Skip to content

P
pytensor
提交 124cffee authored 作者: ChienliMa's avatar ChienliMa
浏览文件
操作

push special case

上级 664d0a96
隐藏空白字符变更
内嵌 并排
正在显示 包含 146 行增加 和 140 行删除
theano/compile/builders.py
浏览文件 @ 124cffee
...@@ -145,6 +145,10 @@ class OpFromGraph(gof.Op): ...@@ -145,6 +145,10 @@ class OpFromGraph(gof.Op):
shape = theano.scan_module.scan_utils.infer_shape(self.new_outputs, shape = theano.scan_module.scan_utils.infer_shape(self.new_outputs,
self.new_inputs, self.new_inputs,
shapes) shapes)
import pdb
pdb.set_trace()
return shape return shape
def grad(self, inputs, output_grads): def grad(self, inputs, output_grads):
......
theano/compile/tests/test_builders.py
浏览文件 @ 124cffee
...@@ -16,152 +16,154 @@ import unittest ...@@ -16,152 +16,154 @@ import unittest
class T_OpFromGraph(unittest_tools.InferShapeTester): class T_OpFromGraph(unittest_tools.InferShapeTester):
def test_straightforward(self): # def test_straightforward(self):
x, y, z = T.matrices('xyz') # x, y, z = T.matrices('xyz')
e = x + y * z # e = x + y * z
op = OpFromGraph([x, y, z], [e]) # op = OpFromGraph([x, y, z], [e])
# (1+3*5=array of 16) - (3+1*5=array of 8) # # (1+3*5=array of 16) - (3+1*5=array of 8)
f = op(x, y, z) - op(y, z, x) # f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f) # fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX) # xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3 # yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5 # zv = numpy.ones((2, 2), dtype=config.floatX)*5
# print function, function.__module__ # # print function, function.__module__
# print fn.maker.fgraph.toposort() # # print fn.maker.fgraph.toposort()
fn(xv, yv, zv) # fn(xv, yv, zv)
assert numpy.all(8.0 == fn(xv, yv, zv)) # assert numpy.all(8.0 == fn(xv, yv, zv))
assert numpy.all(8.0 == fn(xv, yv, zv)) # assert numpy.all(8.0 == fn(xv, yv, zv))
def test_size_changes(self): # def test_size_changes(self):
x, y, z = T.matrices('xyz') # x, y, z = T.matrices('xyz')
e = T.dot(x, y) # e = T.dot(x, y)
op = OpFromGraph([x, y], [e]) # op = OpFromGraph([x, y], [e])
f = op(x, op(y, z)) # f = op(x, op(y, z))
fn = function([x, y, z], f) # fn = function([x, y, z], f)
xv = numpy.ones((2, 3), dtype=config.floatX) # xv = numpy.ones((2, 3), dtype=config.floatX)
yv = numpy.ones((3, 4), dtype=config.floatX)*3 # yv = numpy.ones((3, 4), dtype=config.floatX)*3
zv = numpy.ones((4, 5), dtype=config.floatX)*5 # zv = numpy.ones((4, 5), dtype=config.floatX)*5
res = fn(xv, yv, zv) # res = fn(xv, yv, zv)
assert res.shape == (2, 5) # assert res.shape == (2, 5)
assert numpy.all(180.0 == res) # assert numpy.all(180.0 == res)
res = fn(xv, yv, zv) # res = fn(xv, yv, zv)
assert res.shape == (2, 5) # assert res.shape == (2, 5)
assert numpy.all(180.0 == res) # assert numpy.all(180.0 == res)
def test_grad(self): # def test_grad(self):
x, y, z = T.matrices('xyz') # x, y, z = T.matrices('xyz')
e = x + y * z # e = x + y * z
op = OpFromGraph([x, y, z], [e]) # op = OpFromGraph([x, y, z], [e])
f = op(x, y, z) # f = op(x, y, z)
f = f - T.grad(T.sum(f), y) # f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f) # fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX) # xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3 # yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5 # zv = numpy.ones((2, 2), dtype=config.floatX)*5
assert numpy.all(11.0 == fn(xv, yv, zv)) # assert numpy.all(11.0 == fn(xv, yv, zv))
def test_grad_grad(self): # def test_grad_grad(self):
x, y, z = T.matrices('xyz') # x, y, z = T.matrices('xyz')
e = x + y * z # e = x + y * z
op = OpFromGraph([x, y, z], [e]) # op = OpFromGraph([x, y, z], [e])
f = op(x, y, z) # f = op(x, y, z)
f = f - T.grad(T.sum(f), y) # f = f - T.grad(T.sum(f), y)
f = f - T.grad(T.sum(f), y) # f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f) # fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX) # xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3 # yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5 # zv = numpy.ones((2, 2), dtype=config.floatX)*5
assert numpy.allclose(6.0, fn(xv, yv, zv)) # assert numpy.allclose(6.0, fn(xv, yv, zv))
def test_shared(self): # def test_shared(self):
x, y, z = T.matrices('xyz') # x, y, z = T.matrices('xyz')
s = shared(numpy.random.rand(2, 2).astype(config.floatX)) # s = shared(numpy.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s # e = x + y * z + s
op = OpFromGraph([x, y, z], [e]) # op = OpFromGraph([x, y, z], [e])
