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提交 a3e94b40 authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Test more cases, make sure unimplemented raise error

上级 62c86dcb
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正在显示 包含 103 行增加 和 37 行删除
theano/tensor/nnet/tests/test_abstractconv.py
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...@@ -194,77 +194,143 @@ class TestConv2d(unittest.TestCase): ...@@ -194,77 +194,143 @@ class TestConv2d(unittest.TestCase):
if not dnn_available(): if not dnn_available():
return return
mode=mode_with_gpu mode=mode_with_gpu
# provide_shape is not used by the CuDNN impementation
inputs_shapes = self.inputs_shapes provide_shape = False
filters_shapes = self.filters_shapes
subsamples = self.subsamples for (i, f), s, b, flip in itertools.product(
border_modes = self.border_modes zip(self.inputs_shapes, self.filters_shapes),
for i, f in zip(inputs_shapes[0:1], filters_shapes[0:1]): self.subsamples,
for s in subsamples: self.border_modes,
for b in border_modes: self.filters_flip):
o = self.get_output_shape(i, f, s, b) o = self.get_output_shape(i, f, s, b)
for provide_shape in [False, True]:
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s, self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
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,
filters_flip=flip)
self.run_gradweight(inputs_shape=i, filters_shape=f, self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, output_shape=o, subsample=s,
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,
filters_flip=flip)
self.run_gradinput(inputs_shape=i, filters_shape=f, self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, output_shape=o, subsample=s,
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,
filters_flip=flip)
def test_cormm_conv(self): def test_cormm_conv(self):
mode = mode_with_gpu.excluding('cudnn') mode = mode_with_gpu.excluding('cudnn')
inputs_shapes = self.inputs_shapes for (i, f), s, b, flip, provide_shape in itertools.product(
filters_shapes = self.filters_shapes zip(self.inputs_shapes, self.filters_shapes),
subsamples = self.subsamples self.subsamples,
border_modes = self.border_modes self.border_modes,
for i, f in zip(inputs_shapes, filters_shapes): self.filters_flip,
for s in subsamples: [False, True]):
for b in border_modes:
o = self.get_output_shape(i, f, s, b) o = self.get_output_shape(i, f, s, b)
for provide_shape in [False, True]:
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s, self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
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,
filters_flip=flip)
self.run_gradweight(inputs_shape=i, filters_shape=f, self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, output_shape=o, subsample=s,
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,
filters_flip=flip)
self.run_gradinput(inputs_shape=i, filters_shape=f, self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, output_shape=o, subsample=s,
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,
filters_flip=flip)
def test_cpu_conv(self):
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.filters_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
def test_cpu_conv(self): if b not in ('valid', 'full'):
mode = mode_without_gpu fwd_OK = False
gradweight_OK = False
gradinput_OK = False
inputs_shapes = self.inputs_shapes if (not provide_shape) and (s != (1, 1)) and (b == 'full'):
filters_shapes = self.filters_shapes gradweight_OK = False
subsamples = self.subsamples gradinput_OK = False
border_modes = self.border_modes[:2] # only valid and full are supported
for i, f in zip(inputs_shapes, filters_shapes): if ((s[0] not in (1, 2)) or (s[1] not in (1, 2))) and (b == 'full'):
for s in subsamples: gradweight_OK = False
for b in border_modes: gradinput_OK = False
o = self.get_output_shape(i, f, s, b)
for provide_shape in [False, True]: if fwd_OK:
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s, self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, mode=mode, device='cpu', verify_grad=True, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b) provide_shape=provide_shape, border_mode=b,
filters_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,
filters_flip=flip)
if gradweight_OK:
self.run_gradweight(inputs_shape=i, filters_shape=f, self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, output_shape=o, subsample=s,
verify_grad=False, mode=mode, device='cpu', verify_grad=False, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b) provide_shape=provide_shape, border_mode=b,
filters_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,
filters_flip=flip)
if gradinput_OK:
self.run_gradinput(inputs_shape=i, filters_shape=f, self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, output_shape=o, subsample=s,
verify_grad=False, mode=mode, device='cpu', verify_grad=False, mode=mode, device='cpu',
provide_shape=provide_shape, border_mode=b) provide_shape=provide_shape, border_mode=b,
filters_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,
filters_flip=flip)
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