def adjust_in_out_chw(self, G, node, names):
self.verify_chw(node, names)
trans = self.get_trans(names, ['c', 'h', 'w'])
in_dim = node.in_dims[0]
if in_dim.c != 1:
self.apply_input_trans(node, trans, index=0)
else:
reshape = ReshapeParameters(f'{node.name}_r_chw',
old_shape=in_dim.clone(),
shape=Dim.named_ordered(c=in_dim.c,
h=in_dim.h,
w=in_dim.w))
G.insert_node_before(reshape, node, edge_class=NNEdge)
self.check_quantization(G, node, reshape)
out_dim = node.out_dims[0]
if out_dim.c != 1:
self.apply_output_trans(node, self.invert(trans), index=0)
else:
reshape = ReshapeParameters(f'{node.name}_r_{"".join(names)}',
old_shape=Dim.named_ordered(
c=out_dim.c,
h=out_dim.h,
w=out_dim.w),
shape=out_dim.clone())
G.insert_node_after(node, reshape, edge_class=NNEdge)
self.check_quantization(G, node, reshape, dir='out')
def _common1_11(cls, node, **kwargs):
axis = node.attrs.get('axis', 1)
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
x = all_nodes[node.input[0]]
x_shape = x[2].shape
if axis < 0:
axis += len(x_shape)
old_shape = cls._get_real_dim(x_shape)
# v 1 and 11 work differently to v13. In v1 and v11 the input is collected into a 2D tensor
# based on the axis [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] with axis k
# becomes [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]
# This is used for the softmax
new_pshape = [condense(x_shape[:axis:]), condense(x_shape[axis::])]
new_shape = cls._get_real_dim(new_pshape)
reshape_1 = ReshapeParameters(valid_name + "_reshape1",
old_shape=old_shape,
shape=new_shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=reshape_1, from_idx=x[1], to_idx=0))
# operation axis will either be 1 or 0
softmax = SoftMaxParameters(valid_name, axis=len(new_shape) - 1)
G.add_edge(NNEdge(from_node=reshape_1, to_node=softmax))
reshape_2 = ReshapeParameters(valid_name + "_reshape2",
old_shape=new_shape,
shape=old_shape)
G.add_edge(NNEdge(from_node=softmax, to_node=reshape_2))
all_nodes[node.output[0]] = (reshape_2, 0, ProvisionalDim(x_shape))
return softmax
def _match(self,
G: GraphView,
set_identity: bool = True,
**kwargs) -> bool:
has_modified_graph = False
for node in [
node for node in G.nodes(node_classes=StridedSliceParameters)
]:
if node.slice_shape != tuple(node.in_dims[0].shape):
continue
has_modified_graph = True
nid = NodeId(node)
if node.slice_shape == node.out_shape:
LOG.info(
f'removing strided slice {node.name} that does nothing')
G.remove_and_reconnect(node, edge_class=NNEdge)
if G.quantization and nid in G.quantization:
del G.quantization[nid]
else:
reshape = ReshapeParameters(
G.unique_name(f'{node.name}_reshape'),
old_shape=node.slice_shape,
shape=node.out_shape)
LOG.info(
f'replacing strided slice {node.name} with reshape {reshape.name}'
)
G.replace_node(node, reshape)
if G.quantization and nid in G.quantization:
G.quantization[NodeId(reshape)] = G.quantization[nid]
del G.quantization[nid]
if set_identity:
self.set_identity(G)
return has_modified_graph
def get_state(cls, G, inputs, idx, name, hidden_size, num_directions=1):
if not inputs[idx]:
state = np.zeros((num_directions, hidden_size))
elif cls.is_constant(inputs[idx]):
state = cls.get_constant(inputs[idx])
else:
state_inp = inputs[idx]
if num_directions == 2:
act_slices = (((0, 1, 1), (0, hidden_size, 1)),
((1, 2, 1), (0, hidden_size, 1)))
out_shapes = ((1, hidden_size), (1, hidden_size))
split = SplitParameters(G.unique_name(f'{name}_split'),
act_slices=act_slices,
out_shapes=out_shapes,
axis=0)
G.add_edge(
NNEdge(from_node=state_inp[0],
to_node=split,
from_idx=state_inp[1]))
return {
'forward': {
name: (split, 0)
},
'backward': {
name: (split, 0)
},
}
else:
reshape = ReshapeParameters(G.unique_name(f'{name}_reshape'),
