28602b1084
The testing is for both MLIR-based and TOCO based converters. Calling these "toco" is confusing PiperOrigin-RevId: 351158814 Change-Id: If659d97a2b38feee980d9533db83e73b6798e2d1
127 lines
4.0 KiB
Python
127 lines
4.0 KiB
Python
# 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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"""Make HTML tables that report where TF and TOCO failed to convert models.
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This is primarily used by generate_examples.py. See it or
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`make_report_table` for more details on usage.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import html
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import json
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FAILED = "FAILED"
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SUCCESS = "SUCCESS"
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NOTRUN = "NOTRUN"
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def make_report_table(fp, title, reports):
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"""Make an HTML report of the success/failure reports.
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Args:
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fp: File-like object in which to put the html.
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title: "Title of the zip file this pertains to."
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reports: a list of conversion attempts. (report_args, report_vals) i.e.
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({"shape": [1,2,3], "type": "tf.float32"},
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{"tf": "SUCCESS", "converter": "FAILURE",
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"converter_log": "Unsupported type.", "tf_log": ""})
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"""
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# sort reports by if TOCO failure and then TF failure (reversed)
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reports.sort(key=lambda x: x[1]["converter"], reverse=False)
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reports.sort(key=lambda x: x[1]["tf"], reverse=True)
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def result_cell(x, row, col):
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"""Produce a cell with the condition string `x`."""
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s = html.escape(repr(x), quote=True)
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color = "#44ff44" if x == SUCCESS else (
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"#ff4444" if x == FAILED else "#eeeeee")
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handler = "ShowLog(%d, %d)" % (row, col)
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fp.write("<td style='background-color: %s' onclick='%s'>%s</td>\n" % (
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color, handler, s))
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fp.write("""<html>
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<head>
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<title>tflite report</title>
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<style>
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body { font-family: Arial; }
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th { background-color: #555555; color: #eeeeee; }
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td { vertical-align: top; }
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td.horiz {width: 50%;}
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pre { white-space: pre-wrap; word-break: keep-all; }
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table {width: 100%;}
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</style>
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</head>
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""")
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# Write the log data to a javascript variable and also make a function
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# in javascript to show the log when an item is clicked.
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fp.write("<script> \n")
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fp.write("""
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function ShowLog(row, col) {
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var log = document.getElementById("log");
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log.innerHTML = "<pre>" + data[row][col] + "</pre>";
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}
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""")
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fp.write("var data = \n")
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logs = json.dumps([[html.escape(x[1]["tf_log"], quote=True),
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html.escape(x[1]["converter_log"], quote=True)
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] for x in reports])
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fp.write(logs)
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fp.write(";</script>\n")
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# Write the main table and use onclick on the items that have log items.
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fp.write("""
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<body>
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<h1>TOCO Conversion</h1>
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<h2>%s</h2>
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""" % title)
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# Get a list of keys that are in any of the records.
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param_keys = {}
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for params, _ in reports:
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for k in params.keys():
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param_keys[k] = True
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fp.write("<table>\n")
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fp.write("<tr><td class='horiz'>\n")
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fp.write("<div style='height:1000px; overflow:auto'>\n")
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fp.write("<table>\n")
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fp.write("<tr>\n")
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for p in param_keys:
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fp.write("<th>%s</th>\n" % html.escape(p, quote=True))
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fp.write("<th>TensorFlow</th>\n")
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fp.write("<th>TOCO</th>\n")
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fp.write("</tr>\n")
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for idx, (params, vals) in enumerate(reports):
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fp.write("<tr>\n")
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for p in param_keys:
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fp.write(" <td>%s</td>\n" % html.escape(repr(params[p]), quote=True))
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result_cell(vals["tf"], idx, 0)
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result_cell(vals["converter"], idx, 1)
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fp.write("</tr>\n")
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fp.write("</table>\n")
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fp.write("</div>\n")
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fp.write("</td>\n")
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fp.write("<td class='horiz' id='log'></td></tr>\n")
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fp.write("</table>\n")
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fp.write("<script>\n")
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fp.write("</script>\n")
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fp.write("""
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</body>
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</html>
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""")
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