本文介绍两种卷积:
- 3D卷积
- 去卷积
下篇文章介绍:
- 空洞卷积
- depthwise卷积
3D卷积
最近在读论文的时候，有好几个地方提到了3D卷积，一直不懂是怎么操作的，看了一部分资料:
还是有些懵，又利用tensorflow做了进一步测试学习。
tensorflow中的函数tf.nn.conv3d
tf.nn.conv3d(
input,
filter,
strides,
padding,
data_format='NDHWC',
dilations=[1, 1, 1, 1, 1],
name=None
)
给定一个5-D的输入和滤波器，计算一个3D卷积。
Args:
input:A Tensor. Must be one of the following type, half, bfloat16, float32, float64. Shape [batch, in_depth, in_height, in_width, in_channels]filter:A tensor. Must have the same type as input. Shape [filter_depth, filter_height, filter_width, in_channels, out_channels]. in_channels must match between input and filterstrides:A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.padding:A string from: “SAME”, “VALID”. The type of padding algorithm to use.data_format:An optional string from: “NDHWC”, “NCDHW”. Defaults to “NDHWC”. The data format of the input and output data. With the default format “NDHWC”, the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be “NCDHW”, the data storage order is: [batch, in_channels, in_depth, in_height, in_width].dilations:An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.name:A name for the operation (optional).
Returns:
A Tensor. Has the same type as input
与2D卷积tf.nn.conv2d相比，
tf.nn.conv2d(
input,
filter,
strides,
padding,
use_cudnn_on_gpu=True,
data_format='NHWC',
dilations=[1, 1, 1, 1],
name=None
)
其实差别不大，主要区别在于input和filter两个参数，2D卷积中这两个参数的要求是:
input:A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. [batch, height, width, channels.filter:A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
主要区别是3D卷积中的input参数的in_depth与filter参数里面的filter_depth。
input:A Tensor. Must be one of the following type, half, bfloat16, float32, float64. Shape [batch, in_depth, in_height, in_width, in_channels]- in_depth表示的就是时间维度（如视频里面的帧）或者空间维度（如生物医学里面的多个连续切片），它表示帧的个数或者切片的个数。这个区别与图像的channels，如输入rgb图像的3channels。这里它的意义，卷积在这个维度作用于多少的size，如在平面图像的宽和高。
filter:A tensor. Must have the same type as input. Shape [filter_depth, filter_height, filter_width, in_channels, out_channels]. in_channels must match between input and filter- in_fiter就是说这个滤波器在这个尺度的size，例如滤波器在平面图像的size = 3 x 3,或5 x 5
其它参数和3D卷积类似。
下面就以程序为例做下测试。
import tensorflow as tf
import numpy as np
input = tf.constant(1, shape=[1, 7, 224, 224, 3], dtype=tf.float32)
filter_1_2 = tf.constant(2, shape=[1, 3, 3, 3, 64], dtype=tf.float32)
filter_3_4 = tf.constant(2, shape=[1, 5, 3, 3, 64], dtype=tf.float32)
res_1 = tf.nn.conv3d(input=input,
filter=filter_1_2,
strides=[1, 1, 1, 1, 1],
padding='SAME')
res_2 = tf.nn.conv3d(input=input,
filter=filter_1_2,
strides=[1, 2, 1, 1, 1],
padding='SAME')
res_3 = tf.nn.conv3d(input=input,
filter=filter_3_4,
strides=[1, 1, 1, 1, 1],
padding='SAME')
res_4 = tf.nn.conv3d(input=input,
filter=filter_3_4,
strides=[1, 2, 1, 1, 1],
padding='SAME')
sess = tf.Session()
conv_res_1 = sess.run(res_1)
conv_res_2 = sess.run(res_2)
conv_res_3 = sess.run(res_3)
conv_res_4 = sess.run(res_4)
print conv_res_1.shape
print conv_res_2.shape
print conv_res_3.shape
print conv_res_4.shape
#print conv_res
Output:
(1, 7, 224, 224, 64)
(1, 4, 224, 224, 64)
(1, 7, 224, 224, 64)
(1, 4, 224, 224, 64)
可以看到在第2维的卷积和第3、4维一样的原理。
上图为一个3D滤波器（其实为4D: depth, channel, height, width）在输入数据进行卷积的效果。
如上图，滤波器在第三维（帧，切片）上进行滑动，产生了很多((in_depth - 1) / filter_depth + 1)单通道的feature map。不同的滤波器同样可以产生很多同等个数((in_depth - 1) / filter_depth + 1)的单层feature map。然后就一块组合成了[batch, (in_depth - 1) / filter_depth + 1, (in_height - 1) / filter_height + 1, (in_width - 1) / filter_width + 1, out_channels]的输出，如程序示例所示。
去卷积
去卷积是在语义分割里用的比较多，之前看过一篇专门介绍反卷积的论文，但现在记不太清了，故重新学习一下。
这里的动态图对了解反卷积很形象，看看应该就可以明白。
另外分享一篇论文A guide to convolution arithmetic for deep learning
tensorflow中的tf.nn.conv2d_transpose
tf.nn.conv2d_transpose(
value,
filter,
output_shape,
strides,
padding='SAME',
data_format='NHWC',
name=None
)
Args:
value:A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.filter:A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter’s in_channesl dimension must match that of value.output_shape: A 1-D Tensor representing the output shape of the deconvolution op.strides:A list of ints. The stride of the sliding window for each dimension of the input tensor.padding:A string, either ‘VALID’ or ‘SAME’. The padding algorithm. See the “returns” section of tf.nn.convolution for details.data_format:A string. ‘NHWC’ and ‘NCHW’ are supported.name:Optional name for the returned tensor.
