def test_placeholder(self):
"""Test placeholder functionalities."""
g0_graph = tf.Graph()
with g0_graph.as_default():
tf.constant(1, name="foo")
g0 = gde.Graph(g0_graph)
a0 = g0["foo"].output(0)
# Test placeholder name.
self.assertEqual(gde.util.placeholder_name(a0), "geph__foo_0")
self.assertEqual(gde.util.placeholder_name(None), "geph")
self.assertEqual(gde.util.placeholder_name(a0, scope="foo/"),
"foo/geph__foo_0")
self.assertEqual(gde.util.placeholder_name(a0, scope="foo"),
"foo/geph__foo_0")
self.assertEqual(gde.util.placeholder_name(None, scope="foo/"),
"foo/geph")
self.assertEqual(gde.util.placeholder_name(None, scope="foo"),
"foo/geph")
# Test placeholder creation.
g1_graph = tf.Graph()
with g1_graph.as_default():
tf.constant(1, dtype=tf.float32, name="a1")
g1 = gde.Graph(g1_graph)
a1_tensor = g1["a1"].output(0)
print("Type of a1_tensor is {}".format(type(a1_tensor)))
ph1 = gde.util.make_placeholder_from_tensor(g1, a1_tensor)
ph2 = gde.util.make_placeholder_from_dtype_and_shape(g1,
dtype=tf.float32)
self.assertEqual(ph1.name, "geph__a1_0")
self.assertEqual(ph2.name, "geph")
def test_graph_while_loop(self):
tf_graph = tf.Graph()
with tf_graph.as_default():
max_index = tf.placeholder(dtype=tf.int32, shape=tuple())
index_start = tf.constant(1)
sum_start = tf.constant(0)
_, result = tf.while_loop(
cond=lambda i, unused_s: i <= max_index,
body=lambda i, s: (i + 1, s + i),
loop_vars=[index_start, sum_start])
g = gde.Graph(tf_graph)
result_tensor = g[result.op.name].output(0)
max_index_tensor = g[max_index.op.name].output(0)
g.frozen = True
copied_graph = gde.Graph()
_, copy_info = gde.copy(
g, dst_graph=copied_graph, dst_scope="imported")
copied_result_tensor = copy_info.transformed(result_tensor)
copied_max_index_tensor = copy_info.transformed(max_index_tensor)
tf_copied_graph = tf.Graph()
with tf_copied_graph.as_default():
tf.import_graph_def(copied_graph.to_graph_def(), name="")
with tf.Session() as sess:
n = 10
sum_val = sess.run(copied_result_tensor.name,
feed_dict={copied_max_index_tensor.name: n})
self.assertEqual(sum_val, 55)
def test_graph_cond(self):
tf_g = tf.Graph()
with tf_g.as_default():
choice_tensor = tf.placeholder(shape=(), dtype=tf.bool, name="choice")
_ = tf.identity(
tf.cond(
choice_tensor,
lambda: tf.constant(1),
lambda: tf.constant(2)
),
name="result"
)
g = gde.Graph(tf_g)
choice = g["choice"].output(0)
result = g["result"].output(0)
copied_g = gde.Graph()
_, copy_info = gde.copy(
g, dst_graph=copied_g, dst_scope="imported")
copied_result = copy_info.transformed(result)
copied_choice = copy_info.transformed(choice)
tf_copied_graph = tf.Graph()
with tf_copied_graph.as_default():
tf.import_graph_def(copied_g.to_graph_def(), name="")
with tf.Session() as sess:
res = sess.run(copied_result.name, feed_dict={copied_choice.name: True})
self.assertEqual(res, 1)
res = sess.run(copied_result.name,
feed_dict={copied_choice.name: False})
self.assertEqual(res, 2)
def test_unique_graph(self):
"""Test for gde.util.check_graphs and gde.util.get_unique_graph."""
