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341 lines
11 KiB
341 lines
11 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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from op_test import OpTest
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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# situation 1: have shape( list, no tensor), no actual shape(Tensor)
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class TestReshapeOp(OpTest):
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def setUp(self):
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self.init_data()
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self.op_type = "reshape2"
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self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
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self.attrs = {"shape": self.new_shape}
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.infered_shape),
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'XShape': np.random.random(self.ori_shape).astype("float32")
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}
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def init_data(self):
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self.ori_shape = (2, 60)
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self.new_shape = (12, 10)
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self.infered_shape = (12, 10)
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'])
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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class TestReshapeOpDimInfer1(TestReshapeOp):
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def init_data(self):
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self.ori_shape = (5, 25)
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self.new_shape = (5, -1, 5)
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self.infered_shape = (5, -1, 5)
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class TestReshapeOpDimInfer2(TestReshapeOp):
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def init_data(self):
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self.ori_shape = (2, 2, 6)
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self.new_shape = (2, 0, 3, -1)
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self.infered_shape = (2, 2, 3, -1)
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# situation 2: have shape(list, no tensor), have actual shape(Tensor)
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class TestReshapeOpWithInputShape(OpTest):
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def setUp(self):
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self.init_data()
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self.op_type = "reshape2"
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self.inputs = {
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"X": np.random.random(self.ori_shape).astype("float32"),
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"Shape": np.array(
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self.actual_shape, dtype="int32")
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}
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self.attrs = {"shape": self.new_shape}
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.actual_shape),
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'XShape': np.random.random(self.ori_shape).astype("float32")
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}
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def init_data(self):
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self.ori_shape = (6, 5)
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self.new_shape = (0, -1, 5)
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self.actual_shape = (2, 3, 5)
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'])
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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# Situation 3: have shape(list, have tensor), no actual shape(Tensor)
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class TestReshapeOp_attr_ShapeTensor(OpTest):
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def setUp(self):
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self.init_data()
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self.op_type = "reshape2"
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shape_tensor = []
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for index, ele in enumerate(self.new_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.ori_shape).astype("float32"),
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'ShapeTensor': shape_tensor
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}
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self.attrs = {'shape': self.shape}
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.infered_shape),
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'XShape': np.random.random(self.ori_shape).astype("float32")
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}
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def init_data(self):
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self.ori_shape = (2, 25)
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self.new_shape = (5, 10)
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self.infered_shape = (5, 10)
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self.shape = (-1, -1)
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'])
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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class TestReshapeOpDimInfer1_attr_ShapeTensor(TestReshapeOp_attr_ShapeTensor):
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def init_data(self):
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self.ori_shape = (5, 10)
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self.new_shape = (5, -1, 5)
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self.infered_shape = (5, -1, 5)
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self.shape = (5, -1, -1)
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class TestReshapeOpDimInfer2_attr_ShapeTensor(TestReshapeOp_attr_ShapeTensor):
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def init_data(self):
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self.ori_shape = (2, 2, 6)
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self.new_shape = (2, 0, 3, -1)
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self.infered_shape = (2, 2, 3, -1)
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self.shape = (2, 0, 3, -1)
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# Situation 4: have shape(Tensor), no actual shape(Tensor)
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class TestReshapeOp_attr_OnlyShape(OpTest):
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def setUp(self):
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self.init_data()
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self.op_type = "reshape2"
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self.inputs = {
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"X": np.random.random(self.ori_shape).astype("float32"),
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"Shape": np.array(
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self.new_shape, dtype="int32")
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}
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self.attrs = {}
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.infered_shape),
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'XShape': np.random.random(self.ori_shape).astype("float32")
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}
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def init_data(self):
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self.ori_shape = (2, 25)
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self.new_shape = (5, 10)
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self.infered_shape = (5, 10)
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def test_check_output(self):
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self.check_output(no_check_set=['XShape'])
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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class TestReshapeOpDimInfer1_attr_OnlyShape(TestReshapeOp_attr_OnlyShape):
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def init_data(self):
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self.ori_shape = (5, 10)
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self.new_shape = (5, -1, 5)
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self.infered_shape = (5, -1, 5)
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self.shape = (5, -1, -1)
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class TestReshapeOpDimInfer2_attr_OnlyShape(TestReshapeOp_attr_OnlyShape):
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def init_data(self):
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self.ori_shape = (2, 2, 6)
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self.new_shape = (2, 0, 3, -1)
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self.infered_shape = (2, 2, 3, -1)
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self.shape = (2, 0, 3, -1)
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# test int8 data type on CPU
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class TestReshapeInt8Op(OpTest):
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def setUp(self):
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self.init_dtype()
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self.init_data()
