def test_match_valid_conv():
# Tests that running FilterActs with no padding is the same as running
# theano's conv2D in valid mode
rng = np.random.RandomState([2012, 10, 9])
batch_size = 5
rows = 10
cols = 9
channels = 3
filter_rows = 4
filter_cols = filter_rows
num_filters = 16
images = shared(rng.uniform(
-1., 1., (channels, rows, cols, batch_size)).astype('float32'),
name='images')
filters = shared(rng.uniform(
-1., 1.,
(channels, filter_rows, filter_cols, num_filters)).astype('float32'),
name='filters')
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs()(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters_bc01[:, :, ::-1, ::-1]
output_conv2d = conv2d(images_bc01, filters_bc01, border_mode='valid')
output_conv2d = output_conv2d.dimshuffle(1, 2, 3, 0)
f = function([], [output, output_conv2d])
output, output_conv2d = f()
warnings.warn(
"""test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?
One possibility is that theano is numerically unstable and Alex's code is better.
Probably theano CPU 64 bit is OK but it's worth checking the others."""
)
if np.abs(output - output_conv2d).max() > 2.4e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print 'cuda-convnet shape: ', output.shape
print 'theano shape: ', output_conv2d.shape
assert False
err = np.abs(output - output_conv2d)
print 'absolute error range: ', (err.min(), err.max())
print 'mean absolute error: ', err.mean()
print 'cuda-convnet value range: ', (output.min(), output.max())
print 'theano value range: ', (output_conv2d.min(),
output_conv2d.max())
assert False
def test_match_valid_conv_strided():
# Tests that running FilterActs with stride is the same as running
# theano's conv2D in valid mode and then downsampling
rng = np.random.RandomState([2012,10,9])
batch_size = 5
rows = 9
cols = 9
channels = 3
filter_rows = 3
filter_cols = filter_rows
stride = 3
num_filters = 16
images = shared(rng.uniform(-1., 1., (channels, rows, cols,
batch_size)).astype('float32'), name='images')
filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
filter_cols, num_filters)).astype('float32'), name='filters')
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs(stride=stride)(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3,0,1,2)
filters_bc01 = filters.dimshuffle(3,0,1,2)
filters_bc01 = filters_bc01[:,:,::-1,::-1]
output_conv2d = conv2d(images_bc01, filters_bc01,
border_mode='valid', subsample=(stride, stride))
output_conv2d_orig = output_conv2d.dimshuffle(1,2,3,0)
output_conv2d = output_conv2d_orig # [:, ::stride, ::stride, :]
f = function([], [output, output_conv2d, output_conv2d_orig])
output, output_conv2d, output_conv2d_orig = f()
warnings.warn("""test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?
One possibility is that theano is numerically unstable and Alex's code is better.
Probably theano CPU 64 bit is OK but it's worth checking the others.""")
if np.abs(output - output_conv2d).max() > 2.4e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print 'cuda-convnet shape: ',output.shape
print 'theano shape: ',output_conv2d.shape
assert False
err = np.abs(output - output_conv2d)
print 'absolute error range: ', (err.min(), err.max())
print 'mean absolute error: ', err.mean()
print 'cuda-convnet value range: ', (output.min(), output.max())
print 'theano value range: ', (output_conv2d.min(), output_conv2d.max())
assert False
def test_match_valid_conv():
# Tests that running FilterActs with no padding is the same as running
# theano's conv2D in valid mode
rng = np.random.RandomState([2012, 10, 9])
batch_size = 5
rows = 10
cols = 9
channels = 3
filter_rows = 4
filter_cols = filter_rows
num_filters = 16
images = shared(rng.uniform(-1.0, 1.0, (channels, rows, cols, batch_size)).astype("float32"), name="images")
filters = shared(
rng.uniform(-1.0, 1.0, (channels, filter_rows, filter_cols, num_filters)).astype("float32"), name="filters"
)
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs()(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters_bc01[:, :, ::-1, ::-1]
output_conv2d = conv2d(images_bc01, filters_bc01, border_mode="valid")
output_conv2d = output_conv2d.dimshuffle(1, 2, 3, 0)
f = function([], [output, output_conv2d])
output, output_conv2d = f()
warnings.warn(
"""test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?
One possibility is that theano is numerically unstable and Alex's code is better.
Probably theano CPU 64 bit is OK but it's worth checking the others."""
)
if np.abs(output - output_conv2d).max() > 2.4e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print "cuda-convnet shape: ", output.shape
print "theano shape: ", output_conv2d.shape
assert False
err = np.abs(output - output_conv2d)
print "absolute error range: ", (err.min(), err.max())
print "mean absolute error: ", err.mean()
print "cuda-convnet value range: ", (output.min(), output.max())
print "theano value range: ", (output_conv2d.min(), output_conv2d.max())
assert False
def test_match_valid_conv():
# Tests that running FilterActs with no padding is the same as running
# theano's conv2D in valid mode
rng = np.random.RandomState([2012,10,9])
batch_size = 5
rows = 10
cols = 9
channels = 3
filter_rows = 4
filter_cols = filter_rows
num_filters = 16
images = shared(rng.uniform(-1., 1., (channels, rows, cols,
batch_size)).astype('float32'), name='images')
filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
filter_cols, num_filters)).astype('float32'), name='filters')
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs()(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3,0,1,2)
filters_bc01 = filters.dimshuffle(3,0,1,2)
filters_bc01 = filters_bc01[:,:,::-1,::-1]
output_conv2d = conv2d(images_bc01, filters_bc01,
border_mode='valid')
output_conv2d = output_conv2d.dimshuffle(1,2,3,0)
try:
f = function([], [output, output_conv2d])
except:
raise KnownFailureTest("cuda-convnet code depends on an unmerged theano feature.")