g0_graph = tf.Graph()
with g0_graph.as_default():
tf.constant(1, name="a")
tf.constant(2, name="b")
g1_graph = tf.Graph()
with g1_graph.as_default():
tf.constant(1, name="a")
tf.constant(2, name="b")
g0 = gde.Graph(g0_graph.as_graph_def())
g1 = gde.Graph(g1_graph.as_graph_def())
a0, b0, a1, b1 = (g0["a"], g0["b"], g1["a"], g1["b"])
print("g0['a'] returns {} (type {})".format(g0['a'], type(g0['a'])))
# Same graph, should be fine.
self.assertIsNone(gde.util.check_graphs(a0, b0))
# Two different graphs, should assert.
with self.assertRaises(ValueError):
gde.util.check_graphs(a0, b0, a1, b1)
# a0 and b0 belongs to the same graph, should be fine.
self.assertEqual(gde.util.get_unique_graph([a0, b0]), g0)
# Different graph, should raise an error.
with self.assertRaises(ValueError):
gde.util.get_unique_graph([a0, b0, a1, b1])
def test_copy_with_collection(self):
"""Test for issue #36"""
tmp_graph = tf.Graph()
with tmp_graph.as_default():
c = tf.constant(42, name="FortyTwo")
tmp_graph.add_to_collection("Answers", c)
g = gde.Graph(tmp_graph)
g2 = gde.Graph()
gde.transform.copy(g, g2)
self.assertTrue("Answers" in g2.get_all_collection_keys())
def test_graph_collection_types(self):
# Build a graph with NodeList that has an operation and tensor,
# and ByteList with variable
tf_g = tf.Graph()
with tf_g.as_default():
y_ = tf.placeholder(tf.int64, [None])
x = tf.get_variable("x", [1])
with tf.name_scope('loss'):
cross_entropy = tf.losses.sparse_softmax_cross_entropy(
labels=y_, logits=x)
with tf.name_scope('adam_optimizer'):
_ = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
g = gde.Graph(tf_g)
keys = g.get_all_collection_keys()
# Check that loss tensor added to collection
self.assertIn('losses', keys)
t = g.get_tensor_by_name(
"loss/sparse_softmax_cross_entropy_loss/value:0")
self.assertIn('losses', t.collection_names)
# Check that variable added to collection
self.assertIn('variables', keys)
v = g.get_variable_by_name('x:0')
self.assertIn('variables', v.collection_names)
# Check that op added to collection
self.assertIn('train_op', keys)
n = g.get_node_by_name("adam_optimizer/Adam")
self.assertIn('train_op', n.collection_names)
def test_change_batch_size_variable_size(self):
"""
Verifies that a batch size of None (variable size) works.
Also verifies that passing a tensor instead of node works.
"""
tf_g = tf.Graph()
with tf_g.as_default():
input_tensor = tf.placeholder(dtype=tf.float32,
shape=[32, 1],
name="Input")
result_tensor = input_tensor + 42.0
g = gde.Graph(tf_g)
# Note that we pass a Tensor as the third argument instead of a Node.
gde.rewrite.change_batch_size(g, None, [g[input_tensor.name]])
with g.to_tf_graph().as_default():
with tf.Session() as sess:
result = sess.run(
result_tensor.name,
{input_tensor.name: np.array([0]).reshape([1, 1])})
self.assertTrue(
np.array_equal(result,
np.array([42.]).reshape([1, 1])))
result = sess.run(
result_tensor.name,
{input_tensor.name: np.array([0, 1]).reshape([2, 1])})
self.assertTrue(
np.array_equal(result,
np.array([42., 43.]).reshape([2, 1])))
def test_transform_nodedef_fn(self):
transformer = gde.Transformer()
def nodedef_fn(node_def):
if "_foo" in node_def.attr:
del node_def.attr["_foo"]
node_def.attr["_bar"].s = b"bar"
return node_def
my_copy_op_handler = functools.partial(
gde.transform.copy_op_handler, nodedef_fn=nodedef_fn)
transformer.transform_op_handler = my_copy_op_handler
graph = gde.Graph()
transformer(self.graph, graph, "", "")
c0_before = self.graph["Const"]
c0_after = graph["Const"]
self.assertEqual(c0_before.get_attr("_foo"), "foo")
with self.assertRaises(ValueError):
c0_after.get_attr("_foo")
all_ops = graph.nodes
for op in all_ops:
self.assertEqual(op.get_attr("_bar"), "bar")
def main(_):
# Create a graph
tf_g = tf.Graph()
with tf_g.as_default():
a = tf.constant(1.0, shape=[2, 3], name="a")
c = tf.add(tf.placeholder(dtype=np.float32),
tf.placeholder(dtype=np.float32),
name="c")
# Serialize the graph
g = gde.Graph(tf_g.as_graph_def())
print("Before:\n{}".format(_indent(g.to_graph_def())))
