def convert_transpose(name, tf_node, inputs, uff_graph, **kwargs):
tf_permutation_node = kwargs["tf_nodes"][inputs[1]]
if tf_permutation_node.op != "Const":
raise UffException("Transpose permutation has op " + str(tf_permutation_node.op) + ", expected Const. Only constant permuations are supported in UFF.")
permutation = tf2uff.convert_tf2numpy_const_node(
tf_permutation_node).tolist()
inputs = inputs[:1]
uff_graph.transpose(inputs[0], permutation, name)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def convert_argmin(name, tf_node, inputs, uff_graph, **kwargs):
# Retrieve and remove the axis node.
tf_axis_input_node = kwargs["tf_nodes"][inputs[-1]]
if tf_axis_input_node.op != "Const":
raise UffException("ArgMin Axis node has op " + str(tf_axis_input_node.op) + ", expected Const. The axis must be specified as a Const node.")
axis = int(tf2uff.convert_tf2numpy_const_node(tf_axis_input_node))
inputs.pop(-1)
# Add the op.
uff_graph.argmin(inputs[0], axis, name)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def convert_resource_gather(name, tf_node, inputs, uff_graph, **kwargs):
if len(inputs) > 2:
tf_axis_node = kwargs["tf_nodes"][inputs[-1]]
axis = int(tf2uff.convert_tf2numpy_const_node(tf_axis_node))
inputs = inputs[:-1]
else:
axis = 0
indices_dtype = tf2uff.convert_tf2numpy_dtype(tf_node.attr['Tindices'].type)
params_dtype = tf2uff.convert_tf2numpy_dtype(tf_node.attr['dtype'].type)
uff_graph.gather_v2(inputs, name, axis, indices_dtype, params_dtype)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def convert_concatv2(name, tf_node, inputs, uff_graph, **kwargs):
if "axis" in tf_node.attr:
# Handle cases where the axis is not a node, but an attribute instead.
axis = tf_node.attr["axis"].i
else:
tf_axis_node = kwargs["tf_nodes"][inputs[-1]]
if tf_axis_node.op != "Const":
raise UffException("Concat Axis node has op " + str(tf_axis_node.op) + ", expected Const. The axis for a Concat op must be specified as either an attribute, or a Const node.")
axis = int(tf2uff.convert_tf2numpy_const_node(tf_axis_node))
inputs = inputs[:-1]
uff_graph.concat(inputs, axis, name)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def _reduce_helper(name, tf_node, inputs, uff_graph, **kwargs):
func = kwargs.pop("func")
tf_axes_node = kwargs["tf_nodes"][inputs[1]]
array = tf2uff.convert_tf2numpy_const_node(tf_axes_node)
axes = array.tolist()
inputs = inputs[:1]
keepdims = tf_node.attr['keep_dims'].b
print("Warning: keepdims is ignored by the UFF Parser and defaults to True")
uff_graph.reduce(inputs[0], func, axes, keepdims, name)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def convert_softmax(name, tf_node, inputs, uff_graph, **kwargs):
# Some Softmax ops don't have an axis node.
if len(inputs) > 1:
tf_axis_node = kwargs["tf_nodes"][inputs[-1]]
axis = int(tf2uff.convert_tf2numpy_const_node(tf_axis_node))
inputs = inputs[:-1]
else:
axis = 0
fmt = convert_to_str(tf_node.attr['data_format'].s)
fmt = fmt if fmt else "NHWC"
data_fmt = tf2uff.convert_tf2uff_data_format(fmt)
uff_graph.softmax(inputs[0], axis, data_fmt, name)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def convert_bias_add(name, tf_node, inputs, uff_graph, **kwargs):
fmt = convert_to_str(tf_node.attr['data_format'].s)
fmt = fmt if fmt else "NHWC"
biases_name = inputs[1]
biases_array = tf2uff.convert_tf2numpy_const_node(
kwargs["tf_nodes"][biases_name])
inputs = inputs[:1]
if fmt == 'NCHW':
ndim = 4
new_shape = [-1] + [1] * (ndim - 2)
biases_array = biases_array.reshape(new_shape)
uff_graph.const(biases_array, biases_name)
uff_graph.binary(inputs[0], biases_name, 'add', name)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def convert_const(name, tf_node, inputs, uff_graph, **kwargs):
array = tf2uff.convert_tf2numpy_const_node(tf_node)
uff_node = uff_graph.const(array, name)
uff_node.array = array
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]