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test_base.py
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test_base.py
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# -*- coding: utf-8 -*-
import unittest
import jax.tree_util
import brainpy as bp
import brainpy.math as bm
class TestCollectionFunction(unittest.TestCase):
def test_f_nodes(self):
class C(bp.DynamicalSystem):
def __init__(self):
super(C, self).__init__()
class B(bp.DynamicalSystem):
def __init__(self):
super(B, self).__init__()
self.child1 = C()
self.child2 = C()
class A(bp.DynamicalSystem):
def __init__(self):
super(A, self).__init__()
self.child1 = B()
self.child2 = B()
net = bp.Network(a1=A(), a2=A())
print(net.nodes(level=2))
self.assertTrue(len(net.nodes(level=0)) == 1)
self.assertTrue(len(net.nodes(level=0, include_self=False)) == 0)
self.assertTrue(len(net.nodes(level=1)) == (1 + 2))
self.assertTrue(len(net.nodes(level=1, include_self=False)) == 2)
self.assertTrue(len(net.nodes(level=2)) == (1 + 2 + 4))
self.assertTrue(len(net.nodes(level=2, include_self=False)) == (2 + 4))
self.assertTrue(len(net.nodes(level=3)) == (1 + 2 + 4 + 8))
self.assertTrue(len(net.nodes(level=3, include_self=False)) == (2 + 4 + 8))
def test_f_vars(self):
class C(bp.DynamicalSystem):
def __init__(self):
super(C, self).__init__()
self.var1 = bm.Variable(bm.zeros(1))
self.var2 = bm.Variable(bm.zeros(1))
class B(bp.DynamicalSystem):
def __init__(self):
super(B, self).__init__()
self.child1 = C()
self.child2 = C()
self.var1 = bm.Variable(bm.zeros(1))
self.var2 = bm.Variable(bm.zeros(1))
class A(bp.DynamicalSystem):
def __init__(self):
super(A, self).__init__()
self.child1 = B()
self.child2 = B()
self.var1 = bm.Variable(bm.zeros(1))
self.var2 = bm.Variable(bm.zeros(1))
net = bp.Network(a1=A(), a2=A())
print(net.vars(level=2))
self.assertTrue(len(net.vars(level=0)) == 0)
self.assertTrue(len(net.vars(level=0, include_self=False)) == 0)
self.assertTrue(len(net.vars(level=1)) == 2 * 2)
self.assertTrue(len(net.vars(level=1, include_self=False)) == 2 * 2)
self.assertTrue(len(net.vars(level=2)) == (2 + 4) * 2)
self.assertTrue(len(net.vars(level=2, include_self=False)) == (2 + 4) * 2)
self.assertTrue(len(net.vars(level=3)) == (2 + 4 + 8) * 2)
self.assertTrue(len(net.vars(level=3, include_self=False)) == (2 + 4 + 8) * 2)
class TestNodeList(unittest.TestCase):
def test_NodeList_1(self):
bm.random.seed()
class Object(bp.DynamicalSystem):
def __init__(self):
super().__init__()
self.l1 = bp.layers.Dense(5, 10)
self.ls = bm.NodeList([bp.layers.Dense(10, 4),
bp.layers.Activation(bm.tanh),
bp.layers.Dropout(0.1),
bp.layers.Dense(4, 5),
bp.layers.Activation(bm.relu)])
def update(self, x):
x = self.l1(x)
for l in self.ls:
x = l(x)
return x
with bm.environment(mode=bm.NonBatchingMode()):
obj = Object()
self.assertTrue(len(obj.vars()) == 0)
self.assertTrue(len(obj.nodes()) == 7)
print(obj.nodes().keys())
print("obj.nodes(method='relative'): ", obj.nodes(method='relative').keys())
# print(jax.tree_util.tree_structure(obj))
with bm.environment(mode=bm.TrainingMode()):
obj = Object()
self.assertTrue(len(obj.vars()) == 6)
self.assertTrue(len(obj.nodes()) == 7)
print(obj.nodes().keys())
print("obj.nodes(method='relative'): ", obj.nodes(method='relative').keys())
# print(jax.tree_util.tree_structure(obj))
class TestNodeDict(unittest.TestCase):
def test_NodeDict_1(self):
bm.random.seed()
class Object(bp.DynamicalSystem):
def __init__(self):
super().__init__()
self.l1 = bp.layers.Dense(5, 10)
self.ls = bm.NodeDict(
{
'l1': bp.layers.Dense(10, 4),
'l2': bp.layers.Activation(bm.tanh),
'l3': bp.layers.Dropout(0.1),
'l4': bp.layers.Dense(4, 5),
'l5': bp.layers.Activation(bm.relu)
}
)
def update(self, x):
x = self.l1(x)
for l in self.ls:
x = l(x)
return x
with bm.environment(mode=bm.NonBatchingMode()):
obj = Object()
self.assertTrue(len(obj.vars()) == 0)
self.assertTrue(len(obj.nodes()) == 7)
self.assertTrue(len(jax.tree_util.tree_leaves(obj)) == 1)
print(obj.nodes().keys())
print("obj.nodes(method='relative'): ",
obj.nodes(method='relative').keys())
# print(jax.tree_util.tree_structure(obj))
with bm.environment(mode=bm.TrainingMode()):
obj = Object()
self.assertTrue(len(obj.vars()) == 6)
self.assertTrue(len(obj.nodes()) == 7)
print(obj.nodes().keys())
print("obj.nodes(method='relative'): ",
obj.nodes(method='relative').keys())
# print(jax.tree_util.tree_structure(obj))
class TestVarList(unittest.TestCase):
def test_ListVar_1(self):
bm.random.seed()
class Object(bp.DynamicalSystem):
def __init__(self):
super().__init__()
self.vs = bm.VarList([bm.Variable(1.),
bm.Variable(2.),
bm.Variable(bm.ones(10))])
def update(self):
self.vs[0] += 10.
