wehub-resource-sync
1301 lines
43 KiB
Python
1301 lines
43 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import (
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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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import paddle.nn.functional as F
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from paddle import nn
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from paddle.base import (
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Executor,
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core,
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)
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class TestPad3dOp(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.value = 0.0
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self.initTestCase()
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self.dtype = self.get_dtype()
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self.op_type = "pad3d"
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self.python_api = paddle.nn.functional.pad
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self.inputs = {
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'X': (
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np.random.uniform(-1.0, 1.0, self.shape).astype("float32")
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if self.dtype == np.uint16
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else (
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(
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np.random.uniform(-1.0, 1.0, self.shape)
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+ 1j * np.random.uniform(-1.0, 1.0, self.shape)
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).astype(self.dtype)
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if self.dtype == np.complex64 or self.dtype == np.complex128
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else np.random.uniform(-1.0, 1.0, self.shape).astype(
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self.dtype
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)
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)
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)
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}
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self.attrs = {}
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if self.variable_paddings:
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self.attrs['paddings'] = []
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self.inputs['Paddings'] = (
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np.array(self.paddings).flatten().astype("int32")
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)
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else:
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self.attrs['paddings'] = (
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np.array(self.paddings).flatten().astype("int32")
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)
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self.attrs['value'] = self.value
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self.attrs['mode'] = self.mode
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self.attrs['data_format'] = self.data_format
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if self.data_format == "NCDHW":
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paddings = [
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(0, 0),
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(0, 0),
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(self.paddings[4], self.paddings[5]),
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(self.paddings[2], self.paddings[3]),
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(self.paddings[0], self.paddings[1]),
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]
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else:
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paddings = [
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(0, 0),
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(self.paddings[4], self.paddings[5]),
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(self.paddings[2], self.paddings[3]),
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(self.paddings[0], self.paddings[1]),
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(0, 0),
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]
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if self.mode == "constant":
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out = np.pad(
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self.inputs['X'],
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paddings,
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mode=self.mode,
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constant_values=self.value,
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)
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elif self.mode == "reflect":
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out = np.pad(self.inputs['X'], paddings, mode=self.mode)
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elif self.mode == "replicate":
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out = np.pad(self.inputs['X'], paddings, mode="edge")
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elif self.mode == "circular":
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out = np.pad(self.inputs['X'], paddings, mode="wrap")
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self.outputs = {'Out': out}
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if self.dtype == np.uint16:
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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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(['X'], 'Out', check_pir=True)
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def get_dtype(self):
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return np.float64
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 0, 0, 0, 0, 0]
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self.mode = "constant"
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self.data_format = "NCDHW"
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self.pad_value = 0.0
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self.variable_paddings = False
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class TestCase1(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 2, 3, 4, 5]
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self.mode = "constant"
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self.data_format = "NCDHW"
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self.value = 1.0
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self.variable_paddings = False
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class TestCase2(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [1, 1, 1, 1, 1, 1]
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self.mode = "constant"
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self.data_format = "NDHWC"
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self.value = 1.0
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self.variable_paddings = False
