add modelzoo network deeplabv3 testcase

tests/st/model_zoo_tests/deeplabv3/test_DeeplabV3_voc2007.py

@@ -0,0 +1,68 @@
+# Copyright 2021 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.
+# ============================================================================
+import os
+import pytest
+
+from tests.st.model_zoo_tests import utils
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_single
+def test_DeeplabV3_voc2007():
+    cur_path = os.path.dirname(os.path.abspath(__file__))
+    model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
+    model_name = "deeplabv3"
+    utils.copy_files(model_path, cur_path, model_name)
+    cur_model_path = os.path.join(cur_path, model_name)
+
+    old_list = ['/PATH/TO/EXPERIMENTS_DIR',
+                '/PATH/TO/MODEL_ZOO_CODE',
+                '/PATH/TO/MINDRECORD_NAME',
+                '/PATH/TO/PRETRAIN_MODEL',
+                "\\${train_code_path}/src/tools/rank_table_8p.json"]
+    new_list = [cur_model_path + '/train',
+                cur_model_path,
+                os.path.join(utils.data_root, "voc/voc2012/mindrecord_train/vocaug_mindrecord0"),
+                os.path.join(utils.ckpt_root, "deeplabv3/resnet101_ascend.ckpt"),
+                utils.rank_table_path]
+    utils.exec_sed_command(old_list, new_list,
+                           os.path.join(cur_model_path, "scripts/run_distribute_train_s16_r1.sh"))
+
+    old_list = ['model.train(args.train_epochs',
+                'callbacks=cbs']
+    new_list = ['model.train(70',
+                'callbacks=cbs, sink_size=2']
+    utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "train.py"))
+
+    exec_network_shell = "cd {}; sh scripts/run_distribute_train_s16_r1.sh".format(model_name)
+    ret = os.system(exec_network_shell)
+    assert ret == 0
+    cmd = "ps -ef | grep python | grep train.py | grep -v grep"
+    ret = utils.process_check(100, cmd)
+    assert ret
+
+    log_file = os.path.join(cur_model_path, "train/device{}/log")
+    for i in range(8):
+        per_step_time = utils.get_perf_data(log_file.format(i))
+        print("per_step_time is", per_step_time)
+        assert per_step_time < 530.0
+    loss_list = []
+    for i in range(8):
+        loss = utils.get_loss_data_list(log_file.format(i))
+        print("loss is", loss[-1])
+        loss_list.append(loss[-1])
+    assert sum(loss_list) / len(loss_list) < 2.5