Skip to content

P
pytensor
提交 f2608775 authored 作者: David Warde-Farley's avatar David Warde-Farley
浏览文件
操作

Merge pull request #126 from nouiz/join_test

Split/Join tests
上级 6bc14189 15cc9076
隐藏空白字符变更
内嵌 并排
正在显示 包含 268 行增加 和 232 行删除
theano/sandbox/cuda/opt.py
浏览文件 @ f2608775
......@@ -881,8 +881,8 @@ def local_gpu_join(node):
#print "OPT: axis_and_tensors=", axis_and_tensors
matches = [not t.owner is None and t.owner.op == host_from_gpu for t in axis_and_tensors[1:]]
matches = [(not t.owner is None and t.owner.op == host_from_gpu) or
isinstance(t, gof.Constant) for t in axis_and_tensors[1:]]
#print "OPT: matches =", matches
# if all input tensors are host_from_gpu'ified
......
theano/sandbox/cuda/tests/test_basic_ops.py
浏览文件 @ f2608775
......@@ -646,76 +646,6 @@ def test_hostfromgpu_shape_i():
# -----------------------------------------------------------------------
import theano.sandbox.cuda as cuda_ndarray
from theano.sandbox.cuda.basic_ops import gpu_join, GpuDimShuffle
def test_gpujoin_concatenate_one_element():
m = T.fmatrix()
c = T.concatenate([m])
f = theano.function(inputs=[m], outputs=[c], mode=mode_with_gpu)
topo = f.maker.env.toposort()
assert len(topo)==1
assert isinstance(topo[0].op,theano.compile.DeepCopyOp)
def test_gpujoin_twomatrices_joincolumns():
_a = numpy.asarray([[1,2],[3,4]],dtype='float32')
_b = numpy.asarray([[5,6,7],[8,9,10]],dtype='float32')
a = tcn.shared_constructor(_a)
b = tcn.shared_constructor(_b)
c = gpu_join(1,a,b)
f = theano.function([], c)
assert numpy.all(f() == numpy.concatenate([_a,_b], axis=1))
def test_gpujoin_twomatrices_badshapes():
_a = numpy.asarray([[1,2],[3,4]],dtype='float32')
_b = numpy.asarray([[5,6,7],[8,9,10]],dtype='float32')
a = tcn.shared_constructor(_a)
b = tcn.shared_constructor(_b)
# try to join on dimension 0 where they don't agree (2!=3)
c = gpu_join(0,a,b)
f = theano.function([], c)
try:
f()
assert False
except ValueError:
assert True
def test_gpujoin_preserves_broadcasting():
_a = numpy.asarray([[1,2],[3,4]],dtype='float32')
_b = numpy.asarray([[5,6,7],[8,9,10]],dtype='float32')
a = tcn.shared_constructor(_a)
b = tcn.shared_constructor(_b)
# [0,0] : the two original dims were non-broadcastable
# [1,x,0]: new order and broadcastability
gpu_dimshuffle = GpuDimShuffle([0,0], [1,'x',0])
a_shuffled = gpu_dimshuffle(a)
b_shuffled = gpu_dimshuffle(b)
c = gpu_join(0,a_shuffled,b_shuffled)
assert c.type.broadcastable == (False,True,False)
f = theano.function([], c, mode=mode_with_gpu)
res = f()
a_reshaped = numpy.asarray([[[1,3]],[[2,4]]], dtype='float32')
b_reshaped = numpy.asarray([[[5,8]],[[6,9]],[[7,10]]], dtype='float32')
concat = numpy.concatenate([a_reshaped,b_reshaped], axis=0)
assert numpy.all(res == concat)
def test_gpujoin_assert_cndas():
# this will end up being an ndarray, as it's float64
......@@ -723,7 +653,7 @@ def test_gpujoin_assert_cndas():
a = theano.shared(_a)
try:
c = gpu_join(1,a)
c = cuda.basic_ops.gpu_join(1, a)
# can't "assert False" here, as we want the assertion
# error from gpu_join
except AssertionError:
......@@ -792,6 +722,21 @@ def test_gpualloc_output_to_gpu():
assert numpy.allclose(f(5),f_gpu(5))
import theano.tensor.tests.test_basic
class T_Join_and_Split(theano.tensor.tests.test_basic.T_Join_and_Split):
def setUp(self):
utt.seed_rng()
self.mode = mode_with_gpu.excluding('constant_folding')
