def version_1(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(Conv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
weights_node = filt[0]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_out_c, filt_h, filt_w, filt_in_c = tuple(filt_shape)
# get filter dimensions
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
if len(inputs) > 2:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(
f'{node.name}_bias',
dims=Dim.unnamed([filt_out_c]),
value=np.zeros([filt_out_c], dtype=np.float32)) # TODO - check
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
has_bias=True,
in_dims_hint=SparseList([['h', 'w', 'c'],
cls.TF_LITE_FILTER_ORDER.copy(),
['out_c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]),
constant_store=G.constant_store)
G.add_edge(NNEdge(from_node=weights_node, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=bias_node, to_node=params, to_idx=2))
cls.new_load_filter_parameters(G, params, node.input[0], weights_node,
bias_node, node.output[0], opts)
# if opts.get('load_dequantized'):
# weights_node.value, bias_node.value = cls.load_dequantized_filter_parameters(
# node.input, bias_node.value)
# else:
# qrec, weights_node.value, bias_node.value = cls.load_filter_parameters(G, params, node.input, bias_node.value,
# node.output, opts)
# if qrec:
# G.quantization[NodeId(weights_node)].out_qs[0] = qrec.in_qs[1]
# G.quantization[NodeId(bias_node)].out_qs[0] = qrec.in_qs[2]
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size(
[in_dim,
Dim.unnamed(filt_dim.shape),
Dim.unnamed([filt_out_c])])
pout_dims = ProvisionalDim([in_b] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
params = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def _common(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(DepthwiseConv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x = cls.remove_known_batch_dimension(G, x, node)
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
weights_node = filt[0]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_in_c, filt_h, filt_w, filt_out_c = tuple(filt_shape)
# get filter dimensions
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_DW_FILTER_ORDER)
# multiplier should match filter
check(filt_dim.out_c == node_opts.DepthMultiplier() * in_c,
"invalid multiplier")
groups = filt_dim.out_c // node_opts.DepthMultiplier()
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
if len(inputs) > 2:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(
f'{node.name}_bias',
dims=Dim.unnamed([filt_out_c]),
value=np.zeros([filt_out_c], dtype=np.float32)) # TODO - check
# TFLITE produces single channel input DW convolutions with the
# multiplier equal to the number of out channels. This is just
# a normal convolution and since we don't handle the channel
# multiplier at present (but can) just convert them to normal
# convolutions
convert_to_conv = in_c == 1 and groups == 1
if convert_to_conv:
filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
has_bias=True,
in_dims_hint=[['h', 'w', 'c'],
cls.TF_LITE_FILTER_ORDER.copy(), ['out_c']],
out_dims_hint=[['h', 'w', 'c']])
else:
filt_dim.impose_order(cls.TF_LITE_DW_FILTER_ORDER)
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
groups=groups,
multiplier=node_opts.DepthMultiplier(),
has_bias=True,
tf_depthwise=True,
in_dims_hint=[['h', 'w', 'c'],
cls.TF_LITE_DW_FILTER_ORDER.copy(), ['out_c']],
out_dims_hint=[['h', 'w', 'c']])
G.add_edge(NNEdge(from_node=weights_node, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=bias_node, to_node=params, to_idx=2))
cls.new_load_filter_parameters(G,
params,
params.filter.actual_shape,
params.filter.get_order_idx('out_c'),
node.input[0],
weights_node,
bias_node,
node.output[0],
opts,
dw_to_pw=convert_to_conv)
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size(
[in_dim,
Dim.unnamed(filt_dim.shape),
Dim.unnamed([filt_out_c])])
pout_dims = ProvisionalDim([in_b] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
params = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def _common(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(ConcatenationOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
inp_shapes = [input[2].shape for input in inputs]
buffer_idxes = [tensor.buffer_idx for tensor in node.input]
non_zero_idxes = [idx for idx in buffer_idxes if idx != 0]
duplicates = [
idx for idx, count in Counter(non_zero_idxes).items() if count > 1
]
if duplicates:
LOG.warning(
f'concat {node.name} has duplicate inputs. Inserting copies but this is not very efficient.'