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self.use_mkldnn = True
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self._cpu_only = True
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self.op_type = "reshape2"
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input = np.random.randint(0, 127, self.ori_shape).astype(self.dtype)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
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self.attrs = {
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'shape': self.new_shape,
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'use_mkldnn': self.use_mkldnn,
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}
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self.outputs = {
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"Out": self.inputs["X"].reshape(self.infered_shape),
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'XShape': np.random.random(self.ori_shape).astype(np.float32)
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}
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def init_dtype(self):
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self.dtype = np.int8
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def init_data(self):
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self.ori_shape = (2, 2, 6)
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self.new_shape = (2, 0, 3, -1)
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self.infered_shape = (2, 2, 3, -1)
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def test_check_output(self):
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self.check_output_with_place(
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fluid.core.CPUPlace(), atol=1e-5, no_check_set=['XShape'])
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def test_check_grad(self):
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pass
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# test unt8 data type on CPU
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class TestReshapeUint8Op(TestReshapeInt8Op):
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def init_dtype(self):
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self.dtype = np.uint8
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# Test python API
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class TestReshapeAPI(unittest.TestCase):
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# situation 1: have shape( list, no tensor), no actual shape(Tensor)
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def test_1(self):
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input = np.random.random([2, 25]).astype("float32")
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shape = [2, 5, 5]
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positive_five = fluid.layers.fill_constant([1], "int32", 5)
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x = fluid.layers.data(
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name="x", shape=[2, 25], append_batch_size=False, dtype="float32")
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actual_shape = fluid.layers.data(
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name="shape",
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shape=[1, 3],
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append_batch_size=False,
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dtype="float32")
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# situation 1: have shape( list, no tensor), no actual shape(Tensor)
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out_1 = fluid.layers.reshape(x, shape)
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# situation 2: have shape(list, no tensor), have actual shape(Tensor)
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out_2 = fluid.layers.reshape(x, shape=shape, actual_shape=actual_shape)
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# Situation 3: have shape(list, have tensor), no actual shape(Tensor)
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out_3 = fluid.layers.reshape(x, shape=[positive_five, 10])
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# Situation 4: have shape(Tensor), no actual shape(Tensor)
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out_4 = fluid.layers.reshape(x, shape=actual_shape)
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exe = fluid.Executor(place=fluid.CPUPlace())
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res_1, res_2, res_3, res_4 = exe.run(
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fluid.default_main_program(),
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feed={"x": input,
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"shape": np.array([2, 5, 5]).astype("int32")},
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fetch_list=[out_1, out_2, out_3, out_4])
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assert np.array_equal(res_1, input.reshape(shape))
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assert np.array_equal(res_2, input.reshape(shape))
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assert np.array_equal(res_3, input.reshape([5, 10]))
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assert np.array_equal(res_4, input.reshape(shape))
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# Test Input Error
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class TestReshapeOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The x type of reshape_op must be Variable.
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def test_x_type():
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x1 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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fluid.layers.reshape(x1, shape=[1])
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self.assertRaises(TypeError, test_x_type)
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# The x dtype of reshape_op must be float16, float32, float64, int32 or int64.
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def test_x_dtype():
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x2 = fluid.layers.data(
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name="x2",
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shape=[2, 25],
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append_batch_size=False,
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dtype="bool")
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fluid.layers.reshape(x2, shape=[2, 5, 5])
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self.assertRaises(TypeError, test_x_dtype)
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def test_x_dtype_float16():
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x_float16 = fluid.layers.data(
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name="x_float16",
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shape=[2, 25],
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append_batch_size=False,
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dtype="float16")
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fluid.layers.reshape(x_float16, shape=[2, 5, 5])
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test_x_dtype_float16()
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x3 = fluid.layers.data(
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name="x3",
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shape=[2, 25],
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append_batch_size=False,
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dtype="float32")
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# The argument shape's type of reshape_op must be list, tuple or Variable.
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def test_shape_type():
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fluid.layers.reshape(x3, shape=1)
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self.assertRaises(TypeError, test_shape_type)
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# The argument actual_shape's type of reshape_op must be Variable or None.
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def test_actual_shape_type():
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fluid.layers.reshape(x3, shape=[25, 2], actual_shape=1)
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self.assertRaises(TypeError, test_actual_shape_type)
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# The argument shape have more than one -1.
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def test_shape_1():
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fluid.layers.reshape(x3, shape=[-1, -1, 5])
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self.assertRaises(AssertionError, test_shape_1)
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# The argument shape have element 0 whose index exceed the input dimension.
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def test_shape_2():
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fluid.layers.reshape(x3, [2, 5, 5, 0])
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self.assertRaises(AssertionError, test_shape_2)
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# The argument shape have more than one negtive value.
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def test_shape_3():
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fluid.layers.reshape(x3, [-1, -2, 5])
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self.assertRaises(AssertionError, test_shape_3)
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if __name__ == "__main__":
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unittest.main()
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