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class TestCase3(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 1, 0, 2, 3]
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self.mode = "reflect"
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self.data_format = "NCDHW"
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self.variable_paddings = False
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class TestCase4(TestPad3dOp):
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def initTestCase(self):
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self.shape = (4, 4, 4, 4, 4)
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self.paddings = [0, 1, 2, 1, 2, 3]
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self.mode = "reflect"
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self.data_format = "NDHWC"
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self.variable_paddings = False
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class TestCase5(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 2, 3, 2, 1]
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self.mode = "replicate"
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self.data_format = "NCDHW"
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self.variable_paddings = False
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class TestCase6(TestPad3dOp):
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def initTestCase(self):
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self.shape = (4, 4, 4, 4, 4)
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self.paddings = [5, 4, 2, 1, 2, 3]
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self.mode = "replicate"
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self.data_format = "NDHWC"
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self.variable_paddings = False
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class TestCase7(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 2, 3, 2, 1]
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self.mode = "circular"
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self.data_format = "NCDHW"
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self.variable_paddings = False
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class TestCase8(TestPad3dOp):
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def initTestCase(self):
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self.shape = (4, 4, 4, 4, 4)
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self.paddings = [0, 1, 2, 1, 2, 3]
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self.mode = "circular"
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self.data_format = "NDHWC"
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self.variable_paddings = False
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class TestCase9(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 2, 3, 4, 5]
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self.mode = "constant"
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self.data_format = "NCDHW"
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self.value = 1.0
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self.variable_paddings = True
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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class TestCase10(TestPad3dOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.paddings = [0, 1, 2, 3, 4, 5]
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self.mode = "constant"
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self.data_format = "NDHWC"
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self.value = 1.0
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self.variable_paddings = True
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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# ----------------Pad3d Fp16----------------
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def create_test_fp16(parent):
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@unittest.skipIf(
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not (
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(core.is_compiled_with_cuda() or is_custom_device())
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or is_custom_device()
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),
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"core is not compiled with CUDA",
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)
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class TestPad3dFp16(parent):
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def get_dtype(self):
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return np.float16
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def test_check_output(self):
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self.check_output(
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atol=1e-3,
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check_pir=True,
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check_symbol_infer=(not self.variable_paddings),
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)
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def test_check_grad_normal(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=1.5e-3, check_pir=True
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)
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cls_name = "{}_{}".format(parent.__name__, "FP16OP")
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TestPad3dFp16.__name__ = cls_name
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globals()[cls_name] = TestPad3dFp16
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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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create_test_fp16(TestCase6)
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create_test_fp16(TestCase7)
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create_test_fp16(TestCase8)
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create_test_fp16(TestCase9)
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create_test_fp16(TestCase10)
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# ----------------Pad3d Bf16----------------
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def create_test_bf16(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 TestPad3dBf16(parent):
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def get_dtype(self):
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return np.uint16
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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(
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place,
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atol=1e-2,
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check_pir=True,
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check_symbol_infer=(not self.variable_paddings),
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)
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def test_check_grad_normal(self):
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place = get_device_place()
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self.check_grad_with_place(
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place, ['X'], 'Out', max_relative_error=1e-2, check_pir=True
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)
