def two_conv_graph():
G = NNGraph(name='two_conv_graph')
ti = G.add_input(Dim.unnamed([10, 10, 2]))
c1filt = Conv2DFilterDim(3, 3, 2, in_c=2)
c1filt.impose_order(['out_c', 'h', 'w', 'in_c'])
n1 = Conv2DParameters("node1",
filt=c1filt,
stride=StrideDim(1, 1),
padding=PadDim(0),
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
G.add_node(n1)
w1 = [[0.25, 0.25], [0.25, 0.25], [0.25, 0.25]]
w1 = [w1, w1, w1]
w2 = [[0.75, 0.75], [0.75, 0.75], [0.75, 0.75]]
w2 = [w2, w2, w2]
n1.weights = np.array([w1, w2])
c2filt = Conv2DFilterDim(3, 3, 2, in_c=2)
c2filt.impose_order(['out_c', 'h', 'w', 'in_c'])
n2 = Conv2DParameters("node2",
filt=c2filt,
stride=StrideDim(1, 1),
padding=PadDim(0),
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
G.add_node(n2)
w3 = [[0.75, 0.25], [0.75, 0.25], [0.75, 0.25]]
w3 = [w3, w3, w3]
n2.weights = np.array([w3, w3])
to = G.add_output()
G.add_edge(NNEdge(ti, n1))
G.add_edge(NNEdge(n1, n2))
G.add_edge(NNEdge(n2, to))
G.add_dimensions()
yield G
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
trans_a = node.attrs.get('transA', 0)
trans_b = node.attrs.get('transB', 0)
alpha = node.attrs.get('alpha', 1.0)
beta = node.attrs.get('beta', 1.0)
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
y = inputs[1]
y_shape = y[2].shape
real_x_shape = cls._get_real_dim(x_shape)
real_y_shape = cls._get_real_dim(y_shape)
real_x_shape = [
real_x_shape[1], real_x_shape[0]
] if len(real_x_shape) == 2 and trans_a else real_x_shape
real_y_shape = [
real_y_shape[1], real_y_shape[0]
] if len(real_y_shape) == 2 and trans_b else real_y_shape
if not cls.is_linear(y, real_x_shape, real_y_shape) or trans_a:
raise ValueError(
"GEMM is only currently supported for operations that map onto a linear kernel"
)
if len(inputs) > 2:
has_bias = True
biases = cls.get_constant(inputs[2])
else:
biases = None
has_bias = False
filt_dim = FcFilterDim(real_y_shape[1], real_x_shape[0])
weights = cls.get_constant(y) * alpha
if not trans_b:
weights = np.transpose(weights, [1, 0])
params = FcParameters(valid_name,
filt=filt_dim,
has_bias=has_bias,
in_dims_hint=SparseList([['c']]),
out_dims_hint=SparseList([['c']]),
constant_store=G.constant_store)
params.weights = weights
params.biases = biases * beta
out_dims = params.get_output_size([Dim.unnamed(real_x_shape)])
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
if isinstance(x[2], ProvisionalDim):
out_dim = x[2].infer_mapping(out_dims[0].shape)
else:
out_dim = out_dims[0]
all_nodes[node.output[0]] = (params, 0, out_dim)
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 = cls._get_real_dim(x[2].shape)
y = inputs[1]
y_shape = cls._get_real_dim(y[2].shape)
if cls.is_linear(y, x_shape, y_shape):
filt_dim = FcFilterDim(y_shape[1], x_shape[0])
weights = np.transpose(cls.get_constant(y), [1, 0])
params = FcParameters(valid_name,
filt=filt_dim,
has_bias=False,
in_dims_hint=SparseList([['c']]),
out_dims_hint=SparseList([['c']]),
constant_store=G.constant_store)
params.weights = weights
out_dims = params.get_output_size([Dim.unnamed(x_shape)])
else:
params = MatMulOpParameters(valid_name)
out_dims = params.get_output_size(
[Dim.unnamed(x_shape),
Dim.unnamed(y_shape)])
G.add_edge(
NNEdge(from_node=y[0], to_node=params, from_idx=y[1],
to_idx=1))
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
pout_dims = x[2].infer_mapping(out_dims[0].shape)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def add_pool(G,
tensors,
name,
subgraph,
op_name,
op,
load_tensors=False,
dequantize=False):
pool_opts = Pool2DOptions.Pool2DOptions()
pool_opts.Init(op.BuiltinOptions().Bytes, op.BuiltinOptions().Pos)
pad = get_tf_padding(pool_opts.Padding())
