DanceDB

.gitignore

@@ -1,3 +1,9 @@
+data/DanceDB-bvhs/
+data/DanceDB/
+pre-trained/
+scripts/*.out
+scripts/*.err
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -107,4 +113,4 @@ venv.bak/
 .DS_Store
 .vscode/
 .idea/
-.nosync
+.nosync

bvh/bvh_parser.py

@@ -1,3 +1,4 @@
+from typing import Optional
 import torch
 import bvh.bvh_io as bvh_io
 import numpy as np
@@ -93,8 +94,8 @@ def ee_id(self):
 
 
 class BVH_file:
-    def __init__(self, file_path, no_scale=False, requires_contact=False, joint_reduction=True):
-        self.anim = bvh_io.load(file_path)
+    def __init__(self, file_path, no_scale=False, no_blender_scale=True, requires_contact=False, joint_reduction=True, start: Optional[int] = None, end: Optional[int] = None):
+        self.anim = bvh_io.load(file_path, start=start, end=end)
         self._names = self.anim.names
         self.frametime = self.anim.frametime
         self.skeleton = Skeleton(self.anim.names, self.anim.parent, self.anim.offsets, joint_reduction)
@@ -110,7 +111,7 @@ def __init__(self, file_path, no_scale=False, requires_contact=False, joint_redu
             self.anim.rotations = self.anim.rotations[::2]
 
         # Scale by 1/100 if it's raw exported bvh from blender
-        if not no_scale and self.skeleton.offsets[0, 1] > 10:
+        if not no_scale and not no_blender_scale and self.skeleton.offsets[0, 1] > 10:
             self.scale(1. / 100)
         # Scale by 0.14 for Xia's dataset
         if not no_scale and self.skeleton.skeleton_type == 3:

bvh/skeleton_database.py

@@ -2,6 +2,8 @@
 elephant_corps_name = ['Hips', 'Bip01_Pelvis', 'BN_Tail_01', 'BN_Tail_02', 'BN_Tail_03', 'BN_Tail_04', 'Bip01_Spine', 'Bip01_R_Thigh', 'Bip01_R_Calf', 'Bip01_R_Foot', 'Bip01_R_Toe0', 'Bip01_L_Thigh', 'Bip01_L_Calf', 'Bip01_L_Foot', 'Bip01_L_Toe0', 'Bip01_Spine1', 'Bip01_Spine2', 'Bip01_Neck', 'Bip01_Head', 'BN_Eyebrow_L', 'BN_Eyebrow_R', 'BN_Ear_L_01', 'BN_Ear_L_02', 'BN_Mouth_01', 'BN_Ear_R_01', 'BN_Ear_R_02', 'BN_Nose_01', 'BN_Nose_02', 'BN_Nose_03', 'BN_Nose_04', 'BN_Nose_05', 'BN_Nose_06', 'Bip01_R_Clavicle', 'Bip01_R_UpperArm', 'Bip01_R_Forearm', 'Bip01_R_Hand', 'Bip01_L_Clavicle', 'Bip01_L_UpperArm', 'Bip01_L_Forearm', 'Bip01_L_Hand']
 crab_dance_corps_name = ['ORG_Hips', 'ORG_BN_Bip01_Pelvis', 'DEF_BN_Eye_L_01', 'DEF_BN_Eye_L_02', 'DEF_BN_Eye_L_03', 'DEF_BN_Eye_L_03_end', 'DEF_BN_Eye_R_01', 'DEF_BN_Eye_R_02', 'DEF_BN_Eye_R_03', 'DEF_BN_Eye_R_03_end', 'DEF_BN_Leg_L_11', 'DEF_BN_Leg_L_12', 'DEF_BN_Leg_L_13', 'DEF_BN_Leg_L_14', 'DEF_BN_Leg_L_15', 'DEF_BN_Leg_L_15_end', 'DEF_BN_Leg_R_11', 'DEF_BN_Leg_R_12', 'DEF_BN_Leg_R_13', 'DEF_BN_Leg_R_14', 'DEF_BN_Leg_R_15', 'DEF_BN_Leg_R_15_end', 'DEF_BN_leg_L_01', 'DEF_BN_leg_L_02', 'DEF_BN_leg_L_03', 'DEF_BN_leg_L_04', 'DEF_BN_leg_L_05', 'DEF_BN_leg_L_05_end', 'DEF_BN_leg_L_06', 'DEF_BN_Leg_L_07', 'DEF_BN_Leg_L_08', 'DEF_BN_Leg_L_09', 'DEF_BN_Leg_L_10', 'DEF_BN_Leg_L_10_end', 'DEF_BN_leg_R_01', 'DEF_BN_leg_R_02', 'DEF_BN_leg_R_03', 'DEF_BN_leg_R_04', 'DEF_BN_leg_R_05', 'DEF_BN_leg_R_05_end', 'DEF_BN_leg_R_06', 'DEF_BN_Leg_R_07', 'DEF_BN_Leg_R_08', 'DEF_BN_Leg_R_09', 'DEF_BN_Leg_R_10', 'DEF_BN_Leg_R_10_end', 'DEF_BN_Bip01_Pelvis', 'DEF_BN_Bip01_Pelvis_end', 'DEF_BN_Arm_L_01', 'DEF_BN_Arm_L_02', 'DEF_BN_Arm_L_03', 'DEF_BN_Arm_L_03_end', 'DEF_BN_Arm_R_01', 'DEF_BN_Arm_R_02', 'DEF_BN_Arm_R_03', 'DEF_BN_Arm_R_03_end']
 xia_corps_name = ['Hips', 'LHipJoint', 'LeftUpLeg', 'LeftLeg', 'LeftFoot', 'LeftToeBase', 'RHipJoint', 'RightUpLeg', 'RightLeg', 'RightFoot', 'RightToeBase', 'LowerBack', 'Spine', 'Spine1', 'Neck', 'Neck1', 'Head', 'LeftShoulder', 'LeftArm', 'LeftForeArm', 'LeftHand', 'LeftFingerBase', 'LeftHandIndex1', 'LThumb', 'RightShoulder', 'RightArm', 'RightForeArm', 'RightHand', 'RightFingerBase', 'RightHandIndex1', 'RThumb']
+dancedb_corps_name = ['Hips', 'LHipJoint', 'LeftUpLeg', 'LeftLeg', 'LeftFoot', 'LeftToeBase',  'RHipJoint', 'RightUpLeg', 'RightLeg', 'RightFoot', 'RightToeBase',  'LowerBack', 'Spine', 'Spine1', 'Neck', 'Neck1', 'Head',  'LeftShoulder', 'LeftArm', 'LeftForeArm', 'LeftHand', 'LeftFingerBase', 'LeftHandIndex1',  'LThumb',  'RightShoulder', 'RightArm', 'RightForeArm', 'RightHand', 'RightFingerBase', 'RightHandIndex1',  'RThumb']
+dancedb_contacts = ['LeftFoot', 'LeftToeBase', 'RightFoot', 'RightToeBase']
 
