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GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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__init__.py Executable file
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baselines_with_dialogue_moves.py Executable file
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from glob import glob
import os, json, sys
import torch, random, torch.nn as nn, numpy as np
from torch import optim
from random import shuffle
from sklearn.metrics import accuracy_score, f1_score
from src.data.game_parser import GameParser, make_splits, onehot, DEVICE, set_seed
from src.models.model_with_dialogue_moves import Model
import argparse
def print_epoch(data,acc_loss,lst):
print(f'{acc_loss/len(lst):9.4f}',end='; ',flush=True)
data = list(zip(*data))
for x in data:
a, b = list(zip(*x))
if max(a) <= 1:
print(f'({accuracy_score(a,b):5.3f},{f1_score(a,b,average="weighted"):5.3f},{sum(a)/len(a):5.3f},{sum(b)/len(b):5.3f},{len(b)})', end=' ',flush=True)
else:
print(f'({accuracy_score(a,b):5.3f},{f1_score(a,b,average="weighted"):5.3f},{len(b)})', end=' ',flush=True)
print('', end='; ',flush=True)
def do_split(model,lst,exp,criterion,optimizer=None,global_plan=False, player_plan=False,device=DEVICE):
data = []
acc_loss = 0
for game in lst:
if model.training and (not optimizer is None): optimizer.zero_grad()
l = model(game, global_plan=global_plan, player_plan=player_plan)
prediction = []
ground_truth = []
for gt, prd in l:
lbls = [int(a==b) for a,b in zip(gt[0],gt[1])]
lbls += [['NO', 'MAYBE', 'YES'].index(gt[0][0]),['NO', 'MAYBE', 'YES'].index(gt[0][1])]
if gt[0][2] in game.materials_dict:
lbls.append(game.materials_dict[gt[0][2]])
else:
lbls.append(0)
lbls += [['NO', 'MAYBE', 'YES'].index(gt[1][0]),['NO', 'MAYBE', 'YES'].index(gt[1][1])]
if gt[1][2] in game.materials_dict:
lbls.append(game.materials_dict[gt[1][2]])
else:
lbls.append(0)
prd = prd[exp:exp+1]
lbls = lbls[exp:exp+1]
data.append([(g,torch.argmax(p).item()) for p,g in zip(prd,lbls)])
# p, g = zip(*[(p,torch.eye(p.shape[0]).float()[g]) for p,g in zip(prd,lbls)])
if exp == 0:
pairs = list(zip(*[(pr,gt) for pr,gt in zip(prd,lbls) if gt==0 or (random.random() < 2/3)]))
elif exp == 1:
pairs = list(zip(*[(pr,gt) for pr,gt in zip(prd,lbls) if gt==0 or (random.random() < 5/6)]))
elif exp == 2:
pairs = list(zip(*[(pr,gt) for pr,gt in zip(prd,lbls) if gt==1 or (random.random() < 5/6)]))
else:
pairs = list(zip(*[(pr,gt) for pr,gt in zip(prd,lbls)]))
# print(pairs)
if pairs:
p,g = pairs
else:
continue
# print(p,g)
prediction.append(torch.cat(p))
# ground_truth.append(torch.cat(g))
ground_truth += g
if prediction:
prediction = torch.stack(prediction)
else:
continue
if ground_truth:
# ground_truth = torch.stack(ground_truth).float().to(DEVICE)
ground_truth = torch.tensor(ground_truth).long().to(device)
else:
continue
loss = criterion(prediction,ground_truth)
if model.training and (not optimizer is None):
loss.backward()
# nn.utils.clip_grad_norm_(model.parameters(), 10)
nn.utils.clip_grad_norm_(model.parameters(), 1)
optimizer.step()
acc_loss += loss.item()
# return data, acc_loss + loss.item()
print_epoch(data,acc_loss,lst)
return acc_loss, data
def main(args):
print(args, flush=True)
print(f'PID: {os.getpid():6d}', flush=True)
if isinstance(args.device, int) and args.device >= 0:
DEVICE = f'cuda:{args.device}' if torch.cuda.is_available() else 'cpu'
print(f'Using {DEVICE}')
else:
print('Device must be a zero or positive integer, but got',args.device)
