy()),
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)
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def create_test_zero_size_class(op_type, dtype, shape, axis):
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class Cls(unittest.TestCase):
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def test_zero_size(self):
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paddle.disable_static()
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numpy_tensor_1 = np.random.rand(*shape).astype(dtype)
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paddle_x = paddle.to_tensor(numpy_tensor_1)
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paddle_x.stop_gradient = False
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paddle_api = eval(f"paddle.{op_type}")
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paddle_out = paddle_api(paddle_x, axes=axis)
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numpy_api = eval(f"np.{op_type}")
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numpy_out = numpy_api(numpy_tensor_1, axes=axis)
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loss = paddle.sum(paddle_out)
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loss.backward()
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np.testing.assert_allclose(
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paddle_out.numpy(),
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numpy_out,
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1e-2,
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1e-2,
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)
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np.testing.assert_allclose(
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paddle_out.shape,
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numpy_out.shape,
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)
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np.testing.assert_allclose(
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paddle_x.grad.shape,
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paddle_x.shape,
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)
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cls_name = f"{op_type}{dtype}_ZeroSize"
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Cls.__name__ = cls_name
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globals()[cls_name] = Cls
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create_test_zero_size_class("rot90", "float32", [3, 4, 0], (0, 1))
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create_test_zero_size_class("rot90", "int32", [3, 4, 0, 3, 4], (0, 1))
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if __name__ == "__main__":
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unittest.main()
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