add crop_tensor_op, test=develop, test=document_preview (#19314)
add crop_tensor op. The main difference with crop is : 1. If the argument shape is a list, each element is an integer or a tensor variable with shape: [1]. This way is suitable for the case that the shape may be changed each iteration. 2. If the argument shape is a variable. Its rank must be 1. In crop op, the rank of shape must be the same as x offsets can be a list, in which each element is an integer or a tensor variavle with shape: [1].expand_as_op_1
Zhang Ting
6 years ago
committed by
Aurelius84
parent
bf8367367e
commit
b38889413d
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/operators/crop_tensor_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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crop_tensor,
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ops::CropTensorKernel<paddle::platform::CUDADeviceContext, float>,
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ops::CropTensorKernel<paddle::platform::CUDADeviceContext, double>);
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REGISTER_OP_CUDA_KERNEL(
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crop_tensor_grad,
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ops::CropTensorGradKernel<paddle::platform::CUDADeviceContext, float>,
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ops::CropTensorGradKernel<paddle::platform::CUDADeviceContext, double>);
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# 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 __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid as fluid
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def crop(data, offsets, crop_shape):
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def indexOf(shape, index):
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result = []
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for dim in reversed(shape):
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result.append(index % dim)
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index = index / dim
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return result[::-1]
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result = []
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for i, value in enumerate(data.flatten()):
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index = indexOf(data.shape, i)
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selected = True
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if len(index) == len(offsets):
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for j, offset in enumerate(offsets):
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selected = selected and index[j] >= offset and index[
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j] < crop_shape[j] + offset
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if selected:
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result.append(value)
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return np.array(result).reshape(crop_shape)
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class TestCropTensorOp(OpTest):
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def setUp(self):
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self.op_type = "crop_tensor"
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self.crop_by_1D_shape = False
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self.offset_by_input = False
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self.unk_dim_idx = -1
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self.attrs = {}
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self.initTestCase()
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if self.crop_by_1D_shape:
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self.inputs = {
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'X': np.random.random(self.x_shape).astype("float32"),
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'Shape': np.array(self.crop_shape).astype("int32")
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}
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else:
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self.attrs['shape'] = self.crop_shape
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self.inputs = {
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'X': np.random.random(self.x_shape).astype("float32"),
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}
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if self.offset_by_input:
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self.inputs['Offsets'] = np.array(self.offsets).astype('int32')
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else:
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self.attrs['offsets'] = self.offsets
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if self.unk_dim_idx != -1:
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self.crop_shape[self.unk_dim_idx] = self.x_shape[self.unk_dim_idx]
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self.outputs = {
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'Out': crop(self.inputs['X'], self.offsets, self.crop_shape)
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}
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def initTestCase(self):
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self.x_shape = (8, 8)
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self.crop_shape = [2, 2]
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self.offsets = [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(['X'], 'Out', max_relative_error=0.006)
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class TestCase1(TestCropTensorOp):
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def initTestCase(self):
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self.x_shape = (100)
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self.crop_shape = [64]
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self.offsets = [13]
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class TestCase2(TestCropTensorOp):
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def initTestCase(self):
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self.x_shape = (12, 24)
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self.crop_shape = [-1, 8] #only the first dimension (batch) can be -1
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self.offsets = [0, 0]
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self.unk_dim_idx = 0
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class TestCase3(TestCropTensorOp):
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def initTestCase(self):
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self.x_shape = (4, 8, 16)
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self.crop_shape = [2, 2, 3]
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self.offsets = [1, 5, 3]
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self.crop_by_1D_shape = True
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class TestCase4(TestCropTensorOp):
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def initTestCase(self):
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self.x_shape = (8, 3, 6, 6)
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self.crop_shape = [-1, 3, 4, 4]
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self.offsets = [0, 0, 0, 0]
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self.crop_by_1D_shape = True
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self.unk_dim_idx = 0
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class TestCase5(TestCropTensorOp):
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def initTestCase(self):
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self.x_shape = (2, 4, 5, 8, 8)
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self.crop_shape = [1, 1, 2, 4, 4]
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self.offsets = [1, 0, 0, 2, 2]
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self.offset_by_input = True
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class TestCase6(TestCropTensorOp):
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def initTestCase(self):
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self.x_shape = (2, 2, 4, 4, 4, 2)
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self.crop_shape = [1, 1, 4, 2, 2, 2]
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self.offsets = [0, 0, 0, 0, 0, 0]
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self.crop_by_1D_shape = True
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self.offset_by_input = True
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class TestCropTensorOp_attr_tensor(OpTest):
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def setUp(self):
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self.op_type = "crop_tensor"
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self.mixed_type = False
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self.OffsetsTensor = False
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self.ShapeTensor = True
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self.attrs = {}
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self.initTestCase()
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if self.ShapeTensor:
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shape_tensor = []
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for index, ele in enumerate(self.crop_shape):
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shape_tensor.append(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs = {
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'X': np.random.random(self.x_shape).astype("float32"),
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'ShapeTensor': shape_tensor
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}
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if self.mixed_type:
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self.attrs['shape'] = self.shape_attr
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if self.OffsetsTensor:
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offsets_tensor = []
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for index, ele in enumerate(self.offsets):
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offsets_tensor.append(("x" + str(index), np.ones(
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(1)).astype('int32') * ele))
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self.inputs = {
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'X': np.random.random(self.x_shape).astype("float32"),
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'OffsetsTensor': offsets_tensor
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}
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else:
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self.attrs['offsets'] = self.offsets
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self.outputs = {
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'Out': crop(self.inputs['X'], self.offsets, self.crop_shape)
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}
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def initTestCase(self):
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self.x_shape = (8, 8)
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self.crop_shape = (2, 2)
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self.offsets = [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(["X"], "Out", max_relative_error=0.006)
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class TestCropTensorOp_attr_tensor_case1(TestCropTensorOp_attr_tensor):
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def init_data(self):
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self.x_shape = (16, 8, 32)
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self.crop_shape = [2, 2, 3]
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self.offsets = [1, 5, 3]
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class TestCropTensorOp_attr_tensor_case2(TestCropTensorOp_attr_tensor):
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def init_data(self):
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self.x_shape = (4, 8, 16, 8)
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self.crop_shape = [2, 2, 3, 4]
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self.offsets = [1, 5, 3, 0]
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self.shape_attr = [-1, -1, 3, 4]
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self.mixed_type = True
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class TestCropTensorOp_attr_tensor_case3(TestCropTensorOp_attr_tensor):
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def init_data(self):
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self.x_shape = (16, 8, 32)
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self.crop_shape = [2, 2, 3]
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self.offsets = [1, 5, 3]
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self.ShapeTensor = False
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self.OffsetsTensor = True
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class TestCropTensorOp_attr_tensor_case4(TestCropTensorOp_attr_tensor):
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def init_data(self):
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self.x_shape = (16, 8, 32)
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self.crop_shape = [2, 2, 3]
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self.offsets = [1, 5, 3]
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self.OffsetsTensor = True
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if __name__ == '__main__':
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unittest.main()
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