exit()
if isinstance(args.seed, int) and args.seed >= 0:
seed = set_seed(args.seed)
else:
print('Seed must be a zero or positive integer, but got',args.seed)
exit()
# dataset_splits = make_splits('config/dataset_splits.json')
# dataset_splits = make_splits('config/dataset_splits_dev.json')
# dataset_splits = make_splits('config/dataset_splits_old.json')
dataset_splits = make_splits('config/dataset_splits_new.json')
if args.use_dialogue=='Yes':
d_flag = True
elif args.use_dialogue=='No':
d_flag = False
else:
print('Use dialogue must be in [Yes, No], but got',args.use_dialogue)
exit()
if args.use_dialogue_moves=='Yes':
d_move_flag = True
elif args.use_dialogue_moves=='No':
d_move_flag = False
else:
print('Use dialogue must be in [Yes, No], but got',args.use_dialogue)
exit()
if not args.experiment in list(range(9)):
print('Experiment must be in',list(range(9)),', but got',args.experiment)
exit()
if args.seq_model=='GRU':
seq_model = 0
elif args.seq_model=='LSTM':
seq_model = 1
elif args.seq_model=='Transformer':
seq_model = 2
else:
print('The sequence model must be in [GRU, LSTM, Transformer], but got', args.seq_model)
exit()
if args.plans=='Yes':
global_plan = (args.pov=='Third') or ((args.pov=='None') and (args.experiment in list(range(3))))
player_plan = (args.pov=='First') or ((args.pov=='None') and (args.experiment in list(range(3,9))))
elif args.plans=='No' or args.plans is None:
global_plan = False
player_plan = False
else:
print('Use Plan must be in [Yes, No], but got',args.plan)
exit()
print('global_plan', global_plan, 'player_plan', player_plan)
if args.pov=='None':
val = [GameParser(f,d_flag,0,0,d_move_flag) for f in dataset_splits['validation']]
train = [GameParser(f,d_flag,0,0,d_move_flag) for f in dataset_splits['training']]
if args.experiment > 2:
val += [GameParser(f,d_flag,4,0,d_move_flag) for f in dataset_splits['validation']]
train += [GameParser(f,d_flag,4,0,d_move_flag) for f in dataset_splits['training']]
elif args.pov=='Third':
val = [GameParser(f,d_flag,3,0,d_move_flag) for f in dataset_splits['validation']]
train = [GameParser(f,d_flag,3,0,d_move_flag) for f in dataset_splits['training']]
elif args.pov=='First':
val = [GameParser(f,d_flag,1,0,d_move_flag) for f in dataset_splits['validation']]
train = [GameParser(f,d_flag,1,0,d_move_flag) for f in dataset_splits['training']]
val += [GameParser(f,d_flag,2,0,d_move_flag) for f in dataset_splits['validation']]
train += [GameParser(f,d_flag,2,0,d_move_flag) for f in dataset_splits['training']]
else:
print('POV must be in [None, First, Third], but got', args.pov)
exit()
model = Model(seq_model,DEVICE).to(DEVICE)
print(model)
model.train()
learning_rate = 1e-4
num_epochs = 1000#2#1#
weight_decay=1e-4
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# optimizer = optim.RMSprop(model.parameters(), lr=learning_rate)
# optimizer = optim.Adagrad(model.parameters(), lr=learning_rate)
# optimizer = optim.Adadelta(model.parameters())
# optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=weight_decay)
criterion = nn.CrossEntropyLoss()
# criterion = nn.MSELoss()
print(str(criterion), str(optimizer))
min_acc_loss = 100
max_f1 = 0
epochs_since_improvement = 0
wait_epoch = 100
if args.model_path is not None:
print(f'Loading {args.model_path}')
model.load_state_dict(torch.load(args.model_path))
acc_loss, data = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, device=DEVICE)
data = list(zip(*data))
for x in data:
a, b = list(zip(*x))
f1 = f1_score(a,b,average='weighted')
