基于PyCaffe实现网络可视化

青旅半醒 2022-06-04 00:40 240阅读 0赞

# 前言 #

在进行深度学习网络调试参数的时候，往往是将整个训练过程完了之后查看训练的结果。但是除了这种办法之外还可以通过网络可视化来帮助进行参数调试，这里封装了一个小小的类，提供网络显示的接口，传入网络的名称就好了

# 1. 类实现 #

# -*-coding=utf-8-*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import sys
    sys.path.append('/home/sucker/Desktop/caffe-1.0/python')
    import caffe
    
    class Net_Show:
        def __init__(self, net):
            self.caffe_net = net
            self.layer_dic = { }
    
        # layer info(name&shape)
        def ShowLayer_Info(self):
            layer_name = [key for key, value in self.caffe_net.params.items()]
            layer_param = [value[0].data.shape for key, value in self.caffe_net.params.items()]
            for i in np.arange(0, len(layer_name)):
                self.layer_dic[layer_name[i]] = layer_param[i]
    
            for name in self.layer_dic:
                print name, self.layer_dic[name]
    
        # layer data show
        def show_data(self, data, name, padsize=1, padval=0):
            data -= data.min()
            data /= data.max()
    
            # force the number of filters to be square
            n = int(np.ceil(np.sqrt(data.shape[0])))
            padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)
            data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))
    
            # tile the filters into an image
            data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
            data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
            plt.figure()
            plt.imshow(data, cmap='gray')
            plt.axis('off')
            plt.title(name)
            plt.rcParams['figure.figsize'] = (8, 8)
            plt.rcParams['image.interpolation'] = 'nearest'
            plt.rcParams['image.cmap'] = 'gray'
            plt.show()
    
    	# show layer data
        def ShowBlob_Data(self, layer_name):
            data = self.caffe_net.blobs[layer_name].data[0]
            print_str = layer_name + ' data size is : ' + str(data.shape)
            print print_str
    
            self.show_data(data, layer_name+' data')
    
    	# show layer params
        def ShowBlob_Param(self, layer_name):
            shape_size = np.array(self.layer_dic[layer_name])
            data = self.caffe_net.params[layer_name][0].data
            print_str = layer_name + ' param size is : ' + str(data.shape)
            print print_str
            # data = data.transpose(0, 2, 3, 1)
            data = data.reshape(shape_size[0]*shape_size[1], shape_size[2], shape_size[3])
    
            self.show_data(data, layer_name+' param')

# 2. 使用示例 #

caffe.set_mode_gpu()
    solver = caffe.SGDSolver('solver.prototxt')
    solver.step(10000)
    
    m_show.caffe_net = solver.net
    m_show.ShowBlob_Data('Convolution2')
    m_show.ShowBlob_Param('Convolution1')

![这里写图片描述][SouthEast]

![这里写图片描述][SouthEast 1]

[SouthEast]: /images/20220604/3a3fa607c6704d95a8e70b0f1f1bb627.png
[SouthEast 1]: /images/20220604/3bc60c1d708d4802986ab9ede7a80cc5.png