这个output_shape就很懵，为什么input, filter, padding, stride确定后，output_shape不确定？
import tensorflow as tf
import numpy as np
value = tf.constant(1, shape=[1, 3, 3, 3], dtype=tf.float32)
filter = tf.constant(2, shape=[3, 3, 64, 3],dtype=tf.float32)
output_shape_1 = tf.constant([1, 6, 6, 64])
output_shape_2 = tf.constant([1, 5, 5, 64])
res_1 = tf.nn.conv2d_transpose(
value=value,
filter=filter,
output_shape=output_shape_1,
strides=[1, 2, 2, 1],
padding='SAME'
)
res_2 = tf.nn.conv2d_transpose(
value=value,
filter=filter,
output_shape=output_shape_2,
strides=[1, 2, 2, 1],
padding='SAME'
)
sess = tf.Session()
conv_res_1 = sess.run(res_1)
conv_res_2 = sess.run(res_2)
#conv_res_3 = sess.run(res_3)
#conv_res_4 = sess.run(res_4)
print conv_res_1.shape
print conv_res_2.shape
#print conv_res_3.shape
#print conv_res_4.shape
#print conv_res
Output
(1, 6, 6, 64)
(1, 5, 5, 64)
上面的例子可以看到指定这个output_shape是重要的，因为反卷积后的shape是不确定，可以在可能的集合里来指定。
那么具体反卷积是怎么操作的呢？或者说给定input, filter, stride, padding等信息，反卷积后有哪些可能的output_shape呢?
考虑一下卷积的公式:
conv: i -> o
deconv: o -> i
o = floor(i + 2*p -k) / s + 1， (1)
反卷积是把推导出i的shape，i经过卷积后变成的o.
由(1)可得，
floor(i + 2*p - k) = (o - 1)s
- 如果 s = 1, 那么 i = (o - 1) + k - 2p，i是固定的，这个时候的output_shape是不需要指定的。另外考虑（常用情况） k = 3, p = 1(padding = ‘SAME’)，那么i = o; k = 3, p = 0(padding=‘VALID’), 那么 i = o + 2
- 如果 s = 2，那么 i + 2p - k = 2(o-1)或 i + 2p - k = 2(o-1) + 1，即 i = 2o - 2 + k - 2p 或 i = 2o - 1 + k - 2p。另外考虑（常用情况）k = 3, p = 1(padding=‘SAME’)，那么 i = 2o-1 或者 i = 2*o。k = 3, p = 0(padding=‘VALID’)，那么i = 2o + 1或 i = 2o+2
总结:只考虑k = 3的情况(比较常用)
- s = 1时，
- padding=‘SAME’ -> i = o
- padding=‘VALID’ -> i = o + 2
- s = 2时，
- padding=‘SAME’ -> i = 2o 或 i =2o - 1
- padding = ‘VALID’ -> i = 2o + 1 或 2o+2
程序测试
import tensorflow as tf
import numpy as np
value = tf.constant(1, shape=[1, 64, 64, 3], dtype=tf.float32)
filter = tf.constant(2, shape=[3, 3, 256, 3],dtype=tf.float32)
def deconv_s_1():
# s = 1, padding='SAME'
output_shape_1_same = tf.constant([1, 64, 64, 256])
res_1 = tf.nn.conv2d_transpose(
value=value,
filter=filter,
output_shape=output_shape_1_same,
strides=[1, 1, 1, 1],
padding='SAME'
)
# s = 1, padding='VALID'
output_shape_1_valid = tf.constant([1, 66, 66, 256])
res_2 = tf.nn.conv2d_transpose(
value=value,
filter=filter,
output_shape=output_shape_1_valid,
strides=[1, 1, 1, 1],
padding='VALID'
)
sess = tf.Session()
conv_res_1 = sess.run(res_1)
conv_res_2 = sess.run(res_2)
print "s = 1, padding='SAME', expected: i = o = 64 "
print conv_res_1.shape
print "s = 1, padding='VALID', expected i = o + 2 = 66"
print conv_res_2.shape
def deconv_s_2():
#s = 2, padding='SAME'
output_shape_2_same_1 = tf.constant([1, 128, 128, 256])
res_1 = tf.nn.conv2d_transpose(
value=value,
filter=filter,
output_shape=output_shape_2_same_1,
strides=[1, 2, 2, 1],
padding='SAME'
)
# s = 2, padding='SAME'
output_shape_2_same_2 = tf.constant([1, 127, 127, 256])
res_2 = tf.nn.conv2d_transpose(
value=value,
filter=filter,
output_shape=output_shape_2_same_2,
strides=[1, 2, 2, 1],
padding='SAME'
)
# s = 2, padding='VALID'
output_shape_2_valid_1 = tf.constant([1, 129, 129, 256])
res_3 = tf.nn.conv2d_transpose(