 
 mixamo_contact_name = ['LeftToe_End', 'RightToe_End', 'LeftToeBase', 'RightToeBase']
@@ -11,10 +13,10 @@
 
 
 class SkeletonDatabase:
-    names = ['Mixamo', 'Elephant', 'Crab_dance', 'Xia']
-    corps_names = [mixamo_corps_name, elephant_corps_name, crab_dance_corps_name, xia_corps_name]
-    contact_names = [mixamo_contact_name, elephant_contact_name, crab_dance_contact_name, xia_contact_name]
-    contact_thresholds = [0.018, 0.018, 0.006, 0.018]
+    names = ['Mixamo', 'Elephant', 'Crab_dance', 'Xia', 'DanceDB']
+    corps_names = [mixamo_corps_name, elephant_corps_name, crab_dance_corps_name, xia_corps_name, dancedb_corps_name]
+    contact_names = [mixamo_contact_name, elephant_contact_name, crab_dance_contact_name, xia_contact_name, dancedb_contacts]
+    contact_thresholds = [0.018, 0.018, 0.006, 0.018, 0.018]
 
     @classmethod
     def match(cls, joint_names):
@@ -28,3 +30,4 @@ def match(cls, joint_names):
         max_match = max(n_match)
         max_match_id = n_match.index(max_match)
         return max_match_id, max_match
+

dataset/motion.py

@@ -1,5 +1,6 @@
 import os
 from os.path import join as pjoin
+from typing import Optional
 import numpy as np
 import torch
 import torch.nn.functional as F
@@ -11,9 +12,13 @@
 class MotionData:
     def __init__(self, filename, repr='quat', padding=False,
                  use_velo=False, no_scale=False, contact=False, keep_y_pos=False,
-                 joint_reduction=True):
+                 no_blender_scale=True,
+                 joint_reduction=True,
+                 start: Optional[int] = None, end: Optional[int] = None):
         self.bvh_file = BVH_file(filename, no_scale, requires_contact=contact,
-                                 joint_reduction=joint_reduction)
+                                 no_blender_scale=no_blender_scale,
+                                 joint_reduction=joint_reduction,
+                                 start=start, end=end)
         self.contact = contact
         self.filename = filename
         self.raw_motion = self.bvh_file.to_tensor(repr=repr)

