def test_simple_cnnmodel(self):
model = cnn.CNNModelHelper("NCHW", name="overfeat")
data, label = model.ImageInput(["db"], ["data", "label"], is_test=0)
with core.NameScope("conv1"):
conv1 = model.Conv(data, "conv1", 3, 96, 11, stride=4)
relu1 = model.Relu(conv1, conv1)
pool1 = model.MaxPool(relu1, "pool1", kernel=2, stride=2)
with core.NameScope("classifier"):
fc = model.FC(pool1, "fc", 4096, 1000)
pred = model.Softmax(fc, "pred")
xent = model.LabelCrossEntropy([pred, label], "xent")
loss = model.AveragedLoss(xent, "loss")
model.net.RunAllOnGPU()
model.param_init_net.RunAllOnGPU()
model.AddGradientOperators([loss], skip=1)
track_blob_names = {}
graph = tb.cnn_to_graph_def(
model,
track_blob_names=track_blob_names,
shapes={},
)
self.assertEqual(
track_blob_names['GRADIENTS/conv1/conv1_b_grad'],
'conv1/conv1_b_grad',
)
self.maxDiff = None
# We can't guarantee the order in which they appear, so we sort
# both before we compare them
sep = "node {"
expected = "\n".join(sorted(
sep + "\n " + part.strip()
for part in EXPECTED.strip().split(sep)
if part.strip()
))
actual = "\n".join(sorted(
sep + "\n " + part.strip()
for part in str(graph).strip().split(sep)
if part.strip()
))
self.assertMultiLineEqual(actual, expected)
def test_simple_cnnmodel(self):
model = cnn.CNNModelHelper("NCHW", name="overfeat")
data, label = model.ImageInput(["db"], ["data", "label"], is_test=0)
with core.NameScope("conv1"):
conv1 = model.Conv(data, "conv1", 3, 96, 11, stride=4)
relu1 = model.Relu(conv1, conv1)
pool1 = model.MaxPool(relu1, "pool1", kernel=2, stride=2)
with core.NameScope("classifier"):
fc = model.FC(pool1, "fc", 4096, 1000)
pred = model.Softmax(fc, "pred")
xent = model.LabelCrossEntropy([pred, label], "xent")
loss = model.AveragedLoss(xent, "loss")
model.net.RunAllOnGPU()
model.param_init_net.RunAllOnGPU()
model.AddGradientOperators([loss], skip=1)
track_blob_names = {}
graph = tb.cnn_to_graph_def(
model,
track_blob_names=track_blob_names,
shapes={},
)
self.assertEqual(
track_blob_names['GRADIENTS/conv1/conv1_b_grad'],
'conv1/conv1_b_grad',
)
self.maxDiff = None
# We can't guarantee the order in which they appear, so we sort
# both before we compare them
sep = "node {"
expected = "\n".join(
sorted(sep + "\n " + part.strip()
for part in EXPECTED.strip().split(sep) if part.strip()))
actual = "\n".join(
sorted(sep + "\n " + part.strip()
for part in str(graph).strip().split(sep) if part.strip()))
self.assertMultiLineEqual(actual, expected)
def visualize_cnn(cnn, **kwargs):
g = tb_exporter.cnn_to_graph_def(cnn, **kwargs)
_show_graph(g)
def visualize_cnn(cnn, **kwargs):
g = tb_exporter.cnn_to_graph_def(cnn, **kwargs)
_show_graph(g)