old_shape=(1, hidden_size),
shape=(hidden_size, ))
G.add_edge(
NNEdge(from_node=state_inp[0],
to_node=reshape,
from_idx=state_inp[1]))
return {
'forward': {
name: (reshape, 0)
},
}
state = cls.get_constant(inputs[idx]) if inputs[idx] else np.zeros(
(num_directions, hidden_size))
return {
'forward' if dir == 0 else 'backward': {
name: dir_arr.reshape((hidden_size, ))
}
for dir, dir_arr in enumerate(
np.split(state, num_directions, axis=0))
}
def adjust_in_out_order(self, G, node, names, order):
self.verify_chw(node, names)
trans = self.get_trans(names, order)
in_dim = node.in_dims[0]
if in_dim.c != 1:
self.apply_input_trans(node, trans, index=0)
else:
new_shape = {k: getattr(in_dim, k) for k in order}
reshape = ReshapeParameters(
f'{node.name}_r_{"".join(in_dim.order)}_{"".join(order)}',
old_shape=in_dim.clone(),
shape=Dim.named_ordered(**new_shape)
)
G.insert_node_before(
reshape,
node,
edge_class=NNEdge
)
node.in_dims_hint[0] = order
self.check_quantization(G, node, reshape)
out_dim = node.out_dims[0]
if out_dim.c != 1:
self.apply_output_trans(node, self.invert(trans), index=0)
else:
old_shape = {k: getattr(out_dim, k) for k in order}
node.out_dims_hint[0] = order
reshape = ReshapeParameters(
f'{node.name}_r_{"".join(names)}',
old_shape=Dim.named_ordered(**old_shape),
shape=out_dim.clone()
)
G.insert_node_after(
node,
reshape,
edge_class=NNEdge
)
self.check_quantization(G, node, reshape, direction='out')
def _execute(self, node, G):
info(f"{self}")
direction = self.direction
if self.reshape_from is not None:
params = ReshapeParameters(G.unique_name(f'{node.name}_reshape'),
old_shape=Dim.unnamed(
self.reshape_from),
shape=Dim.reshape_to(self.reshape_to))
self.do_insert(node, G, params, direction=direction)
node = params
direction = "out"
params = TransposeParameters(G.unique_name(f'{node.name}_trans'),
transpose=self.transpose)
self.do_insert(node, G, params, direction=direction)
def _maybe_insert_reshape(cls, G, inp, inp_shape, pout_shape):
out_dim_none = tuple(
[idx for idx, dim in enumerate(pout_shape) if dim is None])
in_dim_none = tuple(
[idx for idx, dim in enumerate(inp_shape) if dim is None])
if out_dim_none == in_dim_none:
return inp[0], inp[1]
old_shape = [dim for dim in inp_shape if dim is not None]
shape = [
dim for idx, dim in enumerate(inp_shape)
if dim is not None and idx not in out_dim_none
]
rparams = ReshapeParameters(G.unique_name(f'{inp[0]}_reshape'),
old_shape=old_shape,
shape=shape)
G.add_edge(NNEdge(from_node=inp[0], to_node=rparams, from_idx=inp[1]))
return rparams, 0
def remove_known_batch_dimension(cls, G, x, node, batch_axis=0):
x_shape = x[2].shape
if x_shape[batch_axis] is not None:
if x_shape[0] > 1:
raise ValueError(
f'multi batch (n={x_shape[batch_axis]}) operations are not supported by {node.name}')
rparams = ReshapeParameters(
f'{node.name}_batch',
old_shape=Dim.unnamed(x_shape),
shape=Dim.unnamed(x_shape[0:batch_axis:]+x_shape[batch_axis+1::]))
if G.quantization:
qrec = G.quantization[NodeId(x[0])]
G.quantization[NodeId(rparams)] = QRec.copy_ktype(
qrec,
in_qs=[qrec.out_qs[0]],
out_qs=[qrec.out_qs[0]])
G.add_edge(
NNEdge(from_node=x[0], to_node=rparams, from_idx=x[1], to_idx=0))
return (rparams, 0, ProvisionalDim(x_shape[0:batch_axis:]+[None]+x_shape[batch_axis+1::]))
else:
return x
def _execute(self, node, G):
info(f"{self}")
if node.name not in G:
return
if self.reshape:
reshape_node = ReshapeParameters(
G.unique_name(f"{node.name}_reshape"),
old_shape=self.reshape[0],
shape=self.reshape[1])
G.replace_node(node.name, reshape_node)
if G.quantization:
G.quantization.copy_qrec(node, 'in', 0, reshape_node)
LOG.info(
f'transpose {node.name} replaced with reshape {reshape_node.name}'
)
else:
