From 5a5042e385b9b810c1eecebabcd5443cc4b71bf6 Mon Sep 17 00:00:00 2001 From: zhanlijun Date: Thu, 13 Jan 2022 21:41:03 +0800 Subject: [PATCH] delete testcase for ops --- tests/ut/python/ops/test_tensor_check.py | 513 ----------------------- 1 file changed, 513 deletions(-) delete mode 100644 tests/ut/python/ops/test_tensor_check.py diff --git a/tests/ut/python/ops/test_tensor_check.py b/tests/ut/python/ops/test_tensor_check.py deleted file mode 100644 index bc1f767e6cc..00000000000 --- a/tests/ut/python/ops/test_tensor_check.py +++ /dev/null @@ -1,513 +0,0 @@ -# Copyright 2020 Huawei Technologies Co., Ltd -# -# 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. -# ============================================================================ -""" test control ops """ -import os -import numpy as np -import pytest - -import mindspore as ms -from mindspore import Tensor -from mindspore import context -from mindspore import nn -from mindspore.common import dtype as mstype -from mindspore.ops import composite as C -from mindspore.ops import operations as P -from mindspore.common.parameter import Parameter - -context.set_context(mode=context.GRAPH_MODE) - -grad_by_list = C.GradOperation(get_by_list=True) -grad_all = C.GradOperation(get_all=True) -grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True) - - -def if_compile_test(x_init, y_init): - """ - Feature: if compile test. - Description: if compile test - Expectation: compile done without error. - """ - class Net(nn.Cell): - def __init__(self): - """""" - super(Net, self).__init__() - self.square = P.Square() - self.add = P.Add() - self.value = Tensor(3, dtype=ms.float32) - self.switch = P.GeSwitch() - self.merge = P.Merge() - self.less = P.Less() - - def construct(self, x, y): - cond = self.less(x, y) - ret = self.value - if cond: - ret = self.add(x, ret) - ret = self.add(y, ret) - else: - ret = self.square(self.value) - return ret - - x = Tensor(x_init, dtype=ms.float32) - y = Tensor(y_init, dtype=ms.float32) - net = Net() - output = net(x, y) - return output - - -def test_if_nested_compile(): - """ - Feature: if nested compile test. - Description: if nested compile test - Expectation: compile done without error. - """ - class Net(nn.Cell): - def __init__(self, auto_prefix=True): - """""" - super().__init__(auto_prefix=auto_prefix) - self.squre = P.Square() - self.value = Tensor(3, dtype=ms.float32) - - def construct(self, x, y): - res = self.value - if x <= y: - res = x + res - res = y + res - else: - if x == y: - res = self.squre(self.value * y) - else: - res = self.squre(self.value) - return res - - x = Tensor(1.0, dtype=ms.float32) - y = Tensor(2.0, dtype=ms.float32) - net = Net() - net(x, y) - - -def test_if_inside_for(): - """ - Feature: if inside test. - Description: if inside test - Expectation: compile done without error. - """ - class Net(nn.Cell): - def __init__(self, auto_prefix=True): - """""" - super().__init__(auto_prefix=auto_prefix) - self.squre = P.Square() - self.value = Tensor(3, dtype=ms.float32) - self.count = 4 - - def construct(self, x, y): - res = 0 - for i in range(self.count): - if i == x: - res = res + x - else: - res = res - y - return res - - c1 = Tensor(1, dtype=ms.int32) - c2 = Tensor(1, dtype=ms.int32) - net = Net() - net(c1, c2) - - -def test_while_with_weight_in_condition(): - """ - Feature: while with weight in condition test. - Description: while with weight in condition test - Expectation: compile done without error. - """ - class Net(nn.Cell): - def __init__(self): - """""" - super(Net, self).__init__() - self.loop = Parameter(Tensor(1, dtype=ms.float32), name="loop") - - def construct(self, x): - while self.loop < 5: - self.loop += 1 - x += 1 - return x - - net = Net() - x = Tensor(-1, dtype=ms.float32) - grad_all(net)(x) - - -def test_while_add(): - """ - Feature: while add test. - Description: while add test - Expectation: compile done without error. - """ - class Net(nn.Cell): - def __init__(self, data): - """""" - super(Net, self).