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']
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 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 create_graph(self, filename, opts) -> NNGraph:
opts = self.get_opts(opts)
model = onnx.load(filename)
# onnx.checker.check_model(model)
try:
model = shape_inference.infer_shapes(model)
except RuntimeError as ex:
msg = "\n".join(f"> {line}" for line in str(ex).split("\n")
if line)
logger.warning(
'shape inference failed on onnx graph. '
f'This may not affect import.\nONNX runtime error was:\n{msg}')
self._name_cache = {}
if model.ir_version < 3:
opset_import = [make_opsetid(defs.ONNX_DOMAIN, 1)]
else:
opset_import = model.opset_import
G = NNGraph(filename=filename, name=opts.get('name'))
G, qrecs = self._import_onnx_model(G, model.graph, opset_import, opts)
G.add_dimensions(quiet=True)
if qrecs:
propagate_qrecs(G, qrecs)
qset = QuantizationSet()
qset.update(qrecs)
qset.scheme_priority = ['SQ8']
qset.schemes_present = {'SQ8'}
G.quantization = qset
try:
quantizer = NewQuantizer(G)
quantizer.quantize()
except ValueError as ex:
logger.warning(
f'unable to import quantization from FakeQuantize nodes correctly - {ex}'
)
clean_dangling_nodes(G)
MatchDuplicateConstants().match(G)
return G
def create_graph(self, filename, opts):
opts = self.get_opts(opts)
self._name_cache = {}
add_sys_path(os.path.dirname(__file__))
buf = open(filename, "rb").read()
model = Model.GetRootAsModel(buf, 0)
LOG.info("Importing TFLITE model version %s", model.Version())
check(model.Version() == 3, "Only support version 3 graphs at present")
if model.SubgraphsLength() > 1:
LOG.warning("nntool only supports one subgraph. There may be errors loading this graph.")
G = NNGraph(model=model, filename=filename, name=opts.get('name'),
constant_store=ConstantStore())
if opts.get('load_quantization'):
G.quantization = QuantizationSet()
G.has_quantized_parameters = True
G.graph_identity.quantization_types.add('SQ8')
self._import_tflite_graph(G, TFLiteGraph.from_model(model, 0), opts)
clean_dangling_nodes(G)
fix_split_in_edges(G)
MatchDuplicateConstants().match(G)
G.add_dimensions()
remove_concats(G)
if opts['remove_quantize_ops']:
RemoveQuantizeOperators().match(G)
G.add_dimensions()
if opts.get('load_quantization'):
# get rid of qrecs on nodes that were not used
to_remove = []
for nid in G.quantization:
if nid.node_name not in G:
to_remove.append(nid)
for nid in to_remove:
del G.quantization[nid]
return G
def create_graph(self, filename, opts):
opts = self.get_opts(opts)
self._name_cache = {}
add_sys_path(os.path.dirname(__file__))
buf = open(filename, "rb").read()
model = Model.GetRootAsModel(buf, 0)
LOG.info("Importing TFLITE model version %s", model.Version())
check(model.Version() == 3, "Only support version 3 graphs at present")
if model.SubgraphsLength() > 1:
LOG.warning(
"nntool only supports one subgraph. There may be errors loading this graph."
)
G = NNGraph(model=model,
filename=filename,
name=opts.get('name'),
constant_store=ConstantStore())
if opts.get('load_quantization'):
G.quantization = QuantizationSet()
G.has_quantized_parameters = True
G.quantization.schemes_present.add('SQ8')
self._import_tflite_graph(G, model, opts)
clean_dangling_nodes(G)
fix_split_in_edges(G)
MatchDuplicateConstants().match(G)
# DrawGraphReporter().report(G)
G.add_dimensions()
remove_concats(G)
if opts['remove_quantize_ops']:
RemoveQuantizeOperators().match(G)
G.add_dimensions()
if opts.get('load_quantization'):
# get rid of qrecs on nodes that were not used
to_remove = []
for nid in G.quantization:
if nid.node_name not in G:
to_remove.append(nid)
for nid in to_remove:
del G.quantization[nid]
nodes_with_bad_quantization = self.find_nodes_with_bad_quantization(
G)
quantizer = UnifiedQuantizer.from_quantized_graph(G)
# check for quantization problems
# 1) need to force softmax/Sigmoid input to POW2 quantization
# 2) need to check that all concats and splits have same input and
# output quantization
# 3) Need to check that all nodes have qrecs and that they are consistent
nodes_with_bad_quantization |= set(
G.nodes(node_classes=(ConcatParameters, SoftMaxParameters,
SplitParameters,
SigmoidActivationParameters)))
G.quantization = quantizer.quantize(
G, start_nodes=nodes_with_bad_quantization)
G.add_dimensions()
return G