From e15b0d7b50398cd4e27d8d7a52da9cc25d3ee416 Mon Sep 17 00:00:00 2001 From: Matteo Bortoletto Date: Tue, 11 Jun 2024 15:36:55 +0200 Subject: [PATCH] up --- LICENSE | 339 ++ __init__.py | 0 baselines_with_dialogue_moves.py | 296 ++ baselines_with_dialogue_moves.sh | 106 + baselines_with_dialogue_moves_graphs.py | 296 ++ baselines_with_dialogue_moves_graphs.sh | 104 + compare_tom_cpa.ipynb | 480 +++ config/dataset_splits.json | 170 + config/dataset_splits_dev.json | 14 + config/dataset_splits_new.json | 170 + config/dataset_splits_old.json | 110 + config/dialogue_act_label_names.json | 21 + config/dialogue_act_labels.json | 3629 +++++++++++++++++ config/dialogue_move_label_names.json | 37 + config/dialogue_move_labels.json | 3629 +++++++++++++++++ config/materials.json | 23 + config/mines.json | 8 + config/tools.json | 14 + intermediate_representations.py | 75 + logistic_regression_tom_feats.py | 383 ++ plan_predictor.py | 416 ++ plan_predictor_graphs.py | 325 ++ plan_predictor_graphs_oracle.py | 283 ++ plan_predictor_graphs_test.py | 201 + plan_predictor_oracle.py | 351 ++ run_plan_predictor.sh | 42 + run_plan_predictor_graphs.sh | 47 + run_plan_predictor_graphs_int0.sh | 44 + run_plan_predictor_graphs_oracle.sh | 36 + run_plan_predictor_graphs_sigmoid.sh | 42 + run_plan_predictor_oracle.sh | 40 + src/.DS_Store | Bin 0 -> 6148 bytes src/__init__.py | 0 src/data/.DS_Store | Bin 0 -> 6148 bytes src/data/__init__.py | 0 src/data/game_parser.py | 761 ++++ src/data/game_parser_graphs_new.py | 837 ++++ src/models/.DS_Store | Bin 0 -> 6148 bytes src/models/__init__.py | 0 src/models/losses.py | 207 + src/models/model_with_dialogue_moves.py | 205 + .../model_with_dialogue_moves_graphs.py | 226 + src/models/plan_model.py | 225 + src/models/plan_model_graphs.py | 230 ++ src/models/plan_model_graphs_oracle.py | 291 ++ src/models/plan_model_oracle.py | 214 + 46 files changed, 14927 insertions(+) create mode 100644 LICENSE create mode 100755 __init__.py create mode 100755 baselines_with_dialogue_moves.py create mode 100755 baselines_with_dialogue_moves.sh create mode 100644 baselines_with_dialogue_moves_graphs.py create mode 100644 baselines_with_dialogue_moves_graphs.sh create mode 100644 compare_tom_cpa.ipynb create mode 100644 config/dataset_splits.json create mode 100644 config/dataset_splits_dev.json create mode 100644 config/dataset_splits_new.json create mode 100644 config/dataset_splits_old.json create mode 100644 config/dialogue_act_label_names.json create mode 100644 config/dialogue_act_labels.json create mode 100644 config/dialogue_move_label_names.json create mode 100644 config/dialogue_move_labels.json create mode 100644 config/materials.json create mode 100644 config/mines.json create mode 100644 config/tools.json create mode 100644 intermediate_representations.py create mode 100644 logistic_regression_tom_feats.py create mode 100644 plan_predictor.py create mode 100644 plan_predictor_graphs.py create mode 100644 plan_predictor_graphs_oracle.py create mode 100644 plan_predictor_graphs_test.py create mode 100644 plan_predictor_oracle.py create mode 100644 run_plan_predictor.sh create mode 100644 run_plan_predictor_graphs.sh create mode 100644 run_plan_predictor_graphs_int0.sh create mode 100644 run_plan_predictor_graphs_oracle.sh create mode 100644 run_plan_predictor_graphs_sigmoid.sh create mode 100644 run_plan_predictor_oracle.sh create mode 100644 src/.DS_Store create mode 100644 src/__init__.py create mode 100644 src/data/.DS_Store create mode 100644 src/data/__init__.py create mode 100755 src/data/game_parser.py create mode 100644 src/data/game_parser_graphs_new.py create mode 100644 src/models/.DS_Store create mode 100644 src/models/__init__.py create mode 100644 src/models/losses.py create mode 100755 src/models/model_with_dialogue_moves.py create mode 100644 src/models/model_with_dialogue_moves_graphs.py create mode 100755 src/models/plan_model.py create mode 100644 src/models/plan_model_graphs.py create mode 100644 src/models/plan_model_graphs_oracle.py create mode 100644 src/models/plan_model_oracle.py diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..d159169 --- /dev/null +++ b/LICENSE @@ -0,0 +1,339 @@ + 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 + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General +Public License instead of this License. diff --git a/__init__.py b/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/baselines_with_dialogue_moves.py b/baselines_with_dialogue_moves.py new file mode 100755 index 0000000..4251153 --- /dev/null +++ b/baselines_with_dialogue_moves.py @@ -0,0 +1,296 @@ +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()) \ No newline at end of file diff --git a/baselines_with_dialogue_moves.sh b/baselines_with_dialogue_moves.sh new file mode 100755 index 0000000..7bffcb8 --- /dev/null +++ b/baselines_with_dialogue_moves.sh @@ -0,0 +1,106 @@ + +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!" + diff --git a/baselines_with_dialogue_moves_graphs.py b/baselines_with_dialogue_moves_graphs.py new file mode 100644 index 0000000..1883c23 --- /dev/null +++ b/baselines_with_dialogue_moves_graphs.py @@ -0,0 +1,296 @@ +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()) \ No newline at end of file diff --git a/baselines_with_dialogue_moves_graphs.sh b/baselines_with_dialogue_moves_graphs.sh new file mode 100644 index 0000000..6a11764 --- /dev/null +++ b/baselines_with_dialogue_moves_graphs.sh @@ -0,0 +1,104 @@ +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!" \ No newline at end of file diff --git a/compare_tom_cpa.ipynb b/compare_tom_cpa.ipynb new file mode 100644 index 0000000..bcd6377 --- /dev/null +++ b/compare_tom_cpa.ipynb @@ -0,0 +1,480 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle \n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import f1_score\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.stats import pearsonr\n", + "import numpy as np " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# task status\n", + "tom6_fname = \"XXX\"\n", + "# task knowledge\n", + "tom7_fname = \"XXX\"\n", + "# task intention\n", + "tom8_fname = \"XXX\"\n", + "# own plan\n", + "exp2_5_fname = \"XXX\"\n", + "exp2_6_fname = \"XXX\"\n", + "exp2_0_fname = \"XXX\"\n", + "# partner plan \n", + "exp3_7_fname = \"XXX\"\n", + "exp3_0_fname = \"XXX\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "with open(tom6_fname, 'rb') as f: \n", + " tom6 = pickle.load(f)\n", + "with open(tom7_fname, 'rb') as f: \n", + " tom7 = pickle.load(f)\n", + "with open(tom8_fname, 'rb') as f: \n", + " tom8 = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "with open(exp2_5_fname, 'rb') as f:\n", + " exp2_5 = pickle.load(f)\n", + "with open(exp2_6_fname, 'rb') as f:\n", + " exp2_6 = pickle.load(f)\n", + "with open(exp2_0_fname, 'rb') as f:\n", + " exp2_0 = pickle.load(f)\n", + "with open(exp3_0_fname, 'rb') as f:\n", + " exp3_0 = pickle.load(f)\n", + "with open(exp3_7_fname, 'rb') as f:\n", + " exp3_7 = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "tom6 = [item[0] for item in tom6 if item and item[0]]\n", + "tom7 = [item[0] for item in tom7 if item and item[0]]\n", + "tom8 = [item[0] for item in tom8 if item and item[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# compute per-game f1 scores\n", + "tom6_f1 = []\n", + "tom6_game_paths = []\n", + "for game in tom6: \n", + " pred = game[0]\n", + " gt = game[1]\n", + " game_path = game[2]\n", + " f1 = f1_score(gt, pred, average=\"weighted\")\n", + " tom6_f1.append(f1)\n", + " tom6_game_paths.append(game_path)\n", + "\n", + "tom7_f1 = []\n", + "tom7_game_paths = []\n", + "for game in tom7: \n", + " pred = game[0]\n", + " gt = game[1]\n", + " game_path = game[2]\n", + " f1 = f1_score(gt, pred, average=\"weighted\")\n", + " tom7_f1.append(f1)\n", + " tom7_game_paths.append(game_path)\n", + "\n", + "tom8_f1 = []\n", + "tom8_game_paths = []\n", + "for game in tom8: \n", + " pred = game[0]\n", + " gt = game[1]\n", + " game_path = game[2]\n", + " f1 = f1_score(gt, pred, average=\"weighted\")\n", + " tom8_f1.append(f1)\n", + " tom8_game_paths.append(game_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tom6_game_paths == tom7_game_paths == tom8_game_paths" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'141_212_108_99_20210325_121618'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "game_ids = [path.split(\"/\")[-1] for path in tom6_game_paths]\n", + "game_ids[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "exp2_5_f1 = [item[-1] for item in exp2_5]\n", + "exp2_6_f1 = [item[-1] for item in exp2_6]\n", + "exp2_0_f1 = [item[-1] for item in exp2_0]\n", + "exp3_0_f1 = [item[-1] for item in exp3_0]\n", + "exp3_7_f1 = [item[-1] for item in exp3_7]\n", + "exp2_game_ids = [item[-3].split(\"/\")[-1] for item in exp2_5]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "exp2_f1_diff_5_0 = [a - b for a, b in zip(exp2_5_f1, exp2_0_f1)]\n", + "exp2_f1_diff_6_0 = [a - b for a, b in zip(exp2_6_f1, exp2_0_f1)]\n", + "exp3_f1_diff_7_0 = [a - b for a, b in zip(exp3_7_f1, exp3_0_f1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(60, 64)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(exp2_game_ids), len(game_ids)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "common_elements = set(exp2_game_ids) & set(game_ids)\n", + "exp2_common_game_ids = []\n", + "exp2_common_f1_diff_5_0 = []\n", + "exp2_common_f1_diff_6_0 = []\n", + "exp3_common_f1_diff_7_0 = []\n", + "for i in range(len(exp2_game_ids)):\n", + " if exp2_game_ids[i] in common_elements:\n", + " exp2_common_game_ids.append(exp2_game_ids[i])\n", + " exp2_common_f1_diff_5_0.append(exp2_f1_diff_5_0[i])\n", + " exp2_common_f1_diff_6_0.append(exp2_f1_diff_6_0[i])\n", + " exp3_common_f1_diff_7_0.append(exp3_f1_diff_7_0[i])\n", + "\n", + "common_game_ids = []\n", + "tom6_common_f1 = []\n", + "tom7_common_f1 = []\n", + "tom8_common_f1 = []\n", + "for i in range(len(game_ids)):\n", + " if game_ids[i] in common_elements:\n", + " common_game_ids.append(game_ids[i])\n", + " tom6_common_f1.append(tom6_f1[i])\n", + " tom7_common_f1.append(tom7_f1[i])\n", + " tom8_common_f1.append(tom8_f1[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp2_common_game_ids == common_game_ids" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Own missing knowledge (exp2)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "tom6correlation_coefficient, tom6p_value = pearsonr(tom6_common_f1, exp2_common_f1_diff_5_0)\n", + "tom7correlation_coefficient, tom7p_value = pearsonr(tom7_common_f1, exp2_common_f1_diff_6_0)\n", + "tom8correlation_coefficient, tom8p_value = pearsonr(tom8_common_f1, exp2_common_f1_diff_6_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.21866640044266308 0.09913002973227936\n", + "0.08225606020725357 0.5393156230207309\n", + "0.15718692888976768 0.23864397213593697\n" + ] + } + ], + "source": [ + "print(tom6correlation_coefficient, tom6p_value)\n", + "print(tom7correlation_coefficient, tom7p_value)\n", + "print(tom8correlation_coefficient, tom8p_value)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fontsize = 19\n", + "\n", + "# Create subplots\n", + "fig, axes = plt.subplots(1, 3, figsize=(19, 5.1))\n", + "\n", + "# Plot for Task Intention\n", + "axes[2].scatter(tom8_common_f1, exp2_common_f1_diff_6_0)\n", + "axes[2].set_title(f'$r={tom8correlation_coefficient:.2f}$, $p={tom8p_value:.2f}$', fontsize=fontsize)\n", + "axes[2].plot(np.unique(tom8_common_f1), np.poly1d(np.polyfit(tom8_common_f1, exp2_common_f1_diff_6_0, 1))(np.unique(tom8_common_f1)), color='red', linestyle='--')\n", + "axes[2].set_xlabel('F1(Task Intention)', fontsize=fontsize)\n", + "#axes[2].set_ylabel('F1(OMK w ToM) - F1(OMK w/ ToM)', fontsize=fontsize)\n", + "#axes[2].legend(fontsize=fontsize, loc='upper center', bbox_to_anchor=(0.5, 1.17))\n", + "axes[2].grid(True, linestyle='--')\n", + "axes[2].tick_params(axis='both', labelsize=fontsize)\n", + "\n", + "# Plot for Task Status\n", + "axes[0].scatter(tom6_common_f1, exp2_common_f1_diff_5_0, c='purple')\n", + "axes[0].set_title(f'$r={tom6correlation_coefficient:.2f}$, $p={tom6p_value:.2f}$', fontsize=fontsize)\n", + "axes[0].plot(np.unique(tom6_common_f1), np.poly1d(np.polyfit(tom6_common_f1, exp2_common_f1_diff_5_0, 1))(np.unique(tom6_common_f1)), color='red', linestyle='--')\n", + "axes[0].set_xlabel('F1(Task Status)', fontsize=fontsize)\n", + "axes[0].set_ylabel('F1(OMK w ToM) $-$ F1(OMK w/ ToM)', fontsize=fontsize)\n", + "#axes[0].legend(fontsize=fontsize, loc='upper center', bbox_to_anchor=(0.5, 1.17))\n", + "axes[0].grid(True, linestyle='--')\n", + "axes[0].tick_params(axis='both', labelsize=fontsize)\n", + "\n", + "# Plot for Task Knowledge\n", + "axes[1].scatter(tom7_common_f1, exp2_common_f1_diff_6_0, c='green')\n", + "axes[1].set_title(f'$r={tom7correlation_coefficient:.2f}$, $p={tom7p_value:.2f}$', fontsize=fontsize)\n", + "axes[1].plot(np.unique(tom7_common_f1), np.poly1d(np.polyfit(tom7_common_f1, exp2_common_f1_diff_6_0, 1))(np.unique(tom7_common_f1)), color='red', linestyle='--')\n", + "axes[1].set_xlabel('F1(Task Knowledge)', fontsize=fontsize)\n", + "#axes[1].set_ylabel('F1(OMK w ToM) - F1(OMK w/ ToM)', fontsize=fontsize)\n", + "#axes[1].legend(fontsize=fontsize, loc='upper center', bbox_to_anchor=(0.5, 1.25))\n", + "axes[1].grid(True, linestyle='--')\n", + "axes[1].tick_params(axis='both', labelsize=fontsize)\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('correlation_tom_feats_exp2.pdf', bbox_inches='tight')\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Partner's missing knowledge (exp3)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "tom6correlation_coefficient, tom6p_value = pearsonr(tom6_common_f1, exp3_common_f1_diff_7_0)\n", + "tom7correlation_coefficient, tom7p_value = pearsonr(tom7_common_f1, exp3_common_f1_diff_7_0)\n", + "tom8correlation_coefficient, tom8p_value = pearsonr(tom8_common_f1, exp3_common_f1_diff_7_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.23820019887745394 0.07176536455128052\n", + "-0.0691280537719391 0.6061193964113574\n", + "0.10181632005409125 0.4469499039738408\n" + ] + } + ], + "source": [ + "print(tom6correlation_coefficient, tom6p_value)\n", + "print(tom7correlation_coefficient, tom7p_value)\n", + "print(tom8correlation_coefficient, tom8p_value)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fontsize = 19\n", + "t_ylim = 0.4\n", + "b_ylim = -0.12\n", + "\n", + "# Create subplots\n", + "fig, axes = plt.subplots(1, 3, figsize=(19, 5.1))\n", + "\n", + "# Plot for Task Intention\n", + "axes[2].scatter(tom8_common_f1, exp3_common_f1_diff_7_0)\n", + "axes[2].set_title(f'$r={tom8correlation_coefficient:.2f}$, $p={tom8p_value:.2f}$', fontsize=fontsize)\n", + "axes[2].plot(np.unique(tom8_common_f1), np.poly1d(np.polyfit(tom8_common_f1, exp3_common_f1_diff_7_0, 1))(np.unique(tom8_common_f1)), color='red', linestyle='--')\n", + "axes[2].set_xlabel('F1(Task Intention)', fontsize=fontsize)\n", + "axes[2].set_ylim(b_ylim, t_ylim)\n", + "#axes[2].set_ylabel('F1(OMK w ToM) - F1(OMK w/ ToM)', fontsize=fontsize)\n", + "#axes[2].legend(fontsize=fontsize, loc='upper center', bbox_to_anchor=(0.5, 1.17))\n", + "axes[2].grid(True, linestyle='--')\n", + "axes[2].tick_params(axis='both', labelsize=fontsize)\n", + "\n", + "# Plot for Task Status\n", + "axes[0].scatter(tom6_common_f1, exp3_common_f1_diff_7_0, c='purple')\n", + "axes[0].set_title(f'$r={tom6correlation_coefficient:.2f}$, $p={tom6p_value:.2f}$', fontsize=fontsize)\n", + "axes[0].plot(np.unique(tom6_common_f1), np.poly1d(np.polyfit(tom6_common_f1, exp3_common_f1_diff_7_0, 1))(np.unique(tom6_common_f1)), color='red', linestyle='--')\n", + "axes[0].set_xlabel('F1(Task Status)', fontsize=fontsize)\n", + "axes[0].set_ylabel('F1(PMK w ToM) $-$ F1(PMK w/ ToM)', fontsize=fontsize)\n", + "axes[0].set_ylim(b_ylim, t_ylim)\n", + "#axes[0].legend(fontsize=fontsize, loc='upper center', bbox_to_anchor=(0.5, 1.17))\n", + "axes[0].grid(True, linestyle='--')\n", + "axes[0].tick_params(axis='both', labelsize=fontsize)\n", + "\n", + "# Plot for Task Knowledge\n", + "axes[1].scatter(tom7_common_f1, exp3_common_f1_diff_7_0, c='green')\n", + "axes[1].set_title(f'$r={tom7correlation_coefficient:.2f}$, $p={tom7p_value:.2f}$', fontsize=fontsize)\n", + "axes[1].plot(np.unique(tom7_common_f1), np.poly1d(np.polyfit(tom7_common_f1, exp3_common_f1_diff_7_0, 1))(np.unique(tom7_common_f1)), color='red', linestyle='--')\n", + "axes[1].set_xlabel('F1(Task Knowledge)', fontsize=fontsize)\n", + "axes[1].set_ylim(b_ylim, t_ylim)\n", + "#axes[1].set_ylabel('F1(OMK w ToM) - F1(OMK w/ ToM)', fontsize=fontsize)\n", + "#axes[1].legend(fontsize=fontsize, loc='upper center', bbox_to_anchor=(0.5, 1.25))\n", + "axes[1].grid(True, linestyle='--')\n", + "axes[1].tick_params(axis='both', labelsize=fontsize)\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('correlation_tom_feats_exp3.pdf', bbox_inches='tight')\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mindcraft_", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/config/dataset_splits.json b/config/dataset_splits.json new file mode 100644 index 0000000..b083de9 --- /dev/null +++ b/config/dataset_splits.json @@ -0,0 +1,170 @@ +{ + "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", + "XXX/main_logs/141_212_108_99_20210408_165949", + "XXX/main_logs/172_31_25_15_20210428_172012", + "XXX/main_logs/141_212_108_99_20210409_130105", + "XXX/saved_logs/141_212_108_99_20210322_141752", + "XXX/main_logs/141_212_108_99_20210405_152436", + "XXX/main_logs/141_212_108_99_20210423_173743", + "XXX/saved_logs/141_212_108_99_20210322_121803", + "XXX/main_logs/141_212_108_99_20210325_175421", + 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a/config/mines.json b/config/mines.json new file mode 100644 index 0000000..9ebcb4b --- /dev/null +++ b/config/mines.json @@ -0,0 +1,8 @@ +[ + "ACACIA_PLANKS", + "BIRCH_PLANKS", + "DARK_OAK_PLANKS", + "JUNGLE_PLANKS", + "OAK_PLANKS", + "SPRUCE_PLANKS" +] \ No newline at end of file diff --git a/config/tools.json b/config/tools.json new file mode 100644 index 0000000..6bb2788 --- /dev/null +++ b/config/tools.json @@ -0,0 +1,14 @@ +[ + "DIAMOND_AXE", + "DIAMOND_HOE", + "DIAMOND_PICKAXE", + "DIAMOND_SHOVEL", + "GOLDEN_AXE", + "GOLDEN_HOE", + "GOLDEN_PICKAXE", + "GOLDEN_SHOVEL", + "IRON_AXE", + "IRON_HOE", + "IRON_PICKAXE", + "IRON_SHOVEL" +] \ No newline at end of file diff --git a/intermediate_representations.py b/intermediate_representations.py new file mode 100644 index 0000000..4baf742 --- /dev/null +++ b/intermediate_representations.py @@ -0,0 +1,75 @@ +from src.models.model_with_dialogue_moves import Model as ToMModel +from src.models.plan_model_graphs import Model as CPAModel +from src.data.game_parser import GameParser +from src.data.game_parser_graphs_new import GameParser as GameParserCPA +import torch +import json +import numpy as np + +DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' +MODEL_TYPES = { + 'GRU' : 0, + 'LSTM' : 1, + 'Transformer' : 2, +} + +def main(): + + model_file = "models/tom_lstm_baseline/tom6_model.torch" + use_dialogue = "Yes" + model_type_name = "LSTM" + model_type = MODEL_TYPES[model_type_name] + model = ToMModel(model_type).to(DEVICE) + model.load_state_dict(torch.load(model_file)) + dataset_splits = json.load(open('config/dataset_splits.json')) + for set in dataset_splits.values(): + for path in set: + for pov in [1, 2]: + out_file = f'{path}/intermediate_ToM6_{path.split("/")[-1]}_player{pov}.npz' + # if os.path.isfile(out_file): + # continue + game = GameParser(path,use_dialogue=='Yes',pov,0,True) + l = model(game, global_plan=False, player_plan=True,intermediate=True).cpu().data.numpy() + np.savez_compressed(open(out_file,'wb'), data=l) + print(out_file,l.shape,model_type_name,use_dialogue,use_dialogue=='Yes') + + model_file = "models/tom_lstm_baseline/tom7_model.torch" + use_dialogue = "Yes" + model_type_name = 'LSTM' + model_type = MODEL_TYPES[model_type_name] + model = ToMModel(model_type).to(DEVICE) + model.load_state_dict(torch.load(model_file)) + dataset_splits = json.load(open('config/dataset_splits.json')) + for set in dataset_splits.values(): + for path in set: + for pov in [1, 2]: + out_file = f'{path}/intermediate_ToM7_{path.split("/")[-1]}_player{pov}.npz' + # if os.path.isfile(out_file): + # continue + game = GameParser(path,use_dialogue=='Yes',4,0,True) + l = model(game, global_plan=False, player_plan=True,intermediate=True).cpu().data.numpy() + np.savez_compressed(open(out_file,'wb'), data=l) + print(out_file,l.shape,model_type_name,use_dialogue,use_dialogue=='Yes') + + model_file = "models/tom_lstm_baseline/tom8_model.torch" + use_dialogue = "Yes" + model_type_name = 'LSTM' + model_type = MODEL_TYPES[model_type_name] + model = ToMModel(model_type).to(DEVICE) + model.load_state_dict(torch.load(model_file)) + dataset_splits = json.load(open('config/dataset_splits.json')) + for set in dataset_splits.values(): + for path in set: + for pov in [1, 2]: + out_file = f'{path}/intermediate_ToM8_{path.split("/")[-1]}_player{pov}.npz' + # if os.path.isfile(out_file): + # continue + game = GameParser(path,use_dialogue=='Yes',4,True) + l = model(game, global_plan=False, player_plan=True,intermediate=True).cpu().data.numpy() + np.savez_compressed(open(out_file,'wb'), data=l) + print(out_file,l.shape,model_type_name,use_dialogue,use_dialogue=='Yes') + + +if __name__ == "__main__": + + main() \ No newline at end of file diff --git a/logistic_regression_tom_feats.py b/logistic_regression_tom_feats.py new file mode 100644 index 0000000..0e2c023 --- /dev/null +++ b/logistic_regression_tom_feats.py @@ -0,0 +1,383 @@ +import argparse +import numpy as np +import pickle +import os +from sklearn.linear_model import LogisticRegression +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score, classification_report, f1_score +from src.data.game_parser_graphs_new import GameParser, make_splits, onehot, set_seed +from tqdm import tqdm +from scipy.stats import wilcoxon +from sklearn.exceptions import ConvergenceWarning +import matplotlib.pyplot as plt +from sklearn.manifold import TSNE +from sklearn.preprocessing import StandardScaler +from sklearn.decomposition import PCA +import umap +import warnings +warnings.simplefilter("ignore", category=ConvergenceWarning) + + +def parse_q(q, game): + if not q is None: + q ,l = q + q = np.concatenate([ + onehot(q[2],2), + onehot(q[3],2), + onehot(q[4][0][0]+1,2), + onehot(game.materials_dict[q[4][0][1]],len(game.materials_dict)), + onehot(q[4][1][0]+1,2), + onehot(game.materials_dict[q[4][1][1]],len(game.materials_dict)), + onehot(q[4][2]+1,2), + onehot(q[5][0][0]+1,2), + onehot(game.materials_dict[q[5][0][1]],len(game.materials_dict)), + onehot(q[5][1][0]+1,2), + onehot(game.materials_dict[q[5][1][1]],len(game.materials_dict)), + onehot(q[5][2]+1,2) + ]) + else: + q = np.zeros(100) + l = None + return q, l + +def cosine_similarity(array1, array2): + """ + Compute the cosine similarity between two arrays. + Parameters: + - array1: First input array + - array2: Second input array + Returns: + - similarity: Cosine similarity between the two arrays + """ + dot_product = np.dot(array1, array2) + norm_array1 = np.linalg.norm(array1) + norm_array2 = np.linalg.norm(array2) + similarity = dot_product / (norm_array1 * norm_array2) + return similarity + +def compute_and_plot_pca(data1, data2, labels=None, fname='pca'): + """ + Compute and plot Principal Component Analysis (PCA) for a given dataset with 2 components. + Parameters: + - data: Input dataset + - labels: Labels for data points (optional) + Returns: + - pca_result: Result of PCA transformation + """ + scaler1 = StandardScaler() + data_standardized1 = scaler1.fit_transform(data1) + scaler2 = StandardScaler() + data_standardized2 = scaler2.fit_transform(data2) + pca1 = PCA(n_components=2) + pca_result1 = pca1.fit_transform(data_standardized1) + pca2 = PCA(n_components=2) + pca_result2 = pca2.fit_transform(data_standardized2) + pca_result = np.concatenate([pca_result1, pca_result2]) + unique_labels = np.unique(labels) if labels is not None else [None] + plt.figure(figsize=(8, 6)) + for unique_label in unique_labels: + mask = (labels == unique_label) if labels is not None else slice(None) + plt.scatter(pca_result[mask, 0], pca_result[mask, 1], label=unique_label) + plt.xlabel('Principal Component 1') + plt.ylabel('Principal Component 2') + if labels is not None: + plt.legend() + os.makedirs("figures/", exist_ok=True) + plt.savefig(f"figures/{fname}.pdf", bbox_inches='tight') + return pca_result + +def compute_and_plot_tsne(data1, data2, labels=None, fname='tsne'): + """ + Compute and plot t-SNE for a given standardized dataset with 2 components. + Parameters: + - data: Input dataset + - labels: Labels for data points (optional) + Returns: + - tsne_result: Result of t-SNE transformation + """ + scaler1 = StandardScaler() + data_standardized1 = scaler1.fit_transform(data1) + tsne1 = TSNE(n_components=2) + tsne_result1 = tsne1.fit_transform(data_standardized1) + scaler2 = StandardScaler() + data_standardized2 = scaler2.fit_transform(data2) + tsne2 = TSNE(n_components=2) + tsne_result2 = tsne2.fit_transform(data_standardized2) + tsne_result = np.concatenate([tsne_result1, tsne_result2]) + unique_labels = np.unique(labels) if labels is not None else [None] + plt.figure(figsize=(8, 6)) + for unique_label in unique_labels: + mask = (labels == unique_label) if labels is not None else slice(None) + plt.scatter(tsne_result[mask, 0], tsne_result[mask, 1], label=unique_label) + plt.xlabel('t-SNE Component 1') + plt.ylabel('t-SNE Component 2') + if labels is not None: + plt.legend() + plt.savefig(f"figures/{fname}.pdf", bbox_inches='tight') + return tsne_result + +def compute_and_plot_umap(data1, data2, labels=None, fname='umap'): + """ + Compute and plot UMAP for a given standardized dataset with 2 components. + Parameters: + - data: Input dataset + - labels: Labels for data points (optional) + Returns: + - umap_result: Result of UMAP transformation + """ + scaler1 = StandardScaler() + data_standardized1 = scaler1.fit_transform(data1) + umap_model1 = umap.UMAP(n_components=2) + umap_result1 = umap_model1.fit_transform(data_standardized1) + scaler2 = StandardScaler() + data_standardized2 = scaler2.fit_transform(data2) + umap_model2 = umap.UMAP(n_components=2) + umap_result2 = umap_model2.fit_transform(data_standardized2) + umap_result = np.concatenate([umap_result1, umap_result2]) + unique_labels = np.unique(labels) if labels is not None else [None] + plt.figure(figsize=(8, 6)) + for unique_label in unique_labels: + mask = (labels == unique_label) if labels is not None else slice(None) + plt.scatter(umap_result[mask, 0], umap_result[mask, 1], label=unique_label) + plt.xlabel('UMAP Component 1') + plt.ylabel('UMAP Component 2') + if labels is not None: + plt.legend() + plt.savefig(f"figures/{fname}.pdf", bbox_inches='tight') + return umap_result + +def prepare_data_tom(mode): + dataset_splits = make_splits('config/dataset_splits_new.json') + d_flag = False + d_move_flag = False + if mode == 'train': + data = [GameParser(f,d_flag,1,7,d_move_flag) for f in tqdm(dataset_splits['training'])] + data += [GameParser(f,d_flag,2,7,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif mode == 'test': + data = [GameParser(f,d_flag,1,7,d_move_flag) for f in tqdm(dataset_splits['test'])] + data += [GameParser(f,d_flag,2,7,d_move_flag) for f in tqdm(dataset_splits['test'])] + else: + raise ValueError('train or test are supported') + tom6repr = [] + tom7repr = [] + tom8repr = [] + tom6labels = [] + tom7labels = [] + tom8labels = [] + for game in data: + _, _, _, q, _, _, interm, _ = zip(*list(game)) + interm = np.array(interm) + # intermediate = np.concatenate([ToM6,ToM7,ToM8,DAct,DMove]) + tom6, tom7, tom8, _, _ = np.split(interm, np.cumsum([1024] * 5)[:-1], axis=1) + q = [parse_q(x, game) for x in q] + q, l = zip(*q) + indexes = [idx for idx, element in enumerate(l) if element is not None] + tom6repr.append(tom6[indexes]) + tom7repr.append(tom7[indexes]) + tom8repr.append(tom8[indexes]) + l = [item[1] for item in l if item is not None] + tom6labels.append([['NO', 'MAYBE', 'YES'].index(item[0]) for item in l]) + tom7labels.append([['NO', 'MAYBE', 'YES'].index(item[1]) for item in l]) + tom8labels.append([game.materials_dict[item[2]] if item[2] in game.materials_dict else 0 for item in l]) + tom6labels = sum(tom6labels, []) + tom7labels = sum(tom7labels, []) + tom8labels = sum(tom8labels, []) + return np.concatenate(tom6repr), tom6labels, np.concatenate(tom7repr), tom7labels, np.concatenate(tom8repr), tom8labels + +def prepare_data_cpa(mode, experiment): + dataset_splits = make_splits('config/dataset_splits_new.json') + d_flag = False + d_move_flag = False + if mode == "train": + data = [GameParser(f,d_flag,1,7,d_move_flag) for f in tqdm(dataset_splits['training'])] + data += [GameParser(f,d_flag,2,7,d_move_flag) for f in tqdm(dataset_splits['training'])] + if experiment == 2: + with open('XXX', 'rb') as f: + feats = pickle.load(f) + elif experiment == 3: + with open('XXX', 'rb') as f: + feats = pickle.load(f) + else: raise ValueError + elif mode == "test": + data = [GameParser(f,d_flag,1,7,d_move_flag) for f in tqdm(dataset_splits['test'])] + data += [GameParser(f,d_flag,2,7,d_move_flag) for f in tqdm(dataset_splits['test'])] + if experiment == 2: + with open('XXX', 'rb') as f: + feats = pickle.load(f) + elif experiment == 3: + with open('XXX', 'rb') as f: + feats = pickle.load(f) + else: raise ValueError + else: + raise ValueError('train or test are supported') + tom6labels = [] + tom7labels = [] + tom8labels = [] + features = [item[0] for item in feats] + game_names = [item[1] for item in feats] + selected_feats = [] + for i, game in enumerate(data): + _, _, _, q, _, _, _, _ = zip(*list(game)) + q = [parse_q(x, game) for x in q] + q, l = zip(*q) + indexes = [idx for idx, element in enumerate(l) if element is not None] + assert game.game_path.split("/")[-1] == game_names[i].split("/")[-1] + selected_feats.append(features[i][indexes]) + l = [item[1] for item in l if item is not None] + tom6labels.append([['NO', 'MAYBE', 'YES'].index(item[0]) for item in l]) + tom7labels.append([['NO', 'MAYBE', 'YES'].index(item[1]) for item in l]) + tom8labels.append([game.materials_dict[item[2]] if item[2] in game.materials_dict else 0 for item in l]) + tom6labels = sum(tom6labels, []) + tom7labels = sum(tom7labels, []) + tom8labels = sum(tom8labels, []) + selected_feats = np.concatenate(selected_feats) + return selected_feats, tom6labels, tom7labels, tom8labels + +def fit_and_test_LR(X_train, y_train, X_test, y_test, max_iter=100): + logreg_model = LogisticRegression(max_iter=max_iter) + logreg_model.fit(X_train, y_train) + y_pred = logreg_model.predict(X_test) + f1 = f1_score(y_test, y_pred, average="weighted", zero_division=1) + # classification_report_output = classification_report(y_test, y_pred) + print("F1 score:", f1) + # print("Classification Report:\n", classification_report_output) + return logreg_model + +def fit_and_test_RF(X_train, y_train, X_test, y_test, n_estimators): + model = RandomForestClassifier(n_estimators=n_estimators) + model.fit(X_train, y_train) + y_pred = model.predict(X_test) + f1 = f1_score(y_test, y_pred, average="weighted", zero_division=1) + print("F1 score:", f1) + return model + +def wilcoxon_test(model1, model2, X_test_1, X_test_2): + probabilities_model1 = model1.predict_proba(X_test_1)[:, 1] + probabilities_model2 = model2.predict_proba(X_test_2)[:, 1] + differences = probabilities_model1 - probabilities_model2 + _, p_value_wilcoxon = wilcoxon(differences) + print("Wilcoxon