G.remove_and_reconnect(node, edge_class=NNEdge)
nid = NodeId(node)
if G.quantization and nid in G.quantization:
del G.quantization[nid]
LOG.info(f'transpose {node.name} removed')
def _maybe_insert_reshape(cls, G, inp, inp_shape, pout_shape):
out_dim_none = tuple(
[idx for idx, dim in enumerate(pout_shape) if dim is None])
in_dim_none = tuple(
[idx for idx, dim in enumerate(inp_shape) if dim is None])
if out_dim_none == in_dim_none:
return inp[0], inp[1]
old_shape = [dim for dim in inp_shape if dim is not None]
new_shape = [
dim for idx, dim in enumerate(inp_shape)
if dim is not None and idx not in out_dim_none
]
if cls.is_constant(inp):
val = np.reshape(cls.get_constant(inp), new_shape)
params = ConstantInputParameters(G.unique_name(inp[0].name),
value=val,
dims=Dim.unnamed(val.shape))
else:
params = ReshapeParameters(G.unique_name(f'{inp[0].name}_reshape'),
old_shape=old_shape,
shape=new_shape)
G.add_edge(
NNEdge(from_node=inp[0], to_node=params, from_idx=inp[1]))
return params, 0
def _execute(self, node, G):
LOGL("%s", str(self))
if isinstance(node, TransposeParameters):
direction = "in"
else:
direction = self.direction
transpose_name = "transpose_" + direction
trans = getattr(node, transpose_name)
if direction == 'in' and isinstance(node, Broadcastable):
node.delete_transpose(self.idx, trans[self.idx])
trans[self.idx] = None
if all(t is None for t in trans):
setattr(node, transpose_name, None)
if self.reshape:
reshape_node = ReshapeParameters(
G.unique_name(f"{node.name}_reshape"),
old_shape=self.reshape[0],
shape=self.reshape[1])
if direction == "in":
in_edge = G.indexed_in_edges(node.name)[self.idx]
G.insert_node_at_edge(reshape_node, in_edge, edge_class=NNEdge)
if G.quantization:
G.quantization.copy_qrec(in_edge.to_node, 'in', self.idx,
reshape_node)
else:
G.insert_node_after(node,
reshape_node,
from_idx=self.idx,
edge_class=NNEdge)
if G.quantization:
G.quantization.copy_qrec(node, 'out', self.idx,
reshape_node)
if isinstance(node, TransposeParameters) and node.transpose_in is None:
LOGL("remove null transpose %s", node.name)
G.remove_and_reconnect(node, edge_class=NNEdge)
LOG.info('transpose is now %s', getattr(node, transpose_name))
def _common(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(PackOptions)
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]
values_count = node_opts.ValuesCount()
check(
len(inputs) == values_count,
"invalid tflite file - values count not equal to inputs")
buffer_idxes = [tensor.buffer_idx for tensor in node.input]
check(
len(set(buffer_idxes)) == len(buffer_idxes),
"packs with multiple versions of the same input are not supported. This is normally a graph design problem."
)
axis = node_opts.Axis()
dimension_size = len(inp_shapes)
if axis < 0:
axis += dimension_size
check(all(shape == inp_shapes[0] for shape in inp_shapes[1::]),
"invalid tflite file - pack inputs not the same")
# prepare shapes of all tensors
pconcat_out_shape = inp_shapes[0].copy()
pconcat_out_shape.insert(axis, values_count)
pconcat_in_shape = inp_shapes[0].copy()
pconcat_in_shape.insert(axis, 1)
preshape_in_shape = inp_shapes[0].copy()
# remove nones from constants
cls.remove_none_from_constants(inputs, preshape_in_shape)
# remove nones from reshape shapes
reshape_in_shape = cls.remove_unspecified_dim(preshape_in_shape)
concat_in_shape = cls.remove_unspecified_dim(pconcat_in_shape)
if all(cls.is_constant(inp) for inp in inputs):
LOG.info("reducing %s to a constant", node.name)
value = np.stack([cls.get_constant(inp) for inp in inputs],
axis=axis)
params = ConstantInputParameters(node.name,
value=value,
constant_store=G.constant_store)
else:
axis -= sum(1 if dim is None else 0
for dim in pconcat_out_shape[:axis:])
