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my_container_sandbox/.singularity.d/runscript

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+#!/bin/sh
+
+    cd /workspace/difftumor
+    python eval2.py --gpu $GPU
+
+

my_container_sandbox/etc/debconf.conf

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+# This is the main config file for debconf. It tells debconf where to
+# store data. The format of this file is a set of stanzas. Each stanza
+# except the first sets up a database for debconf to use. For details, see
+# debconf.conf(5) (in the debconf-doc package).
+#
+# So first things first. This first stanza gives the names of two databases.
+
+# Debconf will use this database to store the data you enter into it,
+# and some other dynamic data.
+Config: configdb
+# Debconf will use this database to store static template data.
+Templates: templatedb
+
+# World-readable, and accepts everything but passwords.
+Name: config
+Driver: File
+Mode: 644
+Reject-Type: password
+Filename: /var/cache/debconf/config.dat
+
+# Not world readable (the default), and accepts only passwords.
+Name: passwords
+Driver: File
+Mode: 600
+Backup: false
+Required: false
+Accept-Type: password
+Filename: /var/cache/debconf/passwords.dat
+
+# Set up the configdb database. By default, it consists of a stack of two
+# databases, one to hold passwords and one for everything else.
+Name: configdb
+Driver: Stack
+Stack: config, passwords
+
+# Set up the templatedb database, which is a single flat text file
+# by default.
+Name: templatedb
+Driver: File
+Mode: 644
+Filename: /var/cache/debconf/templates.dat
+
+# Well that was pretty straightforward, and it will be enough for most
+# people's needs, but debconf's database drivers can be used to do much
+# more interesting things. For example, suppose you want to use config 
+# data from another host, which is mounted over nfs or perhaps the database
+# is accessed via LDAP. You don't want to write to the remote debconf database,
+# just read from it, so you still need a local database for local changes.
+#
+# A remote NFS mounted database, read-only. It is optional; if debconf
+# fails to use it it will not abort.
+#Name: remotedb
+#Driver: DirTree
+#Directory: /mnt/otherhost/var/cache/debconf/config
+#Readonly: true
+#Required: false
+#
+# A remote LDAP database. It is also read-only. The password is really
+# only necessary if the database is not accessible anonymously.
+# Option KeyByKey instructs the backend to retrieve keys from the LDAP
+# server individually (when they are requested), instead of loading all
+# keys at startup. The default is 0, and should only be enabled if you
+# want to track accesses to individual keys on the LDAP server side.
+#Name: remotedb
+#Driver: LDAP
+#Server: remotehost
+#BaseDN: cn=debconf,dc=domain,dc=com
+#BindDN: uid=admin,dc=domain,dc=com
+#BindPasswd: secret
+#KeyByKey: 0
+#
+# A stack consisting of two databases. Values will be read from
+# the first database in the stack to contain a value. In this example,
+# writes always go to the first database.
+#Name: fulldb
+#Driver: Stack
+#Stack: configdb, remotedb
+#
+# In this example, we'd use Config: fulldb at the top of the file
+# to make it use the combination of the databases.
+#
+# Even more complex and interesting setups are possible, see the 
+# debconf.conf(5) page for details.

my_container_sandbox/etc/deluser.conf

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+# /etc/deluser.conf: `deluser' configuration.
+
+# Remove home directory and mail spool when user is removed
+REMOVE_HOME = 0
+
+# Remove all files on the system owned by the user to be removed
+REMOVE_ALL_FILES = 0
+
+# Backup files before removing them. This options has only an effect if
+# REMOVE_HOME or REMOVE_ALL_FILES is set.
+BACKUP = 0
+
+# target directory for the backup file
+BACKUP_TO = "."
+
+# delete a group even there are still users in this group
+ONLY_IF_EMPTY = 0
+
+# exclude these filesystem types when searching for files of a user to backup
+EXCLUDE_FSTYPES = "(proc|sysfs|usbfs|devpts|tmpfs|afs)"

my_container_sandbox/etc/hostname

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+localhost.localdomain

my_container_sandbox/etc/issue.net

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+Ubuntu 20.04.6 LTS

my_container_sandbox/etc/lsb-release

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+DISTRIB_ID=Ubuntu
+DISTRIB_RELEASE=20.04
+DISTRIB_CODENAME=focal
+DISTRIB_DESCRIPTION="Ubuntu 20.04.6 LTS"

my_container_sandbox/etc/mailcap.order

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+###############################################################################
+#
+#  Mailcap.order:  This file allows a system-wide override of MIME program
+#  preferences.  See the mailcap.order(5) man page for more information.
+#
+#  After modifying this file, be sure to run /usr/sbin/update-mime (as root)
+#  to propagate the changes into the /etc/mailcap file.
+#
+################################################################################
+

my_container_sandbox/etc/mke2fs.conf

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+[defaults]
+	base_features = sparse_super,large_file,filetype,resize_inode,dir_index,ext_attr
+	default_mntopts = acl,user_xattr
+	enable_periodic_fsck = 0
+	blocksize = 4096
+	inode_size = 256
+	inode_ratio = 16384
+
+[fs_types]
+	ext3 = {
+		features = has_journal
+	}
+	ext4 = {
+		features = has_journal,extent,huge_file,flex_bg,metadata_csum,64bit,dir_nlink,extra_isize
+		inode_size = 256
+	}
+	small = {
+		inode_size = 128
+		inode_ratio = 4096
+	}
+	floppy = {
+		inode_size = 128
+		inode_ratio = 8192
+	}
+	big = {
+		inode_ratio = 32768
+	}
+	huge = {
+		inode_ratio = 65536
+	}
+	news = {
+		inode_ratio = 4096
+	}
+	largefile = {
+		inode_ratio = 1048576
+		blocksize = -1
+	}
+	largefile4 = {
+		inode_ratio = 4194304
+		blocksize = -1
+	}
+	hurd = {
+	     blocksize = 4096
+	     inode_size = 128
+	}
+
+[options]
+	fname_encoding = utf8

my_container_sandbox/etc/os-release

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+NAME="Ubuntu"
+VERSION="20.04.6 LTS (Focal Fossa)"
+ID=ubuntu
+ID_LIKE=debian
+PRETTY_NAME="Ubuntu 20.04.6 LTS"
+VERSION_ID="20.04"
+HOME_URL="https://www.ubuntu.com/"
+SUPPORT_URL="https://help.ubuntu.com/"
+BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
+PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
+VERSION_CODENAME=focal
+UBUNTU_CODENAME=focal

my_container_sandbox/etc/passwd

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+root:x:0:0:root:/root:/bin/bash
+daemon:x:1:1:daemon:/usr/sbin:/usr/sbin/nologin
+bin:x:2:2:bin:/bin:/usr/sbin/nologin
+sys:x:3:3:sys:/dev:/usr/sbin/nologin
+sync:x:4:65534:sync:/bin:/bin/sync
+games:x:5:60:games:/usr/games:/usr/sbin/nologin
+man:x:6:12:man:/var/cache/man:/usr/sbin/nologin
+lp:x:7:7:lp:/var/spool/lpd:/usr/sbin/nologin
+mail:x:8:8:mail:/var/mail:/usr/sbin/nologin
+news:x:9:9:news:/var/spool/news:/usr/sbin/nologin
+uucp:x:10:10:uucp:/var/spool/uucp:/usr/sbin/nologin
+proxy:x:13:13:proxy:/bin:/usr/sbin/nologin
+www-data:x:33:33:www-data:/var/www:/usr/sbin/nologin
+backup:x:34:34:backup:/var/backups:/usr/sbin/nologin
+list:x:38:38:Mailing List Manager:/var/list:/usr/sbin/nologin
+irc:x:39:39:ircd:/var/run/ircd:/usr/sbin/nologin
+gnats:x:41:41:Gnats Bug-Reporting System (admin):/var/lib/gnats:/usr/sbin/nologin
+nobody:x:65534:65534:nobody:/nonexistent:/usr/sbin/nologin
+_apt:x:100:65534::/nonexistent:/usr/sbin/nologin
+sshd:x:101:65534::/run/sshd:/usr/sbin/nologin

my_container_sandbox/etc/screenrc

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+# $Id: screenrc,v 1.15 2003/10/08 11:39:03 zal Exp $
+#
+# /etc/screenrc
+#
+#   This is the system wide screenrc.
+#
+#   You can use this file to change the default behavior of screen system wide
+#   or copy it to ~/.screenrc and use it as a starting point for your own
+#   settings.
+#
+#   Commands in this file are used to set options, bind screen functions to
+#   keys, redefine terminal capabilities, and to automatically establish one or
+#   more windows at the beginning of your screen session.
+#
+#   This is not a comprehensive list of options, look at the screen manual for
+#   details on everything that you can put in this file.
+#
+
+# ------------------------------------------------------------------------------
+# SCREEN SETTINGS
+# ------------------------------------------------------------------------------
+
+#startup_message off
+#nethack on
+
+#defflow on # will force screen to process ^S/^Q
+deflogin on
+#autodetach off
+
+# turn visual bell on
+vbell on
+vbell_msg "   Wuff  ----  Wuff!!  "
+
+# define a bigger scrollback, default is 100 lines
+defscrollback 1024
+
+# ------------------------------------------------------------------------------
+# SCREEN KEYBINDINGS
+# ------------------------------------------------------------------------------
+
+# Remove some stupid / dangerous key bindings
+bind ^k
+#bind L
+bind ^\
+# Make them better
+bind \\ quit
+bind K kill
+bind I login on
+bind O login off
+bind } history
+
+# An example of a "screen scraper" which will launch urlview on the current
+# screen window
+#
+#bind ^B eval "hardcopy_append off" "hardcopy -h $HOME/.screen-urlview" "screen urlview $HOME/.screen-urlview"
+
+# ------------------------------------------------------------------------------
+# TERMINAL SETTINGS
+# ------------------------------------------------------------------------------
+
+# The vt100 description does not mention "dl". *sigh*
+termcapinfo vt100 dl=5\E[M
+
+# turn sending of screen messages to hardstatus off
+hardstatus off
+# Set the hardstatus prop on gui terms to set the titlebar/icon title
+termcapinfo xterm*|rxvt*|kterm*|Eterm* hs:ts=\E]0;:fs=\007:ds=\E]0;\007
+# use this for the hard status string
+hardstatus string "%h%? users: %u%?"
+
+# An alternative hardstatus to display a bar at the bottom listing the
+# windownames and highlighting the current windowname in blue. (This is only
+# enabled if there is no hardstatus setting for your terminal)
+#
+#hardstatus lastline "%-Lw%{= BW}%50>%n%f* %t%{-}%+Lw%<"
+
+# set these terminals up to be 'optimal' instead of vt100
+termcapinfo xterm*|linux*|rxvt*|Eterm* OP
+
+# Change the xterm initialization string from is2=\E[!p\E[?3;4l\E[4l\E>
+# (This fixes the "Aborted because of window size change" konsole symptoms found
+#  in bug #134198)
+termcapinfo xterm 'is=\E[r\E[m\E[2J\E[H\E[?7h\E[?1;4;6l'
+
+# To get screen to add lines to xterm's scrollback buffer, uncomment the
+# following termcapinfo line which tells xterm to use the normal screen buffer
+# (which has scrollback), not the alternate screen buffer.
+#
+#termcapinfo xterm|xterms|xs|rxvt ti@:te@
+
+# Enable non-blocking mode to better cope with flaky ssh connections.
+defnonblock 5
+
+# ------------------------------------------------------------------------------
+# STARTUP SCREENS
+# ------------------------------------------------------------------------------
+
+# Example of automatically running some programs in windows on screen startup.
+#
+#   The following will open top in the first window, an ssh session to monkey
+#   in the next window, and then open mutt and tail in windows 8 and 9
+#   respectively.
+#
+# screen top
+# screen -t monkey ssh monkey
+# screen -t mail 8 mutt
+# screen -t daemon 9 tail -f /var/log/daemon.log
+