pool_type = TF_POOL_OPS[op_name]
inp = get_input_size(None, subgraph, op, 0, order=TF_LITE_IN_OUT_ORDER)
check(inp['n'] == 1, "Multi batch not supported")
node = PoolingParameters(name,
filt=PoolFilterDim(pool_opts.FilterHeight(),
pool_opts.FilterWidth()),
stride=StrideDim(pool_opts.StrideH(),
pool_opts.StrideW()),
padding=pad,
pool_type=pool_type,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
return fuse_activation(G, pool_opts, name, node)
def add_mean(G,
tensors,
name,
subgraph,
op_name,
op,
load_tensors=False,
dequantize=False):
check(op.InputsLength() == 2,\
"Very odd " + str(op.InputsAsNumpy()))
mean_dims = get_tensor(G.model, tensors, subgraph, op, 1, dequantize=False)
if len(mean_dims) != 2 or mean_dims[0] != 1 or mean_dims[1] != 2:
LOG.warning(
"MEAN operator seen but can't convert to global average pool")
return add_unconverted(G, name, subgraph, op_name, op, load_tensors,
dequantize)
else:
LOG.info("MEAN operator converted to global average pool")
inp = get_input_size(None, subgraph, op, 0, order=TF_LITE_IN_OUT_ORDER)
check(inp['n'] == 1, "Multi batch not supported")
return add_node(
G,
PoolingParameters(name,
filt=PoolFilterDim(inp['h'], inp['w']),
stride=StrideDim(1, 1),
padding=PadDim.valid(),
pool_type="average",
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']])))
def add_fully_connected(G,
tensors,
name,
subgraph,
_,
op,
load_tensors=False,
dequantize=False):
fc_opts = FullyConnectedOptions.FullyConnectedOptions()
fc_opts.Init(op.BuiltinOptions().Bytes, op.BuiltinOptions().Pos)
# get filter dimensions
inp = get_input_size(tensors, subgraph, op, 0)
check(inp[0] == 1, "Multi batch not supported")
filt = get_input_size(tensors, subgraph, op, 1, order=TF_LITE_FC_ORDER)
check(filt['sz'] == reduce(lambda i, j: i * j, inp, 1),
"filter doesn't match input size")
# in the case we get an input of 1 batch with everything flattened fill h and w with 1
if len(inp) == 2:
inp = {'h': 1, 'w': 1, 'c': inp[1]}
elif len(inp) == 4:
inp = {'h': inp[1], 'w': inp[2], 'c': inp[3]}
else:
raise NotImplementedError('FC input size not implemented')
filt_dim = FcFilterDim(inp['h'],
inp['w'],
filt['out_c'],
in_c=inp['c'],
order=TF_LITE_FC_EXP_ORDER)
# does it have biases
has_bias = op.InputsLength() > 2
node = FcParameters(name,
filt=filt_dim,
has_bias=has_bias,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['c']]),
constant_store=G.constant_store)
if load_tensors:
node.weights = get_tensor(G.model,
tensors,
subgraph,
op,
1,
dequantize=dequantize)
if has_bias:
node.biases = get_tensor(G.model,
tensors,
subgraph,
op,
2,
dequantize=dequantize)
return fuse_activation(G, fc_opts, name, node)
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, "only 1D and 2D convolutions supported"
# M x C/group x kH x kW
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
filt_h = weights.shape[2]
filt_w = weights.shape[2]
h = 1 if spatial_size <= 1 else x_shape[2]
w = 1 if spatial_size == 0 else (x_shape[2] if spatial_size == 1 else x_shape[3])
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 = cls.get_constant(inputs[2])
else:
biases = np.zeros([out_c])
dilations = cls.pad_start_with(node.attrs.get("dilations", [1] * spatial_size), [1], 2)
strides = cls.pad_start_with(node.attrs.get("strides", [1] * spatial_size), [1], 2)
pad_dim = cls.calc_pad_dim(node, spatial_size)
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=SparseList([['c', 'h', 'w']]),
out_dims_hint=SparseList([['c', 'h', 'w']]),
constant_store=G.constant_store)
params.weights = weights
params.biases = biases
in_dim = Dim.named_ordered(c=in_c, h=h, w=w)
out_dims = params.get_output_size([in_dim])
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