)
for idx in duplicates:
dup_idxes = [i for i, x in enumerate(buffer_idxes) if x == idx]
for dup_idx in dup_idxes[1:]:
cparams = CopyParameters(
G.unique_name(
f'{node.name}_dup_{dup_idxes[0]}_{dup_idx}'))
dup_inp = inputs[dup_idx]
G.add_edge(
NNEdge(from_node=dup_inp[0],
from_idx=dup_inp[1],
to_node=cparams))
inputs[dup_idx] = tuple([cparams, 0] + list(dup_inp[2:]))
axis = node_opts.Axis()
if any(inp_shape[axis] is None for inp_shape in inp_shapes):
raise ValueError("concat on undefined axis in node %s" % node.name)
def red_func(x, y):
return y.copy() if x is None else [
(elem if y[idx] is not None and elem is not None else None)
if idx != axis else elem + y[axis]
for idx, elem in enumerate(x)
]
pout_shape = reduce(red_func, inp_shapes)
if all(cls.is_constant(inp) for inp in inputs):
# cls.remove_none_from_constants(inputs, pout_shape)
LOG.info("reducing %s to a constant", node.name)
value = np.concatenate([cls.get_constant(inp) for inp in inputs],
axis=axis)
params = ConstantInputParameters(node.name, value=value)
else:
axis -= sum(1 if dim is None else 0 for dim in pout_shape[:axis:])
params = ConcatParameters(node.name, axis=axis, axis_hint=None)
for idx, inp in enumerate(inputs):
inp_node, inp_idx = cls._maybe_insert_reshape(
G, inp, inp_shapes[idx], pout_shape)
G.add_edge(
NNEdge(from_node=inp_node,
to_node=params,
from_idx=inp_idx,
to_idx=idx))
if opts.get('load_quantization'):
G.quantization[NodeId(params)] = cls.load_tf_quantization(
node.input, node.output)
cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (params, 0, ProvisionalDim(pout_shape))
return params
def version_1(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(Conv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
filt_tensor = node.input[1]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_out_c, filt_h, filt_w, filt_in_c = tuple(filt_shape)
# get filter dimensions
filt_tensor.used = True
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
has_bias = len(inputs) > 2
if has_bias:
node.input[2].used = True
params = Conv2DParameters(node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(),
node_opts.StrideW()),
dilation=DilationDim(
node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
has_bias=has_bias,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]),
constant_store=G.constant_store)
if opts.get('load_dequantized'):
cls.load_dequantized_filter_parameters(params, node.input)
else:
cls.load_filter_parameters(G, params, node.input, node.output,
opts)
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size([in_dim])
pout_dims = ProvisionalDim([in_b] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
params = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def new_load_filter_parameters(cls,
G,
params,
filter_shape,
filter_scale_axis,
input_tensor,
weights_node,
bias_node,
output_tensor,
opts,
dw_to_pw=False):
weights_node.meta['filter_params'] = True
bias_node.meta['filter_params'] = True
# if quantizaton is not loaded then the constants will already be dequantized
if dw_to_pw:
# Conv has been converted from depthwise to pointwise so reorder the weights tensor
weights_node.value = np.transpose(weights_node.value,
cls.TF_LITE_DW_FILTER_TRANSPOSE)
weights_node.dims = Dim.unnamed(weights_node.value.shape)
if not opts.get('load_quantization'):
return
wqtype = weights_node.qtype
if wqtype is None:
LOG.warning('quantization is missing on node %s', params.name)
return
# scale weights as requested. change asymmetric and/or unsigned weights to signed symmetric
if wqtype.asymmetric or not wqtype.signed:
if opts.get('rescale_perchannel'):
wqtype = cls.get_weights_qtype_by_channel(
filter_shape, filter_scale_axis, weights_node)
else:
wqtype = cls.get_weights_qtype_by_tensor(weights_node)
else:
if opts.get('rescale_perchannel'):
if len(wqtype.scale) != filter_shape[filter_scale_axis]:
wqtype = cls.get_weights_qtype_by_channel(
filter_shape, filter_scale_axis, weights_node)
else:
if len(wqtype.scale) > 1:
wqtype = cls.get_weights_qtype_by_tensor(weights_node)
iqtype = input_tensor.qtype
# correct input qtype to symmetric tensor scaled
if iqtype.asymmetric or not iqtype.signed or len(iqtype.scale) > 1:
iqtype = QType.from_min_max_sq(min_val=iqtype.min_val,
max_val=iqtype.max_val)
else:
iqtype = deepcopy(iqtype)
oqtype = output_tensor.qtype
# correct output qtype to symmetric tensor scaled
if oqtype.asymmetric or not oqtype.signed or len(oqtype.scale) > 1:
oqtype = QType.from_min_max_sq(min_val=oqtype.min_val,
max_val=oqtype.max_val)
else:
oqtype = deepcopy(oqtype)
# dqbias = bias_node.dqvalue
bias_scale = (iqtype.scale * wqtype.scale).astype(np.float32)
bqtype = QType(dtype=np.int32, scale=bias_scale)
