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#!/usr/bin/env python
# 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.
# ==============================================================================
"""This tool creates an html visualization of a TensorFlow Lite graph.
Example usage:
python visualize.py foo.tflite foo.html
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import sys
# Schema to use for flatbuffers
_SCHEMA = "third_party/tensorflow/contrib/lite/schema/schema.fbs"
# Where the binary will be once built in for the flatc converter
_BINARY = "third_party/flatbuffers/flatc"
# A CSS description for making the visualizer
_CSS = """
<html>
<head>
<style>
body {font-family: sans-serif; background-color: #ffaa00;}
table {background-color: #eeccaa;}
th {background-color: black; color: white;}
h1 {
background-color: ffaa00;
padding:5px;
color: black;
}
div {
border-radius: 5px;
background-color: #ffeecc;
padding:5px;
margin:5px;
}
.tooltip {color: blue;}
.tooltip .tooltipcontent {
visibility: hidden;
color: black;
background-color: yellow;
padding: 5px;
border-radius: 4px;
position: absolute;
z-index: 1;
}
.tooltip:hover .tooltipcontent {
visibility: visible;
}
.edges line {
stroke: #333333;
}
.nodes text {
color: black;
pointer-events: none;
font-family: sans-serif;
font-size: 11px;
}
</style>
<script src="https://d3js.org/d3.v4.min.js"></script>
</head>
<body>
"""
_D3_HTML_TEMPLATE = """
<script>
// Build graph data
var graph = %s;
var svg = d3.select("#subgraph%d");
var width = svg.attr("width");
var height = svg.attr("height");
var color = d3.scaleOrdinal(d3.schemeCategory20);
var simulation = d3.forceSimulation()
.force("link", d3.forceLink().id(function(d) {return d.id;}))
.force("charge", d3.forceManyBody())
.force("center", d3.forceCenter(0.5 * width, 0.5 * height));
function buildGraph() {
var edge = svg.append("g").attr("class", "edges").selectAll("line")
.data(graph.edges).enter().append("line")
// Make the node group
var node = svg.selectAll(".nodes")
.data(graph.nodes)
.enter().append("g")
.attr("class", "nodes")
.call(d3.drag()
.on("start", function(d) {
if(!d3.event.active) simulation.alphaTarget(1.0).restart();
d.fx = d.x;d.fy = d.y;
})
.on("drag", function(d) {
d.fx = d3.event.x; d.fy = d3.event.y;
})
.on("end", function(d) {
if (!d3.event.active) simulation.alphaTarget(0);
d.fx = d.fy = null;
}));
// Within the group, draw a circle for the node position and text
// on the side.
node.append("circle")
.attr("r", "5px")
.attr("fill", function(d) { return color(d.group); })
node.append("text")
.attr("dx", 8).attr("dy", 5).text(function(d) { return d.name; });
// Setup force parameters and update position callback
simulation.nodes(graph.nodes).on("tick", forceSimulationUpdated);
simulation.force("link").links(graph.edges);
function forceSimulationUpdated() {
// Update edges.
edge.attr("x1", function(d) {return d.source.x;})
.attr("y1", function(d) {return d.source.y;})
.attr("x2", function(d) {return d.target.x;})
.attr("y2", function(d) {return d.target.y;});
// Update node positions
node.attr("transform", function(d) { return "translate(" + d.x + "," + d.y + ")"; });
}
}
buildGraph()
</script>
"""
class OpCodeMapper(object):
"""Maps an opcode index to an op name."""
def __init__(self, data):
self.code_to_name = {}
for idx, d in enumerate(data["operator_codes"]):
self.code_to_name[idx] = d["builtin_code"]
def __call__(self, x):
if x not in self.code_to_name:
s = "<UNKNOWN>"
else:
s = self.code_to_name[x]
return "%s (opcode=%d)" % (s, x)
class DataSizeMapper(object):
"""For buffers, report the number of bytes."""
def __call__(self, x):
if x is not None:
return "%d bytes" % len(x)
else:
return "--"
class TensorMapper(object):
"""Maps a list of tensor indices to a tooltip hoverable indicator of more."""
def __init__(self, subgraph_data):
self.data = subgraph_data
def __call__(self, x):
html = ""
html += "<span class='tooltip'><span class='tooltipcontent'>"
for i in x:
tensor = self.data["tensors"][i]
html += str(i) + " "
html += tensor["name"] + " "
html += str(tensor["type"]) + " "
html += repr(tensor["shape"]) + "<br>"
html += "</span>"
html += repr(x)
html += "</span>"
return html
def GenerateGraph(subgraph_idx, g, opcode_mapper):
"""Produces the HTML required to have a d3 visualization of the dag."""