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_shape = weights[2].shape
out_c = weights_shape[0]
filt_dim = FcFilterDim(weights_shape[0], *x_known_shape)
node.input[1].used = True
check(filt_dim.sz == inp_sz, "filter doesn't match input size")
if len(node.input) > 2:
node.input[2].used = True
keep_dims = node_opts.KeepNumDims()
in_hint = [str(i) for i in range(len(x_known_shape) - 1)] + ['c']
out_hint = in_hint.copy() if keep_dims else ['c']
params = FcParameters(node.name,
filt=filt_dim,
has_bias=True,
in_dims_hint=SparseList([in_hint]),
out_dims_hint=SparseList([out_hint]),
constant_store=G.constant_store,
keep_dims=keep_dims)
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)
if x_shape[0] is None:
out_shape = x_shape[:-1:] + [out_c] if keep_dims else [
x_shape[0], out_c
]
else:
out_shape = x_known_shape[:-1:] + [out_c] if keep_dims else [out_c]
pout_dims = ProvisionalDim(out_shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
aparams = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (aparams, 0, pout_dims)
return params
def pool2d(cls, node, pool_type=None, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
opts = kwargs['opts']
node_opts = node.get_options(Pool2DOptions)
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
in_c = x_shape[1]
in_b, h, w, in_c = tuple(x_shape)
filt_h = node_opts.FilterHeight()
filt_w = node_opts.FilterWidth()
stride_h = node_opts.StrideH()
stride_w = node_opts.StrideW()
pad = cls.get_tf_padding(node_opts.Padding())
filter_matches_input = h == filt_h and w == filt_w
stride_is_one = stride_h == 1 and stride_w == 1
if filter_matches_input and stride_is_one:
params = GlobalPoolParameters(node.name,
pool_type=pool_type,
axis=[0, 1],
keep_dims=True,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
else:
params = PoolingParameters(node.name,
filt=PoolFilterDim(filt_h, filt_w),
stride=StrideDim(stride_h, stride_w),
padding=pad,
pool_type=pool_type,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
if opts.get('load_quantization'):
G.quantization[NodeId(params)] = cls.load_tf_quantization(node.input, node.output)
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 add_convolution(G,
tensors,
name,
subgraph,
_,
op,
load_tensors=False,
dequantize=False):
conv_opts = Conv2DOptions.Conv2DOptions()
conv_opts.Init(op.BuiltinOptions().Bytes, op.BuiltinOptions().Pos)
# get filter dimensions
filt = get_input_size(tensors, subgraph, op, 1, order=TF_LITE_FILTER_ORDER)
filt = Conv2DFilterDim(filt['h'], filt['w'],\
filt['out_c'], in_c=filt['in_c'])
filt = filt.impose_order(TF_LITE_FILTER_ORDER)
# compute padding
pad = get_tf_padding(conv_opts.Padding())
# does it have biases
has_bias = op.InputsLength() > 2
node = Conv2DParameters(name,
filt=filt,
stride=StrideDim(conv_opts.StrideH(),
conv_opts.StrideW()),
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 load_tensors:
node.weights = get_tensor(G.model,
tensors,
subgraph,
op,
1,
dequantize=dequantize)
if has_bias:
node.biases = get_tensor(G.model,
tensors,
subgraph,
op,
2,
dequantize=dequantize)
return fuse_activation(G, conv_opts, name, node)
def propagate_upwards(G: NNGraph):
for node in G.dfs(reverse=True):
# First propagate the out dim hints to the in dim hints
# Any node that does not want this to happen should set its in dim hints
if node.out_dims_hint is not None:
if isinstance(node, ReshapeParameters):
if len(node.shape) < len(node.out_dims_hint[0]):
node.shape = Dim.unnamed((
[1] * (len(node.out_dims_hint[0]) - len(node.shape))) +
node.shape.shape)
node.shape.apply_naming_hints(node.out_dims_hint[0])
if node.in_dims_hint is None:
node.in_dims_hint = SparseList(