self.join_op = cuda.GpuJoin
# No gpu split.
self.split_op = tensor.Split
# No Make vector on the gpu, Join used instead
self.make_vector_op = cuda.GpuJoin
self.floatX = "float32"
# In FAST_COMPILE mode, we force the FAST_RUN mode for optimization.
self.hide_error = theano.config.mode not in ['DebugMode', 'DEBUG_MODE']
self.shared = cuda.shared_constructor
# This is to don't duplicate test.
class T_subtensor(theano.tensor.tests.test_basic.T_subtensor):
shared=staticmethod(cuda.shared_constructor)
......
theano/tensor/basic.py
浏览文件 @ f2608775
......@@ -2892,8 +2892,9 @@ def extract_constant(x):
This function is basically a call to tensor.get_constant_value. The
main difference is the behaviour in case of failure. While
get_constant_value raises an TypeError, this function returns x,
as a tensor ( by removing the last scalar_from_tensor ) if needed
or None if that is the value of x.
as a tensor if possible. If x is a ScalarVariable from a
scalar_from_tensor, we remove the conversion. If x is just a
ScalarVariable, we convert it to a tensor with tensor_from_scalar.
'''
try:
x = get_constant_value(x)
......
theano/tensor/tests/test_basic.py
浏览文件 @ f2608775
......@@ -13,7 +13,7 @@ from numpy.testing import dec
from numpy.testing.noseclasses import KnownFailureTest
import theano
from theano import compile, config, function, gof, tensor
from theano import compile, config, function, gof, tensor, shared
from theano.compile.mode import get_default_mode
from theano.gof.python25 import any, all, combinations
from theano.tensor import (_shared, wvector, bvector, autocast_float_as,
......@@ -30,7 +30,7 @@ from theano.tensor import (_shared, wvector, bvector, autocast_float_as,
var, value, Join, shape, MaxAndArgmax, lscalar, zvector, exp,
get_constant_value, ivector, reshape, scalar_from_tensor, scal,
iscalars, arange, dscalars, fvector, imatrix, numeric_grad,
opt, ComplexError, TensorDot, lvector, true_div, max, min)
opt, ComplexError, TensorDot, lvector, true_div, max, min, Split)
from theano.tests import unittest_tools as utt
......@@ -56,7 +56,7 @@ def inplace_func(inputs, outputs, mode=None, allow_input_downcast=False):
def eval_outputs(outputs):
variables = inplace_func([], outputs)()
if isinstance(variables,(tuple,list)) and len(variables) == 1:
if isinstance(variables,(tuple, list)) and len(variables) == 1:
return variables[0]
return variables
......@@ -2536,6 +2536,38 @@ class T_Join_and_Split(unittest.TestCase):
def setUp(self):
Join.debug = False
utt.seed_rng()
self.mode = theano.compile.get_default_mode().excluding(
'constant_folding'
)
self.join_op = Join
self.split_op = Split
self.make_vector_op = opt.MakeVector
self.floatX = config.floatX
self.hide_error = theano.config.mode not in ['DebugMode',
'DEBUG_MODE',
'FAST_COMPILE']
self.shared = shared
def eval_outputs_and_check_join(self, outputs):
f = theano.function([], outputs, self.mode)
topo = f.maker.env.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
variables = f()
if isinstance(variables, (tuple, list)) and len(variables) == 1:
return variables[0]
return variables
def eval_outputs_and_check_vector(self, outputs,
make_vector_op=None):
if make_vector_op is None:
make_vector_op = self.make_vector_op
f = theano.function([], outputs, self.mode)
topo = f.maker.env.toposort()
assert [True for node in topo if isinstance(node.op, make_vector_op)]
variables = f()
if isinstance(variables, (tuple, list)) and len(variables) == 1:
return variables[0]
return variables
def test_join_scalar(self):
a = as_tensor_variable(1)
......@@ -2547,37 +2579,40 @@ class T_Join_and_Split(unittest.TestCase):
self.fail()
def test_stack_mixed_type_constants(self):
# tested only on cpu as gpu support only float32
a = as_tensor_variable(1)
b = as_tensor_variable(2.0)
c = as_tensor_variable(3.0)
c = shared(numpy.asarray(3.0, dtype=self.floatX))
s = stack(a, b, c)
want = numpy.array([1, 2, 3])
self.assertTrue((eval_outputs([s]) == want).all())
out = self.eval_outputs_and_check_vector([s], opt.MakeVector)
self.assertTrue((out == want).all())
def test_stack_scalar(self):
a = as_tensor_variable(1)
b = as_tensor_variable(2)
c = as_tensor_variable(3)
a = self.shared(numpy.asarray(1., dtype=self.floatX))
b = as_tensor_variable(2.)
