Python实现卷积的方法及示例

import numpy as np

import math

class Conv2D(object):

def __init__(self, shape, output_channels, ksize=3, stride=1, method='VALID'):

self.input_shape = shape

self.output_channels = output_channels

self.input_channels = shape[-1]

self.batchsize = shape[0]

self.stride = stride

self.ksize = ksize

self.method = method

weights_scale = math.sqrt(ksize*ksize*self.input_channels/2)

self.weights = np.random.standard_normal((ksize, ksize, self.input_channels, self.output_channels)) // weights_scale

self.bias = np.random.standard_normal(self.output_channels) // weights_scale

if method == 'VALID':

self.eta = np.zeros((shape[0], (shape[1] - ksize ) // self.stride + 1, (shape[1] - ksize ) // self.stride + 1,self.output_channels))

if method == 'SAME':

self.eta = np.zeros((shape[0], shape[1]//self.stride, shape[2]//self.stride,self.output_channels))

self.w_gradient = np.zeros(self.weights.shape)

self.b_gradient = np.zeros(self.bias.shape)

self.output_shape = self.eta.shape

def forward(self,x):

col_weights = self.weights.reshape([-1,self.output_channels])

if self.method == 'SAME':

x = np.pad(x, ((0, 0), (self.ksize // 2, self.ksize // 2), (self.ksize // 2, self.ksize // 2), (0, 0)),'constant', constant_values=0)

self.col_image = []

conv_out = np.zeros(self.eta.shape)

for i in range(self.batchsize):

img_i = x[i][np.newaxis,...]

self.col_image_i = self.im2col(img_i,self.ksize,self.stride)

print(col_weights.shape)

conv_out[i] = np.reshape(np.dot(self.col_image_i,col_weights)+self.bias, self.eta[0].shape)

self.col_image.append(self.col_image_i)

return conv_out

# self.col_image = np.array(self.col_image)

# return conv_out

def im2col(self,image,k_size,stride):

image_col = []

for i in range(0,image.shape[1] - k_size+1,stride):

for j in range(0,image.shape[2]-k_size+1,stride):

# print("......:", image[:,i:i+k_size,j:j+k_size,:].shape)

col = image[:,i:i+k_size,j:j+k_size,:].reshape([-1])

image_col.append(col)

image_col = np.array(image_col)

print(image_col.shape)

return image_col

if __name__ == '__main__':

conv2d = Conv2D([5,10,10,3],32,3,1,'VALID')

input_data = np.random.standard_normal((5,10,10,3))

print("input:",input_data.shape)

conv_out = conv2d.forward(input_data)

print(conv_out.shape)