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504 lines
16 KiB
504 lines
16 KiB
# Copyright (c) 2019 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 op_test import OpTest
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import numpy as np
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import unittest
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import paddle.fluid as fluid
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def strided_slice_native_forward(input, axes, starts, ends, strides):
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dim = input.ndim
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start = []
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end = []
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stride = []
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for i in range(dim):
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start.append(0)
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end.append(input.shape[i])
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stride.append(1)
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for i in range(len(axes)):
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start[axes[i]] = starts[i]
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end[axes[i]] = ends[i]
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stride[axes[i]] = strides[i]
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result = {
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1: lambda input, start, end, stride: input[start[0]:end[0]:stride[0]],
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2: lambda input, start, end, stride: input[start[0]:end[0]:stride[0], \
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start[1]:end[1]:stride[1]],
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3: lambda input, start, end, stride: input[start[0]:end[0]:stride[0], \
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start[1]:end[1]:stride[1], start[2]:end[2]:stride[2]],
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4: lambda input, start, end, stride: input[start[0]:end[0]:stride[0], \
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start[1]:end[1]:stride[1], start[2]:end[2]:stride[2], start[3]:end[3]:stride[3]],
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5: lambda input, start, end, stride: input[start[0]:end[0]:stride[0], \
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start[1]:end[1]:stride[1], start[2]:end[2]:stride[2], start[3]:end[3]:stride[3], start[4]:end[4]:stride[4]],
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6: lambda input, start, end, stride: input[start[0]:end[0]:stride[0], \
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start[1]:end[1]:stride[1], start[2]:end[2]:stride[2], start[3]:end[3]:stride[3], \
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start[4]:end[4]:stride[4], start[5]:end[5]:stride[5]]
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}[dim](input, start, end, stride)
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return result
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class TestStrideSliceOp(OpTest):
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def setUp(self):
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self.initTestCase()
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self.op_type = 'strided_slice'
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self.output = strided_slice_native_forward(
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self.input, self.axes, self.starts, self.ends, self.strides)
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self.inputs = {'Input': self.input}
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self.outputs = {'Out': self.output}
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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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'strides': self.strides,
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'infer_flags': self.infer_flags
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(set(['Input']), 'Out')
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def initTestCase(self):
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self.input = np.random.rand(6)
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self.axes = [0]
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self.starts = [-4]
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self.ends = [-3]
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self.strides = [1]
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self.infer_flags = [1]
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class TestStrideSliceOp1(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(6)
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self.axes = [0]
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self.starts = [3]
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self.ends = [8]
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self.strides = [1]
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self.infer_flags = [1]
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class TestStrideSliceOp2(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(6)
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self.axes = [0]
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self.starts = [5]
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self.ends = [0]
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self.strides = [-1]
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self.infer_flags = [1]
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class TestStrideSliceOp3(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(6)
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self.axes = [0]
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self.starts = [-1]
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self.ends = [-3]
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self.strides = [-1]
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self.infer_flags = [1]
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class TestStrideSliceOp4(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(3, 4, 6)
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self.axes = [0, 1, 2]
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self.starts = [0, -1, 0]
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self.ends = [2, -3, 5]
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self.strides = [1, -1, 1]
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self.infer_flags = [1, 1, 1]
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class TestStrideSliceOp5(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(3, 3, 3)
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self.axes = [0, 1, 2]
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self.starts = [1, 0, 0]
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self.ends = [2, 1, 3]
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self.strides = [1, 1, 1]
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self.infer_flags = [1, 1, 1]
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class TestStrideSliceOp6(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(3, 3, 3)
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self.axes = [0, 1, 2]
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self.starts = [1, -1, 0]
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self.ends = [2, -3, 3]
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self.strides = [1, -1, 1]
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self.infer_flags = [1, 1, 1]
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class TestStrideSliceOp7(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(3, 3, 3)
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self.axes = [0, 1, 2]
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self.starts = [1, 0, 0]
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self.ends = [2, 2, 3]
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self.strides = [1, 1, 1]
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self.infer_flags = [1, 1, 1]
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class TestStrideSliceOp8(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(1, 3, 1)
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self.axes = [1]
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self.starts = [1]
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self.ends = [2]
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self.strides = [1]
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self.infer_flags = [1]
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class TestStrideSliceOp9(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(1, 3, 1)
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self.axes = [1]
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self.starts = [-1]
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self.ends = [-2]
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self.strides = [-1]
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self.infer_flags = [1]
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class TestStrideSliceOp10(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(3, 3)
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self.axes = [0, 1]
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self.starts = [1, 0]
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self.ends = [2, 2]
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self.strides = [1, 1]
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self.infer_flags = [1, 1]
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class TestStrideSliceOp11(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(3, 3, 3, 4)
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self.axes = [0, 1, 2, 3]
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self.starts = [1, 0, 0, 0]
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self.ends = [2, 2, 3, 4]
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self.strides = [1, 1, 1, 2]
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self.infer_flags = [1, 1, 1, 1]
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class TestStrideSliceOp12(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(3, 3, 3, 4, 5)
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self.axes = [0, 1, 2, 3, 4]
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self.starts = [1, 0, 0, 0, 0]
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self.ends = [2, 2, 3, 4, 4]
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self.strides = [1, 1, 1, 1, 1]
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self.infer_flags = [1, 1, 1, 1]
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class TestStrideSliceOp13(TestStrideSliceOp):
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def initTestCase(self):
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self.input = np.random.rand(3, 3, 3, 6, 7, 8)
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self.axes = [0, 1, 2, 3, 4, 5]
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self.starts = [1, 0, 0, 0, 1, 2]
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self.ends = [2, 2, 3, 1, 2, 8]
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self.strides = [1, 1, 1, 1, 1, 2]
