本文整理汇总了Python中tensorflow.python.debug.debug_utils.watch_graph函数的典型用法代码示例。如果您正苦于以下问题:Python watch_graph函数的具体用法?Python watch_graph怎么用?Python watch_graph使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了watch_graph函数的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testWatchingOutputSlotWithoutOutgoingEdge
def testWatchingOutputSlotWithoutOutgoingEdge(self):
"""Test watching output slots not attached to any outgoing edges."""
with session.Session() as sess:
u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
u = constant_op.constant(u_init_val, shape=[2, 2], name="u")
# Create a control edge from a node with an output: From u to z.
# Node u will get executed only because of the control edge. The output
# tensor u:0 is not attached to any outgoing edge in the graph. This test
# checks that the debugger can watch such a tensor.
with ops.control_dependencies([u]):
z = control_flow_ops.no_op(name="z")
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls=self._debug_urls())
run_metadata = config_pb2.RunMetadata()
sess.run(z, options=run_options, run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=run_metadata.partition_graphs)
# Assert that the DebugIdentity watch on u works properly.
self.assertEqual(1, len(dump.dumped_tensor_data))
datum = dump.dumped_tensor_data[0]
self.assertEqual("u", datum.node_name)
self.assertEqual(0, datum.output_slot)
self.assertEqual("DebugIdentity", datum.debug_op)
self.assertAllClose([[5.0, 3.0], [-1.0, 0.0]], datum.get_tensor())
示例2: testDebugNumericSummaryOnInitializedTensorGivesCorrectResult
def testDebugNumericSummaryOnInitializedTensorGivesCorrectResult(self):
with session.Session() as sess:
a = variables.Variable(
[
np.nan, np.nan, 0.0, 0.0, 0.0, -1.0, -3.0, 3.0, 7.0, -np.inf,
-np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.nan, np.nan
],
dtype=np.float32,
name="numeric_summary/a")
b = variables.Variable(
[0.0] * 18, dtype=np.float32, name="numeric_summary/b")
c = math_ops.add(a, b, name="numeric_summary/c")
sess.run(variables.global_variables_initializer())
run_metadata = config_pb2.RunMetadata()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugNumericSummary"],
debug_urls=self._debug_urls())
sess.run(c, options=run_options, run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=run_metadata.partition_graphs)
self.assertTrue(dump.loaded_partition_graphs())
self.assertAllClose([[
1.0, 18.0, 2.0, 2.0, 3.0, 2.0, 5.0, 4.0, -3.0, 7.0, 0.85714286,
8.97959184
]], dump.get_tensors("numeric_summary/a/read", 0, "DebugNumericSummary"))
示例3: testDebugNumericSummaryOnUninitializedTensorGivesCorrectResult
def testDebugNumericSummaryOnUninitializedTensorGivesCorrectResult(self):
with session.Session() as sess:
a = variables.Variable(
[42], dtype=np.float32, name="numeric_summary_uninit/a")
run_metadata = config_pb2.RunMetadata()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugNumericSummary"],
debug_urls=self._debug_urls())
sess.run(a.initializer, options=run_options, run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=run_metadata.partition_graphs)
self.assertTrue(dump.loaded_partition_graphs())
# DebugNumericSummary output should reflect the uninitialized state of
# the watched tensor.
numeric_summary = dump.get_tensors(
"numeric_summary_uninit/a", 0, "DebugNumericSummary")[0]
self.assertAllClose(
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], numeric_summary[0:8])
self.assertTrue(np.isinf(numeric_summary[8]))
self.assertGreater(numeric_summary[8], 0.0)
self.assertTrue(np.isinf(numeric_summary[9]))
self.assertLess(numeric_summary[9], 0.0)
self.assertTrue(np.isnan(numeric_summary[10]))
self.assertTrue(np.isnan(numeric_summary[11]))
示例4: testRunWithError
def testRunWithError(self):
"""Test the debug tensor dumping when error occurs in graph runtime."""