demo.py

@@ -111,7 +111,7 @@ def main():
         conds_rec = None
 
     print('levels:', lengths)
-    save_path = pjoin(args.save_path, 'bvh')
+    save_path = pjoin(args.save_path, test_args.bvh_dir)
     os.makedirs(save_path, exist_ok=True)
 
     base_id = 0

option.py

@@ -96,6 +96,8 @@ def __init__(self):
         self.parser.add_argument('--full_zstar', type=int, default=1)
         self.parser.add_argument('--correct_zstar_gen', type=int, default=0)
         self.parser.add_argument('--use_6d_fk', type=int, default=0)
+        self.parser.add_argument('--start', type=int, default=None)
+        self.parser.add_argument('--end', type=int, default=None)
 
     @staticmethod
     def checker(args):
@@ -116,3 +118,4 @@ def __init__(self):
         self.parser.add_argument('--keyframe_editing', type=str, default='')
         self.parser.add_argument('--conditional_generation', type=str, default='')
         self.parser.add_argument('--interactive', type=int, default=0)
+        self.parser.add_argument('--bvh_dir', type=str, default="bvh")

slurm-scripts/experiments.csv

@@ -0,0 +1,5 @@
+BVH_PREFIX,BVH_NAME,SAVE_PATH,START,END
+./data/DanceDB-bvhs/greek-and-cypriot-dances/,ANDREAS_Poustsenko,res-andreas-poustsenko,10000,13000
+./data/DanceDB-bvhs/greek-and-cypriot-dances/,Clio_Podaraki,res-clio-podaraki,2500,5000
+./data/DanceDB-bvhs/greek-and-cypriot-dances/,Stefanos_Theodorou_1os_Antikristos_Katrsilamas_v1,res-stefanos-theodorou-1os-antikristos-katrsilamas,3000,5500
+

slurm-scripts/generate_samples.sh

@@ -0,0 +1,26 @@
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --gres=gpu:1
+#SBATCH --time=24:00:00
+#SBATCH --mem=16GB
+#SBATCH --ntasks-per-node=8
+#SBATCH --output="scripts/job.train.%j.out"
+#SBATCH --error="scripts/job.train.%j.err"
+#SBATCH --job-name=ganimdemo
+
+# # # # # # # # # # # # # # # # # # # # # #
+# GPU jupyter notebook setup on CYENS HPC #
+# # # # # # # # # # # # # # # # # # # # # #
+
+PROJECT_CONDA_ENV="ganimator"
+PROJECT_DIRECTORY="/lustreFS/data/veupnea/cchadjiminas/projects/ganimator"
+
+## Setup environment
+source ~/.bashrc
+
+echo "WHICH PYTHON: $(which python)"
+cd "$PROJECT_DIRECTORY"
+
+echo python demo.py --save_path="${SAVE_PATH}" --target_length="${TARGET_LENGTH}" --bvh_dir="${BVH_DIR}" --device="cuda"
+python demo.py --save_path="${SAVE_PATH}" --target_length="${TARGET_LENGTH}" --bvh_dir="${BVH_DIR}" --device="cuda"
+

slurm-scripts/jupyter.sh

@@ -0,0 +1,43 @@
+#!/bin/bash -l
+#SBATCH --nodes=1
+#SBATCH --gres=gpu:1
+#SBATCH --time=24:00:00
+#SBATCH --mem=32GB
+#SBATCH --ntasks-per-node=8
+#SBATCH --output="scripts/job.%j.out"
+#SBATCH --error="scripts/job.%j.err"
+#SBATCH --job-name=smdmjp
+
+# # # # # # # # # # # # # # # # # # # # # #
+# GPU jupyter notebook setup on CYENS HPC #
+# # # # # # # # # # # # # # # # # # # # # #
+
+PROJECT_CONDA_ENV="ganimator"
+PROJECT_DIRECTORY="/lustreFS/data/veupnea/cchadjiminas/projects/ganimator"
+
+## Setup environment
+source ~/.bashrc
+conda deactivate 
+conda deactivate 
+conda activate "$PROJECT_CONDA_ENV"
+
+# get tunneling info
+export XDG_RUNTIME_DIR="" node=$(hostname -s)
+user="$(whoami)"
+submit_host="${SLURM_SUBMIT_HOST}"
+port=10501
+echo $node pinned to port $port
+# print tunneling instructions
+
+echo -e "
+To connect to the compute node ${node} on CYENS HPC running your jupyter notebook server, you need to run following two commands in a terminal 1.
+Command to create ssh tunnel from you workstation/laptop to controller1:
+
+ssh -L ${port}:${node}:${port} ${user}@82.116.197.12
+
+Copy the link provided below by jupyter-server and replace the NODENAME with localhost before pasting it in your browser on your workstation/laptop "
+
+# Run Jupyter
+cd "$PROJECT_DIRECTORY"
+jupyter lab --no-browser --port=${port} --port-retries=50 --ip=${node}
+

