智能计算系统实验3.3非实时图像风格迁移.pdf

智能计算系统实验3.3⾮实时图像风格迁移 实实验验 ⽬⽬的的 使⽤Python语⾔numpy 块基于VGG19⽹络 型实现⾮实时图像迁移。 1. 加深对卷积神经⽹络的理解，利⽤VGG19 型进⾏图像特征提取。 2. 使⽤numpy 块实现风格迁移中相关风格和内容损失函数的计算。实现layer2层的反向传播。 3. 对卷积层和池化层实现中的四重循环进⾏改进 （img2col+gemm），提升运算速度。 实实验验代代码码 layer_2.py ：卷积层和池化层的基本实现和加速算法实现 # coding :utf-8 import numpy as np import struct import os import time def im2col(image, ksize, stride) : # image is a 4d tensor( batchsize, channel, height, width]) image_col = ] for b in range(image.shape 0]) : for i in range(0, image.shape 2] - ksize + 1, stride) : for j in range(0, image.shape 3] - ksize + 1, stride) : col = image b, :, i:i + ksize, j :j + ksize].reshape( -1]) image_col.append(col) image_col = np.array(image_col) return image_col # N, ((H-k)/s+1)*((w-k)/s+1), k*k*cin] def im2col_ pool(image, ksize, stride) : # image is a 4d tensor( batchsize, channel, height, width]) image_col = ] for b in range(image.shape 0]) : for i in range(0, image.shape 2] - ksize + 1, stride) : for j in range(0, image.shape 3] - ksize + 1, stride) : col = image b, :, i:i + ksize, j :j + ksize].reshape( image.shape 1],-1]) image_col.append(col) image_col = np.array(image_col) return image_col # N, channel, ((H-k)/s+1)*((w-k)/s+1), k*k] class ConvolutionalLayer(object) : def __ init__ (self, kernel_size, channel_ in, channel_out, padding, stride, type=1) : self.kernel_size = kernel_size self.channel_ in = channel_ in self.channel_out = channel_out self.padding = padding self.stride = stride self.forward = self.forward_ raw self.backward = self.backward_