my_container_sandbox/etc/sensors3.conf

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+# libsensors configuration file
+# -----------------------------
+#
+# This default configuration file only includes statements which do not
+# differ from one mainboard to the next. Only label, compute and set
+# statements for internal voltage and temperature sensors are included.
+#
+# In general, local changes should not be added to this file, but rather
+# placed in custom configuration files located in /etc/sensors.d. This
+# approach makes further updates much easier.
+#
+# Such custom configuration files for specific mainboards can be found in
+# "configs" directory of lm-sensors package.
+#
+# Please contribute back a configuration of your board so other users with
+# the same hardware won't need to recreate it again and again.
+
+chip "lm78-*" "lm79-*" "lm80-*" "lm96080-*"
+
+    label temp1 "M/B Temp"
+
+
+chip "w83792d-*"
+
+    label in0 "VcoreA"
+    label in1 "VcoreB"
+    label in6 "+5V"
+    label in7 "5VSB"
+    label in8 "Vbat"
+
+    set in6_min  5.0 * 0.90
+    set in6_max  5.0 * 1.10
+    set in7_min  5.0 * 0.90
+    set in7_max  5.0 * 1.10
+    set in8_min  3.0 * 0.90
+    set in8_max  3.0 * 1.10
+
+
+chip "w83793-*"
+
+    label in0 "VcoreA"
+    label in1 "VcoreB"
+    label in7 "+5V"
+    label in8 "5VSB"
+    label in9 "Vbat"
+
+    set in7_min  5.0 * 0.90
+    set in7_max  5.0 * 1.10
+    set in8_min  5.0 * 0.90
+    set in8_max  5.0 * 1.10
+    set in9_min  3.0 * 0.90
+    set in9_max  3.0 * 1.10
+
+
+chip "w83795g-*" "w83795adg-*"
+
+    label in12 "+3.3V"
+    label in13 "3VSB"
+    label in14 "Vbat"
+
+    set in12_min  3.3 * 0.90
+    set in12_max  3.3 * 1.10
+    set in13_min  3.3 * 0.90
+    set in13_max  3.3 * 1.10
+    set in14_min  3.0 * 0.90
+    set in14_max  3.3 * 1.10
+
+
+chip "via686a-*"
+
+    label in0 "Vcore"
+    label in2 "+3.3V"
+    label in3 "+5V"
+    label in4 "+12V"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+    set in4_min 12.0 * 0.90
+    set in4_max 12.0 * 1.10
+
+
+chip "adm1025-*" "ne1619-*"
+
+    label in1 "Vcore"
+    label in2 "+3.3V"
+    label in3 "+5V"
+    label in4 "+12V"
+    label in5 "VCC"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+    set in5_min  3.3 * 0.90
+    set in5_max  3.3 * 1.10
+# Depending on how your chip is hardwired, you may or may not have
+# +12V readings.
+#    set in4_min 12.0 * 0.90
+#    set in4_max 12.0 * 1.10
+
+    label temp1 "CPU Temp"
+    label temp2 "M/B Temp"
+
+
+chip "lm87-*" "adm1024-*"
+
+    label in1 "Vcore"
+    label in2 "+3.3V"
+    label in3 "+5V"
+    label in4 "+12V"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+    set in4_min 12.0 * 0.90
+    set in4_max 12.0 * 1.10
+
+    label temp1 "M/B Temp"
+    label temp2 "CPU Temp"
+
+
+chip "it87-*" "it8712-*" "it8716-*" "it8718-*" "it8720-*"
+
+    label in8 "Vbat"
+
+
+chip "fscpos-*" "fscher-*"
+#FSC "Hermes"
+
+    label in0 "+12V"
+    label in1 "+5V"
+    label in2 "Vbat"
+
+    label temp1 "CPU Temp"
+    label temp2 "M/B Temp"
+    label temp3 "Aux Temp"
+
+
+chip "fscscy-*"
+#FSC "Scylla"
+
+    label in0 "+12V"
+    label in1 "+5V"
+    label in2 "+3.3V"
+
+    label temp1 "CPU0 Temp"
+    label temp2 "CPU1 Temp"
+    label temp3 "M/B Temp"
+    label temp4 "Aux Temp"
+
+
+chip "fschds-*"
+# Fujitsu Technology Solutions, "Hades"-Chip
+
+# Temperatures
+    label temp1 "CPU Temp"
+    label temp2 "Super I/O Temp"
+    label temp3 "System Temp"
+
+# Fans
+    label fan1 "PSU Fan"
+    label fan2 "CPU Fan"
+    label fan3 "System FAN2"
+    label fan4 "System FAN3"
+    label fan5 "System FAN4"
+
+# Voltages
+    label in0 "+12V"
+    label in1 "+5V"
+    label in2 "Vbat"
+
+chip "fscsyl-*"
+# Fujitsu Technology Solutions, "Syleus"-Chip
+
+# Temperatures
+    label temp1 "CPU Temp"
+    label temp4 "Super I/O Temp"
+    label temp5 "Northbridge Temp"
+
+# Fans
+    label fan1 "CPU Fan"
+    label fan2 "System FAN2"
+    label fan3 "System FAN3"
+    label fan4 "System FAN4"
+    label fan7 "PSU Fan"
+
+# Voltages
+    label in0 "+12V"
+    label in1 "+5V"
+    label in2 "Vbat"
+    label in3 "+3.3V"
+    label in5 "+3.3V-Aux"
+
+chip "vt1211-*"
+
+    label in5 "+3.3V"
+
+    label temp2 "SIO Temp"
+
+
+chip "vt8231-*"
+
+    label in5 "+3.3V"
+
+
+chip "smsc47m192-*"
+
+    label in1 "Vcore"
+    label in2 "+3.3V"
+    label in3 "+5V"
+    label in4 "+12V"
+    label in5 "VCC"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+    set in4_min 12.0 * 0.90
+    set in4_max 12.0 * 1.10
+    set in5_min  3.3 * 0.90
+    set in5_max  3.3 * 1.10
+
+    label temp1 "SIO Temp"
+
+
+chip "lm85-*" "lm85b-*" "lm85c-*" "adm1027-*" "adt7463-*" "adt7468-*" \
+     "emc6d100-*" "emc6d102-*" "emc6d103-*" "emc6d103s-*" 
+
+    label in1 "Vcore"
+    label in2 "+3.3V"
+    label in3 "+5V"
+    label in4 "+12V"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+# Depending on how your chip is hardwired, you may or may not have
+# +12V readings.
+#    set in4_min 12.0 * 0.90
+#    set in4_max 12.0 * 1.10
+
+    label temp2 "M/B Temp"
+
+
+chip "emc6w201-*"
+
+    label in2 "+3.3V"
+    label in3 "+5V"
+
+    label temp6 "M/B Temp"
+
+
+chip "pc87365-*" "pc87366-*"
+
+# Voltage inputs
+
+    label in7 "3VSB"
+    label in8 "VDD"
+    label in9 "Vbat"
+    label in10 "AVDD"
+
+    compute in7   @*2, @/2
+    compute in8   @*2, @/2
+    compute in10  @*2, @/2
+
+# These are the operating conditions as recommended by National
+# Semiconductor
+    set in7_min   3.0
+    set in7_max   3.6
+    set in8_min   3.0
+    set in8_max   3.6
+    set in10_min  3.0
+    set in10_max  3.6
+# Depending on the hardware setup, the battery voltage may or may not
+# be monitored.
+#    set in9_min   2.4
+#    set in9_max   3.6
+
+    label temp3 "SIO Temp"
+
+    set temp3_min    0
+    set temp3_max   70
+    set temp3_crit  85
+
+
+chip "adm1030-*" "adm1031-*"
+
+    label temp1 "M/B Temp"
+
+
+chip "w83627thf-*"
+
+    label in3 "+5V"
+    label in7 "5VSB"
+    label in8 "Vbat"
+
+    # Internal resistors
+    compute in3  @ * (1 + 34/51), @ / (1 + 34/51)
+    compute in7  @ * (1 + 34/51), @ / (1 + 34/51)
+
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+    set in7_min  5.0 * 0.90
+    set in7_max  5.0 * 1.10
+# The battery voltage may or may not be monitored.
+#    set in8_min  3.0 * 0.90
+#    set in8_max  3.0 * 1.10
+
+
+chip "w83627ehf-*" "w83627dhg-*" "w83667hg-*" "nct6775-*" "nct6776-*" \
+     "nct6779-*" "nct6791-*" "nct6795-*" "nct6796-*"
+
+    label in0 "Vcore"
+    label in2 "AVCC"
+    label in3 "+3.3V"
+    label in7 "3VSB"
+    label in8 "Vbat"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in3_min  3.3 * 0.90
+    set in3_max  3.3 * 1.10
+    set in7_min  3.3 * 0.90
+    set in7_max  3.3 * 1.10
+    set in8_min  3.0 * 0.90
+    set in8_max  3.3 * 1.10
+
+
+chip "w83627uhg-*"
+
+    label in2 "AVCC"
+    label in3 "+5V"
+    label in7 "5VSB"
+    label in8 "Vbat"
+
+    set in2_min  5.0 * 0.90
+    set in2_max  5.0 * 1.10
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+    set in7_min  5.0 * 0.90
+    set in7_max  5.0 * 1.10
+    set in8_min  3.0 * 0.90
+    set in8_max  3.3 * 1.10
+
+
+chip "f71805f-*"
+
+    label in0 "+3.3V"
+
+    set in0_min  3.3 * 0.90
+    set in0_max  3.3 * 1.10
+
+
+chip "f71872f-*"
+
+    label in0 "+3.3V"
+    label in9 "Vbat"
+    label in10 "3VSB"
+
+    set in0_min   3.3 * 0.90
+    set in0_max   3.3 * 1.10
+    set in9_min   3.0 * 0.90
+    set in9_max   3.0 * 1.10
+    set in10_min  3.3 * 0.90
+    set in10_max  3.3 * 1.10
+
+
+chip "k8temp-*"
+
+    label temp1 "Core0 Temp"
+    label temp2 "Core0 Temp"
+    label temp3 "Core1 Temp"
+    label temp4 "Core1 Temp"
+
+
+chip "dme1737-*"
+
+    label in0 "5VSB"
+    label in1 "Vcore"
+    label in2 "+3.3V"
+    label in3 "+5V"
+    label in4 "+12V"
+    label in5 "3VSB"
+    label in6 "Vbat"
+
+    label temp2 "SIO Temp"
+
+    set in0_min  5.0 * 0.90
+    set in0_max  5.0 * 1.10
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+    set in4_min 12.0 * 0.90
+    set in4_max 12.0 * 1.10
+    set in5_min  3.3 * 0.90
+    set in5_max  3.3 * 1.10
+    set in6_min  3.0 * 0.90
+    set in6_max  3.0 * 1.10
+
+
+chip "sch311x-*"
+
+    label in1 "Vcore"
+    label in2 "+3.3V"
+    label in3 "+5V"
+    label in4 "+12V"
+    label in5 "3VSB"
+    label in6 "Vbat"
+
+    label temp2 "SIO Temp"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+    set in4_min 12.0 * 0.90
+    set in4_max 12.0 * 1.10
+    set in5_min  3.3 * 0.90
+    set in5_max  3.3 * 1.10
+    set in6_min  3.0 * 0.90
+    set in6_max  3.0 * 1.10
+
+
+chip "sch5027-*"
+
+    label in0 "5VSB"
+    label in1 "Vcore"
+    label in2 "+3.3V"
+    label in5 "3VSB"
+    label in6 "Vbat"
+
+    label temp2 "SIO Temp"
+
+    set in0_min  5.0 * 0.90
+    set in0_max  5.0 * 1.10
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in5_min  3.3 * 0.90
+    set in5_max  3.3 * 1.10
+    set in6_min  3.0 * 0.90
+    set in6_max  3.0 * 1.10
+
+
+chip "sch5127-*"
+
+    label in2 "+3.3V"
+    label in5 "3VSB"
+    label in6 "Vbat"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in5_min  3.3 * 0.90
+    set in5_max  3.3 * 1.10
+    set in6_min  3.0 * 0.90
+    set in6_max  3.0 * 1.10
+
+
+chip "f71808e-*" "f71808a-*" "f71862fg-*" "f71869-*" "f71869a-*" "f71882fg-*" \
+     "f71889fg-*" "f71889ed-*" "f71889a-*"
+
+    label in0 "+3.3V"
+    label in7 "3VSB"
+    label in8 "Vbat"
+
+    compute in0  @*2, @/2
+    compute in7  @*2, @/2
+    compute in8  @*2, @/2
+
+
+chip "f71858fg-*" "f8000-*"
+
+    label in0 "+3.3V"
+    label in1 "3VSB"
+    label in2 "Vbat"
+
+    compute in0  @*2, @/2
+    compute in1  @*2, @/2
+    compute in2  @*2, @/2
+
+
+chip "f71868a-*"
+
+    label in0 "+3.3V"
+    label in7 "3VSB"
+    label in8 "Vbat"
+    label in9 "5VSB"
+
+    compute in0  @*2, @/2
+    compute in7  @*2, @/2
+    compute in8  @*2, @/2
+    compute in9  @*3, @/3
+
+
+chip "f81865f-*"
+
+    label in0 "+3.3V"
+    label in5 "3VSB"
+    label in6 "Vbat"
+
+    compute in0  @*2, @/2
+    compute in5  @*2, @/2
+    compute in6  @*2, @/2
+
+
+chip "adt7473-*" "adt7475-*"
+
+    label in2 "+3.3V"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+
+    label temp2 "Board Temp"
+
+
+chip "adt7476-*" "adt7490-*"
+
+    label in1 "Vcore"
+    label in2 "+3.3V"
+    label in3 "+5V"
+    label in4 "+12V"
+
+    set in2_min  3.3 * 0.90
+    set in2_max  3.3 * 1.10
+    set in3_min  5.0 * 0.90
+    set in3_max  5.0 * 1.10
+# Depending on how your ADT7476 is hardwired, you may or may not have
+# +12V readings.
+#    set in4_min 12.0 * 0.90
+#    set in4_max 12.0 * 1.10
+
+    label temp2 "M/B Temp"

my_container_sandbox/etc/shadow-

@@ -0,0 +1,20 @@
+root:*:19633:0:99999:7:::
+daemon:*:19633:0:99999:7:::
+bin:*:19633:0:99999:7:::
+sys:*:19633:0:99999:7:::
+sync:*:19633:0:99999:7:::
+games:*:19633:0:99999:7:::
+man:*:19633:0:99999:7:::
+lp:*:19633:0:99999:7:::
+mail:*:19633:0:99999:7:::
+news:*:19633:0:99999:7:::
+uucp:*:19633:0:99999:7:::
+proxy:*:19633:0:99999:7:::
+www-data:*:19633:0:99999:7:::
+backup:*:19633:0:99999:7:::
+list:*:19633:0:99999:7:::
+irc:*:19633:0:99999:7:::
+gnats:*:19633:0:99999:7:::
+nobody:*:19633:0:99999:7:::
+_apt:*:19633:0:99999:7:::
+sshd:*:19800:0:99999:7:::

my_container_sandbox/workspace/.pip/pip.conf

@@ -0,0 +1 @@
+[global]

my_container_sandbox/workspace/anaconda3/LICENSE.txt

@@ -0,0 +1,83 @@
+===================================
+End User License Agreement - Miniconda
+===================================
+
+Copyright 2015-2021, Anaconda, Inc.
+
+All rights reserved under the 3-clause BSD License:
+
+This End User License Agreement (the "Agreement") is a legal agreement between you and Anaconda, Inc. ("Anaconda") and governs your use of Miniconda.
+
+Subject to the terms of this Agreement, Anaconda hereby grants you a non-exclusive, non-transferable license to:
+
+  * Install and use the Miniconda.
+  * Modify and create derivative works of sample source code delivered in Miniconda subject to the Terms of Service for the Repository (as defined hereinafter) available at https://www.anaconda.com/terms-of-service, and
+
+  * Redistribute code files in source (if provided to you by Anaconda as source) and binary forms, with or without modification subject to the requirements set forth below.
+
+Anaconda may, at its option, make available patches, workarounds or other updates to Miniconda. Unless the updates are provided with their separate governing terms, they are deemed part of Miniconda licensed to you as provided in this Agreement. This Agreement does not entitle you to any support for Miniconda.
+
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+  * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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+
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+
+DISCLAIMER
+==========
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+
+Miscellaneous
+=============
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+If you want to terminate this Agreement, you may do so by discontinuing use of Miniconda. Anaconda may, at any time, terminate this Agreement and the license granted hereunder if you fail to comply with any term of this Agreement. Upon any termination of this Agreement, you agree to promptly discontinue use of the Miniconda and destroy all copies in your possession or control. Upon any termination of this Agreement all provisions survive except for the licenses granted to you.
+
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+
+Notice of Third Party Software Licenses
+=======================================
+
+Miniconda provides access to a repository (the "Repository") which contains software packages or tools licensed on an open source basis from third parties and binary packages of these third party tools. These third party software packages or tools are provided on an "as is" basis and are subject to their respective license agreements as well as this Agreement and the Terms of Service for the Repository located at https://www.anaconda.com/terms-of-service; provided, however, no restriction contained in the Terms of Service shall be construed so as to limit Your ability to download the packages contained in Miniconda provided you comply with the license for each such package. These licenses may be accessed from within the Miniconda software[1]  or https://www.anaconda.com/legal. Information regarding which license is applicable is available from within many of the third party software packages and tools and athttps://repo.anaconda.com/pkgs/main/ andhttps://repo.anaconda.com/pkgs/r/. Anaconda reserves the right, in its sole discretion, to change which third party tools are included in the Repository accessible through Miniconda.
+
+
+Intel Math Kernel Library
+-------------------------
+
+Miniconda provides access to re-distributable, run-time, shared-library files from the Intel Math Kernel Library ("MKL binaries").
+
+Copyright 2018 Intel Corporation. License available at https://software.intel.com/en-us/license/intel-simplified-software-license (the "MKL License").
+
+You may use and redistribute the MKL binaries, without modification, provided the following conditions are met:
+
+  * Redistributions must reproduce the above copyright notice and the following terms of use in the MKL binaries and in the documentation and/or other materials provided with the distribution.
+  * Neither the name of Intel nor the names of its suppliers may be used to endorse or promote products derived from the MKL binaries without specific prior written permission.
+  * No reverse engineering, decompilation, or disassembly of the MKL binaries is permitted.
+
+You are specifically authorized to use and redistribute the MKL binaries with your installation of Miniconda subject to the terms set forth in the MKL License. You are also authorized to redistribute the MKL binaries with Miniconda or in the Anaconda package that contains the MKL binaries. If needed, instructions for removing the MKL binaries after installation of Miniconda are available at https://docs.anaconda.com.
+
+cuDNN Software
+--------------
+
+Miniconda also provides access to cuDNN software binaries ("cuDNN binaries") from NVIDIA Corporation. You are specifically authorized to use the cuDNN binaries with your installation of Miniconda subject to your compliance with the license agreement located at https://docs.nvidia.com/deeplearning/sdk/cudnn-sla/index.html. You are also authorized to redistribute the cuDNN binaries with an Miniconda package that contains the cuDNN binaries. You can add or remove the cuDNN binaries utilizing the install and uninstall features in Miniconda.
+
+cuDNN binaries contain source code provided by NVIDIA Corporation.
+
+Export; Cryptography Notice
+===========================
+
+You must comply with all domestic and international export laws and regulations that apply to the software, which include restrictions on destinations, end users, and end use. Miniconda includes cryptographic software. The country in which you currently reside may have restrictions on the import, possession, use, and/or re-export to another country, of encryption software. BEFORE using any encryption software, please check your country's laws, regulations and policies concerning the import, possession, or use, and re-export of encryption software, to see if this is permitted. See the Wassenaar Arrangement http://www.wassenaar.org/ for more information.
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+
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+
+The following packages listed on https://www.anaconda.com/cryptography are included in the repository accessible through Miniconda that relate to cryptography.
+
+Last updated June 24, 2021

my_container_sandbox/workspace/difftumor/eval2.py

@@ -0,0 +1,32 @@
+import os, shutil, argparse, torch
+from tqdm import tqdm
+import sys
+sys.path.append('.')
+# print('sys.path', sys.path)
+join = os.path.join
+
+
+def eval(args, test_data_list):
+    gpu = args.gpu
+    try:
+        os.system('CUDA_VISIBLE_DEVICES={} python organ_mask_access/organ_test.py'.format(gpu))
+
+        os.system('CUDA_VISIBLE_DEVICES={} python tumor_mask_access/liver_tumor.py'.format(gpu))
+        os.system('CUDA_VISIBLE_DEVICES={} python tumor_mask_access/pancreas_tumor.py'.format(gpu))
+        os.system('CUDA_VISIBLE_DEVICES={} python tumor_mask_access/kidney_tumor.py'.format(gpu))
+    except Exception as e:
+        print('error')
+
+            
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--gpu', default=0, type=int, help='evaluation results will be saved in this folder')
+    
+    args = parser.parse_args()
+    data_path = '/workspace/inputs'
+    test_data_list = os.listdir(data_path)
+    test_data_list.sort()
+    eval(args, test_data_list)
+    

my_container_sandbox/workspace/difftumor/organ_mask_access/dataset/dataloader.py

@@ -0,0 +1,152 @@
+from monai.transforms import (
+    AsDiscrete,
+    AddChanneld,
+    Compose,
+    CropForegroundd,
+    LoadImaged,
+    Orientationd,
+    RandFlipd,
+    RandCropByPosNegLabeld,
+    RandShiftIntensityd,
+    ScaleIntensityRanged,
+    Spacingd,
+    RandRotate90d,
+    ToTensord,
+    CenterSpatialCropd,
+    Resized,
+    SpatialPadd,
+    apply_transform,
+    RandZoomd,
+    RandCropByLabelClassesd,
+)
+
+import collections.abc
+import math
+import pickle
+import shutil
+import sys
+import tempfile
+import threading
+import time
+import warnings
+from copy import copy, deepcopy
+import h5py
+
+
+import numpy as np
+import torch
+from typing import IO, TYPE_CHECKING, Any, Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union
+
+sys.path.append("..") 
+from utils.utils import get_key
+
+from torch.utils.data import Subset
+
+from monai.data import DataLoader, Dataset, list_data_collate, DistributedSampler, CacheDataset
+from monai.config import DtypeLike, KeysCollection
+from monai.transforms.transform import Transform, MapTransform
+from monai.utils.enums import TransformBackends
+from monai.config.type_definitions import NdarrayOrTensor
+from monai.transforms.io.array import LoadImage, SaveImage
+from monai.utils import GridSamplePadMode, ensure_tuple, ensure_tuple_rep
+from monai.data.image_reader import ImageReader
+from monai.utils.enums import PostFix
+import os
+
+DEFAULT_POST_FIX = PostFix.meta()
+    
+class LoadImageh5d(MapTransform):
+    def __init__(
+        self,
+        keys: KeysCollection,
+        reader: Optional[Union[ImageReader, str]] = None,
+        dtype: DtypeLike = np.float32,
+        meta_keys: Optional[KeysCollection] = None,
+        meta_key_postfix: str = DEFAULT_POST_FIX,
+        overwriting: bool = False,
+        image_only: bool = False,
+        ensure_channel_first: bool = False,
+        simple_keys: bool = False,
+        allow_missing_keys: bool = False,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__(keys, allow_missing_keys)
+        self._loader = LoadImage(reader, image_only, dtype, ensure_channel_first, simple_keys, *args, **kwargs)
+        if not isinstance(meta_key_postfix, str):
+            raise TypeError(f"meta_key_postfix must be a str but is {type(meta_key_postfix).__name__}.")
+        self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)
+        if len(self.keys) != len(self.meta_keys):
+            raise ValueError("meta_keys should have the same length as keys.")
+        self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix, len(self.keys))
+        self.overwriting = overwriting
+
+
+    def register(self, reader: ImageReader):
+        self._loader.register(reader)
+
+
+    def __call__(self, data, reader: Optional[ImageReader] = None):
+        d = dict(data)
+        for key, meta_key, meta_key_postfix in self.key_iterator(d, self.meta_keys, self.meta_key_postfix):
+            data = self._loader(d[key], reader)
+            if self._loader.image_only:
+                d[key] = data
+            else:
+                if not isinstance(data, (tuple, list)):
+                    raise ValueError("loader must return a tuple or list (because image_only=False was used).")
+                d[key] = data[0]
+                if not isinstance(data[1], dict):
+                    raise ValueError("metadata must be a dict.")
+                meta_key = meta_key or f"{key}_{meta_key_postfix}"
+                if meta_key in d and not self.overwriting:
+                    raise KeyError(f"Metadata with key {meta_key} already exists and overwriting=False.")
+                d[meta_key] = data[1]
+        return d
+
+def get_loader(args, test_data_list):
+    val_transforms = Compose(
+        [
+            LoadImaged(keys=["image"]),
+            AddChanneld(keys=["image"]),
+            Orientationd(keys=["image"], axcodes="RAS"),
+            Spacingd(
+                keys=["image"],
+                pixdim=(args.space_x, args.space_y, args.space_z),
+                mode=("bilinear"),
+            ), 
+            ScaleIntensityRanged(
+                keys=["image"],
+                a_min=args.a_min,
+                a_max=args.a_max,
+                b_min=args.b_min,
+                b_max=args.b_max,
+                clip=True,
+            ),
+            CropForegroundd(keys=["image"], source_key="image"),
+            ToTensord(keys=["image"]),
+        ]
+    )
+
+    ## test dict part
+    test_img = []
+    test_name = []
+    
+    for data_name in test_data_list:
+        name = data_name.split('.nii')[0]
+        test_img.append(os.path.join(args.data_root_path, data_name))
+        test_name.append(name)
+
+    data_dicts_test = [{'image': image, 'name': name}
+            for image, name in zip(test_img ,test_name)]
+    print('test len {}'.format(len(data_dicts_test)))
+
+    test_dataset = Dataset(data=data_dicts_test, transform=val_transforms)
+    test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=4, collate_fn=list_data_collate)
+    return test_loader, val_transforms
+
+if __name__ == "__main__":
+    train_loader, test_loader = partial_label_dataloader()
+    for index, item in enumerate(test_loader):
+        print(item['image'].shape, item['label'].shape, item['task_id'])
+        input()