params = ConcatParameters(node.name, axis=axis, axis_hint=None)
# insert reshapes on each input to add concat axis
for idx, inp in enumerate(inputs):
rparams = ReshapeParameters(node.name + "_%s" % idx,
old_shape=reshape_in_shape,
shape=concat_in_shape)
G.add_edge(
NNEdge(from_node=inp[0], to_node=rparams, from_idx=inp[1]))
G.add_edge(NNEdge(from_node=rparams, to_node=params))
if opts.get('load_quantization'):
G.quantization[NodeId(rparams)] = cls.load_tf_quantization(
[node.input[idx]], [node.input[idx]])
if opts.get('load_quantization'):
G.quantization[NodeId(params)] = cls.load_tf_quantization(
node.input, node.output)
all_nodes[node.output[0]] = (params, 0,
ProvisionalDim(pconcat_out_shape))
return params
def _execute(self, node, G):
info(f"{self}")
params = ReshapeParameters(G.unique_name(f'{node.name}_reshape'),
old_shape=self.in_shape,
shape=self.out_shape)
self.do_insert(node, G, params)
def _common(cls, node, **kwargs):
node_opts = node.get_options(FullyConnectedOptions)
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
x_known_shape = x[2].known_shape
inp_sz = np.prod(np.array(x_known_shape))
weights = inputs[1]
weights_node = weights[0]
weights_shape = weights[2].shape
assert len(weights_shape
) == 2, f'bad filter shape {weights_shape} in {node.name}'
out_c = weights_shape[0]
batch_size = inp_sz // weights_shape[1]
if batch_size > 1:
filt_dim = FcFilterDim(weights_shape[0], weights_shape[1])
else:
filt_dim = FcFilterDim(weights_shape[0], *x_known_shape)
node.input[1].used = True
check(filt_dim.sz * batch_size == inp_sz,
"filter doesn't match input size")
if len(inputs) > 2:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(
f'{node.name}_bias',
dims=Dim.unnamed([out_c]),
value=np.zeros([out_c], dtype=np.float32)) # TODO - check
keep_dims = node_opts.KeepNumDims()
if batch_size > 1:
if keep_dims:
raise ValueError(
f'keep dims on Fully Connected {node.name} with batch size > 1 is not supported'
)
# add a reshape to force the size of the input to batch * in_c
input_shape = (batch_size, weights_shape[1])
if x_known_shape != input_shape:
rparams = ReshapeParameters(
G.unique_name(f'{node.name}_batch'),
old_shape=Dim.unnamed(x_known_shape),
shape=Dim.unnamed(input_shape))
G.add_edge(
NNEdge(from_node=x[0],
to_node=rparams,
from_idx=x[1],
to_idx=0))
link = (rparams, 0)
else:
link = x
# the batched linear is transpose(weights . transpose(input))
params = MatMulOpParameters(node.name)
params.transpose_in = [None, (1, 0), None]
params.transpose_out = [(1, 0)]
cls.new_load_filter_parameters(G, params, weights_shape, 0,
node.input[0], weights_node,
bias_node, node.output[0], opts)
G.add_edge(
NNEdge(from_node=link[0],
to_node=params,
from_idx=link[1],
to_idx=1))
G.add_edge(NNEdge(from_node=weights_node, to_node=params,
to_idx=0))
G.add_edge(NNEdge(from_node=bias_node, to_node=params, to_idx=2))
out_shape = [batch_size, out_c]
else:
# in_hint = [[str(i) for i in range(len(x_known_shape) - 1)] + ['c'],
# ['out_c', 'in_c'], ['out_c']]
in_hint = [None, ['out_c', 'in_c'], ['out_c']]
out_hint = in_hint.copy() if keep_dims else ['c']
ker_in_order = None
ker_out_order = None
link = (x[0], x[1])
params = FcParameters(node.name,
filt=filt_dim,
has_bias=True,
in_dims_hint=in_hint,
out_dims_hint=[out_hint],
ker_in_order=ker_in_order,
ker_out_order=ker_out_order,
batch_size=batch_size,
constant_store=G.constant_store,
keep_dims=keep_dims)
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)
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))
G.add_edge(
NNEdge(from_node=link[0],
to_node=params,
from_idx=link[1],
to_idx=0))
# handle keep_dims
if x_shape[0] is None:
if keep_dims:
out_shape = x_shape[:-1:] + [out_c]
else:
out_shape = [None, out_c]
