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!185 modify comment about normal mode 

Merge pull request !185 from jinyaohui/master
This commit is contained in:
mindspore-ci-bot 2020-04-09 14:24:28 +08:00 committed by Gitee
parent af3484baaf 0790ef8a85
commit 0565e4641e
5 changed files with 8 additions and 8 deletions
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2
example/yolov3_coco2017/train.py
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@ -67,7 +67,7 @@ if __name__ == '__main__':
parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.") parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.") parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or non-sink mode, default is sink") parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink")
parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10") parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.") parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path") parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")

2
mindspore/ccsrc/pipeline/pipeline_ge.cc
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@ -453,7 +453,7 @@ void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr>& info, const py::
} }
// process the first args of tensor // process the first args of tensor
// only in Dataset non-sink Mode, fp_bp graph need input tensors // only in dataset normal(non-sink) mode, fp_bp graph need input tensors
if (ConfigManager::GetInstance().dataset_mode() == DS_NORMAL_MODE) { if (ConfigManager::GetInstance().dataset_mode() == DS_NORMAL_MODE) {
for (std::size_t i = 0; i < size; i++) { for (std::size_t i = 0; i < size; i++) {
ValuePtr converted = nullptr; ValuePtr converted = nullptr;

2
mindspore/ccsrc/transform/convert.cc
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@ -447,7 +447,7 @@ void DfGraphConvertor::InitLoopVar(std::vector<ge::Operator> *init_input) {
if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) { if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
value = ConfigManager::GetInstance().iter_num(); value = ConfigManager::GetInstance().iter_num();
} else { } else {
MS_LOG(INFO) << "Run with non-sink mode, the iterator number will always be 1"; MS_LOG(INFO) << "Run with normal(non-sink) mode, the iterator number will always be 1";
value = 1; value = 1;
ConfigManager::GetInstance().set_iter_num(value); ConfigManager::GetInstance().set_iter_num(value);
} }

2
mindspore/nn/wrap/loss_scale.py
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@ -51,7 +51,7 @@ class DynamicLossScaleUpdateCell(Cell):
In every training step, the loss scaling value will be updated by loss scaling value/`scale_factor` In every training step, the loss scaling value will be updated by loss scaling value/`scale_factor`
when there is overflow. And it will be increased by loss scaling value * `scale_factor` if there is no when there is overflow. And it will be increased by loss scaling value * `scale_factor` if there is no
overflow for a continuous `scale_window` steps. This cell is used for Graph mode training in which all overflow for a continuous `scale_window` steps. This cell is used for Graph mode training in which all
logic will be executed on device side(Another training mode is non-sink mode in which some logic will be logic will be executed on device side(Another training mode is normal(non-sink) mode in which some logic will be
executed on host). executed on host).
Args: Args:

8
tests/ut/python/utils/test_callback.py
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@ -112,8 +112,8 @@ def test_save_checkpoint():
os.remove('./test_files/test_ckpt-model.pkl') os.remove('./test_files/test_ckpt-model.pkl')
def test_loss_monitor_sink_model(): def test_loss_monitor_sink_mode():
"""Test loss monitor sink model.""" """Test loss monitor sink mode."""
cb_params = _InternalCallbackParam() cb_params = _InternalCallbackParam()
cb_params.cur_epoch_num = 4 cb_params.cur_epoch_num = 4
cb_params.cur_step_num = 2 cb_params.cur_step_num = 2
@ -131,8 +131,8 @@ def test_loss_monitor_sink_model():
callbacklist.end(run_context) callbacklist.end(run_context)
def test_loss_monitor_feed_model(): def test_loss_monitor_normal_mode():
"""Test loss monitor non-sink mode.""" """Test loss monitor normal(non-sink) mode."""
cb_params = _InternalCallbackParam() cb_params = _InternalCallbackParam()
run_context = RunContext(cb_params) run_context = RunContext(cb_params)
loss_cb = LossMonitor(1) loss_cb = LossMonitor(1)