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提交 57c8ffdc authored 作者: Benjamin Scellier's avatar Benjamin Scellier
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file theano/gpuarray/tests/test_basic_ops.py

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theano/gpuarray/tests/test_basic_ops.py
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......@@ -4,7 +4,7 @@ from theano.compat import izip
from six import iteritems
import numpy
import numpy as np
import theano
import theano.tensor as T
from theano.tensor import TensorType
......@@ -29,7 +29,7 @@ from .config import mode_with_gpu, mode_without_gpu, test_ctx_name
from pygpu import gpuarray
utt.seed_rng()
rng = numpy.random.RandomState(seed=utt.fetch_seed())
rng = np.random.RandomState(seed=utt.fetch_seed())
def inplace_func(inputs, outputs, mode=None, allow_input_downcast=False,
......@@ -92,7 +92,7 @@ def makeTester(name, op, gpu_op, cases, checks=None, mode_gpu=mode_with_gpu,
for testname, inputs in iteritems(cases):
for _ in range(len(inputs)):
if type(inputs[_]) is float:
inputs[_] = numpy.asarray(inputs[_],
inputs[_] = np.asarray(inputs[_],
dtype=theano.config.floatX)
self.run_case(testname, inputs)
......@@ -177,7 +177,7 @@ def test_transfer_cpu_gpu():
a = T.fmatrix('a')
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
av = numpy.asarray(rng.rand(5, 4), dtype='float32')
av = np.asarray(rng.rand(5, 4), dtype='float32')
gv = gpuarray.array(av, context=get_context(test_ctx_name))
f = theano.function([a], GpuFromHost(test_ctx_name)(a))
......@@ -186,14 +186,14 @@ def test_transfer_cpu_gpu():
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert numpy.all(fv == av)
assert np.all(fv == av)
def test_transfer_gpu_gpu():
g = GpuArrayType(dtype='float32', broadcastable=(False, False),
context_name=test_ctx_name)()
av = numpy.asarray(rng.rand(5, 4), dtype='float32')
av = np.asarray(rng.rand(5, 4), dtype='float32')
gv = gpuarray.array(av, context=get_context(test_ctx_name))
mode = mode_with_gpu.excluding('cut_gpua_host_transfers', 'local_cut_gpua_host_gpua')
f = theano.function([g], GpuToGpu(test_ctx_name)(g), mode=mode)
......@@ -211,7 +211,7 @@ def test_transfer_strided():
a = T.fmatrix('a')
g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
av = numpy.asarray(rng.rand(5, 8), dtype='float32')
av = np.asarray(rng.rand(5, 8), dtype='float32')
gv = gpuarray.array(av, context=get_context(test_ctx_name))
av = av[:, ::2]
......@@ -223,7 +223,7 @@ def test_transfer_strided():
f = theano.function([g], host_from_gpu(g))
fv = f(gv)
assert numpy.all(fv == av)
assert np.all(fv == av)
def gpu_alloc_expected(x, *shp):
......@@ -237,16 +237,16 @@ GpuAllocTester = makeTester(
op=alloc,
gpu_op=GpuAlloc(test_ctx_name),
cases=dict(
correct01=(rand(), numpy.int32(7)),
correct01=(rand(), np.int32(7)),
# just gives a DeepCopyOp with possibly wrong results on the CPU
# correct01_bcast=(rand(1), numpy.int32(7)),
correct02=(rand(), numpy.int32(4), numpy.int32(7)),
correct12=(rand(7), numpy.int32(4), numpy.int32(7)),
correct13=(rand(7), numpy.int32(2), numpy.int32(4),
numpy.int32(7)),
correct23=(rand(4, 7), numpy.int32(2), numpy.int32(4),
numpy.int32(7)),
bad_shape12=(rand(7), numpy.int32(7), numpy.int32(5)),
# correct01_bcast=(rand(1), np.int32(7)),
correct02=(rand(), np.int32(4), np.int32(7)),
correct12=(rand(7), np.int32(4), np.int32(7)),
correct13=(rand(7), np.int32(2), np.int32(4),
np.int32(7)),
correct23=(rand(4, 7), np.int32(2), np.int32(4),
np.int32(7)),
bad_shape12=(rand(7), np.int32(7), np.int32(5)),
)
)
......@@ -282,7 +282,7 @@ def test_shape():
v = gpuarray.zeros((3, 4, 5), dtype='float32', context=get_context(test_ctx_name))
f = theano.function([x], x.shape)
topo = f.maker.fgraph.toposort()
assert numpy.all(f(v) == (3, 4, 5))
assert np.all(f(v) == (3, 4, 5))
if theano.config.mode != 'FAST_COMPILE':
assert len(topo) == 4
assert isinstance(topo[0].op, T.opt.Shape_i)
......@@ -292,7 +292,7 @@ def test_shape():
mode = mode_with_gpu.excluding("local_shape_to_shape_i")
f = theano.function([x], x.shape, mode=mode)
topo = f.maker.fgraph.toposort()
assert numpy.all(f(v) == (3, 4, 5))
assert np.all(f(v) == (3, 4, 5))
assert len(topo) == 1
assert isinstance(topo[0].op, T.Shape)
......@@ -300,7 +300,7 @@ def test_shape():
def test_gpu_contiguous():
a = T.fmatrix('a')
i = T.iscalar('i')
a_val = numpy.asarray(numpy.random.rand(4, 5), dtype='float32')
a_val = np.asarray(np.random.rand(4, 5), dtype='float32')