def TensorName(idx):
return "t%d" % idx
def OpName(idx):
return "o%d" % idx
edges = []
nodes = []
first = {}
pixel_mult = 50 # TODO(aselle): multiplier for initial placement
for op_index, op in enumerate(g["operators"]):
for tensor_input_position, tensor_index in enumerate(op["inputs"]):
if tensor_index not in first:
first[tensor_index] = (
op_index * pixel_mult,
tensor_input_position * pixel_mult - pixel_mult / 2)
edges.append({
"source": TensorName(tensor_index),
"target": OpName(op_index)
})
for tensor_index in op["outputs"]:
edges.append({
"target": TensorName(tensor_index),
"source": OpName(op_index)
})
nodes.append({
"id": OpName(op_index),
"name": opcode_mapper(op["opcode_index"]),
"group": 2,
"x": pixel_mult,
"y": op_index * pixel_mult
})
for tensor_index, tensor in enumerate(g["tensors"]):
initial_y = (
first[tensor_index] if tensor_index in first else len(g["operators"]))
nodes.append({
"id": TensorName(tensor_index),
"name": "%s (%d)" % (tensor["name"], tensor_index),
"group": 1,
"x": 2,
"y": initial_y
})
graph_str = json.dumps({"nodes": nodes, "edges": edges})
html = _D3_HTML_TEMPLATE % (graph_str, subgraph_idx)
return html
def GenerateTableHtml(items, keys_to_print, display_index=True):
"""Given a list of object values and keys to print, make an HTML table.
Args:
items: Items to print an array of dicts.
keys_to_print: (key, display_fn). `key` is a key in the object. i.e.
items[0][key] should exist. display_fn is the mapping function on display.
i.e. the displayed html cell will have the string returned by
`mapping_fn(items[0][key])`.
display_index: add a column which is the index of each row in `items`.
Returns:
An html table.
"""
html = ""
# Print the list of items
html += "<table><tr>\n"
html += "<tr>\n"
if display_index:
html += "<th>index</th>"
for h, mapper in keys_to_print:
html += "<th>%s</th>" % h
html += "</tr>\n"
for idx, tensor in enumerate(items):
html += "<tr>\n"
if display_index:
html += "<td>%d</td>" % idx
# print tensor.keys()
for h, mapper in keys_to_print:
val = tensor[h] if h in tensor else None
val = val if mapper is None else mapper(val)
html += "<td>%s</td>\n" % val
html += "</tr>\n"
html += "</table>\n"
return html
def CreateHtmlFile(tflite_input, html_output):
"""Given a tflite model in `tflite_input` file, produce html description."""
# Convert the model into a JSON flatbuffer using flatc (build if doesn't
# exist.
if not os.path.exists(tflite_input):
raise RuntimeError("Invalid filename %r" % tflite_input)
if tflite_input.endswith(".tflite") or tflite_input.endswith(".bin"):
# Run convert
cmd = (
_BINARY + " -t "
"--strict-json --defaults-json -o /tmp {schema} -- {input}".format(
input=tflite_input, schema=_SCHEMA))
print(cmd)
os.system(cmd)
real_output = ("/tmp/" + os.path.splitext(
os.path.split(tflite_input)[-1])[0] + ".json")
data = json.load(open(real_output))
elif tflite_input.endswith(".json"):
data = json.load(open(tflite_input))
else:
raise RuntimeError("Input file was not .tflite or .json")
html = ""
html += _CSS
html += "<h1>TensorFlow Lite Model</h2>"
data["filename"] = tflite_input # Avoid special case
toplevel_stuff = [("filename", None), ("version", None), ("description",
None)]
html += "<table>\n"
for key, mapping in toplevel_stuff:
if not mapping:
mapping = lambda x: x
html += "<tr><th>%s</th><td>%s</td></tr>\n" % (key, mapping(data[key]))
html += "</table>\n"
# Spec on what keys to display
buffer_keys_to_display = [("data", DataSizeMapper())]
operator_keys_to_display = [("builtin_code", None)]
for subgraph_idx, g in enumerate(data["subgraphs"]):
# Subgraph local specs on what to display
html += "<div class='subgraph'>"
tensor_mapper = TensorMapper(g)
opcode_mapper = OpCodeMapper(data)
op_keys_to_display = [("inputs", tensor_mapper), ("outputs", tensor_mapper),
("builtin_options", None), ("opcode_index",
opcode_mapper)]
tensor_keys_to_display = [("name", None), ("type", None), ("shape", None),
("buffer", None), ("quantization", None)]
html += "<h2>Subgraph %d</h2>\n" % subgraph_idx
# Inputs and outputs.
html += "<h3>Inputs/Outputs</h3>\n"
html += GenerateTableHtml(
[{
"inputs": g["inputs"],
"outputs": g["outputs"]
}], [("inputs", tensor_mapper), ("outputs", tensor_mapper)],
display_index=False)
# Print the tensors.
html += "<h3>Tensors</h3>\n"
html += GenerateTableHtml(g["tensors"], tensor_keys_to_display)
# Print the ops.
html += "<h3>Ops</h3>\n"
html += GenerateTableHtml(g["operators"], op_keys_to_display)
# Visual graph.
html += "<svg id='subgraph%d' width='960' height='1600'></svg>\n" % (
subgraph_idx,)
html += GenerateGraph(subgraph_idx, g, opcode_mapper)
html += "</div>"
# Buffers have no data, but maybe in the future they will
html += "<h2>Buffers</h2>\n"
html += GenerateTableHtml(data["buffers"], buffer_keys_to_display)
# Operator codes
html += "<h2>Operator Codes</h2>\n"
html += GenerateTableHtml(data["operator_codes"], operator_keys_to_display)
html += "</body></html>\n"
open(html_output, "w").write(html)
def main(argv):
try:
tflite_input = argv[1]
html_output = argv[2]
except IndexError:
print("Usage: %s <input tflite> <output html>" % (argv[0]))
else:
CreateHtmlFile(tflite_input, html_output)
if __name__ == "__main__":
main(sys.argv)
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