def add_const_proto_explicit(self,
name,
value,
np_dtype=None,
tf_dtype=None,
onnx_dtype=None):
onnx_dtype = any_dtype_to_onnx_dtype(np_dtype=np_dtype,
tf_dtype=tf_dtype,
onnx_dtype=onnx_dtype)
const_dim = len(value.shape)
if const_dim == 0:
raw_values = [value.tolist()]
values = [value]
else:
raw_values = value.flatten().tolist()
values = value
shape = np.array(values).shape
const_proto = make_tensor(name=name,
data_type=onnx_dtype,
dims=shape,
vals=raw_values)
self._consts_proto.append(const_proto)
def add_input_proto_explicit(self,
name,
shape,
np_dtype=None,
tf_dtype=None,
onnx_dtype=None):
onnx_dtype = any_dtype_to_onnx_dtype(
np_dtype=np_dtype, tf_dtype=tf_dtype, onnx_dtype=onnx_dtype)
input_proto = make_tensor_value_info(name, onnx_dtype, shape)
self._inputs_proto.append(input_proto)
def _create_util_nodes():
util_nodes = [(CONST_MINUS_ONE_INT32, np.array([-1]).astype(np.int32)),
(CONST_ZERO_INT32, np.array([0]).astype(np.int32)),
(CONST_ONE_INT32, np.array([1]).astype(np.int32))]
return [
TensorflowNode(op_type="Const",
name=name,
attr={
"value": value,
"dtype": any_dtype_to_onnx_dtype(value.dtype),
"_output_shapes": [value.shape]
}) for name, value in util_nodes
]
def version_9(cls, node, **kwargs):
unique_suffix = get_unique_suffix()
# Convert to NCHW:
transpose_node = Transpose.handle(TensorflowNode(
name='transopose_input_to_nchw_' + unique_suffix,
inputs=node.inputs[:1] + ["perm"],
outputs=["transposed_input_" + unique_suffix]),
consts={"perm": [0, 3, 1, 2]})
# Get shape of NCHW input tensor:
input_shape_node = Shape.handle(
TensorflowNode(name='get_input_shape_' + unique_suffix,
inputs=transpose_node.output,
outputs=["input_shape_" + unique_suffix],
attr=node.attr))
util_one = OnnxGraph.CONST_ONE_FP32
output_shape_tensor = node.inputs[1]
# Cast output shape (HW dim only) to float32:
out_shape_float = Cast.handle(
TensorflowNode(
name='cast_output_shape_to_fp32_' + unique_suffix,
inputs=[output_shape_tensor],
outputs=["output_shape_float_partial_" + unique_suffix],
attr={"DstT": tf.float32}))
# Cast input shape to float32:
in_shape_cast = Cast.handle(
TensorflowNode(name='cast_input_shape_to_fp32_' + unique_suffix,
inputs=input_shape_node.output,
outputs=["input_shape_float_" + unique_suffix],
attr={"DstT": tf.float32}))
slice_const_items = [
("begin", np.array([2]).astype(np.int32)),
("end", np.array([4]).astype(np.int32)),
("strides", np.array([1]).astype(np.int32)),
]
slice_const_proto = {}
for k, v in slice_const_items:
const_name = "{}_".format(k) + unique_suffix
slice_const_proto[k] = make_node(
"Constant", [], [const_name],
value=make_tensor(const_name,
any_dtype_to_onnx_dtype(np_dtype=v.dtype),
v.shape, v))
in_shape_slices = StridedSlice.handle(
TensorflowNode(
name="stridedslice_input_shape_" + unique_suffix,
inputs=list(in_shape_cast.output) +
[slice_const_proto[k].output[0] for k, v in slice_const_items],
outputs=["sliced_input_shape_" + unique_suffix]),
consts={
slice_const_proto[k].output[0]: v
for k, v in slice_const_items
},
add_consts=True)
# Divide input shape with output shape to get scaling factor:
div_node = Div.handle(
TensorflowNode(name='div_to_get_scale_factor_' + unique_suffix,
inputs=list(out_shape_float.output) +
list(in_shape_slices[-1].output),
outputs=["hw_scale_" + unique_suffix]))
# Prepend 1's in the N, C dimension:
full_scale = Concat.handle(TensorflowNode(
name='prepend_ones_to_scale_factor_' + unique_suffix,
inputs=[util_one, util_one] + list(div_node.output) +
["concat_axis"],
outputs=["scale_" + unique_suffix]),
consts={"concat_axis": 0})
# Upsample with the computed scaling factor:
upsample_node = cls.make_node_from_tf_node(
node,
op_type="Upsample",
mode="bilinear",
inputs=list(transpose_node.output) + list(full_scale.output),
outputs=["upsample_to_tranpose_" + unique_suffix])
# Transpose back to NHWC:
transpose_output_node = Transpose.handle(TensorflowNode(
name='transpose_output_to_nhwc_' + unique_suffix,
inputs=list(upsample_node.output) + ["perm"],
outputs=node.outputs),
consts={"perm": [0, 2, 3, 1]})
transpose_and_get_shapes = [
transpose_node, input_shape_node, out_shape_float, in_shape_cast
]
slice_shape = list(slice_const_proto.values()) + in_shape_slices
get_scale_and_upsample_and_transpose = [
div_node, full_scale, upsample_node, transpose_output_node
]
return transpose_and_get_shapes + slice_shape + get_scale_and_upsample_and_transpose