wehub-resource-sync
674 lines
21 KiB
Python
674 lines
21 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 os
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import unittest
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import numpy as np
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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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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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import paddle.distributed as dist
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from paddle.base import core
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from paddle.framework import in_pir_mode
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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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class TestPadOp(OpTest):
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def setUp(self):
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self.initTestCase()
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self.dtype = self.get_dtype()
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self.op_type = "pad"
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self.python_api = pad_wrapper
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self.inputs = {
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"X": np.random.random(self.shape).astype(self.dtype),
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}
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self.attrs = {}
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self.attrs["paddings"] = list(np.array(self.paddings).flatten())
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self.attrs["pad_value"] = self.pad_value
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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.prim_op_type = "prim"
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self.public_python_api = pad_wrapper
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def get_dtype(self):
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return np.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_normal(self):
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self.check_grad(
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["X"],
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"Out",
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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check_auto_parallel=self.check_auto_parallel,
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)
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def initTestCase(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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self.check_auto_parallel = False
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class TestCase1(TestPadOp):
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def initTestCase(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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self.check_auto_parallel = False
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class TestCase2(TestPadOp):
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def initTestCase(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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self.check_auto_parallel = False
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class TestCase3(TestPadOp):
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def initTestCase(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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self.check_auto_parallel = False
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class TestCase4(TestPadOp):
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def initTestCase(self):
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self.shape = (10, 10)
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self.paddings = [(0, 1), (2, 3)]
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self.pad_value = 1.0
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self.check_auto_parallel = True
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self.placements = {
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'X': [dist.Replicate()],
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}
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class TestCase5(TestPadOp):
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def initTestCase(self):
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self.shape = (10, 10)
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self.paddings = [(0, 0), (2, 3)]
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self.pad_value = 1.0
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self.check_auto_parallel = True
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self.placements = {
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'X': [dist.Shard(0)],
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}
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# ----------------Pad Fp16----------------
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def create_test_fp16(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 TestPadFp16(parent):
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def get_dtype(self):
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return np.float16
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def test_check_grad_normal(self):
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self.check_grad(
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["X"],
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"Out",
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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cls_name = "{}_{}".format(parent.__name__, "Fp16")
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TestPadFp16.__name__ = cls_name
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globals()[cls_name] = TestPadFp16
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create_test_fp16(TestPadOp)
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create_test_fp16(TestCase1)
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create_test_fp16(TestCase2)
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create_test_fp16(TestCase3)
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create_test_fp16(TestCase4)
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create_test_fp16(TestCase5)
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class TestPadOpError(unittest.TestCase):
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def test_errors(self):
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with (
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static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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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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if core.is_compiled_with_cuda() or is_custom_device():
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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(self.temp_dir.name, self.path_prefix())
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def test_static(self):
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with static_guard():
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.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 in_pir_mode():
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self.assertTrue(self.var_prefix() in str(main_prog))
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exe = paddle.static.Executor()
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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 test_pir_static(self):
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with paddle.pir_utils.IrGuard():
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.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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exe = paddle.static.Executor()
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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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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(x, pad=[1, 1, 1, 1], value=padding_value)
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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 = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.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(x, [0, 1, 2, 3], value=pad_value)
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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.CPUPlace())
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exe.run(startup_prog)
