def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
out_rank = len(x_shape) + len(kwargs['axes'])
axes = cls._resolve_negative_ranks(kwargs['axes'], out_rank)
old_shape = x_shape.copy()
new_shape = [
1 if new_idx in axes else old_shape.pop(0)
for new_idx in range(out_rank)
]
pshape = ProvisionalDim(new_shape)
if cls.is_constant(x):
x_val = cls.get_constant(x)
logger.info(
f"reducing {valid_name} to a constant {cls.print_small(x_val)}"
)
params = ConstantInputParameters(valid_name,
value=x_val.reshape(new_shape))
else:
old_shape = cls._get_real_dim(x_shape)
shape = cls._get_real_dim(new_shape)
params = ReshapeParameters(valid_name,
old_shape=old_shape,
shape=shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, pshape, x[3])
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
y = inputs[1]
indices = cls.get_constant(y)
axis = node.attrs.get('axis', 0)
pshape = ProvisionalDim(x_shape[:axis:] + list(indices.shape) +
x_shape[axis + 1:])
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
x_val = cls.get_constant(x)
params = ConstantInputParameters(valid_name,
value=np.take(x_val,
indices,
axis=axis))
else:
axis = cls._trim_axis(axis, x_shape)
params = GatherParametters(valid_name, axis=axis, indices=indices)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, pshape)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
transpose = node.attrs.get('perm', list(range(len(x_shape) - 1, -1, -1)))
transpose = tuple(transpose)
pout_shape = [x_shape[i] for i in transpose]
new_axes = {}
for idx, dim in enumerate(x_shape):
if dim is not None:
new_axes[idx] = len(new_axes)
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
x_val = cls.get_constant(x)
params = ConstantInputParameters(valid_name, value=x_val.transpose(transpose))
else:
transpose = [new_axes[axis] for axis in transpose if x_shape[axis] is not None]
if transpose == sorted(transpose):
params = NoOPParameters(valid_name, desc="transpose does nothing")
else:
params = TransposeParameters(valid_name, transpose=transpose)
G.add_edge(NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
all_nodes[node.output[0]] = (params, 0, ProvisionalDim(pout_shape))
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
valid_name = kwargs['valid_name']
G = kwargs['G']
constant_operation = kwargs.get('constant_operation')
inputs = [all_nodes[inp] for inp in node.input]
# may have more than one input i.e. clip
x = inputs[0]
if cls.is_constant(x) and constant_operation:
res = constant_operation(cls.get_constant(x))
if res.size < 10:
logger.info("reducing %s to a constant %s", valid_name, res)
else:
logger.info("reducing %s to a constant", valid_name)
params = ConstantInputParameters(valid_name,
value=res,
constant_store=G.constant_store)
else:
params_args = kwargs.get('params_args', {})
params = kwargs['params_class'](valid_name, **params_args)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, copy.deepcopy(x[2]))
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
axes = cls._resolve_negative_ranks(kwargs['axes'], len(x_shape))
if len(x_shape) == 0:
assert len(axes) == 1 and axes[0] == 0
new_shape = [1]
else:
new_shape = [
item for sublist in [[1, dim] if idx in axes else [dim]
for idx, dim in enumerate(x_shape)]
for item in sublist
]
pshape = ProvisionalDim(new_shape)
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
x_val = cls.get_constant(x)
params = ConstantInputParameters(valid_name,
value=x_val.reshape(new_shape))
else:
old_shape = cls._get_real_dim(x_shape)
shape = cls._get_real_dim(new_shape)
params = ReshapeParameters(valid_name,
old_shape=old_shape,
shape=shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, pshape)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
to_dtype = node.attrs['to']
if cls.is_constant(x):
x_val = cls.get_constant(x)
x_val = x_val.astype(to_dtype)
if x_val.size < 10:
logger.info("reducing %s to a constant %s", valid_name, x_val)
else:
logger.info("reducing %s to a constant", valid_name)
params = ConstantInputParameters(valid_name,
dims=Dim.unnamed(x_val.shape),
value=x_val)
else:
params = QuantizeParameters(valid_name,
to_qtype=QType(dtype=to_dtype))
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, ProvisionalDim(x_shape), None)
return params
def _common(cls, node, v13=False, **kwargs):
all_nodes = kwargs['all_nodes']
valid_name = kwargs['valid_name']
G = kwargs['G']
inputs = [all_nodes[inp] for inp in node.input]
axis = node.attrs.get('axis', None)
# may have more than one input i.e. clip
x = inputs[0]
x_shape = x[2].shape
if axis and axis < 0:
axis += len(x_shape)
axis = cls._trim_axis(axis, x_shape)
if axis != 0 and not v13:
ValueError(
'LogSoftmax does not support ONNX version < 13 with axis not first'
)
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
params = ConstantInputParameters(
valid_name,
value=np.log(softmax_func(cls.get_constant(x), axis=axis)))
else:
softmax_params = SoftMaxParameters(f'{valid_name}_softmax',
axis=axis)
G.add_edge(
NNEdge(from_node=x[0],
to_node=softmax_params,
from_idx=x[1],
to_idx=0))
params = LogOpParameters(f'{valid_name}_log')
G.add_edge(NNEdge(from_node=softmax_params, to_node=params))
all_nodes[node.output[0]] = (params, 0, copy.deepcopy(x[2]), None)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
axes = cls._resolve_negative_ranks(kwargs['axes'], len(x_shape))
if axes:
if any(x_shape[axis] != 1 for axis in axes):
raise ValueError("axis parameter in node %s is invalid %s" % (valid_name, axes))
new_shape = [dim for idx, dim in enumerate(x_shape) if idx not in axes]
else:
new_shape = [dim for dim in x_shape if dim != 1]
pshape = ProvisionalDim(new_shape)
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
x_val = cls.get_constant(x)
params = ConstantInputParameters(valid_name, value=x_val.reshape(new_shape),
constant_store=G.constant_store)
else:
old_shape = cls._get_real_dim(x_shape)
shape = cls._get_real_dim(new_shape)
params = ReshapeParameters(valid_name, old_shape=old_shape, shape=shape)
G.add_edge(NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
all_nodes[node.output[0]] = (params, 0, pshape)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
if not all(cls.is_constant(inp) for inp in inputs):
raise NotImplementedError(
"nntool does not support import of graphs with evaluated loops"
)
importer = kwargs['importer']
sub_G = NNGraph()
all_nodes_clone = all_nodes.copy()
importer.import_subgraph(sub_G,
node.attrs['body'], {},
all_nodes=all_nodes_clone)
if not all(
isinstance(inp, (InputParameters, ConstantInputParameters))
for inp in sub_G.inputs()):
raise NotImplementedError(
"nntool does not support import of graphs with evaluated loops"
)
sub_G.add_dimensions()
for idx, inp in enumerate(sub_G.inputs()):
inp.index = idx
logger.info(f"reducing loop {valid_name} to a constant")
count = inputs[0][0].value
keep_going = inputs[1][0].value
loop_carried = [inp[0].value for inp in inputs[2:]]
outputs = [np.array([])] * len(node.output)
while keep_going and count > 0:
executer = GraphExecuter(sub_G)
output_tensors = executer.execute([count, keep_going] +
loop_carried,
silent=True)
outp_vals = [
output_tensors[node.step_idx][0] for node in sub_G.outputs()
if not isinstance(node, InputParameters)
]
keep_going = outp_vals[0]
for idx, val in enumerate(outp_vals[1:]):
if idx < len(loop_carried):
loop_carried[idx] = outputs[idx] = val
elif outputs[idx] is None:
outputs[idx] = val
else:
outputs[idx] = np.concatenate((outputs[idx], val))
count -= 1
for idx, outp in enumerate(node.output):
params = ConstantInputParameters(
G.unique_name(f'{valid_name}_out{idx}'),
value=outputs[idx],
dims=Dim.unnamed(outputs[idx].shape))
all_nodes[outp] = (params, 0, ProvisionalDim(outputs[idx].shape),