f1 = f1_score(a,b,average='weighted')
if (max_f1 < f1):
max_f1 = f1
epochs_since_improvement = 0
print('^')
torch.save(model.cpu().state_dict(), args.save_path)
model = model.to(DEVICE)
else:
print('Training model from scratch', flush=True)
# exit()
for epoch in range(num_epochs):
print(f'{os.getpid():6d} {epoch+1:4d},',end=' ', flush=True)
shuffle(train)
model.train()
do_split(model,train,args.experiment,criterion,optimizer=optimizer,global_plan=global_plan, player_plan=player_plan, device=DEVICE)
model.eval()
acc_loss, data = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, device=DEVICE)
data = list(zip(*data))
for x in data:
a, b = list(zip(*x))
f1 = f1_score(a,b,average='weighted')
if (max_f1 < f1):
max_f1 = f1
epochs_since_improvement = 0
print('^')
torch.save(model.cpu().state_dict(), args.save_path)
model = model.to(DEVICE)
else:
epochs_since_improvement += 1
print()
# if (min_acc_loss > acc_loss):
# min_acc_loss = acc_loss
# epochs_since_improvement = 0
# print('^')
# else:
# epochs_since_improvement += 1
# print()
if epoch > wait_epoch and epochs_since_improvement > 20:
break
print()
print('Test')
model.load_state_dict(torch.load(args.save_path))
val = None
train = None
if args.pov=='None':
test = [GameParser(f,d_flag,0,0,d_move_flag) for f in dataset_splits['test']]
if args.experiment > 2:
test += [GameParser(f,d_flag,4,0,d_move_flag) for f in dataset_splits['test']]
elif args.pov=='Third':
test = [GameParser(f,d_flag,3,0,d_move_flag) for f in dataset_splits['test']]
elif args.pov=='First':
test = [GameParser(f,d_flag,1,0,d_move_flag) for f in dataset_splits['test']]
test += [GameParser(f,d_flag,2,0,d_move_flag) for f in dataset_splits['test']]
else:
print('POV must be in [None, First, Third], but got', args.pov)
model.eval()
_, data = do_split(model,test,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, device=DEVICE)
print()
print(data)
print()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--pov', type=str,
help='point of view [None, First, Third]')
parser.add_argument('--use_dialogue', type=str,
help='Use dialogue [Yes, No]')
parser.add_argument('--use_dialogue_moves', type=str,
help='Use dialogue [Yes, No]')
parser.add_argument('--plans', type=str,
help='Use dialogue [Yes, No]')
parser.add_argument('--seq_model', type=str,
help='point of view [GRU, LSTM, Transformer]')
parser.add_argument('--experiment', type=int,
help='point of view [0:AggQ1, 1:AggQ2, 2:AggQ3, 3:P0Q1, 4:P0Q2, 5:P0Q3, 6:P1Q1, 7:P1Q2, 8:P1Q3]')
parser.add_argument('--seed', type=int,
help='Selet random seed by index [0, 1, 2, ...]. 0 -> random seed set to 0. n>0 -> random seed '
'set to n\'th random number with original seed set to 0')
parser.add_argument('--save_path', type=str,
help='path where to save model')
parser.add_argument('--model_path', type=str, default=None,
help='path to the pretrained model to be loaded')
parser.add_argument('--device', type=int, default=0,
help='select cuda device number')
main(parser.parse_args())

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FOLDER="models/tom_lstm_baseline"
mkdir -p $FOLDER
CUDA_DEVICE=$1
SEED=$2
for MODEL in LSTM; do # LSTM; do # LSTM Transformer; do #
for DLGM in Yes; do # No; do # Yes; do # No
for EXP in 6 7 8; do # 2 3; do
DLG="No"
POV="None"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}"
COMM="baselines_with_dialogue_moves.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
DLG="Yes"
POV="None"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}"