__init__() - self.start = Tensor(0, dtype=mstype.int32) - self.end = Tensor(2, dtype=mstype.int32) - self.out = Tensor(np.zeros([2, 3], dtype=np.float32)) - self.add = P.Add() - - def construct(self, inputs): - idx = self.start - end = self.end - out = self.out - while idx < end: - xi = inputs[idx, :, :] - out = self.add(out, xi) - idx = idx + 1 - return out - - x = Tensor(np.arange(10 * 2 * 3).reshape(10, 2, 3).astype(np.float32)) - net = Net(x) - net(x) - - -def test_tensor_all_construct_lack_branch(): - """ - Feature: tensor all construct lack test. - Description: tensor all construct lack test - Expectation: compile done without error. - """ - class NetConditionLackBranch(nn.Cell): - def __init__(self): - """""" - super(NetConditionLackBranch, self).__init__() - self.logicaland = P.LogicalAnd() - self.logicalor = P.LogicalOr() - - def construct(self, input1, input2): - if input1.all(): - return self.logicaland(input1, input2) - while input1.any(): - return self.logicalor(input1, input2) - # NOTICE: here missing return statement, default return None - - input_np_1 = np.random.choice([True], size=(2, 3, 4, 5)) - input_tensor_1 = Tensor(input_np_1) - input_np_2 = np.random.choice([True, False], size=(2, 3, 4, 5)) - input_tensor_2 = Tensor(input_np_2) - net = NetConditionLackBranch() - with pytest.raises(Exception): - net(input_tensor_1, input_tensor_2) - - -def test_parser_switch_layer_func_primitive(): - """ - Feature: parser switch layer func primitive test. - Description: parser switch layer func primitive test - Expectation: compile done without error. - """ - class FinalNet(nn.Cell): - def __init__(self, funcs): - """""" - super().__init__() - self.funcs = funcs - - def construct(self, i, input1): - x = self.funcs[i](input1) - return x - - func1 = P.ReLU() - func2 = P.Softmax() - funcs = (func1, func2) - net = FinalNet(funcs) - - input1 = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32)) - i = Tensor(1, mstype.int32) - - with pytest.raises(ValueError): - net(i, input1) - - -def test_large_for_loop(): - """ - Feature: large for loop test. - Description: large for loop test - Expectation: compile done without error. - """ - class Net(nn.Cell): - def __init__(self): - """""" - super(Net, self).__init__() - self.flatten = P.ReLU() # nn.Flatten() - - def construct(self, x): - for elem in range(1, 1900): - x = self.flatten(x + elem) - return x - - t = Tensor(np.ones([2, 3], dtype=np.float32)) - net = Net() - os.environ['MS_DEV_RECURSIVE_EVAL'] = '1' - old_max_call_depth = context.get_context('max_call_depth') - context.set_context(max_call_depth=60) - with pytest.raises(RuntimeError) as err: - net(t) - context.set_context(max_call_depth=old_max_call_depth) - os.environ['MS_DEV_RECURSIVE_EVAL'] = '0' - assert 'Exceed function call depth limit 60' in str(err.value) - - -def test_large_for_loop_with_continue_break(): - """ - Feature: large for loop with continue break test. - Description: large for loop with continue break test - Expectation: compile done without error. - """ - class Net(nn.Cell): - def __init__(self): - """""" - super(Net, self).__init__() - self.flatten = P.ReLU() # nn.Flatten() - - def construct(self, x): - idx = 0 - for elem1 in range(200): - idx = idx + 1 - if idx < 10: - x = x + 0.5 - continue - if idx > 500: - break - x = self.flatten(x + elem1) - return x - - os.environ['MS_DEV_RECURSIVE_EVAL'] = '1' - old_max_call_depth = context.get_context('max_call_depth') - context.set_context(max_call_depth=2000) - t = Tensor(np.ones([2, 3], dtype=np.float32)) - net = Net() - net(t) - os.environ['MS_DEV_RECURSIVE_EVAL'] = '0' - context.set_context(max_call_depth=old_max_call_depth) - - -def test_recursive_call(): - """ - Feature: recursive call test. - Description: recursive call test - Expectation: compile done without error. - """ - class Net(nn.Cell): - """ Net definition """ - def __init__(self): - """""" - super(Net, self).__init__() - self.fc = nn.Dense(10, 10) # padding=0 - # self.net2 = Net2() - - def construct(self, x): - net2 = Net2() - x = net2(x) - out = self.fc(x) - return out - - class Net2(nn.Cell): - def __init__(self): - super(Net2, self).