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cls_name = "{}_{}".format(parent.__name__, "BF16OP")
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TestPad3dBf16.__name__ = cls_name
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globals()[cls_name] = TestPad3dBf16
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create_test_bf16(TestCase1)
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create_test_bf16(TestCase2)
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create_test_bf16(TestCase3)
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create_test_bf16(TestCase4)
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create_test_bf16(TestCase5)
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create_test_bf16(TestCase6)
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create_test_bf16(TestCase7)
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create_test_bf16(TestCase8)
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create_test_bf16(TestCase9)
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create_test_bf16(TestCase10)
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# ----------------Pad3d complex64----------------
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def create_test_complex64(parent):
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@unittest.skipIf(
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not (
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(core.is_compiled_with_cuda() or is_custom_device())
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or is_custom_device()
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),
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"core is not compiled with CUDA",
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)
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class TestPad3dComplex64(parent):
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def get_dtype(self):
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return np.complex64
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def test_check_output(self):
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self.check_output(
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atol=1e-3,
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check_pir=True,
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check_symbol_infer=(not self.variable_paddings),
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)
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def test_check_grad_normal(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=1.5e-3, check_pir=True
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)
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cls_name = "{}_{}".format(parent.__name__, "Complex64OP")
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TestPad3dComplex64.__name__ = cls_name # 重新修改TestPad3dFp16的类名
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globals()[cls_name] = TestPad3dComplex64
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create_test_complex64(TestCase1)
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create_test_complex64(TestCase2)
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create_test_complex64(TestCase3)
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create_test_complex64(TestCase4)
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create_test_complex64(TestCase5)
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create_test_complex64(TestCase6)
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create_test_complex64(TestCase7)
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create_test_complex64(TestCase8)
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create_test_complex64(TestCase9)
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create_test_complex64(TestCase10)
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# ----------------Pad3d complex128----------------
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def create_test_complex128(parent):
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@unittest.skipIf(
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not (
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(core.is_compiled_with_cuda() or is_custom_device())
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or is_custom_device()
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),
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"core is not compiled with CUDA",
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)
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class TestPad3dComplex128(parent):
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def get_dtype(self):
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return np.complex128
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def test_check_output(self):
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self.check_output(
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atol=1e-3,
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check_pir=True,
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check_symbol_infer=(not self.variable_paddings),
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)
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def test_check_grad_normal(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=1.5e-3, check_pir=True
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)
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cls_name = "{}_{}".format(parent.__name__, "Complex128OP")
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TestPad3dComplex128.__name__ = cls_name # 重新修改TestPad3dFp16的类名
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globals()[cls_name] = TestPad3dComplex128
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create_test_complex128(TestCase1)
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create_test_complex128(TestCase2)
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create_test_complex128(TestCase3)
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create_test_complex128(TestCase4)
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create_test_complex128(TestCase5)
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create_test_complex128(TestCase6)
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create_test_complex128(TestCase7)
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create_test_complex128(TestCase8)
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create_test_complex128(TestCase9)
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create_test_complex128(TestCase10)
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class TestPadAPI(unittest.TestCase):
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def setUp(self):
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self.init_dtype()
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self.places = get_places()
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def init_dtype(self):
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self.dtype = np.float32
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def check_static_result_1(self, place):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input_shape = (1, 2, 3, 4, 5)
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pad = [1, 2, 1, 1, 3, 4]