COMM="baselines_with_dialogue_moves.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --model_path=${FOLDER}/gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_No_pov_None_exp${EXP}_seed_${SEED}.torch"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
DLG="No"
POV="First"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}"
COMM="baselines_with_dialogue_moves.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --model_path=${FOLDER}/gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_No_pov_None_exp${EXP}_seed_${SEED}.torch"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
DLG="Yes"
POV="First"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}_DlgFirst"
COMM="baselines_with_dialogue_moves.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --model_path=${FOLDER}/gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_Yes_pov_None_exp${EXP}_seed_${SEED}.torch"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
DLG="Yes"
POV="First"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}_VidFirst"
COMM="baselines_with_dialogue_moves.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --model_path=${FOLDER}/gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_No_pov_First_exp${EXP}_seed_${SEED}.torch"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
done
done
done
echo "Done!"

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import os
import torch, random, torch.nn as nn, numpy as np
from torch import optim
from random import shuffle
from sklearn.metrics import accuracy_score, f1_score
from src.data.game_parser_graphs_new import GameParser, make_splits, DEVICE, set_seed
from src.models.model_with_dialogue_moves_graphs import Model
import argparse
from tqdm import tqdm
import pickle
def print_epoch(data,acc_loss,lst):
print(f'{acc_loss/len(lst):9.4f}',end='; ',flush=True)
data = list(zip(*data))
for x in data:
a, b = list(zip(*x))
if max(a) <= 1:
print(f'({accuracy_score(a,b):5.3f},{f1_score(a,b,average="weighted"):5.3f},{sum(a)/len(a):5.3f},{sum(b)/len(b):5.3f},{len(b)})', end=' ',flush=True)
else:
print(f'({accuracy_score(a,b):5.3f},{f1_score(a,b,average="weighted"):5.3f},{len(b)})', end=' ',flush=True)
print('', end='; ',flush=True)
def do_split(model,lst,exp,criterion,optimizer=None,global_plan=False, player_plan=False,device=DEVICE):
data = []
acc_loss = 0
for game in lst:
if model.training and (not optimizer is None): optimizer.zero_grad()
l = model(game, global_plan=global_plan, player_plan=player_plan)
prediction = []
ground_truth = []
for gt, prd in l:
lbls = [int(a==b) for a,b in zip(gt[0],gt[1])]
lbls += [['NO', 'MAYBE', 'YES'].index(gt[0][0]),['NO', 'MAYBE', 'YES'].index(gt[0][1])]
if gt[0][2] in game.materials_dict:
lbls.append(game.materials_dict[gt[0][2]])
else:
lbls.append(0)
lbls += [['NO', 'MAYBE', 'YES'].index(gt[1][0]),['NO', 'MAYBE', 'YES'].index(gt[1][1])]
if gt[1][2] in game.materials_dict:
lbls.append(game.materials_dict[gt[1][2]])
else:
lbls.append(0)
prd = prd[exp:exp+1]
lbls = lbls[exp:exp+1]
data.append([(g,torch.argmax(p).item()) for p,g in zip(prd,lbls)])
# p, g = zip(*[(p,torch.eye(p.shape[0]).float()[g]) for p,g in zip(prd,lbls)])
if exp == 0:
pairs = list(zip(*[(pr,gt) for pr,gt in zip(prd,lbls) if gt==0 or (random.random() < 2/3)]))
elif exp == 1:
pairs = list(zip(*[(pr,gt) for pr,gt in zip(prd,lbls) if gt==0 or (random.random() < 5/6)]))
elif exp == 2:
pairs = list(zip(*[(pr,gt) for pr,gt in zip(prd,lbls) if gt==1 or (random.random() < 5/6)]))
else:
pairs = list(zip(*[(pr,gt) for pr,gt in zip(prd,lbls)]))
