Add train resnet test.

test/train.py

@@ -0,0 +1,33 @@
+import torchvision
+import torch
+from torch import nn
+import cv2
+import numpy as np
+import torch.nn.functional as F
+
+resnet = torchvision.models.resnet152(pretrained=False)
+resnet.fc = torch.nn.Linear(2048, 10)
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+weights_path = "weights"
+model = resnet
+
+model.train().cuda()
+
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=0.2, momentum=0.9, weight_decay=5e-4)
+scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20)
+
+# with torch.no_grad():
+img_ori = np.ones([1, 3, 224, 224])
+img_ori = np.float32(img_ori) / 255
+img_ori = torch.tensor(img_ori).cuda()
+output = model(img_ori)
+
+target = torch.ones([1]).to(torch.int64).cuda()
+
+optimizer.zero_grad()
+loss = F.nll_loss(output, target)
+loss.backward()
+optimizer.step()
+print(loss)