部分节点访问与数据比对

code/minist/test.py

@@ -5,6 +5,13 @@ import matplotlib.pyplot as plt
 import torch
 import cv2
 import torchvision.transforms as transforms
+
+import numpy as np
+
+import tkinter
+import tkinter.messagebox #弹窗库
+
+from PyQt5.QtWidgets import *
 #定义网络
 class Net(nn.Module):
     def __init__(self):
@@ -35,6 +42,10 @@ net.to(device)
 net.eval()
 
 
+for name in net.state_dict():
+    print(name)
+print(net.state_dict()['conv1.weight'])
+
 img = cv2.imread('./data/8.png')
 img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
 img = img/255
@@ -49,9 +60,35 @@ out = net(imgTensor)
 
 print(out.data.max(1, keepdim=True)[1][0].item())
 
+tkinter.messagebox.showinfo('pt推理结果', out.data.max(1, keepdim=True)[1][0].item())
+
+
+
+
+# onnx model
 dummy_input = torch.randn(1, 1,28,28).to(device)#输入大小   #data type nchw
 torch.onnx.export(net, dummy_input, "ministNet.onnx", verbose=True, input_names=['input_111'], output_names=['output_111'])
 
+net = cv2.dnn.readNetFromONNX("ministNet.onnx")  # 加载训练好的识别模型
+img = cv2.imread('./data/8.png')
+img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+img = img/255
+
+transf = transforms.ToTensor()
+imgTensor = transf(img).unsqueeze(0)
+
+
+
+im = img[np.newaxis, np.newaxis,:, :]
+im = im.astype(np.float32)
+outNames = net.getUnconnectedOutLayersNames()
+net.setInput(im)
+out = net.forward(outNames)
+
+
+ind  = np.where(out[0][0]==np.max(out[0][0]))
+print(ind[0])
 
+tkinter.messagebox.showinfo('onnx推理结果',ind[0])
 
 

code/minist/testOnnx.py

@@ -1,27 +0,0 @@
-import cv2
-import torchvision.transforms as transforms
-import numpy as np
-net = cv2.dnn.readNetFromONNX("ministNet.onnx")  # 加载训练好的识别模型
-img = cv2.imread('./data/8.png')
-img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-img = img/255
-
-transf = transforms.ToTensor()
-imgTensor = transf(img).unsqueeze(0)
-
-
-
-im = img[np.newaxis, np.newaxis,:, :]
-im = im.astype(np.float32)
-outNames = net.getUnconnectedOutLayersNames()
-net.setInput(im)
-out = net.forward(outNames)
-
-
-ind  = np.where(out[0][0]==np.max(out[0][0]))
-print(ind[0])
-
-
-
-
-