# print(pairs)
if pairs:
p,g = pairs
else:
continue
# print(p,g)
prediction.append(torch.cat(p))
# ground_truth.append(torch.cat(g))
ground_truth += g
if prediction:
prediction = torch.stack(prediction)
else:
continue
if ground_truth:
# ground_truth = torch.stack(ground_truth).float().to(DEVICE)
ground_truth = torch.tensor(ground_truth).long().to(device)
else:
continue
loss = criterion(prediction,ground_truth)
if model.training and (not optimizer is None):
loss.backward()
# nn.utils.clip_grad_norm_(model.parameters(), 10)
nn.utils.clip_grad_norm_(model.parameters(), 1)
optimizer.step()
acc_loss += loss.item()
# return data, acc_loss + loss.item()
print_epoch(data,acc_loss,lst)
return acc_loss, data
def main(args):
print(args, flush=True)
print(f'PID: {os.getpid():6d}', flush=True)
if isinstance(args.device, int) and args.device >= 0:
DEVICE = f'cuda:{args.device}' if torch.cuda.is_available() else 'cpu'
print(f'Using {DEVICE}')
else:
print('Device must be a zero or positive integer, but got',args.device)
exit()
if isinstance(args.seed, int) and args.seed >= 0:
seed = set_seed(args.seed)
else:
print('Seed must be a zero or positive integer, but got',args.seed)
exit()
# dataset_splits = make_splits('config/dataset_splits.json')
# dataset_splits = make_splits('config/dataset_splits_dev.json')
# dataset_splits = make_splits('config/dataset_splits_old.json')
dataset_splits = make_splits('config/dataset_splits_new.json')
if args.use_dialogue=='Yes':
d_flag = True
elif args.use_dialogue=='No':
d_flag = False
else:
print('Use dialogue must be in [Yes, No], but got',args.use_dialogue)
exit()
if args.use_dialogue_moves=='Yes':
d_move_flag = True
elif args.use_dialogue_moves=='No':
d_move_flag = False
else:
print('Use dialogue must be in [Yes, No], but got',args.use_dialogue)
exit()
if not args.experiment in list(range(9)):
print('Experiment must be in',list(range(9)),', but got',args.experiment)
exit()
if args.seq_model=='GRU':
seq_model = 0
elif args.seq_model=='LSTM':
seq_model = 1
elif args.seq_model=='Transformer':
seq_model = 2
else:
print('The sequence model must be in [GRU, LSTM, Transformer], but got', args.seq_model)
exit()
if args.plans=='Yes':
global_plan = (args.pov=='Third') or ((args.pov=='None') and (args.experiment in list(range(3))))
player_plan = (args.pov=='First') or ((args.pov=='None') and (args.experiment in list(range(3,9))))
elif args.plans=='No' or args.plans is None:
global_plan = False
player_plan = False
else:
print('Use Plan must be in [Yes, No], but got',args.plan)
exit()
print('global_plan', global_plan, 'player_plan', player_plan)
if args.pov=='None':
val = [GameParser(f,d_flag,0,0,d_move_flag) for f in tqdm(dataset_splits['validation'])]
train = [GameParser(f,d_flag,0,0,d_move_flag) for f in tqdm(dataset_splits['training'])]
if args.experiment > 2:
val += [GameParser(f,d_flag,4,0,d_move_flag) for f in tqdm(dataset_splits['validation'])]
train += [GameParser(f,d_flag,4,0,d_move_flag) for f in tqdm(dataset_splits['training'])]
elif args.pov=='Third':
val = [GameParser(f,d_flag,3,0,d_move_flag) for f in tqdm(dataset_splits['validation'])]
train = [GameParser(f,d_flag,3,0,d_move_flag) for f in tqdm(dataset_splits['training'])]
elif args.pov=='First':
val = [GameParser(f,d_flag,1,0,d_move_flag) for f in tqdm(dataset_splits['validation'])]
train = [GameParser(f,d_flag,1,0,d_move_flag) for f in tqdm(dataset_splits['training'])]
val += [GameParser(f,d_flag,2,0,d_move_flag) for f in tqdm(dataset_splits['validation'])]