with session.Session() as sess:
ph = tf.placeholder(tf.float32, name="mismatch/ph")
x = tf.transpose(ph, name="mismatch/x")
m = constant_op.constant(
np.array(
[[1.0, 2.0]], dtype=np.float32), name="mismatch/m")
y = math_ops.matmul(m, x, name="mismatch/y")
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls=self._debug_urls())
with self.assertRaises(errors.OpError):
sess.run(y,
options=run_options,
feed_dict={ph: np.array([[-3.0], [0.0]])})
dump = debug_data.DebugDumpDir(self._dump_root)
self.assertFalse(dump.loaded_partition_graphs())
m_dumps = dump.watch_key_to_data("mismatch/m:0:DebugIdentity")
self.assertEqual(1, len(m_dumps))
self.assertAllClose(np.array([[1.0, 2.0]]), m_dumps[0].get_tensor())
x_dumps = dump.watch_key_to_data("mismatch/x:0:DebugIdentity")
self.assertEqual(1, len(x_dumps))
self.assertAllClose(np.array([[-3.0, 0.0]]), x_dumps[0].get_tensor())
示例5: testWatchGraph_allNodes
def testWatchGraph_allNodes(self):
debug_utils.watch_graph(
self._run_options,
self._graph,
debug_ops=["DebugIdentity", "DebugNanCount"],
debug_urls="file:///tmp/tfdbg_1")
self.assertEqual(self._expected_num_nodes,
len(self._run_options.debug_tensor_watch_opts))
# Verify that each of the nodes in the graph with output tensors in the
# graph have debug tensor watch.
node_names = self._verify_watches(self._run_options.debug_tensor_watch_opts,
0, ["DebugIdentity", "DebugNanCount"],
["file:///tmp/tfdbg_1"])
# Verify the node names.
self.assertTrue("a1_init" in node_names)
self.assertTrue("a1" in node_names)
self.assertTrue("a1/Assign" in node_names)
self.assertTrue("a1/read" in node_names)
self.assertTrue("b_init" in node_names)
self.assertTrue("b" in node_names)
self.assertTrue("b/Assign" in node_names)
self.assertTrue("b/read" in node_names)
self.assertTrue("c" in node_names)
self.assertTrue("p1" in node_names)
self.assertTrue("s" in node_names)
示例6: setUpClass
def setUpClass(cls):
cls._dump_root = tempfile.mkdtemp()
with session.Session() as sess:
# 2400 elements should exceed the default threshold (2000).
x = constant_op.constant(np.zeros([300, 8]), name="large_tensors/x")
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls="file://%s" % cls._dump_root)
# Invoke Session.run().
run_metadata = config_pb2.RunMetadata()
sess.run(x, options=run_options, run_metadata=run_metadata)
cls._debug_dump = debug_data.DebugDumpDir(
cls._dump_root, partition_graphs=run_metadata.partition_graphs)
# Construct the analyzer.
cls._analyzer = analyzer_cli.DebugAnalyzer(cls._debug_dump)
# Construct the handler registry.
cls._registry = debugger_cli_common.CommandHandlerRegistry()
# Register command handler.
cls._registry.register_command_handler(
"print_tensor",
cls._analyzer.print_tensor,
cls._analyzer.get_help("print_tensor"),
prefix_aliases=["pt"])
示例7: _session_run_for_graph_structure_lookup
def _session_run_for_graph_structure_lookup(self):
with session.Session() as sess:
u_name = "testDumpGraphStructureLookup/u"
v_name = "testDumpGraphStructureLookup/v"
w_name = "testDumpGraphStructureLookup/w"
u_init = constant_op.constant([2.0, 4.0])
u = variables.Variable(u_init, name=u_name)
v = math_ops.add(u, u, name=v_name)
w = math_ops.add(v, v, name=w_name)
u.initializer.run()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls=self._debug_urls())
run_metadata = config_pb2.RunMetadata()
sess.run(w, options=run_options, run_metadata=run_metadata)
self.assertEqual(self._expected_partition_graph_count,
len(run_metadata.partition_graphs))
dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=run_metadata.partition_graphs)
return u_name, v_name, w_name, dump
示例8: _decorate_run_options
def _decorate_run_options(self,
run_options,
debug_urls,
debug_ops="DebugIdentity",
node_name_regex_whitelist=None,
op_type_regex_whitelist=None):
"""Modify a RunOptions object for debug tensor watching.