slurm-scripts/run_experiments.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+
+# Check if the correct number of arguments is provided
+if [ "$#" -ne 1 ]; then
+  echo "Usage: $0 <experiments.csv>"
+  exit 1
+fi
+
+# Assign the first argument to the input variable
+input="$1"
+
+# Check if the file exists
+if [ ! -f "$input" ]; then
+  echo "File not found: $input"
+  exit 1
+fi
+
+
+# Skip the header and iterate over each line
+tail -n +2 "$input" | while IFS=',' read -r BVH_PREFIX BVH_NAME SAVE_PATH START END
+do
+  # Skip empty lines
+  if [ -z "$BVH_PREFIX" ] || [ -z "$BVH_NAME" ] || [ -z "$SAVE_PATH" ] || [ -z "$START" ] || [ -z "$END" ]; then
+    continue
+  fi
+
+  # Export variables
+  export BVH_PREFIX="$BVH_PREFIX"
+  export BVH_NAME="$BVH_NAME"
+  export SAVE_PATH="$SAVE_PATH"
+  export START="$START"
+  export END="$END"
+
+  echo $BVH_PREFIX $BVH_NAME $SAVE_PATH $START $END
+  # Execute the sbatch command
+  echo $(sbatch scripts/train.sh)
+done
+

slurm-scripts/run_generate_samples.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+
+# Check if the correct number of arguments is provided
+if [ "$#" -ne 1 ]; then
+  echo "Usage: $0 <samples.csv>"
+  exit 1
+fi
+
+# Assign the first argument to the input variable
+input="$1"
+
+# Check if the file exists
+if [ ! -f "$input" ]; then
+  echo "File not found: $input"
+  exit 1
+fi
+
+
+# Skip the header and iterate over each line
+tail -n +2 "$input" | while IFS=',' read -r SAVE_PATH TARGET_LENGTH BVH_DIR
+do
+  # Skip empty lines
+  if [ -z "$SAVE_PATH" ] || [ -z "$TARGET_LENGTH" ] || [ -z "$BVH_DIR" ]; then
+    continue
+  fi
+
+  # Export variables
+  export SAVE_PATH="$SAVE_PATH"
+  export TARGET_LENGTH="$TARGET_LENGTH"
+  export BVH_DIR="$BVH_DIR"
+
+  echo $SAVE_PATH $TARGET_LENGTH $BVH_DIR
+  # Execute the sbatch command
+  echo $(sbatch scripts/generate_samples.sh)
+done
+

slurm-scripts/samples.csv

@@ -0,0 +1,18 @@
+SAVE_PATH,TARGET_LENGTH,BVH_DIR
+240703-1135-res-andreas-poustsenko,600,bvh_600
+240703-1135-res-andreas-poustsenko,1200,bvh_1200
+240703-1135-res-andreas-poustsenko,2400,bvh_2400
+240703-1135-res-andreas-poustsenko,4800,bvh_4800
+240703-1135-res-stefanos-theodorou-1os-antikristos-katrsilamas,600,bvh_600
+240703-1135-res-stefanos-theodorou-1os-antikristos-katrsilamas,1200,bvh_1200
+240703-1135-res-stefanos-theodorou-1os-antikristos-katrsilamas,2400,bvh_2400
+240703-1135-res-stefanos-theodorou-1os-antikristos-katrsilamas,4800,bvh_4800
+240703-1145-res-clio-podaraki,600,bvh_600
+240703-1145-res-clio-podaraki,1200,bvh_1200
+240703-1145-res-clio-podaraki,2400,bvh_2400
+240703-1145-res-clio-podaraki,4800,bvh_4800
+240702-1446-results-clio-rasopoulos,600,bvh_600
+240702-1446-results-clio-rasopoulos,1200,bvh_1200
+240702-1446-results-clio-rasopoulos,2400,bvh_2400
+240702-1446-results-clio-rasopoulos,4800,bvh_4800
+

