def test_graph_datatag_accessors(self):
frame = TFFramework()
frame.load(RunpyLoader, 'test/test_models/simple_nnet.py')
assert frame.get_graph(
mode='training') is not None, 'No training graph returned.'
assert frame.get_graph(
mode='inference') is not None, 'No inference graph returned.'
def _run(self):
frame = TFFramework()
if self.args['list']:
print 'Supported Exemplars:'
for primop in frame.ExemplarRegistry:
print ' ',primop
return 0
elif self.args['primop'] is None:
self.subparser.error('A primop must be specified unless using --list')
primop_t = self.args['primop']
uas = UniformArgSampler()
primop_args = uas.sample(primop_t, n=self.args['n'], seed=self.args['seed'])
Exemplar = frame.ExemplarRegistry.lookup(primop_t)
self.features = Features()
for p_args in primop_args:
exemplar = Exemplar(p_args)
synthmodel = frame.SyntheticModel(exemplar)
frame.set_model(synthmodel)
profile = frame.get_timing(mode='inference', ops='native')
graph = frame.get_graph(mode='inference', scope='dynamic', ops='native')
id = graph.get_vertex_id_from_tf_name( exemplar.get_op_name() )
self.features.append( p_args, profile[id][0] )
if self.args['output'] is None:
self.args['output'] = str(primop_t)+'.features'
self.features.write(self.args['output'])
def test_datatag_graph_all_dynamic_primitive(self, model, mode):
frame = TFFramework(RunpyLoader, model)
g = frame.get_graph(mode, 'dynamic', 'primitive')
assert len(g.ops) > 0, 'No operations in graph.'
