def onnxifi_caffe2_net(pred_net,
input_shapes,
infer_shapes=False,
debug=False,
use_onnx=True):
"""
Transform the caffe2_net by collapsing ONNXIFI-runnable nodes into Onnxifi c2 ops
"""
# Inject an fake input tensor to help popluate the shape if we
# do not do shape inference
shape_hints = {}
external_inputs = []
if not infer_shapes:
for k, v in input_shapes.items():
need_input_tensor = True
if workspace.HasBlob(k):
itensor = workspace.FetchBlob(k)
if itensor.shape == v:
need_input_tensor = False
if need_input_tensor:
workspace.FeedBlob(k, np.random.randn(*v).astype(np.float32))
external_inputs.append(k)
for k, v in input_shapes.items():
shape_hints[k] = v
pred_net_str = C.onnxifi(pred_net.SerializeToString(), external_inputs,
shape_hints, infer_shapes, debug, use_onnx)
pred_net_cut = caffe2_pb2.NetDef()
pred_net_cut.ParseFromString(pred_net_str)
return pred_net_cut
def onnxifi_caffe2_net(pred_net,
input_shapes,
max_batch_size=1,
max_seq_size=1,
debug=False,
use_onnx=True,
merge_fp32_inputs_into_fp16=False,
adjust_batch=True,
black_list=None,
weight_names=None,
timeout=0):
"""
Transform the caffe2_net by collapsing ONNXIFI-runnable nodes into Onnxifi c2 ops
"""
shape_hints = {}
for k, v in input_shapes.items():
shape_hints[k] = v
pred_net_str = C.onnxifi(pred_net.SerializeToString(), shape_hints,
black_list if black_list else [],
weight_names if weight_names is not None else [],
max_batch_size, max_seq_size, timeout,
adjust_batch, debug, merge_fp32_inputs_into_fp16,
use_onnx)
pred_net_cut = caffe2_pb2.NetDef()
pred_net_cut.ParseFromString(pred_net_str)
return pred_net_cut
def onnxifi_caffe2_net(pred_net,
input_shapes,
max_batch_size=1,
max_seq_size=1,
debug=False,
use_onnx=True):
"""
Transform the caffe2_net by collapsing ONNXIFI-runnable nodes into Onnxifi c2 ops
"""
shape_hints = {}
for k, v in input_shapes.items():
shape_hints[k] = v
pred_net_str = C.onnxifi(pred_net.SerializeToString(), shape_hints,
max_batch_size, max_seq_size, debug, use_onnx)
pred_net_cut = caffe2_pb2.NetDef()
pred_net_cut.ParseFromString(pred_net_str)
return pred_net_cut
def onnxifi_caffe2_net(pred_net,
input_shapes,
max_batch_size=1,
max_seq_size=1,
debug=False,
use_onnx=True,
merge_fp32_inputs_into_fp16=False,
adjust_batch=True,
block_list=None,
weight_names=None,
net_ssa_rewritten=False,
timeout=0):
"""
Transform the caffe2_net by collapsing ONNXIFI-runnable nodes into Onnxifi c2 ops
"""
shape_hints = caffe2_pb2.TensorBoundShapes()
if type(input_shapes) is caffe2_pb2.TensorBoundShapes:
shape_hints = input_shapes
elif type(input_shapes) is dict:
for k, v in input_shapes.items():
tbs = caffe2_pb2.TensorBoundShape()
tbs.name = k
tbs.shape.dims.extend(v)
tbs.dim_type.extend([caffe2_pb2.TensorBoundShape.CONSTANT] *
len(tbs.shape.dims))
tbs.dim_type[0] = caffe2_pb2.TensorBoundShape.BATCH
shape_hints.shapes.extend([tbs])
shape_hints.max_batch_size = max_batch_size
shape_hints.max_feature_len = max_seq_size
pred_net_str = C.onnxifi(pred_net.SerializeToString(),
shape_hints.SerializeToString(),
block_list if block_list else [],
weight_names if weight_names is not None else [],
max_batch_size, max_seq_size, timeout,
adjust_batch, debug, merge_fp32_inputs_into_fp16,
net_ssa_rewritten, use_onnx)
pred_net_cut = caffe2_pb2.NetDef()
pred_net_cut.ParseFromString(pred_net_str)
return pred_net_cut
def onnxifi_caffe2_net(pred_net,
input_shapes,
populate_shapes=False,
debug=False):
"""
Transfrom the caffe2_net by collapsing ONNXIFI-runnable nodes into Onnxifi c2 ops
"""
# Hacky way to infer shapes as not all our operators have shape inference function.
# Normally this is not needed
shape_hints = {}
if populate_shapes:
input_data = {}
for k, v in input_shapes.items():
input_data[k] = np.random.randn(*v).astype(np.float32)
shape_hints = _infer_shapes(pred_net, input_data)
for k, v in input_shapes.items():
shape_hints[k] = v
pred_net_str = C.onnxifi(pred_net.SerializeToString(), shape_hints, debug)
pred_net_cut = caffe2_pb2.NetDef()
pred_net_cut.ParseFromString(pred_net_str)
return pred_net_cut