signed-rank test p-value:", p_value_wilcoxon) + return p_value_wilcoxon + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + parser.add_argument("--task", type=str) + parser.add_argument("--seed", type=int) + parser.add_argument("--experiment", type=int) + args = parser.parse_args() + set_seed(args.seed) + task = args.task + experiment = args.experiment + + if task == "tom": + tom6_train_x, tom6_train_labels, tom7_train_x, tom7_train_labels, tom8_train_x, tom8_train_labels = prepare_data_tom("train") + tom6_test_x, tom6_test_labels, tom7_test_x, tom7_test_labels, tom8_test_x, tom8_test_labels = prepare_data_tom("test") + print("=========== EXP 6 ========================================") + # 0.6056079527261083 + fit_and_test_LR(tom6_train_x, tom6_train_labels, tom6_test_x, tom6_test_labels, 100) + print("=========== EXP 7 ========================================") + # 0.5090737845776365 + fit_and_test_LR(tom7_train_x, tom7_train_labels, tom7_test_x, tom7_test_labels, 100) + print("=========== EXP 8 ========================================") + # 0.10206891928130866 + fit_and_test_LR(tom8_train_x, tom8_train_labels, tom8_test_x, tom8_test_labels, 6) + breakpoint() + + elif task == "cpa": + train_x, tom6_train_labels, tom7_train_labels, tom8_train_labels = prepare_data_cpa("train", experiment) + test_x, tom6_test_labels, tom7_test_labels, tom8_test_labels = prepare_data_cpa("test", experiment) + print("=========== EXP 6 ========================================") + # 0.5157497361676466 139 + fit_and_test_LR(train_x, tom6_train_labels, test_x, tom6_test_labels, 139 if experiment == 2 else 11) + print("=========== EXP 7 ========================================") + # 0.49755418256915795 307 + fit_and_test_LR(train_x, tom7_train_labels, test_x, tom7_test_labels, 307 if experiment == 2 else 25) + print("=========== EXP 8 ========================================") + # 0.14099639490943838 23 + fit_and_test_LR(train_x, tom8_train_labels, test_x, tom8_test_labels, 23 if experiment == 2 else 9) + breakpoint() + + elif task == "random": + tom6_train_x, tom6_train_labels, tom7_train_x, tom7_train_labels, tom8_train_x, tom8_train_labels = prepare_data_tom("train") + tom6_test_x, tom6_test_labels, tom7_test_x, tom7_test_labels, tom8_test_x, tom8_test_labels = prepare_data_tom("test") + tom6_train_x = np.random.randn(*tom6_train_x.shape) * 0.1 + tom7_train_x = np.random.randn(*tom7_train_x.shape) * 0.1 + tom8_train_x = np.random.randn(*tom8_train_x.shape) * 0.1 + tom6_test_x = np.random.randn(*tom6_test_x.shape) * 0.1 + tom7_test_x = np.random.randn(*tom7_test_x.shape) * 0.1 + tom8_test_x = np.random.randn(*tom8_test_x.shape) * 0.1 + print("=========== EXP 6 ========================================") + # 0.4573518645097593 + fit_and_test_LR(tom6_train_x, tom6_train_labels, tom6_test_x, tom6_test_labels, 100) + print("=========== EXP 7 ========================================") + # 0.45066310491597705 + fit_and_test_LR(tom7_train_x, tom7_train_labels, tom7_test_x, tom7_test_labels, 100) + print("=========== EXP 8 ========================================") + # 0.09281225255303022 + fit_and_test_LR(tom8_train_x, tom8_train_labels, tom8_test_x, tom8_test_labels, 100) + breakpoint() + + elif task == "all": + ############## TOM + print("############## TOM") + tom6_train_x_tom, tom6_train_labels_tom, tom7_train_x_tom, tom7_train_labels_tom, tom8_train_x_tom, tom8_train_labels_tom = prepare_data_tom("train") + tom6_test_x_tom, tom6_test_labels_tom, tom7_test_x_tom, tom7_test_labels_tom, tom8_test_x_tom, tom8_test_labels_tom = prepare_data_tom("test") + print("=========== EXP 6 ========================================") + model_tom_6 = fit_and_test_LR(tom6_train_x_tom, tom6_train_labels_tom, tom6_test_x_tom, tom6_test_labels_tom, 100) + print("=========== EXP 7 ========================================") + model_tom_7 = fit_and_test_LR(tom7_train_x_tom, tom7_train_labels_tom, tom7_test_x_tom, tom7_test_labels_tom, 100) + print("=========== EXP 8 ========================================") + model_tom_8 = fit_and_test_LR(tom8_train_x_tom, tom8_train_labels_tom, tom8_test_x_tom, tom8_test_labels_tom, 6) + ############## CPA + print("############## CPA") + train_x_cpa, tom6_train_labels_cpa, tom7_train_labels_cpa, tom8_train_labels_cpa = prepare_data_cpa("train", experiment) + test_x_cpa, tom6_test_labels_cpa, tom7_test_labels_cpa, tom8_test_labels_cpa = prepare_data_cpa("test", experiment) + print("=========== EXP 6 ========================================") + model_cpa_6 = fit_and_test_LR(train_x_cpa, tom6_train_labels_cpa, test_x_cpa, tom6_test_labels_cpa, 139) + print("=========== EXP 7 ========================================") + model_cpa_7 = fit_and_test_LR(train_x_cpa, tom7_train_labels_cpa, test_x_cpa, tom7_test_labels_cpa, 307) + print("=========== EXP 8 ========================================") + model_cpa_8 = fit_and_test_LR(train_x_cpa, tom8_train_labels_cpa, test_x_cpa, tom8_test_labels_cpa, 23) + ############## RANDOM + print("############## RANDOM") + tom6_train_x_rand = np.random.randn(*tom6_train_x_tom.shape) * 0.1 + tom7_train_x_rand = np.random.randn(*tom7_train_x_tom.shape) * 0.1 + tom8_train_x_rand = np.random.randn(*tom8_train_x_tom.shape) * 0.1 + tom6_test_x_rand = np.random.randn(*tom6_test_x_tom.shape) * 0.1 + tom7_test_x_rand = np.random.randn(*tom7_test_x_tom.shape) * 0.1 + tom8_test_x_rand = np.random.randn(*tom8_test_x_tom.shape) * 0.1 + print("=========== EXP 6 ========================================") + model_rand_6 = fit_and_test_LR(tom6_train_x_rand, tom6_train_labels_tom, tom6_test_x_rand, tom6_test_labels_tom, 100) + print("=========== EXP 7 ========================================") + model_rand_7 = fit_and_test_LR(tom7_train_x_rand, tom7_train_labels_tom, tom7_test_x_rand, tom7_test_labels_tom, 100) + print("=========== EXP 8 ========================================") + model_rand_8 = fit_and_test_LR(tom8_train_x_rand, tom8_train_labels_tom, tom8_test_x_rand, tom8_test_labels_tom, 100) + wilcoxon_test(model_tom_6, model_cpa_6, tom6_test_x_tom, test_x_cpa) + wilcoxon_test(model_rand_6, model_cpa_6, tom6_test_x_rand, test_x_cpa) + wilcoxon_test(model_rand_6, model_tom_6, tom6_test_x_rand, tom6_test_x_tom) + wilcoxon_test(model_tom_7, model_cpa_7, tom7_test_x_tom, test_x_cpa) + wilcoxon_test(model_rand_7, model_cpa_7, tom7_test_x_rand, test_x_cpa) + wilcoxon_test(model_rand_7, model_tom_7, tom7_test_x_rand, tom7_test_x_tom) + wilcoxon_test(model_tom_8, model_cpa_8, tom8_test_x_tom, test_x_cpa) + wilcoxon_test(model_rand_8, model_cpa_8, tom8_test_x_rand, test_x_cpa) + wilcoxon_test(model_rand_8, model_tom_8, tom8_test_x_rand, tom8_test_x_tom) + scaler = StandardScaler() + scaled_tom6_test_x_tom = scaler.fit_transform(tom6_test_x_tom) + scaler = StandardScaler() + scaled_tom7_test_x_tom = scaler.fit_transform(tom7_test_x_tom) + scaler = StandardScaler() + scaled_tom8_test_x_tom = scaler.fit_transform(tom8_test_x_tom) + scaler = StandardScaler() + scaled_test_x_cpa = scaler.fit_transform(test_x_cpa) + sim6 = [cosine_similarity(t, c) for t, c in zip(scaled_tom6_test_x_tom, scaled_test_x_cpa)] + sim7 = [cosine_similarity(t, c) for t, c in zip(scaled_tom7_test_x_tom, scaled_test_x_cpa)] + sim8 = [cosine_similarity(t, c) for t, c in zip(scaled_tom8_test_x_tom, scaled_test_x_cpa)] + print(f"[tom6] max sim: {np.max(sim6)}, mean sim: {np.mean(sim6)}") + print(f"[tom7] max sim: {np.max(sim7)}, mean sim: {np.mean(sim7)}") + print(f"[tom8] max sim: {np.max(sim8)}, mean sim: {np.mean(sim8)}") + breakpoint() + + else: + raise ValueError diff --git a/plan_predictor.py b/plan_predictor.py new file mode 100644 index 0000000..ecbdfa9 --- /dev/null +++ b/plan_predictor.py @@ -0,0 +1,416 @@ +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, precision_score, recall_score +from src.data.game_parser import GameParser, make_splits, onehot, DEVICE, set_seed +from src.models.plan_model import Model +from src.models.losses import PlanLoss +import argparse +from tqdm import tqdm + +def print_epoch(data,acc_loss,lst,exp, incremental=False): + print(f'{acc_loss:9.4f}',end='; ',flush=True) + acc = [] + prec = [] + rec = [] + f1 = [] + iou = [] + total = [] + predicts = [] + targets = [] + # for x,game in zip(data,lst): + for x in data: + + game = lst[x[2]] + game_mats = game.plan['materials'] + pov_plan = game.plan[f'player{game.pov}'] + pov_plan_mat = game.__dict__[f'player{game.pov}_plan_mat'] + possible_mats = [game.materials_dict[x]-1 for x,_ in zip(game_mats[1:],pov_plan[1:])] + possible_cand = [game.materials_dict[x]-1 for x,y in zip(game_mats[1:],pov_plan[1:]) if y['make'] and y['make'][0][0]==-1] + possible_extra = [game.materials_dict[x]-1 for x,y in zip(game_mats[1:],pov_plan[1:]) if y['make'] and y['make'][0][0]>-1] + a, b = x[:2] + if exp == 3: + a = a.reshape(21,21) + for idx,aa in enumerate(a): + if idx in possible_extra: + cand_idxs = set([i for i,x in enumerate(pov_plan_mat[idx]) if x]) + th, _ = zip(*sorted([(i, x) for i, x in enumerate(aa) if i in possible_mats], key=lambda x:x[1])[-2:]) + if len(cand_idxs.intersection(set(th))): + for jdx, _ in enumerate(aa): + a[idx,jdx] = pov_plan_mat[idx,jdx] + else: + for jdx, _ in enumerate(aa): + a[idx,jdx] = 0 + else: + for jdx, aaa in enumerate(aa): + a[idx,jdx] = 0 + elif exp == 2: + a = a.reshape(21,21) + for idx,aa in enumerate(a): + if idx in possible_cand: + th = [x for i, x in enumerate(aa) if i in possible_mats] + th = sorted(th) + th = th[-2] + th = 1.1 if th < (1/21) else th + for jdx, aaa in enumerate(aa): + if idx in possible_mats: + a[idx,jdx] = 0 if aaa < th else 1 + else: + a[idx,jdx] = 0 + else: + for jdx, aaa in enumerate(aa): + a[idx,jdx] = 0 + + else: + a = a.reshape(21,21) + for idx,aa in enumerate(a): + th = sorted(aa)[-2] + th = 1.1 if th < (2.1/21) else th + for jdx, aaa in enumerate(aa): + a[idx,jdx] = 0 if aaa < th else 1 + a = a.reshape(-1) + predicts.append(np.argmax(a)) + targets.append(np.argmax(a) if np.argmax(a) in [x for x in b if x] else np.argmax(b)) + acc.append(accuracy_score(a,b)) + sa = set([i for i,x in enumerate(a) if x]) + sb = set([i for i,x in enumerate(b) if x]) + i = len(sa.intersection(sb)) + u = len(sa.union(sb)) + if u > 0: + a,b = zip(*[(x,y) for x,y in zip(a,b) if x+y > 0]) + f1.append(f1_score(b,a,zero_division=1)) + prec.append(precision_score(b,a,zero_division=1)) + rec.append(recall_score(b,a,zero_division=1)) + iou.append(i/u if u > 0 else 1) + total.append(sum(a)) + print( + # f'({accuracy_score(targets,predicts):5.3f},' + # f'{np.mean(acc):5.3f},' + # f'{np.mean(prec):5.3f},' + # f'{np.mean(rec):5.3f},' + f'{np.mean(f1):5.3f},' + f'{np.mean(iou):5.3f},' + f'{np.std(iou):5.3f},', + # f'{np.mean(total):5.3f})', + end=' ',flush=True) + print('', end='; ',flush=True) + return accuracy_score(targets,predicts), np.mean(acc), np.mean(f1), np.mean(iou) + +def do_split(model,lst,exp,criterion,optimizer=None,global_plan=False, player_plan=False, incremental=False, device=DEVICE): + data = [] + acc_loss = 0 + p = [] + g = [] + masks = [] + for batch, game in enumerate(lst): + + if model.training and (not optimizer is None): optimizer.zero_grad() + + if exp==0: + ground_truth = torch.tensor(game.global_plan_mat.reshape(-1)).float() + elif exp==1: + ground_truth = torch.tensor(game.partner_plan.reshape(-1)).float() + elif exp==2: + ground_truth = torch.tensor(game.global_diff_plan_mat.reshape(-1)).float() + loss_mask = torch.tensor(game.global_plan_mat.reshape(-1)).float() + else: + ground_truth = torch.tensor(game.partner_diff_plan_mat.reshape(-1)).float() + loss_mask = torch.tensor(game.plan_repr.reshape(-1)).float() + + prediction, _ = model(game, global_plan=global_plan, player_plan=player_plan, incremental=incremental) + + if incremental: + ground_truth = ground_truth.to(device) + g += [ground_truth for _ in prediction] + masks += [loss_mask for _ in prediction] + + p += [x for x in prediction] + + data += list(zip(prediction.cpu().data.numpy(), [ground_truth.cpu().data.numpy()]*len(prediction),[batch]*len(prediction))) + else: + ground_truth = ground_truth.to(device) + g.append(ground_truth) + masks.append(loss_mask) + + p.append(prediction) + + data.append((prediction.cpu().data.numpy(), ground_truth.cpu().data.numpy(),batch)) + + if (batch+1) % 2 == 0: + loss = criterion(torch.stack(p),torch.stack(g), torch.stack(masks)) + + loss += 1e-5 * sum(p.pow(2.0).sum() for p in model.parameters()) + if model.training and (not optimizer is None): + loss.backward() + # nn.utils.clip_grad_norm_(model.parameters(), 1) + # nn.utils.clip_grad_norm_(model.parameters(), 10) + optimizer.step() + acc_loss += loss.item() + p = [] + g = [] + masks = [] + + acc_loss /= len(lst) + + acc0, acc, f1, iou = print_epoch(data,acc_loss,lst,exp) + + return acc0, acc_loss, data, acc, f1, iou + + +def init_weights(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(0.01) + +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 args.seed=='Random': + # pass + # elif args.seed=='Fixed': + # random.seed(0) + # torch.manual_seed(1) + # np.random.seed(0) + # else: + # print('Seed must be in [Random, Fixed], but got',args.seed) + # 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 not args.intermediate in list(range(32)): + print('Intermediate must be in',list(range(32)),', but got',args.intermediate) + 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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,0,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + if args.experiment > 2: + val += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='Third': + val = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='First': + val = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + val += [GameParser(f,d_flag,2,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,2,args.intermediate,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) + model.apply(init_weights) + + print(model) + model.train() + + learning_rate = 1e-5 + 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.BCELoss() + criterion = PlanLoss() + # criterion = torch.hub.load( + # 'adeelh/pytorch-multi-class-focal-loss', + # model='focal_loss', + # alpha=[.25, .75], + # gamma=10, + # reduction='mean', + # device=device, + # dtype=torch.float32, + # force_reload=False + # ) + # criterion = nn.BCEWithLogitsLoss(pos_weight=10*torch.ones(21*21).to(device)) + # criterion = nn.MSELoss() + + print(str(criterion), str(optimizer)) + + num_epochs = 200#1# + min_acc_loss = 1e6 + max_f1 = 0 + epochs_since_improvement = 0 + wait_epoch = 15#150#1000# + max_fails = 5 + + if args.model_path is not None: + print(f'Loading {args.model_path}') + model.load_state_dict(torch.load(args.model_path)) + model.eval() + acc, acc_loss, data, _, f1, iou = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=True, device=DEVICE) + acc, acc_loss0, data, _, f1, iou = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=False, device=DEVICE) + + if np.mean([acc_loss,acc_loss0]) < min_acc_loss: + min_acc_loss = np.mean([acc_loss,acc_loss0]) + epochs_since_improvement = 0 + print('^') + torch.save(model.cpu().state_dict(), args.save_path) + model = model.to(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) + + 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, incremental=True, device=DEVICE) + do_split(model,train,args.experiment,criterion,optimizer=optimizer,global_plan=global_plan, player_plan=player_plan, incremental=False, device=DEVICE) + model.eval() + acc, acc_loss, data, _, f1, iou = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=True, device=DEVICE) + acc, acc_loss0, data, _, f1, iou = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=False, device=DEVICE) + + if np.mean([acc_loss,acc_loss0]) < min_acc_loss: + min_acc_loss = np.mean([acc_loss,acc_loss0]) + epochs_since_improvement = 0 + print('^') + torch.save(model.cpu().state_dict(), args.save_path) + model = model.to(DEVICE) + else: + epochs_since_improvement += 1 + print() + + # test_val = iou + # if (max_f1 < test_val): + # max_f1 = test_val + # epochs_since_improvement = 0 + # print('^') + # if not args.save_path is None: + # torch.save(model.cpu().state_dict(), args.save_path) + # model = model.to(DEVICE) + # else: + # epochs_since_improvement += 1 + # print() + + if epoch > wait_epoch and epochs_since_improvement > max_fails: + 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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + if args.experiment > 2: + test += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='Third': + test = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='First': + test = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + test += [GameParser(f,d_flag,2,args.intermediate,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() + acc, acc_loss, data, _, f1, iou = do_split(model,test,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=True, device=DEVICE) + acc, acc_loss, data, _, f1, iou = do_split(model,test,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=False, 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:Global, 1:Partner, 2:GlobalDif, 3:PartnerDif]') + parser.add_argument('--intermediate', type=int, + help='point of view [0:Global, 1:Partner, 2:GlobalDif, 3:PartnerDif]') + parser.add_argument('--save_path', type=str, + help='path where to save model') + 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('--device', type=int, default=0, + help='select cuda device number') + parser.add_argument('--model_path', type=str, default=None, + help='path to the pretrained model to be loaded') + + main(parser.parse_args()) \ No newline at end of file diff --git a/plan_predictor_graphs.py b/plan_predictor_graphs.py new file mode 100644 index 0000000..080b10b --- /dev/null +++ b/plan_predictor_graphs.py @@ -0,0 +1,325 @@ +import os +import torch, 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, set_seed +from src.models.plan_model_graphs import Model +import argparse +from tqdm import tqdm +import pickle + + +def print_epoch(data, acc_loss): + print(f'{acc_loss:9.4f}',end='; ',flush=True) + acc = [] + f1 = [] + for x in data: + a, b, _, _, _, _, _, _ = x + acc.append(accuracy_score(b, a)) + f1.append(f1_score(b, a, zero_division=1)) + print(f'{np.mean(f1):5.3f},', end=' ', flush=True) + print('', end='; ', flush=True) + return np.mean(acc), np.mean(f1), f1 + +def do_split(model, lst, exp, criterion, device, optimizer=None, global_plan=False, player_plan=False, incremental=False): + data = [] + acc_loss = 0 + for batch, game in enumerate(lst): + if (exp != 2) and (exp != 3): + raise ValueError('This script is only for exp == 2 or exp == 3.') + prediction, ground_truth, sel = model(game, experiment=exp, global_plan=global_plan, player_plan=player_plan, incremental=incremental) + if exp == 2: + if sel[0]: + prediction = prediction[game.player1_plan.edge_index.shape[1]:] + ground_truth = ground_truth[game.player1_plan.edge_index.shape[1]:] + if sel[1]: + prediction = prediction[game.player2_plan.edge_index.shape[1]:] + ground_truth = ground_truth[game.player2_plan.edge_index.shape[1]:] + if prediction.numel() == 0 and ground_truth.numel() == 0: continue + if incremental: + ground_truth = ground_truth.to(device).repeat(prediction.shape[0], 1) + data += list(zip(torch.round(torch.sigmoid(prediction)).float().cpu().data.numpy(), + ground_truth.cpu().data.numpy(), + [game.player1_plan.edge_index.shape[1]]*len(prediction), + [game.player2_plan.edge_index.shape[1]]*len(prediction), + [game.global_plan.edge_index.shape[1]]*len(prediction), + [sel]*len(prediction), + [game.game_path]*len(prediction), + [batch]*len(prediction))) + else: + ground_truth = ground_truth.to(device) + data.append(( + torch.round(torch.sigmoid(prediction)).float().cpu().data.numpy(), + ground_truth.cpu().data.numpy(), + game.player1_plan.edge_index.shape[1], + game.player2_plan.edge_index.shape[1], + game.global_plan.edge_index.shape[1], + sel, + game.game_path, + batch, + )) + loss = criterion(prediction, ground_truth) + # loss += 1e-5 * sum(p.pow(2.0).sum() for p in model.parameters()) + acc_loss += loss.item() + if model.training and (not optimizer is None): + loss.backward() + if (batch+1) % 2 == 0: # gradient accumulation + # nn.utils.clip_grad_norm_(model.parameters(), 1) + optimizer.step() + optimizer.zero_grad() + acc_loss /= len(lst) + acc, f1, f1_list = print_epoch(data, acc_loss) + if not incremental: + data = [data[i] + (f1_list[i],) for i in range(len(data))] + return acc_loss, data, acc, f1 + +def init_weights(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(0.00) + +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_new.json') + # dataset_splits = make_splits('config/dataset_splits_dev.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 not args.intermediate in list(range(32)): + print('Intermediate must be in',list(range(32)),', but got',args.intermediate) + 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.use_int0_instead_of_intermediate: + if args.pov=='None': + val = [GameParser(f,d_flag,0,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,0,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['training'])] + if args.experiment > 2: + val += [GameParser(f,d_flag,4,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,4,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['training'])] + elif args.pov=='Third': + val = [GameParser(f,d_flag,3,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,3,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['training'])] + elif args.pov=='First': + val = [GameParser(f,d_flag,1,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,1,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['training'])] + val += [GameParser(f,d_flag,2,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,2,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['training'])] + else: + print('POV must be in [None, First, Third], but got', args.pov) + exit() + else: + if args.pov=='None': + val = [GameParser(f,d_flag,0,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,0,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + if args.experiment > 2: + val += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='Third': + val = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='First': + val = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + val += [GameParser(f,d_flag,2,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,2,args.intermediate,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) + # model.apply(init_weights) + + print(model) + model.train() + + learning_rate = args.lr + weight_decay = args.weight_decay + optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay) + if args.experiment == 2: + pos_weight = torch.tensor([2.5], device=DEVICE) + if args.experiment == 3: + pos_weight = torch.tensor([10.0], device=DEVICE) + criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weight) + # criterion = nn.BCEWithLogitsLoss() + print(str(criterion), str(optimizer)) + + num_epochs = 200 + min_acc_loss = 1e6 + best_f1 = 0.0 + epochs_since_improvement = 0 + wait_epoch = 15 + max_fails = 5 + + if args.model_path is not None: + print(f'Loading {args.model_path}') + model.load_state_dict(torch.load(args.model_path)) + model.eval() + # acc_loss, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=True) + acc_loss0, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False) + # if np.mean([acc_loss, acc_loss0]) < min_acc_loss: + if f1 > best_f1: + # min_acc_loss = np.mean([acc_loss, acc_loss0]) + best_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) + + 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, device=DEVICE, optimizer=optimizer, global_plan=global_plan, player_plan=player_plan, incremental=True) + do_split(model, train, args.experiment, criterion, device=DEVICE, optimizer=optimizer, global_plan=global_plan, player_plan=player_plan, incremental=False) + model.eval() + # acc_loss, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=True) + acc_loss0, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False) + + # if np.mean([acc_loss, acc_loss0]) < min_acc_loss: + # if acc_loss0 < min_acc_loss: + if f1 > best_f1: + # min_acc_loss = np.mean([acc_loss, acc_loss0]) + # min_acc_loss = acc_loss0 + best_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 epoch > wait_epoch and epochs_since_improvement > max_fails: + break + print() + print('Test') + model.load_state_dict(torch.load(args.save_path)) + + if args.use_int0_instead_of_intermediate: + val = None + train = None + if args.pov=='None': + test = [GameParser(f,d_flag,0,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['test'])] + if args.experiment > 2: + test += [GameParser(f,d_flag,4,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['test'])] + elif args.pov=='Third': + test = [GameParser(f,d_flag,3,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['test'])] + elif args.pov=='First': + test = [GameParser(f,d_flag,1,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['test'])] + test += [GameParser(f,d_flag,2,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['test'])] + else: + print('POV must be in [None, First, Third], but got', args.pov) + else: + val = None + train = None + if args.pov=='None': + test = [GameParser(f,d_flag,0,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + if args.experiment > 2: + test += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='Third': + test = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='First': + test = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + test += [GameParser(f,d_flag,2,args.intermediate,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() + # acc_loss, data, acc, f1 = do_split(model, test, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=True) + acc_loss, data, acc, f1 = do_split(model, test, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False) + + print() + print(data) + print() + + with open(f'{args.save_path[:-6]}_data.pkl', 'wb') as f: + pickle.dump(data, f) + +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:Global, 1:Partner, 2:GlobalDif, 3:PartnerDif]') + parser.add_argument('--intermediate', type=int, + help='point of view [0:Global, 1:Partner, 2:GlobalDif, 3:PartnerDif]') + parser.add_argument('--save_path', type=str, + help='path where to save model') + 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('--device', type=int, default=0, + help='select cuda device number') + parser.add_argument('--model_path', type=str, default=None, + help='path to the pretrained model to be loaded') + parser.add_argument('--weight_decay', type=float, default=0.0) + parser.add_argument('--lr', type=float, default=1e-4) + parser.add_argument('--use_int0_instead_of_intermediate', action='store_true') + + + main(parser.parse_args()) \ No newline at end of file diff --git a/plan_predictor_graphs_oracle.py b/plan_predictor_graphs_oracle.py new file mode 100644 index 0000000..c19c4f3 --- /dev/null +++ b/plan_predictor_graphs_oracle.py @@ -0,0 +1,283 @@ +import os +import torch, 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, set_seed +from src.models.plan_model_graphs_oracle import Model +import argparse +from tqdm import tqdm +import pickle + + +def print_epoch(data, acc_loss): + print(f'{acc_loss:9.4f}',end='; ',flush=True) + acc = [] + f1 = [] + for x in data: + a, b, _, _, _, _, _, _ = x + acc.append(accuracy_score(b, a)) + f1.append(f1_score(b, a, zero_division=1)) + print(f'{np.mean(f1):5.3f},', end=' ', flush=True) + print('', end='; ', flush=True) + return np.mean(acc), np.mean(f1), f1 + +def do_split(model, lst, exp, criterion, device, optimizer=None, global_plan=False, player_plan=False, incremental=False, intermediate=0): + data = [] + acc_loss = 0 + for batch, game in enumerate(lst): + if (exp != 2) and (exp != 3): + raise ValueError('This script is only for exp == 2 or exp == 3.') + prediction, ground_truth, sel = model(game, experiment=exp, global_plan=global_plan, player_plan=player_plan, incremental=incremental, intermediate=intermediate) + if exp == 2: + if sel[0]: + prediction = prediction[game.player1_plan.edge_index.shape[1]:] + ground_truth = ground_truth[game.player1_plan.edge_index.shape[1]:] + if sel[1]: + prediction = prediction[game.player2_plan.edge_index.shape[1]:] + ground_truth = ground_truth[game.player2_plan.edge_index.shape[1]:] + if prediction.numel() == 0 and ground_truth.numel() == 0: continue + if incremental: + ground_truth = ground_truth.to(device).repeat(prediction.shape[0], 1) + data += list(zip(torch.round(torch.sigmoid(prediction)).float().cpu().data.numpy(), + ground_truth.cpu().data.numpy(), + [game.player1_plan.edge_index.shape[1]]*len(prediction), + [game.player2_plan.edge_index.shape[1]]*len(prediction), + [game.global_plan.edge_index.shape[1]]*len(prediction), + [sel]*len(prediction), + [game.game_path]*len(prediction), + [batch]*len(prediction))) + else: + ground_truth = ground_truth.to(device) + data.append(( + torch.round(torch.sigmoid(prediction)).float().cpu().data.numpy(), + ground_truth.cpu().data.numpy(), + game.player1_plan.edge_index.shape[1], + game.player2_plan.edge_index.shape[1], + game.global_plan.edge_index.shape[1], + sel, + game.game_path, + batch, + )) + loss = criterion(prediction, ground_truth) + # loss += 1e-5 * sum(p.pow(2.0).sum() for p in model.parameters()) + acc_loss += loss.item() + if model.training and (not optimizer is None): + loss.backward() + if (batch+1) % 2 == 0: # gradient accumulation + # nn.utils.clip_grad_norm_(model.parameters(), 1) + optimizer.step() + optimizer.zero_grad() + acc_loss /= len(lst) + acc, f1, f1_list = print_epoch(data, acc_loss) + if not incremental: + data = [data[i] + (f1_list[i],) for i in range(len(data))] + return acc_loss, data, acc, f1 + +def init_weights(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(0.00) + +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_new.json') + # dataset_splits = make_splits('config/dataset_splits_dev.