c = as_tensor_variable(3.)
s = stack(a, b, c)
want = numpy.array([1, 2, 3])
self.assertTrue((eval_outputs([s]) == want).all())
out = self.eval_outputs_and_check_vector([s])
self.assertTrue((out == want).all())
def test_stack_scalar_make_vector(self):
'''Test that calling stack() on scalars instantiates MakeVector,
not Join. Test that the floatX dtype stay floatX, not downcasted to int64'''
a = tensor.scalar('a')
b = tensor.scalar('b')
"""Test that calling stack() on scalars instantiates MakeVector,
not Join. Test that the floatX dtype stay floatX, not downcasted
to int64"""
a = tensor.scalar('a', dtype=self.floatX)
b = tensor.scalar('b', dtype=self.floatX)
s = stack(a, b, a, b)
f = function([a,b], s)
val = f(1,2)
f = function([a, b], s, mode=self.mode)
val = f(1, 2)
print val
self.assertTrue(numpy.all(val == [1,2,1,2]))
e = f.maker.env.toposort()
assert len([n for n in e if isinstance(n.op,opt.MakeVector)]) > 0
assert len([n for n in e if isinstance(n, Join)]) == 0
assert f.maker.env.outputs[0].dtype == config.floatX
self.assertTrue(numpy.all(val == [1, 2, 1, 2]))
topo = f.maker.env.toposort()
assert len([n for n in topo if isinstance(n.op, opt.MakeVector)]) > 0
assert len([n for n in topo if isinstance(n, self.join_op)]) == 0
assert f.maker.env.outputs[0].dtype == self.floatX
def test_stack_scalar_make_vector_dtype(self):
'''Test that calling stack() on scalars instantiates MakeVector,
......@@ -2585,12 +2620,12 @@ class T_Join_and_Split(unittest.TestCase):
a = tensor.iscalar('a')
b = tensor.lscalar('b')
s = stack(a, b, a, b)
f = function([a,b], s)
val = f(1,2)
self.assertTrue(numpy.all(val == [1,2,1,2]))
e = f.maker.env.toposort()
assert len([n for n in e if isinstance(n.op,opt.MakeVector)]) > 0
assert len([n for n in e if isinstance(n, Join)]) == 0
f = function([a, b], s, mode=self.mode)
val = f(1, 2)
self.assertTrue(numpy.all(val == [1, 2, 1, 2]))
topo = f.maker.env.toposort()
assert len([n for n in topo if isinstance(n.op, opt.MakeVector)]) > 0
assert len([n for n in topo if isinstance(n, self.join_op)]) == 0
assert f.maker.env.outputs[0].dtype == 'int64'
def test_stack_scalar_make_vector_constant(self):
......@@ -2600,92 +2635,116 @@ class T_Join_and_Split(unittest.TestCase):
b = tensor.lscalar('b')
#test when the constant is the first element.
#The first element is used in a special way
s = stack(10,a,b, numpy.int8(3))
f = function([a,b], s)
val = f(1,2)
self.assertTrue(numpy.all(val == [10,1,2,3]))
e = f.maker.env.toposort()
assert len([n for n in e if isinstance(n.op,opt.MakeVector)]) > 0
assert len([n for n in e if isinstance(n, Join)]) == 0
s = stack(10, a, b, numpy.int8(3))
f = function([a, b], s, mode=self.mode)
val = f(1, 2)
self.assertTrue(numpy.all(val == [10, 1, 2, 3]))
topo = f.maker.env.toposort()
assert len([n for n in topo if isinstance(n.op, opt.MakeVector)]) > 0
assert len([n for n in topo if isinstance(n, self.join_op)]) == 0
assert f.maker.env.outputs[0].dtype == 'int64'
def test_join_concatenate_one_element(self):
''' Fast test of concatenate as this is an alias for join.