# Modify the graph.
# In this case we replace the two input placeholders with constants.
# One of the constants (a) is a node that was in the original graph.
# The other one (b) we create here.
b = gde.make_const(g, "b", np.full([2, 3], 2.0, dtype=np.float32))
gde.swap_inputs(g[c.op.name], [g[a.name], b.output(0)])
print("After:\n{}".format(_indent(g.to_graph_def())))
# Reconstitute the modified serialized graph as TensorFlow graph...
with g.to_tf_graph().as_default():
# ...and print the value of c, which should be 2x3 matrix of 3.0's
with tf.Session() as sess:
res = sess.run(c.name)
print("Result is:\n{}".format(_indent(res)))
def test_graph_replace_gradients(self):
tmp_graph = tf.Graph()
with tmp_graph.as_default():
w_tensor = tf.Variable(0.0, name="w")
y_tensor = tf.multiply(tf.multiply(w_tensor, w_tensor, name="mul1"),
w_tensor, name="mul2")
grad_tensor = tf.gradients(y_tensor, w_tensor, name="gradient")[0]
_ = tf.identity(grad_tensor, "grad")
g = gde.Graph(tmp_graph)
# Extract the operations.
replacement_ts = {g["w/read"].output(0): g["grad"].output(0)}
# Should not raise exception.
res = gde.graph_replace(g["grad"].output(0), replacement_ts,
dst_scope="res")
self.assertNotEqual(res.name, g["grad"].output(0).name)
after_graph = tf.Graph()
with after_graph.as_default():
tf.import_graph_def(g.to_graph_def(), name="")
gde.util.load_variables_to_tf_graph(g)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
g_val, res_val = sess.run([g["grad"].output(0).name, res.name])
self.assertNear(g_val, 0.0, ERROR_TOLERANCE)
self.assertNear(res_val, 0.0, ERROR_TOLERANCE)
def test_fold_batch_norms_mat_mul(self):
"""
Python port of TestFoldBatchNormsMatMul in the TF Graph Transform Tool
tests.
