| # from transformers import CvtModel,CvtConfig,CvtForImageClassification,AutoFeatureExtractor | |
| # import torch | |
| # from PIL import Image | |
| # import requests | |
| # from dnns.vit import vit_b_16 | |
| # torch.cuda.set_device(1) | |
| # device = 'cuda' | |
| # #configuration = CvtConfig(num_labels=5) | |
| # # url = 'http://images.cocodataset.org/val2017/000000039769.jpg' | |
| # # image = Image.open(requests.get(url, stream=True).raw) | |
| # # feature_extractor = AutoFeatureExtractor.from_pretrained('microsoft/cvt-13') | |
| # model = CvtForImageClassification.from_pretrained("/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/cvt_model")#这里是规定最终我输出的分类个数，需要注意的是如果linear最终的输出不匹配的话，需要把第三个参数设置为True | |
| # sample = torch.rand((4, 3, 224, 224)).to(device) | |
| # model3 = vit_b_16(pretrained = True,num_classes=20) | |
| # model2 = torch.load("/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/entry_model/cvt_pretrained.pt",map_location=device) | |
| # model2['main'].train() | |
| # for n, m in model2['main'].named_modules(): | |
| # print(n) | |
| # if n=='cvt.encoder.stages.2.layers.2.attention.attention.convolution_projection_value.linear_projection': | |
| # print(m) | |
| # elif n== 'cvt.encoder.stages.2.layers.0.intermediate.dense': | |
| # print(m) | |
| # outputs = model2['main'](sample) | |
| # # print(**inputs) | |
| import numpy | |
| a = [1,2,3,4,5] | |
| print(a[1:]) | |