wehub-resource-sync
325 lines
9.8 KiB
Python
325 lines
9.8 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 gradient_checker
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import numpy as np
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from decorator_helper import prog_scope
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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get_places,
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is_custom_device,
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)
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import paddle
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from paddle import base
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from paddle.base import core
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class TestFlipOp_API(unittest.TestCase):
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"""Test flip api."""
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def test_static_graph(self):
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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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axis = [0]
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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.flip(input, axis)
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output = paddle.flip(output, -1)
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output = output.flip(0)
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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, 2, 1], [6, 5, 4]]).astype(np.float32)
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self.assertTrue(
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(out_np == out_ref).all(),
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msg='flip output is wrong, out =' + str(out_np),
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)
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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.flip(inputs, [0])
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ret = ret.flip(0)
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ret = paddle.flip(ret, 1)
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out_ref = np.array([[3, 2, 1], [6, 5, 4]]).astype(np.float32)
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self.assertTrue(
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(ret.numpy() == out_ref).all(),
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msg='flip output is wrong, out =' + str(ret.numpy()),
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)
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class TestFlipOp(OpTest):
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def setUp(self):
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self.op_type = 'flip'
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self.python_api = paddle.tensor.flip
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self.init_test_case()
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self.init_attrs()
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self.init_dtype()
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if self.is_bfloat16_op():
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self.input = np.random.random(self.in_shape).astype(np.float32)
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else:
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self.input = np.random.random(self.in_shape).astype(self.dtype)
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output = self.calc_ref_res()
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if self.is_bfloat16_op():
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output = output.astype(np.float32)
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self.inputs = {'X': convert_float_to_uint16(self.input)}
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self.outputs = {'Out': convert_float_to_uint16(output)}
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else:
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self.inputs = {'X': self.input.astype(self.dtype)}
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output = output.astype(self.dtype)
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self.outputs = {'Out': output}
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def init_dtype(self):
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self.dtype = np.float64
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def init_attrs(self):
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self.attrs = {"axis": self.axis}
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def test_check_output(self):
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self.check_output(
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check_cinn=True, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad(["X"], "Out", check_cinn=True, check_pir=True)
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def init_test_case(self):
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self.in_shape = (6, 4, 2, 3)
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self.axis = [0, 1]
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def calc_ref_res(self):
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res = self.input
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if isinstance(self.axis, int):
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return np.flip(res, self.axis)
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for axis in self.axis:
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res = np.flip(res, axis)
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return res
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class TestFlipOpAxis1(TestFlipOp):
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def init_test_case(self):
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self.in_shape = (2, 4, 4)
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self.axis = [0]
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class TestFlipOpAxis2(TestFlipOp):
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def init_test_case(self):
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self.in_shape = (4, 4, 6, 3)
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self.axis = [0, 2]
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class TestFlipOpAxis3(TestFlipOp):
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def init_test_case(self):
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self.in_shape = (4, 3, 1)
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self.axis = [0, 1, 2]
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class TestFlipOpAxis4(TestFlipOp):
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def init_test_case(self):
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self.in_shape = (6, 4, 2, 2)
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self.axis = [0, 1, 2, 3]
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class TestFlipOpEmptyAxis(TestFlipOp):
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def init_test_case(self):
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self.in_shape = (6, 4, 2, 2)
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self.axis = []
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class TestFlipOpNegAxis(TestFlipOp):
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def init_test_case(self):
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self.in_shape = (6, 4, 2, 2)
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self.axis = [-1]
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class TestFlipOp_ZeroSize(TestFlipOp):
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def init_test_case(self):
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self.in_shape = (4, 0, 6, 3)
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self.axis = [0, 2]
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# ----------------flip_fp16----------------
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def create_test_fp16_class(parent):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestFlipFP16(parent):
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def init_dtype(self):
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_output_with_place(
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place, check_cinn=True, check_pir=True
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)
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def test_check_grad(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place, ["X"], "Out", check_cinn=True, check_pir=True
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)
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cls_name = "{}_{}".format(parent.__name__, "FP16OP")
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TestFlipFP16.__name__ = cls_name
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globals()[cls_name] = TestFlipFP16
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create_test_fp16_class(TestFlipOp)
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create_test_fp16_class(TestFlipOpAxis1)
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create_test_fp16_class(TestFlipOpAxis2)
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create_test_fp16_class(TestFlipOpAxis3)
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create_test_fp16_class(TestFlipOpAxis4)
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create_test_fp16_class(TestFlipOpEmptyAxis)
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create_test_fp16_class(TestFlipOpNegAxis)
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# ----------------flip_bf16----------------
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def create_test_bf16_class(parent):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class TestFlipBF16(parent):
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def init_dtype(self):
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self.dtype = np.uint16
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def test_check_output(self):
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place = get_device_place()
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if core.is_bfloat16_supported(place):
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self.check_output_with_place(place, check_pir=True)
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def test_check_grad(self):
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place = get_device_place()
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if core.is_bfloat16_supported(place):
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self.check_grad_with_place(place, ["X"], "Out", check_pir=True)
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cls_name = "{}_{}".format(parent.__name__, "BF16OP")
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TestFlipBF16.__name__ = cls_name
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globals()[cls_name] = TestFlipBF16
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create_test_bf16_class(TestFlipOp)
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create_test_bf16_class(TestFlipOpAxis1)
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create_test_bf16_class(TestFlipOpAxis2)
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create_test_bf16_class(TestFlipOpAxis3)
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create_test_bf16_class(TestFlipOpAxis4)
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create_test_bf16_class(TestFlipOpEmptyAxis)
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create_test_bf16_class(TestFlipOpNegAxis)
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class TestFlipDoubleGradCheck(unittest.TestCase):
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def flip_wrapper(self, x):
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return paddle.flip(x[0], [0, 1])
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@prog_scope()
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def func(self, place):
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# the shape of input variable should be clearly specified, not include -1.
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eps = 0.005
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dtype = np.float32
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data = paddle.static.data('data', [3, 2, 2], dtype)
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data.persistable = True
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out = paddle.flip(data, [0, 1])
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data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
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gradient_checker.double_grad_check(
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[data], out, x_init=[data_arr], place=place, eps=eps
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)
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gradient_checker.double_grad_check_for_dygraph(
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self.flip_wrapper, [data], out, x_init=[data_arr], place=place
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)
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def test_grad(self):
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paddle.enable_static()
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for p in get_places():
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self.func(p)
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class TestFlipTripleGradCheck(unittest.TestCase):
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def flip_wrapper(self, x):
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return paddle.flip(x[0], [0, 1])
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@prog_scope()
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def func(self, place):
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# the shape of input variable should be clearly specified, not include -1.
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eps = 0.005
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dtype = np.float32
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data = paddle.static.data('data', [3, 2, 2], dtype)
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data.persistable = True
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out = paddle.flip(data, [0, 1])
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data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
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gradient_checker.triple_grad_check(
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[data], out, x_init=[data_arr], place=place, eps=eps
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)
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gradient_checker.triple_grad_check_for_dygraph(
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self.flip_wrapper, [data], out, x_init=[data_arr], place=place
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)
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def test_grad(self):
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paddle.enable_static()
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for p in get_places():
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self.func(p)
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class TestFlipError(unittest.TestCase):
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def test_axis(self):
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paddle.enable_static()
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def test_axis_rank():
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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.flip(input, axis=[[0]])
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self.assertRaises(TypeError, test_axis_rank)
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def test_axis_rank2():
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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.flip(input, axis=[[0, 0], [1, 1]])
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self.assertRaises(TypeError, test_axis_rank2)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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