value=value,
filter=filter,
output_shape=output_shape_2_valid_1,
strides=[1, 2, 2, 1],
padding='VALID'
)
# s = 2, padding='VALID'
print "s = 2, padding='VALID'"
output_shape_2_valid_2 = tf.constant([1, 130, 130, 256])
res_4 = tf.nn.conv2d_transpose(
value=value,
filter=filter,
output_shape=output_shape_2_valid_2,
strides=[1, 2, 2, 1],
padding='VALID'
)
sess = tf.Session()
conv_res_1 = sess.run(res_1)
conv_res_2 = sess.run(res_2)
conv_res_3 = sess.run(res_3)
conv_res_4 = sess.run(res_4)
print "s = 2, padding='SAME', expected i = 2o = 128 or i = 2o - 1 = 127 "
print conv_res_1.shape
print conv_res_2.shape
print "s = 2, padding='VALID', expected i = 2o + 1 = 129 or i = 2o + 2 = 130"
print conv_res_3.shape
print conv_res_4.shape
# print conv_res
def deconv_error():
output_shape_2_same_1 = tf.constant([1, 129, 129, 256])
res_1 = tf.nn.conv2d_transpose(
value=value,
filter=filter,
output_shape=output_shape_2_same_1,
strides=[1, 2, 2, 1],
padding='SAME'
)
sess = tf.Session()
conv_res_1 = sess.run(res_1)
print "s = 2, padding='SAME', expected i = 2o = 128 or i = 2o - 1 = 127 "
print conv_res_1.shape
print "input_size = 64:"
print "stride = 1"
deconv_s_1()
print "stride = 2"
deconv_s_2()
deconv_error()
Output
input_size = 64:
2018-12-04 00:04:02.363507: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
stride = 1
s = 1, padding='SAME', expected: i = o = 64
(1, 64, 64, 256)
s = 1, padding='VALID', expected i = o + 2 = 66
(1, 66, 66, 256)
stride = 2
s = 2, padding='VALID'
s = 2, padding='SAME', expected i = 2o = 128 or i = 2o - 1 = 127
(1, 128, 128, 256)
(1, 127, 127, 256)
s = 2, padding='VALID', expected i = 2o + 1 = 129 or i = 2o + 2 = 130
(1, 129, 129, 256)
(1, 130, 130, 256)
2018-12-04 00:04:03.186144: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at conv_grad_input_ops.cc:355 : Invalid argument: Conv2DCustomBackpropInput: Size of out_backprop doesn't match computed: actual = 64, computed = 65 spatial_dim: 1 input: 129 filter: 3 output: 64 stride: 2 dilation: 1
2018-12-04 00:04:03.187537: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at conv_grad_input_ops.cc:355 : Invalid argument: Conv2DCustomBackpropInput: Size of out_backprop doesn't match computed: actual = 64, computed = 65 spatial_dim: 1 input: 129 filter: 3 output: 64 stride: 2 dilation: 1
2018-12-04 00:04:03.188916: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at conv_grad_input_ops.cc:355 : Invalid argument: Conv2DCustomBackpropInput: Size of out_backprop doesn't match computed: actual = 64, computed = 65 spatial_dim: 1 input: 129 filter: 3 output: 64 stride: 2 dilation: 1
2018-12-04 00:04:03.189200: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at conv_grad_input_ops.cc:355 : Invalid argument: Conv2DCustomBackpropInput: Size of out_backprop doesn't match computed: actual = 64, computed = 65 spatial_dim: 1 input: 129 filter: 3 output: 64 stride: 2 dilation: 1
综上，通过上述程序验证了我们的想法。
所以当ksize = 3时，我们选择padding=‘SAME’, stride = 2，可以将输出Osize = 2 * Isize.
以上从卷积<->反卷积的shape的关系进行了解析，并分析了output_shape的可能性。
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