[["%s" % i for i in range(len(node.old_shape))]])
elif isinstance(node, MatrixBroadcastedLinearOpParameters):
node.in_dims_hint = [node.out_dims_hint[0]] * 2
elif isinstance(node, MatrixMulParameters):
continue
elif isinstance(node, GlobalPoolParameters):
if node.keep_dims:
node.in_dims_hint = deepcopy(node.out_dims_hint)
elif isinstance(
node, ConstantInputParameters) and not node.dims.is_named:
node.dims.apply_naming_hints(node.out_dims_hint[0])
else:
if node.in_dims_hint is None:
node.in_dims_hint = deepcopy(node.out_dims_hint)
# if we have an in dim hint then propagate it to upstream nodes
if node.in_dims_hint is not None:
for edge in G.in_edges(node.name):
hint = node.in_dims_hint[edge.to_idx]
if hint is None:
continue
if edge.from_node.out_dims_hint is None:
edge.from_node.out_dims_hint = SparseList()
if edge.from_node.out_dims_hint[edge.from_idx] is None:
edge.from_node.out_dims_hint[edge.from_idx] = hint
if isinstance(edge.from_node, InputParameters):
assert edge.from_idx == 0, "input node should only have one output"
dims_len = len(edge.from_node.dims)
hint_len = len(hint)
if dims_len < hint_len:
edge.from_node.dims = Dim.unnamed(
[1] * (hint_len - dims_len) +
edge.from_node.dims.shape)
def actfusion_graph():
G = NNGraph(name='actfusion_graph')
ti1 = G.add_input(Dim.unnamed([10, 10, 2])).name
ti2 = G.add_input(Dim.unnamed([10, 10, 2])).name
c1filt = Conv2DFilterDim(3, 3, 2, in_c=2)
c1filt.impose_order(['out_c', 'h', 'w', 'in_c'])
n1 = Conv2DParameters("node1",
filt=c1filt,
stride=StrideDim(1, 1),
padding=PadDim(0),
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
G.add_node(n1)
w1 = [[0.25, 0.25], [0.25, 0.25], [0.25, 0.25]]
w1 = [w1, w1, w1]
w2 = [[0.75, 0.75], [0.75, 0.75], [0.75, 0.75]]
w2 = [w2, w2, w2]
n1.weights = np.array([w1, w2])
n1a = ReluActivationParameters("node1a")
G.add_node(n1a)
c2filt = Conv2DFilterDim(3, 3, 2, in_c=2)
c2filt.impose_order(['out_c', 'h', 'w', 'in_c'])
n2 = Conv2DParameters("node2",
filt=c2filt,
stride=StrideDim(1, 1),
padding=PadDim(0),
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
G.add_node(n2)
w3 = [[0.75, 0.25], [0.75, 0.25], [0.75, 0.25]]
w3 = [w3, w3, w3]
n2.weights = np.array([w3, w3])
n3 = MatrixAddParameters("node3")
G.add_node(n3)
n4 = ReluActivationParameters("node4")
G.add_node(n4)
to = G.add_output()
G.add_edge(NNEdge(ti1, n1))
G.add_edge(NNEdge(n1, n1a))
G.add_edge(NNEdge(ti2, n2))
G.add_edge(NNEdge(n1a, n3, to_idx=0))
G.add_edge(NNEdge(n2, n3, to_idx=1))
G.add_edge(NNEdge(n3, n4))
G.add_edge(NNEdge(n4, to))
G.add_dimensions()
yield G
def propagate_downwards(G: NNGraph):
for node in G.dfs():
# First propagate the in dim hints to the out dim hints
# Any node that does not want this to happen should set its out dim hints
if node.in_dims_hint is not None:
if isinstance(node, ReshapeParameters):
if len(node.old_shape) == len(node.in_dims_hint[0]):
LOG.debug("set reshape %s in dims hint %s", node.name,
node.in_dims_hint[0])
node.old_shape.apply_naming_hints(node.in_dims_hint[0])
elif isinstance(node, GlobalPoolParameters):
if node.keep_dims:
node.out_dims_hint = deepcopy(node.in_dims_hint)
elif isinstance(node, MatrixBroadcastedLinearOpParameters):
max_hint = None
for hint in node.in_dims_hint:
if hint is not None and (max_hint is None
or len(hint) > len(max_hint)):
max_hint = hint
if max_hint is not None:
node.out_dims_hint = [max_hint]
elif isinstance(node, ConcatParameters):
# if any incoming edge of the concat doesn't have a hint
# set it the same as the others
any_in_hint = next(
(hint for hint in node.in_dims_hint if hint is not None),
None)
if any_in_hint:
LOG.debug("set concat %s in dims hint %s", node.name,
