def test_instantiation_Conv2D_Pool2D_Flatten_Dense(self):
for i in range(10):
batch = 1
_data = TypeShape(
DFloat,
Shape((DimNames.BATCH, batch), (DimNames.HEIGHT, 32),
(DimNames.WIDTH, 32), (DimNames.CHANNEL, 3)))
_target = TypeShape(
DFloat, Shape(
(DimNames.BATCH, batch),
(DimNames.UNITS, 10),
))
outputs = {'out0': _target}
IOLabel.DS = 'DS'
inputs = {IOLabel.DS: (IOLabel.DATA, _data, 'Dataset')}
functions = [Conv2D, Pooling2D, Flatten, Dense]
NN = NeuralNetwork(
**{
NeuralNetwork.arg_INPUTS: inputs,
NeuralNetwork.arg_OUTPUT_TARGETS: outputs,
NeuralNetwork.arg_FUNCTIONS: functions
})
self.assertIsNotNone(NN)
pb = NN.get_pb()
state = NN.__getstate__()
f_ids = dict([(_id, None) for _, _id in NN.inputs.values()])
for _f in NN.functions:
f_ids[_f.id_name] = _f
for _f in NN.functions:
for _f_input, (other_output, other_id) in _f.inputs.items():
if other_id not in f_ids:
self.assertTrue(False)
stack = [f_id for _, f_id in NN.output_mapping.values()]
required_ids = set()
while stack:
f_id = stack.pop()
required_ids.add(f_id)
f_ = f_ids.get(f_id)
if f_ is not None:
stack.extend([f_id for _, f_id in f_.inputs.values()])
self.assertSetEqual(required_ids, set(f_ids.keys()))
NN_pb = NeuralNetwork.__new__(NeuralNetwork)
NN_pb.__setstate__(pb)
self.assertIsNot(NN, NN_pb)
NN_state = NeuralNetwork.__new__(NeuralNetwork)
NN_state.__setstate__(state)
self.assertIsNot(NN, NN_state)
NN_mut = NN.mutate(100)
self.assertIsNot(NN, NN_mut)
self.assertNotEqual(NN, NN_mut)
NN_mut = NN.mutate(0)
self.assertIsNot(NN, NN_mut)
self.assertNotEqual(NN, NN_mut)
def test_instantiation_USD_ONTS_Dense_Merge(self):
for i in range(10):
batch = 1
_data = TypeShape(
DFloat, Shape((DimNames.BATCH, batch), (DimNames.UNITS, 20)))
IOLabel.DS1 = 'DS1'
IOLabel.DS2 = 'DS2'
inputs = {
IOLabel.DS1: (IOLabel.DATA, _data, 'Dataset'),
IOLabel.DS2: (IOLabel.DATA, _data, 'Dataset')
}
outShape = Shape((DimNames.BATCH, batch), (DimNames.UNITS, 10))
outShape1 = Shape((DimNames.BATCH, batch), (DimNames.UNITS, 15))
outputs = {
'out0': TypeShape(DFloat, outShape),
'out1': TypeShape(DFloat, outShape1)
}
functions = [Merge, Dense]
NN = NeuralNetwork(
**{
NeuralNetwork.arg_INPUTS: inputs,
NeuralNetwork.arg_OUTPUT_TARGETS: outputs,
NeuralNetwork.arg_FUNCTIONS: functions,
NeuralNetwork.arg_MAX_BRANCH: 2
})
self.assertIsNotNone(NN)
pb = NN.get_pb()
state = NN.__getstate__()
NN_pb = NeuralNetwork.__new__(NeuralNetwork)
NN_pb.__setstate__(pb)
self.assertIsNot(NN, NN_pb)
NN_state = NeuralNetwork.__new__(NeuralNetwork)
NN_state.__setstate__(state)
self.assertIsNot(NN, NN_state)
NN_mut = NN.mutate(1)[0]
self.assertEqual(pb, NN.get_pb())
self.assertIsNot(NN, NN_mut)
self.assertNotEqual(NN, NN_mut)
f_ids = dict([(_id, None) for _, _id in NN_mut.inputs.values()])
for _f in NN_mut.functions:
f_ids[_f.id_name] = _f
for _f in NN_mut.functions:
for _f_input, (other_output, other_id) in _f.inputs.items():
if other_id not in f_ids:
self.assertTrue(False)
stack = [f_id for _, f_id in NN_mut.output_mapping.values()]
required_ids = set()
while stack:
f_id = stack.pop()
required_ids.add(f_id)
f_ = f_ids.get(f_id)
if f_ is not None:
stack.extend([f_id for _, f_id in f_.inputs.values()])
self.assertSetEqual(required_ids, set(f_ids.keys()))
NN_mut = NN.mutate(1)[0]
self.assertEqual(pb, NN.get_pb())
self.assertIsNot(NN, NN_mut)
self.assertNotEqual(NN, NN_mut)
f_ids = dict([(_id, None) for _, _id in NN_mut.inputs.values()])
for _f in NN_mut.functions:
f_ids[_f.id_name] = _f
for _f in NN_mut.functions:
for _f_input, (other_output, other_id) in _f.inputs.items():
if other_id not in f_ids:
self.assertTrue(False)
stack = [f_id for _, f_id in NN_mut.output_mapping.values()]
required_ids = set()
while stack:
f_id = stack.pop()
required_ids.add(f_id)
f_ = f_ids.get(f_id)
if f_ is not None:
stack.extend([f_id for _, f_id in f_.inputs.values()])
self.assertSetEqual(required_ids, set(f_ids.keys()))
NN_mut = NN.mutate(0)[0]
NN_mut._id_name = NN._id_name
self.assertNotEqual(NN, NN_mut)