最近几篇较好论文实现代码（附源代码下载）

git clone git@github.com:ma-xu/LIVE.gitcd LIVEconda create -n live python=3.7conda activate liveconda install -y pytorch torchvision -c pytorchconda install -y numpy scikit-imageconda install -y -c anaconda cmakeconda install -y -c conda-forge ffmpegpip install svgwrite svgpathtools cssutils numba torch-tools scikit-fmm easydict visdompip install opencv-python==4.5.4.60  # please install this version to avoid segmentation fault.cd DiffVGgit submodule update --init --recursivepython setup.py installcd ..

conda activate livecd LIVE# Please modify the paramters accordingly.python main.py --config <config.yaml> --experiment <experiment-setting> --signature <given-folder-name> --target <input-image> --log_dir <log-dir># Here is an simple example:python main.py --config config/base.yaml --experiment experiment_5x1 --signature smile --target figures/smile.png --log_dir log/

python3 main.py --dataset HRSS --samples MIXED --k 100

python3 main.py --dataset HRSS --samples MIXED --k 100 --train_new_model

2022-06-03 16:13:46: Running evaluation for results/2022-02-28_08-57-34_yang-icra2021_s0_test2022-06-03 16:13:47: Evaluation results:|  success rate   |    mean accum time (s)    |                    failure (%)                     ||      (%)        |  exec  |  plan  |  total  |  hand contact   |   object drop   |    timeout     ||:---------------:|:------:|:------:|:-------:|:---------------:|:---------------:|:--------------:|| 64.58 ( 93/144) | 4.864  | 0.036  |  4.900  | 17.36 ( 25/144) | 11.81 ( 17/144) | 6.25 (  9/144) |2022-06-03 16:13:47: Printing scene ids2022-06-03 16:13:47: Success (93 scenes):---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  0    1    2    3    4    5    6    7    8    9   10   12   13   15   16   17   18   19   21   22 23   25   26   27   28   30   33   34   35   36   37   38   42   43   46   49   50   53   54   56 59   60   62   63   64   66   68   69   70   71   72   77   81   83   85   87   89   91   92   93 94   95   96   98  103  106  107  108  109  110  111  112  113  114  115  116  117  120  121  123125  126  127  128  130  131  132  133  137  138  139  141  143---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---2022-06-03 16:13:47: Failure - hand contact (25 scenes):---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  --- 11   14   20   29   39   40   41   44   45   47   51   55   57   58   65   67   74   80   82   88102  105  118  124  136---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---2022-06-03 16:13:47: Failure - object drop (17 scenes):---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  --- 24   31   32   52   61   78   79   84   86   97  101  104  119  122  134  140  142---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---2022-06-03 16:13:47: Failure - timeout (9 scenes):---  ---  ---  ---  ---  ---  ---  ---  --- 48   73   75   76   90   99  100  129  135---  ---  ---  ---  ---  ---  ---  ---  ---2022-06-03 16:13:47: Evaluation complete.

from transformers import AutoTokenizer, AutoModel# load model and tokenizertokenizer = AutoTokenizer.from_pretrained('biu-nlp/cdlm')model = AutoModel.from_pretrained('biu-nlp/cdlm')

import torchimport torch.nn as nnfrom gated_tab_transformer import GatedTabTransformer
model = GatedTabTransformer(    categories = (10, 5, 6, 5, 8),      # tuple containing the number of unique values within each category    num_continuous = 10,                # number of continuous values    transformer_dim = 32,               # dimension, paper set at 32    dim_out = 1,                        # binary prediction, but could be anything    transformer_depth = 6,              # depth, paper recommended 6    transformer_heads = 8,              # heads, paper recommends 8    attn_dropout = 0.1,                 # post-attention dropout    ff_dropout = 0.1,                   # feed forward dropout    mlp_act = nn.LeakyReLU(0),          # activation for final mlp, defaults to relu, but could be anything else (selu, etc.)    mlp_depth=4,                        # mlp hidden layers depth    mlp_dimension=32,                   # dimension of mlp layers    gmlp_enabled=True                   # gmlp or standard mlp)
x_categ = torch.randint(0, 5, (1, 5))   # category values, from 0 - max number of categories, in the order as passed into the constructor abovex_cont = torch.randn(1, 10)             # assume continuous values are already normalized individually
pred = model(x_categ, x_cont)print(pred)