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 not args.intermediate in list(range(32)): + print('Intermediate must be in',list(range(32)),', but got',args.intermediate) + 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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,0,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + if args.experiment > 2: + val += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='Third': + val = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='First': + val = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + val += [GameParser(f,d_flag,2,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,2,args.intermediate,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) + # model.apply(init_weights) + + print(model) + model.train() + + learning_rate = 1e-4 + weight_decay = 0.0 #1e-4 + optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay) + criterion = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([2.5], device=DEVICE)) + # criterion = nn.BCEWithLogitsLoss() + print(str(criterion), str(optimizer)) + + num_epochs = 200 + min_acc_loss = 1e6 + best_f1 = 0.0 + epochs_since_improvement = 0 + wait_epoch = 15 + max_fails = 5 + + if args.model_path is not None: + print(f'Loading {args.model_path}') + model.load_state_dict(torch.load(args.model_path)) + model.eval() + # acc_loss, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=True) + acc_loss0, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False) + # if np.mean([acc_loss, acc_loss0]) < min_acc_loss: + if f1 > best_f1: + # min_acc_loss = np.mean([acc_loss, acc_loss0]) + best_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) + + 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, device=DEVICE, optimizer=optimizer, global_plan=global_plan, player_plan=player_plan, incremental=True) + do_split(model, train, args.experiment, criterion, device=DEVICE, optimizer=optimizer, global_plan=global_plan, player_plan=player_plan, incremental=False, intermediate=args.intermediate) + model.eval() + # acc_loss, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=True) + acc_loss0, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False, intermediate=args.intermediate) + + # if np.mean([acc_loss, acc_loss0]) < min_acc_loss: + # if acc_loss0 < min_acc_loss: + if f1 > best_f1: + # min_acc_loss = np.mean([acc_loss, acc_loss0]) + # min_acc_loss = acc_loss0 + best_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 epoch > wait_epoch and epochs_since_improvement > max_fails: + 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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + if args.experiment > 2: + test += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='Third': + test = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='First': + test = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + test += [GameParser(f,d_flag,2,args.intermediate,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() + # acc_loss, data, acc, f1 = do_split(model, test, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=True) + acc_loss, data, acc, f1 = do_split(model, test, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False) + + print() + print(data) + print() + + with open(f'{args.save_path[:-6]}_data.pkl', 'wb') as f: + pickle.dump(data, f) + +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:Global, 1:Partner, 2:GlobalDif, 3:PartnerDif]') + parser.add_argument('--intermediate', type=int, + help='point of view [0:Global, 1:Partner, 2:GlobalDif, 3:PartnerDif]') + parser.add_argument('--save_path', type=str, + help='path where to save model') + 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('--device', type=int, default=0, + help='select cuda device number') + parser.add_argument('--model_path', type=str, default=None, + help='path to the pretrained model to be loaded') + + main(parser.parse_args()) \ No newline at end of file diff --git a/plan_predictor_graphs_test.py b/plan_predictor_graphs_test.py new file mode 100644 index 0000000..759f103 --- /dev/null +++ b/plan_predictor_graphs_test.py @@ -0,0 +1,201 @@ +import os +import torch, torch.nn as nn, numpy as np +from sklearn.metrics import accuracy_score, f1_score +from src.data.game_parser_graphs_new import GameParser, make_splits, set_seed +from src.models.plan_model_graphs import Model +import argparse +import pickle +from tqdm import tqdm + + +def print_epoch(data, acc_loss): + print(f'{acc_loss:9.4f}',end='; ',flush=True) + acc = [] + f1 = [] + for x in data: + a, b, _, _, _, _, _, _ = x + acc.append(accuracy_score(b, a)) + f1.append(f1_score(b, a, zero_division=1)) + print(f'{np.mean(f1):5.3f},', end=' ', flush=True) + print('', end='; ', flush=True) + return np.mean(acc), np.mean(f1), f1 + +def do_split(model, lst, exp, criterion, device, optimizer=None, global_plan=False, player_plan=False, incremental=False): + data = [] + seq2seq_feats = [] + acc_loss = 0 + for batch, game in enumerate(lst): + if (exp != 2) and (exp != 3): + raise ValueError('This script is only for exp == 2 or exp == 3.') + prediction, ground_truth, sel, feats = model(game, experiment=exp, global_plan=global_plan, player_plan=player_plan, incremental=incremental, return_feats=True) + seq2seq_feats.append([feats, game.game_path]) + if exp == 2: + if sel[0]: + prediction = prediction[game.player1_plan.edge_index.shape[1]:] + ground_truth = ground_truth[game.player1_plan.edge_index.shape[1]:] + if sel[1]: + prediction = prediction[game.player2_plan.edge_index.shape[1]:] + ground_truth = ground_truth[game.player2_plan.edge_index.shape[1]:] + if prediction.numel() == 0 and ground_truth.numel() == 0: continue + if incremental: + ground_truth = ground_truth.to(device).repeat(prediction.shape[0], 1) + data += list(zip(torch.round(torch.sigmoid(prediction)).float().cpu().data.numpy(), + ground_truth.cpu().data.numpy(), + [game.player1_plan.edge_index.shape[1]]*len(prediction), + [game.player2_plan.edge_index.shape[1]]*len(prediction), + [game.global_plan.edge_index.shape[1]]*len(prediction), + [sel]*len(prediction), + [game.game_path]*len(prediction), + [batch]*len(prediction))) + else: + ground_truth = ground_truth.to(device) + data.append(( + torch.round(torch.sigmoid(prediction)).float().cpu().data.numpy(), + ground_truth.cpu().data.numpy(), + game.player1_plan.edge_index.shape[1], + game.player2_plan.edge_index.shape[1], + game.global_plan.edge_index.shape[1], + sel, + game.game_path, + batch, + )) + loss = criterion(prediction, ground_truth) + acc_loss += loss.item() + acc_loss /= len(lst) + acc, f1, f1_list = print_epoch(data, acc_loss) + if not incremental: + data = [data[i] + (f1_list[i],) for i in range(len(data))] + return acc_loss, data, acc, f1, seq2seq_feats + +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_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 not args.intermediate in list(range(32)): + print('Intermediate must be in',list(range(32)),', but got',args.intermediate) + 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) + + criterion = nn.BCEWithLogitsLoss() + + model = Model(seq_model, DEVICE).to(DEVICE) + + model.load_state_dict(torch.load(args.model_path)) + model.eval() + + if args.pov=='None': + test = [GameParser(f,d_flag,0,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + if args.experiment > 2: + test += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='Third': + test = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='First': + test = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + test += [GameParser(f,d_flag,2,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + else: + print('POV must be in [None, First, Third], but got', args.pov) + + ######### TEST + acc_loss, data, acc, f1, seq2seq_feats = do_split(model, test, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False) + with open(f'{args.model_path[:-6]}_feats_test.pkl', 'wb') as f: + pickle.dump(seq2seq_feats, f) + + if args.pov=='None': + train = [GameParser(f,d_flag,0,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + if args.experiment > 2: + train += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='Third': + train = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='First': + train = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + train += [GameParser(f,d_flag,2,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + else: + print('POV must be in [None, First, Third], but got', args.pov) + exit() + + ######### TRAIN + acc_loss, data, acc, f1, seq2seq_feats = do_split(model, train, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False) + with open(f'{args.model_path[:-6]}_feats_train.pkl', 'wb') as f: + pickle.dump(seq2seq_feats, f) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Process some integers.') + parser.add_argument('--pov', type=str, default='First', + help='point of view [None, First, Third]') + parser.add_argument('--use_dialogue', type=str, default='Yes', + help='Use dialogue [Yes, No]') + parser.add_argument('--use_dialogue_moves', type=str, default='Yes', + help='Use dialogue [Yes, No]') + parser.add_argument('--plans', type=str, default='Yes', + help='Use dialogue [Yes, No]') + parser.add_argument('--seq_model', type=str, default='Transformer', + help='point of view [GRU, LSTM, Transformer]') + parser.add_argument('--experiment', type=int, default=2, + help='point of view [0:Global, 1:Partner, 2:GlobalDif, 3:PartnerDif]') + parser.add_argument('--intermediate', type=int, + help='') + 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('--device', type=int, default=0, + help='select cuda device number') + parser.add_argument('--model_path', type=str, default=None, + help='path to the pretrained model to be loaded') + + main(parser.parse_args()) \ No newline at end of file diff --git a/plan_predictor_oracle.py b/plan_predictor_oracle.py new file mode 100644 index 0000000..3f68a69 --- /dev/null +++ b/plan_predictor_oracle.py @@ -0,0 +1,351 @@ +import os +import torch, torch.nn as nn, numpy as np +from torch import optim +from random import shuffle +from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score +from src.data.game_parser import GameParser, make_splits, onehot, DEVICE, set_seed +from src.models.plan_model_oracle import Model +from src.models.losses import PlanLoss +import argparse +from tqdm import tqdm + +def print_epoch(data,acc_loss,lst,exp, incremental=False): + print(f'{acc_loss:9.4f}',end='; ',flush=True) + acc = [] + prec = [] + rec = [] + f1 = [] + iou = [] + total = [] + predicts = [] + targets = [] + for x in data: + game = lst[x[2]] + game_mats = game.plan['materials'] + pov_plan = game.plan[f'player{game.pov}'] + pov_plan_mat = game.__dict__[f'player{game.pov}_plan_mat'] + possible_mats = [game.materials_dict[x]-1 for x,_ in zip(game_mats[1:],pov_plan[1:])] + possible_cand = [game.materials_dict[x]-1 for x,y in zip(game_mats[1:],pov_plan[1:]) if y['make'] and y['make'][0][0]==-1] + possible_extra = [game.materials_dict[x]-1 for x,y in zip(game_mats[1:],pov_plan[1:]) if y['make'] and y['make'][0][0]>-1] + a, b = x[:2] + if exp == 3: + a = a.reshape(21,21) + for idx,aa in enumerate(a): + if idx in possible_extra: + cand_idxs = set([i for i,x in enumerate(pov_plan_mat[idx]) if x]) + th, _ = zip(*sorted([(i, x) for i, x in enumerate(aa) if i in possible_mats], key=lambda x:x[1])[-2:]) + if len(cand_idxs.intersection(set(th))): + for jdx, _ in enumerate(aa): + a[idx,jdx] = pov_plan_mat[idx,jdx] + else: + for jdx, _ in enumerate(aa): + a[idx,jdx] = 0 + else: + for jdx, aaa in enumerate(aa): + a[idx,jdx] = 0 + elif exp == 2: + a = a.reshape(21,21) + for idx,aa in enumerate(a): + if idx in possible_cand: + th = [x for i, x in enumerate(aa) if i in possible_mats] + th = sorted(th) + th = th[-2] + th = 1.1 if th < (1/21) else th + for jdx, aaa in enumerate(aa): + if idx in possible_mats: + a[idx,jdx] = 0 if aaa < th else 1 + else: + a[idx,jdx] = 0 + else: + for jdx, aaa in enumerate(aa): + a[idx,jdx] = 0 + + else: + a = a.reshape(21,21) + for idx,aa in enumerate(a): + th = sorted(aa)[-2] + th = 1.1 if th < (2.1/21) else th + for jdx, aaa in enumerate(aa): + a[idx,jdx] = 0 if aaa < th else 1 + a = a.reshape(-1) + predicts.append(np.argmax(a)) + targets.append(np.argmax(a) if np.argmax(a) in [x for x in b if x] else np.argmax(b)) + acc.append(accuracy_score(a,b)) + sa = set([i for i,x in enumerate(a) if x]) + sb = set([i for i,x in enumerate(b) if x]) + i = len(sa.intersection(sb)) + u = len(sa.union(sb)) + if u > 0: + a,b = zip(*[(x,y) for x,y in zip(a,b) if x+y > 0]) + f1.append(f1_score(b,a,zero_division=1)) + prec.append(precision_score(b,a,zero_division=1)) + rec.append(recall_score(b,a,zero_division=1)) + iou.append(i/u if u > 0 else 1) + total.append(sum(a)) + print( + f'{np.mean(f1):5.3f},' + f'{np.mean(iou):5.3f},' + f'{np.std(iou):5.3f},', + end=' ',flush=True) + print('', end='; ',flush=True) + return accuracy_score(targets,predicts), np.mean(acc), np.mean(f1), np.mean(iou) + +def do_split(model,lst,exp,criterion,optimizer=None,global_plan=False, player_plan=False, incremental=False, device=DEVICE, intermediate=0): + data = [] + acc_loss = 0 + p = [] + g = [] + masks = [] + for batch, game in enumerate(lst): + + if model.training and (not optimizer is None): optimizer.zero_grad() + + if exp==0: + ground_truth = torch.tensor(game.global_plan_mat.reshape(-1)).float() + elif exp==1: + ground_truth = torch.tensor(game.partner_plan.reshape(-1)).float() + elif exp==2: + ground_truth = torch.tensor(game.global_diff_plan_mat.reshape(-1)).float() + loss_mask = torch.tensor(game.global_plan_mat.reshape(-1)).float() + else: + ground_truth = torch.tensor(game.partner_diff_plan_mat.reshape(-1)).float() + loss_mask = torch.tensor(game.plan_repr.reshape(-1)).float() + + prediction, _ = model(game, global_plan=global_plan, player_plan=player_plan, incremental=incremental, intermediate=intermediate) + + if incremental: + ground_truth = ground_truth.to(device) + g += [ground_truth for _ in prediction] + masks += [loss_mask for _ in prediction] + + p += [x for x in prediction] + + data += list(zip(prediction.cpu().data.numpy(), [ground_truth.cpu().data.numpy()]*len(prediction),[batch]*len(prediction))) + else: + ground_truth = ground_truth.to(device) + g.append(ground_truth) + masks.append(loss_mask) + + p.append(prediction) + + data.append((prediction.cpu().data.numpy(), ground_truth.cpu().data.numpy(),batch)) + + if (batch+1) % 2 == 0: + loss = criterion(torch.stack(p),torch.stack(g), torch.stack(masks)) + + loss += 1e-5 * sum(p.pow(2.0).sum() for p in model.parameters()) + if model.training and (not optimizer is None): + loss.backward() + optimizer.step() + acc_loss += loss.item() + p = [] + g = [] + masks = [] + + acc_loss /= len(lst) + + acc0, acc, f1, iou = print_epoch(data,acc_loss,lst,exp) + + return acc0, acc_loss, data, acc, f1, iou + + +def init_weights(m): + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(0.01) + +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_dev.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 not args.intermediate in list(range(32)): + print('Intermediate must be in',list(range(32)),', but got',args.intermediate) + 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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,0,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + if args.experiment > 2: + val += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='Third': + val = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + elif args.pov=='First': + val = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])] + val += [GameParser(f,d_flag,2,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])] + train += [GameParser(f,d_flag,2,args.intermediate,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) + model.apply(init_weights) + + print(model) + model.train() + + learning_rate = 1e-5 + weight_decay=1e-4 + optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay) + criterion = PlanLoss() + + print(str(criterion), str(optimizer)) + + num_epochs = 200#1# + min_acc_loss = 1e6 + max_f1 = 0 + epochs_since_improvement = 0 + wait_epoch = 15 + max_fails = 5 + + if args.model_path is not None: + print(f'Loading {args.model_path}') + model.load_state_dict(torch.load(args.model_path)) + model.eval() + acc, acc_loss, data, _, f1, iou = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=True, device=DEVICE, intermediate=args.intermediate) + acc, acc_loss0, data, _, f1, iou = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=False, device=DEVICE, intermediate=args.intermediate) + + if np.mean([acc_loss,acc_loss0]) < min_acc_loss: + min_acc_loss = np.mean([acc_loss,acc_loss0]) + 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) + + 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, incremental=True, device=DEVICE, intermediate=args.intermediate) + do_split(model,train,args.experiment,criterion,optimizer=optimizer,global_plan=global_plan, player_plan=player_plan, incremental=False, device=DEVICE, intermediate=args.intermediate) + model.eval() + acc, acc_loss, data, _, f1, iou = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=True, device=DEVICE, intermediate=args.intermediate) + acc, acc_loss0, data, _, f1, iou = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=False, device=DEVICE, intermediate=args.intermediate) + + if np.mean([acc_loss,acc_loss0]) < min_acc_loss: + min_acc_loss = np.mean([acc_loss,acc_loss0]) + 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 epoch > wait_epoch and