# Copyright (c) 2019 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 paddle.fluid import compiler, Program, program_guard from op_test import OpTest, convert_float_to_uint16 import paddle.fluid.core as core paddle.enable_static() # Correct: General. class TestSqueezeOp(OpTest): def setUp(self): self.op_type = "squeeze" self.init_test_case() 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 = (1, 3, 1, 40) self.axes = (0, 2) self.new_shape = (3, 40) def init_attrs(self): self.attrs = {"axes": self.axes} class TestSqueezeBF16Op(OpTest): def setUp(self): self.op_type = "squeeze" self.dtype = np.uint16 self.init_test_case() x = np.random.random(self.ori_shape).astype("float32") out = x.reshape(self.new_shape) self.inputs = {"X": convert_float_to_uint16(x)} self.init_attrs() self.outputs = {"Out": convert_float_to_uint16(out)} 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 = (1, 3, 1, 40) self.axes = (0, 2) self.new_shape = (3, 40) def init_attrs(self): self.attrs = {"axes": self.axes} # Correct: There is mins axis. class TestSqueezeOp1(TestSqueezeOp): def init_test_case(self): self.ori_shape = (1, 3, 1, 40) self.axes = (0, -2) self.new_shape = (3, 40) # Correct: No axes input. class TestSqueezeOp2(TestSqueezeOp): def init_test_case(self): self.ori_shape = (1, 20, 1, 5) self.axes = () self.new_shape = (20, 5) # Correct: Just part of axes be squeezed. class TestSqueezeOp3(TestSqueezeOp): def init_test_case(self): self.ori_shape = (6, 1, 5, 1, 4, 1) self.axes = (1, -1) self.new_shape = (6, 5, 1, 4) # Correct: The demension of axis is not of size 1 remains unchanged. class TestSqueezeOp4(TestSqueezeOp): def init_test_case(self): self.ori_shape = (6, 1, 5, 1, 4, 1) self.axes = (1, 2) self.new_shape = (6, 5, 1, 4, 1) class TestSqueezeOpError(unittest.TestCase): def test_errors(self): paddle.enable_static() with program_guard(Program(), Program()): # The input type of softmax_op must be Variable. x1 = fluid.create_lod_tensor(np.array([[-1]]), [[1]], paddle.CPUPlace()) self.assertRaises(TypeError, paddle.squeeze, x1) # The input axes of squeeze must be list. x2 = paddle.static.data(name='x2', shape=[4], dtype="int32") self.assertRaises(TypeError, paddle.squeeze, x2, axes=0) # The input dtype of squeeze not support float16. x3 = paddle.static.data(name='x3', shape=[4], dtype="float16") self.assertRaises(TypeError, paddle.squeeze, x3, axes=0) class API_TestSqueeze(unittest.TestCase): def setUp(self): self.executed_api() def executed_api(self): self.squeeze = paddle.squeeze def test_out(self): paddle.enable_static() with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()): data1 = paddle.static.data('data1', shape=[-1, 1, 10], dtype='float64') result_squeeze = self.squeeze(data1, axis=[1]) place = paddle.CPUPlace() exe = paddle.static.Executor(place) input1 = np.random.random([5, 1, 10]).astype('float64') result, = exe.run(feed={"data1": input1}, fetch_list=[result_squeeze]) expected_result = np.squeeze(input1, axis=1) np.testing.assert_allclose(expected_result, result, rtol=1e-05) class API_TestStaticSqueeze_(API_TestSqueeze): def executed_api(self): self.squeeze = paddle.squeeze_ class API_TestDygraphSqueeze(unittest.TestCase): def setUp(self): self.executed_api() def executed_api(self): self.squeeze = paddle.squeeze def test_out(self): paddle.disable_static() input_1 = np.random.random([5, 1, 10]).astype("int32") input = paddle.to_tensor(input_1) output = self.squeeze(input, axis=[1]) out_np = output.numpy() expected_out = np.squeeze(input_1, axis=1) np.testing.assert_allclose(expected_out, out_np, rtol=1e-05) def test_out_int8(self): paddle.disable_static() input_1 = np.random.random([5, 1, 10]).astype("int8") input = paddle.to_tensor(input_1) output = self.squeeze(input, axis=[1]) out_np = output.numpy() expected_out = np.squeeze(input_1, axis=1) np.testing.assert_allclose(expected_out, out_np, rtol=1e-05) def test_out_uint8(self): paddle.disable_static() input_1 = np.random.random([5, 1, 10]).astype("uint8") input = paddle.to_tensor(input_1) output = self.squeeze(input, axis=[1]) out_np = output.numpy() expected_out = np.squeeze(input_1, axis=1) np.testing.assert_allclose(expected_out, out_np, rtol=1e-05) def test_axis_not_list(self): paddle.disable_static() input_1 = np.random.random([5, 1, 10]).astype("int32") input = paddle.to_tensor(input_1) output = self.squeeze(input, axis=1) out_np = output.numpy() expected_out = np.squeeze(input_1, axis=1) np.testing.assert_allclose(expected_out, out_np, rtol=1e-05) def test_dimension_not_1(self): paddle.disable_static() input_1 = np.random.random([5, 1, 10]).astype("int32") input = paddle.to_tensor(input_1) output = self.squeeze(input, axis=(1, 0)) out_np = output.numpy() expected_out = np.squeeze(input_1, axis=1) np.testing.assert_allclose(expected_out, out_np, rtol=1e-05) class API_TestDygraphSqueezeInplace(API_TestDygraphSqueeze): def executed_api(self): self.squeeze = paddle.squeeze_ if __name__ == "__main__": unittest.main()