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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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@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 not support the bfloat16",
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)
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class TestPadBP16Op(OpTest):
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def setUp(self):
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self.initTestCase()
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self.dtype = np.uint16
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self.op_type = "pad"
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self.python_api = pad_wrapper
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x = np.random.random(self.shape).astype(np.float32)
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self.attrs = {}
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self.attrs["paddings"] = list(np.array(self.paddings).flatten())
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self.attrs["pad_value"] = self.pad_value
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out = np.pad(
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x, self.paddings, mode="constant", constant_values=self.pad_value
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)
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self.inputs = {"X": convert_float_to_uint16(x)}
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self.outputs = {"Out": convert_float_to_uint16(out)}
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self.prim_op_type = "prim"
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self.public_python_api = pad_wrapper
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self.if_enable_cinn()
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def if_enable_cinn(self):
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pass
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def initTestCase(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 test_check_output(self):
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place = get_device_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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self.check_grad_with_place(
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place,
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["X"],
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"Out",
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check_prim=True,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestPadOrder2N(unittest.TestCase):
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def init_case(self):
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self.shape = [2, 3]
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self.paddings = [(0, 1), (1, 0)]
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self.pad_value = 0.5
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def test_order_dygraph(self):
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self.init_case()
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place = paddle.CPUPlace()
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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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paddle.disable_static(place)
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x_np = np.random.random(self.shape).astype('float32')
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paddings_np = self.paddings.copy()
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x = paddle.to_tensor(x_np)
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paddings = list(np.array(self.paddings).flatten())
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# pad_from_left_axis
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out_np = np.pad(
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x_np, paddings_np, mode="constant", constant_values=self.pad_value
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)
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out = paddle.nn.functional.pad(
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x,
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paddings,
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mode='constant',
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value=self.pad_value,
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pad_from_left_axis=True,
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)
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np.testing.assert_array_equal(out, out_np)
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# pad_from_right_axis:
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paddings_np.reverse()
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out_np = np.pad(
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x_np, paddings_np, mode="constant", constant_values=self.pad_value
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)
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out = paddle.nn.functional.pad(
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x,
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paddings,
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mode='constant',
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value=self.pad_value,
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pad_from_left_axis=False,
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)
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np.testing.assert_array_equal(out, out_np)
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paddle.enable_static()
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def test_order_static(self):
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self.init_case()
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place = paddle.CPUPlace()
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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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x_np = np.random.random(self.shape).astype('float32')
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paddings_np = self.paddings.copy()
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paddings = list(np.array(self.paddings).flatten())
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with static_guard():
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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x = paddle.static.data(
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name="x", shape=self.shape, dtype="float32"
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)
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y_pad_from_left_axis = paddle.nn.functional.pad(
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x,
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paddings,
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mode='constant',
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value=self.pad_value,
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pad_from_left_axis=True,
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)
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y_pad_from_right_axis = paddle.nn.functional.pad(
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x,
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paddings,
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mode='constant',
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value=self.pad_value,
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pad_from_left_axis=False,
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)
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exe = paddle.static.Executor(place)
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exe.run(startup_prog)
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res = exe.run(
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main_prog,
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feed={"x": x_np},
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fetch_list=[y_pad_from_left_axis, y_pad_from_right_axis],
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)
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pd_out_pad_from_left_axis, pd_out_pad_from_right_axis = res
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out_np_pad_from_left_axis = np.pad(
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x_np,
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paddings_np,
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mode="constant",
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constant_values=self.pad_value,
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)
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paddings_np.reverse()