train += [GameParser(f,d_flag,2,0,d_move_flag) for f in tqdm(dataset_splits['training'])]
else:
print('POV must be in [None, First, Third], but got', args.pov)
exit()
model = Model(seq_model,DEVICE).to(DEVICE)
print(model)
model.train()
learning_rate = 1e-4
num_epochs = 1000#2#1#
weight_decay=1e-4
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# optimizer = optim.RMSprop(model.parameters(), lr=learning_rate)
# optimizer = optim.Adagrad(model.parameters(), lr=learning_rate)
# optimizer = optim.Adadelta(model.parameters())
# optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=weight_decay)
criterion = nn.CrossEntropyLoss()
# criterion = nn.MSELoss()
print(str(criterion), str(optimizer))
min_acc_loss = 100
max_f1 = 0
epochs_since_improvement = 0
wait_epoch = 100
if args.model_path is not None:
print(f'Loading {args.model_path}')
model.load_state_dict(torch.load(args.model_path))
acc_loss, data = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, device=DEVICE)
data = list(zip(*data))
for x in data:
a, b = list(zip(*x))
f1 = f1_score(a,b,average='weighted')
f1 = f1_score(a,b,average='weighted')
if (max_f1 < f1):
max_f1 = f1
epochs_since_improvement = 0
print('^')
torch.save(model.cpu().state_dict(), args.save_path)
model = model.to(DEVICE)
else:
print('Training model from scratch', flush=True)
# exit()
for epoch in range(num_epochs):
print(f'{os.getpid():6d} {epoch+1:4d},',end=' ', flush=True)
shuffle(train)
model.train()
do_split(model,train,args.experiment,criterion,optimizer=optimizer,global_plan=global_plan, player_plan=player_plan, device=DEVICE)
model.eval()
acc_loss, data = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, device=DEVICE)
data = list(zip(*data))
for x in data:
a, b = list(zip(*x))
f1 = f1_score(a,b,average='weighted')
if (max_f1 < f1):
max_f1 = f1
epochs_since_improvement = 0
print('^')
torch.save(model.cpu().state_dict(), args.save_path)
model = model.to(DEVICE)
else:
epochs_since_improvement += 1
print()
# if (min_acc_loss > acc_loss):
# min_acc_loss = acc_loss
# epochs_since_improvement = 0
# print('^')
# else:
# epochs_since_improvement += 1
# print()
if epoch > wait_epoch and epochs_since_improvement > 20:
break
print()
print('Test')
model.load_state_dict(torch.load(args.save_path))
val = None
train = None
if args.pov=='None':
test = [GameParser(f,d_flag,0,0,d_move_flag) for f in tqdm(dataset_splits['test'])]
if args.experiment > 2:
test += [GameParser(f,d_flag,4,0,d_move_flag) for f in tqdm(dataset_splits['test'])]
elif args.pov=='Third':
test = [GameParser(f,d_flag,3,0,d_move_flag) for f in tqdm(dataset_splits['test'])]
elif args.pov=='First':
test = [GameParser(f,d_flag,1,0,d_move_flag) for f in tqdm(dataset_splits['test'])]
test += [GameParser(f,d_flag,2,0,d_move_flag) for f in tqdm(dataset_splits['test'])]
else:
print('POV must be in [None, First, Third], but got', args.pov)
model.eval()
_, data = do_split(model,test,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, device=DEVICE)
print()
print(data)
print()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--pov', type=str,
help='point of view [None, First, Third]')
parser.add_argument('--use_dialogue', type=str,
help='Use dialogue [Yes, No]')
parser.add_argument('--use_dialogue_moves', type=str,
help='Use dialogue [Yes, No]')
parser.add_argument('--plans', type=str,
help='Use dialogue [Yes, No]')
parser.add_argument('--seq_model', type=str,
help='point of view [GRU, LSTM, Transformer]')
parser.add_argument('--experiment', type=int,