Specifies request for outputting partition graphs. Adds
debug_tensor_watch_opts with proper debug URLs.
Args:
run_options: (RunOptions) the modified RunOptions object.
debug_urls: (list of str) debug URLs to be entered in run_options.
debug_tensor_watch_opts.
debug_ops: (str or list of str) debug op(s) to be used by the debugger.
node_name_regex_whitelist: Regular-expression whitelist for node
name.
op_type_regex_whitelist: Regular-expression whitelist for op type.
"""
run_options.output_partition_graphs = True
debug_utils.watch_graph(
run_options,
self._sess.graph,
debug_urls=debug_urls,
debug_ops=debug_ops,
node_name_regex_whitelist=node_name_regex_whitelist,
op_type_regex_whitelist=op_type_regex_whitelist)
示例9: before_run
def before_run(self, run_context):
if not self._wrapper_initialized:
dumping_wrapper.DumpingDebugWrapperSession.__init__(
self,
run_context.session,
self._session_root,
watch_fn=self._watch_fn,
log_usage=self._log_usage)
self._wrapper_initialized = True
self._run_call_count += 1
(debug_urls, debug_ops, node_name_regex_whitelist,
op_type_regex_whitelist) = self._prepare_run_watch_config(
run_context.original_args.fetches, run_context.original_args.feed_dict)
run_options = config_pb2.RunOptions()
debug_utils.watch_graph(
run_options,
run_context.session.graph,
debug_urls=debug_urls,
debug_ops=debug_ops,
node_name_regex_whitelist=node_name_regex_whitelist,
op_type_regex_whitelist=op_type_regex_whitelist)
run_args = session_run_hook.SessionRunArgs(
None, feed_dict=None, options=run_options)
return run_args
示例10: testDumpCausalityCheck
def testDumpCausalityCheck(self):
with session.Session() as sess:
u_name = "testDumpCausalityCheck/u"
v_name = "testDumpCausalityCheck/v"
w_name = "testDumpCausalityCheck/w"
u_init = constant_op.constant([2.0, 4.0])
u = variables.Variable(u_init, name=u_name)
v = math_ops.add(u, u, name=v_name)
w = math_ops.add(v, v, name=w_name)
u.initializer.run()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options, sess.graph, debug_ops=["DebugIdentity"], debug_urls="file://%s" % self._dump_root
)
run_metadata = config_pb2.RunMetadata()
sess.run(w, options=run_options, run_metadata=run_metadata)
self.assertEqual(self._expected_partition_graph_count, len(run_metadata.partition_graphs))
# First, loading the original dump without supplying the
# partition_graphs should not cause a RuntimeError, validation occurs
# only with partition_graphs loaded.
debug_data.DebugDumpDir(self._dump_root)
# Now, loading the original dump with partition graphs supplied should
# succeed. The validation should pass quietly.
dump = debug_data.DebugDumpDir(self._dump_root, partition_graphs=run_metadata.partition_graphs)