also test that we remove the Join op if there is only 1 input'''
m = tensor.fmatrix()
c = tensor.concatenate([m])
f = theano.function(inputs=[m], outputs=[c],
mode=self.mode.including('local_join_1'))
topo = f.maker.env.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op, theano.compile.DeepCopyOp)
def test_join_vector(self):
a = as_tensor_variable(numpy.array([1, 2, 3]))
b = as_tensor_variable(numpy.array([7, 8, 9]))
a = self.shared(numpy.array([1, 2, 3], dtype=self.floatX))
b = as_tensor_variable(numpy.array([7, 8, 9], dtype=self.floatX))
s = join(0, a, b)
want = numpy.array([1, 2, 3, 7, 8, 9])
self.assertTrue((eval_outputs([s]) == want).all())
out = self.eval_outputs_and_check_join([s])
self.assertTrue((out == want).all())
def test_stack_vector(self):
a = as_tensor_variable(numpy.array([1, 2, 3]))
b = as_tensor_variable(numpy.array([7, 8, 9]))
a = self.shared(numpy.array([1, 2, 3], dtype=self.floatX))
b = as_tensor_variable(numpy.array([7, 8, 9], dtype=self.floatX))
s = stack(a, b)
want = numpy.array([[1, 2, 3],[ 7, 8, 9]])
self.assertTrue((eval_outputs([s]) == want).all())
want = numpy.array([[1, 2, 3], [7, 8, 9]])
out = self.eval_outputs_and_check_join([s])
self.assertTrue((out == want).all())
def test_join_matrix0(self):
a = as_tensor_variable(numpy.array([[1, 2, 3], [4, 5, 6]]))
b = as_tensor_variable(numpy.array([[7, 8, 9]]))
a = self.shared(numpy.array([[1, 2, 3], [4, 5, 6]],
dtype=self.floatX))
b = as_tensor_variable(numpy.array([[7, 8, 9]], dtype=self.floatX))
s = join(0, a, b)
want = numpy.array([[1, 2, 3],[4,5,6],[7, 8, 9]])
self.assertTrue((eval_outputs([s]) == want).all())
want = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
out = self.eval_outputs_and_check_join([s])
self.assertTrue((out == want).all())
def test_join_matrix1(self):
av=numpy.array([[1, 2, 3], [4, 5, 6]], dtype='float32')
bv= numpy.array([[7], [8]],dtype='float32')
a = as_tensor_variable(av)
av = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='float32')
bv = numpy.array([[7], [8]], dtype='float32')
a = self.shared(av)
b = as_tensor_variable(bv)
s = join(1, a, b)
want = numpy.array([[1, 2, 3, 7], [4, 5, 6, 8]], dtype='float32')
self.assertTrue((eval_outputs([s]) == want).all())
out = self.eval_outputs_and_check_join([s])
self.assertTrue((out == want).all())
utt.verify_grad(lambda a, b: join(1,a,b), [av, bv], eps=1.0e-4, rel_tol=1.0e-3)
# assert tensor.grad(join(1,a,b), a
utt.verify_grad(lambda a, b: join(1, a, b), [av, bv],
eps=1.0e-4, rel_tol=1.0e-3)
def test_join_matrix1_using_vertical_stack(self):
a = as_tensor_variable(numpy.array([[1, 2, 3], [4, 5, 6]]))
b = as_tensor_variable(numpy.array([[7, 8, 9]]))
c = as_tensor_variable(numpy.array([[9, 8, 7]]))
a = self.shared(numpy.array([[1, 2, 3], [4, 5, 6]], dtype=self.floatX))
b = as_tensor_variable(numpy.array([[7, 8, 9]], dtype=self.floatX))
c = as_tensor_variable(numpy.array([[9, 8, 7]], dtype=self.floatX))
s = vertical_stack(a, b, c)
want = numpy.array([[1, 2, 3],[4,5,6],[7, 8, 9], [9, 8, 7]])
self.assertTrue((eval_outputs([s]) == want).all())
want = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [9, 8, 7]])
out = self.eval_outputs_and_check_join([s])
self.assertTrue((out == want).all())
def test_join_matrix1_using_horizontal_stack(self):
av=numpy.array([[1, 2, 3], [4, 5, 6]], dtype='float32')
bv=numpy.array([[7], [8]],dtype='float32')
cv=numpy.array([[3, 2, 1], [6, 5, 4]], dtype='float32')
a = as_tensor_variable(av)
av = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='float32')
bv = numpy.array([[7], [8]], dtype='float32')
cv = numpy.array([[3, 2, 1], [6, 5, 4]], dtype='float32')
a = self.shared(av)
b = as_tensor_variable(bv)
c = as_tensor_variable(cv)
s = horizontal_stack(a, b, c)
want = numpy.array([[1, 2, 3, 7, 3, 2, 1], [4, 5, 6, 8, 6, 5, 4]], dtype='float32')
self.assertTrue((eval_outputs([s]) == want).all())
want = numpy.array([[1, 2, 3, 7, 3, 2, 1], [4, 5, 6, 8, 6, 5, 4]],
dtype='float32')
out = self.eval_outputs_and_check_join([s])
self.assertTrue((out == want).all())
utt.verify_grad(lambda a, b: join(1,a,b), [av, bv], eps=1.0e-4, rel_tol=1.0e-3)
utt.verify_grad(lambda a, b: join(1, a, b), [av, bv],
eps=1.0e-4, rel_tol=1.0e-3)
def test_join_matrixV(self):
"""variable join axis"""
v = numpy.array([[1., 2., 3.], [4., 5., 6.]])
