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