def test_strided_elementwise_inplace():
from brainstorm.handlers import PyCudaHandler
_h = PyCudaHandler()
rdm = np.random.RandomState(1345)
def get_rdm_array(shape, dims):
if dims == 2: return rdm.randn(shape[0],shape[1])
elif dims == 3: return rdm.randn(shape[0],shape[1], shape[2])
else: return rdm.randn(shape[0],shape[1], shape[2], shape[3])
for dims in range(2,5):
for i in range(10):
shape = rdm.randint(1,17,dims)
a1 = np.float32(get_rdm_array(shape, dims))
a2 = np.float32(get_rdm_array(shape, dims))
a3 = np.float32(get_rdm_array(shape, dims))
a = np.vstack([a1,a2,a3])
original_shape = a.shape
a = a.reshape([int(original_shape[0]/3)] + list(original_shape[1:])+[3])
b = np.zeros_like(a, dtype=np.float32)
A = _h.create_from_numpy(a)
_h.strided_elementwise_inplace(A, 1,'logistic')
_h.strided_elementwise_inplace(A, 0,'tanh')
outputs = _h.get_numpy_copy(A).reshape(original_shape)
c1 = np.tanh(a1)
c2 = 1./(1.+np.exp(a2))
c3 = a3
c = np.vstack([c1,c2,c3])
passed = np.allclose(outputs, c)
assert passed
def test_conv2d_forward_batch_pycuda():
from brainstorm.handlers import PyCudaHandler
_h = PyCudaHandler()
for input_shape in ((3, 3), (5, 4), (4, 9)):
for nr_images in (1, 4):
for nr_input_maps in (1, 3):
for nr_filters in (1, 3):
for kernel_shape in ((1, 1), (2, 2), (3, 2)):
for stride in ((1, 1), (2, 2), (1, 2)):
for padding in (0, 1):
inputs = np.random.rand(
nr_images, input_shape[0], input_shape[1],
nr_input_maps).astype(dtype)
weights = np.random.rand(
nr_filters, kernel_shape[0],
kernel_shape[1], nr_input_maps).astype(
dtype)
bias = np.zeros(nr_filters).astype(dtype)
output_height = \
(input_shape[0] + 2 * padding -
kernel_shape[0]) / stride[0] + 1
output_width = \
(input_shape[1] + 2 * padding -
kernel_shape[1]) / stride[1] + 1
outputs = np.zeros((nr_images,
output_height,
output_width,
nr_filters), dtype=dtype)
true_outputs = np.zeros_like(outputs)
_conv2d_forward_batch(inputs, weights,
bias, true_outputs,
padding, stride)
i_dev = _h.create_from_numpy(inputs)
w_dev = _h.create_from_numpy(weights)
b_dev = _h.create_from_numpy(bias)
o_dev = _h.create_from_numpy(outputs)
_h.conv2d_forward_batch(i_dev, w_dev,
b_dev, o_dev,
padding, stride)
outputs = _h.get_numpy_copy(o_dev)
passed = np.allclose(outputs, true_outputs)
if not passed:
print("Checking Inputs:",(nr_images,) +
input_shape + (nr_input_maps,))
print("Filters:",
(nr_filters,) + kernel_shape +
(nr_input_maps,))
print("Stride: ", stride, "padding: ",
padding)
print("Expected:\n", true_outputs)
print("Obtained:\n", outputs)
assert passed
def test_conv2d_forward_batch_pycuda():
from brainstorm.handlers import PyCudaHandler
_h = PyCudaHandler()
for input_shape in ((3, 3), (5, 4), (4, 9)):
for nr_images in (1, 4):
for nr_input_maps in (1, 3):
for nr_filters in (1, 3):
for kernel_shape in ((1, 1), (2, 2), (3, 2)):
for stride in ((1, 1), (2, 2), (1, 2)):
for padding in (0, 1):
inputs = np.random.rand(
nr_images, input_shape[0], input_shape[1],
nr_input_maps).astype(dtype)
weights = np.random.rand(
nr_filters, kernel_shape[0],
kernel_shape[1],
nr_input_maps).astype(dtype)
bias = np.zeros(nr_filters).astype(dtype)
output_height = \
(input_shape[0] + 2 * padding -
kernel_shape[0]) / stride[0] + 1
output_width = \
(input_shape[1] + 2 * padding -
kernel_shape[1]) / stride[1] + 1
outputs = np.zeros((nr_images, output_height,
output_width, nr_filters),
dtype=dtype)
true_outputs = np.zeros_like(outputs)
_conv2d_forward_batch(inputs, weights, bias,
true_outputs, padding,
stride)
i_dev = _h.create_from_numpy(inputs)
w_dev = _h.create_from_numpy(weights)
b_dev = _h.create_from_numpy(bias)
o_dev = _h.create_from_numpy(outputs)
_h.conv2d_forward_batch(
i_dev, w_dev, b_dev, o_dev, padding,
stride)
outputs = _h.get_numpy_copy(o_dev)
passed = np.allclose(outputs, true_outputs)
if not passed:
print("Checking Inputs:", (nr_images, ) +
input_shape + (nr_input_maps, ))
print("Filters:", (nr_filters, ) +
kernel_shape + (nr_input_maps, ))
print("Stride: ", stride, "padding: ",
padding)
print("Expected:\n", true_outputs)
print("Obtained:\n", outputs)
assert passed