my_container_sandbox/workspace/difftumor/organ_mask_access/model/DiNTS.py

@@ -0,0 +1,672 @@
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from monai.networks.blocks.dints_block import (
+    ActiConvNormBlock,
+    FactorizedIncreaseBlock,
+    FactorizedReduceBlock,
+    P3DActiConvNormBlock,
+)
+from monai.networks.layers.factories import Conv
+from monai.networks.layers.utils import get_act_layer, get_norm_layer
+from monai.utils import optional_import
+
+# solving shortest path problem
+csr_matrix, _ = optional_import("scipy.sparse", name="csr_matrix")
+dijkstra, _ = optional_import("scipy.sparse.csgraph", name="dijkstra")
+
+@torch.jit.interface
+class CellInterface(torch.nn.Module):
+    """interface for torchscriptable Cell"""
+
+    def forward(self, x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:  # type: ignore
+        pass
+
+
+@torch.jit.interface
+class StemInterface(torch.nn.Module):
+    """interface for torchscriptable Stem"""
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:  # type: ignore
+        pass
+
+
+class StemTS(StemInterface):
+    """wrapper for torchscriptable Stem"""
+
+    def __init__(self, *mod):
+        super().__init__()
+        self.mod = torch.nn.Sequential(*mod)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.mod(x)  # type: ignore
+
+
+def _dfs(node, paths):
+    """use depth first search to find all path activation combination"""
+    if node == paths:
+        return [[0], [1]]
+    child = _dfs(node + 1, paths)
+    return [[0] + _ for _ in child] + [[1] + _ for _ in child]
+
+
+class _IdentityWithRAMCost(nn.Identity):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.ram_cost = 0
+
+
+class _CloseWithRAMCost(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.ram_cost = 0
+
+    def forward(self, x):
+        return torch.tensor(0.0, requires_grad=False).to(x)
+
+
+class _ActiConvNormBlockWithRAMCost(ActiConvNormBlock):
+    """The class wraps monai layers with ram estimation. The ram_cost = total_ram/output_size is estimated.
+    Here is the estimation:
+     feature_size = output_size/out_channel
+     total_ram = ram_cost * output_size
+     total_ram = in_channel * feature_size (activation map) +
+                 in_channel * feature_size (convolution map) +
+                 out_channel * feature_size (normalization)
+               = (2*in_channel + out_channel) * output_size/out_channel
+     ram_cost = total_ram/output_size = 2 * in_channel/out_channel + 1
+    """
+
+    def __init__(
+        self,
+        in_channel: int,
+        out_channel: int,
+        kernel_size: int,
+        padding: int,
+        spatial_dims: int = 3,
+        act_name: Union[Tuple, str] = "RELU",
+        norm_name: Union[Tuple, str] = ("INSTANCE", {"affine": True}),
+    ):
+        super().__init__(in_channel, out_channel, kernel_size, padding, spatial_dims, act_name, norm_name)
+        self.ram_cost = 1 + in_channel / out_channel * 2
+
+
+class _P3DActiConvNormBlockWithRAMCost(P3DActiConvNormBlock):
+    def __init__(
+        self,
+        in_channel: int,
+        out_channel: int,
+        kernel_size: int,
+        padding: int,
+        p3dmode: int = 0,
+        act_name: Union[Tuple, str] = "RELU",
+        norm_name: Union[Tuple, str] = ("INSTANCE", {"affine": True}),
+    ):
+        super().__init__(in_channel, out_channel, kernel_size, padding, p3dmode, act_name, norm_name)
+        # 1 in_channel (activation) + 1 in_channel (convolution) +
+        # 1 out_channel (convolution) + 1 out_channel (normalization)
+        self.ram_cost = 2 + 2 * in_channel / out_channel
+
+
+class _FactorizedIncreaseBlockWithRAMCost(FactorizedIncreaseBlock):
+    def __init__(
+        self,
+        in_channel: int,
+        out_channel: int,
+        spatial_dims: int = 3,
+        act_name: Union[Tuple, str] = "RELU",
+        norm_name: Union[Tuple, str] = ("INSTANCE", {"affine": True}),
+    ):
+        super().__init__(in_channel, out_channel, spatial_dims, act_name, norm_name)
+        # s0 is upsampled 2x from s1, representing feature sizes at two resolutions.
+        # 2 * in_channel * s0 (upsample + activation) + 2 * out_channel * s0 (conv + normalization)
+        # s0 = output_size/out_channel
+        self.ram_cost = 2 * in_channel / out_channel + 2
+
+
+class _FactorizedReduceBlockWithRAMCost(FactorizedReduceBlock):
+    def __init__(
+        self,
+        in_channel: int,
+        out_channel: int,
+        spatial_dims: int = 3,
+        act_name: Union[Tuple, str] = "RELU",
+        norm_name: Union[Tuple, str] = ("INSTANCE", {"affine": True}),
+    ):
+        super().__init__(in_channel, out_channel, spatial_dims, act_name, norm_name)
+        # s0 is upsampled 2x from s1, representing feature sizes at two resolutions.
+        # in_channel * s0 (activation) + 3 * out_channel * s1 (convolution, concatenation, normalization)
+        # s0 = s1 * 2^(spatial_dims) = output_size / out_channel * 2^(spatial_dims)
+        self.ram_cost = in_channel / out_channel * 2**self._spatial_dims + 3
+
+
+class MixedOp(nn.Module):
+    """
+    The weighted averaging of cell operations.
+    Args:
+        c: number of output channels.
+        ops: a dictionary of operations. See also: ``Cell.OPS2D`` or ``Cell.OPS3D``.
+        arch_code_c: binary cell operation code. It represents the operation results added to the output.
+    """
+
+    def __init__(self, c: int, ops: dict, arch_code_c=None):
+        super().__init__()
+        if arch_code_c is None:
+            arch_code_c = np.ones(len(ops))
+        self.ops = nn.ModuleList()
+        for arch_c, op_name in zip(arch_code_c, ops):
+            self.ops.append(_CloseWithRAMCost() if arch_c == 0 else ops[op_name](c))
+
+    def forward(self, x: torch.Tensor, weight: torch.Tensor):
+        """
+        Args:
+            x: input tensor.
+            weight: learnable architecture weights for cell operations. arch_code_c are derived from it.
+        Return:
+            out: weighted average of the operation results.
+        """
+        out = 0.0
+        weight = weight.to(x)
+        for idx, _op in enumerate(self.ops):
+            out = out + _op(x) * weight[idx]
+        return out
+
+
+class Cell(CellInterface):
+    """
+    The basic class for cell operation search, which contains a preprocessing operation and a mixed cell operation.
+    Each cell is defined on a `path` in the topology search space.
+    Args:
+        c_prev: number of input channels
+        c: number of output channels
+        rate: resolution change rate. It represents the preprocessing operation before the mixed cell operation.
+            ``-1`` for 2x downsample, ``1`` for 2x upsample, ``0`` for no change of resolution.
+        arch_code_c: cell operation code
+    """
+
+    DIRECTIONS = 3
+    # Possible output paths for `Cell`.
+    #
+    #       - UpSample
+    #      /
+    # +--+/
+    # |  |--- Identity or AlignChannels
+    # +--+\
+    #      \
+    #       - Downsample
+
+    # Define 2D operation set, parameterized by the number of channels
+    OPS2D = {
+        "skip_connect": lambda _c: _IdentityWithRAMCost(),
+        "conv_3x3": lambda c: _ActiConvNormBlockWithRAMCost(c, c, 3, padding=1, spatial_dims=2),
+    }
+
+    # Define 3D operation set, parameterized by the number of channels
+    OPS3D = {
+        "skip_connect": lambda _c: _IdentityWithRAMCost(),
+        "conv_3x3x3": lambda c: _ActiConvNormBlockWithRAMCost(c, c, 3, padding=1, spatial_dims=3),
+        "conv_3x3x1": lambda c: _P3DActiConvNormBlockWithRAMCost(c, c, 3, padding=1, p3dmode=0),
+        "conv_3x1x3": lambda c: _P3DActiConvNormBlockWithRAMCost(c, c, 3, padding=1, p3dmode=1),
+        "conv_1x3x3": lambda c: _P3DActiConvNormBlockWithRAMCost(c, c, 3, padding=1, p3dmode=2),
+    }
+
+    # Define connection operation set, parameterized by the number of channels
+    ConnOPS = {
+        "up": _FactorizedIncreaseBlockWithRAMCost,
+        "down": _FactorizedReduceBlockWithRAMCost,
+        "identity": _IdentityWithRAMCost,
+        "align_channels": _ActiConvNormBlockWithRAMCost,
+    }
+
+    def __init__(
+        self,
+        c_prev: int,
+        c: int,
+        rate: int,
+        arch_code_c=None,
+        spatial_dims: int = 3,
+        act_name: Union[Tuple, str] = "RELU",
+        norm_name: Union[Tuple, str] = ("INSTANCE", {"affine": True}),
+    ):
+        super().__init__()
+        self._spatial_dims = spatial_dims
+        self._act_name = act_name
+        self._norm_name = norm_name
+
+        if rate == -1:  # downsample
+            self.preprocess = self.ConnOPS["down"](
+                c_prev, c, spatial_dims=self._spatial_dims, act_name=self._act_name, norm_name=self._norm_name
+            )
+        elif rate == 1:  # upsample
+            self.preprocess = self.ConnOPS["up"](
+                c_prev, c, spatial_dims=self._spatial_dims, act_name=self._act_name, norm_name=self._norm_name
+            )
+        else:
+            if c_prev == c:
+                self.preprocess = self.ConnOPS["identity"]()
+            else:
+                self.preprocess = self.ConnOPS["align_channels"](
+                    c_prev, c, 1, 0, spatial_dims=self._spatial_dims, act_name=self._act_name, norm_name=self._norm_name
+                )
+
+        # Define 2D operation set, parameterized by the number of channels
+        self.OPS2D = {
+            "skip_connect": lambda _c: _IdentityWithRAMCost(),
+            "conv_3x3": lambda c: _ActiConvNormBlockWithRAMCost(
+                c, c, 3, padding=1, spatial_dims=2, act_name=self._act_name, norm_name=self._norm_name
+            ),
+        }
+
+        # Define 3D operation set, parameterized by the number of channels
+        self.OPS3D = {
+            "skip_connect": lambda _c: _IdentityWithRAMCost(),
+            "conv_3x3x3": lambda c: _ActiConvNormBlockWithRAMCost(
+                c, c, 3, padding=1, spatial_dims=3, act_name=self._act_name, norm_name=self._norm_name
+            ),
+            "conv_3x3x1": lambda c: _P3DActiConvNormBlockWithRAMCost(
+                c, c, 3, padding=1, p3dmode=0, act_name=self._act_name, norm_name=self._norm_name
+            ),
+            "conv_3x1x3": lambda c: _P3DActiConvNormBlockWithRAMCost(
+                c, c, 3, padding=1, p3dmode=1, act_name=self._act_name, norm_name=self._norm_name
+            ),
+            "conv_1x3x3": lambda c: _P3DActiConvNormBlockWithRAMCost(
+                c, c, 3, padding=1, p3dmode=2, act_name=self._act_name, norm_name=self._norm_name
+            ),
+        }
+
+        self.OPS = {}
+        if self._spatial_dims == 2:
+            self.OPS = self.OPS2D
+        elif self._spatial_dims == 3:
+            self.OPS = self.OPS3D
+        else:
+            raise NotImplementedError(f"Spatial dimensions {self._spatial_dims} is not supported.")
+
+        self.op = MixedOp(c, self.OPS, arch_code_c)
+
+    def forward(self, x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: input tensor
+            weight: weights for different operations.
+        """
+        x = self.preprocess(x)
+        x = self.op(x, weight)
+        return x
+
+class TopologyConstruction(nn.Module):
+    """
+    The base class for `TopologyInstance` and `TopologySearch`.
+
+    Args:
+        arch_code: `[arch_code_a, arch_code_c]`, numpy arrays. The architecture codes defining the model.
+            For example, for a ``num_depths=4, num_blocks=12`` search space:
+
+            - `arch_code_a` is a 12x10 (10 paths) binary matrix representing if a path is activated.
+            - `arch_code_c` is a 12x10x5 (5 operations) binary matrix representing if a cell operation is used.
+            - `arch_code` in ``__init__()`` is used for creating the network and remove unused network blocks. If None,
+
+            all paths and cells operations will be used, and must be in the searching stage (is_search=True).
+        channel_mul: adjust intermediate channel number, default is 1.
+        cell: operation of each node.
+        num_blocks: number of blocks (depth in the horizontal direction) of the DiNTS search space.
+        num_depths: number of image resolutions of the DiNTS search space: 1, 1/2, 1/4 ... in each dimension.
+        use_downsample: use downsample in the stem. If False, the search space will be in resolution [1, 1/2, 1/4, 1/8],
+            if True, the search space will be in resolution [1/2, 1/4, 1/8, 1/16].
+        device: `'cpu'`, `'cuda'`, or device ID.
+
+
+    Predefined variables:
+        `filter_nums`: default to 32. Double the number of channels after downsample.
+        topology related variables:
+
+            - `arch_code2in`: path activation to its incoming node index (resolution). For depth = 4,
+              arch_code2in = [0, 1, 0, 1, 2, 1, 2, 3, 2, 3]. The first path outputs from node 0 (top resolution),
+              the second path outputs from node 1 (second resolution in the search space),
+              the third path outputs from node 0, etc.
+            - `arch_code2ops`: path activation to operations of upsample 1, keep 0, downsample -1. For depth = 4,
+              arch_code2ops = [0, 1, -1, 0, 1, -1, 0, 1, -1, 0]. The first path does not change
+              resolution, the second path perform upsample, the third perform downsample, etc.
+            - `arch_code2out`: path activation to its output node index.
+              For depth = 4, arch_code2out = [0, 0, 1, 1, 1, 2, 2, 2, 3, 3],
+              the first and second paths connects to node 0 (top resolution), the 3,4,5 paths connects to node 1, etc.
+    """
+
+    def __init__(
+        self,
+        arch_code: Optional[list] = None,
+        channel_mul: float = 1.0,
+        cell=Cell,
+        num_blocks: int = 6,
+        num_depths: int = 3,
+        spatial_dims: int = 3,
+        act_name: Union[Tuple, str] = "RELU",
+        norm_name: Union[Tuple, str] = ("INSTANCE", {"affine": True}),
+        use_downsample: bool = True,
+        device: str = "cpu",
+    ):
+
+        super().__init__()
+
+        self.filter_nums = [int(n_feat * channel_mul) for n_feat in (32, 64, 128, 256, 512)]
+        self.num_blocks = num_blocks
+        self.num_depths = num_depths
+        self._spatial_dims = spatial_dims
+        self._act_name = act_name
+        self._norm_name = norm_name
+        self.use_downsample = use_downsample
+        self.device = device
+        self.num_cell_ops = 0
+        if self._spatial_dims == 2:
+            self.num_cell_ops = len(cell.OPS2D)
+        elif self._spatial_dims == 3:
+            self.num_cell_ops = len(cell.OPS3D)
+
+        # Calculate predefined parameters for topology search and decoding
+        arch_code2in, arch_code2out = [], []
+        for i in range(Cell.DIRECTIONS * self.num_depths - 2):
+            arch_code2in.append((i + 1) // Cell.DIRECTIONS - 1 + (i + 1) % Cell.DIRECTIONS)
+        arch_code2ops = ([-1, 0, 1] * self.num_depths)[1:-1]
+        for m in range(self.num_depths):
+            arch_code2out.extend([m, m, m])
+        arch_code2out = arch_code2out[1:-1]
+        self.arch_code2in = arch_code2in
+        self.arch_code2ops = arch_code2ops
+        self.arch_code2out = arch_code2out
+
+        # define NAS search space
+        if arch_code is None:
+            arch_code_a = torch.ones((self.num_blocks, len(self.arch_code2out))).to(self.device)
+            arch_code_c = torch.ones((self.num_blocks, len(self.arch_code2out), self.num_cell_ops)).to(self.device)
+        else:
+            arch_code_a = torch.from_numpy(arch_code[0]).to(self.device)
+            arch_code_c = F.one_hot(torch.from_numpy(arch_code[1]).to(torch.int64), self.num_cell_ops).to(self.device)
+
+        self.arch_code_a = arch_code_a
+        self.arch_code_c = arch_code_c
+        # define cell operation on each path
+        self.cell_tree = nn.ModuleDict()
+        for blk_idx in range(self.num_blocks):
+            for res_idx in range(len(self.arch_code2out)):
+                if self.arch_code_a[blk_idx, res_idx] == 1:
+                    self.cell_tree[str((blk_idx, res_idx))] = cell(
+                        self.filter_nums[self.arch_code2in[res_idx] + int(use_downsample)],
+                        self.filter_nums[self.arch_code2out[res_idx] + int(use_downsample)],
+                        self.arch_code2ops[res_idx],
+                        self.arch_code_c[blk_idx, res_idx],
+                        self._spatial_dims,
+                        self._act_name,
+                        self._norm_name,
+                    )
+
+    def forward(self, x):
+        """This function to be implemented by the architecture instances or search spaces."""
+        pass
+
+class TopologyInstance(TopologyConstruction):
+    """
+    Instance of the final searched architecture. Only used in re-training/inference stage.
+    """
+
+    def __init__(
+        self,
+        arch_code=None,
+        channel_mul: float = 1.0,
+        cell=Cell,
+        num_blocks: int = 6,
+        num_depths: int = 3,
+        spatial_dims: int = 3,
+        act_name: Union[Tuple, str] = "RELU",
+        norm_name: Union[Tuple, str] = ("INSTANCE", {"affine": True}),
+        use_downsample: bool = True,
+        device: str = "cpu",
+    ):
+        """
+        Initialize DiNTS topology search space of neural architectures.
+        """
+        if arch_code is None:
+            warnings.warn("arch_code not provided when not searching.")
+
+        super().__init__(
+            arch_code=arch_code,
+            channel_mul=channel_mul,
+            cell=cell,
+            num_blocks=num_blocks,
+            num_depths=num_depths,
+            spatial_dims=spatial_dims,
+            act_name=act_name,
+            norm_name=norm_name,
+            use_downsample=use_downsample,
+            device=device,
+        )
+
+
+    def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
+        """
+        Args:
+            x: input tensor.
+        """
+        # generate path activation probability
+        inputs, outputs = x, [torch.tensor(0.0).to(x[0])] * self.num_depths
+        for blk_idx in range(self.num_blocks):
+            outputs = [torch.tensor(0.0).to(x[0])] * self.num_depths
+            for res_idx, activation in enumerate(self.arch_code_a[blk_idx].data):
+                if activation:
+                    mod: CellInterface = self.cell_tree[str((blk_idx, res_idx))]
+                    _out = mod.forward(
+                        x=inputs[self.arch_code2in[res_idx]], weight=torch.ones_like(self.arch_code_c[blk_idx, res_idx])
+                    )
+                    outputs[self.arch_code2out[res_idx]] = outputs[self.arch_code2out[res_idx]] + _out
+            inputs = outputs
+
+        return inputs
+
+class DiNTS(nn.Module):
+    """
+    Reimplementation of DiNTS based on
+    "DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
+    <https://arxiv.org/abs/2103.15954>".
+
+    The model contains a pre-defined multi-resolution stem block (defined in this class) and a
+    DiNTS space (defined in :py:class:`monai.networks.nets.TopologyInstance` and
+    :py:class:`monai.networks.nets.TopologySearch`).
+
+    The stem block is for: 1) input downsample and 2) output upsample to original size.
+    The model downsamples the input image by 2 (if ``use_downsample=True``).
+    The downsampled image is downsampled by [1, 2, 4, 8] times (``num_depths=4``) and used as input to the
+    DiNTS search space (``TopologySearch``) or the DiNTS instance (``TopologyInstance``).
+
+        - ``TopologyInstance`` is the final searched model. The initialization requires the searched architecture codes.
+        - ``TopologySearch`` is a multi-path topology and cell operation search space.
+          The architecture codes will be initialized as one.
+        - ``TopologyConstruction`` is the parent class which constructs the instance and search space.
+
+    To meet the requirements of the structure, the input size for each spatial dimension should be:
+    divisible by 2 ** (num_depths + 1).
+
+    Args:
+        dints_space: DiNTS search space. The value should be instance of `TopologyInstance` or `TopologySearch`.
+        in_channels: number of input image channels.
+        num_classes: number of output segmentation classes.
+        act_name: activation name, default to 'RELU'.
+        norm_name: normalization used in convolution blocks. Default to `InstanceNorm`.
+        spatial_dims: spatial 2D or 3D inputs.
+        use_downsample: use downsample in the stem.
+            If ``False``, the search space will be in resolution [1, 1/2, 1/4, 1/8],
+            if ``True``, the search space will be in resolution [1/2, 1/4, 1/8, 1/16].
+        node_a: node activation numpy matrix. Its shape is `(num_depths, num_blocks + 1)`.
+            +1 for multi-resolution inputs.
+            In model searching stage, ``node_a`` can be None. In deployment stage, ``node_a`` cannot be None.
+    """
+
+    def __init__(
+        self,
+        dints_space,
+        in_channels: int,
+        num_classes: int,
+        act_name: Union[Tuple, str] = "RELU",
+        norm_name: Union[Tuple, str] = ("INSTANCE", {"affine": True}),
+        spatial_dims: int = 3,
+        use_downsample: bool = True,
+        node_a=None,
+    ):
+        super().__init__()
+
+        self.dints_space = dints_space
+        self.filter_nums = dints_space.filter_nums
+        self.num_blocks = dints_space.num_blocks
+        self.num_depths = dints_space.num_depths
+        if spatial_dims not in (2, 3):
+            raise NotImplementedError(f"Spatial dimensions {spatial_dims} is not supported.")
+        self._spatial_dims = spatial_dims
+        if node_a is None:
+            self.node_a = torch.ones((self.num_blocks + 1, self.num_depths))
+        else:
+            self.node_a = node_a
+
+        # define stem operations for every block
+        conv_type = Conv[Conv.CONV, spatial_dims]
+        self.stem_down = nn.ModuleDict()
+        self.stem_up = nn.ModuleDict()
+        mode = "trilinear" if self._spatial_dims == 3 else "bilinear"
+        for res_idx in range(self.num_depths):
+            # define downsample stems before DiNTS search
+            if use_downsample:
+                self.stem_down[str(res_idx)] = StemTS(
+                    nn.Upsample(scale_factor=1 / (2**res_idx), mode=mode, align_corners=True),
+                    conv_type(
+                        in_channels=in_channels,
+                        out_channels=self.filter_nums[res_idx],
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        groups=1,
+                        bias=False,
+                        dilation=1,
+                    ),
+                    get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[res_idx]),
+                    get_act_layer(name=act_name),
+                    conv_type(
+                        in_channels=self.filter_nums[res_idx],
+                        out_channels=self.filter_nums[res_idx + 1],
+                        kernel_size=3,
+                        stride=2,
+                        padding=1,
+                        groups=1,
+                        bias=False,
+                        dilation=1,
+                    ),
+                    get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[res_idx + 1]),
+                )
+                self.stem_up[str(res_idx)] = StemTS(
+                    get_act_layer(name=act_name),
+                    conv_type(
+                        in_channels=self.filter_nums[res_idx + 1],
+                        out_channels=self.filter_nums[res_idx],
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        groups=1,
+                        bias=False,
+                        dilation=1,
+                    ),
+                    get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[res_idx]),
+                    nn.Upsample(scale_factor=2, mode=mode, align_corners=True),
+                )
+
+            else:
+                self.stem_down[str(res_idx)] = StemTS(
+                    nn.Upsample(scale_factor=1 / (2**res_idx), mode=mode, align_corners=True),
+                    conv_type(
+                        in_channels=in_channels,
+                        out_channels=self.filter_nums[res_idx],
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        groups=1,
+                        bias=False,
+                        dilation=1,
+                    ),
+                    get_norm_layer(name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[res_idx]),
+                )
+                self.stem_up[str(res_idx)] = StemTS(
+                    get_act_layer(name=act_name),
+                    conv_type(
+                        in_channels=self.filter_nums[res_idx],
+                        out_channels=self.filter_nums[max(res_idx - 1, 0)],
+                        kernel_size=3,
+                        stride=1,
+                        padding=1,
+                        groups=1,
+                        bias=False,
+                        dilation=1,
+                    ),
+                    get_norm_layer(
+                        name=norm_name, spatial_dims=spatial_dims, channels=self.filter_nums[max(res_idx - 1, 0)]
+                    ),
+                    nn.Upsample(scale_factor=2 ** (res_idx != 0), mode=mode, align_corners=True),
+                )
+
+    def weight_parameters(self):
+        return [param for name, param in self.named_parameters()]
+
+    def forward(self, x: torch.Tensor):
+        """
+        Prediction based on dynamic arch_code.
+
+        Args:
+            x: input tensor.
+        """
+        inputs = []
+        for d in range(self.num_depths):
+            # allow multi-resolution input
+            _mod_w: StemInterface = self.stem_down[str(d)]
+            x_out = _mod_w.forward(x)
+            if self.node_a[0][d]:
+                inputs.append(x_out)
+            else:
+                inputs.append(torch.zeros_like(x_out))
+
+        outputs = self.dints_space(inputs)
+
+        blk_idx = self.num_blocks - 1
+        start = False
+        _temp: torch.Tensor = torch.empty(0)
+        for res_idx in range(self.num_depths - 1, -1, -1):
+            _mod_up: StemInterface = self.stem_up[str(res_idx)]
+            if start:
+                _temp = _mod_up.forward(outputs[res_idx] + _temp)
+            elif self.node_a[blk_idx + 1][res_idx]:
+                start = True
+                _temp = _mod_up.forward(outputs[res_idx])
+
+        return outputs[-1], _temp
+
+if __name__ == "__main__":
+    ckpt = torch.load('./arch_code_cvpr.pth')
+    node_a = ckpt["node_a"]
+    arch_code_a = ckpt["arch_code_a"]
+    arch_code_c = ckpt["arch_code_c"]
+
+    dints_space = TopologyInstance(
+            channel_mul=1.0,
+            num_blocks=12,
+            num_depths=4,
+            use_downsample=True,
+            arch_code=[arch_code_a, arch_code_c]
+        )
+
+    net = DiNTS(
+            dints_space=dints_space,
+            in_channels=1,
+            num_classes=3,
+            use_downsample=True,
+            node_a=node_a,
+        )
+    input_tensor = torch.zeros(1, 1, 96, 96, 96)
+    net(input_tensor)