# The reshape is needed otherwise we make the subtensor on the CPU
# to transfer less data.
f = theano.function([a, i], gpu_contiguous(a.reshape((5, 4))[::i]),
......@@ -353,22 +353,22 @@ class G_Join_and_Split(test_basic.T_Join_and_Split):
self.shared = gpuarray_shared_constructor
def test_gpusplit_opt(self):
rng = numpy.random.RandomState(seed=utt.fetch_seed())
rng = np.random.RandomState(seed=utt.fetch_seed())
m = self.shared(rng.rand(4, 6).astype(self.floatX))
o = T.Split(2)(m, 0, [2, 2])
f = theano.function([], o, mode=self.mode)
assert any([isinstance(node.op, self.split_op_class)
for node in f.maker.fgraph.toposort()])
o1, o2 = f()
assert numpy.allclose(o1, m.get_value(borrow=True)[:2])
assert numpy.allclose(o2, m.get_value(borrow=True)[2:])
assert np.allclose(o1, m.get_value(borrow=True)[:2])
assert np.allclose(o2, m.get_value(borrow=True)[2:])
def test_gpujoin_gpualloc():
a = T.fmatrix('a')
a_val = numpy.asarray(numpy.random.rand(4, 5), dtype='float32')
a_val = np.asarray(np.random.rand(4, 5), dtype='float32')
b = T.fmatrix('b')
b_val = numpy.asarray(numpy.random.rand(3, 5), dtype='float32')
b_val = np.asarray(np.random.rand(3, 5), dtype='float32')
f = theano.function([a, b], T.join(0, T.zeros_like(a), T.ones_like(b)) + 4,
mode=mode_without_gpu)
......@@ -387,7 +387,7 @@ def test_gpujoin_gpualloc():
for node in f_gpu2.maker.fgraph.toposort()]) == 2
assert sum([node.op == gpu_join
for node in f_gpu2.maker.fgraph.toposort()]) == 1
assert numpy.allclose(f(a_val, b_val), f_gpu2(a_val, b_val))
assert np.allclose(f(a_val, b_val), f_gpu2(a_val, b_val))
def test_gpueye():
......@@ -401,14 +401,14 @@ def test_gpueye():
M = N
N_symb = T.iscalar()
M_symb = T.iscalar()
k_symb = numpy.asarray(0)
k_symb = np.asarray(0)
out = T.eye(N_symb, M_symb, k_symb, dtype=dtype)
f = theano.function([N_symb, M_symb],
T.stack(out),
mode=mode_with_gpu)
result = numpy.asarray(f(N, M))
assert numpy.allclose(result, numpy.eye(N, M_, dtype=dtype))
assert result.dtype == numpy.dtype(dtype)
result = np.asarray(f(N, M))
assert np.allclose(result, np.eye(N, M_, dtype=dtype))
assert result.dtype == np.dtype(dtype)
assert any([isinstance(node.op, GpuEye)
for node in f.maker.fgraph.toposort()])
......@@ -429,8 +429,8 @@ def test_hostfromgpu_shape_i():
'specialize')
a = T.fmatrix('a')
ca = theano.gpuarray.type.GpuArrayType('float32', (False, False))()
av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')
cv = gpuarray.asarray(numpy.random.rand(5, 4),
av = np.asarray(np.random.rand(5, 4), dtype='float32')
cv = gpuarray.asarray(np.random.rand(5, 4),
dtype='float32',
context=get_context(test_ctx_name))
......@@ -464,7 +464,7 @@ def test_Gpujoin_inplace():
non-empty element.
"""
s = T.lscalar()
data = numpy.array([3, 4, 5], dtype=theano.config.floatX)
data = np.array([3, 4, 5], dtype=theano.config.floatX)
x = gpuarray_shared_constructor(data, borrow=True)
z = T.zeros((s,))
......@@ -473,4 +473,4 @@ def test_Gpujoin_inplace():
f = theano.function([s], theano.Out(c, borrow=True))
assert x.get_value(borrow=True, return_internal_type=True) is f(0)
assert numpy.allclose(f(0), [3, 4, 5])
assert np.allclose(f(0), [3, 4, 5])
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