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提交 124cffee authored 作者: ChienliMa's avatar ChienliMa
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push special case

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theano/compile/builders.py
浏览文件 @ 124cffee
......@@ -145,6 +145,10 @@ class OpFromGraph(gof.Op):
shape = theano.scan_module.scan_utils.infer_shape(self.new_outputs,
self.new_inputs,
shapes)
import pdb
pdb.set_trace()
return shape
def grad(self, inputs, output_grads):
......
theano/compile/tests/test_builders.py
浏览文件 @ 124cffee
......@@ -16,152 +16,154 @@ import unittest
class T_OpFromGraph(unittest_tools.InferShapeTester):
def test_straightforward(self):
x, y, z = T.matrices('xyz')
e = x + y * z
op = OpFromGraph([x, y, z], [e])
# (1+3*5=array of 16) - (3+1*5=array of 8)
f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5
# print function, function.__module__
# print fn.maker.fgraph.toposort()
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):
x, y, z = T.matrices('xyz')
e = T.dot(x, y)
op = OpFromGraph([x, y], [e])
f = op(x, op(y, z))
fn = function([x, y, z], f)
xv = numpy.ones((2, 3), dtype=config.floatX)
yv = numpy.ones((3, 4), dtype=config.floatX)*3
zv = numpy.ones((4, 5), dtype=config.floatX)*5
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert numpy.all(180.0 == res)
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert numpy.all(180.0 == res)
def test_grad(self):
x, y, z = T.matrices('xyz')
e = x + y * z
op = OpFromGraph([x, y, z], [e])
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5
assert numpy.all(11.0 == fn(xv, yv, zv))
def test_grad_grad(self):
x, y, z = T.matrices('xyz')
e = x + y * z
op = OpFromGraph([x, y, z], [e])
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)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5
assert numpy.allclose(6.0, fn(xv, yv, zv))
def test_shared(self):
x, y, z = T.matrices('xyz')
s = shared(numpy.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = OpFromGraph([x, y, z], [e])
# (1+3*5=array of 16) - (3+1*5=array of 8)
f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX)*3
zv = numpy.ones((2, 2), dtype=config.floatX)*5
# print function, function.__module__
# print fn.maker.fgraph.toposort()
assert numpy.allclose(8.0, fn(xv, yv, zv))
assert numpy.allclose(8.0, fn(xv, yv, zv))
def test_shared_grad(self):
x, y, z = T.matrices('xyz')
s = shared(numpy.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = OpFromGraph([x, y, z], [e])
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
assert numpy.allclose(11.0 + s.get_value(), fn(xv, yv, zv))
# grad again the shared variable
f = op(x, y, z)
f = f - T.grad(T.sum(f), s)
fn = function([x, y, z], f)
assert numpy.allclose(15.0 + s.get_value(),
fn(xv, yv, zv))
# def test_straightforward(self):
# x, y, z = T.matrices('xyz')
# e = x + y * z
# op = OpFromGraph([x, y, z], [e])
# # (1+3*5=array of 16) - (3+1*5=array of 8)
# f = op(x, y, z) - op(y, z, x)
# fn = function([x, y, z], f)
# xv = numpy.ones((2, 2), dtype=config.floatX)
# yv = numpy.ones((2, 2), dtype=config.floatX)*3
# zv = numpy.ones((2, 2), dtype=config.floatX)*5
# # print function, function.__module__
# # print fn.maker.fgraph.toposort()
# 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):
# x, y, z = T.matrices('xyz')
# e = T.dot(x, y)
# op = OpFromGraph([x, y], [e])
# f = op(x, op(y, z))
# fn = function([x, y, z], f)
# xv = numpy.ones((2, 3), dtype=config.floatX)
# yv = numpy.ones((3, 4), dtype=config.floatX)*3
# zv = numpy.ones((4, 5), dtype=config.floatX)*5
# res = fn(xv, yv, zv)
# assert res.shape == (2, 5)
# assert numpy.all(180.0 == res)
# res = fn(xv, yv, zv)
# assert res.shape == (2, 5)
# assert numpy.all(180.0 == res)
# def test_grad(self):
# x, y, z = T.matrices('xyz')
# e = x + y * z
# op = OpFromGraph([x, y, z], [e])
# f = op(x, y, z)
# f = f - T.grad(T.sum(f), y)
# fn = function([x, y, z], f)
# xv = numpy.ones((2, 2), dtype=config.floatX)
# yv = numpy.ones((2, 2), dtype=config.floatX)*3
# zv = numpy.ones((2, 2), dtype=config.floatX)*5
# assert numpy.all(11.0 == fn(xv, yv, zv))
# def test_grad_grad(self):
# x, y, z = T.matrices('xyz')
# e = x + y * z
# op = OpFromGraph([x, y, z], [e])
# 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)
# xv = numpy.ones((2, 2), dtype=config.floatX)
# yv = numpy.ones((2, 2), dtype=config.floatX)*3
# zv = numpy.ones((2, 2), dtype=config.floatX)*5
# assert numpy.allclose(6.0, fn(xv, yv, zv))
# def test_shared(self):
# x, y, z = T.matrices('xyz')
# s = shared(numpy.random.rand(2, 2).astype(config.floatX))
# e = x + y * z + s
# op = OpFromGraph([x, y, z], [e])
# # (1+3*5=array of 16) - (3+1*5=array of 8)
# f = op(x, y, z) - op(y, z, x)
# fn = function([x, y, z], f)
# xv = numpy.ones((2, 2), dtype=config.floatX)
# yv = numpy.ones((2, 2), dtype=config.floatX)*3
# zv = numpy.ones((2, 2), dtype=config.floatX)*5
# # print function, function.__module__
# # print fn.maker.fgraph.toposort()
# assert numpy.allclose(8.0, fn(xv, yv, zv))
# assert numpy.allclose(8.0, fn(xv, yv, zv))
# def test_shared_grad(self):
# x, y, z = T.matrices('xyz')
# s = shared(numpy.random.rand(2, 2).astype(config.floatX))
# e = x + y * z + s
# op = OpFromGraph([x, y, z], [e])
# f = op(x, y, z)
# f = f - T.grad(T.sum(f), y)
# fn = function([x, y, z], f)
# xv = numpy.ones((2, 2), dtype=config.floatX)
# yv = numpy.ones((2, 2), dtype=config.floatX) * 3
# zv = numpy.ones((2, 2), dtype=config.floatX) * 5
# assert numpy.allclose(11.0 + s.get_value(), fn(xv, yv, zv))
# # grad again the shared variable
# f = op(x, y, z)
# f = f - T.grad(T.sum(f), s)
# fn = function([x, y, z], f)
# assert numpy.allclose(15.0 + s.get_value(),
# fn(xv, yv, zv))
def test_connection_pattern(self):
# Basic case
x, y, z = T.matrices('xyz')
out1 = x * y
out2 = y * z
op1 = OpFromGraph([x ,y, z], [out1, out2])
results = op1.connection_pattern(None)
expect_result = [[True, False],
[True, True],
[False, True]]
assert results == expect_result
# Graph with ops that don't have a 'full' connection pattern
# and with ops that have multiple outputs
m, n, p, q = T.matrices('mnpq')
o1, o2 = op1(m, n, p)
out1, out2 = op1(o1, q, o2)
op2 = OpFromGraph([m, n, p, q], [out1, out2])
results = op2.connection_pattern(None)
expect_result = [[True, False],
[True, True],
[False, True],
[True, True]]
assert results == expect_result
# Inner graph where some computation doesn't rely on explicit inputs
srng = RandomStreams(seed=234)
rv_u = srng.uniform((2,2))
x, y = T.matrices('xy')
out1 = x + rv_u
out2 = y + 3
out3 = 3 + rv_u
op3 = OpFromGraph([x, y], [out1, out2, out3])
results = op3.connection_pattern(None)
expect_result = [[True, False, False],
[False, True, False],
[True, False, True]]
assert results == expect_result
# def test_connection_pattern(self):
# # Basic case
# x, y, z = T.matrices('xyz')
# out1 = x * y
# out2 = y * z
# op1 = OpFromGraph([x ,y, z], [out1, out2])
# results = op1.connection_pattern(None)
# expect_result = [[True, False],
# [True, True],
# [False, True]]
# assert results == expect_result
# # Graph with ops that don't have a 'full' connection pattern
# # and with ops that have multiple outputs
# m, n, p, q = T.matrices('mnpq')
# o1, o2 = op1(m, n, p)
# out1, out2 = op1(o1, q, o2)
# op2 = OpFromGraph([m, n, p, q], [out1, out2])
# results = op2.connection_pattern(None)
# expect_result = [[True, False],
# [True, True],
# [False, True],
# [True, True]]
# assert results == expect_result
# # Inner graph where some computation doesn't rely on explicit inputs
# srng = RandomStreams(seed=234)
# rv_u = srng.uniform((2,2))
# x, y = T.matrices('xy')
# out1 = x + rv_u
# out2 = y + 3
# out3 = 3 + rv_u
# op3 = OpFromGraph([x, y], [out1, out2, out3])
# results = op3.connection_pattern(None)
# expect_result = [[True, False, False],
# [False, True, False],
# [True, False, True]]
# assert results == expect_result
def test_infer_shape(self):
x = T.matrix('x')
y = x+x
z = x*x
op_graph = OpFromGraph([x], [y,z])
y = T.matrix('y')
o1 = x+y
o2 = x*y
op_graph = OpFromGraph([x,y], [o1,o2])
q = T.matrix('q')
self._compile_and_check([q],
[op_graph(q)[0],op_graph(q)[1]],
p = T.matrix('p')
self._compile_and_check([q,p],
[op_graph(q,p)[0],op_graph(q,p)[1]],
[numpy.ones([3,4], dtype=config.floatX)],
OpFromGraph)
......
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