a = as_tensor_variable(v.copy())
v = numpy.array([[1., 2., 3.], [4., 5., 6.]], dtype=self.floatX)
a = self.shared(v.copy())
b = as_tensor_variable(v.copy())
ax = lscalar()
s = join(ax, a, b)
f = inplace_func([ax], [s])
f = inplace_func([ax], [s], mode=self.mode)
topo = f.maker.env.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
want = numpy.array([[1, 2, 3], [4, 5, 6] ,[1, 2, 3], [4, 5, 6]])
want = numpy.array([[1, 2, 3], [4, 5, 6], [1, 2, 3], [4, 5, 6]])
got = f(0)
self.assertTrue((got == want).all(), (got, want))
want = numpy.array([[ 1, 2, 3, 1, 2, 3], [4, 5, 6, 4, 5, 6]])
want = numpy.array([[1, 2, 3, 1, 2, 3], [4, 5, 6, 4, 5, 6]])
got = f(1)
self.assertTrue((got == want).all(), (got, want))
utt.verify_grad(lambda a, b: join(0,a,b), [v, 2*v])
utt.verify_grad(lambda a, b: join(1,a,b), [v, 2*v])
utt.verify_grad(lambda a, b: join(0, a, b), [v, 2 * v])
utt.verify_grad(lambda a, b: join(1, a, b), [v, 2 * v])
def test_vector_len(self):
x = lscalar('x')
......@@ -2694,8 +2753,11 @@ class T_Join_and_Split(unittest.TestCase):
triple = as_tensor_variable((x, y, 9.0))
assert 3 == get_vector_length(triple)
a,b,c = triple
f = function([x,y], [b,c,a])
a, b, c = triple
f = function([x, y], [b, c, a], mode=self.mode)
topo = f.maker.env.toposort()
assert [True for node in topo if isinstance(node.op, opt.MakeVector)]
assert numpy.allclose(f(4, 5), [5, 9, 4])
def test_broadcastable_flag_assignment_mixed_otheraxes(self):
......@@ -2704,32 +2766,38 @@ class T_Join_and_Split(unittest.TestCase):
a join operation on non-join axes are True if one or
more inputs is broadcastable on that dimension.