"""
input_data = (np.array(
[1., 4., 2., 5., 3., 6., -1., -4., -2., -5., -3., -6.],
dtype=np.float32).reshape([6, 2]))
weights_data = (np.array([1., 2., 3., 4.],
dtype=np.float32).reshape([2, 2]))
mul_values_data = (np.array([2., 3.], dtype=np.float32).reshape([2]))
# Create and run graph:
# (input, weights) --> MatMul --> Mul(const)
tf_g = tf.Graph()
with tf_g.as_default():
in_t = tf.constant(input_data, name="input_op")
weights_t = tf.constant(weights_data, name="weights_op")
matmul_t = tf.linalg.matmul(in_t, weights_t, name="matmul_op")
mul_values_t = tf.constant(mul_values_data, name="mul_values")
output_t = tf.multiply(matmul_t, mul_values_t, name="output")
with tf.Session(graph=tf_g) as sess:
original_outputs = sess.run(output_t)
# Rewrite and compare results
g = gde.Graph(tf_g)
gde.rewrite.fold_batch_norms(g)
with tf.Session(graph=g.to_tf_graph()) as sess:
fused_outputs = sess.run(output_t.name)
self.assertClose(original_outputs, fused_outputs, delta=1e-5)
# Make sure the rewrite happened
for n in g.nodes:
self.assertNotEqual(n.op_type, "Mul")
def test_fold_batch_norms_up_fused(self):
"""
Test of the fold_batch_norms_up() rewrite with the pattern:
FusedBatchNorm => Relu => Conv2D
"""
input_data = (np.array(
[1., 4., 2., 5., 3., 6., -1., -4., -2., -5., -3., -6.],
dtype=np.float32).reshape([1, 1, 6, 2]))
weights_data = (np.array([1., 2., 3., 4., 0.1, 0.2, 0.3, 0.4],
dtype=np.float32).reshape([1, 2, 2, 2]))
mean_data = np.array([10., 20.], dtype=np.float32).reshape([2])
variance_data = np.array([0.25, 0.5], dtype=np.float32).reshape([2])
beta_data = np.array([0.1, 0.6], dtype=np.float32).reshape([2])
gamma_data = np.array([1., 2.], dtype=np.float32).reshape([2])
# Create the non-deprecated part of the graph
# input -> Relu -> Conv2D
tf_g = tf.Graph()
with tf_g.as_default():
in_t = tf.constant(input_data, name="input_op")
relu_t = tf.nn.relu(in_t, name="relu_op")
weights_t = tf.constant(weights_data, name="weights_op")
conv_t = tf.nn.conv2d(relu_t,
weights_t, [1, 1, 1, 1],
"VALID",
name="output")
mean_t = tf.constant(mean_data, name="mean_op")
variance_t = tf.constant(variance_data, name="variance_op")
beta_t = tf.constant(beta_data, name="beta_op")
gamma_t = tf.constant(gamma_data, name="gamma_op")
g = gde.Graph(tf_g)
# Add fused batch norm node manually because there's no Python API to add
# this op directly.
batch_norm_node = g.add_node("batch_norm_op", "FusedBatchNorm")
batch_norm_node.set_inputs([
g[in_t.name], g[gamma_t.name], g[beta_t.name], g[mean_t.name],
g[variance_t.name]
])
batch_norm_node.add_attr("T", tf.float32)
batch_norm_node.add_attr("epsilon", 0.00001)
batch_norm_node.add_attr("is_training", False)
batch_norm_node.infer_outputs()
# Redirect the input of the ReLU to our new batch norm
g.get_node_by_name(relu_t.op.name).set_inputs(
[batch_norm_node.output(0)])
# Run the graph before and after the rewrite and compare results
with tf.Session(graph=g.to_tf_graph()) as sess:
original_outputs = sess.run("output:0")
gde.rewrite.fold_batch_norms_up(g)
with tf.Session(graph=g.to_tf_graph()) as sess:
fused_outputs = sess.run("output:0")
self.assertClose(original_outputs, fused_outputs, delta=1e-5)
# Make sure the rewrite happened
for n in g.nodes:
self.assertNotEqual(n.op_type, "FusedBatchNorm")
def build_graph(self):
tf_g = tf.Graph()
with tf_g.as_default():
a = tf.constant(1, name="a")
b = tf.constant(2, name="b")
c = tf.constant(10, name="c")
add_res = tf.add(a, b, name="add")
res = tf.multiply(add_res, c, name="mult")
g = gde.Graph(g=tf_g)
return g
def test_fold_fused_batch_norms(self):
"""
Version of test_fold_old_batch_norms() with a FusedBatchNorms op instead
of BatchNormWithGlobalNormalization
"""
input_data = (np.array(
[1., 4., 2., 5., 3., 6., -1., -4., -2., -5., -3., -6.],
dtype=np.float32).reshape([1, 1, 6, 2]))
weights_data = (np.array([1., 2., 3., 4., 0.1, 0.2, 0.3, 0.4],
dtype=np.float32).reshape([1, 2, 2, 2]))
mean_data = np.array([10., 20.], dtype=np.float32).reshape([2])
variance_data = np.array([0.25, 0.5], dtype=np.float32).reshape([2])
beta_data = np.array([0.1, 0.6], dtype=np.float32).reshape([2])
gamma_data = np.array([1., 2.], dtype=np.float32).reshape([2])