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mode = "constant"
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value = 100
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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input_data = (
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np.random.rand(*input_shape)
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+ 1j * np.random.rand(*input_shape)
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).astype(self.dtype)
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x = paddle.static.data(
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name="x", shape=input_shape, dtype=self.dtype
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)
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result = F.pad(
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x=x, pad=pad, value=value, mode=mode, data_format="NCDHW"
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)
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exe = Executor(place)
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"x": input_data},
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fetch_list=[result],
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)
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np_out = self._get_numpy_out(input_data, pad, mode, value)
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np.testing.assert_allclose(fetches[0], np_out, rtol=1e-05)
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def check_static_result_2(self, place):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input_shape = (2, 3, 4, 5, 6)
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pad = [1, 2, 1, 1, 1, 2]
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mode = "reflect"
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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input_data = (
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np.random.rand(*input_shape)
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+ 1j * np.random.rand(*input_shape)
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).astype(self.dtype)
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x = paddle.static.data(
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name="x", shape=input_shape, dtype=self.dtype
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)
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result1 = F.pad(x=x, pad=pad, mode=mode, data_format="NCDHW")
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result2 = F.pad(x=x, pad=pad, mode=mode, data_format="NDHWC")
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result3 = F.pad(x=x, pad=pad, mode=mode)
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exe = Executor(place)
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"x": input_data},
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fetch_list=[result1, result2, result3],
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)
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np_out1 = self._get_numpy_out(
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input_data, pad, mode, data_format="NCDHW"
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)
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np_out2 = self._get_numpy_out(
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input_data, pad, mode, data_format="NDHWC"
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)
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np.testing.assert_allclose(fetches[0], np_out1, rtol=1e-05)
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np.testing.assert_allclose(fetches[1], np_out2, rtol=1e-05)
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np.testing.assert_allclose(fetches[2], np_out1, rtol=1e-05)
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def check_static_result_3(self, place):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input_shape = (2, 3, 4, 5, 6)
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pad = [1, 2, 1, 1, 3, 4]
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mode = "replicate"
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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input_data = (
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np.random.rand(*input_shape)
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+ 1j * np.random.rand(*input_shape)
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).astype(self.dtype)
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x = paddle.static.data(
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name="x", shape=input_shape, dtype=self.dtype
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)
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result1 = F.pad(x=x, pad=pad, mode=mode, data_format="NCDHW")
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result2 = F.pad(x=x, pad=pad, mode=mode, data_format="NDHWC")
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result3 = F.pad(x=x, pad=pad, mode=mode)
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exe = Executor(place)
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fetches = exe.run(
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paddle.static.default_main_program(),
|
|
feed={"x": input_data},
|
|
fetch_list=[result1, result2, result3],
|
|
)
|
|
|
|
np_out1 = self._get_numpy_out(
|
|
input_data, pad, mode, data_format="NCDHW"
|
|
)
|
|
np_out2 = self._get_numpy_out(
|
|
input_data, pad, mode, data_format="NDHWC"
|
|
)
|
|
np.testing.assert_allclose(fetches[0], np_out1, rtol=1e-05)
|
|
np.testing.assert_allclose(fetches[1], np_out2, rtol=1e-05)
|
|
np.testing.assert_allclose(fetches[2], np_out1, rtol=1e-05)
|
|
|
|
def check_static_result_4(self, place):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
input_shape = (2, 3, 4, 5, 6)
|
|
pad = [1, 2, 1, 1, 3, 4]
|
|
mode = "circular"
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
if self.dtype == np.complex64 or self.dtype == np.complex128:
|
|
input_data = (
|
|
np.random.rand(*input_shape)
|
|
+ 1j * np.random.rand(*input_shape)
|
|
).astype(self.dtype)
|
|
x = paddle.static.data(
|
|
name="x", shape=input_shape, dtype=self.dtype
|
|
)
|
|
result1 = F.pad(x=x, pad=pad, mode=mode, data_format="NCDHW")
|
|
result2 = F.pad(x=x, pad=pad, mode=mode, data_format="NDHWC")
|
|
result3 = F.pad(x=x, pad=pad, mode=mode)
|
|
exe = Executor(place)
|
|
fetches = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"x": input_data},
|
|
fetch_list=[result1, result2, result3],
|
|
)
|
|
|
|
np_out1 = self._get_numpy_out(
|
|
input_data, pad, mode, data_format="NCDHW"
|
|
)
|
|
np_out2 = self._get_numpy_out(
|
|
input_data, pad, mode, data_format="NDHWC"
|
|
)
|
|
np.testing.assert_allclose(fetches[0], np_out1, rtol=1e-05)
|
|
np.testing.assert_allclose(fetches[1], np_out2, rtol=1e-05)
|
|
np.testing.assert_allclose(fetches[2], np_out1, rtol=1e-05)
|
|
|
|
def _get_numpy_out(
|
|
self, input_data, pad, mode, value=0, data_format="NCDHW"
|
|
):
|
|
if mode == "constant" and len(pad) == len(input_data.shape) * 2:
|
|
pad = np.reshape(pad, (-1, 2)).tolist()
|
|
elif data_format == "NCDHW":
|
|
pad = [
|
|
(0, 0),
|
|
(0, 0),
|
|
(pad[4], pad[5]),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
]
|
|
elif data_format == "NDHWC":
|
|
pad = [
|
|
(0, 0),
|
|
(pad[4], pad[5]),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
