def _get_type_val(self, value):
if isinstance(value, (float, np.float64)):
value = np.float32(value)
elif isinstance(value, bool):
value = np.bool(value)
elif isinstance(value, (int, np.int64)):
value = np.int32(value)
elif isinstance(value, (tuple, list, np.ndarray)):
value = np.array(value)
if value.dtype == np.int64:
# We use int32 by default.
value = value.astype(np.int32)
if value.dtype == np.float64:
# We use float32 by default.
value = value.astype(np.float32)
elif isinstance(value, mil_list):
# if val that was passed in is of type mil_list, which is just a wrapper on top of python list
# then construct the list type
list_value = value.ls
if len(list_value) == 0:
raise ValueError("'mil_list' points to an empty list")
builtin_elem_type, _ = self._get_type_val(list_value[0])
from coremltools.converters.mil.mil.types.type_list import list as types_list
builtin_type = types_list(builtin_elem_type, init_length=len(list_value), dynamic_length=False)
return builtin_type, value
if not isinstance(value, (np.generic, np.ndarray, str, bool, mil_list)):
raise ValueError("Unknown value for constant: {}".format(value))
_, builtin_type = numpy_val_to_builtin_val(value)
return builtin_type, value
def _get_type_val(self, value):
if isinstance(value, (float, np.float64)):
value = np.float32(value)
elif isinstance(value, bool):
value = np.bool(value)
elif isinstance(value, (int, np.int64)):
value = np.int32(value)
elif isinstance(value, (tuple, list, np.ndarray)):
value = np.array(value)
# For the int type, we use int32 by default
if value.dtype in [
np.uint8, np.int8, np.uint16, np.int16, np.uint32,
np.uint64, np.int64
]:
if value.dtype in [np.uint64, np.int64]:
msg = "Downcast const op {} data int64 as int32".format(
self.name)
logging.debug(msg)
value = value.astype(np.int32)
# For the float type, we use float32 by default
elif value.dtype == np.float64:
msg = "Downcast const op {} data fp64 as fp32".format(
self.name)
logging.debug(msg)
value = value.astype(np.float32)
elif isinstance(value, mil_list):
# if val that was passed in is of type mil_list, which is just a wrapper on top of python list
# then construct the list type
list_value = value.ls
if len(list_value) == 0:
raise ValueError("'mil_list' points to an empty list")
builtin_elem_type, _ = self._get_type_val(list_value[0])
# mil_list is a special case that we want to preserve the int64 element type
if isinstance(list_value[0], np.int64):
builtin_elem_type = types.int64
from coremltools.converters.mil.mil.types.type_list import list as types_list
builtin_type = types_list(builtin_elem_type,
init_length=len(list_value),
dynamic_length=False)
return builtin_type, value
if not isinstance(value,
(np.generic, np.ndarray, str, bool, mil_list)):
raise ValueError("Unknown value for constant: {}".format(value))
_, builtin_type = numpy_val_to_builtin_val(value)
return builtin_type, value
def _get_type_val(self, value):
if isinstance(value, (float, np.float64)):
value = np.float32(value)
elif isinstance(value, bool):
value = np.bool(value)
elif isinstance(value, (six.integer_types, np.int64)):
value = np.int32(value)
elif isinstance(value, (tuple, list, np.ndarray)):
value = np.array(value)
if value.dtype == np.int64:
# We use int32 by default.
value = value.astype(np.int32)
if value.dtype == np.float64:
# We use float32 by default.
value = value.astype(np.float32)
if not isinstance(value, (np.generic, np.ndarray, six.string_types, bool)):
raise ValueError("Unknown value for constant: {}".format(value))
_, builtin_type = numpy_val_to_builtin_val(value)
return builtin_type, value
def _constant_propagation(fn, new_graph, constant_nodes, constant_node_num_outputs):
try:
if len(constant_nodes) > 0:
with tf.Graph().as_default() as graph:
tf.import_graph_def(new_graph, name="")
# We're only making one call to `sess.run()` in order to compute constant values.
# In this context, the default optimization settings make everything dramatically
# slower and more memory-intensive.
if tf.__version__ < _StrictVersion("1.13.1"):
session_config = tf.ConfigProto()
session_config.graph_options.optimizer_options.opt_level = (
tf.OptimizerOptions.L0
)
sess = tf.Session(graph=graph, config=session_config)
else:
session_config = tf.compat.v1.ConfigProto()
session_config.graph_options.optimizer_options.opt_level = (
tf.compat.v1.OptimizerOptions.L0
)
session_config.graph_options.rewrite_options.disable_meta_optimizer = (
True
)
sess = tf.compat.v1.Session(graph=graph, config=session_config)
query_list = list()
control_flow_ops = list()
for c in constant_nodes:
for j in range(constant_node_num_outputs[c]):
query = c + ":" + str(j)
lower_query = query.lower()
if "switch" in lower_query or "cond" in lower_query:
control_flow_ops.append(query)
else:
query_list.append(query)
result_list = sess.run(query_list)
result = {
query_list[i]: result_list[i] for i in range(len(query_list))
}
# propagate switch one by one
for op in control_flow_ops:
try:
res = sess.run([op])
result.update({op: res[0]})
except:
logging.warning(
'[Constant Propagation] Skip "dead" tensor: {}'.format(
op
)
)
result.update({op: None})
sess.close()
for k, v in fn.graph.items():
if k in constant_node_num_outputs:
if constant_node_num_outputs[k] == 1:
result_entry = k + ":0"
try:
v.value, v.datatype = numpy_val_to_builtin_val(
result[result_entry]
)
except:
logging.error(result_entry)
logging.error(result[result_entry])
else:
values = [
result[k + ":" + str(i)]
for i in range(constant_node_num_outputs[k])
]
try:
npval = [numpy_val_to_builtin_val(i) for i in values]
v.datatype = types.tuple(tuple([val[1] for val in npval]))
v.value = v.datatype()
for idx, val in enumerate(npval):
v.value.val[idx] = val[0]
except:
logging.error(values)
for k, v in fn.graph.items():
if v.op == "get_tuple":
inp = fn.graph[v.inputs[0]]
idx = v.attr["index"]
if inp.value is not None:
v.value = inp.value.val[idx]
v.datatype = inp.datatype.T[idx]
except Exception as e:
logging.exception("Constant Propagation pass failed: {}".format(e))
