Tensorflow卷积实现原理+手写python代码实现卷积教程

#输入，shape=[c,h,w]
input_data=[
  [[1,0,1,2,1],
  [0,2,1,0,1],
  [1,1,0,2,0],
  [2,2,1,1,0],
  [2,0,1,2,0]],

  [[2,0,2,1,1],
  [0,1,0,0,2],
  [1,0,0,2,1],
  [1,1,2,1,0],
  [1,0,1,1,1]],
 
  ]

#卷积核，shape=[in_c,k,k]=[2,3,3]
weights_data=[ 
  [[ 1, 0, 1],
  [-1, 1, 0],
  [ 0,-1, 0]],
  [[-1, 0, 1],
  [ 0, 0, 1],
  [ 1, 1, 1]] 
  ]

def get_shape(tensor):
 [s1,s2,s3]= tensor.get_shape() 
 s1=int(s1)
 s2=int(s2)
 s3=int(s3)
 return s1,s2,s3

def chw2hwc(chw_tensor): 
 [c,h,w]=get_shape(chw_tensor) 
 cols=[]
 
 for i in range(c):
 #每个通道里面的二维数组转为[w*h,1]即1列 
 line = tf.reshape(chw_tensor[i],[h*w,1])
 cols.append(line)

 #横向连接，即将所有竖直数组横向排列连接
 input = tf.concat(cols,1)#[w*h,c]
 #[w*h,c]-->[h,w,c]
 input = tf.reshape(input,[h,w,c])
 return input

import tensorflow as tf
import numpy as np
input_data=[
  [[1,0,1,2,1],
  [0,2,1,0,1],
  [1,1,0,2,0],
  [2,2,1,1,0],
  [2,0,1,2,0]],

  [[2,0,2,1,1],
  [0,1,0,0,2],
  [1,0,0,2,1],
  [1,1,2,1,0],
  [1,0,1,1,1]],
 
  ]
weights_data=[ 
  [[ 1, 0, 1],
  [-1, 1, 0],
  [ 0,-1, 0]],
  [[-1, 0, 1],
  [ 0, 0, 1],
  [ 1, 1, 1]] 
  ]
def get_shape(tensor):
 [s1,s2,s3]= tensor.get_shape() 
 s1=int(s1)
 s2=int(s2)
 s3=int(s3)
 return s1,s2,s3

def chw2hwc(chw_tensor): 
 [c,h,w]=get_shape(chw_tensor) 
 cols=[]
 
 for i in range(c):
 #每个通道里面的二维数组转为[w*h,1]即1列 
 line = tf.reshape(chw_tensor[i],[h*w,1])
 cols.append(line)

 #横向连接，即将所有竖直数组横向排列连接
 input = tf.concat(cols,1)#[w*h,c]
 #[w*h,c]-->[h,w,c]
 input = tf.reshape(input,[h,w,c])
 return input

def hwc2chw(hwc_tensor):
 [h,w,c]=get_shape(hwc_tensor) 
 cs=[] 
 for i in range(c): 
 #[h,w]-->[1,h,w] 
 channel=tf.expand_dims(hwc_tensor[:,:,i],0)
 cs.append(channel)
 #[1,h,w]...[1,h,w]---->[c,h,w]
 input = tf.concat(cs,0)#[c,h,w]
 return input

def tf_conv2d(input,weights):
 conv = tf.nn.conv2d(input, weights, strides=[1, 1, 1, 1], padding='SAME')
 return conv

def main(): 
 const_input = tf.constant(input_data , tf.float32)
 const_weights = tf.constant(weights_data , tf.float32 )

 
 input = tf.Variable(const_input,name="input")
 #[2,5,5]------>[5,5,2]
 input=chw2hwc(input)
 #[5,5,2]------>[1,5,5,2]
 input=tf.expand_dims(input,0)

 
 weights = tf.Variable(const_weights,name="weights")
 #[2,3,3]-->[3,3,2]
 weights=chw2hwc(weights)
 #[3,3,2]-->[3,3,2,1]
 weights=tf.expand_dims(weights,3) 

 #[b,h,w,c]
 conv=tf_conv2d(input,weights)
 rs=hwc2chw(conv[0]) 

 init=tf.global_variables_initializer()
 sess=tf.Session()
 sess.run(init)
 conv_val = sess.run(rs)
 
 print(conv_val[0]) 


if __name__=='__main__':
 main()

[[ 2. 0. 2. 4. 0.]
 [ 1. 4. 4. 3. 5.]
 [ 4. 3. 5. 9. -1.]
 [ 3. 4. 6. 2. 1.]
 [ 5. 3. 5. 1. -2.]]

import numpy as np
input_data=[
  [[1,0,1,2,1],
  [0,2,1,0,1],
  [1,1,0,2,0],
  [2,2,1,1,0],
  [2,0,1,2,0]],

  [[2,0,2,1,1],
  [0,1,0,0,2],
  [1,0,0,2,1],
  [1,1,2,1,0],
  [1,0,1,1,1]] 
  ]
weights_data=[ 
  [[ 1, 0, 1],
  [-1, 1, 0],
  [ 0,-1, 0]],
  [[-1, 0, 1],
  [ 0, 0, 1],
  [ 1, 1, 1]] 

  ]

#fm:[h,w]
#kernel:[k,k]
#return rs:[h,w] 
def compute_conv(fm,kernel):
 [h,w]=fm.shape
 [k,_]=kernel.shape 
 r=int(k/2)
 #定义边界填充0后的map
 padding_fm=np.zeros([h+2,w+2],np.float32)
 #保存计算结果
 rs=np.zeros([h,w],np.float32)
 #将输入在指定该区域赋值，即除了4个边界后，剩下的区域
 padding_fm[1:h+1,1:w+1]=fm 
 #对每个点为中心的区域遍历
 for i in range(1,h+1):
 for j in range(1,w+1): 
  #取出当前点为中心的k*k区域
  roi=padding_fm[i-r:i+r+1,j-r:j+r+1]
  #计算当前点的卷积,对k*k个点点乘后求和
  rs[i-1][j-1]=np.sum(roi*kernel)
 
 return rs
 
def my_conv2d(input,weights):
 [c,h,w]=input.shape
 [_,k,_]=weights.shape
 outputs=np.zeros([h,w],np.float32)

 #对每个feature map遍历，从而对每个feature map进行卷积
 for i in range(c):
 #feature map==>[h,w]
 f_map=input[i]
 #kernel ==>[k,k]
 w=weights[i]
 rs =compute_conv(f_map,w)
 outputs=outputs+rs 

 return outputs

def main(): 
 
 #shape=[c,h,w]
 input = np.asarray(input_data,np.float32)
 #shape=[in_c,k,k]
 weights = np.asarray(weights_data,np.float32) 
 rs=my_conv2d(input,weights) 
 print(rs) 


if __name__=='__main__':
 main() 

[[ 2. 0. 2. 4. 0.]
 [ 1. 4. 4. 3. 5.]
 [ 4. 3. 5. 9. -1.]
 [ 3. 4. 6. 2. 1.]
 [ 5. 3. 5. 1. -2.]]