# Get the dump file names and compute their timestamps.
self.assertEqual(1, len(dump.get_tensor_file_paths(u_name, 0, "DebugIdentity")))
u_file_path = dump.get_tensor_file_paths(u_name, 0, "DebugIdentity")[0]
self.assertEqual(1, len(dump.get_tensor_file_paths(v_name, 0, "DebugIdentity")))
v_file_path = dump.get_tensor_file_paths(v_name, 0, "DebugIdentity")[0]
u_timestamp = int(u_file_path[u_file_path.rindex("_") + 1 :])
v_timestamp = int(v_file_path[v_file_path.rindex("_") + 1 :])
# Swap the time stamps
new_u_file_path = u_file_path[: u_file_path.rindex("_")] + "_%d" % v_timestamp
new_v_file_path = v_file_path[: v_file_path.rindex("_")] + "_%d" % u_timestamp
os.rename(u_file_path, new_u_file_path)
os.rename(v_file_path, new_v_file_path)
# Load the dump directory again. Now a ValueError is expected to be
# raised due to the timestamp swap.
with self.assertRaisesRegexp(ValueError, "Causality violated"):
dump = debug_data.DebugDumpDir(self._dump_root, partition_graphs=run_metadata.partition_graphs)
# Loading the dump directory with kwarg "validate" set explicitly to
# False should get rid of the error.
dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=run_metadata.partition_graphs, validate=False
)
示例11: setUpClass
def setUpClass(cls):
cls._dump_root = tempfile.mkdtemp()
cls._is_gpu_available = test.is_gpu_available()
if cls._is_gpu_available:
cls._main_device = "/job:localhost/replica:0/task:0/gpu:0"
else:
cls._main_device = "/job:localhost/replica:0/task:0/cpu:0"
with session.Session() as sess:
u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
v_init_val = np.array([[2.0], [-1.0]])
u_name = "simple_mul_add/u"
v_name = "simple_mul_add/v"
u_init = constant_op.constant(u_init_val, shape=[2, 2])
u = variables.Variable(u_init, name=u_name)
v_init = constant_op.constant(v_init_val, shape=[2, 1])
v = variables.Variable(v_init, name=v_name)
w = math_ops.matmul(u, v, name="simple_mul_add/matmul")
x = math_ops.add(w, w, name="simple_mul_add/add")
u.initializer.run()
v.initializer.run()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls="file://%s" % cls._dump_root)
# Invoke Session.run().
run_metadata = config_pb2.RunMetadata()
sess.run(x, options=run_options, run_metadata=run_metadata)
debug_dump = debug_data.DebugDumpDir(
cls._dump_root, partition_graphs=run_metadata.partition_graphs)
# Construct the analyzer.
analyzer = analyzer_cli.DebugAnalyzer(debug_dump)
# Construct the handler registry.
cls._registry = debugger_cli_common.CommandHandlerRegistry()
# Register command handlers.
cls._registry.register_command_handler(
"list_tensors",
analyzer.list_tensors,
analyzer.get_help("list_tensors"),
prefix_aliases=["lt"])
cls._registry.register_command_handler(
"node_info",
analyzer.node_info,
analyzer.get_help("node_info"),
prefix_aliases=["ni"])
示例12: testFindNodesWithBadTensorValues
def testFindNodesWithBadTensorValues(self):
with session.Session() as sess:
u_name = "testFindNodesWithBadTensorValues/u"
v_name = "testFindNodesWithBadTensorValues/v"
w_name = "testFindNodesWithBadTensorValues/w"
x_name = "testFindNodesWithBadTensorValues/x"
y_name = "testFindNodesWithBadTensorValues/y"
z_name = "testFindNodesWithBadTensorValues/z"
u_init = constant_op.constant([2.0, 4.0])
u = variables.Variable(u_init, name=u_name)
v_init = constant_op.constant([2.0, 1.0])
v = variables.Variable(v_init, name=v_name)
# Expected output: [0.0, 3.0]
w = math_ops.sub(u, v, name=w_name)
# Expected output: [inf, 1.3333]
x = math_ops.div(u, w, name=x_name)
# Expected output: [nan, 4.0]
y = math_ops.mul(w, x, name=y_name)
z = math_ops.mul(y, y, name=z_name)
u.initializer.run()
v.initializer.run()
run_options = config_pb2.RunOptions()
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls="file://%s" % self._dump_root)
run_metadata = config_pb2.RunMetadata()
sess.run(z, options=run_options, run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(self._dump_root)
def has_bad_value(_, tensor):
return np.any(np.isnan(tensor)) or np.any(np.isinf(tensor))
