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提交 49ffd9b8 authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Adapt the tests for gpuarray.

上级 ccd25be8
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正在显示 包含 36 行增加 和 51 行删除
theano/sandbox/gpuarray/tests/test_dnn.py
浏览文件 @ 49ffd9b8
......@@ -11,26 +11,16 @@ import theano.tests.unittest_tools as utt
from theano.sandbox.neighbours import images2neibs
from theano.tensor.signal.downsample import max_pool_2d
from theano.tensor.signal.downsample import DownsampleFactorMaxGrad
import theano.sandbox.cuda.dnn as dnn
from theano.sandbox.cuda.basic_ops import GpuAllocEmpty, gpu_alloc_empty
# Skip test if cuda_ndarray is not available.
import theano.sandbox.cuda as cuda
if not cuda.cuda_available:
raise SkipTest('Optional package cuda disabled')
from .. import dnn
from ..basic_ops import GpuAllocEmpty
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
mode_without_gpu = theano.compile.mode.get_mode(
'FAST_RUN').excluding('gpu')
else:
mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')
from .test_basic_ops import mode_with_gpu, mode_without_gpu
def test_dnn_conv_desc_merge():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img_shp = T.as_tensor_variable(
numpy.asarray([2, 1, 8, 8]).astype('int64'))
kern_shp = T.as_tensor_variable(
......@@ -51,14 +41,9 @@ def test_dnn_conv_desc_merge():
def test_dnn_conv_merge():
"""This test that we merge correctly multiple dnn_conv.
This can is more difficult due to GpuEmptyAlloc that aren't
merged.
"""
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
# This test that we merge correctly multiple dnn_conv.
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img_shp = [2, 5, 6, 8]
kern_shp = [3, 5, 5, 6]
img = T.ftensor4('img')
......@@ -96,8 +81,8 @@ def test_dnn_conv_inplace():
GpuAllocEmpty get merged together.
"""
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img_shp = [2, 5, 6, 8]
kern_shp = [3, 5, 5, 6]
img = T.ftensor4('img')
......@@ -121,7 +106,7 @@ def test_dnn_conv_inplace():
assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
# Test grad w op
out = gpu_alloc_empty(*kern.shape)
out = GpuAllocEmpty(kern.dtype)(*kern.shape)
o1 = dnn.GpuDnnConvGradW()(img, kern, out, desc1)
o2 = dnn.GpuDnnConvGradW()(img, kern, out, desc2)
f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
......@@ -132,7 +117,7 @@ def test_dnn_conv_inplace():
assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
# Test grad i op
out = gpu_alloc_empty(*img.shape)
out = GpuAllocEmpty(img.dtype)(*img.shape)
o1 = dnn.GpuDnnConvGradI()(img, kern, out, desc1)
o2 = dnn.GpuDnnConvGradI()(img, kern, out, desc2)
f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
......@@ -179,8 +164,8 @@ def pool_2d_i2n(input, ds=(2, 2), strides=None,
def test_pooling():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
x = T.ftensor4()
for mode, pad in product(('max', 'average_inc_pad', 'average_exc_pad'),
......@@ -189,7 +174,7 @@ def test_pooling():
func = T.max
else:
func = T.mean
if pad != (0, 0) and cuda.dnn.version() == -1:
if pad != (0, 0) and dnn.version() == -1:
continue
if pad != (0, 0) and func is T.mean:
......@@ -213,10 +198,10 @@ def test_pooling():
mode_without_gpu2 = mode_without_gpu.including()
mode_without_gpu2.check_isfinite = False
f1 = theano.function([x], out1, mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
assert any([isinstance(node.op, dnn.GpuDnnPool)
for node in f1.maker.fgraph.apply_nodes])
f2 = theano.function([x], out2, mode=mode_without_gpu2)
assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
assert not any([isinstance(node.op, dnn.GpuDnnPool)
for node in f2.maker.fgraph.apply_nodes])
for shp in [(1, 10, 100, 100),
(1, 3, 99, 99),
......@@ -250,12 +235,12 @@ def test_pooling():