None)
return None
def gen_concat(cls, node, inputs, axis, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
input_shapes = [inp[2].shape for inp in inputs]
axis_sum = sum(shape[axis] for shape in input_shapes)
axis = axis if axis >= 0 else len(input_shapes[0]) + axis
output_shape = [
axis_sum if idx == axis else dim
for idx, dim in enumerate(input_shapes[0])
]
pout_dim = ProvisionalDim(output_shape)
none_dims = sum(
[1 if dim is None else 0 for dim in output_shape[:axis:]])
if all(cls.is_constant(inp) for inp in inputs):
value = np.concatenate([cls.get_constant(inp) for inp in inputs],
axis=axis)
logger.info(
f"reducing {valid_name} to a constant {print_small(value)}")
params = ConstantInputParameters(valid_name, value=value)
else:
params = ConcatParameters(valid_name, axis=axis - none_dims)
for idx, inp in enumerate(inputs):
G.add_edge(
NNEdge(from_node=inp[0],
to_node=params,
from_idx=inp[1],
to_idx=idx))
all_nodes[node.output[0]] = (params, 0, pout_dim, inputs[0][3])
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
valid_name = kwargs['valid_name']
G = kwargs['G']
constant_operation = kwargs.get('constant_operation')
inputs = [all_nodes[inp] for inp in node.input]
assert len(inputs) == 2
if all(cls.is_constant(inp) for inp in inputs) and constant_operation:
logger.info("reducing %s to a constant", valid_name)
values = [cls.get_constant(inp) for inp in inputs]
outputs = cls.implied_broadcast(inputs)
params = ConstantInputParameters(valid_name, value=constant_operation(*values),
dims=Dim.unnamed(outputs[0].known_shape),
constant_store=G.constant_store)
else:
params_args = kwargs.get('params_args', {})
params = kwargs['params_class'](valid_name, **params_args)
outputs = cls.implied_broadcast(inputs)
shapes = []
for idx, inp in enumerate(inputs):
G.add_edge(NNEdge(from_node=inp[0], to_node=params, from_idx=inp[1], to_idx=idx))
shapes.append(inp[2].known_shape)
if isinstance(params, Broadcastable):
params.set_broadcast(shapes)
all_nodes[node.output[0]] = (params, 0, outputs[0])
return params
def _common(cls, node, starts, ends, axes, steps, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
x = all_nodes[node.input[0]]
x_shape = np.array(x[2].shape)
x_rank = len(x_shape)
axes = cls._resolve_negative_ranks(
axes, len(x_shape)) if axes else tuple(range(x_rank))
axes_rank = len(axes)
steps = steps if steps else [1] * axes_rank
slices = np.stack([starts, ends, steps]).transpose((1, 0))
p_slices = []
p_shape = []
for idx, dim in enumerate(x_shape):
try:
if dim is None:
p_slices.append(None)
p_shape.append(None)
else:
slice_idx = axes.index(idx)
begin, end, step = slices[slice_idx]
begin = max(min(begin if begin >= 0 else dim + begin, dim),
0)
end = max(min(end if end >= 0 else dim + end, dim), -1)
# -sys.maxsize is used to indicate 0 in the reverse slice direction
# this makes it compatible with the numpy slice
p_slices.append(
(begin, -sys.maxsize if end == -1 else end, step))
if step < 0:
p_shape.append((begin - end) // -step)
else:
p_shape.append((end - begin) // step)
except ValueError:
p_slices.append((0, dim, 1))
p_shape.append(dim)
slices = cls._get_real_dim(p_slices)
shape = cls._get_real_dim(p_shape)
params = StridedSliceParameters(valid_name,
act_slice=slices,
out_shape=shape)
if cls.is_constant(x):
x_val = cls.get_constant(x)
x_val = params.numpy_slice(x_val)
if x_val.size < 10:
logger.info("reducing %s to a constant %s", valid_name, x_val)
else:
logger.info("reducing %s to a constant", valid_name)
params = ConstantInputParameters(valid_name,
dims=Dim.unnamed(x_val.shape),
value=x_val,
constant_store=G.constant_store)
else:
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, ProvisionalDim(p_shape))
return params
def _common(cls, node, copy_qtype=False, quantized_args=None, **kwargs):
all_nodes = kwargs['all_nodes']
valid_name = kwargs['valid_name']
