290 lines
10 KiB
Python
290 lines
10 KiB
Python
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# Copyright 2017 The TensorFlow 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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# ==============================================================================
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"""The TensorBoard Text plugin."""
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import textwrap
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# pylint: disable=g-bad-import-order
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# Necessary for an internal test with special behavior for numpy.
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import numpy as np
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# pylint: enable=g-bad-import-order
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from werkzeug import wrappers
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from tensorboard import plugin_util
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from tensorboard.backend import http_util
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from tensorboard.data import provider
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from tensorboard.plugins import base_plugin
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from tensorboard.plugins.text import metadata
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# HTTP routes
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TAGS_ROUTE = "/tags"
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TEXT_ROUTE = "/text"
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WARNING_TEMPLATE = textwrap.dedent(
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"""\
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**Warning:** This text summary contained data of dimensionality %d, but only \
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2d tables are supported. Showing a 2d slice of the data instead."""
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)
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_DEFAULT_DOWNSAMPLING = 100 # text tensors per time series
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def make_table_row(contents, tag="td"):
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"""Given an iterable of string contents, make a table row.
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Args:
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contents: An iterable yielding strings.
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tag: The tag to place contents in. Defaults to 'td', you might want 'th'.
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Returns:
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A string containing the content strings, organized into a table row.
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Example: make_table_row(['one', 'two', 'three']) == '''
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<tr>
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<td>one</td>
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<td>two</td>
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<td>three</td>
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</tr>'''
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"""
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columns = ("<%s>%s</%s>\n" % (tag, s, tag) for s in contents)
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return "<tr>\n" + "".join(columns) + "</tr>\n"
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def make_table(contents, headers=None):
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"""Given a numpy ndarray of strings, concatenate them into a html table.
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Args:
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contents: A np.ndarray of strings. May be 1d or 2d. In the 1d case, the
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table is laid out vertically (i.e. row-major).
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headers: A np.ndarray or list of string header names for the table.
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Returns:
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A string containing all of the content strings, organized into a table.
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Raises:
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ValueError: If contents is not a np.ndarray.
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ValueError: If contents is not 1d or 2d.
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ValueError: If contents is empty.
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ValueError: If headers is present and not a list, tuple, or ndarray.
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ValueError: If headers is not 1d.
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ValueError: If number of elements in headers does not correspond to number
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of columns in contents.
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"""
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if not isinstance(contents, np.ndarray):
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raise ValueError("make_table contents must be a numpy ndarray")
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if contents.ndim not in [1, 2]:
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raise ValueError(
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"make_table requires a 1d or 2d numpy array, was %dd"
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% contents.ndim
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)
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if headers:
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if isinstance(headers, (list, tuple)):
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headers = np.array(headers)
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if not isinstance(headers, np.ndarray):
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raise ValueError(
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"Could not convert headers %s into np.ndarray" % headers
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)
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if headers.ndim != 1:
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raise ValueError("Headers must be 1d, is %dd" % headers.ndim)
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expected_n_columns = contents.shape[1] if contents.ndim == 2 else 1
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if headers.shape[0] != expected_n_columns:
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raise ValueError(
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"Number of headers %d must match number of columns %d"
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% (headers.shape[0], expected_n_columns)
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)
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header = "<thead>\n%s</thead>\n" % make_table_row(headers, tag="th")
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else:
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header = ""
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n_rows = contents.shape[0]
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if contents.ndim == 1:
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# If it's a vector, we need to wrap each element in a new list, otherwise
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# we would turn the string itself into a row (see test code)
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rows = (make_table_row([contents[i]]) for i in range(n_rows))
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else:
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rows = (make_table_row(contents[i, :]) for i in range(n_rows))
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return "<table>\n%s<tbody>\n%s</tbody>\n</table>" % (header, "".join(rows))
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def reduce_to_2d(arr):
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"""Given a np.npdarray with nDims > 2, reduce it to 2d.
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It does this by selecting the zeroth coordinate for every dimension greater
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than two.
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Args:
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arr: a numpy ndarray of dimension at least 2.
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Returns:
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A two-dimensional subarray from the input array.
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Raises:
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ValueError: If the argument is not a numpy ndarray, or the dimensionality
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is too low.
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"""
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if not isinstance(arr, np.ndarray):
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raise ValueError("reduce_to_2d requires a numpy.ndarray")
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ndims = len(arr.shape)
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if ndims < 2:
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raise ValueError("reduce_to_2d requires an array of dimensionality >=2")
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# slice(None) is equivalent to `:`, so we take arr[0,0,...0,:,:]
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slices = ([0] * (ndims - 2)) + [slice(None), slice(None)]
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return arr[tuple(slices)]
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def text_array_to_html(text_arr, enable_markdown):
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"""Take a numpy.ndarray containing strings, and convert it into html.