# Find all "offending tensors".
bad_data = dump.find(has_bad_value)
# Verify that the nodes with bad values are caught through running find
# on the debug dump.
self.assertEqual(3, len(bad_data))
self.assertEqual(x_name, bad_data[0].node_name)
self.assertEqual(y_name, bad_data[1].node_name)
self.assertEqual(z_name, bad_data[2].node_name)
# Test first_n kwarg of find(): Find the first offending tensor.
first_bad_datum = dump.find(has_bad_value, first_n=1)
self.assertEqual(1, len(first_bad_datum))
self.assertEqual(x_name, first_bad_datum[0].node_name)
示例13: _decorate_options_for_debug
def _decorate_options_for_debug(self, options, graph):
"""Modify RunOptions.debug_options.debug_tensor_watch_opts for debugging.
Args:
options: (config_pb2.RunOptions) The RunOptions instance to be modified.
graph: A TensorFlow Graph object.
"""
debug_utils.watch_graph(
options, graph, debug_urls=self._get_run_debug_urls())
options.output_partition_graphs = True
示例14: testWatchGraph_opTypeWhitelist
def testWatchGraph_opTypeWhitelist(self):
debug_utils.watch_graph(
self._run_options,
self._graph,
debug_urls="file:///tmp/tfdbg_1",
op_type_regex_whitelist="(Variable|MatMul)")
node_names = self._verify_watches(self._run_options.debug_tensor_watch_opts,
0, ["DebugIdentity"],
["file:///tmp/tfdbg_1"])
self.assertEqual(sorted(["a1", "b", "p1"]), sorted(node_names))
示例15: testWatchingVariableUpdateOpsSeesUpdatedValues
def testWatchingVariableUpdateOpsSeesUpdatedValues(self):
"""Watch output slots on Variable-updating ops, with no emitted edges."""
with session.Session() as sess:
u_init = constant_op.constant(10.0)
u = variables.Variable(u_init, name="gdo/u")
v_init = constant_op.constant(20.0)
v = variables.Variable(v_init, name="gdo/v")
w = math_ops.multiply(u, v, name="gdo/w")
# gdo stands for GradientDescentOptimizer.
train_op = gradient_descent.GradientDescentOptimizer(
learning_rate=0.1).minimize(
w, name="gdo/train")
u.initializer.run()
v.initializer.run()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(
run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls=self._debug_urls())
run_metadata = config_pb2.RunMetadata()
sess.run(train_op, options=run_options, run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=run_metadata.partition_graphs)
update_u_data = dump.watch_key_to_data(
"gdo/train/update_gdo/u/ApplyGradientDescent:0:DebugIdentity")
self.assertEqual(1, len(update_u_data))
# Gradient descent on u: w = u * v, so dw / du = v.
# Updated value of u should be:
# 10.0 - learning_rate * v = 10.0 - 0.1 * 20.0 = 8.0
self.assertAllClose(8.0, update_u_data[0].get_tensor())
update_v_data = dump.watch_key_to_data(
"gdo/train/update_gdo/v/ApplyGradientDescent:0:DebugIdentity")
self.assertEqual(1, len(update_v_data))
# Gradient descent on u: w = u * v, so dw / dv = u.
# Updated value of u should be:
# 20.0 - learning_rate * u = 20.0 - 0.1 * 10.0 = 19.0
self.assertAllClose(19.0, update_v_data[0].get_tensor())
# Verify that the Variables u and v are updated properly.
self.assertAllClose(8.0, sess.run(u))
self.assertAllClose(19.0, sess.run(v))