help='point of view [0:AggQ1, 1:AggQ2, 2:AggQ3, 3:P0Q1, 4:P0Q2, 5:P0Q3, 6:P1Q1, 7:P1Q2, 8:P1Q3]')
parser.add_argument('--seed', type=int,
help='Selet random seed by index [0, 1, 2, ...]. 0 -> random seed set to 0. n>0 -> random seed '
'set to n\'th random number with original seed set to 0')
parser.add_argument('--save_path', type=str,
help='path where to save model')
parser.add_argument('--model_path', type=str, default=None,
help='path to the pretrained model to be loaded')
parser.add_argument('--device', type=int, default=0,
help='select cuda device number')
main(parser.parse_args())

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FOLDER="models/gt_dialogue_moves_bootstrap_DlgMove_Graphs"
mkdir -p $FOLDER
CUDA_DEVICE=$1
SEED=$2
for MODEL in LSTM; do
for DLGM in Yes; do
for EXP in 6 7 8; do
DLG="No"
POV="None"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}"
COMM="baselines_with_dialogue_moves_graphs.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
DLG="Yes"
POV="None"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}"
COMM="baselines_with_dialogue_moves_graphs.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --model_path=${FOLDER}/gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_No_pov_None_exp${EXP}_seed_${SEED}.torch"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
DLG="No"
POV="First"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}"
COMM="baselines_with_dialogue_moves_graphs.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --model_path=${FOLDER}/gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_No_pov_None_exp${EXP}_seed_${SEED}.torch"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
DLG="Yes"
POV="First"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}_DlgFirst"
COMM="baselines_with_dialogue_moves_graphs.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --model_path=${FOLDER}/gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_Yes_pov_None_exp${EXP}_seed_${SEED}.torch"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
DLG="Yes"
POV="First"
FILE_NAME="gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_${DLG}_pov_${POV}_exp${EXP}_seed_${SEED}_VidFirst"
COMM="baselines_with_dialogue_moves_graphs.py"
COMM=$COMM" --device=${CUDA_DEVICE}"
COMM=$COMM" --seed=${SEED}"
COMM=$COMM" --use_dialogue_moves=${DLGM}"
COMM=$COMM" --use_dialogue=${DLG}"
COMM=$COMM" --pov=${POV}"
COMM=$COMM" --experiment=${EXP}"
COMM=$COMM" --seq_model=${MODEL}"
COMM=$COMM" --plan=Yes"
COMM=$COMM" --model_path=${FOLDER}/gt_dialogue_moves_${MODEL}_dlgMove_${DLGM}_dlg_No_pov_First_exp${EXP}_seed_${SEED}.torch"
COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch"
echo $(date +%F\ %T) $COMM" > ${FOLDER}/${FILE_NAME}.log"
nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log
done
done
done
echo "Done!"

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{
"test": [
"XXX/main_logs/141_212_108_99_20210325_121618",
"XXX/main_logs/141_212_108_99_20210325_170350",
"XXX/main_logs/141_212_108_99_20210429_151811",
"XXX/main_logs/141_212_108_99_20210408_161512",
"XXX/saved_logs/141_212_108_99_20210317_125416",
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14
config/dataset_splits_dev.json Normal file
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@ -0,0 +1,14 @@
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170
config/dataset_splits_new.json Normal file
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@ -0,0 +1,170 @@
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