"""
a = TensorType(dtype=config.floatX, broadcastable=[0, 0, 1])()
b = TensorType(dtype=config.floatX, broadcastable=[1, 0, 1])()
c = join(1, a, b)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 4, 1).astype(self.floatX)
b_val = rng.rand(1, 3, 1).astype(self.floatX)
a = self.shared(a_val, broadcastable=(False, False, True))
b = self.shared(b_val, broadcastable=(True, False, True))
c = self.join_op()(1, a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
# Opt can remplace the int by a Theano constant
c = join(tensor.constant(1), a, b)
c = self.join_op()(theano.tensor.constant(1), a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
# In case futur opt insert other useless stuff
c = join(tensor.cast(tensor.constant(1), dtype="int32"),
c = self.join_op()(theano.tensor.cast(theano.tensor.constant(1),
dtype="int32"),
a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
f = function([a,b], c)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 4, 1).astype(config.floatX)
b_val = rng.rand(1, 3, 1).astype(config.floatX)
f(a_val, b_val)
utt.verify_grad((lambda a,b: join(1,a,b)), [a_val, b_val], rng=rng)
f = function([], c, mode=self.mode)
topo = f.maker.env.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
f()
utt.verify_grad((lambda a, b: join(1, a, b)), [a_val, b_val], rng=rng)
# Should raise an error if dimension 0 does not match
bad_a_val = rng.rand(2, 4, 1).astype(config.floatX)
self.assertRaises(ValueError, f, bad_a_val, b_val)
a.set_value(rng.rand(2, 4, 1).astype(self.floatX))
self.assertRaises(ValueError, f)
def test_broadcastable_flag_assignment_mixed_thisaxes(self):
"""
......@@ -2737,19 +2805,30 @@ class T_Join_and_Split(unittest.TestCase):
is False when some inputs are broadcastable on that
dimension.
"""
a = TensorType(dtype=config.floatX, broadcastable=[0, 0, 1])()
b = TensorType(dtype=config.floatX, broadcastable=[1, 0, 1])()
c = join(0, a, b)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(2, 4, 1).astype(self.floatX)
b_val = rng.rand(1, 4, 1).astype(self.floatX)
a = self.shared(a_val, broadcastable=(False, False, True))
b = self.shared(b_val, broadcastable=(True, False, True))
c = self.join_op()(0, a, b)
assert not c.type.broadcastable[0]
f = function([a,b], c)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(2, 4, 1).astype(config.floatX)
b_val = rng.rand(1, 4, 1).astype(config.floatX)
f(a_val, b_val)
utt.verify_grad((lambda a,b: join(0,a,b)), [a_val, b_val], rng=rng)
f = function([], c, mode=self.mode)
topo = f.maker.env.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
f()
utt.verify_grad((lambda a, b: join(0, a, b)), [a_val, b_val], rng=rng)
# Should raise an error if b_val.shape[0] is not 1
bad_b_val = rng.rand(3, 4, 1).astype(config.floatX)
# We can't set the value|
self.assertRaises(TypeError, b.set_value,
rng.rand(3, 4, 1).astype(self.floatX))
a = TensorType(dtype=self.floatX, broadcastable=[0, 0, 1])()
b = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1])()
c = join(0, a, b)
f = function([a, b], c, mode=self.mode)
bad_b_val = rng.rand(3, 4, 1).astype(self.floatX)
self.assertRaises(TypeError, f, a_val, bad_b_val)
def test_broadcastable_flags_all_broadcastable_on_joinaxis(self):
......@@ -2758,53 +2837,57 @@ class T_Join_and_Split(unittest.TestCase):
broadcastable on the join dimension results in the output
being non-broadcastable on the join dimension.
"""
a = TensorType(dtype=config.floatX, broadcastable=[1, 0, 1])()
b = TensorType(dtype=config.floatX, broadcastable=[1, 0, 1])()
c = join(0, a, b)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 4, 1).astype(self.floatX)
b_val = rng.rand(1, 4, 1).astype(self.floatX)
a = self.shared(a_val, broadcastable=(True, False, True))
b = self.shared(b_val, broadcastable=(True, False, True))
c = self.join_op()(0, a, b)
assert not c.type.broadcastable[0]
f = function([a,b], c)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 4, 1).astype(config.floatX)
b_val = rng.rand(1, 4, 1).astype(config.floatX)
f(a_val, b_val)
utt.verify_grad((lambda a,b: join(0,a,b)), [a_val, b_val], rng=rng)
# Should raise an error if length of dimension 0 is not 1
bad_a_val = rng.rand(2, 4, 1).astype(config.floatX)
bad_b_val = rng.rand(3, 4, 1).astype(config.floatX)
self.assertRaises(TypeError, f, bad_a_val, b_val)
self.assertRaises(TypeError, f, a_val, bad_b_val)
f = function([], c, mode=self.mode)
topo = f.maker.env.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
f()
utt.verify_grad((lambda a, b: join(0, a, b)), [a_val, b_val], rng=rng)
def test_broadcastable_single_input_broadcastable_dimension(self):
"""
Test that all broadcastable flags are preserved by a
single-input join.