else:
if keep_dims:
out_shape = [None] + x_shape[1:-1:] + [out_c]
else:
out_shape = [None, out_c]
pout_dims = ProvisionalDim(out_shape)
aparams = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (aparams, 0, pout_dims)
return params
def _execute(self, node, G):
LOGL("%s", str(self))
params = ReshapeParameters(G.unique_name(f'{node.name}'),
old_shape=self.in_shape,
shape=self.out_shape)
G.insert_node_at_edge(params, self.edge, edge_class=NNEdge)
def conv(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 N x C x H x W
x = inputs[0]
x_rank = len(x[2].shape)
x_shape = x[2].shape
spatial_size = x_rank - 2
assert spatial_size == 2 or spatial_size == 1, "only 1D and 2D convolutions supported"
# M x C/group x kH x kW
weights_node = inputs[1][0]
weights_node.name = f'{valid_name}_weights'
weights = cls.get_constant(inputs[1])
out_c = weights.shape[0]
group = node.attrs.get("group", 1)
in_c = x_shape[1]
filt_in_c = in_c // group
if in_c != weights.shape[1] * group:
raise ValueError(
f'node {valid_name} has incorrect input channel '
f'dimension {in_c} expecting {weights.shape[1] * group}')
if spatial_size == 1:
filt_w = weights.shape[-1]
filt_h = 1
# create a new constant node since we are changing the shape
weights = np.reshape(weights, (out_c, filt_in_c, filt_h, filt_w))
weights_node = ConstantInputParameters(
f'{valid_name}_weights',
value=weights,
dims=Dim.unnamed(weights.shape),
constant_store=G.constant_store)
else:
filt_h = weights.shape[-2]
filt_w = weights.shape[-1]
h = 1 if spatial_size == 1 else x_shape[-2]
w = x_shape[-1]
filt_dim = Conv2DFilterDim(filt_h, filt_w, out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.ONNX_FILTER_ORDER)
if len(inputs) > 2:
biases_node = inputs[2][0]
biases = cls.get_constant(inputs[2])
else:
biases = np.zeros([out_c], dtype=np.float32)
biases_node = ConstantInputParameters(
f'{valid_name}_biases',
value=biases,
dims=Dim.unnamed(biases.shape),
constant_store=G.constant_store)
dilations = cls.pad_start_with(node.attrs.get("dilations", []), [1], 2)
strides = cls.pad_start_with(node.attrs.get("strides", []), [1], 2)
pad_dim = cls.calc_pad_dim(node, 4)
params = Conv2DParameters(valid_name,
filt=filt_dim,
stride=StrideDim(strides[0], strides[1]),
dilation=DilationDim(dilations[0],
dilations[1]),
groups=group,
padding=pad_dim,
has_bias=True,
in_dims_hint=[['c', 'h', 'w'],
cls.ONNX_FILTER_ORDER, ['c']],
out_dims_hint=[['c', 'h', 'w']],
constant_store=G.constant_store)
in_dim = Dim.named_ordered(c=in_c, h=h, w=w)
w_dim = Dim.named_ordered(out_c=out_c,
in_c=filt_in_c,
h=filt_h,
w=filt_w)
b_dim = Dim.named_ordered(c=out_c)
out_dims = params.get_output_size([in_dim, w_dim, b_dim])
G.add_edge(
NNEdge(from_node=weights_node,
to_node=params,
from_idx=0,
to_idx=1))
G.add_edge(
NNEdge(from_node=biases_node, to_node=params, from_idx=0,
to_idx=2))
if spatial_size == 1:
oned_in_shape = [in_c, w]
twod_in_shape = [in_c, 1, w]
oned_out_shape = [out_dims[0].c, out_dims[0].w]
r1_params = ReshapeParameters(f'{valid_name}_reshape2d',
old_shape=Dim.unnamed(oned_in_shape),
shape=Dim.unnamed(twod_in_shape))
r2_params = ReshapeParameters(f'{valid_name}_reshape1d',
old_shape=out_dims[0],
shape=Dim.unnamed(oned_out_shape))
G.add_edge(
NNEdge(from_node=x[0],
to_node=r1_params,
from_idx=x[1],
to_idx=0))
G.add_edge(
NNEdge(from_node=r1_params,
to_node=params,
from_idx=0,
to_idx=0))
G.add_edge(
NNEdge(from_node=params,
to_node=r2_params,
from_idx=0,
to_idx=0))
pout_dims = ProvisionalDim([x_shape[0]] + oned_out_shape)
all_nodes[node.output[0]] = (r2_params, 0, pout_dims)
return r2_params
else:
pout_dims = ProvisionalDim([x_shape[0]] + out_dims[0].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, pout_dims)
return params
