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533 lines
17 KiB
533 lines
17 KiB
# 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from op_test import OpTest
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import paddle.fluid as fluid
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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 TestSliceOp(OpTest):
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def setUp(self):
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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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}
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype("float32")
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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()
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def test_check_grad_normal(self):
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self.check_grad(['Input'], 'Out', max_relative_error=0.006)
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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("float32")
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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("float32")
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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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# 1.2 with attr(decrease)
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class TestSliceOp_decs_dim(OpTest):
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def setUp(self):
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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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}
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype("float32")
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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()
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def test_check_grad_normal(self):
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self.check_grad(['Input'], 'Out', max_relative_error=0.006)
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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("float32")
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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("float32")
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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("float32")
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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, 3:4]
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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("float32")
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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("float32")
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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, 3:4]
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# Situation 2: starts(list, have tensor), ends(list, no tensor)
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# without attr(decrease)
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class TestSliceOp_starts_ListTensor(OpTest):
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def setUp(self):
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self.op_type = "slice"
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self.config()
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starts_tensor = []
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for index, ele in enumerate(self.starts):
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starts_tensor.append(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
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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_infer,
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'ends': self.ends,
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'infer_flags': self.infer_flags
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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("float32")
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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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self.starts_infer = [-1, 0, -1]
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['Input'], 'Out', max_relative_error=0.006)
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# Situation 2: starts(list, have tensor), ends(list, no tensor)
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# with attr(decrease)
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class TestSliceOp_decs_dim_starts_ListTensor(OpTest):
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def setUp(self):
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self.op_type = "slice"
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self.config()
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starts_tensor = []
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for index, ele in enumerate(self.starts):
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starts_tensor.append(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
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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_infer,
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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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}
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype("float32")
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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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self.starts_infer = [1, -1, 2]
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['Input'], 'Out', max_relative_error=0.006)
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class TestSliceOp_decs_dim_5_starts_ListTensor(
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TestSliceOp_decs_dim_starts_ListTensor):
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype("float32")
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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]
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self.out = self.input[:, :, :, -1]
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self.starts_infer = [-1]
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# Situation 3: starts(tensor), ends(list, no tensor)
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# with attr(decrease)
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class TestSliceOp_decs_dim_starts_OneTensor(OpTest):
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def setUp(self):
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self.op_type = "slice"
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self.config()
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self.inputs = {
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'Input': self.input,
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"StartsTensor": np.array(
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self.starts, dtype="int32")
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}
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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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}
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype("float32")
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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()
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def test_check_grad_normal(self):
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self.check_grad(['Input'], 'Out', max_relative_error=0.006)
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# Situation 4: starts(tensor), ends(tensor)
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# without attr(decrease)
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class TestSliceOp_starts_OneTensor_ends_OneTensor(OpTest):
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def setUp(self):
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self.op_type = "slice"
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self.config()
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self.inputs = {
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'Input': self.input,
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"StartsTensor": np.array(
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self.starts, dtype="int32"),
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"EndsTensor": np.array(
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self.ends, dtype="int32")