my_container_sandbox/workspace/difftumor/organ_mask_access/model/Unetpp.py

@@ -0,0 +1,152 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Sequence, Union
+
+import torch
+import torch.nn as nn
+
+from monai.networks.layers.factories import Conv
+from monai.networks.nets.basic_unet import Down, TwoConv, UpCat
+from monai.utils import ensure_tuple_rep
+
+
+class BasicUNetPlusPlus(nn.Module):
+    def __init__(
+        self,
+        spatial_dims: int = 3,
+        in_channels: int = 1,
+        out_channels: int = 2,
+        features: Sequence[int] = (32, 32, 64, 128, 256, 32),
+        deep_supervision: bool = False,
+        act: Union[str, tuple] = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}),
+        norm: Union[str, tuple] = ("instance", {"affine": True}),
+        bias: bool = True,
+        dropout: Union[float, tuple] = 0.0,
+        upsample: str = "deconv",
+    ):
+        """
+        A UNet++ implementation with 1D/2D/3D supports.
+
+        Based on:
+
+            Zhou et al. "UNet++: A Nested U-Net Architecture for Medical Image
+            Segmentation". 4th Deep Learning in Medical Image Analysis (DLMIA)
+            Workshop, DOI: https://doi.org/10.48550/arXiv.1807.10165
+
+
+        Args:
+            spatial_dims: number of spatial dimensions. Defaults to 3 for spatial 3D inputs.
+            in_channels: number of input channels. Defaults to 1.
+            out_channels: number of output channels. Defaults to 2.
+            features: six integers as numbers of features.
+                Defaults to ``(32, 32, 64, 128, 256, 32)``,
+
+                - the first five values correspond to the five-level encoder feature sizes.
+                - the last value corresponds to the feature size after the last upsampling.
+
+            deep_supervision: whether to prune the network at inference time. Defaults to False. If true, returns a list,
+                whose elements correspond to outputs at different nodes.
+            act: activation type and arguments. Defaults to LeakyReLU.
+            norm: feature normalization type and arguments. Defaults to instance norm.
+            bias: whether to have a bias term in convolution blocks. Defaults to True.
+                According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,
+                if a conv layer is directly followed by a batch norm layer, bias should be False.
+            dropout: dropout ratio. Defaults to no dropout.
+            upsample: upsampling mode, available options are
+                ``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.
+
+        Examples::
+
+            # for spatial 2D
+            >>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128))
+
+            # for spatial 2D, with deep supervision enabled
+            >>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), deep_supervision=True)
+
+            # for spatial 2D, with group norm
+            >>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), norm=("group", {"num_groups": 4}))
+
+            # for spatial 3D
+            >>> net = BasicUNetPlusPlus(spatial_dims=3, features=(32, 32, 64, 128, 256, 32))
+
+        See Also
+            - :py:class:`monai.networks.nets.BasicUNet`
+            - :py:class:`monai.networks.nets.DynUNet`
+            - :py:class:`monai.networks.nets.UNet`
+
+        """
+        super().__init__()
+
+        self.deep_supervision = deep_supervision
+
+        fea = ensure_tuple_rep(features, 6)
+        print(f"BasicUNetPlusPlus features: {fea}.")
+
+        self.conv_0_0 = TwoConv(spatial_dims, in_channels, fea[0], act, norm, bias, dropout)
+        self.conv_1_0 = Down(spatial_dims, fea[0], fea[1], act, norm, bias, dropout)
+        self.conv_2_0 = Down(spatial_dims, fea[1], fea[2], act, norm, bias, dropout)
+        self.conv_3_0 = Down(spatial_dims, fea[2], fea[3], act, norm, bias, dropout)
+        self.conv_4_0 = Down(spatial_dims, fea[3], fea[4], act, norm, bias, dropout)
+
+        self.upcat_0_1 = UpCat(spatial_dims, fea[1], fea[0], fea[0], act, norm, bias, dropout, upsample, halves=False)
+        self.upcat_1_1 = UpCat(spatial_dims, fea[2], fea[1], fea[1], act, norm, bias, dropout, upsample)
+        self.upcat_2_1 = UpCat(spatial_dims, fea[3], fea[2], fea[2], act, norm, bias, dropout, upsample)
+        self.upcat_3_1 = UpCat(spatial_dims, fea[4], fea[3], fea[3], act, norm, bias, dropout, upsample)
+
+        self.upcat_0_2 = UpCat(
+            spatial_dims, fea[1], fea[0] * 2, fea[0], act, norm, bias, dropout, upsample, halves=False
+        )
+        self.upcat_1_2 = UpCat(spatial_dims, fea[2], fea[1] * 2, fea[1], act, norm, bias, dropout, upsample)
+        self.upcat_2_2 = UpCat(spatial_dims, fea[3], fea[2] * 2, fea[2], act, norm, bias, dropout, upsample)
+
+        self.upcat_0_3 = UpCat(
+            spatial_dims, fea[1], fea[0] * 3, fea[0], act, norm, bias, dropout, upsample, halves=False
+        )
+        self.upcat_1_3 = UpCat(spatial_dims, fea[2], fea[1] * 3, fea[1], act, norm, bias, dropout, upsample)
+
+        self.upcat_0_4 = UpCat(
+            spatial_dims, fea[1], fea[0] * 4, fea[5], act, norm, bias, dropout, upsample, halves=False
+        )
+
+
+    def forward(self, x: torch.Tensor):
+        """
+        Args:
+            x: input should have spatially N dimensions
+                ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N-1])``, N is defined by `dimensions`.
+                It is recommended to have ``dim_n % 16 == 0`` to ensure all maxpooling inputs have
+                even edge lengths.
+
+        Returns:
+            A torch Tensor of "raw" predictions in shape
+            ``(Batch, out_channels, dim_0[, dim_1, ..., dim_N-1])``.
+        """
+        x_0_0 = self.conv_0_0(x)
+        x_1_0 = self.conv_1_0(x_0_0)
+        x_0_1 = self.upcat_0_1(x_1_0, x_0_0)
+
+        x_2_0 = self.conv_2_0(x_1_0)
+        x_1_1 = self.upcat_1_1(x_2_0, x_1_0)
+        x_0_2 = self.upcat_0_2(x_1_1, torch.cat([x_0_0, x_0_1], dim=1))
+
+        x_3_0 = self.conv_3_0(x_2_0)
+        x_2_1 = self.upcat_2_1(x_3_0, x_2_0)
+        x_1_2 = self.upcat_1_2(x_2_1, torch.cat([x_1_0, x_1_1], dim=1))
+        x_0_3 = self.upcat_0_3(x_1_2, torch.cat([x_0_0, x_0_1, x_0_2], dim=1))
+
+        x_4_0 = self.conv_4_0(x_3_0)
+        x_3_1 = self.upcat_3_1(x_4_0, x_3_0)
+        x_2_2 = self.upcat_2_2(x_3_1, torch.cat([x_2_0, x_2_1], dim=1))
+        x_1_3 = self.upcat_1_3(x_2_2, torch.cat([x_1_0, x_1_1, x_1_2], dim=1))
+        x_0_4 = self.upcat_0_4(x_1_3, torch.cat([x_0_0, x_0_1, x_0_2, x_0_3], dim=1))
+
+        return x_4_0, x_0_4

