the result of side_output5 is bad about rcf HOT 9 CLOSED

How do you deal with the dilation operation in conv5? Besides, what are the learning rates in conv5?

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Thanks for your answer. I will check my code and report later.
I used tf.nn.conv2d_transpose() to do dilation operation. The weights was bilinear kernel.
TF was hard to set different learning rate for different layer, so I set the all trainable weights as the same leraning rate which are 1e-6 divide by 10 per 10k iteration.

`def upsample_filt(size):
"""
Make a 2D bilinear kernel suitable for upsampling of the given (h, w) size.
"""
factor = (size + 1) // 2
if size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = np.ogrid[:size, :size]
return (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)

def bilinear_upsample_weights(factor, number_of_classes):
"""
Create weights matrix for transposed convolution with bilinear filter
initialization.
"""
filter_size = get_kernel_size(factor)

weights = np.zeros((filter_size,
                    filter_size,
                    number_of_classes,
                    number_of_classes), dtype=np.float32)

upsample_kernel = upsample_filt(filter_size)

for i in range(number_of_classes):
    weights[:, :, i, i] = upsample_kernel

return weights

def deconv(inputs, upsample_factor):
input_shape = tf.shape(inputs)

# Calculate the ouput size of the upsampled tensor
upsampled_shape = tf.stack([input_shape[0],
                           input_shape[1] * upsample_factor,
                           input_shape[2] * upsample_factor,
                           1])

upsample_filter_np = bilinear_upsample_weights(upsample_factor, 1)
upsample_filter_tensor = tf.constant(upsample_filter_np)

# Perform the upsampling
upsampled_inputs = tf.nn.conv2d_transpose(inputs, upsample_filter_tensor,
                                          output_shape=upsampled_shape,
                                          strides=[1, upsample_factor, upsample_factor, 1])

return upsampled_inputs`

from rcf.

As I know, tf.nn.conv2d_transpose() is for deconvolution, not for dilation operation. You may misunderstand me. Maybe you can remove the dilation operation, but please note the upsampling scale.

from rcf.

Thanks. I found my biggest mistake is set the vgg conv_layer's lr = 0 in my code. After fixed, the results are better than before. Next question maybe to set the different learning_rate for different layer. After study this konwledge point, I will report my results.
I'm wondering that can we consider dilation as a resize operation use bilinear kernel.
If this opinion is true, then we set tf.nn.conv2d_transpose(trainable=False) maybe the right way.
I rear your code and found that at Deconv step, you set lr = 0, I guess that is the same as trainable=False in tensorflow operation.

from rcf.

But dilation is different from resizing.

from rcf.

OK..... I will learn the code carefully.

from rcf.

Hi,
I've read the paper 'HED' and 'Fully Convolutional Networks for Semantic Segmentation' .
I think dilation is an simple bilinear interpolation with kernel size f. In the FCN, we call it as backwards convolution (sometimes called deconvolution). Weights can be learned or not is not important. HED proved that learned weights provide no noticeable improvements.
Am I right?

from rcf.

I don't know where you got this information, but dilation is nothing with the deconvolution or bilinear interpolation at all. Dilation is just a Caffe implementation of the well-known hole algorithm. Please refer to some tutorials of deep learning for more help.

from rcf.

Sorry to waste your time...I misunderstand your mean. you mean atrous algorithm. I used
tf.nn.atrous_conv2d(bottom, filt, rate=2, padding='SAME') to do dilation operation.
hanks for your help.
Its very kind of you!

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