wehub-resource-sync
346 lines
13 KiB
Python
346 lines
13 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 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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check_run_big_shape_test,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import XPUOpTest
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import paddle
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paddle.enable_static()
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# Situation 1: starts(list, no tensor), ends(list, no tensor)
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# 1.1 without attr(decrease)
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class XPUTestSliceOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'slice'
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self.use_dynamic_create_class = False
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class TestSliceOp(XPUOpTest):
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def setUp(self):
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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self.op_type = "slice"
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self.config()
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self.inputs = {'Input': self.input}
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self.outputs = {'Out': self.out}
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self.attrs = {
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'axes': self.axes,
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'starts': self.starts,
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'ends': self.ends,
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'infer_flags': self.infer_flags,
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"use_xpu": True,
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}
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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self.starts = [1, 0, 2]
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self.ends = [3, 3, 4]
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self.axes = [0, 1, 2]
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self.infer_flags = [1, 1, 1]
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self.out = self.input[1:3, 0:3, 2:4, :]
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def test_check_output(self):
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self.check_output_with_place(self.place)
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def test_check_grad_normal(self):
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if self.dtype == np.float16:
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self.check_grad_with_place(self.place, ['Input'], 'Out')
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else:
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user_defined_grad_outputs = np.random.random(
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self.out.shape
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).astype(self.dtype)
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self.check_grad_with_place(
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self.place,
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['Input'],
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'Out',
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user_defined_grad_outputs=user_defined_grad_outputs,
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)
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class TestCase1(TestSliceOp):
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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self.starts = [-3, 0, 2]
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self.ends = [3, 100, -1]
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self.axes = [0, 1, 2]
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self.infer_flags = [1, 1, 1]
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self.out = self.input[-3:3, 0:100, 2:-1, :]
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class TestCase2(TestSliceOp):
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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self.starts = [-3, 0, 2]
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self.ends = [3, 100, -1]
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self.axes = [0, 1, 3]
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self.infer_flags = [1, 1, 1]
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self.out = self.input[-3:3, 0:100, :, 2:-1]
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@check_run_big_shape_test()
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class TestCaseLargeShape1(TestSliceOp):
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def config(self):
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self.input = np.random.random([8192, 5120])
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self.starts = [0, 5119]
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self.ends = [8192, 5120]
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self.axes = [0, 1]
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self.infer_flags = [1, 1]
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self.out = self.input[:, -1:]
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@check_run_big_shape_test()
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class TestCaseLargeShape2(TestSliceOp):
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def config(self):
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self.input = np.random.random([2, 1, 8192, 1, 128])
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self.starts = [0, 0, 0, 0, 0]
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self.ends = [2, 1, 1, 1, 128]
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self.axes = [0, 1, 2, 3, 4]
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self.infer_flags = [1, 1, 1, 1, 1]
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self.out = self.input[:, :, -1:, :, :]
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# 1.2 with attr(decrease)
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class XPUTestSliceOp_decs_dim(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'slice'
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self.use_dynamic_create_class = False
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class TestSliceOp_decs_dim(XPUOpTest):
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def setUp(self):
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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self.op_type = "slice"
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self.config()
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self.inputs = {'Input': self.input}
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self.outputs = {'Out': self.out}
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self.attrs = {
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'axes': self.axes,
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'starts': self.starts,
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'ends': self.ends,
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'infer_flags': self.infer_flags,
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'decrease_axis': self.decrease_axis,
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"use_xpu": True,
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}
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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self.starts = [1, 0, 2]
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self.ends = [2, 3, 4]
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self.axes = [0, 1, 2]
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self.decrease_axis = [0]
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self.infer_flags = [1, 1, 1]
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self.out = self.input[1, 0:3, 2:4, :]
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def test_check_output(self):
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self.check_output_with_place(self.place)
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def test_check_grad_normal(self):
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if self.dtype == np.float16:
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self.check_grad_with_place(self.place, ['Input'], 'Out')
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else:
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user_defined_grad_outputs = np.random.random(
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self.out.shape
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).astype(self.dtype)
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self.check_grad_with_place(
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self.place,
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['Input'],
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'Out',
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user_defined_grad_outputs=user_defined_grad_outputs,
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)
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class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim):
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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self.starts = [1, 0, 2]
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self.ends = [2, 1, 4]
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self.axes = [0, 1, 2]
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self.decrease_axis = [0, 1]
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self.infer_flags = [1, 1, 1]
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self.out = self.input[1, 0, 2:4, :]
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class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim):
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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self.starts = [-1, 0, 2]
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self.ends = [1000000, 1, 4]
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self.axes = [0, 1, 2]
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self.decrease_axis = [0, 1]
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self.infer_flags = [1, 1, 1]
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self.out = self.input[-1, 0, 2:4, :]
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class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim):
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def config(self):
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self.input = np.random.random([3, 4, 5, 7]).astype(self.dtype)
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self.starts = [0, 1, 2, 3]
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self.ends = [1, 2, 3, 4]
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self.axes = [0, 1, 2, 3]
