readme typo

yswi · yswi · commit 6b81dba724f9 · 2022-10-10T17:01:30.000-04:00
diff --git a/README.md b/README.md
@@ -1,58 +1,29 @@
 # VIRDO
-
-<<<<<<< HEAD
 ## Quick Start
 **Reconstruction & latent space composition** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/15T89qRkZuOFfcHYEa24mlZUuFeni1QqI#scrollTo=izxG2oGAriLK&uniqifier=1)
 
 **inference using partial pointcloud** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ZY5LVsKR8qN99C0EeyyqVnsWWg4v6vPN#scrollTo=f53ea8fc)
 
-=======
-<<<<<<< HEAD
->>>>>>> no lfs
 ## Step 0: Set up the environment
 ```angular2html
 conda create -n virdo python=3.8
 conda activate virdo
-<<<<<<< HEAD
-=======
-```
-## Colab Examples
-### Reconstruction & Latent Space Composition
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/15T89qRkZuOFfcHYEa24mlZUuFeni1QqI#scrollTo=izxG2oGAriLK&uniqifier=1)
-
-### Inference Using Partial Pointcloud
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ZY5LVsKR8qN99C0EeyyqVnsWWg4v6vPN#scrollTo=f53ea8fc)
-
-
-## Preparation
-Datasets and pretrained models can be downloaded from [here](https://www.dropbox.com/sh/4gnme6f0srhnk23/AAABlA6n8cfyo-GsaiDEqLoba?dl=0). Then put the  files as below:
-```
->>>>>>> no lfs
 conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=11.0 -c pytorch
 conda install pytorch3d=0.5.0 -c pytorch3d
 pip install open3d==0.14.1
 pip install plyfile==0.7.4
 pip install scikit-image
 ```
 
-<<<<<<< HEAD
-## Step 1: Download pretrained model and dataset
-Make sure to install wget ```$ apt-get install wget``` and unzip ```$ apt-get install unzip```
-=======
 
 ## Step 1: Download pretrained model and dataset
-Makesure to install curl ```$ apt-get install curl``` and unzip ```$ apt-get install unzip```
->>>>>>> no lfs
+Make sure to install wget ```$ apt-get install wget``` and unzip ```$ apt-get install unzip```
 ```angular2html
 source download.sh
 download_dataset
 download_pretrained
 ```
-<<<<<<< HEAD
-=======
 ### (Optionally) Manual
->>>>>>> no lfs
 Alternatively, you can manually download the datasets and pretrained models from [here](https://www.dropbox.com/sh/4gnme6f0srhnk23/AAABlA6n8cfyo-GsaiDEqLoba?dl=0). Then put the files as below:
 ```
 ── VIRDO
@@ -68,7 +39,6 @@ Alternatively, you can manually download the datasets and pretrained models from
 ## Step 2: Pretrain nominal shapes
 ```
 python pretrain.py --name <log name> --gpu_id 0
-<<<<<<< HEAD
 ```
 If you want to check the result of your pretrained model, 
 ```
@@ -80,6 +50,4 @@ then you will see the nominal reconstructions in /output/ directory.
 ## Step 3: Train entire dataset
 ```angular2html
 python train.py --pretrain_path <path>  --checkpoints_dir <dir>
-=======
->>>>>>> no lfs
 ```
diff --git a/download.sh b/download.sh
@@ -1,23 +1,14 @@
 download_dataset(){
   (
     cd 'data'
-<<<<<<< HEAD
     wget https://www.dropbox.com/s/xtyotb99gqb72xp/virdo_simul_dataset.pickle
-=======
-    curl https://www.dropbox.com/s/xtyotb99gqb72xp/virdo_simul_dataset.pickle?dl=0 -L -O -J
-    unzip -o data.zip virdo_simul_dataset.pickle
->>>>>>> no lfs
   )
 }
 
 download_pretrained(){
   (
     cd 'pretrained_model'
-<<<<<<< HEAD
     wget https://www.dropbox.com/s/7h2sqc6ouzlk94y/pretrained_model.zip
-=======
-    curl https://www.dropbox.com/sh/s1r9gxd9dz4wdkk/AADK6mHvrPtFYo_xD4EmZZdda?dl=0 -L -O -J
->>>>>>> no lfs
     unzip -o pretrained_model.zip
   )
 }

Original file line number	Diff line number	Diff line change
`@@ -1,23 +1,14 @@`
`1`	`1`	`download_dataset(){`
`2`	`2`	`(`
`3`	`3`	`cd 'data'`
`4`		`-<<<<<<< HEAD`
`5`	`4`	`wget https://www.dropbox.com/s/xtyotb99gqb72xp/virdo_simul_dataset.pickle`
`6`		`-=======`
`7`		`- curl https://www.dropbox.com/s/xtyotb99gqb72xp/virdo_simul_dataset.pickle?dl=0 -L -O -J`
`8`		`- unzip -o data.zip virdo_simul_dataset.pickle`
`9`		`->>>>>>> no lfs`
`10`	`5`	`)`
`11`	`6`	`}`
`12`	`7`
`13`	`8`	`download_pretrained(){`
`14`	`9`	`(`
`15`	`10`	`cd 'pretrained_model'`
`16`		`-<<<<<<< HEAD`
`17`	`11`	`wget https://www.dropbox.com/s/7h2sqc6ouzlk94y/pretrained_model.zip`
`18`		`-=======`
`19`		`- curl https://www.dropbox.com/sh/s1r9gxd9dz4wdkk/AADK6mHvrPtFYo_xD4EmZZdda?dl=0 -L -O -J`
`20`		`->>>>>>> no lfs`
`21`	`12`	`unzip -o pretrained_model.zip`
`22`	`13`	`)`
`23`	`14`	`}`