slurm-scripts/tensorboard.sh

@@ -0,0 +1,54 @@
+#!/bin/bash -l
+#SBATCH --nodes=1
+#SBATCH --time=24:00:00
+#SBATCH --mem=32GB
+#SBATCH --ntasks-per-node=2
+#SBATCH --output="scripts/job.%j.out"
+#SBATCH --error="scripts/job.%j.err"
+#SBATCH --job-name=ganimtb
+
+# # # # # # # INPUTS  # # # # # # # # # #
+LOG_DIR="${LOG_DIR:-"results-clio-rasopoulos-3/logs/"}"
+
+
+PORT="${PORT:-$(availport)}"
+PROJECT_CONDA_ENV="${PROJECT_CONDA_ENV:-"ganimator"}"
+PROJECT_DIRECTORY="${PROJECT_DIRECTORY:-"/lustreFS/data/veupnea/cchadjiminas/projects/ganimator"}"
+# # # # # # # # # #  # # # # # # # # # #
+
+cat << EOF
+LOG_DIR "${LOG_DIR}"
+
+PROJECT_DIRECTORY "${PROJECT_DIRECTORY}"
+PROJECT_CONDA_ENV "${PROJECT_CONDA_ENV}"
+PORT "${PORT}"
+EOF
+
+## Setup environment
+source ~/.bashrc
+conda deactivate 
+conda deactivate 
+conda activate "$PROJECT_CONDA_ENV"
+
+# get tunneling info
+export XDG_RUNTIME_DIR="" node=$(hostname -s)
+user="$(whoami)"
+submit_host="${SLURM_SUBMIT_HOST}"
+echo $node pinned to PORT $PORT
+# print tunneling instructions
+
+echo -e "
+To connect to the compute node ${node} on CYENS HPC running your jupyter notebook server, you need to run following two commands in a terminal 1.
+Command to create ssh tunnel from you workstation/laptop to controller1:
+
+ssh -L ${PORT}:${node}:${PORT} ${user}@82.116.197.12
+
+Copy the link provided below in your local browser"
+
+echo "http://localhost:${PORT}/#scalars"
+
+
+# Run Tensorboard
+cd "$PROJECT_DIRECTORY"
+tensorboard --port="${PORT}" --logdir "${LOG_DIR}" --host="${node}"
+

slurm-scripts/train.sh

@@ -0,0 +1,35 @@
+#!/bin/bash -l
+#SBATCH --nodes=1
+#SBATCH --gres=gpu:1
+#SBATCH --time=24:00:00
+#SBATCH --mem=32GB
+#SBATCH --ntasks-per-node=8
+#SBATCH --output="scripts/job.train.%j.out"
+#SBATCH --error="scripts/job.train.%j.err"
+#SBATCH --job-name=ganim
+
+# # # # # # # # # # # # # # # # # # # # # #
+# GPU jupyter notebook setup on CYENS HPC #
+# # # # # # # # # # # # # # # # # # # # # #
+
+PROJECT_CONDA_ENV="ganimator"
+PROJECT_DIRECTORY="/lustreFS/data/veupnea/cchadjiminas/projects/ganimator"
+
+## Setup environment
+source ~/.bashrc
+source /lustreFS/data/veupnea/cchadjiminas/miniconda3/etc/profile.d/conda.sh
+conda deactivate 
+conda deactivate 
+conda activate "$PROJECT_CONDA_ENV"
+
+cd "$PROJECT_DIRECTORY"
+
+START_OPT=${START:+--start=${START}}
+END_OPT=${END:+--end=${END}}
+
+BVH_PREFIX="${BVH_PREFIX:-"./data/DanceDB/"}"
+SAVE_PATH="${SAVE_PATH:-"./results"}"
+
+echo python train.py  --device="cuda" --bvh_prefix="${BVH_PREFIX}" --bvh_name="${BVH_NAME}" --save_path="${SAVE_PATH}" "${START_OPT}" "${END_OPT}"
+python train.py  --device="cuda" --bvh_prefix="${BVH_PREFIX}" --bvh_name="${BVH_NAME}" --save_path="${SAVE_PATH}" "${START_OPT}" "${END_OPT}"
+