G = kwargs['G']
constant_operation = kwargs.get('constant_operation')
constant_int_operation = kwargs.get('constant_int_operation')
inputs = [all_nodes[inp] for inp in node.input]
if quantized_args:
args = [inputs[quantized_args[0][0]], inputs[quantized_args[1][0]]]
inp_qtypes = [
cls.get_qtype(inputs[quantized_args[0][1]], inputs[quantized_args[0][2]]),
cls.get_qtype(inputs[quantized_args[1][1]], inputs[quantized_args[1][2]])
]
out_qtype = cls.get_qtype(inputs[quantized_args[2][0]], inputs[quantized_args[2][1]])
else:
args = inputs
assert len(args) == 2
out_qtype = None
if all(cls.is_constant(inp) for inp in args) and constant_operation:
values = [cls.get_constant(inp) for inp in args]
if quantized_args:
values = [inp_qtype.dequantize(val) for inp_qtype, val in zip(inp_qtypes, values)]
outputs = cls.implied_broadcast(inputs)
if constant_int_operation and all(np.issubdtype(val.dtype, np.integer) for val in values):
res = constant_int_operation(*values)
else:
res = constant_operation(*values)
if quantized_args:
res = out_qtype.quantize(res)
if res.size < 10:
logger.info("reducing %s to a constant %s", valid_name, res)
else:
logger.info("reducing %s to a constant", valid_name)
params = ConstantInputParameters(valid_name, value=res,
dims=Dim.unnamed(outputs[0].known_shape),
qtype=out_qtype)
else:
params_args = kwargs.get('params_args', {})
params = kwargs['params_class'](valid_name, **params_args)
outputs = cls.implied_broadcast(inputs)
shapes = []
for idx, inp in enumerate(args):
G.add_edge(NNEdge(from_node=inp[0], to_node=params, from_idx=inp[1], to_idx=idx))
shapes.append(inp[2].known_shape)
if isinstance(params, Broadcastable):
params.set_broadcast(shapes)
if quantized_args:
for qtype, inp in zip(inp_qtypes, args):
if cls.is_constant(inp):
inp[0].qtype = qtype
qrecs = kwargs['qrecs']
qrecs[NodeId(params)] = QRec.scaled(in_qs=inp_qtypes, out_qs=[out_qtype])
if copy_qtype:
out_qtype = inputs[0][3] if inputs[0][3] is not None else inputs[1][3]
all_nodes[node.output[0]] = (params, 0, outputs[0], out_qtype)
return params
def _common(cls,
node,
mode='constant',
pads=None,
constant_value=0,
**kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
apads = np.array(pads).reshape((-1, 2))
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
val = cls.get_constant(x)
if mode == 'constant':
val = np.pad(val,
apads,
mode=mode,
constant_values=constant_value)
else:
val = np.pad(val, apads, mode=mode)
params = ConstantInputParameters(valid_name,
value=val,
constant_store=G.constant_store)
all_nodes[node.output[0]] = (params, 0, ProvisionalDim(x_shape))
return params
if mode != 'constant':
raise ValueError('%s - pad mode %s is not supported' %
(valid_name, mode))
if constant_value != 0:
raise ValueError('%s - only zero padding is supported' %
valid_name)
trimmed_pads = tuple(
[pad for idx, pad in enumerate(apads) if x_shape[idx] is not None])
if all(sum(trimmed_pad) == 0 for trimmed_pad in trimmed_pads):
params = NoOPParameters(valid_name, desc="eliminated pad of 0")
pshape = x_shape
else:
pshape = [
dim + sum(apads[idx]) if dim is not None else None
for idx, dim in enumerate(x_shape)
]
# pshape = [None if dim is None else dim + sum(apads[idx]) for idx, dim in enumerate(x_shape)]
padvals = [(constant_value, constant_value)] * len(trimmed_pads)
params = PadParameters(valid_name,
padding=trimmed_pads,
pad_vals=padvals)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
all_nodes[node.output[0]] = (params, 0, ProvisionalDim(pshape))
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
if cls.SINCE_VERSION == 1:
shape = np.array(node.attrs["shape"])
else: # since_version >= 5
shape = cls.get_constant(inputs[1])
input_shape = np.array(inputs[0][2].shape)
shape = [
dim if dim != 0 else input_shape[idx]
for idx, dim in enumerate(shape)
]
if -1 in shape:
wild_index = shape.index(-1)