any_in_hint)
for edge in G.in_edges(node.name):
if not node.in_dims_hint[edge.to_idx]:
node.in_dims_hint[edge.to_idx] = any_in_hint
node.out_dims_hint = [any_in_hint]
else:
if node.out_dims_hint is None:
node.out_dims_hint = deepcopy(node.in_dims_hint)
# if we have an out dim hint then propagate it to downstream nodes
if node.out_dims_hint is not None:
LOG.debug("propagate down hint from %s", node.name)
for edge in G.out_edges(node.name):
hint = node.out_dims_hint[edge.from_idx]
if hint is None:
continue
if edge.to_node.in_dims_hint is None:
edge.to_node.in_dims_hint = SparseList()
if edge.to_node.in_dims_hint[edge.to_idx] is None:
edge.to_node.in_dims_hint[edge.to_idx] = hint
def propagate_upwards(G: NNGraph):
for node in G.dfs(reverse=True):
# First propagate the out dim hints to the in dim hints
# Any node that does not want this to happen should set its in dim hints
if node.out_dims_hint is not None:
if isinstance(node, ReshapeParameters):
assert len(node.shape) == len(node.out_dims_hint[0])
node.shape.apply_naming_hints(node.out_dims_hint[0])
if node.in_dims_hint is None:
node.in_dims_hint = SparseList([["%s" % i for i in range(len(node.old_shape))]])
else:
if node.in_dims_hint is None:
node.in_dims_hint = deepcopy(node.out_dims_hint)
# if we have an in dim hint then propagate it to upstream nodes
if node.in_dims_hint is not None:
for edge in G.in_edges(node.name):
hint = node.in_dims_hint[edge.to_idx]
if edge.from_node.out_dims_hint is None:
edge.from_node.out_dims_hint = SparseList()
if edge.from_node.out_dims_hint[edge.from_idx] is None:
edge.from_node.out_dims_hint[edge.from_idx] = hint
def test1():
sl = SparseList()
sl[2] = True
assert sl[1] is None
assert len(sl) == 3
assert sl[2] == True
sl[5] = False
assert len(sl) == 6
assert sl[5] == False
del sl[2]
assert len(sl) == 5
assert sl[2] is None
assert sl[4] == False
tl = [v for v in sl]
assert tl == [None, None, None, None, False]
def test1():
sparse_list = SparseList()
sparse_list[2] = True
assert sparse_list[1] is None
assert len(sparse_list) == 3
assert sparse_list[2]
sparse_list[5] = False
assert len(sparse_list) == 6
assert not sparse_list[5]
del sparse_list[2]
assert len(sparse_list) == 5
assert sparse_list[2] is None
assert not sparse_list[4]
iter_sparse_list = [v for v in sparse_list]
assert iter_sparse_list == [None, None, None, None, False]
def propagate_downwards(G: NNGraph):
for node in G.dfs():
# First propagate the in dim hints to the out dim hints
# Any node that does not want this to happen should set its out dim hints
if node.in_dims_hint is not None:
if isinstance(node, ReshapeParameters):
assert len(node.old_shape) == len(node.in_dims_hint[0]), "reshape doesn't match input"
node.old_shape.apply_naming_hints(node.in_dims_hint[0])
else:
if node.out_dims_hint is None:
node.out_dims_hint = deepcopy(node.in_dims_hint)
# if we have an out dim hint then propagate it to downstream nodes
if node.out_dims_hint is not None:
for edge in G.out_edges(node.name):
hint = node.out_dims_hint[edge.from_idx]
if edge.to_node.in_dims_hint is None:
edge.to_node.in_dims_hint = SparseList()
if edge.to_node.in_dims_hint[edge.to_idx] is None:
edge.to_node.in_dims_hint[edge.to_idx] = hint
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
out_c = weights_shape[0]
filt_dim = FcFilterDim(weights_shape[0], *x_known_shape)
node.input[1].used = True
check(filt_dim.sz == 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()
in_hint = [str(i) for i in range(len(x_known_shape) - 1)] + ['c']
out_hint = in_hint.copy() if keep_dims else ['c']
params = FcParameters(node.name,
filt=filt_dim,
has_bias=True,
in_dims_hint=SparseList(
