diff --git a/mindspore/train/summary/_summary_adapter.py b/mindspore/train/summary/_summary_adapter.py index 47ed0a7b90484a0b462879a3781c3160f95b7f0c..fc4a2302bd7f75cbc9adc54de2aba43f6a9e1a24 100644 --- a/mindspore/train/summary/_summary_adapter.py +++ b/mindspore/train/summary/_summary_adapter.py @@ -113,7 +113,7 @@ def package_summary_event(data_list, step): data = value["data"] tag = value["name"] - logger.debug("Now process %r summary, tag = %r", summary_type, tag) + logger.debug(f"Now process {summary_type} summary, tag = {tag}") summary_value = summary.value.add() summary_value.tag = tag @@ -130,7 +130,7 @@ def package_summary_event(data_list, step): _fill_histogram_summary(tag, data, summary_value.histogram) else: # The data is invalid ,jump the data - logger.error("Summary type(%r) is error, tag = %r", summary_type, tag) + logger.error(f"Summary type({summary_type}) is error, tag = {tag}") del summary.value[-1] return summary_event @@ -186,17 +186,17 @@ def _fill_scalar_summary(tag: str, np_value, summary): Returns: Summary, return scalar summary content. """ - logger.debug("Set(%r) the scalar summary value", tag) + logger.debug(f"Set({tag}) the scalar summary value") if np_value.size == 1: # is scalar summary.scalar_value = np_value.item() return True if np_value.size > 1: - logger.warning("The tensor is not a single scalar, tag = %r, ndim = %r, shape = %r", tag, np_value.ndim, - np_value.shape) + logger.warning( + f"The tensor is not a single scalar, tag = {tag}, ndim = {np_value.ndim}, shape = {np_value.shape}") summary.scalar_value = next(np_value.flat).item() return True - logger.error("There no values inside tensor, tag = %r, size = %r", tag, np_value.size) + logger.error(f"There no values inside tensor, tag = {tag}, size = {np_value.size}") return False @@ -212,7 +212,7 @@ def _fill_tensor_summary(tag: str, np_value, summary_tensor): Retruns: Summary, return tensor summary content. """ - logger.debug("Set(%r) the tensor summary value", tag) + logger.debug(f"Set({tag}) the tensor summary value") # get tensor dtype tensor_dtype = _nptype_to_prototype(np_value) summary_tensor.data_type = DataType.Value(tensor_dtype) @@ -266,7 +266,7 @@ def _fill_histogram_summary(tag: str, np_value: np.ndarray, summary) -> None: np_value (np.ndarray): Summary data. summary (summary_pb2.Summary.Histogram): Summary histogram data. """ - logger.debug("Set(%r) the histogram summary value", tag) + logger.debug(f"Set({tag}) the histogram summary value") # Default bucket for tensor with no valid data. ma_value = np.ma.masked_invalid(np_value) total, valid = np_value.size, ma_value.count() @@ -281,7 +281,7 @@ def _fill_histogram_summary(tag: str, np_value: np.ndarray, summary) -> None: summary.count = total summary.nan_count, summary.pos_inf_count, summary.neg_inf_count = invalids if not valid: - logger.warning('There are no valid values in the ndarray(size=%d, shape=%d)', total, np_value.shape) + logger.warning(f'There are no valid values in the ndarray(size={total}, shape={np_value.shape})') # summary.{min, max, sum} are 0s by default, no need to explicitly set else: # BUG: max of a masked array with dtype np.float16 returns inf @@ -290,9 +290,8 @@ def _fill_histogram_summary(tag: str, np_value: np.ndarray, summary) -> None: summary.min = ma_value.min(fill_value=np.PINF) summary.max = ma_value.max(fill_value=np.NINF) if summary.min < F32_MIN or summary.max > F32_MAX: - logger.warning( - 'Values(%r, %r) are too large, ' - 'you may encounter some undefined behaviours hereafter.', summary.min, summary.max) + logger.warning(f'Values({summary.min}, {summary.max}) are too large, ' + f'you may encounter some undefined behaviours hereafter.') else: summary.min = ma_value.min() summary.max = ma_value.max() @@ -327,14 +326,14 @@ def _fill_image_summary(tag: str, np_value, summary_image, input_format='NCHW'): Returns: Summary, return image summary content. """ - logger.debug("Set(%r) the image summary value", tag) + logger.debug(f"Set({tag}) the image summary value") if np_value.ndim != 4 or np_value.shape[1] not in (1, 3): - logger.error("The value is not Image, tag = %r, ndim = %r, shape=%r", tag, np_value.ndim, np_value.shape) + logger.error(f"The value is not Image, tag = {tag}, ndim = {np_value.ndim}, shape={np_value.shape}") return False if np_value.ndim != len(input_format): - logger.error("The tensor with dim(%r) can't convert the format(%r) because dim not same", np_value.ndim, - input_format) + logger.error( + f"The tensor with dim({np_value.ndim}) can't convert the format({input_format}) because dim not same") return False # convert the tensor format