def do_fusions(self, args):
"""
Carry out the default set of fusions on the graph"""
self._check_graph()
if args.list:
self.ppaged("\n".join(
["%s - %s" % (name, desc) for name, desc in get_fusions()]))
return
if args.apply:
fusions = [get_fusion(name) for name in args.apply]
if not fusions:
self.perror('fusion %s not found' % args.apply)
return
elif args.pow2:
fusions = [get_pow2_match_group()]
elif args.scale8:
fusions = [get_scale8_match_group()]
else:
self.perror(
"No fusion set selected. Nothing to do. Select --pow2 or --scale8."
)
return
for fusion in fusions:
fusion.match(self.G)
self.G.add_dimensions()
if self.G.quantization and not self.G.quantization.verify_quantization(
self.G):
self.G.quantization = None
def test_mobv2_quant_asym_tf1_15_vwwvehicle():
graph = 'tests/mobv2_valid/mobv2_vwwvehicle_quant_asym.tflite'
tfi = TfliteImporter()
G = tfi.create_graph(graph, {
'load_tensors': True,
'load_quantization': True
})
G.add_dimensions()
G.adjust_order()
matcher = get_scale8_match_group()
matcher.match(G)
G.add_dimensions()
def test_adjust9(mn3q_graph, caplog):
caplog.set_level(logging.INFO)
tfi = TfliteImporter()
G = tfi.create_graph(mn3q_graph, {
'load_tensors': True,
'load_quantization': True
})
G.add_dimensions()
G.adjust_order()
matcher = get_scale8_match_group()
matcher.match(G)
G.add_dimensions()
def test_adjust10(caplog):
caplog.set_level(logging.INFO)
tfi = TfliteImporter()
G = tfi.create_graph(
"tests/graph/ssdlite_v2_quant_ocr_nopostprocess.tflite", {
'load_tensors': True,
'load_quantization': True
})
G.add_dimensions()
G.adjust_order()
matcher = get_scale8_match_group()
matcher.match(G)
G.add_dimensions()
def do_fusions(self, args):
"""
Carry out the default set of fusions on the graph"""
if args.list:
table = texttable.Texttable()
table.set_cols_align(['l', 'l'])
table.set_max_width(120)
table.add_rows([['Name', 'Description']] + get_fusions())
self.ppaged(table.draw())
return
self._check_graph()
state = ConstantInputParameters.save_compression_state(self.G)
try:
if args.apply:
fusions = [get_fusion(name) for name in args.apply]
invalid_names = [
args.apply[idx] for idx, fusion in enumerate(fusions)
if fusion is None
]
if invalid_names:
self.perror(
f'fusion{"s" if len(invalid_names) > 1 else ""} {", ".join(invalid_names)} not found'
)
return
elif args.pow2:
fusions = [get_pow2_match_group()]
elif args.scale8:
fusions = [get_scale8_match_group()]
else:
self.perror(
"No fusion set selected. Nothing to do. Select --pow2 or --scale8."
)
return
for fusion in fusions:
fusion.match(self.G)
self.G.add_dimensions()
if self.G.quantization and verify_quantization(self.G):
quantizer = NewQuantizer(self.G)
quantizer.quantize()
problems = verify_quantization(self.G)
if problems:
self.perror('quantization issue after fusions')
for problem in problems:
self.perror(problem)
finally:
ConstantInputParameters.restore_compression_state(self.G, state)
def test_external_biases_sq8(qvww_graph):
# this model has at the end an external biases layer as constant add
tfi = TfliteImporter()
G = tfi.create_graph(qvww_graph, {"load_quantization": True, "load_tensors": True})
G.add_dimensions()
matcher = get_scale8_match_group()
matcher.match(G)
G.add_dimensions()
image = 'tests/vwwimages/COCO_val2014_000000174838_1.png'
img_in = Image.open(image)
img_in = img_in.resize((238, 208))
input_tensor = np.array(img_in, dtype=np.uint8)
input_tensor = (input_tensor.astype(np.float32) - 128) / 128
executer = GraphExecuter(G, qrecs=G.quantization)
# check if nntool can execute
qoutput_tensors = executer.execute([input_tensor], qmode=QuantizationMode.all_dequantize())
foutput_tensors = executer.execute([input_tensor], qmode=None)
diff = [q[0]-f[0] for q,f in zip(qoutput_tensors, foutput_tensors)]
assert max([np.max(d) for d in diff]) < 2.2
def test_gen_vergesense(caplog):
caplog.set_level(logging.DEBUG)
tfi = TfliteImporter()
G = tfi.create_graph("tests/graph/marco_17_04.tflite", {
'load_tensors': True,
'load_quantization': True
})
G.add_dimensions()
G.adjust_order()
matcher = get_scale8_match_group()
matcher.match(G)
G.add_dimensions()
with tempfile.TemporaryDirectory() as tempdir:
opts = {
'default_input_location': 'ARG_LOC_L2',
'default_output_location': 'ARG_LOC_L2',
'default_global_location': 'ARG_LOC_L3_HFLASH',
'default_local_location': 'AT_MEM_UNDEF',
'tensor_directory': tempdir
}
code_gen = CodeGenerator(G, DefaultNamingConvension(G), opts)
default_template(G, code_generator=code_gen)
code_gen.write_constants()
def do_fusions(self, args):
"""
Carry out the default set of fusions on the graph"""
if args.list:
table = texttable.Texttable()
table.set_cols_align(['l', 'l'])
table.set_max_width(120)
table.add_rows([['Name', 'Description']] + get_fusions())
self.ppaged(table.draw())
return
self._check_graph()
if args.apply:
fusions = [get_fusion(name) for name in args.apply]
invalid_names = [
args.apply[idx] for idx, fusion in enumerate(fusions)
if fusion is None
]
if invalid_names:
self.perror(
f'fusion{"s" if len(invalid_names) > 1 else ""} {", ".join(invalid_names)} not found'
)
return
elif args.pow2:
fusions = [get_pow2_match_group()]
elif args.scale8:
fusions = [get_scale8_match_group()]
else:
self.perror(
"No fusion set selected. Nothing to do. Select --pow2 or --scale8."
)
return
for fusion in fusions:
fusion.match(self.G)
self.G.add_dimensions()
if self.G.quantization and not self.G.quantization.verify_quantization(
self.G):
self.G.quantization = None
