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
def quantize(self, G: NNGraph) -> OrderedDict:
'''quantize the graph'''
if G.has_quantized_parameters:
self.dequantize(G)
G.has_quantized_parameters = False
G.quantization = None
self.qrecs = QuantizationSet()
edge_recs = {}
opts = {
'force_width': self._force_width,
'quantized_dimension': self._quantized_dimension,
'narrow_weights': self._narrow_weights
}
opts.update(self._options)
quant_kwargs = {
'opts': opts,
'all_stats': self._activation_stats,
'G': G,
'qrecs': self.qrecs
}
dtype = WIDTH_TO_DTYPE[self._force_width]
self.quantize_forward(edge_recs, dtype=dtype, **quant_kwargs)
self.qrecs['__quantizer'] = self
G.graph_identity.quantization_type = 'SQ8'
return self.qrecs
def quantize(self, G: NNGraph) -> OrderedDict:
'''quantize the graph'''
if G.has_quantized_parameters:
self.dequantize(G)
G.has_quantized_parameters = False
G.quantization = None
edge_recs = {}
dtype = WIDTH_TO_DTYPE[self._force_width]
qrecs = self.quantize_forward(G, edge_recs, dtype)
qrecs['__quantizer'] = self
G.graph_identity.quantization_type = 'SQ8'
return qrecs
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