refactor
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zhangmozhe
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# Copyright 2016 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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"""## Generation of summaries.
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### Class for writing Summaries
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@@FileWriter
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@@FileWriterCache
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### Summary Ops
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@@tensor_summary
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@@scalar
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@@histogram
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@@audio
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@@image
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@@merge
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@@merge_all
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## Utilities
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@@get_summary_description
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"""
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from __future__ import absolute_import, division, print_function
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import bisect
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import logging
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import re as _re
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import numpy as np
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from six import StringIO
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from six.moves import range
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from .src.plugin_pr_curve_pb2 import PrCurvePluginData
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# pylint: disable=unused-import
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from .src.summary_pb2 import HistogramProto, Summary, SummaryMetadata
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from .src.tensor_pb2 import TensorProto
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from .src.tensor_shape_pb2 import TensorShapeProto
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from .x2num import makenp
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_INVALID_TAG_CHARACTERS = _re.compile(r"[^-/\w\.]")
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def _clean_tag(name):
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# In the past, the first argument to summary ops was a tag, which allowed
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# arbitrary characters. Now we are changing the first argument to be the node
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# name. This has a number of advantages (users of summary ops now can
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# take advantage of the tf name scope system) but risks breaking existing
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# usage, because a much smaller set of characters are allowed in node names.
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# This function replaces all illegal characters with _s, and logs a warning.
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# It also strips leading slashes from the name.
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if name is not None:
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new_name = _INVALID_TAG_CHARACTERS.sub("_", name)
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new_name = new_name.lstrip("/") # Remove leading slashes
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if new_name != name:
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logging.info("Summary name %s is illegal; using %s instead." % (name, new_name))
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name = new_name
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return name
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def scalar(name, scalar, collections=None):
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"""Outputs a `Summary` protocol buffer containing a single scalar value.
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The generated Summary has a Tensor.proto containing the input Tensor.
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Args:
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name: A name for the generated node. Will also serve as the series name in
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TensorBoard.
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tensor: A real numeric Tensor containing a single value.
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collections: Optional list of graph collections keys. The new summary op is
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added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
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Returns:
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A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
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Raises:
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ValueError: If tensor has the wrong shape or type.
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"""
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name = _clean_tag(name)
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scalar = makenp(scalar)
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assert scalar.squeeze().ndim == 0, "scalar should be 0D"
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scalar = float(scalar)
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return Summary(value=[Summary.Value(tag=name, simple_value=scalar)])
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def histogram(name, values, bins, collections=None):
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# pylint: disable=line-too-long
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"""Outputs a `Summary` protocol buffer with a histogram.
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The generated
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[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
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has one summary value containing a histogram for `values`.
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This op reports an `InvalidArgument` error if any value is not finite.
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Args:
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name: A name for the generated node. Will also serve as a series name in
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TensorBoard.
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values: A real numeric `Tensor`. Any shape. Values to use to
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build the histogram.
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collections: Optional list of graph collections keys. The new summary op is
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added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
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Returns:
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A scalar `Tensor` of type `string`. The serialized `Summary` protocol
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buffer.
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"""
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name = _clean_tag(name)
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values = makenp(values)
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hist = make_histogram(values.astype(float), bins)
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return Summary(value=[Summary.Value(tag=name, histo=hist)])
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def make_histogram(values, bins):
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"""Convert values into a histogram proto using logic from histogram.cc."""
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values = values.reshape(-1)
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counts, limits = np.histogram(values, bins=bins)
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limits = limits[1:]
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sum_sq = values.dot(values)
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return HistogramProto(
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min=values.min(),
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max=values.max(),
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num=len(values),
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sum=values.sum(),
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sum_squares=sum_sq,
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bucket_limit=limits,
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bucket=counts,
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)
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def image(tag, tensor):
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"""Outputs a `Summary` protocol buffer with images.
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The summary has up to `max_images` summary values containing images. The
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images are built from `tensor` which must be 3-D with shape `[height, width,
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channels]` and where `channels` can be:
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* 1: `tensor` is interpreted as Grayscale.
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* 3: `tensor` is interpreted as RGB.
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* 4: `tensor` is interpreted as RGBA.
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The `name` in the outputted Summary.Value protobufs is generated based on the
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name, with a suffix depending on the max_outputs setting:
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* If `max_outputs` is 1, the summary value tag is '*name*/image'.
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* If `max_outputs` is greater than 1, the summary value tags are
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generated sequentially as '*name*/image/0', '*name*/image/1', etc.
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Args:
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tag: A name for the generated node. Will also serve as a series name in
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TensorBoard.
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tensor: A 3-D `uint8` or `float32` `Tensor` of shape `[height, width,
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channels]` where `channels` is 1, 3, or 4.