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}
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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_infer,
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'infer_flags': self.infer_flags
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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("float32")
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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()
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def test_check_grad_normal(self):
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self.check_grad(['Input'], 'Out', max_relative_error=0.006)
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# Situation 5: starts(tensor), ends(tensor)
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# with attr(decrease)
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class TestSliceOp_decs_dim_starts_and_ends_OneTensor(OpTest):
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def setUp(self):
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self.op_type = "slice"
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self.config()
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self.inputs = {
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'Input': self.input,
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"StartsTensor": np.array(
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self.starts, dtype="int32"),
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"EndsTensor": np.array(
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self.ends, dtype="int32")
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}
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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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}
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def config(self):
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self.input = np.random.random([3, 4, 5, 6]).astype("float32")
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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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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['Input'], 'Out', max_relative_error=0.006)
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# Situation 6: starts(tensor), ends(list, have tensor)
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# without attr(decrease)
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class TestSliceOp_starts_OneTensor_ends_ListTensor(OpTest):
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def setUp(self):
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self.op_type = "slice"
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self.config()
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ends_tensor = []
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for index, ele in enumerate(self.ends):
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ends_tensor.append(("y" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs = {
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'Input': self.input,
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"StartsTensor": np.array(
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self.starts, dtype="int32"),
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'EndsTensorList': ends_tensor
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}
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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_infer,
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'infer_flags': self.infer_flags
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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("float32")
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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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self.ends_infer = [-1, 3, 4]
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['Input'], 'Out', max_relative_error=0.006)
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# Test CUDA float16
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestFP16(OpTest):
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def setUp(self):
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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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}
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def config(self):
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self.dtype = "float16"
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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.out = self.input[-3:3, 0:100, :, 2:-1]
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self.infer_flags = [1, 1, 1]
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def test_check_output(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-5)
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def test_check_grad_normal(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place, ['Input'], 'Out', max_relative_error=0.006)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestFP16_2(OpTest):
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def setUp(self):
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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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}
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def config(self):
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self.dtype = "float16"
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self.input = np.random.random([3, 4, 5]).astype(self.dtype)
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self.starts = [0]
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self.ends = [1]
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self.axes = [1]
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self.out = self.input[:, 0:1, :]
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self.infer_flags = [1]
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def test_check_output(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-5)
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def test_check_grad_normal(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place, ['Input'],
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'Out',
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max_relative_error=0.006,
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numeric_grad_delta=0.5)
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# Test python API
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class TestSliceAPI(unittest.TestCase):
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def test_1(self):
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input = np.random.random([3, 4, 5, 6]).astype("float32")
|
|
minus_1 = fluid.layers.fill_constant([1], "int32", -1)
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|
minus_3 = fluid.layers.fill_constant([1], "int32", -3)
|
|
starts = fluid.layers.data(
|
|
name='starts', shape=[1, 3], append_batch_size=False)
|
|
ends = fluid.layers.data(
|
|
name='ends', shape=[3], append_batch_size=False)
|
|
|
|
x = fluid.layers.data(
|
|
name="x",
|
|
shape=[3, 4, 5, 6],
|
|
append_batch_size=False,
|
|
dtype="float32")
|
|
|
|
out_1 = fluid.layers.slice(
|
|
x, axes=[0, 1, 2], starts=[-3, 0, 2], ends=[3, 100, -1])
|
|
out_2 = fluid.layers.slice(
|
|
x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, -1])
|
|
out_3 = fluid.layers.slice(
|
|
x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, minus_1])
|
|
out_4 = fluid.layers.slice(x, axes=[0, 1, 2], starts=starts, ends=ends)
|
|
|
|
out_5 = x[-3:3, 0:100, 2:-1]
|
|
out_6 = x[minus_3:3, 0:100, :, 2:-1]
|
|
out_7 = x[minus_1, 0:100, :, 2:minus_1]
|
|
|
|
exe = fluid.Executor(place=fluid.CPUPlace())
|
|
res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
|
|
fluid.default_main_program(),
|
|
feed={
|
|
"x": input,
|
|
'starts': np.array([-3, 0, 2]).astype("int32"),
|
|
'ends': np.array([3, 100, -1]).astype("int32")
|
|
},
|
|
fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7])
|
|
|
|
assert np.array_equal(res_1, input[-3:3, 0:100, 2:-1, :])
|
|
assert np.array_equal(res_2, input[-3:3, 0:100, :, 2:-1])
|
|
assert np.array_equal(res_3, input[-3:3, 0:100, :, 2:-1])
|
|
assert np.array_equal(res_4, input[-3:3, 0:100, 2:-1, :])
|
|
assert np.array_equal(res_5, input[-3:3, 0:100, 2:-1, :])
|
|
assert np.array_equal(res_6, input[-3:3, 0:100, :, 2:-1])
|
|
assert np.array_equal(res_7, input[-1, 0:100, :, 2:-1])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|