1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
|
import click.testing
import numpy as np
import os
import tempfile
import unittest
from caffe2.python import brew, core, model_helper
import caffe2.contrib.tensorboard.tensorboard as tb
import caffe2.contrib.tensorboard.tensorboard_exporter as tb_exporter
try:
# tensorboard>=1.14.0
from tensorboard.compat.proto.graph_pb2 import GraphDef
except ImportError:
from tensorflow import GraphDef
def load_events(filename):
try:
# tensorboard>=1.14.0
from tensorboard.backend.event_processing import event_file_loader
loader = event_file_loader.EventFileLoader(filename)
return list(loader.Load())
except ImportError:
import tensorflow as tf
return list(tf.train.summary_iterator(filename))
class TensorboardTest(unittest.TestCase):
def test_events(self):
runner = click.testing.CliRunner()
c2_dir = tempfile.mkdtemp()
np.random.seed(1701)
n_iters = 2
blobs = ["w", "b"]
data = np.random.randn(len(blobs), n_iters, 10)
for i, blob in enumerate(blobs):
with open(os.path.join(c2_dir, blob), "w") as f:
for row in data[i]:
stats = [row.min(), row.max(), row.mean(), row.std()]
f.write(" ".join(str(s) for s in stats) + "\n")
# Test error handling path
with open(os.path.join(c2_dir, "not-a-summary"), "w") as f:
f.write("not-a-summary")
tf_dir = tempfile.mkdtemp()
result = runner.invoke(
tb.cli,
["tensorboard-events", "--c2-dir", c2_dir, "--tf-dir", tf_dir])
self.assertEqual(result.exit_code, 0)
entries = list(os.walk(tf_dir))
self.assertEqual(len(entries), 1)
((d, _, (fname,)),) = entries
self.assertEqual(tf_dir, d)
events = load_events(os.path.join(tf_dir, fname))
self.assertEqual(len(events), n_iters + 1)
events = events[1:]
self.maxDiff = None
self.assertEqual(len(events), 2)
def test_tensorboard_graphs(self):
model = model_helper.ModelHelper(name="overfeat")
data, label = brew.image_input(
model, ["db"], ["data", "label"], is_test=0
)
with core.NameScope("conv1"):
conv1 = brew.conv(model, data, "conv1", 3, 96, 11, stride=4)
relu1 = brew.relu(model, conv1, conv1)
pool1 = brew.max_pool(model, relu1, "pool1", kernel=2, stride=2)
with core.NameScope("classifier"):
fc = brew.fc(model, pool1, "fc", 4096, 1000)
pred = brew.softmax(model, fc, "pred")
xent = model.LabelCrossEntropy([pred, label], "xent")
loss = model.AveragedLoss(xent, "loss")
model.AddGradientOperators([loss], skip=1)
c2_dir = tempfile.mkdtemp()
tf_dir = tempfile.mkdtemp()
with open(os.path.join(c2_dir, "init"), "w") as f:
f.write(str(model.param_init_net.Proto()))
with open(os.path.join(c2_dir, "net"), "w") as f:
f.write(str(model.net.Proto()))
runner = click.testing.CliRunner()
result = runner.invoke(
tb.cli,
["tensorboard-graphs",
"--c2-netdef", os.path.join(c2_dir, "init"),
"--c2-netdef", os.path.join(c2_dir, "net"),
"--tf-dir", tf_dir])
self.assertEqual(result.exit_code, 0)
entries = list(os.walk(tf_dir))
self.assertEqual(len(entries), 1)
((d, _, (fname,)),) = entries
self.assertEqual(tf_dir, d)
events = load_events(os.path.join(tf_dir, fname))
self.assertEqual(len(events), 3)
events = events[1:]
nets = [model.param_init_net, model.net]
for i, (event, net) in enumerate(zip(events, nets), start=1):
self.assertEqual(event.step, i)
self.assertEqual(event.wall_time, i)
g = GraphDef()
g.ParseFromString(event.graph_def)
self.assertMultiLineEqual(
str(g),
str(tb_exporter.nets_to_graph_def([net])))
if __name__ == "__main__":
unittest.main()
|