__init__() - self.net = Net() - self.fc = nn.Dense(10, 10) - - def construct(self, x): - x = self.net(x) - out = self.fc(x) - return out - - context.set_context(mode=context.GRAPH_MODE) - os.environ['MS_DEV_RECURSIVE_EVAL'] = '1' - old_max_call_depth = context.get_context('max_call_depth') - context.set_context(max_call_depth=80) - input_data = Tensor(np.identity(10).astype(np.float32)) - net = Net2() - with pytest.raises(RuntimeError): - net(input_data) - os.environ['MS_DEV_RECURSIVE_EVAL'] = '0' - context.set_context(max_call_depth=old_max_call_depth) - - -def test_pow(): - """ - Feature: pow test. - Description: pow test - Expectation: compile done without error. - """ - input_tensor = Tensor(np.array([[2, 2], [3, 3]])) - power = Tensor(np.array(3.0, np.int64)) - testpow = P.Pow() - expect = np.array([[8, 8], [27, 27]]) - result = testpow(input_tensor, power) - assert np.all(result.asnumpy() == expect) - - -def test_pow1(): - """ - Feature: pow one test. - Description: pow one test - Expectation: compile done without error. - """ - input_tensor = Tensor(np.array([[2, 2], [2, 2]])) - power = Tensor(np.array(3.0, np.int64)) - testpow = P.Pow() - expect = np.array([[8, 8], [8, 8]]) - result = testpow(input_tensor, power) - assert np.all(result.asnumpy() == expect) - - -def test_pow2(): - """ - Feature: pow two test. - Description: pow two test - Expectation: compile done without error. - """ - input_tensor = Tensor(np.array([[1, 1], [2, 2]])) - power = Tensor(np.array(3.0, np.int64)) - testpow = P.Pow() - expect = np.array([[1, 1], [8, 8]]) - result = testpow(input_tensor, power) - assert np.all(result.asnumpy() == expect) - - -def test_pow3(): - """ - Feature: pow three test. - Description: pow three test - Expectation: compile done without error. - """ - input_tensor = Tensor(np.array([[2, 2], [1, 1]])) - power = Tensor(np.array(3.0, np.int64)) - testpow = P.Pow() - expect = np.array([[8, 8], [1, 1]]) - result = testpow(input_tensor, power) - assert np.all(result.asnumpy() == expect) - - -def test_exp(): - """ - Feature: exp test. - Description: exp test - Expectation: compile done without error. - """ - input_tensor = Tensor(np.array([[2, 2], [3, 3]])) - testexp = P.Exp() - result = testexp(input_tensor) - expect = np.exp(np.array([[2, 2], [3, 3]])) - assert np.all(result.asnumpy() == expect) - - -def test_exp1(): - """ - Feature: exp one test. - Description: exp one test - Expectation: compile done without error. - """ - input_tensor = Tensor(np.array([[2, 2], [3, 3]])) - testexp = P.Exp() - result = testexp(input_tensor) - expect = np.exp(np.array([[2, 2], [3, 3]])) - assert np.all(result.asnumpy() == expect) - - -def test_realdiv(): - """ - Feature: realdiv test. - Description: realdiv test - Expectation: compile done without error. - """ - x = Tensor(2048.0) - y = Tensor(128.0) - div = P.RealDiv() - result = div(x, y) - x = x.asnumpy() - y = y.asnumpy() - expect = x / y - assert np.all(result.asnumpy() == expect) - - -def test_realdiv1(): - """ - Feature: realdiv one test. - Description: realdiv one test - Expectation: compile done without error. - """ - x = Tensor(256.0) - y = Tensor(128.0) - div = P.RealDiv() - result = div(x, y) - x = x.asnumpy() - y = y.asnumpy() - expect = x / y - assert np.all(result.asnumpy() == expect) - - -def test_eye(): - """ - Feature: eye test. - Description: eye test - Expectation: compile done without error. - """ - x = np.arange(3) - expect = np.ones_like(x) - expect = np.diag(expect) - eye = P.Eye() - eye_output = eye(3, 3, ms.float32) - assert np.all(eye_output.asnumpy() == expect) - - -def test_sub(): - """ - Feature: sub test. - Description: sub test - Expectation: compile done without error. - """ - input_x = Tensor(np.ones(shape=[3])) - input_y = Tensor(np.zeros(shape=[3])) - - sub = P.Sub() - result = sub(input_x, input_y) - expect = np.ones(shape=[3]) - assert np.all(result.asnumpy() == expect) - - -def test_square(): - """ - Feature: square test. - Description: square test - Expectation: compile done without error. - """ - input_tensor = Tensor(np.array([[1, 2, 3], [4, 5, 6]])) - square = P.Square() - result = square(input_tensor) - expect = np.array([[1, 4, 9], [16, 25, 36]]) - assert np.all(result.asnumpy() == expect) - - -def test_sqrt(): - """ - Feature: sqrt test. - Description: sqrt test - Expectation: compile done without error. - """ - input_tensor = Tensor(np.array([[4, 4], [9, 9]])) - - sqrt = P.Sqrt() - expect = np.array([[2, 2], [3, 3]]) - result = sqrt(input_tensor) - assert np.all(result.asnumpy() == expect)