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out_np_pad_from_right_axis = np.pad(
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x_np,
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paddings_np,
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mode="constant",
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constant_values=self.pad_value,
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)
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np.testing.assert_array_equal(
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pd_out_pad_from_left_axis, out_np_pad_from_left_axis
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)
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np.testing.assert_array_equal(
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pd_out_pad_from_right_axis, out_np_pad_from_right_axis
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)
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# test padding order for cases when length of padding is not 2(N-2) or 2N
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class TestPadOrder(unittest.TestCase):
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def init_case(self):
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self.shape = [2, 3]
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self.paddings = [(0, 1)]
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self.pad_value = 0.5
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def test_order_dygraph(self):
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self.init_case()
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place = paddle.CPUPlace()
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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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paddle.disable_static(place)
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x_np = np.random.random(self.shape).astype('float32')
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paddings_np = self.paddings.copy()
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paddings_np += [(0, 0)] * (len(self.shape) - len(paddings_np))
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x = paddle.to_tensor(x_np)
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paddings = list(np.array(self.paddings).flatten())
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# pad from last axis by default
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paddings_np.reverse()
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out_np = np.pad(
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x_np, paddings_np, mode="constant", constant_values=self.pad_value
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)
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out = paddle.nn.functional.pad(
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x, paddings, mode='constant', value=self.pad_value
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)
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np.testing.assert_array_equal(out, out_np)
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def test_order_static(self):
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self.init_case()
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place = paddle.CPUPlace()
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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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paddle.disable_static(place)
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x_np = np.random.random(self.shape).astype('float32')
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paddings_np = self.paddings.copy()
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paddings_np += [(0, 0)] * (len(self.shape) - len(paddings_np))
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paddings = list(np.array(self.paddings).flatten())
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with static_guard():
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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x = paddle.static.data(
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name="x", shape=self.shape, dtype="float32"
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)
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y = paddle.nn.functional.pad(
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x, paddings, mode='constant', value=self.pad_value
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)
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exe = paddle.static.Executor(place)
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|
exe.run(startup_prog)
|
|
res = exe.run(main_prog, feed={"x": x_np}, fetch_list=[y])
|
|
paddings_np.reverse()
|
|
out_np = np.pad(
|
|
x_np,
|
|
paddings_np,
|
|
mode="constant",
|
|
constant_values=self.pad_value,
|
|
)
|
|
np.testing.assert_array_equal(res[0], out_np)
|
|
|
|
|
|
class TestPadOrder2N3D(TestPadOrder2N):
|
|
def init_case(self):
|
|
self.shape = [2, 3, 4]
|
|
self.paddings = [(0, 1), (2, 3), (2, 1)]
|
|
self.pad_value = 0.5
|
|
|
|
|
|
class TestPadOrder2N4D(TestPadOrder2N):
|
|
def init_case(self):
|
|
self.shape = [2, 3, 4, 5]
|
|
self.paddings = [(0, 1), (2, 3), (2, 1), (1, 1)]
|
|
self.pad_value = 0.5
|
|
|
|
|
|
class TestPadOrder2N5D(TestPadOrder2N):
|
|
def init_case(self):
|
|
self.shape = [1, 2, 3, 4, 5]
|
|
self.paddings = [(0, 1), (2, 3), (2, 1), (1, 1), (1, 0)]
|
|
self.pad_value = 0.5
|
|
|
|
|
|
class TestPadOrder1(TestPadOrder):
|
|
def init_case(self):
|
|
self.shape = [2, 3, 4]
|
|
self.paddings = [(0, 1), (2, 3)]
|
|
self.pad_value = 0.5
|
|
|
|
|
|
class TestPadOrder2(TestPadOrder):
|
|
def init_case(self):
|
|
self.shape = [2, 3, 4, 5]
|
|
self.paddings = [(0, 1), (2, 3), (2, 1)]
|
|
self.pad_value = 0.5
|
|
|
|
|
|
class TestPadOrder3(TestPadOrder):
|
|
def init_case(self):
|
|
self.shape = [2, 3, 4, 5]
|
|
self.paddings = [(0, 1)]
|
|
self.pad_value = 0.5
|
|
|
|
|
|
class TestPadOp_ZeroSize(unittest.TestCase):
|
|
def init_case(self):
|
|
self.shape = [0, 16]
|
|
self.paddings = [(0, 1), (2, 3)]
|
|
self.paddings_empty_tensor = False
|
|
self.pad_value = 0.5
|
|
|
|
def test_dygraph(self):
|
|
self.init_case()
|
|
for place in get_places():
|
|
paddle.disable_static(place)
|
|
x_np = np.random.random(self.shape).astype('float32')
|
|
paddings_np = self.paddings.copy()
|
|
x = paddle.to_tensor(x_np)
|
|
x.stop_gradient = False
|
|
paddings = list(np.array(self.paddings).flatten())
|
|
if self.paddings_empty_tensor:
|
|
paddings = paddle.to_tensor(paddings)
|
|
# output the same as x
|
|
out_np = x_np
|
|
else:
|
|
out_np = np.pad(
|
|
x_np,
|
|
paddings_np,
|
|
mode="constant",
|
|
constant_values=self.pad_value,
|
|
)
|
|
out = paddle.nn.functional.pad(
|
|
x,
|
|
paddings,
|
|
mode='constant',
|
|
value=self.pad_value,
|
|
pad_from_left_axis=True,
|
|
)
|
|
np.testing.assert_array_equal(out, out_np)
|
|
out.sum().backward()
|
|
np.testing.assert_allclose(x.grad.numpy(), np.ones(self.shape))
|
|
|
|
|
|
class TestPadOp_ZeroSize2(TestPadOp_ZeroSize):
|
|
def init_case(self):
|
|
self.shape = [4, 6, 6]
|
|
self.paddings = []
|
|
self.paddings_empty_tensor = True
|
|
self.pad_value = 0.5
|
|
|
|
|
|
class TestPadAliasSupport(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
self.shape = (2, 3)
|
|
self.paddings = [1, 2, 3, 4]
|
|
self.value = 0.5
|
|
self.x = np.random.random(self.shape).astype('float32')
|
|
|
|
def test_no_param_name(self):
|
|
out = paddle.nn.functional.pad(
|
|
paddle.to_tensor(self.x), self.paddings, value=self.value
|
|
)
|
|
expected = np.pad(
|
|
self.x,
|
|
[(1, 2), (3, 4)],
|
|
mode='constant',
|
|
constant_values=self.value,
|
|
)
|
|
np.testing.assert_array_equal(out.numpy(), expected)
|
|
|
|
def test_x_param_name(self):
|
|
out = paddle.nn.functional.pad(
|
|
x=paddle.to_tensor(self.x), pad=self.paddings, value=self.value
|
|
)
|
|
expected = np.pad(
|
|
self.x,
|
|
[(1, 2), (3, 4)],
|
|
mode='constant',
|
|
constant_values=self.value,
|
|
)
|
|
np.testing.assert_array_equal(out.numpy(), expected)
|
|
|
|
def test_input_param_name(self):
|
|
out = paddle.nn.functional.pad(
|
|
input=paddle.to_tensor(self.x), pad=self.paddings, value=self.value
|
|
)
|
|
expected = np.pad(
|
|
self.x,
|
|
[(1, 2), (3, 4)],
|
|
mode='constant',
|
|
constant_values=self.value,
|
|
)
|
|
np.testing.assert_array_equal(out.numpy(), expected)
|
|
|
|
def test_both_param_name(self):
|
|
with self.assertRaises(ValueError) as context:
|
|
paddle.nn.functional.pad(
|
|
x=paddle.to_tensor(self.x),
|
|
input=paddle.to_tensor(self.x),
|
|
pad=self.paddings,
|
|
value=self.value,
|
|
)
|
|
self.assertIn(
|
|
"Cannot specify both 'x' and its alias 'input'",
|
|
str(context.exception),
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# paddle.enable_static()
|
|
unittest.main()
|