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pytensor
提交 041a5d75 authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron 提交者： Pascal Lamblin
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Move the test for cpu implementations of abstractconv over to the gpuarray backend.

上级 28e9fdb0
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内嵌 并排
正在显示 包含 31 行增加 和 124 行删除
theano/sandbox/cuda/tests/test_abstractconv.py
浏览文件 @ 041a5d75
...@@ -237,124 +237,3 @@ class TestConv2d(unittest.TestCase): ...@@ -237,124 +237,3 @@ class TestConv2d(unittest.TestCase):
verify_grad=True, mode=mode, device='gpu', verify_grad=True, mode=mode, device='gpu',
provide_shape=provide_shape, border_mode=b, provide_shape=provide_shape, border_mode=b,
filter_flip=flip) filter_flip=flip)
def test_cormm_conv(self):
if not dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
mode = mode_without_gpu
for (i, f), s, b, flip, provide_shape in itertools.product(
zip(self.inputs_shapes, self.filters_shapes),
self.subsamples,
self.border_modes,
self.filter_flip,
[False, True]):
o = self.get_output_shape(i, f, s, b)
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
def test_cpu_conv(self):
if not dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
mode = mode_without_gpu.excluding('conv_gemm')
for (i, f), s, b, flip, provide_shape in itertools.product(
zip(self.inputs_shapes, self.filters_shapes),
self.subsamples,
self.border_modes,
self.filter_flip,
[False, True]):
o = self.get_output_shape(i, f, s, b)
fwd_OK = True
gradweight_OK = True
gradinput_OK = True
if not flip:
fwd_OK = False
gradweight_OK = False
gradinput_OK = False
if b not in ('valid', 'full'):
fwd_OK = False
gradweight_OK = False
gradinput_OK = False
if (not provide_shape) and (s != (1, 1)) and (b == 'full'):
gradweight_OK = False
gradinput_OK = False
if ((s[0] not in (1, 2)) or (s[1] not in (1, 2))) and (b == 'full'):
gradweight_OK = False
gradinput_OK = False
if fwd_OK:
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
else:
self.assertRaises(NotImplementedError,
self.run_fwd,
inputs_shape=i,
filters_shape=f,
subsample=s,
verify_grad=False,
mode=mode,
device='cpu',
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
if gradweight_OK:
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=False, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
else:
self.assertRaises(NotImplementedError,
self.run_gradweight,
inputs_shape=i,
filters_shape=f,
output_shape=o,
subsample=s,
verify_grad=False,
mode=mode,
device='cpu',
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
if gradinput_OK:
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=False, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
else:
self.assertRaises(NotImplementedError,
self.run_gradinput,
inputs_shape=i,
filters_shape=f,
output_shape=o,
subsample=s,
verify_grad=False,
mode=mode,
device='cpu',
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
theano/sandbox/gpuarray/tests/test_abstractconv.py
浏览文件 @ 041a5d75
...@@ -11,7 +11,7 @@ from theano.compile import shared as cpu_shared ...@@ -11,7 +11,7 @@ from theano.compile import shared as cpu_shared
from ..type import gpuarray_shared_contructor as gpu_shared from ..type import gpuarray_shared_contructor as gpu_shared
from ..dnn import dnn_available, dnn_conv, dnn_gradweight, dnn_gradinput from ..dnn import dnn_available, dnn_conv, dnn_gradweight, dnn_gradinput
from .config import mode_with_gpu, mode_without_gpu, test_ctx from .config import mode_with_gpu, mode_without_gpu, test_ctx_name
class TestConv2d(unittest.TestCase): class TestConv2d(unittest.TestCase):
...@@ -177,7 +177,7 @@ class TestConv2d(unittest.TestCase): ...@@ -177,7 +177,7 @@ class TestConv2d(unittest.TestCase):
mode=mode, eps=1) mode=mode, eps=1)
def test_dnn_conv(self): def test_dnn_conv(self):
if not dnn_available(): if not dnn_available(test_ctx_name):
raise SkipTest(cuda.dnn.dnn_available.msg) raise SkipTest(cuda.dnn.dnn_available.msg)
mode = mode_with_gpu mode = mode_with_gpu
# provide_shape is not used by the CuDNN impementation # provide_shape is not used by the CuDNN impementation
...@@ -204,8 +204,36 @@ class TestConv2d(unittest.TestCase): ...@@ -204,8 +204,36 @@ class TestConv2d(unittest.TestCase):
provide_shape=provide_shape, border_mode=b, provide_shape=provide_shape, border_mode=b,
filter_flip=flip) filter_flip=flip)
def test_cormm_conv(self):
if not dnn_available(test_ctx_name):
raise SkipTest(cuda.dnn.dnn_available.msg)
mode = mode_without_gpu
for (i, f), s, b, flip, provide_shape in itertools.product(
zip(self.inputs_shapes, self.filters_shapes),
self.subsamples,
self.border_modes,
self.filter_flip,
[False, True]):
o = self.get_output_shape(i, f, s, b)
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b,
filter_flip=flip)
def test_cpu_conv(self): def test_cpu_conv(self):
if not dnn_available(): if not dnn_available(test_ctx_name):
raise SkipTest(cuda.dnn.dnn_available.msg) raise SkipTest(cuda.dnn.dnn_available.msg)
mode = mode_without_gpu.excluding('conv_gemm') mode = mode_without_gpu.excluding('conv_gemm')
......
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