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If the ndarray contains a single scalar string, that string is converted to
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html via our sanitized markdown parser. If it contains an array of strings,
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the strings are individually converted to html and then composed into a table
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using make_table. If the array contains dimensionality greater than 2,
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all but two of the dimensions are removed, and a warning message is prefixed
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to the table.
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Args:
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text_arr: A numpy.ndarray containing strings.
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enable_markdown: boolean, whether to enable Markdown
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Returns:
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The array converted to html.
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"""
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if not text_arr.shape:
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# It is a scalar. No need to put it in a table.
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if enable_markdown:
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return plugin_util.markdown_to_safe_html(text_arr.item())
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else:
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return plugin_util.safe_html(text_arr.item())
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warning = ""
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if len(text_arr.shape) > 2:
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warning = plugin_util.markdown_to_safe_html(
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WARNING_TEMPLATE % len(text_arr.shape)
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)
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text_arr = reduce_to_2d(text_arr)
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if enable_markdown:
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table = plugin_util.markdowns_to_safe_html(
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text_arr.reshape(-1),
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lambda xs: make_table(np.array(xs).reshape(text_arr.shape)),
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)
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else:
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# Convert utf-8 bytes to str. The built-in np.char.decode doesn't work on
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# object arrays, and converting to an numpy chararray is lossy.
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decode = lambda bs: bs.decode("utf-8") if isinstance(bs, bytes) else bs
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text_arr_str = np.array(
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[decode(bs) for bs in text_arr.reshape(-1)]
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).reshape(text_arr.shape)
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table = plugin_util.safe_html(make_table(text_arr_str))
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return warning + table
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def process_event(wall_time, step, string_ndarray, enable_markdown):
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"""Convert a text event into a JSON-compatible response."""
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html = text_array_to_html(string_ndarray, enable_markdown)
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return {
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"wall_time": wall_time,
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"step": step,
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"text": html,
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}
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class TextPlugin(base_plugin.TBPlugin):
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"""Text Plugin for TensorBoard."""
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plugin_name = metadata.PLUGIN_NAME
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def __init__(self, context):
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"""Instantiates TextPlugin via TensorBoard core.
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Args:
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context: A base_plugin.TBContext instance.
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"""
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self._downsample_to = (context.sampling_hints or {}).get(
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self.plugin_name, _DEFAULT_DOWNSAMPLING
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)
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self._data_provider = context.data_provider
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self._version_checker = plugin_util._MetadataVersionChecker(
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data_kind="text",
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latest_known_version=0,
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)
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def is_active(self):
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return False # `list_plugins` as called by TB core suffices
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def frontend_metadata(self):
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return base_plugin.FrontendMetadata(element_name="tf-text-dashboard")
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def index_impl(self, ctx, experiment):
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mapping = self._data_provider.list_tensors(
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ctx,
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experiment_id=experiment,
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plugin_name=metadata.PLUGIN_NAME,
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)
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result = {run: [] for run in mapping}
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for (run, tag_to_content) in mapping.items():
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for (tag, metadatum) in tag_to_content.items():
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md = metadata.parse_plugin_metadata(metadatum.plugin_content)
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if not self._version_checker.ok(md.version, run, tag):
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continue
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result[run].append(tag)
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return result
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@wrappers.Request.application
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def tags_route(self, request):
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ctx = plugin_util.context(request.environ)
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experiment = plugin_util.experiment_id(request.environ)
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index = self.index_impl(ctx, experiment)
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return http_util.Respond(request, index, "application/json")
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def text_impl(self, ctx, run, tag, experiment, enable_markdown):
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all_text = self._data_provider.read_tensors(
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ctx,
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experiment_id=experiment,
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plugin_name=metadata.PLUGIN_NAME,
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downsample=self._downsample_to,
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run_tag_filter=provider.RunTagFilter(runs=[run], tags=[tag]),
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)
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text = all_text.get(run, {}).get(tag, None)
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if text is None:
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return []
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return [
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process_event(d.wall_time, d.step, d.numpy, enable_markdown)
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for d in text
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]
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@wrappers.Request.application
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def text_route(self, request):
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ctx = plugin_util.context(request.environ)
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experiment = plugin_util.experiment_id(request.environ)
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run = request.args.get("run")
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tag = request.args.get("tag")
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markdown_arg = request.args.get("markdown")
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enable_markdown = markdown_arg != "false" # Default to enabled.
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response = self.text_impl(ctx, run, tag, experiment, enable_markdown)
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return http_util.Respond(request, response, "application/json")
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def get_plugin_apps(self):
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return {
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TAGS_ROUTE: self.tags_route,
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TEXT_ROUTE: self.text_route,
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}
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