[in_hint, ['out_c', 'in_c'], ['out_c']]),
out_dims_hint=SparseList([out_hint]),
constant_store=G.constant_store,
keep_dims=keep_dims)
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]
if x_shape[0] is None:
out_shape = x_shape[:-1:] + [out_c] if keep_dims else [
x_shape[0], out_c
]
else:
out_shape = x_known_shape[:-1:] + [out_c] if keep_dims else [out_c]
pout_dims = ProvisionalDim(out_shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
aparams = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (aparams, 0, pout_dims)
return params
def _common(cls, node, **kwargs):
node_opts = node.get_options(UnidirectionalSequenceLSTMOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes.get(t) for t in node.input]
x = inputs[0]
x_shape = x[2].shape
time_major = node_opts.TimeMajor()
max_time = int(x_shape[0 if time_major else 1])
n_cells = int(node.input[2].shape[0])
n_inputs = int(x_shape[2])
pout_dims = ProvisionalDim([x_shape[0], x_shape[1], n_cells])
params = LSTMParameters(node.name,
in_dims_hint=SparseList([['sz', 'c']]),
out_dims_hint=SparseList([['sz', 'c']]),
constant_store=G.constant_store,
cell_clip=node_opts.CellClip(),
proj_clip=node_opts.ProjClip(),
n_input_cells=max_time,
n_cells=max_time, # TF says max_time - we say cells
n_inputs=n_inputs, # Input will be n_input_cells, n_inputs
n_output_cells=max_time, # Output will be n_output_cells, n_states
n_states=n_cells, # TF says cells - we say states
activation=cls.TF_ACTIVATIONS[node_opts.FusedActivationFunction()])
constant_nodes = cls.get_all_const_inputs(
G,
all_nodes,
opts,
node,
params,
exclude=[0],
names=["%s_%s" % (in_name, node.name)
for in_name in LSTMParameters.INPUT_NAMES],
short_names=LSTMParameters.INPUT_NAMES,
adjust_transposes=[False] * len(node.input),
load_quantization_if_present=True,
skip_empty_tensors=False)
# trim batch dimension from state values
for state_node_name in ['i_state', 'c_state']:
state_node = constant_nodes[LSTMParameters.INPUT_NAMES.index(state_node_name)]
if opts.get('load_tensors'):
state_node.value = state_node.value[0]
state_node.dims = Dim(list(state_node.value.shape), is_ordered=True)
# set by default as allocated
state_node.at_options.allocate = True
state_node.is_constant = False
# reset state after each invocation
state_node.always_copy = True
# add a single reset
state_node.reset_name = "Reset"
# Link the state weights to the input weights
# The autotiler expects the state and input weights to be
# concatenated. This tells the constant code generator to do this
for gate in ['i', 'o', 'c', 'f']:
i_w_node = constant_nodes[LSTMParameters.INPUT_NAMES.index('i_2_%s_w' % gate)]
r_w_node = constant_nodes[LSTMParameters.INPUT_NAMES.index('r_2_%s_w' % gate)]
r_w_node.concated_nodes.append(i_w_node)
i_w_node.generate_value = False
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
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_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)
# TODO - reorder weights for node converted to convolution (perhaps just dequantize)
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)
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=SparseList([['h', 'w', 'c'],
cls.TF_LITE_DW_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,
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 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[0] * 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=SparseList([['c', 'h', 'w'], cls.ONNX_FILTER_ORDER,
['c']]),
out_dims_hint=SparseList([['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
def add_depthwise_convolution(G,
tensors,
name,
subgraph,
_,
op,
load_tensors=False,
dequantize=False):
