onnxTest

.gitignore

@@ -152,3 +152,4 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+/*.pth

code/minist/Minist.py

@@ -16,7 +16,7 @@ random_seed = 1
 
 torch.manual_seed(random_seed)
 
-
+#下载数据，预处理
 train_loader = torch.utils.data.DataLoader(
   torchvision.datasets.MNIST('./data/', train=True, download=True,
                              transform=torchvision.transforms.Compose([
@@ -34,12 +34,11 @@ test_loader = torch.utils.data.DataLoader(
                              ])),
   batch_size=batch_size_test, shuffle=True)
 
+#查看数据
 examples = enumerate(test_loader)
 batch_idx, (example_data, example_targets) = next(examples)
 print(example_targets)
 print(example_data.shape)
-
-
 fig = plt.figure()
 for i in range(6):
   plt.subplot(2,3,i+1)
@@ -50,9 +49,12 @@ for i in range(6):
   plt.yticks([])
 plt.show()
 
+train_losses = []
+train_counter = []
+test_losses = []
+test_counter = [i*len(train_loader.dataset) for i in range(n_epochs + 1)]
 
-
+#定义网络
-
 class Net(nn.Module):
     def __init__(self):
         super(Net, self).__init__()
@@ -70,6 +72,10 @@ class Net(nn.Module):
         x = self.fc2(x)
         return F.log_softmax(x)
 
+network = Net()
+optimizer = optim.SGD(network.parameters(), lr=learning_rate,
+                      momentum=momentum)
+# 定义训练
 def train(epoch):
   network.train()
   for batch_idx, (data, target) in enumerate(train_loader):
@@ -90,6 +96,7 @@ def train(epoch):
       
 #train(1)
 
+#测试
 def test():
   network.eval()
   test_loss = 0
@@ -105,15 +112,10 @@ def test():
   print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
     test_loss, correct, len(test_loader.dataset),
     100. * correct / len(test_loader.dataset)))
+test()
+
 
-network = Net()
-optimizer = optim.SGD(network.parameters(), lr=learning_rate,
-                      momentum=momentum)
 
-train_losses = []
-train_counter = []
-test_losses = []
-test_counter = [i*len(train_loader.dataset) for i in range(n_epochs + 1)]
 
 
 

code/minist/test.py

@@ -0,0 +1,57 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import torch
+import cv2
+import torchvision.transforms as transforms
+#定义网络
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+        self.conv2_drop = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.fc2 = nn.Linear(50, 10)
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(self.conv1(x), 2))
+        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+        x = x.view(-1, 320)
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return F.log_softmax(x)
+
+network = Net()
+
+
+
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+net = Net()
+net.load_state_dict(torch.load('model.pth'))
+net.to(device)
+net.eval()
+
+
+img = cv2.imread('./data/8.png')
+img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+img = img/255
+# cv2.namedWindow('img',0)
+# cv2.imshow('img',img)
+# cv2.waitKey(0)
+transf = transforms.ToTensor()
+imgTensor = transf(img).unsqueeze(0)
+imgTensor = imgTensor.type(torch.FloatTensor).to(device)
+out = net(imgTensor)
+
+
+print(out.data.max(1, keepdim=True)[1][0].item())
+
+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'])
+
+
+
+

code/minist/testOnnx.py

@@ -0,0 +1,27 @@
+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])
+
+
+
+
+