def test_local_response_normalization(device_id, precision):
dtype = PRECISION_TO_TYPE[precision]
dev = cntk_device(device_id)
def lrn(x, depth_radius, bias, alpha, beta, name=''):
x2 = C.square(x)
# reshape to insert a fake singleton reduction dimension after the 3th axis (channel axis). Note Python axis order and BrainScript are reversed.
x2s = C.reshape(x2, (1, C.InferredDimension), 0, 1)
W = C.constant(alpha / (2 * depth_radius + 1),
shape=(1, 2 * depth_radius + 1, 1, 1),
dtype=dtype,
name='W')
# 3D convolution with a filter that has a non 1-size only in the 3rd axis, and does not reduce since the reduction dimension is fake and 1
y = C.convolution(W, x2s)
# reshape back to remove the fake singleton reduction dimension
b = C.reshape(y, C.InferredDimension, 0, 2)
den = C.exp(beta * C.log(bias + b))
return C.element_divide(x, den)
from cntk import local_response_normalization
img_shape = (64, 32, 32)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=dtype)
x_gt = C.input_variable(shape=img_shape, dtype=dtype)
x_r = C.input_variable(shape=img_shape, dtype=dtype)
gt = lrn(x_gt, 2, 1.0, 0.0001, 0.75)
r = local_response_normalization(x_r, 2, 1.0, 0.0001, 0.75)
ss = gt.eval({x_gt: img})
sa = r.eval({x_r: img})
assert np.allclose(r.eval({x_r: img}), gt.eval({x_gt: img}))
def test_local_response_normalization(device_id, precision):
dtype = PRECISION_TO_TYPE[precision]
dev = cntk_device(device_id)
def lrn(x, depth_radius, bias, alpha, beta, name=''):
x2 = C.square(x)
# reshape to insert a fake singleton reduction dimension after the 3th axis (channel axis). Note Python axis order and BrainScript are reversed.
x2s = C.reshape(x2, (1, C.InferredDimension), 0, 1)
W = C.constant(alpha/(2*depth_radius+1), shape=(1,2*depth_radius+1,1,1), dtype=dtype, name='W')
# 3D convolution with a filter that has a non 1-size only in the 3rd axis, and does not reduce since the reduction dimension is fake and 1
y = C.convolution (W, x2s)
# reshape back to remove the fake singleton reduction dimension
b = C.reshape(y, C.InferredDimension, 0, 2)
den = C.exp(beta * C.log(bias + b))
return C.element_divide(x, den)
from cntk import local_response_normalization
img_shape = (64, 32, 32)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=dtype)
x_gt = C.input_variable(shape=img_shape, dtype=dtype)
x_r = C.input_variable(shape=img_shape, dtype=dtype)
gt = lrn(x_gt, 2, 1.0, 0.0001, 0.75)
r = local_response_normalization(x_r, 2, 1.0, 0.0001, 0.75)
ss = gt.eval({x_gt:img})
sa = r.eval({x_r:img})
assert np.allclose(r.eval({x_r:img}), gt.eval({x_gt:img}))
def test_LRN(tmpdir, dtype, device_id):
if device_id == -1 and dtype == np.float16:
pytest.skip('Test is skipped on CPU with float16 data')
device = cntk_device(device_id)
with C.default_options(dtype=dtype):
img_shape = (64, 32, 32)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=dtype)
x_r = C.input_variable(shape=img_shape, dtype=dtype)
model = C.local_response_normalization(x_r, 2, 1.0, 0.0001, 0.75)
verify_one_input(model, img, tmpdir, 'LRN_1', device)
def test_LRN(tmpdir, dtype, device_id):
if device_id == -1 and dtype == np.float16:
pytest.skip('Test is skipped on CPU with float16 data, because it uses convolution.')
device = cntk_device(device_id)
with C.default_options(dtype=dtype):
img_shape = (64, 32, 32)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=dtype)
x_r = C.input_variable(shape=img_shape, dtype=dtype)
model = C.local_response_normalization(x_r, 2, 1.0, 0.0001, 0.75)
verify_one_input(model, img, tmpdir, 'LRN_1', device)
def test_LRN(tmpdir):
img_shape = (64, 32, 32)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=np.float32)
x_r = C.input_variable(shape=img_shape, dtype=np.float32)
model = C.local_response_normalization(x_r, 2, 1.0, 0.0001, 0.75)
verify_one_input(model, img, tmpdir, 'LRN_1')
def _lrn(x):
return cntk.local_response_normalization(x, int(n - 1), k, alpha, beta)
def _lrn(x):
return cntk.local_response_normalization(x, int(n - 1), k, alpha,
beta)
def test_LRN(tmpdir):
img_shape = (64, 32, 32)
img = np.asarray(np.random.uniform(-1, 1, img_shape), dtype=np.float32)
x_r = C.input_variable(shape=img_shape, dtype=np.float32)
model = C.local_response_normalization(x_r, 2, 1.0, 0.0001, 0.75)
verify_one_input(model, img, tmpdir, 'LRN_1')
