How to use the tf2onnx.utils.get_tf_const_value function in tf2onnx
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onnx / keras-onnx / keras2onnx / ktf2onnx / tf2onnx / shape_inference.py View on Github 
axis_op = op.inputs[2].op
if not utils.is_tf_const_op(axis_op):
return False
axis = utils.get_tf_const_value(axis_op)
else:
axis = 0
shape = shape_params[:axis] + shape_indices + shape_params[axis + 1:]
op.outputs[0].set_shape(shape)
return True
if op.type in ["All", "Any", "Max", "Min"]:
axis_op = op.inputs[1].op
if not utils.is_tf_const_op(axis_op):
return False
axis = utils.get_tf_const_value(axis_op)
if not isinstance(axis, list):
axis = [axis]
keep_dims = op.get_attr("keep_dims")
shape = utils.get_tf_tensor_shape(op.inputs[0])
for i, _ in enumerate(axis):
if axis[i] < 0:
axis[i] += len(shape)
new_shape = []
for i, _ in enumerate(shape):
if i in axis:
if keep_dims:
new_shape.append(1)
else:
new_shape.append(shape[i])axis_op = op.inputs[2].op
if not utils.is_tf_const_op(axis_op):
return False
axis = utils.get_tf_const_value(axis_op)
else:
axis = 0
shape = shape_params[:axis] + shape_indices + shape_params[axis + 1:]
op.outputs[0].set_shape(shape)
return True
if op.type in ["All", "Any", "Max", "Min"]:
axis_op = op.inputs[1].op
if not utils.is_tf_const_op(axis_op):
return False
axis = utils.get_tf_const_value(axis_op)
if not isinstance(axis, list):
axis = [axis]
keep_dims = op.get_attr("keep_dims")
shape = utils.get_tf_tensor_shape(op.inputs[0])
for i, _ in enumerate(axis):
if axis[i] < 0:
axis[i] += len(shape)
new_shape = []
for i, _ in enumerate(shape):
if i in axis:
if keep_dims:
new_shape.append(1)
else:
new_shape.append(shape[i])new_shape.append(1)
else:
new_shape.append(shape[i])
op.outputs[0].set_shape(new_shape)
logger.debug("set %s op [%s] with new shape %s", op.type, op.outputs[0].name, new_shape)
return True
if op.type == "ExpandDims":
# https://www.tensorflow.org/api_docs/python/tf/expand_dims
input_shape = utils.get_tf_tensor_shape(op.inputs[0])
dim_op = op.inputs[1].op
if input_shape is None or not utils.is_tf_const_op(dim_op):
return False
dim = utils.get_tf_const_value(dim_op)
if dim < 0:
dim = dim + len(input_shape) + 1
new_shape = input_shape[:dim] + [1] + input_shape[dim:]
op.outputs[0].set_shape(new_shape)
logger.debug("set [%s] with new shape %s", op.outputs[0].name, new_shape)
return True
if op.type == "Unpack":
input_shape = utils.get_tf_tensor_shape(op.inputs[0])
if input_shape is None:
return False
axis = op.get_attr("axis")
axis = axis if axis >= 0 else axis + len(input_shape)
# the link below says that the rank of output is "rank(input) -1",if utils.is_tf_concat_op(op):
data_inputs = op.inputs[:-1]
input_shapes = [utils.get_tf_tensor_shape(inp) for inp in data_inputs]
input_shapes = [shape for shape in input_shapes if shape is not None]
if not input_shapes:
logger.debug("all input shapes of concat op %s are None, can't infer its output shape", op.name)
return False
new_shape = input_shapes[0]
axis_op = op.inputs[-1]
rank = len(new_shape)
if not utils.is_tf_const_op(axis_op):
op.outputs[0].set_shape([-1] * rank)
return True
axis = utils.get_tf_const_value(axis_op)
axis = axis if axis >= 0 else axis + rank
new_shape[axis] = -1
if len(input_shapes) == len(data_inputs): # all input shapes are known
concat_dim_vals = list(np.array(input_shapes)[:, axis])
# only when inputs' shape are known, then val of concat dim can be calculated
if concat_dim_vals.count(-1) == 0:
new_shape[axis] = sum(concat_dim_vals)