(0, 0),
|
|
]
|
|
elif data_format == "NCHW":
|
|
pad = [
|
|
(0, 0),
|
|
(0, 0),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
]
|
|
elif data_format == "NHWC":
|
|
pad = [
|
|
(0, 0),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
(0, 0),
|
|
]
|
|
elif data_format == "NCL":
|
|
pad = [
|
|
(0, 0),
|
|
(0, 0),
|
|
(pad[0], pad[1]),
|
|
]
|
|
elif data_format == "NLC":
|
|
pad = [
|
|
(0, 0),
|
|
(pad[0], pad[1]),
|
|
(0, 0),
|
|
]
|
|
|
|
if mode == "constant":
|
|
out = np.pad(input_data, pad, mode=mode, constant_values=value)
|
|
elif mode == "reflect":
|
|
out = np.pad(input_data, pad, mode=mode)
|
|
elif mode == "replicate":
|
|
out = np.pad(input_data, pad, mode="edge")
|
|
elif mode == "circular":
|
|
out = np.pad(input_data, pad, mode="wrap")
|
|
|
|
return out
|
|
|
|
def test_static(self):
|
|
for place in self.places:
|
|
self.check_static_result_1(place=place)
|
|
self.check_static_result_2(place=place)
|
|
self.check_static_result_3(place=place)
|
|
self.check_static_result_4(place=place)
|
|
|
|
def test_dygraph_1(self):
|
|
paddle.disable_static()
|
|
input_shape = (1, 2, 3, 4, 5)
|
|
pad = [1, 2, 1, 1, 3, 4]
|
|
pad_3 = [1, 2, 1, 1, 3, 4, 5, 6, 7, 8]
|
|
mode = "constant"
|
|
value = 100
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
if self.dtype == np.complex64 or self.dtype == np.complex128:
|
|
input_data = (
|
|
np.random.rand(*input_shape) + 1j * np.random.rand(*input_shape)
|
|
).astype(self.dtype)
|
|
np_out1 = self._get_numpy_out(
|
|
input_data, pad, mode, value, data_format="NCDHW"
|
|
)
|
|
np_out2 = self._get_numpy_out(
|
|
input_data, pad, mode, value, data_format="NDHWC"
|
|
)
|
|
np_out3 = self._get_numpy_out(
|
|
input_data, pad_3, mode, value, data_format="NCDHW"
|
|
)
|
|
tensor_data = paddle.to_tensor(input_data)
|
|
|
|
y1 = F.pad(
|
|
tensor_data, pad=pad, mode=mode, value=value, data_format="NCDHW"
|
|
)
|
|
y2 = F.pad(
|
|
tensor_data, pad=pad, mode=mode, value=value, data_format="NDHWC"
|
|
)
|
|
y3 = F.pad(
|
|
tensor_data, pad=pad_3, mode=mode, value=value, data_format="NCDHW"
|
|
)
|
|
y4 = F.pad(tensor_data, pad=pad, mode=mode, value=value)
|
|
|
|
np.testing.assert_allclose(y1.numpy(), np_out1, rtol=1e-05)
|
|
np.testing.assert_allclose(y2.numpy(), np_out2, rtol=1e-05)
|
|
np.testing.assert_allclose(y3.numpy(), np_out3, rtol=1e-05)
|
|
np.testing.assert_allclose(y4.numpy(), np_out1, rtol=1e-05)
|
|
|
|
def test_dygraph_2(self):
|
|
paddle.disable_static()
|
|
input_shape = (2, 3, 4, 5)
|
|
pad = [1, 1, 3, 4]
|
|
pad_3 = [1, 2, 1, 1, 3, 4, 5, 6]
|
|
mode = "constant"
|
|
value = 100
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
np_out1 = self._get_numpy_out(
|
|
input_data, pad, mode, value, data_format="NCHW"
|
|
)
|
|
np_out2 = self._get_numpy_out(
|
|
input_data, pad, mode, value, data_format="NHWC"
|
|
)
|
|
np_out3 = self._get_numpy_out(
|
|
input_data, pad_3, mode, value, data_format="NCHW"
|
|
)
|
|
|
|
tensor_data = paddle.to_tensor(input_data)
|
|
tensor_pad = paddle.to_tensor(pad, dtype="int32")
|
|
|
|
y1 = F.pad(
|
|
tensor_data,
|
|
pad=tensor_pad,
|
|
mode=mode,
|
|
value=value,
|
|
data_format="NCHW",
|
|
)
|
|
y2 = F.pad(
|
|
tensor_data,
|
|
pad=tensor_pad,
|
|
mode=mode,
|
|
value=value,
|
|
data_format="NHWC",
|
|
)
|
|
y3 = F.pad(
|
|
tensor_data, pad=pad_3, mode=mode, value=value, data_format="NCHW"
|
|
)
|
|
y4 = F.pad(
|
|
tensor_data,
|
|
pad=tensor_pad,
|
|
mode=mode,
|
|
value=value,
|
|
)
|
|
np.testing.assert_allclose(y1.numpy(), np_out1, rtol=1e-05)
|
|
np.testing.assert_allclose(y2.numpy(), np_out2, rtol=1e-05)
|
|
np.testing.assert_allclose(y3.numpy(), np_out3, rtol=1e-05)
|
|
np.testing.assert_allclose(y4.numpy(), np_out1, rtol=1e-05)
|
|
|
|
def test_dygraph_3(self):
|
|
paddle.disable_static()
|
|
input_shape = (3, 4, 5)
|
|
pad = [3, 4]
|
|
pad_3 = [3, 4, 5, 6, 7, 8]
|
|
mode = "constant"
|
|
value = 100
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
if self.dtype == np.complex64 or self.dtype == np.complex128:
|
|
input_data = (
|
|
np.random.rand(*input_shape) + 1j * np.random.rand(*input_shape)
|
|
).astype(self.dtype)
|
|
np_out1 = self._get_numpy_out(
|
|
input_data, pad, mode, value, data_format="NCL"
|
|
)
|
|
np_out2 = self._get_numpy_out(
|
|
input_data, pad, mode, value, data_format="NLC"
|
|
)
|
|
np_out3 = self._get_numpy_out(
|
|
input_data, pad_3, mode, value, data_format="NCL"
|
|
)
|
|
tensor_data = paddle.to_tensor(input_data)
|
|
tensor_pad = paddle.to_tensor(pad, dtype="int32")
|
|
|
|
y1 = F.pad(
|
|
tensor_data,
|
|
pad=tensor_pad,
|
|
mode=mode,
|
|
value=value,
|
|
data_format="NCL",
|
|
)
|
|
y2 = F.pad(
|
|
tensor_data,
|
|
pad=tensor_pad,
|
|
mode=mode,
|
|
value=value,
|
|
data_format="NLC",
|
|
)
|
|
y3 = F.pad(
|
|
tensor_data, pad=pad_3, mode=mode, value=value, data_format="NCL"
|
|
)
|
|
y4 = F.pad(
|
|
tensor_data,
|
|
pad=tensor_pad,
|
|
mode=mode,
|
|
value=value,
|
|
)
|
|
|
|
np.testing.assert_allclose(y1.numpy(), np_out1, rtol=1e-05)
|
|
np.testing.assert_allclose(y2.numpy(), np_out2, rtol=1e-05)
|
|
np.testing.assert_allclose(y3.numpy(), np_out3, rtol=1e-05)
|
|
np.testing.assert_allclose(y4.numpy(), np_out1, rtol=1e-05)
|
|
|
|
|
|
class TestPadAPI_complex64(TestPadAPI):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex64
|
|
|
|
|
|
class TestPadAPI_complex128(TestPadAPI):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex128
|
|
|
|
|
|
class TestPad1dAPI(unittest.TestCase):
|
|
def _get_numpy_out(
|
|
self, input_data, pad, mode, value=0.0, data_format="NCL"
|
|
):
|
|
if data_format == "NCL":
|
|
pad = [
|
|
(0, 0),
|
|
(0, 0),
|
|
(pad[0], pad[1]),
|
|
]
|
|
else:
|
|
pad = [
|
|
(0, 0),
|
|
(pad[0], pad[1]),
|
|
(0, 0),
|
|
]
|
|
|
|
if mode == "constant":
|
|
out = np.pad(input_data, pad, mode=mode, constant_values=value)
|
|
elif mode == "reflect":
|
|
out = np.pad(input_data, pad, mode=mode)
|
|
elif mode == "replicate":
|
|
out = np.pad(input_data, pad, mode="edge")
|
|
elif mode == "circular":
|
|
out = np.pad(input_data, pad, mode="wrap")
|
|
|
|
return out
|
|
|
|
def setUp(self):
|
|
self.init_dtype()
|
|
self.places = get_places()
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float32
|
|
|
|
def test_class(self):
|
|
paddle.disable_static()
|
|
for place in self.places:
|
|
input_shape = (3, 4, 5)
|
|
pad = [1, 2]
|
|
pad_int = 1
|
|
value = 100
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
if self.dtype == np.complex64 or self.dtype == np.complex128:
|
|
input_data = (
|
|
np.random.rand(*input_shape)
|
|
+ 1j * np.random.rand(*input_shape)
|
|
).astype(self.dtype)
|
|
pad_reflection = nn.Pad1D(padding=pad, mode="reflect")
|
|
pad_replication = nn.Pad1D(padding=pad, mode="replicate")
|
|
pad_constant = nn.Pad1D(padding=pad, mode="constant", value=value)
|
|
pad_constant_int = nn.Pad1D(
|
|
padding=pad_int, mode="constant", value=value
|
|
)
|
|
pad_circular = nn.Pad1D(padding=pad, mode="circular")
|
|
|
|
data = paddle.to_tensor(input_data)
|
|
|
|
output = pad_reflection(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "reflect", data_format="NCL"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_replication(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "replicate", data_format="NCL"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "constant", value=value, data_format="NCL"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant_int(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data,
