wehub-resource-sync
332 lines
9.9 KiB
Python
332 lines
9.9 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test import convert_float_to_uint16
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from op_test_xpu import XPUOpTest
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import paddle
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paddle.enable_static()
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class XPUTestFlattenOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'flatten_contiguous_range'
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self.use_dynamic_create_class = False
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class TestFlattenOp(XPUOpTest):
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def setUp(self):
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self.set_xpu()
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self.op_type = "flatten_contiguous_range"
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self.place = paddle.XPUPlace(0)
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self.use_xpu = True
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self.use_onednn = False
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self.start_axis = 0
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self.stop_axis = -1
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self.dtype = self.in_type
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self.init_test_case()
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if self.dtype == np.uint16:
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data = np.random.random(self.in_shape).astype(np.float32)
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self.inputs = {"X": convert_float_to_uint16(data)}
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else:
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self.inputs = {
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"X": np.random.random(self.in_shape).astype(self.dtype)
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}
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self.init_attrs()
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.new_shape),
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"XShape": np.random.random(self.in_shape).astype(self.dtype),
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}
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def set_xpu(self):
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self.__class__.use_xpu = True
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def test_check_output(self):
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self.check_output_with_place(self.place, no_check_set=["XShape"])
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def test_check_grad(self):
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self.check_grad_with_place(self.place, ["X"], "Out")
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = -1
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self.new_shape = 120
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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'use_xpu': True,
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}
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class TestFlattenOp_1(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 1
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self.stop_axis = 2
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self.new_shape = (3, 10, 4)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenOp_2(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = 1
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self.new_shape = (6, 5, 4)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenOp_3(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = 2
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self.new_shape = (30, 4)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenOp_4(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = -2
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self.stop_axis = -1
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self.new_shape = (3, 2, 20)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenOp_5(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 2
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self.stop_axis = 2
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self.new_shape = (3, 2, 5, 4)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenOpSixDims(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 3, 2, 4, 4)
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self.start_axis = 3
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self.stop_axis = 5
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self.new_shape = (3, 2, 3, 32)
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenOp_Float32(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = 1
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self.new_shape = (6, 5, 4)
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self.dtype = np.float32
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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class TestFlattenOp_int32(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = 1
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self.new_shape = (6, 5, 4)
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self.dtype = np.int32
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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'use_xpu': True,
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}
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def test_check_grad(self):
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pass
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class TestFlattenOp_int8(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = 1
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self.new_shape = (6, 5, 4)
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self.dtype = np.int8
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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def test_check_grad(self):
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pass
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class TestFlattenOp_int64(TestFlattenOp):
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def init_test_case(self):
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self.in_shape = (3, 2, 5, 4)
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self.start_axis = 0
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self.stop_axis = 1
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self.new_shape = (6, 5, 4)
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self.dtype = np.int64
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def init_attrs(self):
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self.attrs = {
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"start_axis": self.start_axis,
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"stop_axis": self.stop_axis,
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}
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def test_check_grad(self):
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pass
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class TestFlatten2OpError(unittest.TestCase):
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def test_errors(self):
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image_shape = (2, 3, 4, 4)
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x = (
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np.arange(
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image_shape[0]
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* image_shape[1]
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* image_shape[2]
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* image_shape[3]
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).reshape(image_shape)
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/ 100.0
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)
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x = x.astype('float32')
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def test_ValueError1():
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x_var = paddle.static.data(
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name="x", shape=image_shape, dtype='float32'
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)
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out = paddle.flatten(x_var, start_axis=2, stop_axis=1)
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self.assertRaises(ValueError, test_ValueError1)
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def test_ValueError2():
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x_var = paddle.static.data(
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name="x", shape=image_shape, dtype='float32'
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)
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paddle.flatten(x_var, start_axis=10, stop_axis=1)
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self.assertRaises(ValueError, test_ValueError2)
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def test_ValueError3():
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x_var = paddle.static.data(
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name="x", shape=image_shape, dtype='float32'
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)
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paddle.flatten(x_var, start_axis=2, stop_axis=10)
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self.assertRaises(ValueError, test_ValueError3)
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def test_InputError():
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out = paddle.flatten(x)
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self.assertRaises(ValueError, test_InputError)
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class TestStaticFlattenPythonAPI(unittest.TestCase):
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def execute_api(self, x, start_axis=0, stop_axis=-1):
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return paddle.flatten(x, start_axis, stop_axis)
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def test_static_api(self):
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paddle.enable_static()
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np_x = np.random.rand(2, 3, 4, 4).astype('float32')
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main_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, paddle.static.Program()):
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x = paddle.static.data(
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name="x", shape=[2, 3, 4, 4], dtype='float32'
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)
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out = self.execute_api(x, start_axis=-2, stop_axis=-1)
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exe = paddle.static.Executor(place=paddle.XPUPlace(0))
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fetch_out = exe.run(main_prog, feed={"x": np_x}, fetch_list=[out])
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self.assertTrue((2, 3, 16) == fetch_out[0].shape)
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class TestStaticInplaceFlattenPythonAPI(TestStaticFlattenPythonAPI):
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def execute_api(self, x, start_axis=0, stop_axis=-1):
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return x.flatten_(start_axis, stop_axis)
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class TestFlattenPython(unittest.TestCase):
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def test_python_api(self):
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image_shape = (2, 3, 4, 4)
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x = (
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np.arange(
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image_shape[0]
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* image_shape[1]
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* image_shape[2]
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* image_shape[3]
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).reshape(image_shape)
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/ 100.0
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)
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x = x.astype('float32')
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def test_InputError():
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out = paddle.flatten(x)
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self.assertRaises(ValueError, test_InputError)
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def test_Negative():
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paddle.disable_static(paddle.XPUPlace(0))
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img = paddle.to_tensor(x)
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out = paddle.flatten(img, start_axis=-2, stop_axis=-1)
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return out.numpy().shape
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res_shape = test_Negative()
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self.assertTrue((2, 3, 16) == res_shape)
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support_types = get_xpu_op_support_types('flatten_contiguous_range')
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for stype in support_types:
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create_test_class(globals(), XPUTestFlattenOp, stype)
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if __name__ == "__main__":
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unittest.main()
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