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I am trying to create a fixed convolutional kernel that applies a blur filter to each channel separately:
# inputs = <previous layer>
kernel_weights = np.array([[1, 2, 1],
[2, 4, 2],
[1, 2, 1]])
kernel_weights = kernel_weights / np.sum(kernel_weights)
kernel_weights = np.reshape(kernel_weights, (*kernel_weights.shape, 1, 1))
kernel_weights = np.tile(kernel_weights, (1, 1, inputs.get_shape().as_list()[3], 1))
return tf.nn.depthwise_conv2d_native(max_pool, kernel_weights, strides=(1, 2, 2, 1), padding="SAME")
I'm currently under the impression that this convolutional kernel can/will change during training - how can I prevent it from doing so?
Would it be sufficient to wrap it in a tf.constant before passing it to the conv2d layer? Like so:
kernel_weights = tf.constant(kernel_weights)
Thanks!
As GPhilo's comment correctly identifies: just passing the kernel as a tf.constant (or plain numpy array) works, verified by plotting the histogram for the kernel in tensorboard.
From the accepted answer in this question,
given the following
input and kernel matrices, the output of tf.nn.conv2d is
[[14 6]
[6 12]]
which makes sense. However, when I make the input and kernel matrices have 3-channels each (by repeating each original matrix), and run the same code:
# the previous input
i_grey = np.array([
[4, 3, 1, 0],
[2, 1, 0, 1],
[1, 2, 4, 1],
[3, 1, 0, 2]
])
# copy to 3-dimensions
i_rgb = np.repeat( np.expand_dims(i_grey, axis=0), 3, axis=0 )
# convert to tensor
i_rgb = tf.constant(i_rgb, dtype=tf.float32)
# make kernel depth match input; same process as input
k = np.array([
[1, 0, 1],
[2, 1, 0],
[0, 0, 1]
])
k_rgb = np.repeat( np.expand_dims(k, axis=0), 3, axis=0 )
# convert to tensor
k_rgb = tf.constant(k_rgb, dtype=tf.float32)
here's what my input and kernel matrices look like at this point
# reshape input to format: [batch, in_height, in_width, in_channels]
image_rgb = tf.reshape(i_rgb, [1, 4, 4, 3])
# reshape kernel to format: [filter_height, filter_width, in_channels, out_channels]
kernel_rgb = tf.reshape(k_rgb, [3, 3, 3, 1])
conv_rgb = tf.squeeze( tf.nn.conv2d(image_rgb, kernel_rgb, [1,1,1,1], "VALID") )
with tf.Session() as sess:
conv_result = sess.run(conv_rgb)
print(conv_result)
I get the final output:
[[35. 15.]
[35. 26.]]
But I was expecting the original output*3:
[[42. 18.]
[18. 36.]]
because from my understanding, each channel of the kernel is convolved with each channel of the input, and the resultant matrices are summed to get the final output.
Am I missing something from this process or the tensorflow implementation?
Reshape is a tricky function. It will produce you the shape you want, but can easily ground things together. In cases like yours, one should avoid using reshape by all means.
In that particular case instead, it is better to duplicate the arrays along the new axis. When using [batch, in_height, in_width, in_channels] channels is the last dimension and it should be used in repeat() function. Next code should better reflect the logic behind it:
i_grey = np.expand_dims(i_grey, axis=0) # add batch dim
i_grey = np.expand_dims(i_grey, axis=3) # add channel dim
i_rgb = np.repeat(i_grey, 3, axis=3 ) # duplicate along channels dim
And likewise with filters:
k = np.expand_dims(k, axis=2) # input channels dim
k = np.expand_dims(k, axis=3) # output channels dim
k_rgb = np.repeat(k, 3, axis=2) # duplicate along the input channels dim
In numpy, it could be easily done as
>>> img
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]], dtype=int32)
>>> img[img>5] = [1,2,3,4]
>>> img
array([[1, 2, 3],
[4, 5, 1],
[2, 3, 4]], dtype=int32)
However, there seems not exist similar operation in tensorflow.
You can never assign a value to a tensor in tensorflow as the change in tensor value is not traceable by backpropagation, but you can still get another tensor from origin tensor, here is a solution
import tensorflow as tf
tf.enable_eager_execution()
img = tf.constant(list(range(1, 10)), shape=[3, 3])
replace_mask = img > 5
keep_mask = tf.logical_not(replace_mask)
keep = tf.boolean_mask(img, keep_mask)
keep_index = tf.where(keep_mask)
replace_index = tf.where(replace_mask)
replace = tf.random_uniform((tf.shape(replace_index)[0],), 0, 10, tf.int32)
updates = tf.concat([keep, replace], axis=0)
indices = tf.concat([keep_index, replace_index], axis=0)
result = tf.scatter_nd(tf.cast(indices, tf.int32), updates, shape=tf.shape(img))
Actually there is a way to achieve this. Very similar to #Jie.Zhou's answer, you can replace tf.constant with tf.Variable, then replace tf.scatter_nd with tf.scatter_nd_update
I want to take use of tensorflow to implement fully convolutional network. There is a function
tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding, name),
which could be used to take bilinear upsampling. However, I am confused as to how to use it? The input is an image with a single channel, and the output is also an image with a single channel, whose size is two times the one of the input.