# Create the non-deprecated part of the graph
# (input, weights) --> Conv2D --> [...], plus inputs to [...]
tf_g = tf.Graph()
with tf_g.as_default():
in_t = tf.constant(input_data, name="input_op")
weights_t = tf.constant(weights_data, name="weights_op")
conv_t = tf.nn.conv2d(in_t,
weights_t, [1, 1, 1, 1],
"VALID",
name="conv_op")
mean_t = tf.constant(mean_data, name="mean_op")
variance_t = tf.constant(variance_data, name="variance_op")
beta_t = tf.constant(beta_data, name="beta_op")
gamma_t = tf.constant(gamma_data, name="gamma_op")
g = gde.Graph(tf_g)
# Add fused batch norm node manually because there's no Python API to add
# this op directly.
batch_norm_node = g.add_node("output", "FusedBatchNorm")
batch_norm_node.set_inputs([
g[conv_t.name], g[gamma_t.name], g[beta_t.name], g[mean_t.name],
g[variance_t.name]
])
batch_norm_node.add_attr("T", tf.float32)
batch_norm_node.add_attr("epsilon", 0.00001)
batch_norm_node.add_attr("is_training", False)
batch_norm_node.infer_outputs()
# Run the graph before and after the rewrite and compare results
with tf.Session(graph=g.to_tf_graph()) as sess:
original_outputs = sess.run("output:0")
gde.rewrite.fold_old_batch_norms(g)
with tf.Session(graph=g.to_tf_graph()) as sess:
fused_outputs = sess.run("output:0")
self.assertClose(original_outputs, fused_outputs, delta=1e-5)
# Make sure the rewrite happened
for n in g.nodes:
self.assertNotEqual(n.op_type, "FusedBatchNorm")
def setUp(self):
tf_graph = tf.Graph()
with tf_graph.as_default():
c0 = tf.constant(1.0, shape=[10], name="Const")
c0.op._set_attr("_foo", tf.AttrValue(s=b"foo"))
c1 = tf.constant(1.0, shape=[10], name="Const")
c2 = tf.constant(1.0, shape=[10], name="Const")
i = tf.constant(1.0, shape=[10], name="Input")
tf.identity(tf.add(c2, tf.add(c1, tf.add(c0, i))), name="o")
self.graph = gde.Graph(tf_graph)
self.o = self.graph["o"]
def test_graph_replace_missing(self):
tmp_graph = tf.Graph()
with tmp_graph.as_default():
a_tensor = tf.constant(1.0, name="a")
b_tensor = tf.constant(2.0, name="b")
_ = tf.add(a_tensor, 2 * b_tensor, name="c")
_ = tf.constant(2.0, name="d")
g = gde.Graph(tmp_graph)
res = gde.graph_replace([g["b"].output(0), g["c"].output(0)],
{g["a"].output(0): g["d"].output(0)})
self.assertEqual(res[0].name, "b:0")
self.assertEqual(res[1].name, "c_1:0")
def test_node_collection_type_unique(self):
g = gde.Graph()
a = g.add_node("a", "a_op")
a.set_outputs_from_pairs([(tf.int32, tf.TensorShape([]))])
a.add_to_collection("mixed_collection")
b = g.add_node("b", "b_op")
b.set_outputs_from_pairs([(tf.int32, tf.TensorShape([]))])
t = b.outputs[0]
t.add_to_collection("mixed_collection")
with self.assertRaisesRegex(
TypeError, "Node collections cannot be Nodes and Tensors.*"):
g.get_collection_by_name("mixed_collection")
def test_make_list_of_node(self):
"""Test for gde.util.make_list_of_op."""
