forked from mindspore-Ecosystem/mindspore
!1883 Change '%d' to '%r': shape is a tuple, not a integer
Merge pull request !1883 from 李鸿章/fix_type_error
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09433cdd06
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@ -113,7 +113,7 @@ def package_summary_event(data_list, step):
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data = value["data"]
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tag = value["name"]
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logger.debug("Now process %r summary, tag = %r", summary_type, tag)
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logger.debug(f"Now process {summary_type} summary, tag = {tag}")
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summary_value = summary.value.add()
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summary_value.tag = tag
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@ -130,7 +130,7 @@ def package_summary_event(data_list, step):
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_fill_histogram_summary(tag, data, summary_value.histogram)
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else:
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# The data is invalid ,jump the data
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logger.error("Summary type(%r) is error, tag = %r", summary_type, tag)
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logger.error(f"Summary type({summary_type}) is error, tag = {tag}")
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del summary.value[-1]
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return summary_event
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@ -186,17 +186,17 @@ def _fill_scalar_summary(tag: str, np_value, summary):
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Returns:
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Summary, return scalar summary content.
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"""
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logger.debug("Set(%r) the scalar summary value", tag)
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logger.debug(f"Set({tag}) the scalar summary value")
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if np_value.size == 1:
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# is scalar
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summary.scalar_value = np_value.item()
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return True
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if np_value.size > 1:
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logger.warning("The tensor is not a single scalar, tag = %r, ndim = %r, shape = %r", tag, np_value.ndim,
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np_value.shape)
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logger.warning(
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f"The tensor is not a single scalar, tag = {tag}, ndim = {np_value.ndim}, shape = {np_value.shape}")
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summary.scalar_value = next(np_value.flat).item()
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return True
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logger.error("There no values inside tensor, tag = %r, size = %r", tag, np_value.size)
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logger.error(f"There no values inside tensor, tag = {tag}, size = {np_value.size}")
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return False
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@ -212,7 +212,7 @@ def _fill_tensor_summary(tag: str, np_value, summary_tensor):
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Retruns:
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Summary, return tensor summary content.
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"""
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logger.debug("Set(%r) the tensor summary value", tag)
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logger.debug(f"Set({tag}) the tensor summary value")
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# get tensor dtype
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tensor_dtype = _nptype_to_prototype(np_value)
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summary_tensor.data_type = DataType.Value(tensor_dtype)
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@ -266,7 +266,7 @@ def _fill_histogram_summary(tag: str, np_value: np.ndarray, summary) -> None:
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np_value (np.ndarray): Summary data.
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summary (summary_pb2.Summary.Histogram): Summary histogram data.
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"""
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logger.debug("Set(%r) the histogram summary value", tag)
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logger.debug(f"Set({tag}) the histogram summary value")
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# Default bucket for tensor with no valid data.
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ma_value = np.ma.masked_invalid(np_value)
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total, valid = np_value.size, ma_value.count()
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@ -281,7 +281,7 @@ def _fill_histogram_summary(tag: str, np_value: np.ndarray, summary) -> None:
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summary.count = total
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summary.nan_count, summary.pos_inf_count, summary.neg_inf_count = invalids
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if not valid:
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logger.warning('There are no valid values in the ndarray(size=%d, shape=%d)', total, np_value.shape)
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logger.warning(f'There are no valid values in the ndarray(size={total}, shape={np_value.shape})')
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# summary.{min, max, sum} are 0s by default, no need to explicitly set
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else:
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# BUG: max of a masked array with dtype np.float16 returns inf
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@ -290,9 +290,8 @@ def _fill_histogram_summary(tag: str, np_value: np.ndarray, summary) -> None:
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summary.min = ma_value.min(fill_value=np.PINF)
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summary.max = ma_value.max(fill_value=np.NINF)
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if summary.min < F32_MIN or summary.max > F32_MAX:
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logger.warning(
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'Values(%r, %r) are too large, '
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'you may encounter some undefined behaviours hereafter.', summary.min, summary.max)
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logger.warning(f'Values({summary.min}, {summary.max}) are too large, '
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f'you may encounter some undefined behaviours hereafter.')
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else:
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summary.min = ma_value.min()
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summary.max = ma_value.max()
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@ -327,14 +326,14 @@ def _fill_image_summary(tag: str, np_value, summary_image, input_format='NCHW'):
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Returns:
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Summary, return image summary content.
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"""
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logger.debug("Set(%r) the image summary value", tag)
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logger.debug(f"Set({tag}) the image summary value")
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if np_value.ndim != 4 or np_value.shape[1] not in (1, 3):
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logger.error("The value is not Image, tag = %r, ndim = %r, shape=%r", tag, np_value.ndim, np_value.shape)
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logger.error(f"The value is not Image, tag = {tag}, ndim = {np_value.ndim}, shape={np_value.shape}")
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return False
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if np_value.ndim != len(input_format):
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logger.error("The tensor with dim(%r) can't convert the format(%r) because dim not same", np_value.ndim,
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input_format)
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logger.error(
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f"The tensor with dim({np_value.ndim}) can't convert the format({input_format}) because dim not same")
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return False
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# convert the tensor format
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