|
|
[pad_int] * 2,
|
|
"constant",
|
|
value=value,
|
|
data_format="NCL",
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_circular(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "circular", value=value, data_format="NCL"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
|
|
class TestPad1dAPI_complex64(TestPad1dAPI):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex64
|
|
|
|
|
|
class TestPad1dAPI_complex128(TestPad1dAPI):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex128
|
|
|
|
|
|
class TestPad2dAPI(unittest.TestCase):
|
|
def _get_numpy_out(
|
|
self, input_data, pad, mode, value=0.0, data_format="NCHW"
|
|
):
|
|
if data_format == "NCHW":
|
|
pad = [
|
|
(0, 0),
|
|
(0, 0),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
]
|
|
else:
|
|
pad = [
|
|
(0, 0),
|
|
(pad[2], pad[3]),
|
|
(pad[0], pad[1]),
|
|
(0, 0),
|
|
]
|
|
|
|
if mode == "constant":
|
|
out = np.pad(input_data, pad, mode=mode, constant_values=value)
|
|
elif mode == "reflect":
|
|
out = np.pad(input_data, pad, mode=mode)
|
|
elif mode == "replicate":
|
|
out = np.pad(input_data, pad, mode="edge")
|
|
elif mode == "circular":
|
|
out = np.pad(input_data, pad, mode="wrap")
|
|
|
|
return out
|
|
|
|
def setUp(self):
|
|
self.init_dtype()
|
|
self.places = get_places()
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.float32
|
|
|
|
def test_class(self):
|
|
paddle.disable_static()
|
|
for place in self.places:
|
|
input_shape = (3, 4, 5, 6)
|
|
pad = [1, 2, 2, 1]
|
|
pad_int = 1
|
|
value = 100
|
|
input_data = np.random.rand(*input_shape).astype(self.dtype)
|
|
if self.dtype == np.complex64 or self.dtype == np.complex128:
|
|
input_data = (
|
|
np.random.rand(*input_shape)
|
|
+ 1j * np.random.rand(*input_shape)
|
|
).astype(self.dtype)
|
|
pad_reflection = nn.Pad2D(padding=pad, mode="reflect")
|
|
pad_replication = nn.Pad2D(padding=pad, mode="replicate")
|
|
pad_constant = nn.Pad2D(padding=pad, mode="constant", value=value)
|
|
pad_constant_int = nn.Pad2D(
|
|
padding=pad_int, mode="constant", value=value
|
|
)
|
|
pad_circular = nn.Pad2D(padding=pad, mode="circular")
|
|
|
|
data = paddle.to_tensor(input_data)
|
|
|
|
output = pad_reflection(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "reflect", data_format="NCHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_replication(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "replicate", data_format="NCHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "constant", value=value, data_format="NCHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_constant_int(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data,
|
|
[pad_int] * 4,
|
|
"constant",
|
|
value=value,
|
|
data_format="NCHW",
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
output = pad_circular(data)
|
|
np_out = self._get_numpy_out(
|
|
input_data, pad, "circular", data_format="NCHW"
|
|
)
|
|
np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
|
|
|
|
|
|
class TestPad2dAPI_complex64(TestPad2dAPI):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex64
|
|
|
|
|
|
class TestPad2dAPI_complex128(TestPad2dAPI):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex128
|
|
|
|
|
|
class TestPad3dAPI(unittest.TestCase):
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def _get_numpy_out(
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self, input_data, pad, mode, value=0.0, data_format="NCDHW"
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):
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if data_format == "NCDHW":
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pad = [
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(0, 0),
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(0, 0),
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(pad[4], pad[5]),
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(pad[2], pad[3]),
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(pad[0], pad[1]),
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]
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else:
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pad = [
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(0, 0),
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(pad[4], pad[5]),
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(pad[2], pad[3]),
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(pad[0], pad[1]),
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(0, 0),
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]
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|
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if mode == "constant":
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out = np.pad(input_data, pad, mode=mode, constant_values=value)
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elif mode == "reflect":
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out = np.pad(input_data, pad, mode=mode)
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elif mode == "replicate":
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out = np.pad(input_data, pad, mode="edge")
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elif mode == "circular":
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out = np.pad(input_data, pad, mode="wrap")
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return out
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def setUp(self):
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self.init_dtype()
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self.places = get_places()
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def init_dtype(self):
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self.dtype = np.float32
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def test_class(self):
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paddle.disable_static()
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for place in self.places:
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input_shape = (3, 4, 5, 6, 7)
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pad = [1, 2, 2, 1, 1, 0]
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pad_int = 1
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value = 100
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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input_data = (
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np.random.rand(*input_shape)
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+ 1j * np.random.rand(*input_shape)
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).astype(self.dtype)
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pad_reflection = nn.Pad3D(padding=pad, mode="reflect")
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pad_replication = nn.Pad3D(padding=pad, mode="replicate")
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pad_constant = nn.Pad3D(padding=pad, mode="constant", value=value)
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pad_constant_int = nn.Pad3D(
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padding=pad_int, mode="constant", value=value
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)
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pad_circular = nn.Pad3D(padding=pad, mode="circular")
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data = paddle.to_tensor(input_data)
