话不多说,代码说话:
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)
