wehub-resource-sync
266 lines
8.8 KiB
Python
266 lines
8.8 KiB
Python
# Copyright (c) 2023 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 os
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import sys
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import unittest
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import XPUOpTest
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sys.path.append("../legacy_test")
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from test_attribute_var import UnittestBase
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from utils import static_guard
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import paddle
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from paddle.base import Program, program_guard
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def pad_wrapper(x, paddings, pad_value):
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return paddle.nn.functional.pad(
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x, pad=list(paddings), mode='constant', value=pad_value
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)
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paddle.enable_static()
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class XPUTestPadOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = "pad"
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self.use_dynamic_create_class = False
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class TestPadOp(XPUOpTest):
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def setUp(self):
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self.op_type = "pad"
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self.place = paddle.XPUPlace(0)
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self.python_api = pad_wrapper
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self.public_python_api = pad_wrapper
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self.init_dtype()
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self.init_test_case()
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self.init_data()
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def init_dtype(self):
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self.dtype = self.in_type
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def init_test_case(self):
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self.shape = (16, 16)
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self.paddings = [(0, 1), (2, 3)]
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self.pad_value = 0.0
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def init_data(self):
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self.inputs = {'X': np.random.random(self.shape).astype(self.dtype)}
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self.outputs = {
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'Out': np.pad(
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self.inputs['X'],
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self.paddings,
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mode='constant',
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constant_values=self.pad_value,
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)
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}
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self.attrs = {
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'paddings': list(np.array(self.paddings).flatten()),
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'pad_value': self.pad_value,
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}
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def test_check_output(self):
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self.check_output_with_place(self.place)
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def test_check_grad_normal(self):
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self.check_grad_with_place(self.place, ['X'], 'Out')
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class TestCase1(TestPadOp):
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def init_test_case(self):
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self.shape = (2, 3, 4, 5)
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self.paddings = [(0, 1), (2, 3), (2, 1), (1, 1)]
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self.pad_value = 0.5
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class TestCase2(TestPadOp):
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def init_test_case(self):
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self.shape = (5, 5, 5)
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self.paddings = [(0, 0), (0, 0), (1, 2)]
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self.pad_value = 1.0
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class TestCase3(TestPadOp):
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def init_test_case(self):
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self.shape = 100
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self.paddings = [(0, 1)]
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self.pad_value = 0.9
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class TestPadOpError(unittest.TestCase):
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def test_errors(self):
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with static_guard():
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with program_guard(Program(), Program()):
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input_data = np.random.random((2, 2)).astype("float32")
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def test_Variable():
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paddle.nn.functional.pad(x=input_data, pad=[1, 1, 1, 1])
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self.assertRaises(TypeError, test_Variable)
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data = paddle.static.data(
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name='data', shape=[4], dtype='float16'
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)
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paddle.nn.functional.pad(x=data, pad=[0, 1])
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class TestPaddingValueTensor(UnittestBase):
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def init_info(self):
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self.shapes = [[2, 4]]
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self.save_path = os.path.join(
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self.temp_dir.name, self.path_prefix()
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)
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def test_static(self):
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with static_guard():
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main_prog = Program()
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startup_prog = Program()
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with program_guard(main_prog, startup_prog):
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fc = paddle.nn.Linear(4, 10)
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x = paddle.randn([2, 4])
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x.stop_gradient = False
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feat = fc(x) # [2,3,10]
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out = self.call_func(feat)
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sgd = paddle.optimizer.SGD()
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sgd.minimize(paddle.mean(out))
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if not paddle.framework.use_pir_api():
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self.assertTrue(self.var_prefix() in str(main_prog))
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exe = paddle.static.Executor(paddle.XPUPlace(0))
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exe.run(startup_prog)
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res = exe.run(fetch_list=[feat, out])
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gt = np.pad(
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res[0], [1, 1], 'constant', constant_values=[1.0, 1.0]
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)
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np.testing.assert_allclose(res[1], gt)
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paddle.static.save_inference_model(
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self.save_path, [x], [feat, out], exe
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)
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# Test for Inference Predictor
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infer_outs = self.infer_prog()
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gt = np.pad(
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infer_outs[0],
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[1, 1],
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'constant',
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constant_values=[1.0, 1.0],
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)
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np.testing.assert_allclose(infer_outs[1], gt)
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def path_prefix(self):
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return 'padding_value'
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def var_prefix(self):
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return "Var["
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def call_func(self, x):
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padding_value = paddle.assign([1.0])
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out = paddle.nn.functional.pad(
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x, pad=[1, 1, 1, 1], value=padding_value, mode='constant'
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)
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return out
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class TestPaddingValueTensor2(TestPaddingValueTensor):
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def call_func(self, x):
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padding_value = paddle.assign([1.0])
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# test for int value
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tmp = paddle.nn.functional.pad(x, pad=[1, 1, 1, 1], value=1)
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out = paddle.nn.functional.pad(
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x, pad=[1, 1, 1, 1], value=padding_value
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)
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return out
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class TestPaddingValueTensor3(unittest.TestCase):
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def test_static(self):
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with static_guard():
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np_x = np.random.random((16, 16)).astype('float32')
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main_prog = Program()
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startup_prog = Program()
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with program_guard(main_prog, startup_prog):
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x = paddle.assign(np_x).astype('float32')
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pad_value = paddle.assign([0.0]).astype('float64')
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y = paddle.nn.functional.pad(
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x, [0, 1, 2, 3], value=pad_value
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)
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loss = y.sum()
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optimize_ops, params_grads = paddle.optimizer.SGD(
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0.01
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).minimize(loss)
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exe = paddle.static.Executor(paddle.XPUPlace(0))
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res = exe.run(
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main_prog, fetch_list=[y] + [g for p, g in params_grads]
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)
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pd_out = res[0]
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np_out = np.pad(np_x, [(0, 1), (2, 3)], constant_values=0.0)
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np.testing.assert_allclose(pd_out, np_out)
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support_types = get_xpu_op_support_types("pad")
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real_types = [t for t in support_types if t != 'complex64']
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for stype in real_types:
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create_test_class(globals(), XPUTestPadOp, stype)
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if 'complex64' in support_types:
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class TestPadComplexOp(XPUTestPadOp.TestPadOp):
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def init_dtype(self):
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self.dtype = np.complex64
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def init_data(self):
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self.inputs = {
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'X': (
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np.random.random(self.shape)
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+ 1j * np.random.random(self.shape)
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).astype(self.dtype)
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}
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self.outputs = {
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'Out': np.pad(
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self.inputs['X'],
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self.paddings,
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mode='constant',
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constant_values=self.pad_value,
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)
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}
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self.attrs = {
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'paddings': list(np.array(self.paddings).flatten()),
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'pad_value': self.pad_value,
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}
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class TestComplexCase1(TestPadComplexOp):
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def init_test_case(self):
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self.shape = (2, 3, 4, 5)
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self.paddings = [(0, 1), (2, 3), (2, 1), (1, 1)]
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self.pad_value = 0.5
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class TestComplexCase2(TestPadComplexOp):
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def init_test_case(self):
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self.shape = (5, 5, 5)
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self.paddings = [(0, 0), (0, 0), (1, 2)]
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self.pad_value = 1.0
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class TestComplexCase3(TestPadComplexOp):
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def init_test_case(self):
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self.shape = 100
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self.paddings = [(0, 1)]
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self.pad_value = 0.9
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if __name__ == "__main__":
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unittest.main()
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