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output = pad_reflection(data)
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np_out = self._get_numpy_out(
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input_data, pad, "reflect", data_format="NCDHW"
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)
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np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
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|
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output = pad_replication(data)
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np_out = self._get_numpy_out(
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input_data, pad, "replicate", data_format="NCDHW"
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)
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np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
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|
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output = pad_constant(data)
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np_out = self._get_numpy_out(
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input_data, pad, "constant", value=value, data_format="NCDHW"
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)
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np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
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|
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output = pad_constant_int(data)
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np_out = self._get_numpy_out(
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input_data,
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[pad_int] * 6,
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"constant",
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value=value,
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data_format="NCDHW",
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)
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np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
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|
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output = pad_circular(data)
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np_out = self._get_numpy_out(
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input_data, pad, "circular", data_format="NCDHW"
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)
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np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
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|
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def test_pad_tensor(self):
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paddle.disable_static()
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for place in self.places:
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input_shape = (3, 4, 5, 6, 7)
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pad = [1, 2, 2, 1, 1, 0]
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pad_tensor = paddle.to_tensor(pad)
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input_data = np.random.rand(*input_shape).astype(self.dtype)
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if self.dtype == np.complex64 or self.dtype == np.complex128:
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input_data = (
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np.random.rand(*input_shape)
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|
+ 1j * np.random.rand(*input_shape)
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|
).astype(self.dtype)
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pad_reflection_ncdhw = nn.Pad3D(
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padding=pad_tensor, mode="reflect", data_format="NCDHW"
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)
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pad_reflection_ndhwc = nn.Pad3D(
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padding=pad_tensor, mode="reflect", data_format="NDHWC"
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)
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data = paddle.to_tensor(input_data)
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|
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output = pad_reflection_ncdhw(data)
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np_out = self._get_numpy_out(
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input_data, pad, "reflect", data_format="NCDHW"
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|
)
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np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
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|
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output = pad_reflection_ndhwc(data)
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np_out = self._get_numpy_out(
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|
input_data, pad, "reflect", data_format="NDHWC"
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|
)
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np.testing.assert_allclose(output.numpy(), np_out, rtol=1e-05)
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|
|
|
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|
class TestPad3dAPI_complex64(TestPad3dAPI):
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|
def init_dtype(self):
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|
self.dtype = np.complex64
|
|
|
|
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|
class TestPad3dAPI_complex128(TestPad3dAPI):
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|
def init_dtype(self):
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|
self.dtype = np.complex128
|
|
|
|
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|
class TestPad3dOpError(unittest.TestCase):
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|
def setUp(self):
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|
self.init_dtype()
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|
self.places = get_places()
|
|
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|
def init_dtype(self):
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|
self.dtype = np.float32
|
|
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|
def test_errors(self):
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|
def test_variable():
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|
input_shape = (1, 2, 3, 4, 5)
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|
data = np.random.rand(*input_shape).astype(self.dtype)
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|
if self.dtype == np.complex64 or self.dtype == np.complex128:
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|
data = (
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|
np.random.rand(*input_shape)
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|
+ 1j * np.random.rand(*input_shape)
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|
).astype(self.dtype)
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|
y = F.pad(x=data, pad=[1, 1, 1, 1, 1, 1], data_format="NCDHW")
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|
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|
def test_reflect_1():
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|