my_container_sandbox/workspace/difftumor/organ_mask_access/organ_test.py

@@ -0,0 +1,135 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+import numpy as np
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+import os
+import argparse
+import time
+from monai.inferers import sliding_window_inference
+
+from model.Universal_model import Universal_model
+from dataset.dataloader import get_loader
+from utils.utils import visualize_label
+from utils.utils import TEMPLATE, ORGAN_NAME, NUM_CLASS
+
+torch.multiprocessing.set_sharing_strategy('file_system')
+
+def cal_dice(pred, true):
+    intersection = np.sum(pred[true==1]) * 2.0
+    dice = intersection / (np.sum(pred) + np.sum(true))
+    return dice
+
+def validation(model, ValLoader, val_transforms, args):
+    
+    model.eval()
+    dice_list = {}
+    for key in TEMPLATE.keys():
+        dice_list[key] = np.zeros((2, NUM_CLASS)) # 1st row for dice, 2nd row for count
+    for index, batch in enumerate(tqdm(ValLoader)):
+        image, name = batch["image"].cuda(), batch["name"]
+        print(batch["name"])
+        name = name[0]
+        save_dir = os.path.join(args.save_path, name, 'predictions')
+        os.makedirs(save_dir, exist_ok=True)
+        with torch.no_grad():
+            pred = sliding_window_inference(image, (args.roi_x, args.roi_y, args.roi_z), 1, model, overlap=0.5, mode='gaussian')
+            pred_sigmoid = F.sigmoid(pred)
+
+        pred_hard = (pred_sigmoid>0.5).to(pred_sigmoid)
+        pred_hard = pred_hard.cpu()
+        torch.cuda.empty_cache()
+
+        liver_mask = pred_hard[0][5]
+        pancreas_mask = pred_hard[0][10]
+        kidney_mask = pred_hard[0][1]
+        left_kidney_mask = pred_hard[0][2]
+        kidney_mask[left_kidney_mask==1] = 1
+        torch.cuda.empty_cache()
+        
+
+        batch['liver'] = liver_mask[None,None,:]
+        batch['pancreas'] = pancreas_mask[None,None,:]
+        batch['kidney'] = kidney_mask[None,None,:]
+
+
+        visualize_label(batch, save_dir, val_transforms)
+        import gzip, shutil
+        # if os.path.exists(os.path.join(args.data_root_path, name+'.nii.gz')):
+        #     with open(os.path.join(args.data_root_path, name+'.nii.gz'), 'rb') as f_in:
+        #         with gzip.open(os.path.join(args.save_path, name, 'ct.nii.gz'), 'wb') as f_out:
+        #             shutil.copyfileobj(f_in, f_out)
+        # elif os.path.exists(os.path.join(args.data_root_path, name+'.nii')):
+        #     with open(os.path.join(args.data_root_path, name+'.nii'), 'rb') as f_in:
+        #         with gzip.open(os.path.join(args.save_path, name, 'ct.nii.gz'), 'wb') as f_out:
+        #             shutil.copyfileobj(f_in, f_out)
+
+
+        if os.path.exists(os.path.join(args.data_root_path, name+'.nii.gz')):
+            shutil.copyfile(os.path.join(args.data_root_path, name+'.nii.gz'), os.path.join(args.save_path, name, 'ct.nii.gz'))
+        elif os.path.exists(os.path.join(args.data_root_path, name+'.nii')):
+            shutil.copyfile(os.path.join(args.data_root_path, name+'.nii'), os.path.join(args.save_path, name, 'ct.nii'))
+                    
+        os.rename(os.path.join(save_dir, name+'_liver.nii.gz'), os.path.join(save_dir, 'liver.nii.gz'))
+        os.rename(os.path.join(save_dir, name+'_pancreas.nii.gz'), os.path.join(save_dir, 'pancreas.nii.gz'))
+        os.rename(os.path.join(save_dir, name+'_kidney.nii.gz'), os.path.join(save_dir, 'kidney.nii.gz'))
+        breakpoint()
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    ## dataset
+    parser.add_argument('--data_root_path', default='/workspace/inputs/', help='data root path')
+    parser.add_argument('--save_path', default='/workspace/outputs/', help='data root path')
+    parser.add_argument('--batch_size', default=1, type=int, help='batch size')
+    parser.add_argument('--num_workers', default=8, type=int, help='workers numebr for DataLoader')
+    parser.add_argument('--a_min', default=-175, type=float, help='a_min in ScaleIntensityRanged')
+    parser.add_argument('--a_max', default=250, type=float, help='a_max in ScaleIntensityRanged')
+    parser.add_argument('--b_min', default=0.0, type=float, help='b_min in ScaleIntensityRanged')
+    parser.add_argument('--b_max', default=1.0, type=float, help='b_max in ScaleIntensityRanged')
+    parser.add_argument('--space_x', default=1.5, type=float, help='spacing in x direction')
+    parser.add_argument('--space_y', default=1.5, type=float, help='spacing in y direction')
+    parser.add_argument('--space_z', default=1.5, type=float, help='spacing in z direction')
+    parser.add_argument('--roi_x', default=96, type=int, help='roi size in x direction')
+    parser.add_argument('--roi_y', default=96, type=int, help='roi size in y direction')
+    parser.add_argument('--roi_z', default=96, type=int, help='roi size in z direction')
+    parser.add_argument('--num_samples', default=1, type=int, help='sample number in each ct')
+
+    args = parser.parse_args()
+
+    # prepare the 3D model
+    model = Universal_model(img_size=(args.roi_x, args.roi_y, args.roi_z),
+                    in_channels=1,
+                    out_channels=NUM_CLASS,
+                    backbone='swinunetr',
+                    encoding='word_embedding'
+                    )
+    
+    #Load pre-trained weights
+    store_dict = model.state_dict()
+    checkpoint = torch.load('organ_mask_access/swinunetr.pth')
+    load_dict = checkpoint['net']
+
+    for key, value in load_dict.items():
+        if 'swinViT' in key or 'encoder' in key or 'decoder' in key:
+            name = '.'.join(key.split('.')[1:])
+            name = 'backbone.' + name
+        else:
+            name = '.'.join(key.split('.')[1:])
+        store_dict[name] = value
+
+
+    model.load_state_dict(store_dict)
+    print('Use pretrained weights')
+
+    model.cuda()
+
+    test_data_list = os.listdir(args.data_root_path)
+    test_data_list.sort()
+    test_loader, val_transforms = get_loader(args, test_data_list)
+
+    validation(model, test_loader, val_transforms, args)
+
+if __name__ == "__main__":
+    main()

my_container_sandbox/workspace/difftumor/organ_mask_access/pretrained_weights/clip_embedding.py

@@ -0,0 +1,27 @@
+import os
+import clip
+import torch
+
+
+## PAOT
+ORGAN_NAME = ['Spleen', 'Right Kidney', 'Left Kidney', 'Gall Bladder', 'Esophagus', 
+                'Liver', 'Stomach', 'Arota', 'Postcava', 'Portal Vein and Splenic Vein',
+                'Pancreas', 'Right Adrenal Gland', 'Left Adrenal Gland', 'Duodenum', 'Hepatic Vessel',
+                'Right Lung', 'Left Lung', 'Colon', 'Intestine', 'Rectum', 
+                'Bladder', 'Prostate', 'Left Head of Femur', 'Right Head of Femur', 'Celiac Truck',
+                'Kidney Tumor', 'Liver Tumor', 'Pancreas Tumor', 'Hepatic Vessel Tumor', 'Lung Tumor', 
+                'Colon Tumor', 'Kidney Cyst']
+
+# Load the model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load('ViT-B/32', device)
+
+
+text_inputs = torch.cat([clip.tokenize(f'A computerized tomography of a {item}') for item in ORGAN_NAME]).to(device)
+
+# Calculate text embedding features
+with torch.no_grad():
+    text_features = model.encode_text(text_inputs)
+    print(text_features.shape, text_features.dtype)
+    torch.save(text_features, 'txt_encoding.pth')
+

my_container_sandbox/workspace/difftumor/organ_mask_access/requirements.txt

@@ -0,0 +1,7 @@
+connected-components-3d
+h5py==3.6.0
+monai==0.9.0
+torch==1.11.0
+tqdm
+fastremap
+simpleitk

my_container_sandbox/workspace/difftumor/organ_mask_access/utils/label_transfer.py

@@ -0,0 +1,310 @@
+from monai.transforms import (
+    AsDiscrete,
+    AddChanneld,
+    Compose,
+    CropForegroundd,
+    LoadImaged,
+    Orientationd,
+    RandFlipd,
+    RandCropByPosNegLabeld,
+    RandShiftIntensityd,
+    ScaleIntensityRanged,
+    Spacingd,
+    RandRotate90d,
+    ToTensord,
+    CenterSpatialCropd,
+    Resized,
+    SpatialPadd,
+    apply_transform,
+)
+
+import collections.abc
+import math
+import pickle
+import shutil
+import sys
+import tempfile
+import threading
+import time
+from copy import copy, deepcopy
+import cc3d
+import argparse
+import os
+import h5py
+import warnings
+warnings.filterwarnings("ignore")
+
+import numpy as np
+import torch
+from typing import IO, TYPE_CHECKING, Any, Callable, Dict, Hashable, List, Mapping, Optional, Sequence, Tuple, Union
+
+torch.multiprocessing.set_sharing_strategy('file_system')
+
+from monai.data import DataLoader, Dataset, list_data_collate, DistributedSampler
+from monai.config import DtypeLike, KeysCollection
+from monai.transforms.transform import Transform, MapTransform
+from monai.utils.enums import TransformBackends
+from monai.config.type_definitions import NdarrayOrTensor
+
+from utils.utils import get_key
+
+ORGAN_DATASET_DIR = '/home/jliu288/data/whole_organ/'
+ORGAN_LIST = 'dataset/dataset_list/PAOT.txt'
+NUM_WORKER = 8
+NUM_CLASS = 32
+TRANSFER_LIST = ['10_08']
+## full list
+# TRANSFER_LIST = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10_03', '10_06', '10_07', '10_08', '10_09', '10_10', '12', '13', '14']
+
+TEMPLATE={
+    '01': [1,2,3,4,5,6,7,8,9,10,11,12,13,14],
+    '02': [1,0,3,4,5,6,7,0,0,0,11,0,0,14],
+    '03': [6],
+    '04': [6,27],       # post process
+    '05': [2,26,32],       # post process
+    '07': [6,1,3,2,7,4,5,11,14,18,19,12,20,21,23,24],
+    '08': [6, 2, 1, 11],
+    '09': [1,2,3,4,5,6,7,8,9,11,12,13,14,21,22],
+    '12': [6,21,16,2],  
+    '13': [6,2,1,11,8,9,7,4,5,12,13,25], 
+    '14': [11,11,28,28,28],     # Felix data, post process
+    '10_03': [6, 27],   # post process
+    '10_06': [30],
+    '10_07': [11, 28],  # post process
+    '10_08': [15, 29],  # post process
+    '10_09': [1],
+    '10_10': [31]
+}
+
+POST_TUMOR_DICT = {
+    '04': [(2,27)],
+    '05': [(2,26), (3,32)],
+    '10_03': [(2,27)], 
+    '10_07': [(2,28)]
+}
+
+def rl_split(input_data, organ_index, right_index, left_index, name):
+    '''
+    input_data: 3-d tensor [w,h,d], after transform 'Orientationd(keys=["label"], axcodes="RAS")'
+    oragn_index: the organ index of interest
+    right_index and left_index: the corresponding index in template
+    return [1, w, h, d]
+    '''
+    RIGHT_ORGAN = right_index
+    LEFT_ORGAN = left_index
+    label_raw = input_data.copy()
+    label_in = np.zeros(label_raw.shape)
+    label_in[label_raw == organ_index] = 1
+    
+    label_out = cc3d.connected_components(label_in, connectivity=26)
+    # print('label_out', organ_index, np.unique(label_out), np.unique(label_in), label_out.shape, np.sum(label_raw == organ_index))
+    # assert len(np.unique(label_out)) == 3, f'more than 2 component in this ct for {name} with {np.unique(label_out)} component'
+    if len(np.unique(label_out)) > 3:
+        count_sum = 0
+        values, counts = np.unique(label_out, return_counts=True)
+        num_list_sorted = sorted(values, key=lambda x: counts[x])[::-1]
+        for i in num_list_sorted[3:]:
+            label_out[label_out==i] = 0
+            count_sum += counts[i]
+        label_new = np.zeros(label_out.shape)
+        for tgt, src in enumerate(num_list_sorted[:3]):
+            label_new[label_out==src] = tgt
+        label_out = label_new
+        print(f'In {name}. Delete {len(num_list_sorted[3:])} small regions with {count_sum} voxels')
+    a1,b1,c1 = np.where(label_out==1)
+    a2,b2,c2 = np.where(label_out==2)
+    
+    label_new = np.zeros(label_out.shape)
+    if np.mean(a1) < np.mean(a2):
+        label_new[label_out==1] = LEFT_ORGAN
+        label_new[label_out==2] = RIGHT_ORGAN
+    else:
+        label_new[label_out==1] = RIGHT_ORGAN
+        label_new[label_out==2] = LEFT_ORGAN
+    
+    return label_new[None]
+
+class ToTemplatelabel(Transform):
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
+
+    def __call__(self, lbl: NdarrayOrTensor, totemplate: List, tumor=False, tumor_list=None) -> NdarrayOrTensor:
+        new_lbl = np.zeros(lbl.shape)
+        for src, tgt in enumerate(totemplate):
+            new_lbl[lbl == (src+1)] = tgt
+        # unique,count=np.unique(new_lbl,return_counts=True)
+        # data_count=dict(zip(unique,count))
+        # print(data_count)
+        # unique,count=np.unique(lbl,return_counts=True)
+        # data_count=dict(zip(unique,count))
+        # print(data_count)
+        if tumor:
+            for src, item in tumor_list:
+                new_lbl[new_lbl == item] = totemplate[0]
+        return new_lbl
+
+class ToTemplatelabeld(MapTransform):
+    '''
+    Comment: spleen to 1
+    '''
+    backend = ToTemplatelabel.backend
+    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False) -> None:
+        super().__init__(keys, allow_missing_keys)
+        self.totemplate = ToTemplatelabel()
+
+    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
+        d = dict(data)
+        dataset_index = int(d['name'][0:2])
+        TUMOR = False
+        tumor_list = None
+        if dataset_index == 1 or dataset_index == 2:
+            template_key = d['name'][0:2]
+            pass
+        elif dataset_index == 10:
+            template_key = d['name'][0:2] + '_' + d['name'][17:19]
+        else:
+            template_key = d['name'][0:2]
+        if template_key in ['04', '05', '10_03', '10_07', '14']:
+            TUMOR = True
+            tumor_list = POST_TUMOR_DICT[template_key]
+        d['label'] = self.totemplate(d['label'], TEMPLATE[template_key], tumor=TUMOR, tumor_list=tumor_list)
+        return d
+
+class RL_Split(Transform):
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
+
+    def __call__(self, lbl: NdarrayOrTensor, organ_list: List, name) -> NdarrayOrTensor:
+        lbl_new = lbl.copy()
+        for organ in organ_list:
+            organ_index = organ
+            right_index = organ
+            left_index = organ + 1
+            lbl_post = rl_split(lbl_new[0], organ_index, right_index, left_index, name)
+            lbl_new[lbl_post == left_index] = left_index
+        return lbl_new
+
+class RL_Splitd(MapTransform):
+    backend = ToTemplatelabel.backend
+    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False) -> None:
+        super().__init__(keys, allow_missing_keys)
+        self.spliter = RL_Split()
+
+    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
+        d = dict(data)
+        dataset_index = int(d['name'][0:2])
+        # print(d['name'], dataset_index)
+        if dataset_index in [5,8,13]:
+            # print(d['name'], np.unique(d['label']))
+            d['label'] = self.spliter(d['label'], [2], d['name'])
+            # print(d['name'], np.unique(d['label']))
+        elif dataset_index == 7:
+            d['label'] = self.spliter(d['label'], [12], d['name'])
+        elif dataset_index == 12:
+            d['label'] = self.spliter(d['label'], [2, 16], d['name'])
+        else:
+            pass
+        return d
+
+def generate_label(input_lbl, num_classes, name, TEMPLATE, raw_lbl):
+    """
+    Convert class index tensor to one hot encoding tensor with -1 (ignored).
+    Args:
+         input: A tensor of shape [bs, 1, *]
+         num_classes: An int of number of class
+    Returns:
+        A tensor of shape [bs, num_classes, *]
+    Comment: spleen to 0
+    """
+    shape = np.array(input_lbl.shape)
+    shape[1] = num_classes
+    shape = tuple(shape)
+    result = torch.zeros(shape)
+    input_lbl = input_lbl.long()
+
+    ## generate binary cross entropy label and assign -1 to ignored organ
+    B = result.shape[0]
+    for b in range(B):
+        dataset_index = int(name[b][0:2])
+        if dataset_index == 10:
+            template_key = name[b][0:2] + '_' + name[b][17:19]
+        else:
+            template_key = name[b][0:2]
+        
+        # for organ split case
+        if dataset_index == 5:
+            organ_list = [2,3,26,32]
+        elif dataset_index == 7:
+            organ_list = [6,1,3,2,7,4,5,11,14,18,19,12,13,20,21,23,24]
+        elif dataset_index == 8:
+            organ_list = [6, 2, 3, 1, 11]
+        elif dataset_index == 12:
+            organ_list = [6,21,16,17,2,3]
+        elif dataset_index == 13:
+            organ_list = [6,2,3,1,11,8,9,7,4,5,12,13,25]
+        else:
+            organ_list = TEMPLATE[template_key]
+        
+        # -1 for organ not labeled
+        for i in range(num_classes):
+            if (i+1) not in organ_list:
+                result[b, i] = -1
+            else:
+                result[b, i] = (input_lbl[b][0] ==  (i+1))
+        
+        # for tumor case
+        if template_key in ['04', '05', '10_03', '10_07']:
+            tumor_list = POST_TUMOR_DICT[template_key]
+            for src, item in tumor_list:
+                result[b, item - 1] = (raw_lbl[b][0] == src)
+
+        if template_key in ['14']:
+            tumor_lbl = torch.zeros(raw_lbl.shape)
+            tumor_lbl[raw_lbl == 3] = 1
+            tumor_lbl[raw_lbl == 4] = 1
+            tumor_lbl[raw_lbl == 5] = 1
+            result[b, organ_list[-1] - 1] = tumor_lbl[b][0]
+    return result
+
+label_process = Compose(
+    [
+        LoadImaged(keys=["image", "label", "label_raw"]),
+        AddChanneld(keys=["image", "label", "label_raw"]),
+        Orientationd(keys=["image", "label", "label_raw"], axcodes="RAS"),
+        ToTemplatelabeld(keys=['label']),
+        RL_Splitd(keys=['label']),
+        Spacingd(
+            keys=["image", "label", "label_raw"], 
+            pixdim=(1.5, 1.5, 1.5), 
+            mode=("bilinear", "nearest", "nearest"),), # process h5 to here
+    ]
+)
+
+train_img = []
+train_lbl = []
+train_name = []
+
+for line in open(ORGAN_LIST):
+    key = get_key(line.strip().split()[0])
+    if key in TRANSFER_LIST:
+        train_img.append(ORGAN_DATASET_DIR + line.strip().split()[0])
+        train_lbl.append(ORGAN_DATASET_DIR + line.strip().split()[1])
+        train_name.append(line.strip().split()[1].split('.')[0])
+data_dicts_train = [{'image': image, 'label': label, 'label_raw': label, 'name': name}
+            for image, label, name in zip(train_img, train_lbl, train_name)]
+print('train len {}'.format(len(data_dicts_train)))
+
+train_dataset = Dataset(data=data_dicts_train, transform=label_process)
+train_loader = DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=NUM_WORKER, 
+                            collate_fn=list_data_collate)
+
+for index, batch in enumerate(train_loader):
+    x, y, y_raw, name = batch["image"], batch["label"], batch['label_raw'], batch['name']
+    y = generate_label(y, NUM_CLASS, name, TEMPLATE, y_raw)
+    name = batch['name'][0].replace('label', 'post_label')
+    print(name)
+    post_dir = ORGAN_DATASET_DIR + '/'.join(name.split('/')[:-1])
+    store_y = y.numpy().astype(np.uint8)
+    if not os.path.exists(post_dir):
+        os.makedirs(post_dir)
+    with h5py.File(ORGAN_DATASET_DIR + name + '.h5', 'w') as f:
+        f.create_dataset('post_label', data=store_y, compression='gzip', compression_opts=9)
+        f.close()

