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254 lines
9.2 KiB
254 lines
9.2 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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from op_test import OpTest
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paddle.enable_static()
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# Correct: General.
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class TestUnsqueezeOp(OpTest):
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def setUp(self):
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self.init_test_case()
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self.op_type = "unsqueeze"
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self.inputs = {"X": np.random.random(self.ori_shape).astype("float64")}
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self.init_attrs()
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self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
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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(["X"], "Out")
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def init_test_case(self):
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self.ori_shape = (3, 40)
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self.axes = (1, 2)
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self.new_shape = (3, 1, 1, 40)
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def init_attrs(self):
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self.attrs = {"axes": self.axes}
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# Correct: Single input index.
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class TestUnsqueezeOp1(TestUnsqueezeOp):
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (-1, )
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self.new_shape = (20, 5, 1)
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# Correct: Mixed input axis.
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class TestUnsqueezeOp2(TestUnsqueezeOp):
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def init_test_case(self):
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self.ori_shape = (20, 5)
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self.axes = (0, -1)
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self.new_shape = (1, 20, 5, 1)
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# Correct: There is duplicated axis.
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class TestUnsqueezeOp3(TestUnsqueezeOp):
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def init_test_case(self):
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self.ori_shape = (10, 2, 5)
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self.axes = (0, 3, 3)
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self.new_shape = (1, 10, 2, 1, 1, 5)
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# Correct: Reversed axes.
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class TestUnsqueezeOp4(TestUnsqueezeOp):
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def init_test_case(self):
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self.ori_shape = (10, 2, 5)
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self.axes = (3, 1, 1)
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self.new_shape = (10, 1, 1, 2, 5, 1)
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class API_TestUnsqueeze(unittest.TestCase):
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def test_out(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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data1 = paddle.static.data('data1', shape=[-1, 10], dtype='float64')
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result_squeeze = paddle.unsqueeze(data1, axis=[1])
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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input1 = np.random.random([5, 1, 10]).astype('float64')
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input = np.squeeze(input1, axis=1)
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result, = exe.run(feed={"data1": input},
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fetch_list=[result_squeeze])
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self.assertTrue(np.allclose(input1, result))
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class TestUnsqueezeOpError(unittest.TestCase):
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def test_errors(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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# The type of axis in split_op should be int or Variable.
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def test_axes_type():
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x6 = paddle.static.data(
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shape=[-1, 10], dtype='float16', name='x3')
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paddle.unsqueeze(x6, axis=3.2)
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self.assertRaises(TypeError, test_axes_type)
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class API_TestUnsqueeze2(unittest.TestCase):
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def test_out(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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data1 = paddle.static.data('data1', shape=[-1, 10], dtype='float64')
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data2 = paddle.static.data('data2', shape=[1], dtype='int32')
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result_squeeze = paddle.unsqueeze(data1, axis=data2)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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input1 = np.random.random([5, 1, 10]).astype('float64')
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input2 = np.array([1]).astype('int32')
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input = np.squeeze(input1, axis=1)
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result1, = exe.run(feed={"data1": input,
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"data2": input2},
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fetch_list=[result_squeeze])
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self.assertTrue(np.allclose(input1, result1))
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class API_TestUnsqueeze3(unittest.TestCase):
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def test_out(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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data1 = paddle.static.data('data1', shape=[-1, 10], dtype='float64')
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data2 = paddle.static.data('data2', shape=[1], dtype='int32')
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result_squeeze = paddle.unsqueeze(data1, axis=[data2, 3])
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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input1 = np.random.random([5, 1, 10, 1]).astype('float64')
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input2 = np.array([1]).astype('int32')
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input = np.squeeze(input1)
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result1, = exe.run(feed={"data1": input,
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"data2": input2},
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fetch_list=[result_squeeze])
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self.assertTrue(np.array_equal(input1, result1))
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self.assertEqual(input1.shape, result1.shape)
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class API_TestDyUnsqueeze(unittest.TestCase):
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def test_out(self):
