# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Make HTML tables that report where TF and TFLite failed to convert models. This is primarily used by generate_examples.py. See it or `make_report_table` for more details on usage. """ import html import json import re FAILED = "FAILED" SUCCESS = "SUCCESS" NOTRUN = "NOTRUN" def make_report_table(fp, title, reports): """Make an HTML report of the success/failure reports. Args: fp: File-like object in which to put the html. title: "Title of the zip file this pertains to." reports: a list of conversion attempts. (report_args, report_vals) i.e. ({"shape": [1,2,3], "type": "tf.float32"}, {"tf": "SUCCESS", "tflite_converter": "FAILURE", "tf_log": "", "tflite_converter_log": "Unsupported type."}) """ # sort reports by if TFLite converter failure and then TF failure (reversed) reports.sort(key=lambda x: x[1]["tflite_converter"], reverse=False) reports.sort(key=lambda x: x[1]["tf"], reverse=True) def result_cell(x, row, col): """Produce a cell with the condition string `x`.""" s = html.escape(repr(x), quote=True) color = "#44ff44" if x == SUCCESS else ( "#ff4444" if x == FAILED else "#eeeeee") handler = "ShowLog(%d, %d)" % (row, col) fp.write("%s\n" % ( color, handler, s)) fp.write(""" tflite report """) # Write the log data to a javascript variable and also make a function # in javascript to show the log when an item is clicked. fp.write("\n") # Write the main table and use onclick on the items that have log items. fp.write("""

TensorFlow Lite Conversion

%s

""" % title) # Get a list of keys that are in any of the records. param_keys = {} for params, _ in reports: for k in params.keys(): param_keys[k] = True fp.write("\n") fp.write("\n") fp.write("\n") fp.write("
\n") fp.write("
\n") fp.write("\n") fp.write("\n") for p in param_keys: fp.write("\n" % html.escape(p, quote=True)) fp.write("\n") fp.write("\n") fp.write("\n") for idx, (params, vals) in enumerate(reports): fp.write("\n") for p in param_keys: fp.write(" \n" % html.escape(repr(params.get(p, None)), quote=True)) result_cell(vals["tf"], idx, 0) result_cell(vals["tflite_converter"], idx, 1) fp.write("\n") fp.write("
%sTensorFlowTensorFlow Lite Converter
%s
\n") fp.write("
\n") fp.write("
\n") fp.write("\n") fp.write(""" """) def escape_and_normalize(log): # These logs contain paths like /tmp/tmpgmypg3xa that are inconsistent between # builds. This replaces these inconsistent paths with a consistent placeholder # so the output is deterministic. log = re.sub(r"/tmp/[^ ]+ ", "/NORMALIZED_TMP_FILE_PATH ", log) log = re.sub(r"/build/work/[^/]+", "/NORMALIZED_BUILD_PATH", log) return html.escape(log, quote=True)