"""
a = TensorType(dtype=config.floatX, broadcastable=[1, 0, 1])()
b = join(0, a)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 4, 1).astype(self.floatX)
a = self.shared(a_val, broadcastable=(True, False, True))
b = self.join_op()(0, a)
assert b.type.broadcastable[0]
assert b.type.broadcastable[2]
assert not b.type.broadcastable[1]
f = function([a], b)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 4, 1).astype(config.floatX)
f(a_val)
utt.verify_grad((lambda a: join(0,a)), [a_val], rng=rng)
f = function([], b, mode=self.mode)
topo = f.maker.env.toposort()
if theano.config.mode != 'FAST_COMPILE':
assert not [True for node in topo if isinstance(node.op, self.join_op)]
f()
utt.verify_grad((lambda a: join(0, a)), [a_val], rng=rng)
# Should raise an error if length of dimension 0 is not 1
bad_a_val = rng.rand(2, 4, 1).astype(config.floatX)
self.assertRaises(TypeError, f, bad_a_val)
self.assertRaises(TypeError, a.set_value,
rng.rand(2, 4, 1).astype(self.floatX))
#self.assertRaises(TypeError, f, bad_a_val)
def test_broadcastable_flags_many_dims_and_inputs(self):
"""
Test that the right broadcastable flags get set for a join
with many inputs and many input dimensions.
"""
a = TensorType(dtype=config.floatX, broadcastable=[1, 0, 1, 0, 0, 0])()
b = TensorType(dtype=config.floatX, broadcastable=[1, 1, 1, 0, 0, 0])()
c = TensorType(dtype=config.floatX, broadcastable=[1, 0, 0, 0, 0, 0])()
d = TensorType(dtype=config.floatX, broadcastable=[1, 0, 1, 1, 0, 1])()
e = TensorType(dtype=config.floatX, broadcastable=[1, 0, 1, 0, 0, 1])()
a = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1, 0, 0, 0])()
b = TensorType(dtype=self.floatX, broadcastable=[1, 1, 1, 0, 0, 0])()
c = TensorType(dtype=self.floatX, broadcastable=[1, 0, 0, 0, 0, 0])()
d = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1, 1, 0, 1])()
e = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1, 0, 0, 1])()
f = join(0, a, b, c, d, e)
fb = f.type.broadcastable
assert not fb[0] and fb[1] and fb[2] and fb[3] and not fb[4] and fb[5]
......@@ -2815,70 +2898,77 @@ class T_Join_and_Split(unittest.TestCase):
hb = h.type.broadcastable
assert hb[0] and hb[1] and hb[2] and hb[3] and not hb[4] and hb[5]
g = function([a,b,c,d,e], f)
f = function([a, b, c, d, e], f, mode=self.mode)
topo = f.maker.env.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 1, 1, 1, 2, 1).astype(config.floatX)
b_val = rng.rand(1, 1, 1, 1, 2, 1).astype(config.floatX)
c_val = rng.rand(1, 1, 1, 1, 2, 1).astype(config.floatX)
d_val = rng.rand(1, 1, 1, 1, 2, 1).astype(config.floatX)
e_val = rng.rand(1, 1, 1, 1, 2, 1).astype(config.floatX)
g(a_val, b_val, c_val, d_val, e_val)
utt.verify_grad((lambda a,b,c,d,e: join(0,a,b,c,d,e)),
[a_val, b_val, c_val, d_val, e_val], rng=rng)
a_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX)
b_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX)
c_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX)
d_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX)
e_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX)
f(a_val, b_val, c_val, d_val, e_val)
utt.verify_grad((lambda a, b, c, d, e: join(0, a, b, c, d, e)),
[a_val, b_val, c_val, d_val, e_val], rng=rng)
# Should raise an error if length of dimension 0 is not 1
bad_val = rng.rand(2, 1, 1, 1, 2, 1).astype(config.floatX)
bad_val = rng.rand(2, 1, 1, 1, 2, 1).astype(self.floatX)