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self.infer_flags = [1, 1, 1, 1, 1]
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class TestStridedSliceOp_starts_ListTensor(OpTest):
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def setUp(self):
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self.op_type = "strided_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.output}
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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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'strides': self.strides,
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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.strides = [1, 1, 1]
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self.infer_flags = [1, -1, 1]
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self.output = strided_slice_native_forward(
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self.input, self.axes, self.starts, self.ends, self.strides)
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self.starts_infer = [1, 10, 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 TestStridedSliceOp_ends_ListTensor(OpTest):
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def setUp(self):
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self.op_type = "strided_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(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs = {'Input': self.input, 'EndsTensorList': ends_tensor}
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self.outputs = {'Out': self.output}
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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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'strides': self.strides,
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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, 0]
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self.ends = [3, 3, 4]
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self.axes = [0, 1, 2]
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self.strides = [1, 1, 2]
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self.infer_flags = [1, -1, 1]
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self.output = strided_slice_native_forward(
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self.input, self.axes, self.starts, self.ends, self.strides)
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self.ends_infer = [3, 1, 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 TestStridedSliceOp_starts_Tensor(OpTest):
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def setUp(self):
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self.op_type = "strided_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.output}
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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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'strides': self.strides,
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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 = [2, 3, 4]
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self.axes = [0, 1, 2]
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self.strides = [1, 1, 1]
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self.infer_flags = [-1, -1, -1]
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self.output = strided_slice_native_forward(
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self.input, self.axes, self.starts, self.ends, self.strides)
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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 TestStridedSliceOp_ends_Tensor(OpTest):
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def setUp(self):
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self.op_type = "strided_slice"
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self.config()
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self.inputs = {
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'Input': self.input,
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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.output}
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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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'strides': self.strides,
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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 = [2, 3, 4]
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self.axes = [0, 1, 2]
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self.strides = [1, 1, 1]
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self.infer_flags = [-1, -1, -1]
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self.output = strided_slice_native_forward(
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self.input, self.axes, self.starts, self.ends, self.strides)
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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 TestStridedSliceOp_listTensor_Tensor(OpTest):
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def setUp(self):
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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(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.op_type = "strided_slice"
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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.output}
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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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'strides': self.strides,
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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 = [2, 3, 4]
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self.axes = [0, 1, 2]
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self.strides = [1, 1, 1]
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self.infer_flags = [-1, -1, -1]
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self.output = strided_slice_native_forward(
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self.input, self.axes, self.starts, self.ends, self.strides)
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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 TestStridedSliceOp_strides_Tensor(OpTest):
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def setUp(self):
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self.op_type = "strided_slice"
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self.config()
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self.inputs = {
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'Input': self.input,
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"StridesTensor": np.array(
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self.strides, dtype="int32")
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}
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self.outputs = {'Out': self.output}
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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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#'strides': self.strides,
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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, -1, 2]
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self.ends = [2, 0, 4]
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self.axes = [0, 1, 2]
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self.strides = [1, -1, 1]
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self.infer_flags = [-1, -1, -1]
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self.output = strided_slice_native_forward(
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self.input, self.axes, self.starts, self.ends, self.strides)
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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 python API
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class TestStridedSliceAPI(OpTest):
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def test_1(self):
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input = np.random.random([3, 4, 5, 6]).astype("float32")
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minus_1 = fluid.layers.fill_constant([1], "int32", -1)
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minus_3 = fluid.layers.fill_constant([1], "int32", -3)
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starts = fluid.layers.data(
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name='starts', shape=[3], append_batch_size=False)
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ends = fluid.layers.data(
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name='ends', shape=[3], append_batch_size=False)
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strides = fluid.layers.data(
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name='strides', shape=[3], append_batch_size=False)
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x = fluid.layers.data(
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name="x",
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shape=[3, 4, 5, 6],
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append_batch_size=False,
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dtype="float32")
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out_1 = fluid.layers.strided_slice(
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x,
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axes=[0, 1, 2],
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starts=[-3, 0, 2],
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ends=[3, 100, -1],
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strides=[1, 1, 1])
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out_2 = fluid.layers.strided_slice(
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x,
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axes=[0, 1, 3],
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starts=[minus_3, 0, 2],
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ends=[3, 100, -1],
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strides=[1, 1, 1])
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out_3 = fluid.layers.strided_slice(
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x,
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axes=[0, 1, 3],
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starts=[minus_3, 0, 2],
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ends=[3, 100, minus_1],
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strides=[1, 1, 1])
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out_4 = fluid.layers.strided_slice(
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x, axes=[0, 1, 2], starts=starts, ends=ends, strides=strides)
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out_5 = x[-3:3, 0:100:2, -1:2:-1]
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out_6 = x[minus_3:3:1, 0:100:2, :, minus_1:2:minus_1]
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out_7 = x[minus_1, 0:100:2, :, -1:2:-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"),
|
|
'strides': np.array([1, 1, 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, -1:2:-1, :])
|
|
assert np.array_equal(res_6, input[-3:3, 0:100:2, :, -1:2:-1])
|
|
assert np.array_equal(res_7, input[-1, 0:100:2, :, -1:2:-1])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|