conv_opts = DepthwiseConv2DOptions.DepthwiseConv2DOptions()
conv_opts.Init(op.BuiltinOptions().Bytes, op.BuiltinOptions().Pos)
# get filter dimensions
inp = get_input_size(tensors, subgraph, op, 0, order=TF_LITE_IN_OUT_ORDER)
filt = get_input_size(tensors,
subgraph,
op,
1,
order=TF_LITE_DW_FILTER_ORDER)
filt = Conv2DFilterDim(filt['h'], filt['w'],\
filt['out_c'], in_c=1)
# multiplier should match filter
check(filt.out_c == conv_opts.DepthMultiplier() * inp['c'],
"invalid multiplier")
groups = filt.out_c // conv_opts.DepthMultiplier()
# compute padding
pad = get_tf_padding(conv_opts.Padding())
# does it have biases
has_bias = op.InputsLength() > 2
# 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 = inp['c'] == 1 and groups == 1
if convert_to_conv:
filt.impose_order(TF_LITE_FILTER_ORDER)
node = Conv2DParameters(name,
filt=filt,
stride=StrideDim(conv_opts.StrideH(),
conv_opts.StrideW()),
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.impose_order(TF_LITE_DW_FILTER_ORDER)
node = Conv2DParameters(name,
filt=filt,
stride=StrideDim(conv_opts.StrideH(),
conv_opts.StrideW()),
padding=pad,
groups=groups,
multiplier=conv_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 load_tensors:
node.weights = get_tensor(G.model,
tensors,
subgraph,
op,
1,
dequantize=dequantize)
# If we've converted to a normal conv then change the weight order
if convert_to_conv:
node.weights = node.weights.transpose(TF_LITE_DW_FILTER_TRANSPOSE)
if has_bias:
node.biases = get_tensor(G.model,
tensors,
subgraph,
op,
2,
dequantize=dequantize)
return fuse_activation(G, conv_opts, name, node)
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
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
trans_a = node.attrs.get('transA', 0)
trans_b = node.attrs.get('transB', 0)
alpha = node.attrs.get('alpha', 1.0)
beta = node.attrs.get('beta', 1.0)
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
y = inputs[1]
y_shape = y[2].shape
real_x_shape = cls._get_real_dim(x_shape)
real_y_shape = cls._get_real_dim(y_shape)
real_x_shape = [
real_x_shape[1], real_x_shape[0]
] if len(real_x_shape) == 2 and trans_a else real_x_shape
real_y_shape = [
real_y_shape[1], real_y_shape[0]
] if len(real_y_shape) == 2 and trans_b else real_y_shape
if not cls.is_linear(y, real_x_shape, real_y_shape) or trans_a:
raise ValueError(
"GEMM is only currently supported for operations that map onto a linear kernel"
)
if len(inputs) > 2:
biases = cls.get_constant(inputs[2])
else:
biases = np.zeros([real_y_shape[1]], dtype=np.float32)
filt_dim = FcFilterDim(real_y_shape[1], real_x_shape[0])
# always create new constants since they may be modified by this not and could be linked elsewhere
weights = cls.get_constant(y) * alpha
if not trans_b:
weights = np.transpose(weights, [1, 0])
weights_params = ConstantInputParameters(f'{valid_name}_weights',
dims=Dim.unnamed(
weights.shape),
value=weights)
biases = biases * beta
biases_params = ConstantInputParameters(f'{valid_name}_biases',
dims=Dim.unnamed(biases.shape),
value=biases)
params = FcParameters(valid_name,
filt=filt_dim,
has_bias=True,
in_dims_hint=SparseList([['c']]),
out_dims_hint=SparseList([['c']]),
constant_store=G.constant_store)
G.add_edge(NNEdge(from_node=weights_params, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=biases_params, to_node=params, to_idx=2))
out_dims = params.get_output_size([Dim.unnamed(real_x_shape)])
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
if isinstance(x[2], ProvisionalDim):
out_dim = x[2].infer_mapping(out_dims[0].shape)
else:
out_dim = out_dims[0]
all_nodes[node.output[0]] = (params, 0, out_dim)
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