def common_quantize(cls, in_qtype, out_qtype, node, **kwargs):
all_nodes = kwargs['all_nodes']
opts = kwargs['opts']
G = kwargs['G']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
in_qtype = in_qtype.make_symmetric_signed()
out_qtype = out_qtype.make_symmetric_signed()
if cls.is_constant(x):
LOG.info("reducing %s to a constant", node.name)
if out_qtype:
val = x[0].value_as(out_qtype)
else:
val = cls.get_constant(x)
params = ConstantInputParameters(node.name,
value=val,
dims=Dim.unnamed(val.shape),
qtype=out_qtype,
constant_store=G.constant_store)
if opts.get('load_quantization'):
G.quantization[NodeId(params)] = QRec.scaled(
in_qs=[out_qtype], out_qs=[out_qtype])
else:
if in_qtype == out_qtype:
LOG.info('removing (de)quantize node %s with no effect',
node.name)
params = NoOPParameters(node.name,
desc="quantize with no effect")
elif in_qtype.dtype == out_qtype.dtype:
LOG.info('removing (de)quantize node %s with scale change',
node.name)
params = NoOPParameters(node.name,
desc="quantize with scale change")
out_qtype = in_qtype
else:
params = QuantizeParameters(node.name,
from_qtype=in_qtype,
to_qtype=out_qtype)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1],
to_idx=0))
if opts.get('load_quantization'):
G.quantization[NodeId(params)] = QRec.scaled(
in_qs=[in_qtype], out_qs=[out_qtype])
all_nodes[node.output[0]] = (params, 0, deepcopy(x[2]))
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: TFLiteNode, **kwargs):
node_opts = node.get_options(CastOptions)
G = kwargs['G']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
if node_opts:
in_qtype = QType(dtype=TFLiteTensorWrapper.TF_TO_NUMPY_TYPE[node_opts.InDataType()])
out_qtype = QType(dtype=TFLiteTensorWrapper.TF_TO_NUMPY_TYPE[node_opts.OutDataType()])
return cls.common_quantize(in_qtype, out_qtype, node, **kwargs)
params = NoOPParameters(node.name, desc='cast with no type information')
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, deepcopy(x[2]))
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