delete longtime python ut

tests/ut/python/train/summary/test_summary_performance.py

@@ -1,97 +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.
-# ============================================================================
-"""
-@File  : test_summary.py
-@Author:
-@Date  : 2019-07-4
-@Desc  : test summary function
-"""
-import os
-import logging
-import time
-import numpy as np
-from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
-from mindspore.common.tensor import Tensor
-
-CUR_DIR = os.getcwd()
-SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
-
-log = logging.getLogger("test")
-log.setLevel(level=logging.ERROR)
-
-def get_now_time_ns():
-    """get the time of second"""
-    time_second = int(time.time_ns())
-    return time_second
-
-def get_test_data(step):
-    """ get_test_data """
-    # pylint: disable=unused-argument
-    test_data_list = []
-    tag1 = "xt1[:Tensor]"
-    tag2 = "xt2[:Tensor]"
-    tag3 = "xt3[:Tensor]"
-    np1 = np.random.random((5, 4, 3, 5))
-    np2 = np.random.random((5, 5, 3, 5))
-    np3 = np.random.random((4, 5, 3, 5))
-
-    dict1 = {}
-    dict1["name"] = tag1
-    dict1["data"] = Tensor(np1)
-
-    dict2 = {}
-    dict2["name"] = tag2
-    dict2["data"] = Tensor(np2)
-
-    dict3 = {}
-    dict3["name"] = tag3
-    dict3["data"] = Tensor(np3)
-
-    test_data_list.append(dict1)
-    test_data_list.append(dict2)
-
-    return test_data_list
-
-
-# Test 1: summary sample of scalar
-def test_summary_performance():
-    """ test_summary_performance """
-    log.debug("begin test_scalar_summary_sample")
-    current_time = time.time()
-    print("time = ", current_time)
-    # step 0: create the thread
-    test_writer = SummaryRecord(SUMMARY_DIR, flush_time=120)
-
-    # step 1: create the test data for summary
-    old_time = get_now_time_ns()
-    # step 2: create the Event
-    for i in range(1, 10):
-        test_data = get_test_data(i)
-        _cache_summary_tensor_data(test_data)
-        test_writer.record(i)
-        now_time = get_now_time_ns()
-        consume_time = (now_time - old_time)/1000/1000
-        old_time = now_time
-        print("step test_summary_performance conusmer time is:", consume_time)
-
-
-    # step 3: send the event to mq
-
-    # step 4: accept the event and write the file
-    test_writer.flush()
-    test_writer.close()
-    current_time = time.time() - current_time
-    print("consume time = ", current_time)
-    log.debug("finished test_scalar_summary_sample")

tests/ut/python/train/test_amp.py

@@ -66,6 +66,7 @@ def test_amp_o2():
     train_network = amp.build_train_network(net, optimizer, level="O2")
     output = train_network(inputs, label)
 
+
 def test_amp_o2_loss():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -75,14 +76,6 @@ def test_amp_o2_loss():
     train_network = amp.build_train_network(net, optimizer, loss, level="O2")
     output = train_network(inputs, label)
 
-def test_amp_resnet50_loss():
-    inputs = Tensor(np.ones([2, 3, 224, 224]).astype(np.float32))
-    label = Tensor(np.zeros([2, 10]).astype(np.float32))
-    net = resnet50()
-    loss = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
-    optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
-    train_network = amp.build_train_network(net, optimizer, loss, level="O2")
-    train_network(inputs, label)
 
 def test_amp_o0_loss():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))