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self.decrease_axis = [0, 1, 2]
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self.infer_flags = [1, 1, 1]
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self.out = self.input[0, 1, 2, 3:4]
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class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim):
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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self.starts = [-1]
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self.ends = [1000000]
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self.axes = [3]
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self.decrease_axis = [3]
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self.infer_flags = [1, 1, 1]
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self.out = self.input[:, :, :, -1]
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class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim):
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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self.starts = [0, 1, 2, 3]
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self.ends = [1, 2, 3, 4]
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self.axes = [0, 1, 2, 3]
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self.decrease_axis = [0, 1, 2, 3]
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self.infer_flags = [1, 1, 1]
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self.out = self.input[0, 1, 2, 3]
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support_types = get_xpu_op_support_types('slice')
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real_types = [t for t in support_types if t != 'complex64']
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for stype in real_types:
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create_test_class(globals(), XPUTestSliceOp, stype)
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create_test_class(globals(), XPUTestSliceOp_decs_dim, stype)
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if 'complex64' in support_types:
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class TestSliceOpComplex(XPUOpTest):
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def setUp(self):
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self.dtype = np.float32
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self.place = paddle.XPUPlace(0)
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self.op_type = "slice"
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self.python_api = paddle.slice
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def _test_check_output(self):
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self.check_output_with_place(self.place)
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def _test_check_grad_normal(self):
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real_grad = np.random.random(self.out.shape).astype(self.dtype)
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imag_grad = np.random.random(self.out.shape).astype(self.dtype)
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user_defined_grad_outputs = real_grad + 1j * imag_grad
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self.check_grad_with_place(
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self.place,
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['Input'],
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'Out',
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user_defined_grad_outputs=user_defined_grad_outputs,
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)
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def test_with_all_possible_start_end(self):
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real_part = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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imag_part = np.random.random([3, 4, 5, 6]).astype(self.dtype)
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np_data = real_part + 1j * imag_part
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dim_size = np_data.shape[2]
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for st in [*list(range(-dim_size - 1, dim_size + 2)), None]:
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for ed in [*list(range(-dim_size - 1, dim_size + 2)), None]:
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try:
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np_res = np_data[:, :, st:ed, :]
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except Exception as e:
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# skip the invalid case use try-except strategy
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continue
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self.axes = [2]
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self.starts = [st if st is not None else 0]
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self.ends = [ed if ed is not None else dim_size]
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self.infer_flags = [1]
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self.input = np_data
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self.out = np_res
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self.inputs = {'Input': self.input}
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self.outputs = {'Out': self.out}
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self.attrs = {
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'axes': self.axes,
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'starts': self.starts,
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'ends': self.ends,
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'infer_flags': self.infer_flags,
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"use_xpu": True,
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}
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self._test_check_output()
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self._test_check_grad_normal()
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def test_with_all_possible_start_end_zero_size(self):
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real_part = np.random.random([0, 4, 5, 6]).astype(self.dtype)
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imag_part = np.random.random([0, 4, 5, 6]).astype(self.dtype)
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np_data = real_part + 1j * imag_part
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dim_size = np_data.shape[2]
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for st in [*list(range(-dim_size - 1, dim_size + 2)), None]:
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for ed in [*list(range(-dim_size - 1, dim_size + 2)), None]:
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try:
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np_res = np_data[:, :, st:ed, :]
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except Exception as e:
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# skip the invalid case use try-except strategy
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continue
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self.axes = [2]
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self.starts = [st if st is not None else 0]
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self.ends = [ed if ed is not None else dim_size]
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self.infer_flags = [1]
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self.input = np_data
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self.out = np_res
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self.inputs = {'Input': self.input}
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self.outputs = {'Out': self.out}
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self.attrs = {
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'axes': self.axes,
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'starts': self.starts,
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'ends': self.ends,
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'infer_flags': self.infer_flags,
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"use_xpu": True,
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}
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self._test_check_output()
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self._test_check_grad_normal()
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def test_with_all_possible_start_end_zero_size_self(self):
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real_part = np.random.random([3, 4, 0, 6]).astype(self.dtype)
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imag_part = np.random.random([3, 4, 0, 6]).astype(self.dtype)
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np_data = real_part + 1j * imag_part
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dim_size = np_data.shape[2]
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for st in [*list(range(-dim_size - 1, dim_size + 2)), None]:
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for ed in [*list(range(-dim_size - 1, dim_size + 2)), None]:
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try:
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np_res = np_data[:, :, st:ed, :]
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except Exception as e:
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# skip the invalid case use try-except strategy
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continue
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self.axes = [2]
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self.starts = [st if st is not None else 0]
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self.ends = [ed if ed is not None else dim_size]
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self.infer_flags = [1]
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self.input = np_data
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self.out = np_res
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self.inputs = {'Input': self.input}
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self.outputs = {'Out': self.out}
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self.attrs = {
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'axes': self.axes,
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'starts': self.starts,
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'ends': self.ends,
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'infer_flags': self.infer_flags,
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"use_xpu": True,
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}
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self._test_check_output()
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self._test_check_grad_normal()
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if __name__ == '__main__':
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unittest.main()
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