my_container_sandbox/workspace/difftumor/organ_mask_access/utils/utils.py

@@ -0,0 +1,767 @@
+import os, sys
+import cc3d
+import fastremap
+import csv
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.ensemble import IsolationForest
+#from pyod.models.knn import KNN
+from math import ceil
+from scipy.ndimage.filters import gaussian_filter
+import warnings
+from typing import Any, Callable, Dict, List, Mapping, Sequence, Tuple, Union
+from scipy import ndimage
+
+from monai.data.utils import compute_importance_map, dense_patch_slices, get_valid_patch_size
+from monai.transforms import Resize, Compose
+from monai.utils import (
+    BlendMode,
+    PytorchPadMode,
+    convert_data_type,
+    ensure_tuple,
+    fall_back_tuple,
+    look_up_option,
+    optional_import,
+)
+
+from monai.data import decollate_batch
+from monai.transforms import Invertd, SaveImaged
+
+NUM_CLASS = 32
+
+
+
+TEMPLATE={
+    '01': [1,2,3,4,5,6,7,8,9,10,11,12,13,14],
+    '01_2': [1,3,4,5,6,7,11,14],
+    '02': [1,3,4,5,6,7,11,14],
+    '03': [6],
+    '04': [6,27], # post process
+    '05': [2,3,26,32], # post process
+    '06': [1,2,3,4,6,7,11,16,17],
+    '07': [6,1,3,2,7,4,5,11,14,18,19,12,13,20,21,23,24],
+    '08': [6, 2, 3, 1, 11],
+    '09': [1,2,3,4,5,6,7,8,9,11,12,13,14,21,22],
+    '12': [6,21,16,17,2,3],  
+    '13': [6,2,3,1,11,8,9,7,4,5,12,13,25], 
+    '14': [11, 28],
+    '10_03': [6, 27], # post process
+    '10_06': [30],
+    '10_07': [11, 28], # post process
+    '10_08': [15, 29], # post process
+    '10_09': [1],
+    # '10_10': [31],
+    '10_10': [18],
+    '15': [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] ## total segmentation
+}
+
+ORGAN_NAME = ['Spleen', 'Right Kidney', 'Left Kidney', 'Gall Bladder', 'Esophagus', 
+                'Liver', 'Stomach', 'Aorta', 'Postcava', 'Portal Vein and Splenic Vein',
+                'Pancreas', 'Right Adrenal Gland', 'Left Adrenal Gland', 'Duodenum', 'Hepatic Vessel',
+                'Right Lung', 'Left Lung', 'Colon', 'Intestine', 'Rectum', 
+                'Bladder', 'Prostate', 'Left Head of Femur', 'Right Head of Femur', 'Celiac Truck',
+                'Kidney Tumor', 'Liver Tumor', 'Pancreas Tumor', 'Hepatic Vessel Tumor', 'Lung Tumor', 'Colon Tumor', 'Kidney Cyst']
+
+## mapping to original setting
+MERGE_MAPPING_v1 = {
+    '01': [(1,1), (2,2), (3,3), (4,4), (5,5), (6,6), (7,7), (8,8), (9,9), (10,10), (11,11), (12,12), (13,13), (14,14)],
+    '02': [(1,1), (3,3), (4,4), (5,5), (6,6), (7,7), (11,11), (14,14)],
+    '03': [(6,1)],
+    '04': [(6,1), (27,2)],
+    '05': [(2,1), (3,1), (26, 2), (32,3)],
+    '06': [(1,1), (2,2), (3,3), (4,4), (6,5), (7,6), (11,7), (16,8), (17,9)],
+    '07': [(1,2), (2,4), (3,3), (4,6), (5,7), (6,1), (7,5), (11,8), (12,12), (13,12), (14,9), (18,10), (19,11), (20,13), (21,14), (23,15), (24,16)],
+    '08': [(1,3), (2,2), (3,2), (6,1), (11,4)],
+    '09': [(1,1), (2,2), (3,3), (4,4), (5,5), (6,6), (7,7), (8,8), (9,9), (11,10), (12,11), (13,12), (14,13), (21,14), (22,15)],
+    '10_03': [(6,1), (27,2)],
+    '10_06': [(30,1)],
+    '10_07': [(11,1), (28,2)],
+    '10_08': [(15,1), (29,2)],
+    '10_09': [(1,1)],
+    # '10_10': [(31,1)],
+    '10_10': [(18,1)],
+    '12': [(2,4), (3,4), (21,2), (6,1), (16,3), (17,3)],  
+    '13': [(1,3), (2,2), (3,2), (4,8), (5,9), (6,1), (7,7), (8,5), (9,6), (11,4), (12,10), (13,11), (25,12)],
+    '15': [(1,1), (2,2), (3,3), (4,4), (5,5), (6,6), (7,7), (8,8), (9,9), (10,10), (11,11), (12,12), (13,13), (14,14), (16,16), (17,17), (18,18)],
+}
+
+## split left and right organ more than dataset defined
+## expand on the original class number 
+MERGE_MAPPING_v2 = {
+    '01': [(1,1), (2,2), (3,3), (4,4), (5,5), (6,6), (7,7), (8,8), (9,9), (10,10), (11,11), (12,12), (13,13), (14,14)],
+    '02': [(1,1), (3,3), (4,4), (5,5), (6,6), (7,7), (11,11), (14,14)],
+    '03': [(6,1)],
+    '04': [(6,1), (27,2)],
+    '05': [(2,1), (3,3), (26, 2), (32,3)],
+    '06': [(1,1), (2,2), (3,3), (4,4), (6,5), (7,6), (11,7), (16,8), (17,9)],
+    '07': [(1,2), (2,4), (3,3), (4,6), (5,7), (6,1), (7,5), (11,8), (12,12), (13,17), (14,9), (18,10), (19,11), (20,13), (21,14), (23,15), (24,16)],
+    '08': [(1,3), (2,2), (3,5), (6,1), (11,4)],
+    '09': [(1,1), (2,2), (3,3), (4,4), (5,5), (6,6), (7,7), (8,8), (9,9), (11,10), (12,11), (13,12), (14,13), (21,14), (22,15)],
+    '10_03': [(6,1), (27,2)],
+    '10_06': [(30,1)],
+    '10_07': [(11,1), (28,2)],
+    '10_08': [(15,1), (29,2)],
+    '10_09': [(1,1)],
+    '10_10': [(31,1)],
+    '12': [(2,4), (3,5), (21,2), (6,1), (16,3), (17,6)],  
+    '13': [(1,3), (2,2), (3,13), (4,8), (5,9), (6,1), (7,7), (8,5), (9,6), (11,4), (12,10), (13,11), (25,12)],
+    '15': [(1,1), (2,2), (3,3), (4,4), (5,5), (6,6), (7,7), (8,8), (9,9), (10,10), (11,11), (12,12), (13,13), (14,14), (16,16), (17,17), (18,18)],
+}
+
+THRESHOLD_DIC = {
+    'Spleen': 0.5,
+    'Right Kidney': 0.5,
+    'Left Kidney': 0.5,
+    'Gall Bladder': 0.5,
+    'Esophagus': 0.5, 
+    'Liver': 0.5,
+    'Stomach': 0.5,
+    'Arota': 0.5, 
+    'Postcava': 0.5, 
+    'Portal Vein and Splenic Vein': 0.5,
+    'Pancreas': 0.5, 
+    'Right Adrenal Gland': 0.5, 
+    'Left Adrenal Gland': 0.5, 
+    'Duodenum': 0.5, 
+    'Hepatic Vessel': 0.5,
+    'Right Lung': 0.5, 
+    'Left Lung': 0.5, 
+    'Colon': 0.5, 
+    'Intestine': 0.5, 
+    'Rectum': 0.5, 
+    'Bladder': 0.5, 
+    'Prostate': 0.5, 
+    'Left Head of Femur': 0.5, 
+    'Right Head of Femur': 0.5, 
+    'Celiac Truck': 0.5,
+    'Kidney Tumor': 0.5, 
+    'Liver Tumor': 0.5, 
+    'Pancreas Tumor': 0.5, 
+    'Hepatic Vessel Tumor': 0.5, 
+    'Lung Tumor': 0.5, 
+    'Colon Tumor': 0.5, 
+    'Kidney Cyst': 0.5
+}
+
+TUMOR_SIZE = {
+    'Kidney Tumor': 80, 
+    'Liver Tumor': 20, 
+    'Pancreas Tumor': 100, 
+    'Hepatic Vessel Tumor': 80, 
+    'Lung Tumor': 30, 
+    'Colon Tumor': 100, 
+    'Kidney Cyst': 20
+}
+
+TUMOR_NUM = {
+    'Kidney Tumor': 5, 
+    'Liver Tumor': 20, 
+    'Pancreas Tumor': 1, 
+    'Hepatic Vessel Tumor': 10, 
+    'Lung Tumor': 10, 
+    'Colon Tumor': 3, 
+    'Kidney Cyst': 20
+}
+
+TUMOR_ORGAN = {
+    'Kidney Tumor': [2,3], 
+    'Liver Tumor': [6], 
+    'Pancreas Tumor': [11], 
+    'Hepatic Vessel Tumor': [15], 
+    'Lung Tumor': [16,17], 
+    'Colon Tumor': [18], 
+    'Kidney Cyst': [2,3]
+}
+
+
+def organ_post_process(pred_mask, organ_list, save_dir, args):
+    post_pred_mask = np.zeros(pred_mask.shape)
+    plot_save_path = save_dir
+    log_path = args.log_name
+    dataset_id = save_dir.split('/')[-2]
+    case_id = save_dir.split('/')[-1]
+    if not os.path.isdir(plot_save_path):
+        os.makedirs(plot_save_path)
+    for b in range(pred_mask.shape[0]):
+        for organ in organ_list:
+            if organ == 11: # both process pancreas and Portal vein and splenic vein
+                post_pred_mask[b,10] = extract_topk_largest_candidates(pred_mask[b,10], 1) # for pancreas
+                if 10 in organ_list:
+                    post_pred_mask[b,9] = PSVein_post_process(pred_mask[b,9], post_pred_mask[b,10])
+            elif organ == 16:
+                try:
+                    left_lung_mask, right_lung_mask = lung_post_process(pred_mask[b])
+                    post_pred_mask[b,16] = left_lung_mask
+                    post_pred_mask[b,15] = right_lung_mask
+                except IndexError:
+                    print('this case does not have lungs!')
+                    shape_temp = post_pred_mask[b,16].shape
+                    post_pred_mask[b,16] = np.zeros(shape_temp)
+                    post_pred_mask[b,15] = np.zeros(shape_temp)
+                    with open(log_path + '/' + dataset_id +'/anomaly.csv','a',newline='') as f:
+                        writer = csv.writer(f)
+                        content = case_id
+                        writer.writerow([content])
+
+                right_lung_size = np.sum(post_pred_mask[b,15],axis=(0,1,2))
+                left_lung_size = np.sum(post_pred_mask[b,16],axis=(0,1,2))
+                
+                print('left lung size: '+str(left_lung_size))
+                print('right lung size: '+str(right_lung_size))
+
+                #knn_model = KNN(n_neighbors=5,contamination=0.00001)
+                right_lung_save_path = plot_save_path+'/right_lung.png'
+                left_lung_save_path = plot_save_path+'/left_lung.png'
+                total_anomly_slice_number=0
+
+                if right_lung_size>left_lung_size:
+                    if right_lung_size/left_lung_size > 4:
+                        mid_point = int(right_lung_mask.shape[0]/2)
+                        left_region = np.sum(right_lung_mask[:mid_point,:,:],axis=(0,1,2))
+                        right_region = np.sum(right_lung_mask[mid_point:,:,:],axis=(0,1,2))
+                        
+                        if (right_region+1)/(left_region+1)>4:
+                            print('this case only has right lung')
+                            post_pred_mask[b,15] = right_lung_mask
+                            post_pred_mask[b,16] = np.zeros(right_lung_mask.shape)
+                        elif (left_region+1)/(right_region+1)>4:
+                            print('this case only has left lung')
+                            post_pred_mask[b,16] = right_lung_mask
+                            post_pred_mask[b,15] = np.zeros(right_lung_mask.shape)
+                        else:
+                            print('need anomly detection')
+                            print('start anomly detection at right lung')
+                            try:
+                                left_lung_mask,right_lung_mask,total_anomly_slice_number = anomly_detection(
+                                    pred_mask,post_pred_mask[b,15],right_lung_save_path,b,total_anomly_slice_number)
+                                post_pred_mask[b,16] = left_lung_mask
+                                post_pred_mask[b,15] = right_lung_mask
+                                right_lung_size = np.sum(post_pred_mask[b,15],axis=(0,1,2))
+                                left_lung_size = np.sum(post_pred_mask[b,16],axis=(0,1,2))
+                                while right_lung_size/left_lung_size>4 or left_lung_size/right_lung_size>4:
+                                    print('still need anomly detection')
+                                    if right_lung_size>left_lung_size:
+                                        left_lung_mask,right_lung_mask,total_anomly_slice_number = anomly_detection(
+                                        pred_mask,post_pred_mask[b,15],right_lung_save_path,b,total_anomly_slice_number)
+                                    else:
+                                        left_lung_mask,right_lung_mask,total_anomly_slice_number = anomly_detection(
+                                        pred_mask,post_pred_mask[b,16],right_lung_save_path,b,total_anomly_slice_number)
+                                    post_pred_mask[b,16] = left_lung_mask
+                                    post_pred_mask[b,15] = right_lung_mask
+                                    right_lung_size = np.sum(post_pred_mask[b,15],axis=(0,1,2))
+                                    left_lung_size = np.sum(post_pred_mask[b,16],axis=(0,1,2))
+                                print('lung seperation complete')
+                            except IndexError:
+                                left_lung_mask, right_lung_mask = lung_post_process(pred_mask[b])
+                                post_pred_mask[b,16] = left_lung_mask
+                                post_pred_mask[b,15] = right_lung_mask
+                                print("cannot seperate two lungs, writing csv")
+                                with open(log_path + '/' + dataset_id +'/anomaly.csv','a',newline='') as f:
+                                    writer = csv.writer(f)
+                                    content = case_id
+                                    writer.writerow([content])
+                else:
+                    if left_lung_size/right_lung_size > 4:
+                        mid_point = int(left_lung_mask.shape[0]/2)
+                        left_region = np.sum(left_lung_mask[:mid_point,:,:],axis=(0,1,2))
+                        right_region = np.sum(left_lung_mask[mid_point:,:,:],axis=(0,1,2))
+                        if (right_region+1)/(left_region+1)>4:
+                            print('this case only has right lung')
+                            post_pred_mask[b,15] = left_lung_mask
+                            post_pred_mask[b,16] = np.zeros(left_lung_mask.shape)
+                        elif (left_region+1)/(right_region+1)>4:
+                            print('this case only has left lung')
+                            post_pred_mask[b,16] = left_lung_mask
+                            post_pred_mask[b,15] = np.zeros(left_lung_mask.shape)
+                        else:
+
+                            print('need anomly detection')
+                            print('start anomly detection at left lung')
+                            try:
+                                left_lung_mask,right_lung_mask,total_anomly_slice_number = anomly_detection(
+                                    pred_mask,post_pred_mask[b,16],left_lung_save_path,b,total_anomly_slice_number)
+                                post_pred_mask[b,16] = left_lung_mask
+                                post_pred_mask[b,15] = right_lung_mask
+                                right_lung_size = np.sum(post_pred_mask[b,15],axis=(0,1,2))
+                                left_lung_size = np.sum(post_pred_mask[b,16],axis=(0,1,2))
+                                while right_lung_size/left_lung_size>4 or left_lung_size/right_lung_size>4:
+                                    print('still need anomly detection')
+                                    if right_lung_size>left_lung_size:
+                                        left_lung_mask,right_lung_mask,total_anomly_slice_number = anomly_detection(
+                                        pred_mask,post_pred_mask[b,15],right_lung_save_path,b,total_anomly_slice_number)
+                                    else:
+                                        left_lung_mask,right_lung_mask,total_anomly_slice_number = anomly_detection(
+                                        pred_mask,post_pred_mask[b,16],right_lung_save_path,b,total_anomly_slice_number)
+                                    post_pred_mask[b,16] = left_lung_mask
+                                    post_pred_mask[b,15] = right_lung_mask
+                                    right_lung_size = np.sum(post_pred_mask[b,15],axis=(0,1,2))
+                                    left_lung_size = np.sum(post_pred_mask[b,16],axis=(0,1,2))
+
+                                print('lung seperation complete')
+                            except IndexError:
+                                left_lung_mask, right_lung_mask = lung_post_process(pred_mask[b])
+                                post_pred_mask[b,16] = left_lung_mask
+                                post_pred_mask[b,15] = right_lung_mask
+                                print("cannot seperate two lungs, writing csv")
+                                with open(log_path + '/' + dataset_id +'/anomaly.csv','a',newline='') as f:
+                                    writer = csv.writer(f)
+                                    content = case_id
+                                    writer.writerow([content])
+                print('find number of anomaly slice: '+str(total_anomly_slice_number))
+            elif organ == 17:
+                continue ## the le
+            elif organ in [1,2,3,4,5,6,7,8,9,12,13,14,18,19,20,21,22,23,24,25]: ## rest organ index
+                post_pred_mask[b,organ-1] = extract_topk_largest_candidates(pred_mask[b,organ-1], 1)
+            # elif organ in [28,29,30,31,32]:
+            #     post_pred_mask[b,organ-1] = extract_topk_largest_candidates(pred_mask[b,organ-1], TUMOR_NUM[ORGAN_NAME[organ-1]], area_least=TUMOR_SIZE[ORGAN_NAME[organ-1]])
+            elif organ in [26,27]:
+                organ_mask = merge_and_top_organ(pred_mask[b], TUMOR_ORGAN[ORGAN_NAME[organ-1]])
+                post_pred_mask[b,organ-1] = organ_region_filter_out(pred_mask[b,organ-1], organ_mask)
+                # post_pred_mask[b,organ-1] = extract_topk_largest_candidates(post_pred_mask[b,organ-1], TUMOR_NUM[ORGAN_NAME[organ-1]], area_least=TUMOR_SIZE[ORGAN_NAME[organ-1]])
+            else:
+                post_pred_mask[b,organ-1] = pred_mask[b,organ-1]
+    return post_pred_mask
+
+def lung_overlap_post_process(pred_mask):
+    new_mask = np.zeros(pred_mask.shape, np.uint8)
+    new_mask[pred_mask==1] = 1
+    label_out = cc3d.connected_components(new_mask, connectivity=26)
+
+    areas = {}
+    for label, extracted in cc3d.each(label_out, binary=True, in_place=True):
+        areas[label] = fastremap.foreground(extracted)
+    candidates = sorted(areas.items(), key=lambda item: item[1], reverse=True)
+    num_candidates = len(candidates)
+    if num_candidates!=1:
+        print('start separating two lungs!')
+        ONE = int(candidates[0][0])
+        TWO = int(candidates[1][0])
+
+
+        print('number of connected components:'+str(len(candidates)))
+        a1,b1,c1 = np.where(label_out==ONE)
+        a2,b2,c2 = np.where(label_out==TWO)
+        
+        left_lung_mask = np.zeros(label_out.shape)
+        right_lung_mask = np.zeros(label_out.shape)
+
+        if np.mean(a1) < np.mean(a2):