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Returns:
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A scalar `Tensor` of type `string`. The serialized `Summary` protocol
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buffer.
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"""
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tag = _clean_tag(tag)
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tensor = makenp(tensor, "IMG")
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tensor = tensor.astype(np.float32)
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tensor = (tensor * 255).astype(np.uint8)
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image = make_image(tensor)
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return Summary(value=[Summary.Value(tag=tag, image=image)])
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def make_image(tensor):
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"""Convert an numpy representation image to Image protobuf"""
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from PIL import Image
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height, width, channel = tensor.shape
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image = Image.fromarray(tensor)
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import io
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output = io.BytesIO()
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image.save(output, format="PNG")
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image_string = output.getvalue()
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output.close()
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return Summary.Image(height=height, width=width, colorspace=channel, encoded_image_string=image_string)
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def audio(tag, tensor, sample_rate=44100):
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tensor = makenp(tensor)
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tensor = tensor.squeeze()
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if abs(tensor).max() > 1:
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print("warning: audio amplitude out of range, auto clipped.")
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tensor = tensor.clip(-1, 1)
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assert tensor.ndim == 1, "input tensor should be 1 dimensional."
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tensor_list = [int(32767.0 * x) for x in tensor]
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import io
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import struct
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import wave
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fio = io.BytesIO()
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Wave_write = wave.open(fio, "wb")
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Wave_write.setnchannels(1)
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Wave_write.setsampwidth(2)
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Wave_write.setframerate(sample_rate)
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tensor_enc = b""
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for v in tensor_list:
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tensor_enc += struct.pack("<h", v)
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Wave_write.writeframes(tensor_enc)
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Wave_write.close()
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audio_string = fio.getvalue()
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fio.close()
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audio = Summary.Audio(
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sample_rate=sample_rate,
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num_channels=1,
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length_frames=len(tensor_list),
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encoded_audio_string=audio_string,
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content_type="audio/wav",
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)
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return Summary(value=[Summary.Value(tag=tag, audio=audio)])
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def text(tag, text):
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import json
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PluginData = [SummaryMetadata.PluginData(plugin_name="text")]
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smd = SummaryMetadata(plugin_data=PluginData)
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tensor = TensorProto(
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dtype="DT_STRING",
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string_val=[text.encode(encoding="utf_8")],
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tensor_shape=TensorShapeProto(dim=[TensorShapeProto.Dim(size=1)]),
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)
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return Summary(value=[Summary.Value(node_name=tag, metadata=smd, tensor=tensor)])
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def pr_curve(tag, labels, predictions, num_thresholds=127, weights=None):
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if num_thresholds > 127: # wierd, value > 127 breaks protobuf
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num_thresholds = 127
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data = compute_curve(labels, predictions, num_thresholds=num_thresholds, weights=weights)
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pr_curve_plugin_data = PrCurvePluginData(version=0, num_thresholds=num_thresholds).SerializeToString()
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PluginData = [SummaryMetadata.PluginData(plugin_name="pr_curves", content=pr_curve_plugin_data)]
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smd = SummaryMetadata(plugin_data=PluginData)
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tensor = TensorProto(
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dtype="DT_FLOAT",
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float_val=data.reshape(-1).tolist(),
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tensor_shape=TensorShapeProto(
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dim=[TensorShapeProto.Dim(size=data.shape[0]), TensorShapeProto.Dim(size=data.shape[1])]
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),
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)
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return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor)])
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# https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/summary.py
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def compute_curve(labels, predictions, num_thresholds=None, weights=None):
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_MINIMUM_COUNT = 1e-7
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if weights is None:
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weights = 1.0
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# Compute bins of true positives and false positives.
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bucket_indices = np.int32(np.floor(predictions * (num_thresholds - 1)))
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float_labels = labels.astype(np.float)
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histogram_range = (0, num_thresholds - 1)
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tp_buckets, _ = np.histogram(
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bucket_indices, bins=num_thresholds, range=histogram_range, weights=float_labels * weights
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)
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fp_buckets, _ = np.histogram(
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bucket_indices, bins=num_thresholds, range=histogram_range, weights=(1.0 - float_labels) * weights
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)
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# Obtain the reverse cumulative sum.
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tp = np.cumsum(tp_buckets[::-1])[::-1]
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fp = np.cumsum(fp_buckets[::-1])[::-1]
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tn = fp[0] - fp
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fn = tp[0] - tp
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precision = tp / np.maximum(_MINIMUM_COUNT, tp + fp)
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recall = tp / np.maximum(_MINIMUM_COUNT, tp + fn)
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return np.stack((tp, fp, tn, fn, precision, recall))
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