g0_graph = tf.Graph()
with g0_graph.as_default():
tf.constant(1, name="a0")
tf.constant(2, name="b0")
g0 = gde.Graph(g0_graph)
# Should extract the ops from the graph.
self.assertEqual(len(gde.util.make_list_of_op(g0)), 2)
# Should extract the ops from the tuple.
self.assertEqual(len(gde.util.make_list_of_op((g0["a0"], g0["b0"]))),
2)
def _make_javascript_deployable_graph(frozen_graph_def, graph_gen, temp_dir,
saved_model_location):
# type: (tf.GraphDef, prepost.GraphGen, str, str) -> None
"""
Prepare a SavedModel directory with a graph that is deployable via
TensorFlow.js
Args:
frozen_graph_def: Base starter graph produced by inference, after turning
variables to constants but before other rewrites.
graph_gen: Callbacks for the current model
temp_dir: Temporary directory in which to dump intermediate results in
case they are needed for debugging.
saved_model_location: Location where the final output SavedModel should go
Returns:
A graph that has been optimized. No preprocessing or postprocessing ops
are attached, as the ops we would like to use for those purposes are not
currently implemented in TensorFlow.js
"""
g = gde.Graph(frozen_graph_def)
print(" Number of ops in frozen graph: {}".format(
len(frozen_graph_def.node)))
g = _apply_generic_deployment_rewrites(g, graph_gen, temp_dir)
# Graph preparation complete. Create a SavedModel "file" (actually a
# directory)
saved_model_graph = tf.Graph()
with saved_model_graph.as_default():
with tf.Session() as sess:
tf.import_graph_def(g.to_graph_def(), name="")
# simple_save needs pointers to tensors, so pull input and output
# tensors out of the graph.
inputs_dict = {
n: saved_model_graph.get_tensor_by_name(n + ":0")
for n in graph_gen.input_node_names()
}
outputs_dict = {
n: saved_model_graph.get_tensor_by_name(n + ":0")
for n in graph_gen.output_node_names()
}
if os.path.isdir(saved_model_location):
shutil.rmtree(saved_model_location)
tf.saved_model.simple_save(sess,
export_dir=saved_model_location,
inputs=inputs_dict,
outputs=outputs_dict)
print("SavedModel written to {}".format(saved_model_location))
def build_graph_with_function(self):
"""Builds a tf graph for function (x + y) * 10.0 ."""
@tf.function
def multiplier_function(v):
return tf.constant(10.0, name="function_multiplier") * v
tf_g = tf.Graph()
with tf_g.as_default():
x = tf.placeholder(name="x", dtype=tf.float32, shape=[])
y = tf.placeholder(name="y", dtype=tf.float32, shape=[])
result_op = tf.add(x, y, name="add")
func_call_op = multiplier_function(result_op)
_ = tf.identity(func_call_op, name="output")
return gde.Graph(g=tf_g)
def test_compute_boundary_ts_2(self):
"""Test for ge.compute_boundary_ts."""
tf_graph = tf.Graph()
with tf_graph.as_default():
a_tensor = tf.constant(1, name="a")
b_tensor = tf.constant(1, name="b")
c_tensor = tf.add(a_tensor, b_tensor, name="c")
_ = a_tensor + c_tensor
g = gde.Graph(tf_graph)
input_ts, output_ts, inside_ts = gde.compute_boundary_ts([g["a"], g["c"]])
self.assertEqual(list(input_ts), [g["b"].output(0)])
self.assertEqual(list(output_ts), [g["a"].output(0), g["c"].output(0)])
self.assertEqual(list(inside_ts), [g["a"].output(0)])
def _graft_pre_and_post_processing_to_main_graph(g):
# type: (gde.Graph) -> None
"""
Attach pre- and post-processing subgraphs to the main graph.
Args:
g: GDE representation of the core graph. Modified in place.