+            left_lung_mask[label_out==ONE] = 1
+            right_lung_mask[label_out==TWO] = 1
+        else:
+            right_lung_mask[label_out==ONE] = 1
+            left_lung_mask[label_out==TWO] = 1
+        erosion_left_lung_size = np.sum(left_lung_mask,axis=(0,1,2))
+        erosion_right_lung_size = np.sum(right_lung_mask,axis=(0,1,2))
+        print('erosion left lung size:'+str(erosion_left_lung_size))
+        print('erosion right lung size:'+ str(erosion_right_lung_size))
+        return num_candidates,left_lung_mask, right_lung_mask
+    else:
+        print('current iteration cannot separate lungs, erosion iteration + 1')
+        ONE = int(candidates[0][0])
+        print('number of connected components:'+str(len(candidates)))
+        lung_mask = np.zeros(label_out.shape)
+        lung_mask[label_out == ONE]=1
+        lung_overlapped_mask_size = np.sum(lung_mask,axis=(0,1,2))
+        print('lung overlapped mask size:' + str(lung_overlapped_mask_size))
+
+        return num_candidates,lung_mask
+
+def find_best_iter_and_masks(lung_mask):
+    iter=1
+    print('current iteration:' + str(iter))
+    struct2 = ndimage.generate_binary_structure(3, 3)
+    erosion_mask= ndimage.binary_erosion(lung_mask, structure=struct2,iterations=iter)
+    candidates_and_masks = lung_overlap_post_process(erosion_mask)
+    while candidates_and_masks[0]==1:
+        iter +=1
+        print('current iteration:' + str(iter))
+        erosion_mask= ndimage.binary_erosion(lung_mask, structure=struct2,iterations=iter)
+        candidates_and_masks = lung_overlap_post_process(erosion_mask)
+    print('check if components are valid')
+    left_lung_erosion_mask = candidates_and_masks[1]
+    right_lung_erosion_mask = candidates_and_masks[2]
+    left_lung_erosion_mask_size = np.sum(left_lung_erosion_mask,axis = (0,1,2))
+    right_lung_erosion_mask_size = np.sum(right_lung_erosion_mask,axis = (0,1,2))
+    while left_lung_erosion_mask_size/right_lung_erosion_mask_size>4 or right_lung_erosion_mask_size/left_lung_erosion_mask_size>4:
+        print('components still have large difference, erosion interation + 1')
+        iter +=1
+        print('current iteration:' + str(iter))
+        erosion_mask= ndimage.binary_erosion(lung_mask, structure=struct2,iterations=iter)
+        candidates_and_masks = lung_overlap_post_process(erosion_mask)
+        while candidates_and_masks[0]==1:
+            iter +=1
+            print('current iteration:' + str(iter))
+            erosion_mask= ndimage.binary_erosion(lung_mask, structure=struct2,iterations=iter)
+            candidates_and_masks = lung_overlap_post_process(erosion_mask)
+        left_lung_erosion_mask = candidates_and_masks[1]
+        right_lung_erosion_mask = candidates_and_masks[2]
+        left_lung_erosion_mask_size = np.sum(left_lung_erosion_mask,axis = (0,1,2))
+        right_lung_erosion_mask_size = np.sum(right_lung_erosion_mask,axis = (0,1,2))
+    print('erosion done, best iteration: '+str(iter))
+
+
+
+    print('start dilation')
+    left_lung_erosion_mask = candidates_and_masks[1]
+    right_lung_erosion_mask = candidates_and_masks[2]
+
+    erosion_part_mask = lung_mask - left_lung_erosion_mask - right_lung_erosion_mask
+    left_lung_dist = np.ones(left_lung_erosion_mask.shape)
+    right_lung_dist = np.ones(right_lung_erosion_mask.shape)
+    left_lung_dist[left_lung_erosion_mask==1]=0
+    right_lung_dist[right_lung_erosion_mask==1]=0
+    left_lung_dist_map = ndimage.distance_transform_edt(left_lung_dist)
+    right_lung_dist_map = ndimage.distance_transform_edt(right_lung_dist)
+    left_lung_dist_map[erosion_part_mask==0]=0
+    right_lung_dist_map[erosion_part_mask==0]=0
+    left_lung_adding_map = left_lung_dist_map < right_lung_dist_map
+    right_lung_adding_map = right_lung_dist_map < left_lung_dist_map
+    
+    left_lung_erosion_mask[left_lung_adding_map==1]=1
+    right_lung_erosion_mask[right_lung_adding_map==1]=1
+
+    left_lung_mask = left_lung_erosion_mask
+    right_lung_mask = right_lung_erosion_mask
+    # left_lung_mask = ndimage.binary_dilation(left_lung_erosion_mask, structure=struct2,iterations=iter)
+    # right_lung_mask = ndimage.binary_dilation(right_lung_erosion_mask, structure=struct2,iterations=iter)
+    print('dilation complete')
+    left_lung_mask_fill_hole = ndimage.binary_fill_holes(left_lung_mask)
+    right_lung_mask_fill_hole = ndimage.binary_fill_holes(right_lung_mask)
+    left_lung_size = np.sum(left_lung_mask_fill_hole,axis=(0,1,2))
+    right_lung_size = np.sum(right_lung_mask_fill_hole,axis=(0,1,2))
+    print('new left lung size:'+str(left_lung_size))
+    print('new right lung size:' + str(right_lung_size))
+    return left_lung_mask_fill_hole,right_lung_mask_fill_hole
+
+
+# def anomly_detection(pred_mask,post_pred_mask,model,save_path,batch):
+#     lung_df = get_dataframe(post_pred_mask)
+#     lung_pred_df = fit_model(model,lung_df)
+#     plot_anomalies(lung_pred_df,save_dir=save_path)
+#     anomly_df = lung_pred_df[lung_pred_df['Predictions']==1]
+#     anomly_slice = anomly_df['slice_index'].to_numpy()
+#     for s in anomly_slice:
+#         pred_mask[batch,15,:,:,s]=0
+#         pred_mask[batch,16,:,:,s]=0
+#     left_lung_mask, right_lung_mask = lung_post_process(pred_mask[batch])
+#     return left_lung_mask, right_lung_mask
+
+def anomly_detection(pred_mask, post_pred_mask, save_path, batch, anomly_num):
+    total_anomly_slice_number = anomly_num
+    df = get_dataframe(post_pred_mask)
+    # lung_pred_df = fit_model(model,lung_df)
+    lung_df = df[df['array_sum']!=0]
+    lung_df['SMA20'] = lung_df['array_sum'].rolling(20,min_periods=1,center=True).mean()
+    lung_df['STD20'] = lung_df['array_sum'].rolling(20,min_periods=1,center=True).std()
+    lung_df['SMA7'] = lung_df['array_sum'].rolling(7,min_periods=1,center=True).mean()
+    lung_df['upper_bound'] = lung_df['SMA20']+2*lung_df['STD20']
+    lung_df['Predictions'] = lung_df['array_sum']>lung_df['upper_bound']
+    lung_df['Predictions'] = lung_df['Predictions'].astype(int)
+    lung_df.dropna(inplace=True)
+    anomly_df = lung_df[lung_df['Predictions']==1]
+    anomly_slice = anomly_df['slice_index'].to_numpy()
+    anomly_value = anomly_df['array_sum'].to_numpy()
+    anomly_SMA7 = anomly_df['SMA7'].to_numpy()
+
+    print('decision made')
+    if len(anomly_df)!=0:
+        print('anomaly point detected')
+        print('check if the anomaly points are real')
+        real_anomly_slice = []
+        for i in range(len(anomly_df)):
+            if anomly_value[i] > anomly_SMA7[i]+200:
+                print('the anomaly point is real')
+                real_anomly_slice.append(anomly_slice[i])
+                total_anomly_slice_number+=1
+        
+        if len(real_anomly_slice)!=0:
+
+            
+            plot_anomalies(lung_df,save_dir=save_path)
+            print('anomaly detection plot created')
+            for s in real_anomly_slice:
+                pred_mask[batch,15,:,:,s]=0
+                pred_mask[batch,16,:,:,s]=0
+            left_lung_mask, right_lung_mask = lung_post_process(pred_mask[batch])
+            left_lung_size = np.sum(left_lung_mask,axis=(0,1,2))
+            right_lung_size = np.sum(right_lung_mask,axis=(0,1,2))
+            print('new left lung size:'+str(left_lung_size))
+            print('new right lung size:' + str(right_lung_size))
+            return left_lung_mask,right_lung_mask,total_anomly_slice_number
+        else: 
+            print('the anomaly point is not real, start separate overlapping')
+            left_lung_mask,right_lung_mask = find_best_iter_and_masks(post_pred_mask)
+            return left_lung_mask,right_lung_mask,total_anomly_slice_number
+
+
+    print('overlap detected, start erosion and dilation')
+    left_lung_mask,right_lung_mask = find_best_iter_and_masks(post_pred_mask)
+
+    return left_lung_mask,right_lung_mask,total_anomly_slice_number
+
+def get_dataframe(post_pred_mask):
+    target_array = post_pred_mask
+    target_array_sum = np.sum(target_array,axis=(0,1))
+    slice_index = np.arange(target_array.shape[-1])
+    df = pd.DataFrame({'slice_index':slice_index,'array_sum':target_array_sum})
+    return df
+
+def plot_anomalies(df, x='slice_index', y='array_sum',save_dir=None):
+    # categories will be having values from 0 to n
+    # for each values in 0 to n it is mapped in colormap
+    categories = df['Predictions'].to_numpy()
+    colormap = np.array(['g', 'r'])
+
+    f = plt.figure(figsize=(12, 4))
+    f = plt.plot(df[x],df['SMA20'],'b')
+    f = plt.plot(df[x],df['upper_bound'],'y')
+    f = plt.scatter(df[x], df[y], c=colormap[categories],alpha=0.3)
+    f = plt.xlabel(x)
+    f = plt.ylabel(y)
+    plt.legend(['Simple moving average','upper bound','predictions'])
+    if save_dir is not None:
+        plt.savefig(save_dir)
+    plt.clf()
+
+def merge_and_top_organ(pred_mask, organ_list):
+    ## merge 
+    out_mask = np.zeros(pred_mask.shape[1:], np.uint8)
+    for organ in organ_list:
+        out_mask = np.logical_or(out_mask, pred_mask[organ-1])
+    ## select the top k, for righr left case
+    out_mask = extract_topk_largest_candidates(out_mask, len(organ_list))
+
+    return out_mask
+
+def organ_region_filter_out(tumor_mask, organ_mask):
+    ## dialtion
+    organ_mask = ndimage.binary_closing(organ_mask, structure=np.ones((5,5,5)))
+    organ_mask = ndimage.binary_dilation(organ_mask, structure=np.ones((5,5,5)))
+    ## filter out
+    tumor_mask = organ_mask * tumor_mask
+
+    return tumor_mask
+
+
+def PSVein_post_process(PSVein_mask, pancreas_mask):
+    xy_sum_pancreas = pancreas_mask.sum(axis=0).sum(axis=0)
+    z_non_zero = np.nonzero(xy_sum_pancreas)
+    z_value = np.min(z_non_zero) ## the down side of pancreas
+    new_PSVein = PSVein_mask.copy()
+    new_PSVein[:,:,:z_value] = 0
+    return new_PSVein
+
+def lung_post_process(pred_mask):
+    new_mask = np.zeros(pred_mask.shape[1:], np.uint8)
+    new_mask[pred_mask[15] == 1] = 1
+    new_mask[pred_mask[16] == 1] = 1
+    label_out = cc3d.connected_components(new_mask, connectivity=26)
+    
+    areas = {}
+    for label, extracted in cc3d.each(label_out, binary=True, in_place=True):
+        areas[label] = fastremap.foreground(extracted)
+    candidates = sorted(areas.items(), key=lambda item: item[1], reverse=True)
+
+    ONE = int(candidates[0][0])
+    TWO = int(candidates[1][0])
+    
+    a1,b1,c1 = np.where(label_out==ONE)
+    a2,b2,c2 = np.where(label_out==TWO)
+    
+    left_lung_mask = np.zeros(label_out.shape)
+    right_lung_mask = np.zeros(label_out.shape)
+
+    if np.mean(a1) < np.mean(a2):
+        left_lung_mask[label_out==ONE] = 1
+        right_lung_mask[label_out==TWO] = 1
+    else:
+        right_lung_mask[label_out==ONE] = 1
+        left_lung_mask[label_out==TWO] = 1
+    
+    return left_lung_mask, right_lung_mask
+
+def extract_topk_largest_candidates(npy_mask, organ_num, area_least=0):
+    ## npy_mask: w, h, d
+    ## organ_num: the maximum number of connected component
+    out_mask = np.zeros(npy_mask.shape, np.uint8)
+    t_mask = npy_mask.copy()
+    keep_topk_largest_connected_object(t_mask, organ_num, area_least, out_mask, 1)
+
+    return out_mask
+
+
+def keep_topk_largest_connected_object(npy_mask, k, area_least, out_mask, out_label):
+    labels_out = cc3d.connected_components(npy_mask, connectivity=26)
+    areas = {}
+    for label, extracted in cc3d.each(labels_out, binary=True, in_place=True):
+        areas[label] = fastremap.foreground(extracted)
+    candidates = sorted(areas.items(), key=lambda item: item[1], reverse=True)
+
+    for i in range(min(k, len(candidates))):
+        if candidates[i][1] > area_least:
+            out_mask[labels_out == int(candidates[i][0])] = out_label
+
+def threshold_organ(data, organ=None, threshold=None):
+    ### threshold the sigmoid value to hard label
+    ## data: sigmoid value
+    ## threshold_list: a list of organ threshold
+    B = data.shape[0]
+    threshold_list = []
+    if organ:
+        THRESHOLD_DIC[organ] = threshold
+    for key, value in THRESHOLD_DIC.items():
+        threshold_list.append(value)
+    threshold_list = torch.tensor(threshold_list).repeat(B, 1).reshape(B,len(threshold_list),1,1,1).cuda()
+    pred_hard = data > threshold_list
+    return pred_hard
+
+
+def visualize_label(batch, save_dir, input_transform):
+    ### function: save the prediction result into dir
+    ## Input
+    ## batch: the batch dict output from the monai dataloader
+    ## one_channel_label: the predicted reuslt with same shape as label
+    ## save_dir: the directory for saving
+    ## input_transform: the dataloader transform
+    post_transforms = Compose([
+        Invertd(
+            keys=['liver'], #, 'split_label' "label", 'one_channel_label_v1', 'one_channel_label_v2'
+            transform=input_transform,
+            orig_keys="image",
+            nearest_interp=True,
+            to_tensor=True,
+        ),
+        Invertd(
+            keys=['pancreas'], #, 'split_label' "label", 'one_channel_label_v1', 'one_channel_label_v2'
+            transform=input_transform,
+            orig_keys="image",
+            nearest_interp=True,
+            to_tensor=True,
+        ),
+        Invertd(
+            keys=['kidney'], #, 'split_label' "label", 'one_channel_label_v1', 'one_channel_label_v2'
+            transform=input_transform,
+            orig_keys="image",
+            nearest_interp=True,
+            to_tensor=True,
+        ),
+        SaveImaged(keys='liver', 
+                meta_keys="image_meta_dict" , 
+                output_dir=save_dir, 
+                output_postfix="liver", #organ_pseudo
+                resample=False,
+                separate_folder=False,
+                output_dtype=np.uint8
+        ),
+        SaveImaged(keys='pancreas', 
+                meta_keys="image_meta_dict" , 
+                output_dir=save_dir, 
+                output_postfix="pancreas", #organ_pseudo
+                resample=False,
+                separate_folder=False,
+                output_dtype=np.uint8
+        ),
+        SaveImaged(keys='kidney', 
+                meta_keys="image_meta_dict" , 
+                output_dir=save_dir, 
+                output_postfix="kidney", #organ_pseudo
+                resample=False,
+                separate_folder=False,
+                output_dtype=np.uint8
+        ),
+    ])
+    
+    batch = [post_transforms(i) for i in decollate_batch(batch)]
+
+def get_key(name):
+    ## input: name
+    ## output: the corresponding key
+    dataset_index = int(name[0:2])
+    if dataset_index == 10:
+        template_key = name[0:2] + '_' + name[17:19]
+    else:
+        template_key = name[0:2]
+    return template_key
+
+
+def dice_score(preds, labels, spe_sen=False):  # on GPU
+    ### preds: w,h,d; label: w,h,d
+    assert preds.shape[0] == labels.shape[0], "predict & target batch size don't match"
+    preds = torch.where(preds > 0.5, 1., 0.)
+    predict = preds.contiguous().view(1, -1)
+    target = labels.contiguous().view(1, -1)
+
+    tp = torch.sum(torch.mul(predict, target))
+    fn = torch.sum(torch.mul(predict!=1, target))
+    fp = torch.sum(torch.mul(predict, target!=1))
+    tn = torch.sum(torch.mul(predict!=1, target!=1))
+
+    den = torch.sum(predict) + torch.sum(target) + 1
+
+    dice = 2 * tp / den
+    recall = tp/(tp+fn)
+    precision = tp/(tp+fp)
+    specificity = tn/(fp + tn)
+
+
+    # print(dice, recall, precision)
+    if spe_sen:
+        return dice, recall, precision, specificity
+    else:
+        return dice, recall, precision
+
+
+def _get_gaussian(patch_size, sigma_scale=1. / 8) -> np.ndarray:
+    tmp = np.zeros(patch_size)
+    center_coords = [i // 2 for i in patch_size]
+    sigmas = [i * sigma_scale for i in patch_size]
+    tmp[tuple(center_coords)] = 1
+    gaussian_importance_map = gaussian_filter(tmp, sigmas, 0, mode='constant', cval=0)
+    gaussian_importance_map = gaussian_importance_map / np.max(gaussian_importance_map) * 1
+    gaussian_importance_map = gaussian_importance_map.astype(np.float32)
+
+    # gaussian_importance_map cannot be 0, otherwise we may end up with nans!
+    gaussian_importance_map[gaussian_importance_map == 0] = np.min(
+        gaussian_importance_map[gaussian_importance_map != 0])
+
+    return gaussian_importance_map
+
+
+def multi_net(net_list, img, task_id):
+    # img = torch.from_numpy(img).cuda()
+
+    padded_prediction = net_list[0](img, task_id)
+    padded_prediction = F.sigmoid(padded_prediction)
+    for i in range(1, len(net_list)):
+        padded_prediction_i = net_list[i](img, task_id)
+        padded_prediction_i = F.sigmoid(padded_prediction_i)
+        padded_prediction += padded_prediction_i
+    padded_prediction /= len(net_list)
+    return padded_prediction#.cpu().data.numpy()
+
+
+def check_data(dataset_check):
+    img = dataset_check[0]["image"]
+    label = dataset_check[0]["label"]
+    print(dataset_check[0]["name"])
+    img_shape = img.shape
+    label_shape = label.shape
+    print(f"image shape: {img_shape}, label shape: {label_shape}")
+    print(torch.unique(label[0, :, :, 150]))
+    plt.figure("image", (18, 6))
+    plt.subplot(1, 2, 1)
+    plt.title("image")
+    plt.imshow(img[0, :, :, 150].detach().cpu(), cmap="gray")
+    plt.subplot(1, 2, 2)
+    plt.title("label")
+    plt.imshow(label[0, :, :, 150].detach().cpu())
+    plt.show()
+
+if __name__ == "__main__":
+    threshold_organ(torch.zeros(1,12,1))    