op.outputs[0].set_shape(new_shape)
logger.debug("set Concat op [%s] with new shape %s", op.outputs[0].name, new_shape)
return True
if op.type == "Select":
new_shape = utils.get_tf_tensor_shape(op.inputs[1])
if new_shape is None:
new_shape = utils.get_tf_tensor_shape(op.inputs[2])if utils.is_tf_concat_op(op):
data_inputs = op.inputs[:-1]
input_shapes = [utils.get_tf_tensor_shape(inp) for inp in data_inputs]
input_shapes = [shape for shape in input_shapes if shape is not None]
if not input_shapes:
logger.debug("all input shapes of concat op %s are None, can't infer its output shape", op.name)
return False
new_shape = input_shapes[0]
axis_op = op.inputs[-1]
rank = len(new_shape)
if not utils.is_tf_const_op(axis_op):
op.outputs[0].set_shape([-1] * rank)
return True
axis = utils.get_tf_const_value(axis_op)
axis = axis if axis >= 0 else axis + rank
new_shape[axis] = -1
if len(input_shapes) == len(data_inputs): # all input shapes are known
concat_dim_vals = list(np.array(input_shapes)[:, axis])
# only when inputs' shape are known, then val of concat dim can be calculated
if concat_dim_vals.count(-1) == 0:
new_shape[axis] = sum(concat_dim_vals)
op.outputs[0].set_shape(new_shape)
logger.debug("set Concat op [%s] with new shape %s", op.outputs[0].name, new_shape)
return True
if op.type == "Select":
new_shape = utils.get_tf_tensor_shape(op.inputs[1])
if new_shape is None:
new_shape = utils.get_tf_tensor_shape(op.inputs[2])new_shape.append(1)
else:
new_shape.append(shape[i])
op.outputs[0].set_shape(new_shape)
logger.debug("set %s op [%s] with new shape %s", op.type, op.outputs[0].name, new_shape)
return True
if op.type == "ExpandDims":
# https://www.tensorflow.org/api_docs/python/tf/expand_dims
input_shape = utils.get_tf_tensor_shape(op.inputs[0])
dim_op = op.inputs[1].op
if input_shape is None or not utils.is_tf_const_op(dim_op):
return False
dim = utils.get_tf_const_value(dim_op)
if dim < 0:
dim = dim + len(input_shape) + 1
new_shape = input_shape[:dim] + [1] + input_shape[dim:]
op.outputs[0].set_shape(new_shape)
logger.debug("set [%s] with new shape %s", op.outputs[0].name, new_shape)
return True
if op.type == "Unpack":
input_shape = utils.get_tf_tensor_shape(op.inputs[0])
if input_shape is None:
return False
axis = op.get_attr("axis")
axis = axis if axis >= 0 else axis + len(input_shape)
# the link below says that the rank of output is "rank(input) -1",if not shape_op or shape_op.type != "Shape":
return False
return set_shape_from_input(shape_op.inputs[0], op.outputs[0])
if op.type == "Gather":
# uses the follwing link to know how to infer shape of output
# https://www.tensorflow.org/api_docs/python/tf/gather
shape_params = utils.get_tf_tensor_shape(op.inputs[0])
shape_indices = utils.get_tf_tensor_shape(op.inputs[1])
# gather can only have 2 inputs
# https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/gather.html
if len(op.inputs) == 3:
axis_op = op.inputs[2].op
if not utils.is_tf_const_op(axis_op):
return False
axis = utils.get_tf_const_value(axis_op)
else:
axis = 0
shape = shape_params[:axis] + shape_indices + shape_params[axis + 1:]
op.outputs[0].set_shape(shape)
return True
if op.type in ["All", "Any", "Max", "Min"]:
axis_op = op.inputs[1].op
if not utils.is_tf_const_op(axis_op):
return False
axis = utils.get_tf_const_value(axis_op)
if not isinstance(axis, list):
axis = [axis]
keep_dims = op.get_attr("keep_dims")
shape = utils.get_tf_tensor_shape(op.inputs[0])
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