"""
# Build the pre- and post-processing subgraphs and import into GDE
pre_g = gde.Graph(_build_preprocessing_graph_def())
post_g = gde.Graph(_build_postprocessing_graph_def())
# Replace the graph's input placeholder with the contents of our
# pre-processing graph.
name_of_input_node = _INPUT_NODE_NAMES[0]
gde.copy(pre_g, g)
gde.reroute_ts(
g.get_node_by_name("preprocessed_image").output(0),
g.get_node_by_name(name_of_input_node).output(0))
g.remove_node_by_name(name_of_input_node)
g.rename_node("raw_image", name_of_input_node)
# Tack on the postprocessing graph at the original output and rename
# the postprocessed output to the original output's name
# The original graph produces an output called "detection_classes".
# The postprocessing graph goes from "detection_classes" to
# "decoded_detection_classes".
# The graph after modification produces decoded classes under the original
# "detection_classes" name. The original output is renamed to
# "raw_detection_classes".
g.rename_node("detection_classes", "raw_detection_classes")
gde.copy(post_g, g)
gde.reroute_ts(
g.get_node_by_name("raw_detection_classes").output(0),
g.get_node_by_name("detection_classes").output(0))
g.remove_node_by_name("detection_classes")
g.rename_node("decoded_detection_classes", "detection_classes")
def _create_replace_graph():
"""Subroutine of the next few tests. Creates the graph that all these
tests use. Since the tests modify the graph, it needs to be recreated
each time.
Returns:
(Graph object, c, target tensor to replace, new value, output tensor)"""
tmp_graph = tf.Graph()
with tmp_graph.as_default():
a = tf.constant(1.0, name="a")
b = tf.Variable(1.0, name="b")
eps = tf.constant(0.001, name="eps")
tf.identity(a + b + eps, name="c")
tf.constant(2.0, name="a_new")
ret = gde.Graph(tmp_graph)
return ret, ret["a"].output(0), ret["a_new"].output(0), ret["c"].output(0)
def test_identity(self):
tf_g = tf.Graph()
with tf_g.as_default():
c = tf.constant(42)
i1 = tf.identity(c, name="identity_tf")
g = gde.Graph(tf_g)
i2_node = gde.util.make_identity(g, "identity_gde",
g.get_tensor_by_name(c.name))
i2 = i2_node.outputs[0]
with g.to_tf_graph().as_default():
with tf.Session() as sess:
result1 = sess.run(i1.name)
result2 = sess.run(i2.name)
self.assertEqual(result1, result2)
def test_get_generating_consuming(self):
"""Test for gde.util.get_generating_ops and gde.util.get_generating_ops."""
g0_graph = tf.Graph()
with g0_graph.as_default():
a0_tensor = tf.constant(1, name="a0")
b0_tensor = tf.constant(2, name="b0")
tf.add(a0_tensor, b0_tensor, name="c0")
g0 = gde.Graph(g0_graph)
a0 = g0["a0"].output(0)
b0 = g0["b0"].output(0)
c0 = g0["c0"].output(0)
self.assertEqual(len(gde.util.get_generating_ops([a0, b0])), 2)
self.assertEqual(len(gde.util.get_consuming_ops([a0, b0])), 1)
self.assertEqual(len(gde.util.get_generating_ops([c0])), 1)
self.assertEqual(gde.util.get_consuming_ops([c0]), [])
def test_transform(self):
transformer = gde.Transformer()
def my_transform_op_handler(info, op, new_inputs):
add_noise = op.name.startswith("Add")
op_, op_outputs_ = gde.transform.copy_op_handler(
info, op, new_inputs)
if not add_noise:
return op_, op_outputs_
# add some noise to op
# Old code:
# with info.graph_.as_default():
# t_ = math_ops.add(
# constant_op.constant(1.0, shape=[10], name="Noise"),
# op_.outputs[0],
# name="AddNoise")
noise_op = gde.make_const(info.graph_,