I tried to use the function as follows, but got an IndexError: list index out of range:
with tf.name_scope('deconv') as scope:
deconv = tf.nn.conv2d_transpose(conv6, [3, 3, 1, 1],
[1, 26, 20, 1], 2, padding='SAME', name=None)
Got it! (assuming input_size = [1, 13, 10,1])
with tf.name_scope('deconv') as scope:
deconv = tf.nn.conv2d_transpose(input_layer, [3, 3, 1, 1],
[1, 26, 20, 1], [1, 2, 2, 1], padding='SAME', name=None)
I'm getting this error message when using conv2d_transpose:
W tensorflow/core/common_runtime/executor.cc:1102] 0x7fc81f0d6250 Compute status: Invalid argument: Conv2DBackpropInput: Number of rows of out_backprop doesn't match computed: actual = 32, computed = 4
[[Node: generator/g_h1/conv2d_transpose = Conv2DBackpropInput[T=DT_FLOAT, padding="SAME", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/cpu:0"](generator/g_h1/conv2d_transpose/output_shape, generator/g_h1/w/read, _recv_l_0)]]
However, it occurs after the graph is built while compiling the loss function (Adam). Any ideas on what would cause this? I suspect it's related to the input dimensions but I'm not sure exactly why.
Full error: https://gist.github.com/jimfleming/75d88e888044615dd6e3
Relevant code:
# l shape: [batch_size, 32, 32, 4]
output_shape = [self.batch_size, 8, 8, 128]
filter_shape = [7, 7, 128, l.get_shape()[-1]]
strides = [1, 2, 2, 1]
with tf.variable_scope("g_h1"):
w = tf.get_variable('w', filter_shape, initializer=tf.random_normal_initializer(stddev=0.02))
h1 = tf.nn.conv2d_transpose(l, w, output_shape=output_shape, strides=strides, padding='SAME')
h1 = tf.nn.relu(h1)
output_shape = [self.batch_size, 16, 16, 128]
filter_shape = [7, 7, 128, h1.get_shape()[-1]]
strides = [1, 2, 2, 1]
with tf.variable_scope("g_h2"):
w = tf.get_variable('w', filter_shape, initializer=tf.random_normal_initializer(stddev=0.02))
h2 = tf.nn.conv2d_transpose(h1, w,output_shape=output_shape, strides=strides, padding='SAME')
h2 = tf.nn.relu(h2)
output_shape = [self.batch_size, 32, 32, 3]
filter_shape = [5, 5, 3, h2.get_shape()[-1]]
strides = [1, 2, 2, 1]
with tf.variable_scope("g_h3"):
w = tf.get_variable('w', filter_shape, initializer=tf.random_normal_initializer(stddev=0.02))
h3 = tf.nn.conv2d_transpose(h2, w,output_shape=output_shape, strides=strides, padding='SAME')
h3 = tf.nn.tanh(h3)
Thanks for the question! You're exactly right---the problem is that the input and output dimensions being passed to tf.nn.conv2d_transpose don't agree. (The error may be detected when computing gradients, but the gradient computation isn't the problem.)
Let's look at just the first part of your code, and simplify it a little bit:
sess = tf.Session()
batch_size = 3
output_shape = [batch_size, 8, 8, 128]
strides = [1, 2, 2, 1]
l = tf.constant(0.1, shape=[batch_size, 32, 32, 4])
w = tf.constant(0.1, shape=[7, 7, 128, 4])
h1 = tf.nn.conv2d_transpose(l, w, output_shape=output_shape, strides=strides, padding='SAME')
print sess.run(h1)
I replaced the variables with constants --- it's easier to see what's going on.
If you try to run this code, you get a similar error:
InvalidArgumentError: Conv2DCustomBackpropInput: Size of out_backprop doesn't match computed: actual = 32, computed = 4
[[Node: conv2d_transpose_6 = Conv2DBackpropInput[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/cpu:0"](conv2d_transpose_6/output_shape, Const_25, Const_24)]]
Now, the error is a little misleading --- it talks about the 'out_backprop' argument to 'Conv2DCustomBackpropInput'. The key is that tf.nn.conv2d_transpose is actually just the gradient of tf.nn.conv2d, so Tensorflow uses the same code internally (Conv2DCustomBackpropInput) to compute the gradient of tf.nn.conv2d and to compute tf.nn.conv2d_transpose.
The error means that the 'output_shape' you requested is not possible, given the shapes of 'l' and 'w'.
Since tf.nn.conv2d_transpose is the backward (gradient) counterpart of tf.nn.conv2d, one way to see what the correct shapes should be is to use the corresponding forward operation:
output = tf.constant(0.1, shape=output_shape)
expected_l = tf.nn.conv2d(output, w, strides=strides, padding='SAME')
print expected_l.get_shape()
# Prints (3, 4, 4, 4)
That is, in the forward direction, if you provided a tensor of shape 'output_shape', you would get out a tensor of shape (3, 4, 4, 4).
So one way to fix the problem is to change the shape of 'l' to (3, 4, 4, 4); if you change the code above to:
l = tf.constant(0.1, shape=[batch_size, 4, 4, 4])
everything works fine.
In general, try using tf.nn.conv2d to get a feel for what the relationship between the tensor shapes is. Since tf.nn.conv2d_transpose is its backward counterpart, it has the same relationship between input, output and filter shapes (but with the roles of the input and output reversed.)
Hope that helps!
Using padding='SAME' in tf.nn.conv2d_transpose() function may works too