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