"Noise",
np.full([10], 1., dtype=np.float32),
uniquify_name=True)
add_noise_op = info.graph_.add_node("AddNoise",
"Add",
uniquify_name=True)
add_noise_op.add_attr("T", tf.float32)
add_noise_op.set_inputs([noise_op.outputs[0], op_.outputs[0]])
add_noise_op.infer_outputs()
t_ = add_noise_op.outputs[0]
# return the "noisy" op
return op_, [t_]
transformer.transform_op_handler = my_transform_op_handler
graph = gde.Graph()
transformer(self.graph, graph, "", "")
matcher0 = gde.OpMatcher("AddNoise").input_ops(
"Noise",
gde.OpMatcher("Add").input_ops("Const", "Input"))
matcher1 = gde.OpMatcher("AddNoise_1").input_ops(
"Noise_1",
gde.OpMatcher("Add_1").input_ops("Const_1", matcher0))
matcher2 = gde.OpMatcher("AddNoise_2").input_ops(
"Noise_2",
gde.OpMatcher("Add_2").input_ops("Const_2", matcher1))
top = gde.select_ops("^AddNoise_2$", graph=graph)[0]
self.assertTrue(matcher2(top))
def setUp(self):
tf_graph = tf.Graph()
with tf_graph.as_default():
a = tf.constant([1., 1.], shape=[2], name="a")
with tf.name_scope("foo"):
b = tf.constant([2., 2.], shape=[2], name="b")
c = tf.add(a, b, name="c")
d = tf.constant([3., 3.], shape=[2], name="d")
with tf.name_scope("bar"):
_ = tf.add(c, d, name="e")
f = tf.add(c, d, name="f")
g = tf.add(c, a, name="g")
with tf.control_dependencies([c.op]):
_ = tf.add(f, g, name="h")
self.graph = gde.Graph(tf_graph)
self.f_op = self.graph[f.op.name]
def test_control_outputs(self):
"""Test for the gde.util.ControlOutputs class."""
g0_graph = tf.Graph()
with g0_graph.as_default():
a0_tensor = tf.constant(1, name="a0")
b0_tensor = tf.constant(2, name="b0")
x0_tensor = tf.constant(3, name="x0")
with tf.control_dependencies([x0_tensor.op]):
tf.add(a0_tensor, b0_tensor, name="c0")
g0 = gde.Graph(g0_graph)
x0_node = g0["x0"]
c0_node = g0["c0"]
control_outputs = gde.util.ControlOutputs(g0).get_all()
self.assertEqual(len(control_outputs), 1)
self.assertEqual(len(control_outputs[x0_node]), 1)
self.assertIs(list(control_outputs[x0_node])[0], c0_node)
def test_copy_assert(self):
tf_g = tf.Graph()
with tf_g.as_default():
a = tf.constant(1, name="a")
b = tf.constant(1, name="b")
eq = tf.equal(a, b, name="EQ")
assert_tf_op = tf.Assert(eq, [a, b])
with tf.control_dependencies([assert_tf_op]):
_ = tf.add(a, b)
assert_op_name = assert_tf_op.name
g = gde.Graph(tf_g)
assert_op = g[assert_op_name]
sgv = gde.make_view([assert_op, g["EQ"], g["a"], g["b"]])
copier = gde.Transformer()
_, info = copier(sgv, sgv.graph, "", "")
new_assert_op = info.transformed(assert_op)
self.assertIsNotNone(new_assert_op)
def test_function_rewrite(self):
tf_g = self.build_tf_graph()
self.assertEqual(30.0, self.run_tf_graph(tf_g, 1.0, 2.0))
graph = gde.Graph(tf_g)
add_op = graph.get_node_by_name("add")
function_name = add_op.outputs[0].consumers()[0].get_attr("f").name
self.assertIn(function_name, graph.function_names)
function_graph = graph.get_function_graph_by_name(function_name)
function_multiplier_op = \
function_graph.get_node_by_name("function_multiplier")
self.assertEqual(10.0, function_multiplier_op.get_attr("value"))
function_multiplier_op.replace_attr("value",
np.array(1000.0, dtype=np.float32))
self.assertEqual(3000.0,
self.run_tf_graph(graph.to_tf_graph(), 1.0, 2.0))
return graph