input_shape = (1, 2, 3, 4, 5)
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|
data = np.random.rand(*input_shape).astype(self.dtype)
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|
if self.dtype == np.complex64 or self.dtype == np.complex128:
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|
data = (
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|
np.random.rand(*input_shape)
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|
+ 1j * np.random.rand(*input_shape)
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|
).astype(self.dtype)
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|
x = paddle.to_tensor(data)
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|
y = F.pad(
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|
x,
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pad=[5, 6, 1, 1, 1, 1],
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|
value=1,
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|
mode='reflect',
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|
data_format="NCDHW",
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|
)
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|
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|
def test_reflect_2():
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|
input_shape = (1, 2, 3, 4, 5)
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|
data = np.random.rand(*input_shape).astype(self.dtype)
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|
if self.dtype == np.complex64 or self.dtype == np.complex128:
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|
data = (
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|
np.random.rand(*input_shape)
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|
+ 1j * np.random.rand(*input_shape)
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|
).astype(self.dtype)
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|
x = paddle.to_tensor(data)
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|
y = F.pad(
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|
x,
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|
pad=[1, 1, 4, 3, 1, 1],
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|
value=1,
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|
mode='reflect',
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|
data_format="NCDHW",
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|
)
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|
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|
def test_reflect_3():
|
|
input_shape = (1, 2, 3, 4, 5)
|
|
data = np.random.rand(*input_shape).astype(self.dtype)
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|
if self.dtype == np.complex64 or self.dtype == np.complex128:
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|
data = (
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|
np.random.rand(*input_shape)
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|
+ 1j * np.random.rand(*input_shape)
|
|
).astype(self.dtype)
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|
x = paddle.to_tensor(data)
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|
y = F.pad(
|
|
x,
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|
pad=[1, 1, 1, 1, 2, 3],
|
|
value=1,
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|
mode='reflect',
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|
data_format="NCDHW",
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|
)
|
|
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|
def test_circular_1():
|
|
input_shape = (1, 2, 0, 4, 5)
|
|
data = np.random.rand(*input_shape).astype(self.dtype)
|
|
if self.dtype == np.complex64 or self.dtype == np.complex128:
|
|
data = (
|
|
np.random.rand(*input_shape)
|
|
+ 1j * np.random.rand(*input_shape)
|
|
).astype(self.dtype)
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|
x = paddle.to_tensor(data)
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|
y = F.pad(
|
|
x, pad=[1, 1, 1, 1, 2, 3], mode='circular', data_format="NCDHW"
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|
)
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|
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|
def test_replicate_1():
|
|
input_shape = (1, 2, 0, 4, 5)
|
|
data = np.random.rand(*input_shape).astype(self.dtype)
|
|
if self.dtype == np.complex64 or self.dtype == np.complex128:
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|
data = (
|
|
np.random.rand(*input_shape)
|
|
+ 1j * np.random.rand(*input_shape)
|
|
).astype(self.dtype)
|
|
x = paddle.to_tensor(data)
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|
y = F.pad(
|
|
x, pad=[1, 1, 1, 1, 2, 3], mode='replicate', data_format="NCDHW"
|
|
)
|
|
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|
paddle.disable_static()
|
|
for _ in self.places:
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"pad3d\(\): argument 'x' \(position 0\) must be Tensor, but got numpy.ndarray",
|
|
test_variable,
|
|
)
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|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"The width of Input\(X\)'s dimension should be greater than pad_left in reflect mode",
|
|
test_reflect_1,
|
|
)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"The height of Input\(X\)'s dimension should be greater than pad_top in reflect mode",
|
|
test_reflect_2,
|
|
)
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"The depth of Input\(X\)'s dimension should be greater than pad_back in reflect mode",
|
|
test_reflect_3,
|
|
)
|
|
# comment out because pad3d support 0-size now.
|
|
# self.assertRaises(Exception, test_circular_1)
|
|
# self.assertRaises(Exception, test_replicate_1)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestPad3dOpError_complex64(TestPad3dOpError):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex64
|
|
|
|
|
|
class TestPad3dOpError_complex128(TestPad3dOpError):
|
|
def init_dtype(self):
|
|
self.dtype = np.complex128
|
|
|
|
|
|
class TestPadDataformatError(unittest.TestCase):
|
|
def test_errors(self):
|
|
def test_ncl():
|
|
input_shape = (1, 2, 3, 4)
|
|
pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32'))
|
|
data = (
|
|
np.arange(np.prod(input_shape), dtype=np.float64).reshape(
|
|
input_shape
|
|
)
|
|
+ 1
|
|
)
|
|
my_pad = nn.Pad1D(padding=pad, mode="replicate", data_format="NCL")
|
|
data = paddle.to_tensor(data)
|
|
result = my_pad(data)
|
|
|
|
def test_nchw():
|
|
input_shape = (1, 2, 4)
|
|
pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32'))
|
|
data = (
|
|
np.arange(np.prod(input_shape), dtype=np.float64).reshape(
|
|
input_shape
|
|
)
|
|
+ 1
|
|
)
|
|
my_pad = nn.Pad1D(padding=pad, mode="replicate", data_format="NCHW")
|
|
data = paddle.to_tensor(data)
|
|
result = my_pad(data)
|
|
|
|
def test_ncdhw():
|
|
input_shape = (1, 2, 3, 4)
|
|
pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32'))
|
|
data = (
|
|
np.arange(np.prod(input_shape), dtype=np.float64).reshape(
|
|
input_shape
|
|
)
|
|
+ 1
|
|
)
|
|
my_pad = nn.Pad1D(
|
|
padding=pad, mode="replicate", data_format="NCDHW"
|
|
)
|
|
data = paddle.to_tensor(data)
|
|
result = my_pad(data)
|
|
|
|
self.assertRaises(AssertionError, test_ncl)
|
|
|
|
self.assertRaises(AssertionError, test_nchw)
|
|
|
|
self.assertRaises(AssertionError, test_ncdhw)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|