# Confirm that the opt would have inserted it.
fg = theano.function([x], theano.grad(fn(x).sum(), x),
mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
assert any([isinstance(node.op, dnn.GpuDnnPoolGrad)
for node in fg.maker.fgraph.toposort()])
# Test the GPU grad + GPU implementation
def fn(x):
dnn_op = cuda.dnn.dnn_pool(
dnn_op = dnn.dnn_pool(
x, ws=(ws, ws),
stride=(stride, stride),
pad=pad,
......@@ -268,7 +253,7 @@ def test_pooling():
# Confirm that we get the good op.
fg = theano.function([x], theano.grad(fn(x).sum(), x),
mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
assert any([isinstance(node.op, dnn.GpuDnnPoolGrad)
for node in fg.maker.fgraph.toposort()])
g_out = fg(data)
......@@ -285,8 +270,8 @@ def test_pooling():
def test_pooling_opt():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
x = T.ftensor4()
......@@ -295,7 +280,7 @@ def test_pooling_opt():
max_pool_2d(x, ds=(2, 2), ignore_border=True),
mode=mode_with_gpu)
assert any([isinstance(n.op, cuda.dnn.GpuDnnPool)
assert any([isinstance(n.op, dnn.GpuDnnPool)
for n in f.maker.fgraph.toposort()])
f = theano.function(
......@@ -303,7 +288,7 @@ def test_pooling_opt():
T.grad(max_pool_2d(x, ds=(2, 2), ignore_border=True).sum(), x),
mode=mode_with_gpu.including("cudnn"))
assert any([isinstance(n.op, cuda.dnn.GpuDnnPoolGrad)
assert any([isinstance(n.op, dnn.GpuDnnPoolGrad)
for n in f.maker.fgraph.toposort()])
......@@ -327,7 +312,7 @@ def test_dnn_tag():
max_pool_2d(x, ds=(2, 2), ignore_border=True),
mode=mode_with_gpu.including("cudnn"))
except (AssertionError, RuntimeError):
assert not cuda.dnn.dnn_available()
assert not dnn.dnn_available()
raised = True
finally:
theano.config.on_opt_error = old
......@@ -336,8 +321,8 @@ def test_dnn_tag():
logging.getLogger('theano').addHandler(theano.logging_default_handler)
if not raised:
assert cuda.dnn.dnn_available()
assert any([isinstance(n.op, cuda.dnn.GpuDnnPool)
assert dnn.dnn_available()
assert any([isinstance(n.op, dnn.GpuDnnPool)
for n in f.maker.fgraph.toposort()])
......@@ -579,8 +564,8 @@ class TestDnnInferShapes(utt.InferShapeTester):
# this has been a problem in the past
def test_dnn_conv_border_mode():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4()
kern = T.ftensor4()
......@@ -591,8 +576,8 @@ def test_dnn_conv_border_mode():
def test_dnn_conv_alpha_output_merge():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4()
kern = T.ftensor4()
out = T.ftensor4()
......@@ -615,7 +600,7 @@ def test_dnn_conv_alpha_output_merge():
lr = numpy.asarray(0.05, dtype='float32')
if cuda.dnn.version() == -1:
if dnn.version() == -1:
# Can't merge alpha with cudnn v1
fr = conv + out
wr = kern + gw
......@@ -660,7 +645,7 @@ def test_dnn_conv_alpha_output_merge():
def test_dnn_conv_grad():
if not cuda.dnn.dnn_available() or dnn.version() == -1:
if not dnn.dnn_available() or dnn.version() == -1:
raise SkipTest('alpha != 1.0 not supported in cudnn v1')
b = 1
......@@ -698,6 +683,6 @@ def test_dnn_conv_grad():
def test_version():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
assert isinstance(cuda.dnn.version(), (int, tuple))
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
assert isinstance(dnn.version(), (int, tuple))
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