self.vs[1] += 10.
self.vs[2] += 10.
obj = Object()
self.assertTrue(len(obj.vars()) == 3)
self.assertTrue(len(obj.nodes()) == 1)
@bm.jit
def f2():
obj()
f2()
print(obj.vs)
self.assertTrue(obj.vs[0] == 11.)
self.assertTrue(obj.vs[1] == 12.)
self.assertTrue(bm.allclose(obj.vs[2], bm.ones(10) * 11.))
class TestVarDict(unittest.TestCase):
def test_DictVar_1(self):
bm.random.seed()
class Object(bp.DynamicalSystem):
def __init__(self):
super().__init__()
self.vs = bm.VarDict({'a': bm.Variable(1.),
'b': bm.Variable(2.),
'c': bm.Variable(bm.ones(10))})
def update(self):
self.vs['a'] += 10.
self.vs['b'] += 10.
self.vs['c'] += 10.
obj = Object()
print(obj.vars())
self.assertTrue(len(obj.vars()) == 3)
self.assertTrue(len(obj.nodes()) == 1)
@bm.jit
def f1():
obj()
f1()
print(obj.vs)
self.assertTrue(obj.vs['a'] == 11.)
self.assertTrue(obj.vs['b'] == 12.)
self.assertTrue(bm.allclose(obj.vs['c'], bm.ones(10) * 11.))
class TestRegisterBPObjectAsPyTree(unittest.TestCase):
def test1(self):
bm.set(bp_object_as_pytree=True)
hh = bp.dyn.HH(1)
hh.reset()
tree = jax.tree.structure(hh)
leaves = jax.tree.leaves(hh)
print(tree)
print(leaves)
print(jax.tree.unflatten(tree, leaves))
print()
class TestStateSavingAndLoading(unittest.TestCase):
def test_load_states(self):
class Object(bp.DynamicalSystem):
def __init__(self):
super().__init__()
self.l1 = bp.layers.Dense(5, 10)
self.ls = bm.NodeList([bp.layers.Dense(10, 4),
bp.layers.Activation(bm.tanh),
bp.layers.Dropout(0.1),
bp.layers.Dense(4, 5),
bp.layers.Activation(bm.relu)])
self.lif = bp.dyn.LifRef(5)
def update(self, x):
x = self.l1(x)
for l in self.ls:
x = l(x)
return x
with bm.training_environment():
obj = Object()
variables = {k: dict(n.vars()) for k, n in obj.nodes(include_self=False).items()}
variables = {k: v for k, v in variables.items() if len(v) > 0}
all_states = obj.state_dict()
all_states = {k: v for k, v in all_states.items() if len(v) > 0}
print(set(all_states.keys()))
print(set(variables.keys()))
def not_close(x, y):
assert not bm.allclose(x, y)
def all_close(x, y):
assert bm.allclose(x, y)
jax.tree.map(all_close, all_states, variables, is_leaf=bm.is_bp_array)
random_state = jax.tree.map(bm.random.rand_like, all_states, is_leaf=bm.is_bp_array)
jax.tree.map(not_close, random_state, variables, is_leaf=bm.is_bp_array)
obj.load_state_dict(random_state)
jax.tree.map(all_close, random_state, variables, is_leaf=bm.is_bp_array)