in_size = prod([1 if dim is None else dim for dim in input_shape])
shape_size = prod(
[1 if dim is None or dim <= 0 else dim for dim in shape])
if in_size % shape_size != 0:
raise ValueError('invalid reshape')
shape[wild_index] = in_size // shape_size
shape = np.array(shape)
if cls.is_constant(inputs[0]):
logger.info("reducing %s to a constant", valid_name)
params = ConstantInputParameters(valid_name,
value=cls.get_constant(
inputs[0]).reshape(shape),
dims=Dim.unnamed(shape),
constant_store=G.constant_store)
pshape = ProvisionalDim(shape)
all_nodes[node.output[0]] = (params, 0, pshape)
return params
# TODO - There must be a better way of doing this
# This hacks around the fact that the batch dimension will be in the reshape
if input_shape[0] is None and shape[0] == 1:
shape = np.array([None] + list(shape[1::]))
pshape = ProvisionalDim(shape)
# pylint: disable=singleton-comparison
old_shape = Dim.unnamed(list(input_shape[input_shape != None]))
shape = Dim.unnamed(list(shape[shape != None]))
params = ReshapeParameters(valid_name,
old_shape=old_shape,
shape=shape)
inp = inputs[0]
G.add_edge(
NNEdge(from_node=inp[0], to_node=params, from_idx=inp[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, pshape)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
logger.info("reducing %s to a constant", valid_name)
x_shape = [dim if dim else 1 for dim in x[2].shape]
sz = np.array(prod(x_shape))
params = ConstantInputParameters(valid_name, value=sz)
all_nodes[node.output[0]] = (params, 0, ProvisionalDim([]), None)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
# this process can leave dangling nodes without outputs
# we clean them after we have finished loading
x_shape = [dim if dim else 1 for dim in x[2].shape]
params = ConstantInputParameters(valid_name, value=np.array(x_shape))
logger.info("reducing %s to a constant %s", valid_name, x_shape)
all_nodes[node.output[0]] = (params, 0, ProvisionalDim([len(x_shape)]), None)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
if not cls.is_constant(x):
# TODO - clear unique paths to this node here
pass
logger.info("reducing %s to a constant", valid_name)
x_shape = [dim if dim else 1 for dim in x[2].shape]
params = ConstantInputParameters(valid_name,
value=np.array(prod(x_shape)))
all_nodes[node.output[0]] = (params, 0, ProvisionalDim([]))
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
input_shapes = [inp[2].shape for inp in inputs]
axis = node.attrs['axis']
new_axis = node.attrs.get('new_axis', 0)
# if new_axis is false this is the same as concat
if not new_axis:
return cls.gen_concat(node, inputs, axis)
# if it is true then it's different
if not all(shape == input_shapes[0] for shape in input_shapes[1::]):
raise ValueError(
'all shapes must be the same in ConcatFromSequence with new axis'
)
# reduce to a constant if we can
if all(cls.is_constant(inp) for inp in inputs):
logger.info("reducing %s to a constant", valid_name)
value = np.concatenate([cls.get_constant(inp) for inp in inputs],
axis=axis)
params = ConstantInputParameters(valid_name, value=value)
all_nodes[node.output[0]] = (params, 0,
ProvisionalDim(value.shape),
inputs[0][3])
return params
# add the axis into the shape
new_shape = input_shapes[0].copy()
new_shape = new_shape[:axis:] + [1] + new_shape[axis::]
old_shape = cls._get_real_dim(input_shapes[0])
shape = cls._get_real_dim(new_shape)
# create a reshape on each input and pass the outputs to the concat mixin
#pylint: disable=consider-using-enumerate
for idx in range(len(inputs)):
inp = inputs[idx]
rparams = ReshapeParameters("%s_reshape_%s" % (valid_name, idx),
old_shape=old_shape,
shape=shape)
G.add_edge(
NNEdge(from_node=inp[0],
to_node=rparams,
from_idx=inp[1],
to_idx=0))
inputs[idx] = (rparams, 0, ProvisionalDim(new_shape), inp[3])
return cls.gen_concat(node, inputs, axis)