self.assertRaises(TypeError, g, bad_val, b_val, c_val, d_val, e_val)
self.assertRaises(TypeError, g, a_val, bad_val, c_val, d_val, e_val)
self.assertRaises(TypeError, g, a_val, b_val, bad_val, d_val, e_val)
self.assertRaises(TypeError, g, a_val, b_val, c_val, bad_val, e_val)
self.assertRaises(TypeError, g, a_val, b_val, c_val, d_val, bad_val)
# Should raise an error if any dimension other than 4 has length != 1
bad_a_val = rng.rand(1, 2, 1, 1, 2, 1).astype(config.floatX)
bad_b_val = rng.rand(1, 1, 1, 1, 2, 2).astype(config.floatX)
bad_c_val = rng.rand(1, 1, 2, 1, 2, 1).astype(config.floatX)
bad_d_val = rng.rand(1, 2, 1, 1, 2, 1).astype(config.floatX)
bad_e_val = rng.rand(1, 1, 1, 2, 2, 1).astype(config.floatX)
self.assertRaises(ValueError, g, bad_a_val, b_val, c_val, d_val, e_val)
self.assertRaises(ValueError, g, a_val, bad_b_val, c_val, d_val, e_val)
self.assertRaises(ValueError, g, a_val, b_val, bad_c_val, d_val, e_val)
self.assertRaises(ValueError, g, a_val, b_val, c_val, bad_d_val, e_val)
self.assertRaises(ValueError, g, a_val, b_val, c_val, d_val, bad_e_val)
bad_a_val = rng.rand(1, 2, 1, 1, 2, 1).astype(self.floatX)
bad_b_val = rng.rand(1, 1, 1, 1, 2, 2).astype(self.floatX)
bad_c_val = rng.rand(1, 1, 2, 1, 2, 1).astype(self.floatX)
bad_d_val = rng.rand(1, 2, 1, 1, 2, 1).astype(self.floatX)
bad_e_val = rng.rand(1, 1, 1, 2, 2, 1).astype(self.floatX)
self.assertRaises(ValueError, f, bad_a_val, b_val, c_val, d_val, e_val)
self.assertRaises(ValueError, f, a_val, bad_b_val, c_val, d_val, e_val)
self.assertRaises(ValueError, f, a_val, b_val, bad_c_val, d_val, e_val)
self.assertRaises(ValueError, f, a_val, b_val, c_val, bad_d_val, e_val)
self.assertRaises(ValueError, f, a_val, b_val, c_val, d_val, bad_e_val)
def test_infer_shape_join(self):
x1 = matrix()
x2 = matrix()
x3 = matrix()
def get_mat(s1,s2):
return numpy.asarray( numpy.random.uniform(size=(s1,s2)),
dtype= config.floatX)
def get_mat(s1, s2):
return numpy.asarray(numpy.random.uniform(size=(s1, s2)),
dtype=self.floatX)
# Test dim 0
z = join(0,x1,x2,x3)
f = theano.function([x1,x2,x3], z.shape)
out = f( get_mat(3,4), get_mat(2,4), get_mat(1,4))
assert (out == [6,4]).all()
z = join(0, x1, x2, x3)
f = theano.function([x1, x2, x3], z.shape, mode=self.mode)
topo = f.maker.env.toposort()
out = f(get_mat(3, 4), get_mat(2, 4), get_mat(1, 4))
assert (out == [6, 4]).all()
if theano.config.mode != 'FAST_COMPILE':
for node in f.maker.env.toposort():
assert not isinstance(node.op, tensor.Join)
# Test dim 1
z = join(1,x1,x2,x3)
f = theano.function([x1,x2,x3], z.shape)
out = f( get_mat(3,4), get_mat(3,4), get_mat(3,5))
assert (out == [3,13]).all()
z = join(1, x1, x2, x3)
f = theano.function([x1, x2, x3], z.shape, mode=self.mode)
topo = f.maker.env.toposort()
out = f( get_mat(3, 4), get_mat(3, 4), get_mat(3, 5))
assert (out == [3, 13]).all()
if theano.config.mode != 'FAST_COMPILE':
for node in f.maker.env.toposort():
assert not isinstance(node.op, tensor.Join)
# Test hide error
if theano.config.mode in ['DebugMode', 'DEBUG_MODE', 'FAST_COMPILE']:
self.assertRaises(ValueError, f, get_mat(3,4), get_mat(3,4), get_mat(2,5))
if not self.hide_error:
self.assertRaises(ValueError, f, get_mat(3, 4), get_mat(3, 4),
get_mat(2, 5))
else:
f(get_mat(3,4), get_mat(3,4), get_mat(2,5))
f(get_mat(3, 4), get_mat(3, 4), get_mat(2, 5))
class test_comparison(unittest.TestCase):
......
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