init

README.md

 # torch_inference_resnet50
 
+## 从光合开发者社区安装 torch、torchvision
+
+## 验证:
+
+```bash
+git clone http://developer.hpccube.com/codes/wangkx1/torch_inference_resnet50.git
+cd torch_inference_resnet50
+python torch_verify.py
+```
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torch_verify.py

+import torch
+from torchvision import models, transforms
+from PIL import Image
+
+
+# Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
+# 加载预训练的ResNet50模型，并指定不使用GPU
+model = models.resnet50(pretrained=True)
+model.eval()  # 将模型设置为评估模式
+device = torch.device("cpu")  # 指定设备为CPU
+model.to(device)  # 将模型转移到CPU上
+
+# 图片预处理
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+image_path = 'dog.jpg'  # 替换为你的图片路径
+image = Image.open(image_path).convert('RGB')
+image = transform(image)
+
+# 添加batch维度
+image = image.unsqueeze(0)
+
+# 在CPU上进行推理
+with torch.no_grad():
+    outputs = model(image.to(device))
+    
+# 获取预测类别
+_, predicted_class = torch.max(outputs, 1)
+predicted_class_idx = predicted_class.item()
+
+# 打印预测类别
+print(f"Predicted class: {predicted_class_idx}")
+
+# 验证分类结果:
+# https://github.com/HoldenCaulfieldRye/caffe/blob/master/data/ilsvrc12/synset_words.txt
+synset_words_path = "synset_words.txt"
+with open(synset_words_path, 'r') as f:
+    synset_words = f.readlines()
+
+# 每行前去除空格和制表符，并根据索引获取类别名称
+class_names = [line.strip() for line in synset_words]
+predicted_class_name = class_names[predicted_class_idx+1]  # 注意索引可能从1开始，所以+1
+print(f"Predicted class name: {predicted_class_name}")  # 彭布罗克，彭布罗克威尔士柯基犬
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