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git clone [email protected]: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)