output, output_conv2d = f()
warnings.warn("test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?")
if np.abs(output - output_conv2d).max() > 2.4e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print 'cuda-convnet shape: ',output.shape
print 'theano shape: ',output_conv2d.shape
assert False
err = np.abs(output - output_conv2d)
print 'absolute error range: ', (err.min(), err.max())
print 'mean absolute error: ', err.mean()
print 'cuda-convnet value range: ', (output.min(), output.max())
print 'theano value range: ', (output_conv2d.min(), output_conv2d.max())
assert False
def test_match_grad_valid_conv():
# Tests that weightActs is the gradient of FilterActs
# with respect to the weights.
for partial_sum in [0, 1, 4]:
rng = np.random.RandomState([2012, 10, 9])
batch_size = 3
rows = 7
cols = 9
channels = 8
filter_rows = 4
filter_cols = filter_rows
num_filters = 16
images = shared(rng.uniform(-1., 1., (channels, rows, cols,
batch_size)).astype('float32'),
name='images')
filters = rng.uniform(-1., 1.,
(channels, filter_rows,
filter_cols, num_filters)).astype('float32')
filters = shared(filters, name='filters')
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs(partial_sum=partial_sum)(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters_bc01[:, :, ::-1, ::-1]
output_conv2d = conv2d(images_bc01, filters_bc01,
border_mode='valid')
output_conv2d = output_conv2d.dimshuffle(1, 2, 3, 0)
theano_rng = MRG_RandomStreams(2013 + 1 + 31)
coeffs = theano_rng.normal(avg=0., std=1.,
size=output_conv2d.shape, dtype='float32')
cost_conv2d = (coeffs * output_conv2d).sum()
weights_grad_conv2d = T.grad(cost_conv2d, filters)
cost = (coeffs * output).sum()
hid_acts_grad = T.grad(cost, output)
weights_grad = WeightActs(partial_sum=partial_sum)(
gpu_images,
gpu_from_host(hid_acts_grad),
as_tensor_variable((4, 4))
)[0]
weights_grad = host_from_gpu(weights_grad)
f = function([], [output, output_conv2d, weights_grad,
weights_grad_conv2d])
output, output_conv2d, weights_grad, weights_grad_conv2d = f()
if np.abs(output - output_conv2d).max() > 8e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print('cuda-convnet shape: ', output.shape)
print('theano shape: ', output_conv2d.shape)
assert False
err = np.abs(output - output_conv2d)
print('absolute error range: ', (err.min(), err.max()))
print('mean absolute error: ', err.mean())
print('cuda-convnet value range: ', (output.min(), output.max()))
print('theano value range: ', (output_conv2d.min(),
output_conv2d.max()))
assert False
warnings.warn(
"test_match_grad_valid_conv success criterion is not very strict."
" Can we verify that this is OK? One possibility is that theano"
" is numerically unstable and Alex's code is better. Probably"
" theano CPU 64 bit is OK but it's worth checking the others.")
if np.abs(weights_grad - weights_grad_conv2d).max() > 8.6e-6:
if type(weights_grad) != type(weights_grad_conv2d):
raise AssertionError("weights_grad is of type " +
str(weights_grad))
assert weights_grad.dtype == weights_grad_conv2d.dtype
if weights_grad.shape != weights_grad_conv2d.shape:
print('cuda-convnet shape: ', weights_grad.shape)
print('theano shape: ', weights_grad_conv2d.shape)
assert False
err = np.abs(weights_grad - weights_grad_conv2d)
print('absolute error range: ', (err.min(), err.max()))
print('mean absolute error: ', err.mean())
print('cuda-convnet value range: ', (weights_grad.min(),
weights_grad.max()))
print('theano value range: ', (weights_grad_conv2d.min(),
weights_grad_conv2d.max()))
assert False
def test_match_valid_conv():
# Tests that running FilterActs with no padding is the same as running
# theano's conv2D in valid mode
rng = np.random.RandomState([2012, 10, 9])
batch_size = 5
rows = 10
cols = 9
channels = 3
filter_rows = 4
filter_cols = filter_rows
num_filters = 16
images = shared(rng.uniform(
-1., 1., (channels, rows, cols, batch_size)).astype('float32'),
name='images')
filters = shared(rng.uniform(
-1., 1.,
(channels, filter_rows, filter_cols, num_filters)).astype('float32'),
name='filters')
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs()(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters_bc01[:, :, ::-1, ::-1]
output_conv2d = conv2d(images_bc01, filters_bc01, border_mode='valid')
output_conv2d = output_conv2d.dimshuffle(1, 2, 3, 0)
try:
f = function([], [output, output_conv2d])
except:
raise KnownFailureTest(
"cuda-convnet code depends on an unmerged theano feature.")
output, output_conv2d = f()
warnings.warn(
"test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?"
)
if np.abs(output - output_conv2d).max() > 2.4e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print 'cuda-convnet shape: ', output.shape
print 'theano shape: ', output_conv2d.shape
assert False
err = np.abs(output - output_conv2d)
print 'absolute error range: ', (err.min(), err.max())
print 'mean absolute error: ', err.mean()
print 'cuda-convnet value range: ', (output.min(), output.max())
print 'theano value range: ', (output_conv2d.min(),
output_conv2d.max())
assert False