epochs_since_improvement > max_fails: + 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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + if args.experiment > 2: + test += [GameParser(f,d_flag,4,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='Third': + test = [GameParser(f,d_flag,3,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + elif args.pov=='First': + test = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])] + test += [GameParser(f,d_flag,2,args.intermediate,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() + acc, acc_loss, data, _, f1, iou = do_split(model,test,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=True, device=DEVICE, intermediate=args.intermediate) + acc, acc_loss, data, _, f1, iou = do_split(model,test,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=False, device=DEVICE, intermediate=args.intermediate) + + 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:Global, 1:Partner, 2:GlobalDif, 3:PartnerDif]') + parser.add_argument('--intermediate', type=int, + help='point of view [0:Global, 1:Partner, 2:GlobalDif, 3:PartnerDif]') + parser.add_argument('--save_path', type=str, + help='path where to save model') + 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('--device', type=int, default=0, + help='select cuda device number') + parser.add_argument('--model_path', type=str, default=None, + help='path to the pretrained model to be loaded') + + main(parser.parse_args()) \ No newline at end of file diff --git a/run_plan_predictor.sh b/run_plan_predictor.sh new file mode 100644 index 0000000..46344e3 --- /dev/null +++ b/run_plan_predictor.sh @@ -0,0 +1,42 @@ +echo $$ +CUDA_DEVICE=$1 +DMOVE=$2 +DLG=$3 + +echo $$ $CUDA_DEVICE $DMOVE $DLG + +FOLDER="models/baselines" +# FOLDER="models/test_loss" +# FOLDER="models/incremental_pretrained_2_attn_new" +mkdir -p $FOLDER +for SEED in 42; do #1 2 3 4 5; do #0; do # + for MODEL in LSTM; do # LSTM; do # Transformer; do # + for EXP in 3; do # 2 3; do + # for DMOVE in "No" "Yes"; do + # for DLG in "No" "Yes"; do + for INT in 0 1 2 3 4 5 6 7; do + FILE_NAME="plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int${INT}_seed_${SEED}" + COMM="plan_predictor.py" + COMM=$COMM" --seed=${SEED}" + COMM=$COMM" --device=${CUDA_DEVICE}" + COMM=$COMM" --use_dialogue_moves=${DMOVE}" + COMM=$COMM" --use_dialogue=${DLG}" + COMM=$COMM" --experiment=${EXP}" + COMM=$COMM" --seq_model=${MODEL}" + COMM=$COMM" --pov=First" + COMM=$COMM" --plan=Yes" + COMM=$COMM" --intermediate=${INT}" + if [ $INT -gt 0 ]; then + COMM=$COMM" --model_path=${FOLDER}/plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int0_seed_${SEED}.torch" + fi + COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch" + echo $(date +%F\ %T)" python3 ${COMM} > ${FOLDER}/${FILE_NAME}.log" + nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log + done + # done + # done + done + done +done + +echo 'Done!' \ No newline at end of file diff --git a/run_plan_predictor_graphs.sh b/run_plan_predictor_graphs.sh new file mode 100644 index 0000000..ba5a16f --- /dev/null +++ b/run_plan_predictor_graphs.sh @@ -0,0 +1,47 @@ +echo $$ +CUDA_DEVICE=$1 +DMOVE=$2 +DLG=$3 +LR=$4 + +echo $$ $CUDA_DEVICE $DMOVE $DLG + +# FOLDER="models/incremental_pretrained_2_new" +# FOLDER="models/incremental_pretrained_2_new_fullsampling_ln" +# FOLDER="models/exp2" +# FOLDER="models/exp3" +FOLDER="models/exp3_test" +# FOLDER="models/exp2_test" +mkdir -p $FOLDER +for SEED in 7 42 123; do #1 2 3 4 5; do #0; do # + for MODEL in Transformer; do # LSTM; do # Transformer; do # + for EXP in 3; do # 2 3; do + # for DMOVE in "No" "Yes"; do + # for DLG in "No" "Yes"; do + for INT in 0 1 2 3 4 5 6 7; do + FILE_NAME="plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int${INT}_seed_${SEED}" + COMM="plan_predictor_graphs.py" + COMM=$COMM" --seed=${SEED}" + COMM=$COMM" --device=${CUDA_DEVICE}" + COMM=$COMM" --use_dialogue_moves=${DMOVE}" + COMM=$COMM" --use_dialogue=${DLG}" + COMM=$COMM" --experiment=${EXP}" + COMM=$COMM" --seq_model=${MODEL}" + COMM=$COMM" --pov=First" + COMM=$COMM" --plan=Yes" + COMM=$COMM" --lr=${LR}" + COMM=$COMM" --intermediate=${INT}" + if [ $INT -gt 0 ]; then + COMM=$COMM" --model_path=${FOLDER}/plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int0_seed_${SEED}.torch" + fi + COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch" + echo $(date +%F\ %T)" python3 ${COMM} > ${FOLDER}/${FILE_NAME}.log" + nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log + done + # done + # done + done + done +done + +echo 'Done!' \ No newline at end of file diff --git a/run_plan_predictor_graphs_int0.sh b/run_plan_predictor_graphs_int0.sh new file mode 100644 index 0000000..f71f951 --- /dev/null +++ b/run_plan_predictor_graphs_int0.sh @@ -0,0 +1,44 @@ +echo Traning on int0! +CUDA_DEVICE=$1 +DMOVE=$2 +DLG=$3 +LR=$4 + +echo $$ $CUDA_DEVICE $DMOVE $DLG + +# FOLDER="models/incremental_pretrained_2_new" +# FOLDER="models/incremental_pretrained_2_new_fullsampling_ln" +# FOLDER="models/exp2" +# FOLDER="models/exp3" +FOLDER="models/exp2_int0exp2" +# FOLDER="models/exp2_test" +mkdir -p $FOLDER +for SEED in 7 42 123; do + for MODEL in Transformer; do + for EXP in 2; do # 2 3; do + for INT in 0; do + FILE_NAME="plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int${INT}_seed_${SEED}" + COMM="plan_predictor_graphs.py" + COMM=$COMM" --seed=${SEED}" + COMM=$COMM" --device=${CUDA_DEVICE}" + COMM=$COMM" --use_dialogue_moves=${DMOVE}" + COMM=$COMM" --use_dialogue=${DLG}" + COMM=$COMM" --experiment=${EXP}" + COMM=$COMM" --seq_model=${MODEL}" + COMM=$COMM" --pov=First" + COMM=$COMM" --plan=Yes" + COMM=$COMM" --lr=${LR}" + COMM=$COMM" --use_int0_instead_of_intermediate" + COMM=$COMM" --intermediate=${INT}" + if [ $INT -gt 0 ]; then + COMM=$COMM" --model_path=${FOLDER}/plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int0_seed_${SEED}.torch" + fi + COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch" + echo $(date +%F\ %T)" python3 ${COMM} > ${FOLDER}/${FILE_NAME}.log" + nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log + done + done + done +done + +echo 'Done!' \ No newline at end of file diff --git a/run_plan_predictor_graphs_oracle.sh b/run_plan_predictor_graphs_oracle.sh new file mode 100644 index 0000000..7505ff1 --- /dev/null +++ b/run_plan_predictor_graphs_oracle.sh @@ -0,0 +1,36 @@ +echo $$ +CUDA_DEVICE=$1 +DMOVE=$2 +DLG=$3 + +echo $$ $CUDA_DEVICE $DMOVE $DLG + +FOLDER="models/exp3_oracle" +mkdir -p $FOLDER +for SEED in 7 42 123; do + for MODEL in Transformer; do + for EXP in 3; do + for INT in 0 1 2 3 4 5 6 7; do + FILE_NAME="plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int${INT}_seed_${SEED}" + COMM="plan_predictor_graphs_oracle.py" + COMM=$COMM" --seed=${SEED}" + COMM=$COMM" --device=${CUDA_DEVICE}" + COMM=$COMM" --use_dialogue_moves=${DMOVE}" + COMM=$COMM" --use_dialogue=${DLG}" + COMM=$COMM" --experiment=${EXP}" + COMM=$COMM" --seq_model=${MODEL}" + COMM=$COMM" --pov=First" + COMM=$COMM" --plan=Yes" + COMM=$COMM" --intermediate=${INT}" + if [ $INT -gt 0 ]; then + COMM=$COMM" --model_path=${FOLDER}/plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int0_seed_${SEED}.torch" + fi + COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch" + echo $(date +%F\ %T)" python3 ${COMM} > ${FOLDER}/${FILE_NAME}.log" + nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log + done + done + done +done + +echo 'Done!' \ No newline at end of file diff --git a/run_plan_predictor_graphs_sigmoid.sh b/run_plan_predictor_graphs_sigmoid.sh new file mode 100644 index 0000000..163f4d9 --- /dev/null +++ b/run_plan_predictor_graphs_sigmoid.sh @@ -0,0 +1,42 @@ +echo $$ +CUDA_DEVICE=$1 +DMOVE=$2 +DLG=$3 + +echo $$ $CUDA_DEVICE $DMOVE $DLG + +# FOLDER="models/incremental_pretrained_2" +# FOLDER="models/tests" +FOLDER="models/incremental_pretrained_2_sigmoid" +mkdir -p $FOLDER +for SEED in 7 42 73; do #1 2 3 4 5; do #0; do # + for MODEL in Transformer; do # LSTM; do # Transformer; do # + for EXP in 2; do # 2 3; do + # for DMOVE in "No" "Yes"; do + # for DLG in "No" "Yes"; do + for INT in 0 7; do + FILE_NAME="plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int${INT}_seed_${SEED}" + COMM="plan_predictor_graphs_sigmoid.py" + COMM=$COMM" --seed=${SEED}" + COMM=$COMM" --device=${CUDA_DEVICE}" + COMM=$COMM" --use_dialogue_moves=${DMOVE}" + COMM=$COMM" --use_dialogue=${DLG}" + COMM=$COMM" --experiment=${EXP}" + COMM=$COMM" --seq_model=${MODEL}" + COMM=$COMM" --pov=First" + COMM=$COMM" --plan=Yes" + COMM=$COMM" --intermediate=${INT}" + if [ $INT -gt 0 ]; then + COMM=$COMM" --model_path=${FOLDER}/plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int0_seed_${SEED}.torch" + fi + COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch" + echo $(date +%F\ %T)" python3 ${COMM} > ${FOLDER}/${FILE_NAME}.log" + nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log + done + # done + # done + done + done +done + +echo 'Done!' \ No newline at end of file diff --git a/run_plan_predictor_oracle.sh b/run_plan_predictor_oracle.sh new file mode 100644 index 0000000..b2dadd5 --- /dev/null +++ b/run_plan_predictor_oracle.sh @@ -0,0 +1,40 @@ +echo $$ +CUDA_DEVICE=$1 +DMOVE=$2 +DLG=$3 + +echo $$ $CUDA_DEVICE $DMOVE $DLG + +FOLDER="models/baselines_oracle" +mkdir -p $FOLDER +for SEED in 123; do #1 2 3 4 5; do #0; do # + for MODEL in LSTM; do # LSTM; do # Transformer; do # + for EXP in 2; do # 2 3; do + # for DMOVE in "No" "Yes"; do + # for DLG in "No" "Yes"; do + for INT in 0 1 2 3 4 5 6 7; do + FILE_NAME="plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int${INT}_seed_${SEED}" + COMM="plan_predictor_oracle.py" + COMM=$COMM" --seed=${SEED}" + COMM=$COMM" --device=${CUDA_DEVICE}" + COMM=$COMM" --use_dialogue_moves=${DMOVE}" + COMM=$COMM" --use_dialogue=${DLG}" + COMM=$COMM" --experiment=${EXP}" + COMM=$COMM" --seq_model=${MODEL}" + COMM=$COMM" --pov=First" + COMM=$COMM" --plan=Yes" + COMM=$COMM" --intermediate=${INT}" + if [ $INT -gt 0 ]; then + COMM=$COMM" --model_path=${FOLDER}/plan_exp${EXP}_${MODEL}_dlg_${DLG}_move_${DMOVE}_int0_seed_${SEED}.torch" + fi + COMM=$COMM" --save_path=${FOLDER}/${FILE_NAME}.torch" + echo $(date +%F\ %T)" python3 ${COMM} > ${FOLDER}/${FILE_NAME}.log" + nice -n 5 python3 $COMM > ${FOLDER}/${FILE_NAME}.log + done + # done + # done + done + done +done + +echo 'Done!' diff --git a/src/.DS_Store b/src/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..185c9720343804f9aa8dea2f8c48ee9168be7397 GIT binary patch literal 6148 zcmeHKJ5Iwu5SX4-xU~p`H>=h$zDl935f$M#L^-M@FhS2D0o?uNO_#bqyX& z{s#l{?#5KpobGAfe}7wLb2lv)UAqeR;p^9aK-(2X)pQkf*;Dy=w%I&iruO|(gZz@F);>=0b^hQ3}~6o zP6lunyMi%b4E!zw@_ld^g1MrP7(N}CLJI&KLmdQb=_Mq_DdvhkA}kP&p}-i*+G21F zhu*1QuIMAiaAIvfSWjl{P&k~9{atV;&Li4v3>X6`0|)VOBK7}jfB&Bb*^@C~3=E0^ z9v5XX!H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 0: + retval[x-1] = 1 + return retval + +class GameParser: + tokenizer = None + model = None + def __init__(self, game_path, load_dialogue=True, pov=0, intermediate=0, use_dialogue_moves=False): + # print(game_path,end = ' ') + self.load_dialogue = load_dialogue + if pov not in (0,1,2,3,4): + print('Point of view must be in (0,1,2,3,4), but got ', pov) + exit() + self.pov = pov + self.use_dialogue_moves = use_dialogue_moves + self.load_player1 = pov==1 + self.load_player2 = pov==2 + self.load_third_person = pov==3 + self.game_path = game_path + # print(game_path) + self.dialogue_file = glob(os.path.join(game_path,'mcc*log'))[0] + self.questions_file = glob(os.path.join(game_path,'web*log'))[0] + self.plan_file = glob(os.path.join(game_path,'plan*json'))[0] + self.plan = json.load(open(self.plan_file)) + self.img_w = 96 + self.img_h = 96 + self.intermediate = intermediate + + self.flip_video = False + for l in open(self.dialogue_file): + if 'HAS JOINED' in l: + player_name = l.strip().split()[1] + self.flip_video = player_name[-1] == '2' + break + + if not os.path.isfile("config/materials.json") or \ + not os.path.isfile("config/mines.json") or \ + not os.path.isfile("config/tools.json"): + plan_files = sorted(glob('data/*_logs/*/plan*.json')) + materials = [] + tools = [] + mines = [] + for plan_file in plan_files: + plan = json.load(open(plan_file)) + materials += plan['materials'] + tools += plan['tools'] + mines += plan['mines'] + materials = sorted(list(set(materials))) + tools = sorted(list(set(tools))) + mines = sorted(list(set(mines))) + json.dump(materials, open('config/materials.json','w'), indent=4) + json.dump(mines, open('config/mines.json','w'), indent=4) + json.dump(tools, open('config/tools.json','w'), indent=4) + + materials = json.load(open('config/materials.json')) + mines = json.load(open('config/mines.json')) + tools = json.load(open('config/tools.json')) + + self.materials_dict = {x:i+1 for i,x in enumerate(materials)} + self.mines_dict = {x:i+1 for i,x in enumerate(mines)} + self.tools_dict = {x:i+1 for i,x in enumerate(tools)} + + self.__load_dialogue_act_labels() + self.__load_dialogue_move_labels() + self.__parse_dialogue() + self.__parse_questions() + self.__parse_start_end() + self.__parse_question_pairs() + self.__load_videos() + self.__assign_dialogue_act_labels() + self.__assign_dialogue_move_labels() + self.__load_replay_data() + self.__load_intermediate() + + # print(len(self.materials_dict)) + + self.global_plan = [] + self.global_plan_mat = np.zeros((21,21)) + mine_counter = 0 + for n,v in zip(self.plan['materials'],self.plan['full']): + if v['make']: + mine = 0 + m1 = self.materials_dict[self.plan['materials'][v['make'][0][0]]] + m2 = self.materials_dict[self.plan['materials'][v['make'][0][1]]] + self.global_plan_mat[self.materials_dict[n]-1][m1-1] = 1 + self.global_plan_mat[self.materials_dict[n]-1][m2-1] = 1 + else: + mine = self.mines_dict[self.plan['mines'][mine_counter]] + mine_counter += 1 + m1 = 0 + m2 = 0 + mine = onehot(mine, len(self.mines_dict)) + m1 = onehot(m1,len(self.materials_dict)) + m2 = onehot(m2,len(self.materials_dict)) + mat = onehot(self.materials_dict[n],len(self.materials_dict)) + t = onehot(self.tools_dict[self.plan['tools'][v['tools'][0]]],len(self.tools_dict)) + step = np.concatenate((mat,m1,m2,mine,t)) + self.global_plan.append(step) + + self.player1_plan = [] + self.player1_plan_mat = np.zeros((21,21)) + mine_counter = 0 + for n,v in zip(self.plan['materials'],self.plan['player1']): + if v['make']: + mine = 0 + if v['make'][0][0] < 0: + m1 = 0 + m2 = 0 + else: + m1 = self.materials_dict[self.plan['materials'][v['make'][0][0]]] + m2 = self.materials_dict[self.plan['materials'][v['make'][0][1]]] + self.player1_plan_mat[self.materials_dict[n]-1][m1-1] = 1 + self.player1_plan_mat[self.materials_dict[n]-1][m2-1] = 1 + else: + mine = self.mines_dict[self.plan['mines'][mine_counter]] + mine_counter += 1 + m1 = 0 + m2 = 0 + mine = onehot(mine, len(self.mines_dict)) + m1 = onehot(m1,len(self.materials_dict)) + m2 = onehot(m2,len(self.materials_dict)) + mat = onehot(self.materials_dict[n],len(self.materials_dict)) + t = onehot(self.tools_dict[self.plan['tools'][v['tools'][0]]],len(self.tools_dict)) + step = np.concatenate((mat,m1,m2,mine,t)) + self.player1_plan.append(step) + + self.player2_plan = [] + self.player2_plan_mat = np.zeros((21,21)) + mine_counter = 0 + for n,v in zip(self.plan['materials'],self.plan['player2']): + if v['make']: + mine = 0 + if v['make'][0][0] < 0: + m1 = 0 + m2 = 0 + else: + m1 = self.materials_dict[self.plan['materials'][v['make'][0][0]]] + m2 = self.materials_dict[self.plan['materials'][v['make'][0][1]]] + self.player2_plan_mat[self.materials_dict[n]-1][m1-1] = 1 + self.player2_plan_mat[self.materials_dict[n]-1][m2-1] = 1 + else: + mine = self.mines_dict[self.plan['mines'][mine_counter]] + mine_counter += 1 + m1 = 0 + m2 = 0 + mine = onehot(mine, len(self.mines_dict)) + m1 = onehot(m1,len(self.materials_dict)) + m2 = onehot(m2,len(self.materials_dict)) + mat = onehot(self.materials_dict[n],len(self.materials_dict)) + t = onehot(self.tools_dict[self.plan['tools'][v['tools'][0]]],len(self.tools_dict)) + step = np.concatenate((mat,m1,m2,mine,t)) + self.player2_plan.append(step) + # print(self.global_plan_mat.reshape(-1)) + # print(self.player1_plan_mat.reshape(-1)) + # print(self.player2_plan_mat.reshape(-1)) + # for x in zip(self.global_plan_mat.reshape(-1),self.player1_plan_mat.reshape(-1),self.player2_plan_mat.reshape(-1)): + # if sum(x) > 0: + # print(x) + # exit() + if self.load_player1: + self.plan_repr = self.player1_plan_mat + self.partner_plan = self.player2_plan_mat + elif self.load_player2: + self.plan_repr = self.player2_plan_mat + self.partner_plan = self.player1_plan_mat + else: + self.plan_repr = self.global_plan_mat + self.partner_plan = self.global_plan_mat + self.global_diff_plan_mat = self.global_plan_mat - self.plan_repr + self.partner_diff_plan_mat = self.global_plan_mat - self.partner_plan + + self.