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Python可视化resnet50所有层特征图使用pytorch中预训练模型,在网络inference的过程中显示特征图的每个通道.文章目录代码input image [1,3,224,224]conv1 [1,64,112,112]bn1_relu [1,64,112,112]maxpool [1,64,56,56]layer1 [1,256,56,56]layer2 [1,512,28,28]layer3 [1,1024,14,14]layer4 [1,2048,7,7]avgpool [1,2048]fc [1,1000]代码import cv2import timeimport osimport matplotlib.pyplot as pltimport torchfrom torch import nnimport torchvision.models as modelsimport torchvision.transforms as transformsimport numpy as npsavepath='vis_resnet50/features_elephant'if not os.path.exists(savepath):    os.mkdir(savepath)def draw_features(width,height,x,savename):    tic=time.time()    fig = plt.figure(figsize=(16, 16))    fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace

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