def test_gradient(self):
for kernel_h in range(3, 5):
for kernel_w in range(3, 5):
channels = 12
height = 3
width = 5
bottom = Array.zeros((5, channels, height, width), np.int32)
bottom_diff = Array.zeros_like(bottom)
for n in range(5):
for c in range(channels):
bottom[n, c] = Array.array([[1, 2, 5, 2, 3],
[9, 4, 1, 4, 8],
[1, 2, 5, 2,
3]]).astype(np.int32)
param = self.layer[5]
param.pooling_param.kernel_h = kernel_h
param.pooling_param.kernel_w = kernel_w
param.pooling_param.stride = 2
param.pooling_param.pad = 1
layer = PoolingLayer(param)
top_shape = layer.get_top_shape(bottom)
top = Array.zeros(top_shape, np.int32)
top_diff = Array.zeros_like(top)
checker = GradientChecker(1e-4, 1e-2)
checker.check_gradient_exhaustive(layer, bottom, bottom_diff,
top, top_diff)
def test_gradient(self):
for kernel_h in range(3, 5):
for kernel_w in range(3, 5):
channels = 12
height = 3
width = 5
bottom = Array.zeros((5, channels, height, width), np.int32)
bottom_diff = Array.zeros_like(bottom)
for n in range(5):
for c in range(channels):
bottom[n, c] = Array.array(
[[1, 2, 5, 2, 3],
[9, 4, 1, 4, 8],
[1, 2, 5, 2, 3]]).astype(np.int32)
param = self.layer[5]
param.pooling_param.kernel_h = kernel_h
param.pooling_param.kernel_w = kernel_w
param.pooling_param.stride = 2
param.pooling_param.pad = 1
layer = PoolingLayer(param)
top_shape = layer.get_top_shape(bottom)
top = Array.zeros(top_shape, np.int32)
top_diff = Array.zeros_like(top)
checker = GradientChecker(1e-4, 1e-2)
checker.check_gradient_exhaustive(layer, bottom, bottom_diff,
top, top_diff)
def test_simple(self):
channels = 12
height = 3
width = 5
bottom = Array.zeros((5, channels, height, width), np.int32)
for n in range(5):
for c in range(channels):
bottom[n, c] = Array.array([[1, 2, 5, 2, 3], [9, 4, 1, 4, 8],
[1, 2, 5, 2, 3]]).astype(np.int32)
param = self.layer[5]
param.pooling_param.kernel_size = 2
param.pooling_param.stride = 1
layer = PoolingLayer(param)
actual_shape = layer.get_top_shape(bottom)
actual = Array.zeros(actual_shape, np.int32)
layer.setup(bottom, actual)
layer.forward(bottom, actual)
for n in range(5):
for c in range(channels):
np.testing.assert_array_equal(
actual[n, c],
np.array([[9, 5, 5, 8], [9, 5, 5, 8]]).astype(np.int32))
bottom = Array.zeros_like(bottom)
for n in range(5):
for c in range(channels):
actual[n, c] = Array.array([[1, 1, 1, 1],
[1, 1, 1, 1]]).astype(np.int32)
layer.backward(bottom, actual)
for n in range(5):
for c in range(channels):
np.testing.assert_array_equal(
bottom[n, c],
np.array([[0, 0, 2, 0, 0], [2, 0, 0, 0, 2],
[0, 0, 2, 0, 0]]).astype(np.int32))
def test_simple(self):
bottom = Array.rand(3, 8, 32, 32).astype(np.float32)
actual = Array.zeros_like(bottom)
layer = LRNLayer(self.layer[4])
param = layer.layer_param.lrn_param
alpha = param.alpha
size = param.local_size
beta = param.beta
layer.setup(bottom, actual)
layer.forward(bottom, actual)
expected = Array.zeros_like(bottom)
for n in range(bottom.shape[0]):
for c in range(bottom.shape[1]):
for h in range(bottom.shape[2]):
for w in range(bottom.shape[3]):
c_start = c - (size - 1) // 2
c_end = min(c_start + size, bottom.shape[1])
scale = 1
for i in range(c_start, c_end):
value = bottom[n, i, h, w]
scale += value * value * alpha / size
expected = bottom[n, c, h, w] / pow(scale, beta)
self.assertTrue(
abs(actual[n, c, h, w] - expected) < 1e-4)
def test_simple(self):
channels = 12
height = 3
width = 5
bottom = Array.zeros((5, channels, height, width), np.int32)
for n in range(5):
for c in range(channels):
bottom[n, c] = Array.array(
[[1, 2, 5, 2, 3],
[9, 4, 1, 4, 8],
[1, 2, 5, 2, 3]]).astype(np.int32)
param = self.layer[5]
param.pooling_param.kernel_size = 2
param.pooling_param.stride = 1
layer = PoolingLayer(param)
actual_shape = layer.get_top_shape(bottom)
actual = Array.zeros(actual_shape, np.int32)
layer.setup(bottom, actual)
layer.forward(bottom, actual)
for n in range(5):
for c in range(channels):
np.testing.assert_array_equal(
actual[n, c],
np.array([
[9, 5, 5, 8],
[9, 5, 5, 8]
]).astype(np.int32))
bottom = Array.zeros_like(bottom)
for n in range(5):
for c in range(channels):
actual[n, c] = Array.array(
[[1, 1, 1, 1],
[1, 1, 1, 1]]).astype(np.int32)
layer.backward(bottom, actual)
for n in range(5):
for c in range(channels):
np.testing.assert_array_equal(
bottom[n, c],
np.array([[0, 0, 2, 0, 0],
[2, 0, 0, 0, 2],
[0, 0, 2, 0, 0]]).astype(np.int32))
def setup(self, bottom, top):
self.scale = Array.zeros_like(bottom)
def setup(self, bottom_data, bottom_label, top):
self.prob = Array.zeros_like(bottom_data)
self.softmax_layer.setup(bottom_data, self.prob)
def setup(self, bottom_data, bottom_label, top):
self.prob = Array.zeros_like(bottom_data)
self.softmax_layer.setup(bottom_data, self.prob)
