Refine the format of printing tensor (#27673)
* add sumary feature * refine printting tensor * add sci_mode * add sample code * fix indent error * fix _format_item * polish code * support item indent * add ut * set place for ut * fix py2 issue * fix utmy_2.0rc
Leo Chen
4 years ago
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GitHub
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c90d35564b
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049696bf67
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import numpy as np
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from paddle.fluid.layers import core
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from paddle.fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
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__all__ = ['set_printoptions']
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class PrintOptions(object):
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precision = 8
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threshold = 1000
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edgeitems = 3
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linewidth = 80
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sci_mode = False
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DEFAULT_PRINT_OPTIONS = PrintOptions()
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def set_printoptions(precision=None,
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threshold=None,
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edgeitems=None,
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sci_mode=None):
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"""Set the printing options for Tensor.
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NOTE: The function is similar with numpy.set_printoptions()
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Args:
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precision (int, optional): Number of digits of the floating number, default 8.
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threshold (int, optional): Total number of elements printed, default 1000.
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edgeitems (int, optional): Number of elements in summary at the begining and end of each dimension, defalt 3.
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sci_mode (bool, optional): Format the floating number with scientific notation or not, default False.
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Returns:
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None.
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Examples:
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.. code-block:: python
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import paddle
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paddle.manual_seed(10)
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a = paddle.rand([10, 20])
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paddle.set_printoptions(4, 100, 3)
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print(a)
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'''
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Tensor: dygraph_tmp_0
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- place: CPUPlace
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- shape: [10, 20]
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- layout: NCHW
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- dtype: float32
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- data: [[0.2727, 0.5489, 0.8655, ..., 0.2916, 0.8525, 0.9000],
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[0.3806, 0.8996, 0.0928, ..., 0.9535, 0.8378, 0.6409],
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[0.1484, 0.4038, 0.8294, ..., 0.0148, 0.6520, 0.4250],
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...,
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[0.3426, 0.1909, 0.7240, ..., 0.4218, 0.2676, 0.5679],
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[0.5561, 0.2081, 0.0676, ..., 0.9778, 0.3302, 0.9559],
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[0.2665, 0.8483, 0.5389, ..., 0.4956, 0.6862, 0.9178]]
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'''
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"""
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kwargs = {}
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if precision is not None:
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check_type(precision, 'precision', (int), 'set_printoptions')
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DEFAULT_PRINT_OPTIONS.precision = precision
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kwargs['precision'] = precision
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if threshold is not None:
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check_type(threshold, 'threshold', (int), 'set_printoptions')
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DEFAULT_PRINT_OPTIONS.threshold = threshold
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kwargs['threshold'] = threshold
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if edgeitems is not None:
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check_type(edgeitems, 'edgeitems', (int), 'set_printoptions')
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DEFAULT_PRINT_OPTIONS.edgeitems = edgeitems
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kwargs['edgeitems'] = edgeitems
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if sci_mode is not None:
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check_type(sci_mode, 'sci_mode', (bool), 'set_printoptions')
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DEFAULT_PRINT_OPTIONS.sci_mode = sci_mode
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kwargs['sci_mode'] = sci_mode
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#TODO(zhiqiu): support linewidth
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core.set_printoptions(**kwargs)
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def _to_sumary(var):
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edgeitems = DEFAULT_PRINT_OPTIONS.edgeitems
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if len(var.shape) == 0:
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return var
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elif len(var.shape) == 1:
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if var.shape[0] > 2 * edgeitems:
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return paddle.concat([var[:edgeitems], var[-edgeitems:]])
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else:
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return var
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else:
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# recursively handle all dimensions
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if var.shape[0] > 2 * edgeitems:
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begin = [x for x in var[:edgeitems]]
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end = [x for x in var[-edgeitems:]]
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return paddle.stack([_to_sumary(x) for x in (begin + end)])
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else:
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return paddle.stack([_to_sumary(x) for x in var])
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def _format_item(np_var, max_width=0):
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if np_var.dtype == np.float32 or np_var.dtype == np.float64 or np_var.dtype == np.float16:
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if DEFAULT_PRINT_OPTIONS.sci_mode:
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item_str = '{{:.{}e}}'.format(
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DEFAULT_PRINT_OPTIONS.precision).format(np_var)
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elif np.ceil(np_var) == np_var:
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item_str = '{:.0f}.'.format(np_var)
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else:
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item_str = '{{:.{}f}}'.format(
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DEFAULT_PRINT_OPTIONS.precision).format(np_var)
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else:
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item_str = '{}'.format(np_var)
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if max_width > len(item_str):
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return '{indent}{data}'.format(
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indent=(max_width - len(item_str)) * ' ', data=item_str)
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else:
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return item_str
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def _get_max_width(var):
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max_width = 0
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for item in np.nditer(var.numpy()):
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item_str = _format_item(item)
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max_width = max(max_width, len(item_str))
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return max_width
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def _format_tensor(var, sumary, indent=0):
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edgeitems = DEFAULT_PRINT_OPTIONS.edgeitems
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max_width = _get_max_width(_to_sumary(var))
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if len(var.shape) == 0:
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return _format_item(var.numpy.items(0), max_width)
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elif len(var.shape) == 1:
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if sumary and var.shape[0] > 2 * edgeitems:
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items = [
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_format_item(item, max_width)
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for item in list(var.numpy())[:DEFAULT_PRINT_OPTIONS.edgeitems]
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] + ['...'] + [
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_format_item(item, max_width)
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for item in list(var.numpy())[-DEFAULT_PRINT_OPTIONS.edgeitems:]
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]
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else:
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items = [
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_format_item(item, max_width) for item in list(var.numpy())
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]
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s = ', '.join(items)
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return '[' + s + ']'
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else:
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# recursively handle all dimensions
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if sumary and var.shape[0] > 2 * edgeitems:
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vars = [
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_format_tensor(x, sumary, indent + 1) for x in var[:edgeitems]
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] + ['...'] + [
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_format_tensor(x, sumary, indent + 1) for x in var[-edgeitems:]
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]
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else:
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vars = [_format_tensor(x, sumary, indent + 1) for x in var]
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return '[' + (',' + '\n' * (len(var.shape) - 1) + ' ' *
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(indent + 1)).join(vars) + ']'
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def to_string(var, prefix='Tensor'):
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indent = len(prefix) + 1
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_template = "{prefix}(shape={shape}, dtype={dtype}, place={place}, stop_gradient={stop_gradient},\n{indent}{data})"
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tensor = var.value().get_tensor()
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if not tensor._is_initialized():
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return "Tensor(Not initialized)"
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if len(var.shape) == 0:
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size = 0
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else:
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size = 1
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for dim in var.shape:
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size *= dim
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sumary = False
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if size > DEFAULT_PRINT_OPTIONS.threshold:
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sumary = True
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data = _format_tensor(var, sumary, indent=indent)
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return _template.format(
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prefix=prefix,
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shape=var.shape,
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dtype=convert_dtype(var.dtype),
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place=var._place_str,
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stop_gradient=var.stop_gradient,
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indent=' ' * indent,
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data=data)
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