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paddle.disable_static()
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input_1 = np.random.random([5, 1, 10]).astype("int32")
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input1 = np.expand_dims(input_1, axis=1)
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input = paddle.to_tensor(input_1)
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output = paddle.unsqueeze(input, axis=[1])
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out_np = output.numpy()
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self.assertTrue(np.array_equal(input1, out_np))
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self.assertEqual(input1.shape, out_np.shape)
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class API_TestDyUnsqueeze2(unittest.TestCase):
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def test_out(self):
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paddle.disable_static()
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input1 = np.random.random([5, 10]).astype("int32")
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out1 = np.expand_dims(input1, axis=1)
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input = paddle.to_tensor(input1)
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output = paddle.unsqueeze(input, axis=1)
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out_np = output.numpy()
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self.assertTrue(np.array_equal(out1, out_np))
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self.assertEqual(out1.shape, out_np.shape)
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class API_TestDyUnsqueezeAxisTensor(unittest.TestCase):
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def test_out(self):
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paddle.disable_static()
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input1 = np.random.random([5, 10]).astype("int32")
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out1 = np.expand_dims(input1, axis=1)
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out1 = np.expand_dims(out1, axis=2)
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input = paddle.to_tensor(input1)
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output = paddle.unsqueeze(input, axis=paddle.to_tensor([1, 2]))
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out_np = output.numpy()
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self.assertTrue(np.array_equal(out1, out_np))
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self.assertEqual(out1.shape, out_np.shape)
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class API_TestDyUnsqueezeAxisTensorList(unittest.TestCase):
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def test_out(self):
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paddle.disable_static()
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input1 = np.random.random([5, 10]).astype("int32")
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# Actually, expand_dims supports tuple since version 1.18.0
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out1 = np.expand_dims(input1, axis=1)
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out1 = np.expand_dims(out1, axis=2)
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input = paddle.to_tensor(input1)
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output = paddle.unsqueeze(
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paddle.to_tensor(input1),
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axis=[paddle.to_tensor([1]), paddle.to_tensor([2])])
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out_np = output.numpy()
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self.assertTrue(np.array_equal(out1, out_np))
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self.assertEqual(out1.shape, out_np.shape)
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class API_TestDygraphUnSqueeze(unittest.TestCase):
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def test_out(self):
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paddle.disable_static()
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input_1 = np.random.random([5, 1, 10]).astype("int32")
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input = paddle.to_tensor(input_1)
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output = paddle.unsqueeze(input, axis=[1])
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out_np = output.numpy()
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expected_out = np.expand_dims(input_1, axis=1)
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self.assertTrue(np.allclose(expected_out, out_np))
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def test_out_int8(self):
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paddle.disable_static()
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input_1 = np.random.random([5, 1, 10]).astype("int8")
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input = paddle.to_tensor(input_1)
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output = paddle.unsqueeze(input, axis=[1])
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out_np = output.numpy()
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expected_out = np.expand_dims(input_1, axis=1)
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self.assertTrue(np.allclose(expected_out, out_np))
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def test_out_uint8(self):
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paddle.disable_static()
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input_1 = np.random.random([5, 1, 10]).astype("uint8")
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input = paddle.to_tensor(input_1)
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output = paddle.unsqueeze(input, axis=1)
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out_np = output.numpy()
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expected_out = np.expand_dims(input_1, axis=1)
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self.assertTrue(np.allclose(expected_out, out_np))
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def test_axis_not_list(self):
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paddle.disable_static()
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input_1 = np.random.random([5, 1, 10]).astype("int32")
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input = paddle.to_tensor(input_1)
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output = paddle.unsqueeze(input, axis=1)
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out_np = output.numpy()
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expected_out = np.expand_dims(input_1, axis=1)
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self.assertTrue(np.allclose(expected_out, out_np))
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def test_dimension_not_1(self):
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paddle.disable_static()
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input_1 = np.random.random([5, 1, 10]).astype("int32")
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input = paddle.to_tensor(input_1)
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output = paddle.unsqueeze(input, axis=(1, 2))
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out_np = output.numpy()
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expected_out = np.expand_dims(input_1, axis=1)
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self.assertTrue(np.allclose(expected_out, out_np))
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if __name__ == "__main__":
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unittest.main()
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