first commit

featuremap.py

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+# 返回输出结果
+import cv2
+import torchvision
+import torch
+from matplotlib import pyplot as plt
+import numpy as np
+import torchvision.transforms as transforms
+import torch
+import torchvision
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.models as models
+from matplotlib import pyplot as plt
+import random
+#定义函数，随机从0-end的一个序列中抽取size个不同的数
+def random_num(size,end):
+    range_ls=[i for i in range(end)]
+    num_ls=[]
+    for i in range(size):
+        num=random.choice(range_ls)
+        range_ls.remove(num)
+        num_ls.append(num)
+    return num_ls
+    
+
+
+path = "/data1/Hatcher/DATA/test13/train_data/images/HatIMG_668.jpg"
+transformss = transforms.Compose(
+    [transforms.ToTensor(),
+     transforms.Resize((224, 224)),
+     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
+
+#注意如果有中文路径需要先解码，最好不要用中文
+img = cv2.imread(path)
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#转换维度
+img = transformss(img).unsqueeze(0)
+
+model = torchvision.models.resnet50(pretrained=True)
+#new_model = torchvision.models._utils.IntermediateLayerGetter(model, {'layer1': '1', 'layer2': '2',"layer3":"3"})
+out = model(img)
+
+tensor_ls=[(k,v) for  k,v in out.items()]
+
+#这里选取layer2的输出画特征图
+v=tensor_ls[1][1]
+
+"""
+如果要选layer3的输出特征图只需把第一个索引值改为2，即：
+v=tensor_ls[2][1]
+只需把第一个索引更换为需要输出的特征层对应的位置索引即可
+"""
+#取消Tensor的梯度并转成三维tensor，否则无法绘图
+v=v.data.squeeze(0)
+
+print(v.shape)  # torch.Size([512, 28, 28])
+
+
+#随机选取25个通道的特征图
+channel_num = random_num(25,v.shape[0])
+plt.figure(figsize=(10, 10))
+for index, channel in enumerate(channel_num):
+    ax = plt.subplot(5, 5, index+1,)
+    plt.imshow(v[channel, :, :])
+plt.savefig("feature.jpg",dpi=300)
+