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 = list(tf.train.summary_iterator(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 = tf.GraphDef()
g.ParseFromString(event.graph_def)
self.assertMultiLineEqual(
str(g),
str(tb_exporter.nets_to_graph_def([net])))
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])))
def tensorboard_graphs(c2_netdef, tf_dir):
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
def parse_net_def(path):
import google.protobuf.text_format
net_def = caffe2_pb2.NetDef()
with open(path) as f:
google.protobuf.text_format.Merge(f.read(), net_def)
return core.Net(net_def)
graph_defs = [tb_exporter.nets_to_graph_def([parse_net_def(path)])
for path in c2_netdef]
events = [graph_def_to_event(i, graph_def)
for (i, graph_def) in enumerate(graph_defs, start=1)]
write_events(tf_dir, events)
log.info("Wrote %s graphs to logdir %s", len(events), tf_dir)
def tensorboard_graphs(c2_netdef, tf_dir):
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
def parse_net_def(path):
import google.protobuf.text_format # type: ignore[import]
net_def = caffe2_pb2.NetDef()
with open(path) as f:
google.protobuf.text_format.Merge(f.read(), net_def)
return core.Net(net_def)
graph_defs = [tb_exporter.nets_to_graph_def([parse_net_def(path)])
for path in c2_netdef]
events = [graph_def_to_event(i, graph_def)
for (i, graph_def) in enumerate(graph_defs, start=1)]
write_events(tf_dir, events)
log.info("Wrote %s graphs to logdir %s", len(events), tf_dir)
def visualize_net(nets, **kwargs):
g = tb_exporter.nets_to_graph_def(nets, **kwargs)
_show_graph(g)
def visualize_net(nets, **kwargs):
g = tb_exporter.nets_to_graph_def(nets, **kwargs)
_show_graph(g)
