feat: test torchvision's resnet model

src/test_backbone.py

@@ -0,0 +1,44 @@
+import torch
+import torchvision
+import torchvision.models as models
+import time
+from torchinfo import summary
+import torchvision.transforms as transforms
+from PIL import Image
+import matplotlib.pyplot as plt
+
+device = torch.device('cuda')
+
+# 創建 resnet50 模型
+model = models.resnet50(weights=torchvision.models.ResNet50_Weights).to(device)
+model = torch.nn.Sequential(*list(model.children())[:-3])                           # 去除 FC & global pooling  && 一層
+
+# 定義 transform
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
+                         std=[0.229, 0.224, 0.225])
+])
+
+
+for name, param in model.named_parameters():
+    param.requires_grad = False
+    # print(name, param.requires_grad)
+# 將模型設置為 evaluation 模式
+model.eval()
+
+# summary(model, input_size=(32, 3, 224, 224))
+# time.sleep(2)
+
+with torch.no_grad():
+    image = Image.open('../data/textcaps/train_images/{}'.format('0a9d5e54fcf25702.jpg'))
+    image = transform(image).to(device).unsqueeze(0)
+    
+    output = model(image)
+    output = output[0].to('cpu')
+    for index, feature in enumerate(output):
+        plt.imshow(feature)
+        plt.savefig("result/tmp/{}.jpg".format(index))
+    print(output.shape)
+# print(model)