def _run_compiled(self, compiled):
plan, output_list, workspace_type = compiled
# make sure the output blobs belong to the parent workspace
outputs = []
for name in output_list.names():
self._ws.create_blob(str(name))
outputs.append(core.BlobReference(str(name)))
output_list.set_values(outputs, _fetch_func=self._fetch_output)
task_ws = (workspace.C.Workspace(self._ws)
if workspace_type == WorkspaceType.PRIVATE else self._ws)
with workspace.WorkspaceGuard(task_ws):
task_ws.run(plan)
def _infer_shape_from_initializer(self):
for arg in self.initializer.arg:
if arg.name == 'shape':
return list(arg.ints)
with workspace.WorkspaceGuard("model_init_by_loading_params"):
try:
net = core.Net("shape_checker")
net._net.op.extend([self.initializer])
shape_blob = net.NextScopedBlob(self.parameter + "_shape")
net.Shape([self.parameter], shape_blob)
workspace.RunNetOnce(net)
return workspace.FetchBlob(shape_blob).tolist()
except RuntimeError:
logger.warning(
"Cannot infer the shape of blob {} from operator {}".
format(self.parameter, self.initializer.type))
workspace.ResetWorkspace()
return None
def _run_task_group(self, task_group):
if task_group not in self._plan_caches:
task = task_group.to_task()
plan = core.Plan('task_group_plan')
plan.AddStep(task.get_step())
self._plan_caches[task_group] = (plan, task)
plan, task = self._plan_caches[task_group]
# make sure the output blobs belong to the parent workspace
outputs = []
for name in task.output_names():
self._ws.create_blob(str(name))
outputs.append(core.BlobReference(str(name)))
task.set_outputs(outputs, _fetch_func=self._fetch_output)
task_ws = (workspace.C.Workspace(self._ws) if task.workspace_type
== WorkspaceType.PRIVATE else self._ws)
with workspace.WorkspaceGuard(task_ws):
task_ws.run(plan)
def test_relu(self, X, engine):
X += 0.02 * np.sign(X)
X[X == 0.0] += 0.02
output = Functional.Relu(X)
Y_l = output[0]
Y_d = output["output_0"]
with workspace.WorkspaceGuard("tmp_workspace"):
op = core.CreateOperator("Relu", ["X"], ["Y"], engine=engine)
workspace.FeedBlob("X", X)
workspace.RunOperatorOnce(op)
Y_ref = workspace.FetchBlob("Y")
np.testing.assert_array_equal(
Y_l, Y_ref, err_msg='Functional Relu result mismatch')
np.testing.assert_array_equal(
Y_d, Y_ref, err_msg='Functional Relu result mismatch')
def run_conv_or_fc(
test_case,
init_net,
net,
X,
W,
b,
op_type,
engine,
order,
gc,
outputs,
scale=None,
zero_point=None,
):
if order:
# Conv
Output = collections.namedtuple("Output",
["Y", "op_type", "engine", "order"])
else:
# FC
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
# We run DNNLOWP ops multiple times to test their first runs that
# do caching so exercises different code paths from the subsequent
# runs
# self.ws.run re-creates operator every time so this test covers
# cases when we have multiple nets sharing the same workspace
test_case.ws.create_blob("X").feed(X, device_option=gc)
test_case.ws.create_blob("W").feed(W, device_option=gc)
test_case.ws.create_blob("b").feed(b, device_option=gc)
if scale is not None and zero_point is not None:
with workspace.WorkspaceGuard(test_case.ws):
dnnlowp_pybind11.CreateInt8QuantParamsBlob("quant_param",
float(scale),
int(zero_point))
if init_net:
test_case.ws.run(init_net)
for i in range(1 if engine == "" else 2):
test_case.ws.run(net)
Y = test_case.ws.blobs["Y"].fetch()
if order:
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
else:
outputs.append(Output(Y=Y, op_type=op_type, engine=engine))
# workspace.CreateNet + workspace.RunNet reuses the same operator
if engine != "":
workspace.FeedBlob("X", X)
workspace.FeedBlob("W", W)
workspace.FeedBlob("b", b)
if scale is not None and zero_point is not None:
dnnlowp_pybind11.CreateInt8QuantParamsBlob("quant_param",
float(scale),
int(zero_point))
if init_net:
workspace.RunNetOnce(init_net)
workspace.CreateNet(net)
for i in range(2):
workspace.RunNet(net)
Y = workspace.FetchBlob("Y")
if order:
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
else:
outputs.append(Output(Y=Y, op_type=op_type, engine=engine))