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
y = inputs[1]
shape = cls.get_constant(y)
pshape = cls.broadcast_to(x, shape)
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
x_val = cls.get_constant(x)
params = ConstantInputParameters(valid_name, value=x_val * np.ones(shape))
else:
raise ValueError("Expand is only implemented on constants")
all_nodes[node.output[0]] = (params, 0, pshape)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
if not all(cls.is_constant(inp) for inp in inputs):
raise NotImplementedError("Tile is only implemented on constants")
inp_vals = [cls.get_constant(inp) for inp in inputs]
out_val = np.tile(inp_vals[0], inp_vals[1])
logger.info("reducing %s to a constant", valid_name)
params = ConstantInputParameters(
valid_name,
value=out_val
)
pshape = ProvisionalDim(out_val.shape)
all_nodes[node.output[0]] = (params, 0, pshape, inputs[0][3])
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
y = inputs[1]
indices = cls.get_constant(y)
axis = node.attrs.get('axis', 0)
pshape = ProvisionalDim(x_shape[:axis:] + list(indices.shape) +
x_shape[axis + 1:])
if cls.is_constant(x):
x_val = cls.get_constant(x)
logger.info(
f"reducing {valid_name} to a constant {cls.print_small(x_val)}"
)
params = ConstantInputParameters(valid_name,
value=np.take(x_val,
indices,
axis=axis))
else:
if np.ndim(indices) <= 1:
idx = np.asscalar(indices)
act_slice = tuple([(0, dim, 1) if i != axis else
(idx, idx + 1, 1)
for i, dim in enumerate(x_shape)
if dim is not None])
out_shape = pshape.known_shape.copy()
params = StridedSliceParameters(valid_name,
act_slice=act_slice,
out_shape=out_shape)
else:
axis = cls._trim_axis(axis, x_shape)
params = GatherParameters(valid_name,
axis=axis,
indices=indices)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, pshape, x[3])
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
x_val = cls.get_constant(x)
params = ConstantInputParameters(valid_name,
dims=Dim.unnamed(x_val.shape),
value=np.floor(x_val),
constant_store=G.constant_store)
else:
raise ValueError("ONNX floor operator is not implemented")
all_nodes[node.output[0]] = (params, 0, ProvisionalDim(x_shape))
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
valid_name = kwargs['valid_name']
G = kwargs['G']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
y = inputs[1]
shape = cls.get_constant(y)
pshape = cls.broadcast_to(x, shape)
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
x_val = cls.get_constant(x)
params = ConstantInputParameters(valid_name, value=x_val * np.ones(shape))
else:
params = ExpandParameters(valid_name, shape=shape)
G.add_edge(NNEdge(x[0], params, from_idx=x[1]))
all_nodes[node.output[0]] = (params, 0, pshape, x[3])
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
axes = cls._resolve_negative_ranks(kwargs['axes'], len(x_shape))
if len(x_shape) == 0:
assert len(axes) == 1 and axes[0] == 0
new_shape = [1]
else:
new_shape = []
old_shape = x_shape.copy()
axes_copy = axes.copy()
idx = 0
while axes_copy or old_shape:
if idx in axes_copy:
axes_copy.remove(idx)
new_shape.append(1)
else:
if not old_shape:
raise ValueError(f'error in unsqueeze inshape {x_shape} axes {axes}')
new_shape.append(old_shape.pop(0))
idx += 1
pshape = ProvisionalDim(new_shape)
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
x_val = cls.get_constant(x)
params = ConstantInputParameters(valid_name, value=x_val.reshape(new_shape),
constant_store=G.constant_store)
else:
old_shape = cls._get_real_dim(x_shape)
shape = cls._get_real_dim(new_shape)
params = ReshapeParameters(valid_name, old_shape=old_shape, shape=shape)
G.add_edge(NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
all_nodes[node.output[0]] = (params, 0, pshape)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
axis = node.attrs.get('axis', 1)
if axis < 0:
axis += len(x_shape)
old_shape = cls._get_real_dim(x_shape)