# NOTE: In some tensorflow graphs the biases are hugely negative or hugely
# positive. I've never seen this without a relun after and the weights on
# these channels were 0. Actually they should be pruned.
# don't overwrite the quantized values since we may move around quantization later
# bias_node.value = bqtype.quantize(dqbias)
# bias_node.qtype = bqtype
if dw_to_pw and wqtype.quantized_dimension:
wqtype.quantized_dimension = 0
mulbiases_q = MultMulBiasScaleQType.from_filter(
iqtype, wqtype, oqtype, params)
qrec = QRec.scaled(in_qs=[iqtype, wqtype, bqtype],
out_qs=[oqtype],
calc_q=bqtype,
acc_q=bqtype,
mul_biases_q=mulbiases_q)
# now set the quantization records on the node and its constants
G.quantization[NodeId(params)] = qrec
G.quantization[NodeId(weights_node)] = QRec.scaled(
out_qs=[deepcopy(wqtype)])
G.quantization[NodeId(bias_node)] = QRec.scaled(
out_qs=[deepcopy(bqtype)])
def version_1(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(Conv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x = cls.remove_known_batch_dimension(G, x, node)
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
weights_node = filt[0]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_out_c, filt_h, filt_w, filt_in_c = tuple(filt_shape)
# get filter dimensions
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
if len(inputs) > 2:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(
f'{node.name}_bias',
dims=Dim.unnamed([filt_out_c]),
value=np.zeros([filt_out_c], dtype=np.float32)) # TODO - check
groups = in_c // filt_in_c
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
groups=groups,
padding=pad,
has_bias=True,
in_dims_hint=[['h', 'w', 'c'],
cls.TF_LITE_FILTER_ORDER.copy(), ['out_c']],
out_dims_hint=[['h', 'w', 'c']])
G.add_edge(NNEdge(from_node=weights_node, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=bias_node, to_node=params, to_idx=2))
cls.new_load_filter_parameters(G, params, params.filter.actual_shape,
params.filter.get_order_idx('out_c'),
node.input[0], weights_node, bias_node,
node.output[0], opts)
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size(
[in_dim,
Dim.unnamed(filt_dim.shape),
Dim.unnamed([filt_out_c])])
pout_dims = ProvisionalDim([None] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
oparams = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (oparams, 0, pout_dims)
return oparams
def _common(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(DepthwiseConv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
filt_tensor = node.input[1]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_in_c, filt_h, filt_w, filt_out_c = tuple(filt_shape)
# get filter dimensions
filt_tensor.used = True
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_DW_FILTER_ORDER)
# multiplier should match filter
check(filt_dim.out_c == node_opts.DepthMultiplier() * in_c,
"invalid multiplier")
groups = filt_dim.out_c // node_opts.DepthMultiplier()
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
has_bias = len(inputs) > 2
if has_bias:
node.input[2].used = True
# TFLITE produces single channel input DW convolutions with the
# multiplier equal to the number of out channels. This is just
# a normal convolution and since we don't handle the channel
# multiplier at present (but can) just convert them to normal
# convolutions
convert_to_conv = in_c == 1 and groups == 1
if convert_to_conv:
filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
has_bias=has_bias,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]),
constant_store=G.constant_store)
else:
filt_dim.impose_order(cls.TF_LITE_DW_FILTER_ORDER)
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
groups=groups,
multiplier=node_opts.DepthMultiplier(),
has_bias=has_bias,
tf_depthwise=True,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]),
constant_store=G.constant_store)
if opts.get('load_dequantized'):
cls.load_dequantized_filter_parameters(params,
node.input,
convert_to_conv,
is_dw=True)
else:
cls.load_filter_parameters(G,
params,
node.input,
node.output,
opts,
converted_to_conv=convert_to_conv)
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size([in_dim])
pout_dims = ProvisionalDim([in_b] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
params = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