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/calculate_task_params.py

@@ -0,0 +1,87 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+
+from monai.data import (
+    Dataset,
+    DatasetSummary,
+    load_decathlon_datalist,
+    load_decathlon_properties,
+)
+from monai.transforms import LoadImaged
+
+from task_params import task_name
+
+
+def get_task_params(args):
+    """
+    This function is used to achieve the spacings of decathlon dataset.
+    In addition, for CT images (task 03, 06, 07, 08, 09 and 10), this function
+    also prints the mean and std values (used for normalization), and the min (0.5 percentile)
+    and max(99.5 percentile) values (used for clip).
+
+    """
+    task_id = args.task_id
+    root_dir = args.root_dir
+    datalist_path = args.datalist_path
+    dataset_path = os.path.join(root_dir, task_name[task_id])
+    datalist_name = "dataset_task{}.json".format(task_id)
+
+    # get all training data
+    datalist = load_decathlon_datalist(os.path.join(datalist_path, datalist_name), True, "training", dataset_path)
+
+    # get modality info.
+    properties = load_decathlon_properties(os.path.join(datalist_path, datalist_name), "modality")
+
+    dataset = Dataset(
+        data=datalist,
+        transform=LoadImaged(keys=["image", "label"]),
+    )
+
+    calculator = DatasetSummary(dataset, num_workers=4)
+    target_spacing = calculator.get_target_spacing()
+    print("spacing: ", target_spacing)
+    if properties["modality"]["0"] == "CT":
+        print("CT input, calculate statistics:")
+        calculator.calculate_statistics()
+        print("mean: ", calculator.data_mean, " std: ", calculator.data_std)
+        calculator.calculate_percentiles(sampling_flag=True, interval=10, min_percentile=0.5, max_percentile=99.5)
+        print(
+            "min: ",
+            calculator.data_min_percentile,
+            " max: ",
+            calculator.data_max_percentile,
+        )
+    else:
+        print("non CT input, skip calculating.")
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+    parser.add_argument("-task_id", "--task_id", type=str, default="04", help="task 01 to 10")
+    parser.add_argument(
+        "-root_dir",
+        "--root_dir",
+        type=str,
+        default="/home/v-qichen2/qic/data/CT/10_Decathlon/",
+        help="dataset path",
+    )
+    parser.add_argument(
+        "-datalist_path",
+        "--datalist_path",
+        type=str,
+        default="config/",
+    )
+
+    args = parser.parse_args()
+    get_task_params(args)

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task01/finetune_multi_gpu.sh

@@ -0,0 +1,23 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# train step 2, finetune with small learning rate
+# please replace the weight variable into your actual weight
+
+lr=1e-2
+fold=0
+weight=model.pt
+
+python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 --node_rank=0 \
+    --master_addr="localhost" --master_port=1234 \
+    train.py -fold $fold -train_num_workers 4 -interval 10 -num_samples 1 \
+    -learning_rate $lr -max_epochs 1000 -task_id 01 -pos_sample_num 1 \
+    -expr_name baseline -tta_val True -checkpoint $weight -multi_gpu True

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task02/finetune.sh

@@ -0,0 +1,22 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# train step 2, finetune with small learning rate
+# please replace the weight variable into your actual weight
+
+lr=1e-2
+fold=0
+weight=model.pt
+
+python train.py -fold $fold -train_num_workers 4 -interval 1 -num_samples 4 \
+-learning_rate $lr -max_epochs 500 -task_id 02 -pos_sample_num 1 \
+-expr_name baseline -tta_val True -checkpoint $weight -determinism_flag True \
+-determinism_seed 0

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task03/finetune_multi_gpu.sh

@@ -0,0 +1,24 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# train step 2, finetune with small learning rate
+# please replace the weight variable into your actual weight
+
+lr=1e-2
+fold=0
+weight=model.pt
+
+python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 --node_rank=0 \
+    --master_addr="localhost" --master_port=1234 \
+    train.py -fold $fold -train_num_workers 8 -interval 20 -num_samples 1 \
+    -learning_rate $lr -max_epochs 2000 -task_id 03 -pos_sample_num 1 \
+    -expr_name baseline -tta_val True -checkpoint $weight -multi_gpu True \
+    -eval_overlap 0.5 -sw_batch_size 2 -batch_dice True

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task04/train.sh

@@ -0,0 +1,19 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# train step 1, with large learning rate
+
+lr=1e-1
+fold=0
+
+python train.py -fold $fold -train_num_workers 4 -interval 1 -num_samples 1 \
+-learning_rate $lr -max_epochs 500 -task_id 04 -pos_sample_num 2 \
+-expr_name baseline -tta_val True -determinism_flag True -determinism_seed 0

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task04/train_multi_gpu.sh

@@ -0,0 +1,21 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# train step 1, with large learning rate
+
+lr=1e-1
+fold=0
+
+python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 --node_rank=0 \
+    --master_addr="localhost" --master_port=1234 \
+	train.py -fold $fold -train_num_workers 4 -interval 1 -num_samples 1 \
+	-learning_rate $lr -max_epochs 500 -task_id 04 -pos_sample_num 2 \
+	-expr_name baseline -tta_val True -multi_gpu True

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task04/val_multi_gpu.sh

@@ -0,0 +1,20 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# please replace the weight variable into your actual weight
+
+weight=model.pt
+fold=0
+
+python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 --node_rank=0 \
+    --master_addr="localhost" --master_port=1234 \
+    train.py -fold $fold -expr_name baseline -task_id 04 -tta_val True \
+    -checkpoint $weight -mode val -multi_gpu True

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task05/finetune.sh

@@ -0,0 +1,22 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# train step 2, finetune with small learning rate
+# please replace the weight variable into your actual weight
+
+lr=1e-2
+fold=0
+weight=model.pt
+
+python train.py -fold $fold -train_num_workers 4 -interval 1 -num_samples 4 \
+-learning_rate $lr -max_epochs 1000 -task_id 05 -pos_sample_num 1 \
+-expr_name baseline -tta_val True -checkpoint $weight -determinism_flag True \
+-determinism_seed 0

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task06/finetune.sh

@@ -0,0 +1,22 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# train step 2, finetune with small learning rate
+# please replace the weight variable into your actual weight
+
+lr=1e-3
+fold=0
+weight=model.pt
+
+python train.py -fold $fold -train_num_workers 4 -interval 5 -num_samples 1 \
+-learning_rate $lr -max_epochs 1000 -task_id 06 -pos_sample_num 1 \
+-expr_name baseline -tta_val True -checkpoint $weight -determinism_flag True \
+-determinism_seed 0 -batch_dice True

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task07/finetune_multi_gpu.sh

@@ -0,0 +1,28 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# train step 2, finetune with small learning rate
+# please replace the weight variable into your actual weight
+# since this task uses lr scheduler, please set the lr and max epochs
+# here according to the step 1 training results. The value of max epochs equals
+# to 2000 minus the best epoch in step 1.
+
+lr=5e-3
+max_epochs=1000
+fold=0
+weight=model.pt
+
+python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 --node_rank=0 \
+    --master_addr="localhost" --master_port=1234 \
+    train.py -fold $fold -train_num_workers 4 -interval 10 -num_samples 1 \
+    -learning_rate $lr -max_epochs $max_epochs -task_id 07 -pos_sample_num 1 \
+    -expr_name baseline -tta_val True -checkpoint $weight -multi_gpu True \
+    -lr_decay True

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/commands/task08/finetune_multi_gpu.sh

@@ -0,0 +1,28 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# train step 2, finetune with small learning rate
+# please replace the weight variable into your actual weight
+# since this task uses lr scheduler, please set the lr and max epochs
+# here according to the step 1 training results. The value of max epochs equals
+# to 2000 minus the best epoch in step 1.
+
+lr=5e-3
+max_epochs=1000
+fold=0
+weight=model.pt
+
+python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 --node_rank=0 \
+    --master_addr="localhost" --master_port=1234 \
+    train.py -fold $fold -train_num_workers 4 -interval 10 -num_samples 1 \
+    -learning_rate $lr -max_epochs $max_epochs -task_id 08 -pos_sample_num 1 \
+    -expr_name baseline -tta_val True -checkpoint $weight -multi_gpu True \
+    -lr_decay True

my_container_sandbox/workspace/difftumor/tumor_mask_access/dynunet_pipeline/config/dataset_task02.json

@@ -0,0 +1 @@
+{"name": "LeftAtrium", "description": "Left atrium segmentation", "tensorImageSize": "3D", "reference": "King\u2019s College London", "licence": "CC-BY-SA 4.0", "relase": "1.0 04/05/2018", "modality": {"0": "MRI"}, "labels": {"0": "background", "1": "left atrium"}, "numTraining": 20, "numTest": 10, "training": [{"image": "./imagesTr/la_007.nii.gz", "label": "./labelsTr/la_007.nii.gz"}, {"image": "./imagesTr/la_019.nii.gz", "label": "./labelsTr/la_019.nii.gz"}, {"image": "./imagesTr/la_023.nii.gz", "label": "./labelsTr/la_023.nii.gz"}, {"image": "./imagesTr/la_005.nii.gz", "label": "./labelsTr/la_005.nii.gz"}, {"image": "./imagesTr/la_009.nii.gz", "label": "./labelsTr/la_009.nii.gz"}, {"image": "./imagesTr/la_017.nii.gz", "label": "./labelsTr/la_017.nii.gz"}, {"image": "./imagesTr/la_021.nii.gz", "label": "./labelsTr/la_021.nii.gz"}, {"image": "./imagesTr/la_029.nii.gz", "label": "./labelsTr/la_029.nii.gz"}, {"image": "./imagesTr/la_003.nii.gz", "label": "./labelsTr/la_003.nii.gz"}, {"image": "./imagesTr/la_011.nii.gz", "label": "./labelsTr/la_011.nii.gz"}, {"image": "./imagesTr/la_030.nii.gz", "label": "./labelsTr/la_030.nii.gz"}, {"image": "./imagesTr/la_022.nii.gz", "label": "./labelsTr/la_022.nii.gz"}, {"image": "./imagesTr/la_014.nii.gz", "label": "./labelsTr/la_014.nii.gz"}, {"image": "./imagesTr/la_018.nii.gz", "label": "./labelsTr/la_018.nii.gz"}, {"image": "./imagesTr/la_020.nii.gz", "label": "./labelsTr/la_020.nii.gz"}, {"image": "./imagesTr/la_004.nii.gz", "label": "./labelsTr/la_004.nii.gz"}, {"image": "./imagesTr/la_016.nii.gz", "label": "./labelsTr/la_016.nii.gz"}, {"image": "./imagesTr/la_024.nii.gz", "label": "./labelsTr/la_024.nii.gz"}, {"image": "./imagesTr/la_010.nii.gz", "label": "./labelsTr/la_010.nii.gz"}, {"image": "./imagesTr/la_026.nii.gz", "label": "./labelsTr/la_026.nii.gz"}], "test": ["./imagesTs/la_015.nii.gz", "./imagesTs/la_025.nii.gz", "./imagesTs/la_013.nii.gz", "./imagesTs/la_001.nii.gz", "./imagesTs/la_027.nii.gz", "./imagesTs/la_006.nii.gz", "./imagesTs/la_008.nii.gz", "./imagesTs/la_012.nii.gz", "./imagesTs/la_028.nii.gz", "./imagesTs/la_002.nii.gz"], "validation_fold0": [{"image": "./imagesTr/la_007.nii.gz", "label": "./labelsTr/la_007.nii.gz"}, {"image": "./imagesTr/la_016.nii.gz", "label": "./labelsTr/la_016.nii.gz"}, {"image": "./imagesTr/la_021.nii.gz", "label": "./labelsTr/la_021.nii.gz"}, {"image": "./imagesTr/la_024.nii.gz", "label": "./labelsTr/la_024.nii.gz"}], "train_fold0": [{"image": "./imagesTr/la_003.nii.gz", "label": "./labelsTr/la_003.nii.gz"}, {"image": "./imagesTr/la_004.nii.gz", "label": "./labelsTr/la_004.nii.gz"}, {"image": "./imagesTr/la_005.nii.gz", "label": "./labelsTr/la_005.nii.gz"}, {"image": "./imagesTr/la_009.nii.gz", "label": "./labelsTr/la_009.nii.gz"}, {"image": "./imagesTr/la_010.nii.gz", "label": "./labelsTr/la_010.nii.gz"}, {"image": "./imagesTr/la_011.nii.gz", "label": "./labelsTr/la_011.nii.gz"}, {"image": "./imagesTr/la_014.nii.gz", "label": "./labelsTr/la_014.nii.gz"}, {"image": "./imagesTr/la_017.nii.gz", "label": "./labelsTr/la_017.nii.gz"}, {"image": "./imagesTr/la_018.nii.gz", "label": "./labelsTr/la_018.nii.gz"}, {"image": "./imagesTr/la_019.nii.gz", "label": "./labelsTr/la_019.nii.gz"}, {"image": "./imagesTr/la_020.nii.gz", "label": "./labelsTr/la_020.nii.gz"}, {"image": "./imagesTr/la_022.nii.gz", "label": "./labelsTr/la_022.nii.gz"}, {"image": "./imagesTr/la_023.nii.gz", "label": "./labelsTr/la_023.nii.gz"}, {"image": "./imagesTr/la_026.nii.gz", "label": "./labelsTr/la_026.nii.gz"}, {"image": "./imagesTr/la_029.nii.gz", "label": "./labelsTr/la_029.nii.gz"}, {"image": "./imagesTr/la_030.nii.gz", "label": "./labelsTr/la_030.nii.gz"}], "validation_fold1": [{"image": "./imagesTr/la_003.nii.gz", "label": "./labelsTr/la_003.nii.gz"}, {"image": "./imagesTr/la_018.nii.gz", "label": "./labelsTr/la_018.nii.gz"}, {"image": "./imagesTr/la_020.nii.gz", "label": "./labelsTr/la_020.nii.gz"}, {"image": "./imagesTr/la_023.nii.gz", 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