if axis == 0:
shape = [1, prod(old_shape)]
pshape = shape
else:
start = x_shape[:axis:]
end = x_shape[axis::]
pshape = list(start) + [prod(cls._get_real_dim(end))]
shape = cls._get_real_dim(pshape)
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
params = ConstantInputParameters(
valid_name,
value=cls.get_constant(x).reshape(shape),
constant_store=G.constant_store
)
pshape = ProvisionalDim(shape)
else:
params = ReshapeParameters(valid_name, old_shape=Dim.unnamed(
old_shape), shape=Dim.unnamed(shape))
pshape = ProvisionalDim(pshape)
G.add_edge(NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
all_nodes[node.output[0]] = (params, 0, pshape)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
valid_name = kwargs['valid_name']
G = kwargs['G']
constant_operation = kwargs.get('constant_operation')
inputs = [all_nodes[inp] for inp in node.input]
assert len(inputs) == 2
if all(cls.is_constant(inp) for inp in inputs) and constant_operation:
logger.info("reducing %s to a constant", valid_name)
values = [cls.get_constant(inp) for inp in inputs]
params = ConstantInputParameters(valid_name,
value=constant_operation(*values))
else:
params_args = kwargs.get('params_args', {})
params = kwargs['params_class'](valid_name, **params_args)
for idx, inp in enumerate(inputs):
G.add_edge(
NNEdge(from_node=inp[0],
to_node=params,
from_idx=inp[1],
to_idx=idx))
outputs = cls.implied_broadcast(inputs)
all_nodes[node.output[0]] = (params, 0, outputs[0])
return params
def _common(cls,
node,
mode='constant',
pads=None,
constant_value=0,
**kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
ndim = len(x_shape)
npad = len(pads) // 2
if npad != ndim:
if all(not pad for pad in pads):
logger.warning(
f'Pad {valid_name} has {npad} pad values and {ndim} input rank. '
'Since pad is zero this is ignored but it probably indicates a bug in the ONNX graph.'
)
else:
raise ValueError(
f'Eroor in ONNX graph - pad {valid_name} has {npad} pad values and {ndim} input rank.'
)
apads = np.array([[pads[idx], pads[idx + ndim]]
for idx in range(ndim)])
# apads = np.array(pads).reshape((-1, 2))
if cls.is_constant(x):
logger.info("reducing %s to a constant", valid_name)
val = cls.get_constant(x)
if mode == 'constant':
val = np.pad(val,
apads,
mode=mode,
constant_values=constant_value)
else:
val = np.pad(val, apads, mode=mode)
params = ConstantInputParameters(valid_name, value=val)
pshape = [
dim + sum(apads[idx]) if dim is not None else None
for idx, dim in enumerate(x_shape)
]
all_nodes[node.output[0]] = (params, 0, ProvisionalDim(pshape),
x[3])
return params
if mode != 'constant':
raise ValueError('%s - pad mode %s is not supported' %
(valid_name, mode))
if any(
sum(pad) > 0 and x_shape[idx] is None
for idx, pad in enumerate(apads)):
raise ValueError(
f'unknown/batch axis is being padded in {valid_name}. Manipulation of '
'unknown/batch axis is not supported')
trimmed_pads = tuple(
[pad for idx, pad in enumerate(apads) if x_shape[idx] is not None])
if all(sum(trimmed_pad) == 0 for trimmed_pad in trimmed_pads):
params = NoOPParameters(valid_name, desc="eliminated pad of 0")
pshape = x_shape
else:
pshape = [
dim + sum(apads[idx]) if dim is not None else None
for idx, dim in enumerate(x_shape)
]
# pshape = [None if dim is None else dim + sum(apads[idx]) for idx, dim in enumerate(x_shape)]
padvals = [(constant_value, constant_value)] * len(trimmed_pads)
params = PadParameters(valid_name,
padding=trimmed_pads,
pad_vals=padvals)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
all_nodes[node.output[0]] = (params, 0, ProvisionalDim(pshape), x[3])
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
axis = node.attrs.get('axis', 0)
if axis < 0:
axis += len(x_shape)
assert axis < len(x_shape) and axis >= 0,\
"axis %s is out of bounds - input dims %s in node %s" % (axis, x_shape, valid_name)
split_dim = x_shape[axis]