# (1+3*5=array of 16) - (3+1*5=array of 8) # # (1+3*5=array of 16) - (3+1*5=array of 8)
f = op(x, y, z) - op(y, z, x) # f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f) # fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX) # xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3 # yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5 # zv = numpy.ones((2, 2), dtype=config.floatX)*5
# print function, function.__module__ # # print function, function.__module__
# print fn.maker.fgraph.toposort() # # print fn.maker.fgraph.toposort()
assert numpy.allclose(8.0, fn(xv, yv, zv)) # assert numpy.allclose(8.0, fn(xv, yv, zv))
assert numpy.allclose(8.0, fn(xv, yv, zv)) # assert numpy.allclose(8.0, fn(xv, yv, zv))
def test_shared_grad(self): # def test_shared_grad(self):
x, y, z = T.matrices('xyz') # x, y, z = T.matrices('xyz')
s = shared(numpy.random.rand(2, 2).astype(config.floatX)) # s = shared(numpy.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s # e = x + y * z + s
op = OpFromGraph([x, y, z], [e]) # op = OpFromGraph([x, y, z], [e])
f = op(x, y, z) # f = op(x, y, z)
f = f - T.grad(T.sum(f), y) # f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f) # fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX) # xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3 # yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5 # zv = numpy.ones((2, 2), dtype=config.floatX) * 5
assert numpy.allclose(11.0 + s.get_value(), fn(xv, yv, zv)) # assert numpy.allclose(11.0 + s.get_value(), fn(xv, yv, zv))
# grad again the shared variable # # grad again the shared variable
f = op(x, y, z) # f = op(x, y, z)
f = f - T.grad(T.sum(f), s) # f = f - T.grad(T.sum(f), s)
fn = function([x, y, z], f) # fn = function([x, y, z], f)
assert numpy.allclose(15.0 + s.get_value(), # assert numpy.allclose(15.0 + s.get_value(),
fn(xv, yv, zv)) # fn(xv, yv, zv))
def test_connection_pattern(self): # def test_connection_pattern(self):
# Basic case # # Basic case
x, y, z = T.matrices('xyz') # x, y, z = T.matrices('xyz')
out1 = x * y # out1 = x * y
out2 = y * z # out2 = y * z
op1 = OpFromGraph([x ,y, z], [out1, out2]) # op1 = OpFromGraph([x ,y, z], [out1, out2])
results = op1.connection_pattern(None) # results = op1.connection_pattern(None)
expect_result = [[True, False], # expect_result = [[True, False],
[True, True], # [True, True],
[False, True]] # [False, True]]
assert results == expect_result # assert results == expect_result
# Graph with ops that don't have a 'full' connection pattern # # Graph with ops that don't have a 'full' connection pattern
# and with ops that have multiple outputs # # and with ops that have multiple outputs
m, n, p, q = T.matrices('mnpq') # m, n, p, q = T.matrices('mnpq')
o1, o2 = op1(m, n, p) # o1, o2 = op1(m, n, p)
out1, out2 = op1(o1, q, o2) # out1, out2 = op1(o1, q, o2)
op2 = OpFromGraph([m, n, p, q], [out1, out2]) # op2 = OpFromGraph([m, n, p, q], [out1, out2])
results = op2.connection_pattern(None) # results = op2.connection_pattern(None)
expect_result = [[True, False], # expect_result = [[True, False],
[True, True], # [True, True],
[False, True], # [False, True],
[True, True]] # [True, True]]
assert results == expect_result # assert results == expect_result
# Inner graph where some computation doesn't rely on explicit inputs # # Inner graph where some computation doesn't rely on explicit inputs
srng = RandomStreams(seed=234) # srng = RandomStreams(seed=234)
rv_u = srng.uniform((2,2)) # rv_u = srng.uniform((2,2))
x, y = T.matrices('xy') # x, y = T.matrices('xy')
out1 = x + rv_u # out1 = x + rv_u
out2 = y + 3 # out2 = y + 3
out3 = 3 + rv_u # out3 = 3 + rv_u
op3 = OpFromGraph([x, y], [out1, out2, out3]) # op3 = OpFromGraph([x, y], [out1, out2, out3])
results = op3.connection_pattern(None) # results = op3.connection_pattern(None)
expect_result = [[True, False, False], # expect_result = [[True, False, False],
[False, True, False], # [False, True, False],
[True, False, True]] # [True, False, True]]
assert results == expect_result # assert results == expect_result
def test_infer_shape(self): def test_infer_shape(self):
x = T.matrix('x') x = T.matrix('x')
y = x+x y = T.matrix('y')
z = x*x o1 = x+y
op_graph = OpFromGraph([x], [y,z]) o2 = x*y
op_graph = OpFromGraph([x,y], [o1,o2])
q = T.matrix('q') q = T.matrix('q')
self._compile_and_check([q], p = T.matrix('p')
[op_graph(q)[0],op_graph(q)[1]], self._compile_and_check([q,p],
[op_graph(q,p)[0],op_graph(q,p)[1]],
[numpy.ones([3,4], dtype=config.floatX)], [numpy.ones([3,4], dtype=config.floatX)],
OpFromGraph) OpFromGraph)
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论