milopyp

#!/usr/bin/env bash

## MiloPYP Quick tutorials
## https://nextpyp.app/milopyp/quick_tutorial/
## 
## Download tutorial data in cwd and run Cellular Content Exploration
## Takes ~ 40 minutes on one NVIDIA GeForce GTX 1080 Ti
##
## James Vincent help@sbgrid.org
## Nov 1, 2024

export SBGRID_ALLOW=true    # sbgrid internal only

## Start SBGrid environment
source /programs/sbgrid.shrc
export MILOPYP_X=0.5.0_cu11.8

## Set location of milopyp python scripts:
milo_dir=/programs//x86_64-linux/milopyp/${MILOPYP_X}/cet_pick/cet_pick

## Download globular tutorial data | Globular-shaped particles (EMPIAR-10304)
wget https://nextpyp.app/files/data/milopyp_globular_tutorial.tbz
tar xvfz milopyp_globular_tutorial.tbz
mkdir data sample_data
mv *.txt ./data
mv *.rec *.ali *.tlt  ./sample_data


## -- Cellular content exploration module --

## Specify GPU to use - this must be set
export CUDA_VISIBLE_DEVICES=0

## Training 2d3d
python.milopyp  ${milo_dir}/simsiam_main.py simsiam2d3d --num_epochs 20 --exp_id test_sample --bbox 36 --dataset simsiam2d3d --arch simsiam2d3d_18 --lr 1e-3 --train_img_txt sample_train_explore_img.txt --batch_size 256 --val_intervals 20 --save_all --gauss 0.8 --dog 3,5

## Inference 2d3d
python.milopyp ${milo_dir}/simsiam_test_hm_2d3d.py simsiam2d3d --exp_id test_sample --bbox 36 --dataset simsiam2d3d --arch simsiam2d3d_18 --test_img_txt sample_train_explore_img.txt --load_model exp/simsiam2d3d/test_sample/model_20.pth --gauss 0.8 --dog 3,5