assert split_dim is not None, "split dimension must be defined"
split = None
if cls.SINCE_VERSION >= 13:
if len(inputs) > 1:
split = cls.get_constant(inputs[1])
else:
split = node.attrs.get('split')
if split:
split = np.array(split)
assert sum(
split
) == split_dim, "split sizes should add up to total size %s" % valid_name
assert np.all(
split > 0
), "split sizes should be greater than zero %s" % valid_name
else:
num_outputs = len(node.output)
assert split_dim % num_outputs == 0,\
"no split attribute or value and dimension is not divisible by number of outputs %s" % valid_name
split = np.array([split_dim // num_outputs] * num_outputs)
split = split.tolist()
act_slices = []
out_shapes = []
out_pshapes = []
cur = 0
for idx, split_dim in enumerate(split):
act_slices.append(
tuple(
(cur, cur + split_dim, 1) if didx == axis else (0, dim, 1)
for didx, dim in enumerate(x_shape) if dim is not None))
out_pshape = tuple(split_dim if didx == axis else dim
for didx, dim in enumerate(x_shape))
out_shapes.append(
tuple(dim for dim in out_pshape if dim is not None))
out_pshapes.append(ProvisionalDim(out_pshape))
cur += split_dim
axis -= sum(1 if dim is None else 0 for dim in x_shape[:axis:])
params = SplitParameters(valid_name,
act_slices=act_slices,
out_shapes=out_shapes,
axis=axis)
if cls.is_constant(x):
logger.info("reducing %s to %s constant(s)", valid_name,
len(out_shapes))
values = params.numpy_split(cls.get_constant(x))
for idx, out_pshape in enumerate(out_pshapes):
cparams = ConstantInputParameters(
valid_name,
value=values[idx],
constant_store=G.constant_store)
all_nodes[node.output[idx]] = (cparams, 0, out_pshape)
return None
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
for idx, out_pshape in enumerate(out_pshapes):
all_nodes[node.output[idx]] = (params, idx, out_pshape)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
if cls.SINCE_VERSION == 1:
shape = np.array(node.attrs["shape"])
else: # since_version >= 5
shape = cls.get_constant(inputs[1])
input_shape = np.array(inputs[0][2].shape)
shape = [
dim if dim != 0 else input_shape[idx]
for idx, dim in enumerate(shape)
]
# this catches a special case where inp is something like [None, 2, 4] and shape is [2, -1, 4]
# The -1 is clearly the None moving so move it
if cls.moves_unknown(input_shape, shape):
shape = np.array([None if dim == -1 else dim for dim in shape])
else:
if -1 in shape:
new_shape_size = reduce(
lambda x, y: x * 1
if y is None or y == -1 else x * y, shape, 1)
inp_size = reduce(lambda x, y: x * y
if y is not None else x, input_shape, 1)
in_size = prod(
[1 if dim is None else dim for dim in input_shape])
shape_size = prod([1 if dim is None else dim for dim in shape])
if in_size % shape_size != 0:
raise ValueError('invalid reshape')
shape[shape.index(-1)] = inp_size // new_shape_size
shape = np.array(shape)
# TODO - There must be a better way of doing this
# This hacks around the fact that the batch dimension will be in the reshape
if input_shape[0] is None and shape[0] == 1:
shape = np.array([None] + list(shape[1::]))
inp = inputs[0]
if cls.is_constant(inp):
# there should be no None in shape since a constant always has known size
logger.info("reducing %s to a constant", valid_name)
params = ConstantInputParameters(
valid_name,
value=cls.get_constant(inp).reshape(shape),
dims=Dim.unnamed(shape))
pshape = ProvisionalDim(shape)
all_nodes[node.output[0]] = (params, 0, pshape, inp[3])
return params
pshape = ProvisionalDim(shape)
# pylint: disable=singleton-comparison
old_shape = Dim.unnamed(list(input_shape[input_shape != None]))
shape = Dim.unnamed(list(shape[shape != None]))
params = ReshapeParameters(valid_name,
old_shape=old_shape,
shape=shape)
G.add_edge(
NNEdge(from_node=inp[0], to_node=params, from_idx=inp[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, pshape, inp[3])
return params
