我试图使用找到的代码来实现LeCun本地对比度规范化,但得到的结果不正确。代码在Python中,使用theano
库。
def lecun_lcn(input, img_shape, kernel_shape, threshold=1e-4):
"""
Yann LeCun's local contrast normalization
Orginal code in Theano by: Guillaume Desjardins
"""
input = input.reshape(input.shape[0], 1, img_shape[0], img_shape[1])
X = T.matrix(dtype=theano.config.floatX)
X = X.reshape(input.shape)
filter_shape = (1, 1, kernel_shape, kernel_shape)
filters = gaussian_filter(kernel_shape).reshape(filter_shape)
convout = conv.conv2d(input=X,
filters=filters,
image_shape=(input.shape[0], 1, img_shape[0], img_shape[1]),
filter_shape=filter_shape,
border_mode='full')
# For each pixel, remove mean of 9x9 neighborhood
mid = int(np.floor(kernel_shape / 2.))
centered_X = X - convout[:, :, mid:-mid, mid:-mid]
# Scale down norm of 9x9 patch if norm is bigger than 1
sum_sqr_XX = conv.conv2d(input=centered_X ** 2,
filters=filters,
image_shape=(input.shape[0], 1, img_shape[0], img_shape[1]),
filter_shape=filter_shape,
border_mode='full')
denom = T.sqrt(sum_sqr_XX[:, :, mid:-mid, mid:-mid])
per_img_mean = denom.mean(axis=[1, 2])
divisor = T.largest(per_img_mean.dimshuffle(0, 'x', 'x', 1), denom)
divisor = T.maximum(divisor, threshold)
new_X = centered_X / divisor
new_X = new_X.dimshuffle(0, 2, 3, 1)
new_X = new_X.flatten(ndim=3)
f = theano.function([X], new_X)
return f(input)
以下是测试代码:
x_img_origin = plt.imread("..//data//Lenna.png")
x_img = plt.imread("..//data//Lenna.png")
x_img_real_result = plt.imread("..//data//Lenna_Processed.png")
x_img = x_img.reshape(1, x_img.shape[0], x_img.shape[1], x_img.shape[2])
for d in range(3):
x_img[:, :, :, d] = tools.lecun_lcn(x_img[:, :, :, d], (x_img.shape[1], x_img.shape[2]), 9)
x_img = x_img[0]
pylab.subplot(1, 3, 1); pylab.axis('off'); pylab.imshow(x_img_origin)
pylab.gray()
pylab.subplot(1, 3, 2); pylab.axis('off'); pylab.imshow(x_img)
pylab.subplot(1, 3, 3); pylab.axis('off'); pylab.imshow(x_img_real_result)
pylab.show()
结果如下:
(从左到右:原点,我的结果,预期结果)
有人能告诉我密码出了什么问题吗?
下面是我如何实现Jarrett et al(http://yann.lecun.com/exdb/publis/pdf/jarrett-iccv-09.pdf)中报告的局部对比度规范化。可以将其用作单独的层。
我用雷诺的LeNet教程中的代码测试了它,在该教程中,我将LCN应用于输入和每个卷积层,从而获得稍好的结果。
你可以在这里找到完整的代码: https://github.com/jostosh/theano_utils/blob/master/lcn.py
我认为这两行在矩阵轴上可能有一些错误:
它应该重写为:
相关问题 更多 >
编程相关推荐