wehub-resource-sync
340 lines
11 KiB
Python
340 lines
11 KiB
Python
# 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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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
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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 = (
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selected
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and index[j] >= offset
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and index[j] < crop_shape[j] + offset
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)
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if selected:
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result.append(value)
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# data 0-size
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if 0 in data.shape:
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for i, value in enumerate(data.shape):
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if value == 0:
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crop_shape[i] = 0
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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.shape_by_input = 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.python_api = paddle.crop
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self.dtype = "float64"
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self.initTestCase()
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if self.shape_by_input:
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self.inputs = {
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'X': np.random.random(self.x_shape).astype(self.dtype),
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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(self.dtype),
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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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crop_shape = list(self.crop_shape)
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for i in range(len(self.crop_shape)):
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if self.crop_shape[i] == -1:
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crop_shape[i] = self.x_shape[i] - self.offsets[i]
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self.outputs = {'Out': crop(self.inputs['X'], self.offsets, crop_shape)}
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def initTestCase(self):
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self.x_shape = (10, 10)
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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(check_pir=True)
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def test_check_grad_normal(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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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]
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self.offsets = [0, 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.shape_by_input = True
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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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, -1, 4]
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self.offsets = [0, 0, 1, 0]
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self.shape_by_input = True
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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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.shape_by_input = True
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self.offset_by_input = True
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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class TestCase_ZeroSize(TestCropTensorOp):
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def initTestCase(self):
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self.__class__.exist_fp64_check_grad = True
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self.x_shape = (0, 0, 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 TestCase_ZeroSize2(TestCropTensorOp):
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def initTestCase(self):
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paddle.disable_static()
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self.__class__.exist_fp64_check_grad = True
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# x_grad return NAN
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self.x_shape = (2, 4, 5, 8, 8)
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self.crop_shape = [0, 0, 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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self.dtype = "float32"
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def test_check_grad_normal(self):
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grad = paddle.zeros(self.x_shape).numpy()
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self.check_grad(['X'], 'Out', user_defined_grads=[grad], check_pir=True)
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class TestCropTensorOpTensorAttr(OpTest):
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def setUp(self):
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self.op_type = "crop_tensor"
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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.python_api = paddle.crop
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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(
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("x" + str(index), np.ones(1).astype('int32') * ele)
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)
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self.inputs = {
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'X': np.random.random(self.x_shape).astype("float64"),
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'ShapeTensor': shape_tensor,
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}
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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(
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("x" + str(index), np.ones(1).astype('int32') * ele)
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)
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self.inputs = {
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'X': np.random.random(self.x_shape).astype("float64"),
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'OffsetsTensor': offsets_tensor,
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}
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self.attrs['offsets'] = self.offsets_attr
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self.attrs['shape'] = self.crop_shape
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self.attrs['offsets'] = self.offsets
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crop_shape = list(self.crop_shape)
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for i in range(len(self.crop_shape)):
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if self.crop_shape[i] == -1:
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crop_shape[i] = self.x_shape[i] - self.offsets[i]
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self.outputs = {'Out': crop(self.inputs['X'], self.offsets, crop_shape)}
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def initTestCase(self):
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self.x_shape = (10, 10)
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self.crop_shape = (2, 2)
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self.offsets = [1, 2]
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self.shape_attr = [0, 0]
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad_normal(self):
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self.check_grad(["X"], "Out", check_pir=True)
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class TestCropTensorOpTensorAttrCase1(TestCropTensorOpTensorAttr):
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def initTestCase(self):
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self.x_shape = (16, 8, 32)
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self.crop_shape = [-1, -1, 3]
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self.offsets = [1, 5, 3]
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self.shape_attr = [-1, -1, 3]
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class TestCropTensorOpTensorAttrCase2(TestCropTensorOpTensorAttr):
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def initTestCase(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 = [0, 0, 3, 4]
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class TestCropTensorOpTensorAttrCase3(TestCropTensorOpTensorAttr):
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def initTestCase(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.offsets_attr = [-1, -1, 3]
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self.ShapeTensor = False
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self.OffsetsTensor = True
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=True)
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class TestCropTensorOpTensorAttrCase4(TestCropTensorOpTensorAttr):
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def initTestCase(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.shape_attr = [0, 2, 3]
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self.offsets = [1, 5, 3]
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self.offsets_attr = [-1, -1, 3]
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self.OffsetsTensor = True
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=True)
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class TestCropTensorException(unittest.TestCase):
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def test_exception(self):
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paddle.enable_static()
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input1 = paddle.static.data(
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name="input1", shape=[2, 3, 6, 6], dtype="float32"
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)
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input2 = paddle.static.data(
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name="input2", shape=[2, 3, 6, 6], dtype="float16"
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)
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dim = paddle.static.data(name='dim', shape=[1], dtype='int32')
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offset = paddle.static.data(name='offset', shape=[1], dtype='int32')
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def attr_shape_type():
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out = paddle.crop(input1, shape=3)
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def attr_shape_dtype():
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out = paddle.crop(input1, shape=[2, 2.0, 3, 3])
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def attr_shape_value1():
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out = paddle.crop(input1, shape=[2, -2, dim, 3])
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def attr_offsets_type():
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out = paddle.crop(input1, shape=[2, 2, 3, 3], offsets=0)
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def attr_offsets_dtype():
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out = paddle.crop(
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input1, shape=[2, 2, 3, 3], offsets=[0, 1.0, 0, 0]
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)
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def attr_offsets_value():
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out = paddle.crop(
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input1, shape=[2, 2, 3, 3], offsets=[0, -1, offset, 0]
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)
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def input_dtype():
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out = paddle.crop(input2, shape=[2, 2, 3, 3])
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self.assertRaises(TypeError, attr_shape_type)
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self.assertRaises(TypeError, attr_shape_dtype)
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self.assertRaises(ValueError, attr_shape_value1)
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self.assertRaises(TypeError, attr_offsets_type)
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self.assertRaises(TypeError, attr_offsets_dtype)
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self.assertRaises(ValueError, attr_offsets_value)
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self.assertRaises(TypeError, input_dtype)
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class TestCropWithUnknownShape(unittest.TestCase):
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def test_crop_with_unknown_shape(self):
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paddle.enable_static()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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x = paddle.static.data(name='x', shape=[-1, 4, 4], dtype='float32')
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shape = paddle.static.data(name='shape', shape=[3], dtype='int32')
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out = paddle.crop(x, shape=shape, offsets=[1, 1, 1])
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exe = paddle.static.Executor(paddle.CPUPlace())
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x_np = np.random.random((4, 4, 4)).astype('float32')
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shape_np = np.array([2, 2, 2]).astype('int32')
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(out_np,) = exe.run(
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feed={'x': x_np, 'shape': shape_np}, fetch_list=[out]
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)
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self.assertEqual(tuple(out.shape), (-1, -1, -1))
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self.assertEqual(out_np.shape, (2, 2, 2))
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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