another way to visualize NN weights

Code/1_data_prepare/1_5_compute_mean.py

@@ -32,6 +32,7 @@
         imgs = np.concatenate((imgs, img), axis=3)
         print(i)
 
+imgs = np.delete(imgs, 0, axis=3)
 imgs = imgs.astype(np.float32)/255.
 
 

Code/4_viewer/2_1_visual_weights.py

@@ -0,0 +1,51 @@
+# coding: utf-8
+import torch
+import torchvision.utils as vutils
+import torch.nn as nn
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool1 = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.pool2 = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool1(F.relu(self.conv1(x)))
+        x = self.pool2(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+    # 定义权值初始化
+    def initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                torch.nn.init.xavier_normal_(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                torch.nn.init.normal_(m.weight.data, 0, 0.01)
+                m.bias.data.zero_()
+
+
+net = Net()     # 创建一个网络
+
+kernels = net.conv1.weight.detach().clone()
+kernels = kernels.view(-1, 1, 5, 5)
+img = vutils.make_grid(kernels, normalize=True, scale_each=True, nrow=3)
+plt.imshow(img.permute(1, 2, 0))
+plt.axis('off')
+plt.show()

Code/4_viewer/3_1_visual_featuremaps.py

@@ -0,0 +1,56 @@
+# coding: utf-8
+import torch
+import torchvision.utils as vutils
+import numpy as np
+from tensorboardX import SummaryWriter
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+import sys
+sys.path.append("..")
+from utils.utils import MyDataset, Net, normalize_invert
+from torch.utils.data import DataLoader
+import matplotlib.pyplot as plt
+
+vis_layer = 'conv1'
+log_dir = '../../Result/visual_featuremaps'
+txt_path = '../../Data/visual.txt'
+pretrained_path = '../../Data/net_params_72p.pkl'
+
+net = Net()
+pretrained_dict = torch.load(pretrained_path)
+net.load_state_dict(pretrained_dict)
+
+# 数据预处理
+normMean = [0.49139968, 0.48215827, 0.44653124]
+normStd = [0.24703233, 0.24348505, 0.26158768]
+normTransform = transforms.Normalize(normMean, normStd)
+testTransform = transforms.Compose([
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    normTransform
+])
+# 载入数据
+test_data = MyDataset(txt_path=txt_path, transform=testTransform)
+test_loader = DataLoader(dataset=test_data, batch_size=1)
+img, label = iter(test_loader).next()
+
+x = img
+
+
+# Visualize feature maps
+features_dict = {}
+def get_features(name):
+    def hook(model, input, output):
+        features_dict[name] = output.detach()
+    return hook
+
+
+net.conv1.register_forward_hook(get_features('ext_conv1'))
+output = net(x)
+
+features = features_dict['ext_conv1'].view(-1, 1, 28, 28)
+
+img = vutils.make_grid(features, normalize=True, scale_each=True, nrow=3)
+plt.imshow(img.permute(1, 2, 0))
+plt.axis('off')
+plt.show()