__iter_ts = self.start_ts + + # self.action_labels = sorted([t for a in self.actions for t in a if t.PacketData in ['BlockChangeData']], key=lambda x: x.TickIndex) + self.action_labels = None + # for tick in ticks: + # print(int(tick.TickIndex/30), self.plan['materials'].index( tick.items[0]), int(tick.Name[-1])) + # print(self.start_ts, self.end_ts, self.start_ts - self.end_ts, int(ticks[-1].TickIndex/30) if ticks else 0,self.action_file) + # exit() + self.materials = sorted(self.plan['materials']) + + def __len__(self): + return self.end_ts - self.start_ts + + def __next__(self): + if self.__iter_ts < self.end_ts: + + if self.load_dialogue: + d = [x for x in self.dialogue_events if x[0] == self.__iter_ts] + l = [x for x in self.dialogue_act_labels if x[0] == self.__iter_ts] + d = d if d else None + l = l if l else None + else: + d = None + l = None + + if self.use_dialogue_moves: + m = [x for x in self.dialogue_move_labels if x[0] == self.__iter_ts] + m = m if m else None + else: + m = None + + if self.action_labels: + a = [x for x in self.action_labels if (x.TickIndex//30 + self.start_ts) >= self.__iter_ts] + if a: + try: + while not a[0].items: + a = a[1:] + al = self.materials.index(a[0].items[0]) if a else 0 + except Exception: + print(a) + print(a[0]) + print(a[0].items) + print(a[0].items[0]) + exit() + at = a[0].TickIndex//30 + self.start_ts + an = int(a[0].Name[-1]) + a = [(at,al,an)] + else: + a = [(self.__iter_ts, self.materials.index(self.plan['materials'][0]), 1)] + a = None + else: + if self.end_ts - self.__iter_ts < 10: + # a = [(self.__iter_ts, self.materials.index(self.plan['materials'][0]), 1)] + a = None + else: + a = None + # if not self.__iter_ts % 30 == 0: + # a= None + if not a is None: + if not a[0][0] == self.__iter_ts: + a = None + + # q = [x for x in self.question_pairs if (x[0][0] < self.__iter_ts) and (x[0][1] > self.__iter_ts)] + q = [x for x in self.question_pairs if (x[0][1] == self.__iter_ts)] + q = q[0] if q else None + frame_idx = self.__iter_ts - self.start_ts + if self.load_third_person: + frames = self.third_pers_frames + elif self.load_player1: + frames = self.player1_pov_frames + elif self.load_player2: + frames = self.player2_pov_frames + else: + frames = np.array([0]) + if len(frames) == 1: + f = np.zeros((self.img_h,self.img_w,3)) + else: + if frame_idx < frames.shape[0]: + f = frames[frame_idx] + else: + f = np.zeros((self.img_h,self.img_w,3)) + if self.do_upperbound: + if not q is None: + qnum = 0 + base_rep = np.concatenate([ + onehot(q[0][2],2), + onehot(q[0][3],2), + onehot(q[0][4][qnum][0]+1,2), + onehot(self.materials_dict[q[0][5][qnum][1]],len(self.materials_dict)), + onehot(q[0][4][qnum][0]+1,2), + onehot(self.materials_dict[q[0][5][qnum][1]],len(self.materials_dict)), + onehot(['YES','MAYBE','NO'].index(q[1][0][qnum])+1,3), + onehot(['YES','MAYBE','NO'].index(q[1][1][qnum])+1,3) + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + ToM6 = base_rep if self.ToM6 is not None else np.zeros(1024) + qnum = 1 + base_rep = np.concatenate([ + onehot(q[0][2],2), + onehot(q[0][3],2), + onehot(q[0][4][qnum][0]+1,2), + onehot(self.materials_dict[q[0][5][qnum][1]],len(self.materials_dict)), + onehot(q[0][4][qnum][0]+1,2), + onehot(self.materials_dict[q[0][5][qnum][1]],len(self.materials_dict)), + onehot(['YES','MAYBE','NO'].index(q[1][0][qnum])+1,3), + onehot(['YES','MAYBE','NO'].index(q[1][1][qnum])+1,3) + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + ToM7 = base_rep if self.ToM7 is not None else np.zeros(1024) + qnum = 2 + base_rep = np.concatenate([ + onehot(q[0][2],2), + onehot(q[0][3],2), + onehot(q[0][4][qnum]+1,2), + onehot(q[0][4][qnum]+1,2), + onehot(self.materials_dict[q[1][0][qnum]] if q[1][0][qnum] in self.materials_dict else len(self.materials_dict)+1,len(self.materials_dict)+1), + onehot(self.materials_dict[q[1][1][qnum]] if q[1][1][qnum] in self.materials_dict else len(self.materials_dict)+1,len(self.materials_dict)+1) + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + ToM8 = base_rep if self.ToM8 is not None else np.zeros(1024) + else: + ToM6 = np.zeros(1024) + ToM7 = np.zeros(1024) + ToM8 = np.zeros(1024) + if not l is None: + base_rep = np.concatenate([ + onehot(l[0][1],2), + onehot(l[0][2],len(self.dialogue_act_labels_dict)) + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + DAct = base_rep if self.DAct is not None else np.zeros(1024) + else: + DAct = np.zeros(1024) + if not m is None: + base_rep = np.concatenate([ + onehot(m[0][1],2), + onehot(m[0][2][0],len(self.dialogue_move_labels_dict)), + onehot(m[0][2][1],len(self.tools_dict) + len(self.materials_dict) + len(self.mines_dict)+1), + onehot(m[0][2][2],len(self.tools_dict) + len(self.materials_dict) + len(self.mines_dict)+1), + onehot(m[0][2][3],len(self.tools_dict) + len(self.materials_dict) + len(self.mines_dict)+1), + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + DMove = base_rep if self.DMove is not None else np.zeros(1024) + else: + DMove = np.zeros(1024) + else: + ToM6 = self.ToM6[frame_idx] if self.ToM6 is not None else np.zeros(1024) + ToM7 = self.ToM7[frame_idx] if self.ToM7 is not None else np.zeros(1024) + ToM8 = self.ToM8[frame_idx] if self.ToM8 is not None else np.zeros(1024) + DAct = self.DAct[frame_idx] if self.DAct is not None else np.zeros(1024) + DMove = self.DAct[frame_idx] if self.DMove is not None else np.zeros(1024) + # if not m is None: + # base_rep = np.concatenate([ + # onehot(m[0][1],2), + # onehot(m[0][2][0],len(self.dialogue_move_labels_dict)), + # onehot(m[0][2][1],len(self.tools_dict) + len(self.materials_dict) + len(self.mines_dict)+1), + # onehot(m[0][2][2],len(self.tools_dict) + len(self.materials_dict) + len(self.mines_dict)+1), + # onehot(m[0][2][3],len(self.tools_dict) + len(self.materials_dict) + len(self.mines_dict)+1), + # ]) + # base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + # DMove = base_rep if self.DMove is not None else np.zeros(1024) + # else: + # DMove = np.zeros(1024) + intermediate = np.concatenate([ToM6,ToM7,ToM8,DAct,DMove]) + retval = ((self.__iter_ts,self.pov),d,l,q,f,a,intermediate,m) + self.__iter_ts += 1 + return retval + self.__iter_ts = self.start_ts + raise StopIteration() + + def __iter__(self): + return self + + def __load_videos(self): + d = self.end_ts - self.start_ts + + if self.load_third_person: + try: + self.third_pers_file = glob(os.path.join(self.game_path,'third*gif'))[0] + np_file = self.third_pers_file[:-3]+'npz' + if os.path.isfile(np_file): + self.third_pers_frames = np.load(np_file)['data'] + else: + frames = imageio.get_reader(self.third_pers_file, '.gif') + reshaper = lambda x: cv2.resize(x,(self.img_h,self.img_w)) + if 'main' in self.game_path: + self.third_pers_frames = np.array([reshaper(f[95:4*95,250:-249,2::-1]) for f in frames]) + else: + self.third_pers_frames = np.array([reshaper(f[-3*95:,250:-249,2::-1]) for f in frames]) + print(np_file,end=' ') + np.savez_compressed(open(np_file,'wb'), data=self.third_pers_frames) + print('saved') + except Exception as e: + self.third_pers_frames = np.array([0]) + + if self.third_pers_frames.shape[0]//d < 10: + self.third_pov_frame_rate = 6 + else: + if self.third_pers_frames.shape[0]//d < 20: + self.third_pov_frame_rate = 12 + else: + if self.third_pers_frames.shape[0]//d < 45: + self.third_pov_frame_rate = 30 + else: + self.third_pov_frame_rate = 60 + self.third_pers_frames = self.third_pers_frames[::self.third_pov_frame_rate] + else: + self.third_pers_frames = np.array([0]) + + if self.load_player1: + try: + search_str = 'play2*gif' if self.flip_video else 'play1*gif' + self.player1_pov_file = glob(os.path.join(self.game_path,search_str))[0] + np_file = self.player1_pov_file[:-3]+'npz' + if os.path.isfile(np_file): + self.player1_pov_frames = np.load(np_file)['data'] + else: + frames = imageio.get_reader(self.player1_pov_file, '.gif') + reshaper = lambda x: cv2.resize(x,(self.img_h,self.img_w)) + self.player1_pov_frames = np.array([reshaper(f[:,:,2::-1]) for f in frames]) + print(np_file,end=' ') + np.savez_compressed(open(np_file,'wb'), data=self.player1_pov_frames) + print('saved') + except Exception as e: + self.player1_pov_frames = np.array([0]) + + if self.player1_pov_frames.shape[0]//d < 10: + self.player1_pov_frame_rate = 6 + else: + if self.player1_pov_frames.shape[0]//d < 20: + self.player1_pov_frame_rate = 12 + else: + if self.player1_pov_frames.shape[0]//d < 45: + self.player1_pov_frame_rate = 30 + else: + self.player1_pov_frame_rate = 60 + self.player1_pov_frames = self.player1_pov_frames[::self.player1_pov_frame_rate] + else: + self.player1_pov_frames = np.array([0]) + + if self.load_player2: + try: + search_str = 'play1*gif' if self.flip_video else 'play2*gif' + self.player2_pov_file = glob(os.path.join(self.game_path,search_str))[0] + np_file = self.player2_pov_file[:-3]+'npz' + if os.path.isfile(np_file): + self.player2_pov_frames = np.load(np_file)['data'] + else: + frames = imageio.get_reader(self.player2_pov_file, '.gif') + reshaper = lambda x: cv2.resize(x,(self.img_h,self.img_w)) + self.player2_pov_frames = np.array([reshaper(f[:,:,2::-1]) for f in frames]) + print(np_file,end=' ') + np.savez_compressed(open(np_file,'wb'), data=self.player2_pov_frames) + print('saved') + except Exception as e: + self.player2_pov_frames = np.array([0]) + + if self.player2_pov_frames.shape[0]//d < 10: + self.player2_pov_frame_rate = 6 + else: + if self.player2_pov_frames.shape[0]//d < 20: + self.player2_pov_frame_rate = 12 + else: + if self.player2_pov_frames.shape[0]//d < 45: + self.player2_pov_frame_rate = 30 + else: + self.player2_pov_frame_rate = 60 + self.player2_pov_frames = self.player2_pov_frames[::self.player2_pov_frame_rate] + else: + self.player2_pov_frames = np.array([0]) + + def __parse_question_pairs(self): + question_dict = {} + for q in self.questions: + k = q[2][0][1] + q[2][1][1] + if not k in question_dict: + question_dict[k] = [] + question_dict[k].append(q) + + self.question_pairs = [] + for k,v in question_dict.items(): + if len(v) == 2: + if v[0][1]+v[1][1] == 3: + self.question_pairs.append(v) + else: + while len(v) > 1: + pair = [] + pair.append(v.pop(0)) + pair.append(v.pop(0)) + while not pair[0][1]+pair[1][1] == 3: + if not v: + break + # print(game_path,pair) + pair.append(v.pop(0)) + pair.pop(0) + if not v: + break + self.question_pairs.append(pair) + self.question_pairs = sorted(self.question_pairs, key=lambda x: x[0][0]) + if self.load_player2 or self.pov==4: + self.question_pairs = [sorted(q, key=lambda x: x[1],reverse=True) for q in self.question_pairs] + else: + self.question_pairs = [sorted(q, key=lambda x: x[1]) for q in self.question_pairs] + + + self.question_pairs = [((a[0], b[0], a[1], b[1], a[2], b[2]), (a[3], b[3])) for a,b in self.question_pairs] + + def __parse_dialogue(self): + self.dialogue_events = [] + # if not self.load_dialogue: + # return + save_path = os.path.join(self.game_path,f'dialogue_{self.game_path.split("/")[-1]}.pkl') + # print(save_path) + # exit() + if os.path.isfile(save_path): + self.dialogue_events = pickle.load(open( save_path, "rb" )) + return + for x in open(self.dialogue_file): + if '[Async Chat Thread' in x: + ts = list(map(int,x.split(' [')[0].strip('[]').split(':'))) + ts = 3600*ts[0] + 60*ts[1] + ts[2] + player, event = x.strip().split('/INFO]: [] ',1) + event = event.lower() + event = ''.join([x if x in string.ascii_lowercase else f' {x} ' for x in event]).strip() + event = event.replace(' ',' ').replace(' ',' ') + player = int(player) + if GameParser.tokenizer is None: + GameParser.tokenizer = BertTokenizer.from_pretrained('bert-large-uncased', do_lower_case=True) + if self.model is None: + GameParser.model = BertModel.from_pretrained('bert-large-uncased', output_hidden_states=True).to(DEVICE) + encoded_dict = GameParser.tokenizer.encode_plus( + event, # Sentence to encode. + add_special_tokens=True, # Add '[CLS]' and '[SEP]' + return_tensors='pt', # Return pytorch tensors. + ) + token_ids = encoded_dict['input_ids'].to(DEVICE) + segment_ids = torch.ones(token_ids.size()).long().to(DEVICE) + GameParser.model.eval() + with torch.no_grad(): + outputs = GameParser.model(input_ids=token_ids, token_type_ids=segment_ids) + outputs = outputs[1][0].cpu().data.numpy() + self.dialogue_events.append((ts,player,event,outputs)) + pickle.dump(self.dialogue_events, open( save_path, "wb" )) + print(f'Saved to {save_path}',flush=True) + + def __parse_questions(self): + self.questions = [] + for x in open(self.questions_file): + if x[0] == '#': + ts, qs = x.strip().split(' Number of records inserted: 1 # player') + # print(ts,qs) + + ts = list(map(int,ts.split(' ')[5].split(':'))) + ts = 3600*ts[0] + 60*ts[1] + ts[2] + + player = int(qs[0]) + questions = qs[2:].split(';') + answers =[x[7:] for x in questions[3:]] + questions = [x[9:].split(' ') for x in questions[:3]] + questions[0] = (int(questions[0][0] == 'Have'), questions[0][-3]) + questions[1] = (int(questions[1][2] == 'know'), questions[1][-1]) + questions[2] = int(questions[2][1] == 'are') + + self.questions.append((ts,player,questions,answers)) + def __parse_start_end(self): + self.start_ts = [x.strip() for x in open(self.dialogue_file) if 'THEY ARE PLAYER' in x][1] + self.start_ts = list(map(int,self.start_ts.split('] [')[0][1:].split(':'))) + self.start_ts = 3600*self.start_ts[0] + 60*self.start_ts[1] + self.start_ts[2] + try: + self.start_ts = max(self.start_ts, self.questions[0][0]-75) + except Exception as e: + pass + + self.end_ts = [x.strip() for x in open(self.dialogue_file) if 'Stopping' in x] + if self.end_ts: + self.end_ts = self.end_ts[0] + self.end_ts = list(map(int,self.end_ts.split('] [')[0][1:].split(':'))) + self.end_ts = 3600*self.end_ts[0] + 60*self.end_ts[1] + self.end_ts[2] + else: + self.end_ts = self.dialogue_events[-1][0] + try: + self.end_ts = max(self.end_ts, self.questions[-1][0]) + 1 + except Exception as e: + pass + + def __load_dialogue_act_labels(self): + file_name = 'config/dialogue_act_labels.json' + if not os.path.isfile(file_name): + files = sorted(glob('/home/*/MCC/*done.txt')) + dialogue_act_dict = {} + for file in files: + game_str = '' + for line in open(file): + line = line.strip() + if '_logs/' in line: + game_str = line + else: + if line: + line = line.split() + key = f'{game_str}#{line[0]}' + dialogue_act_dict[key] = line[-1] + json.dump(dialogue_act_dict,open(file_name,'w'), indent=4) + self.dialogue_act_dict = json.load(open(file_name)) + self.dialogue_act_labels_dict = {l : i for i, l in enumerate(sorted(list(set(self.dialogue_act_dict.values()))))} + self.dialogue_act_bias = {l : sum([int(x==l) for x in self.dialogue_act_dict.values()]) for l in self.dialogue_act_labels_dict.keys()} + json.dump(self.dialogue_act_labels_dict,open('config/dialogue_act_label_names.json','w'), indent=4) + # print(self.dialogue_act_bias) + # print(self.dialogue_act_labels_dict) + # exit() + + def __assign_dialogue_act_labels(self): + + # log_file = glob('/'.join([self.game_path,'mcc*log']))[0][5:] + log_file = glob('/'.join([self.game_path,'mcc*log']))[0].split('mindcraft/')[1] + self.dialogue_act_labels = [] + for emb in self.dialogue_events: + ts = emb[0] + h = ts//3600 + m = (ts%3600)//60 + s = ts%60 + key = f'{log_file}#[{h:02d}:{m:02d}:{s:02d}]:{emb[1]}>' + self.dialogue_act_labels.append((emb[0],emb[1],self.dialogue_act_labels_dict[self.dialogue_act_dict[key]])) + + def __load_dialogue_move_labels(self): + file_name = "config/dialogue_move_labels.json" + dialogue_move_dict = {} + if not os.path.isfile(file_name): + file_text = '' + dialogue_moves = set() + for line in open("XXX"): + line = line.strip() + if not line: + continue + if line[0] == '#': + continue + if line[0] == '[': + tag_text = glob(f'data/*/*/mcc_{file_text}.log')[0].split('/',1)[-1] + key = f'{tag_text}#{line.split()[0]}' + value = line.split()[-1].split('#') + if len(value) < 4: + value += ['IGNORE']*(4-len(value)) + dialogue_moves.add(value[0]) + value = '#'.join(value) + dialogue_move_dict[key] = value + # print(key,value) + # break + else: + file_text = line + # print(line) + dialogue_moves = sorted(list(dialogue_moves)) + # print(dialogue_moves) + + json.dump(dialogue_move_dict,open(file_name,'w'), indent=4) + self.dialogue_move_dict = json.load(open(file_name)) + self.dialogue_move_labels_dict = {l : i for i, l in enumerate(sorted(list(set([lbl.split('#')[0] for lbl in self.dialogue_move_dict.values()]))))} + self.dialogue_move_bias = {l : sum([int(x==l) for x in self.dialogue_move_dict.values()]) for l in self.dialogue_move_labels_dict.keys()} + json.dump(self.dialogue_move_labels_dict,open('config/dialogue_move_label_names.json','w'), indent=4) + + def __assign_dialogue_move_labels(self): + + # log_file = glob('/'.join([self.game_path,'mcc*log']))[0][5:] + log_file = glob('/'.join([self.game_path,'mcc*log']))[0].split('mindcraft/')[1] + self.dialogue_move_labels = [] + for emb in self.dialogue_events: + ts = emb[0] + h = ts//3600 + m = (ts%3600)//60 + s = ts%60 + key = f'{log_file}#[{h:02d}:{m:02d}:{s:02d}]:{emb[1]}>' + move = self.dialogue_move_dict[key].split('#') + move[0] = self.dialogue_move_labels_dict[move[0]] + for i,m in enumerate(move[1:]): + if m == 'IGNORE': + move[i+1] = 0 + elif m in self.materials_dict: + move[i+1] = self.materials_dict[m] + elif m in self.mines_dict: + move[i+1] = self.mines_dict[m] + len(self.materials_dict) + elif m in self.tools_dict: + move[i+1] = self.tools_dict[m] + len(self.materials_dict) + len(self.mines_dict) + else: + print(move) + exit() + # print(move,self.dialogue_move_dict[key],key) + # exit() + self.dialogue_move_labels.append((emb[0],emb[1],move)) + + def __load_replay_data(self): + # self.action_file = "data/ReplayData/ActionsData_mcc_" + self.game_path.split('/')[-1] + # with open(self.action_file) as f: + # data = ' '.join(x.strip() for x in f).split('action') + # # preface = data[0] + # self.actions = list(map(proc_action, data[1:])) + self.actions = None + + def __load_intermediate(self): + if self.intermediate > 15: + self.do_upperbound = True + else: + self.do_upperbound = False + if self.pov in [1,2]: + self.ToM6 = np.load(glob(f'{self.game_path}/intermediate_baseline_ToM6*player{self.pov}.npz')[0])['data'] if self.intermediate % 2 else None + self.intermediate = self.intermediate // 2 + self.ToM7 = np.load(glob(f'{self.game_path}/intermediate_baseline_ToM7*player{self.pov}.npz')[0])['data'] if self.intermediate % 2 else None + self.intermediate = self.intermediate // 2 + self.ToM8 = np.load(glob(f'{self.game_path}/intermediate_baseline_ToM8*player{self.pov}.npz')[0])['data'] if self.intermediate % 2 else None + self.intermediate = self.intermediate // 2 + self.DAct = np.load(glob(f'{self.game_path}/intermediate_baseline_DAct*player{self.pov}.npz')[0])['data'] if self.intermediate % 2 else None + self.intermediate = self.intermediate // 2 + self.DMove = None + # print(self.ToM6) + # print(self.ToM7) + # print(self.ToM8) + # print(self.DAct) + else: + self.ToM6 = None + self.ToM7 = None + self.ToM8 = None + self.DAct = None + self.DMove = None + # exit() + diff --git a/src/data/game_parser_graphs_new.py b/src/data/game_parser_graphs_new.py new file mode 100644 index 0000000..7dd6860 --- /dev/null +++ b/src/data/game_parser_graphs_new.py @@ -0,0 +1,837 @@ +from glob import glob +import os, string, json, pickle +import torch, random, numpy as np +from transformers import BertTokenizer, BertModel +import cv2 +import imageio +import networkx as nx +from torch_geometric.utils.convert import from_networkx +import matplotlib.pyplot as plt +from torch_geometric.utils import degree + + +DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' + +# def set_seed(seed_idx): +# seed = 0 +# random.seed(0) +# for _ in range(seed_idx): +# seed = random.random() +# random.seed(seed) +# torch.manual_seed(seed) +# print('Random seed set to', seed) +# return seed + +def set_seed(seed): + os.environ['PYTHONHASHSEED'] = str(seed) + torch.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + print('Random seed set to', seed) + return seed + +def make_splits(split_file = 'config/dataset_splits.json'): + if not os.path.isfile(split_file): + dirs = sorted(glob('data/saved_logs/*') + glob('data/main_logs/*')) + games = sorted(list(map(GameParser, dirs)), key=lambda x: len(x.question_pairs), reverse=True) + + test = games[0::5] + val = games[1::5] + train = games[2::5]+games[3::5]+games[4::5] + + dataset_splits = {'test' : [g.game_path for g in test], 'validation' : [g.game_path for g in val], 'training' : [g.game_path for g in train]} + json.dump(dataset_splits, open('config/dataset_splits_old.json','w'), indent=4) + + dirs = sorted(glob('data/new_logs/*')) + games = sorted(list(map(GameParser, dirs)), key=lambda x: len(x.question_pairs), reverse=True) + + test = games[0::5] + val = games[1::5] + train = games[2::5]+games[3::5]+games[4::5] + + dataset_splits['test'] += [g.game_path for g in test] + dataset_splits['validation'] += [g.game_path for g in val] + dataset_splits['training'] += [g.game_path for g in train] + json.dump(dataset_splits, open('config/dataset_splits_new.json','w'), indent=4) + json.dump(dataset_splits, open('config/dataset_splits.json','w'), indent=4) + + dataset_splits['test'] = dataset_splits['test'][:2] + dataset_splits['validation'] = dataset_splits['validation'][:2] + dataset_splits['training'] = dataset_splits['training'][:2] + json.dump(dataset_splits, open('config/dataset_splits_dev.json','w'), indent=4) + + dataset_splits = json.load(open(split_file)) + + return dataset_splits + +def onehot(x,n): + retval = np.zeros(n) + if x > 0: + retval[x-1] = 1 + return retval + +class GameParser: + tokenizer = None + model = None + def __init__(self, game_path, load_dialogue=True, pov=0, intermediate=0, use_dialogue_moves=False, load_int0_feats=False): + self.load_dialogue = load_dialogue + if pov not in (0,1,2,3,4): + print('Point of view must be in (0,1,2,3,4), but got ', pov) + exit() + self.pov = pov + self.use_dialogue_moves = use_dialogue_moves + self.load_player1 = pov==1 + self.load_player2 = pov==2 + self.load_third_person = pov==3 + self.game_path = game_path + self.dialogue_file = glob(os.path.join(game_path,'mcc*log'))[0] + self.questions_file = glob(os.path.join(game_path,'web*log'))[0] + self.plan_file = glob(os.path.join(game_path,'plan*json'))[0] + self.plan = json.load(open(self.plan_file)) + self.img_w = 96 + self.img_h = 96 + self.intermediate = intermediate + + self.flip_video = False + for l in open(self.dialogue_file): + if 'HAS JOINED' in l: + player_name = l.strip().split()[1] + self.flip_video = player_name[-1] == '2' + break + + if not os.path.isfile("config/materials.json") or \ + not os.path.isfile("config/mines.json") or \ + not os.path.isfile("config/tools.json"): + plan_files = sorted(glob('data/*_logs/*/plan*.json')) + materials = [] + tools = [] + mines = [] + for plan_file in plan_files: + plan = json.load(open(plan_file)) + materials += plan['materials'] + tools += plan['tools'] + mines += plan['mines'] + materials = sorted(list(set(materials))) + tools = sorted(list(set(tools))) + mines = sorted(list(set(mines))) + json.dump(materials, open('config/materials.json','w'), indent=4) + json.dump(mines, open('config/mines.json','w'), indent=4) + json.dump(tools, open('config/tools.json','w'), indent=4) + + materials = json.load(open('config/materials.json')) + mines = json.load(open('config/mines.json')) + tools = json.load(open('config/tools.json')) + + self.materials_dict = {x:i+1 for i,x in enumerate(materials)} + self.mines_dict = {x:i+1 for i,x in enumerate(mines)} + self.tools_dict = {x:i+1 for i,x in enumerate(tools)} + + # NOTE new + shift_value = max(self.materials_dict.values()) + self.materials_mines_dict = {**self.materials_dict, **{key: value + shift_value for key, value in self.mines_dict.items()}} + self.inverse_materials_mines_dict = {v: k for k, v in self.materials_mines_dict.items()} + #  + + self.__load_dialogue_act_labels() + self.__load_dialogue_move_labels() + self.__parse_dialogue() + self.__parse_questions() + self.__parse_start_end() + self.__parse_question_pairs() + self.__load_videos() + self.__assign_dialogue_act_labels() + self.__assign_dialogue_move_labels() + self.__load_replay_data() + self.__load_intermediate() + self.load_int0 = load_int0_feats + if load_int0_feats: + self.__load_int0_feats() + + ############################################# + ################## GRAPHS ################### + + ############################################# + ################ Global Plan ################ + self.global_plan = nx.DiGraph() + mine_counter = 0 + for n, v in zip(self.plan['materials'], self.plan['full']): + mat = onehot(self.materials_mines_dict[n],len(self.materials_mines_dict)) + t = onehot(self.tools_dict[self.plan['tools'][v['tools'][0]]], len(self.tools_dict)) + self.global_plan = self._add_node(self.global_plan, n, features=mat) + if v['make']: + #print(n, v, self.plan['materials'][v['make'][0][0]], self.plan['materials'][v['make'][0][1]]) + mine = 0 + m1 = self.materials_dict[self.plan['materials'][v['make'][0][0]]] + m2 = self.materials_dict[self.plan['materials'][v['make'][0][1]]] + m1 = onehot(m1, len(self.materials_mines_dict)) + m2 = onehot(m2, len(self.materials_mines_dict)) + self.global_plan = self._add_node(self.global_plan, self.plan['materials'][v['make'][0][0]], features=m1) + self.global_plan = self._add_node(self.global_plan, self.plan['materials'][v['make'][0][1]], features=m2) + # m1 -> mat + self.global_plan = self._add_edge(self.global_plan, self.plan['materials'][v['make'][0][0]], n, tool=t) + # m2 -> mat + self.global_plan = self._add_edge(self.global_plan, self.plan['materials'][v['make'][0][1]], n, tool=t) + else: + #print(n, v, self.plan['mines'][mine_counter]) + mine = self.materials_mines_dict[self.plan['mines'][mine_counter]] + mine_counter += 1 + mine = onehot(mine, len(self.materials_mines_dict)) + self.global_plan = self._add_node(self.global_plan, self.plan['mines'][mine_counter], features=mine) + self.global_plan = self._add_edge(self.global_plan, self.plan['mines'][mine_counter], n, tool=t) + #self._plot_plan_graph(self.global_plan, filename=f"plots/global_{game_path.split('/')[-2]}_{game_path.split('/')[-1]}.png") + self.global_plan = from_networkx(self.global_plan) # NOTE: I modified /torch_geometric/utils/convert.py, line 250 + ############################################# + + ############################################# + ############### Player 1 Plan ############### + self.player1_plan = nx.DiGraph() + mine_counter = 0 + for n,v in zip(self.plan['materials'], self.plan['player1']): + if v['make']: + mine = 0 + if v['make'][0][0] < 0: + #print(n, v, "unknown", "unknown") + m1 = 0 + m2 = 0 + mat = onehot(self.materials_mines_dict[n],len(self.materials_mines_dict)) + self.player1_plan = self._add_node(self.player1_plan, n, features=mat) + else: + #print(n, v, self.plan['materials'][v['make'][0][0]], self.plan['materials'][v['make'][0][1]]) + mat = onehot(self.materials_mines_dict[n],len(self.materials_mines_dict)) + t = onehot(self.tools_dict[self.plan['tools'][v['tools'][0]]], len(self.tools_dict)) + self.player1_plan = self._add_node(self.player1_plan, n, features=mat) + m1 = self.materials_dict[self.plan['materials'][v['make'][0][0]]] + m2 = self.materials_dict[self.plan['materials'][v['make'][0][1]]] + m1 = onehot(m1, len(self.materials_mines_dict)) + m2 = onehot(m2, len(self.materials_mines_dict)) + self.player1_plan = self._add_node(self.player1_plan, self.plan['materials'][v['make'][0][0]], features=m1) + self.player1_plan = self._add_node(self.player1_plan, self.plan['materials'][v['make'][0][1]], features=m2) + # m1 -> mat + self.player1_plan = self._add_edge(self.player1_plan, self.plan['materials'][v['make'][0][0]], n, tool=t) + # m2 -> mat + self.player1_plan = self._add_edge(self.player1_plan, self.plan['materials'][v['make'][0][1]], n, tool=t) + else: + #print(n, v, self.plan['mines'][mine_counter]) + mat = onehot(self.materials_mines_dict[n],len(self.materials_mines_dict)) + t = onehot(self.tools_dict[self.plan['tools'][v['tools'][0]]], len(self.tools_dict)) + self.player1_plan = self._add_node(self.player1_plan, n, features=mat) + mine = self.materials_mines_dict[self.plan['mines'][mine_counter]] + mine_counter += 1 + mine = onehot(mine, len(self.materials_mines_dict)) + self.player1_plan = self._add_node(self.player1_plan, self.plan['mines'][mine_counter], features=mine) + self.player1_plan = self._add_edge(self.player1_plan, self.plan['mines'][mine_counter], n, tool=t) + #self._plot_plan_graph(self.player1_plan, filename=f"plots/player1_{game_path.split('/')[-2]}_{game_path.split('/')[-1]}.png") + self.player1_plan = from_networkx(self.player1_plan) + ############################################# + + ############################################# + ############### Player 2 Plan ############### + self.player2_plan = nx.DiGraph() + mine_counter = 0 + for n,v in zip(self.plan['materials'], self.plan['player2']): + if v['make']: + mine = 0 + if v['make'][0][0] < 0: + #print(n, v, "unknown", "unknown") + m1 = 0 + m2 = 0 + mat = onehot(self.materials_mines_dict[n],len(self.materials_mines_dict)) + self.player2_plan = self._add_node(self.player2_plan, n, features=mat) + else: + #print(n, v, self.plan['materials'][v['make'][0][0]], self.plan['materials'][v['make'][0][1]]) + mat = onehot(self.materials_mines_dict[n],len(self.materials_mines_dict)) + t = onehot(self.tools_dict[self.plan['tools'][v['tools'][0]]], len(self.tools_dict)) + self.player2_plan = self._add_node(self.player2_plan, n, features=mat) + m1 = self.materials_dict[self.plan['materials'][v['make'][0][0]]] + m2 = self.materials_dict[self.plan['materials'][v['make'][0][1]]] + m1 = onehot(m1, len(self.materials_mines_dict)) + m2 = onehot(m2, len(self.materials_mines_dict)) + self.player2_plan = self._add_node(self.player2_plan, self.plan['materials'][v['make'][0][0]], features=m1) + self.player2_plan = self._add_node(self.player2_plan, self.plan['materials'][v['make'][0][1]], features=m2) + # m1 -> mat + self.player2_plan = self._add_edge(self.player2_plan, self.plan['materials'][v['make'][0][0]], n, tool=t) + # m2 -> mat + self.player2_plan = self._add_edge(self.player2_plan, self.plan['materials'][v['make'][0][1]], n, tool=t) + else: + #print(n, v, self.plan['mines'][mine_counter]) + mat = onehot(self.materials_mines_dict[n],len(self.materials_mines_dict)) + t = onehot(self.tools_dict[self.plan['tools'][v['tools'][0]]], len(self.tools_dict)) + self.player2_plan = self._add_node(self.player2_plan, n, features=mat) + mine = self.materials_mines_dict[self.plan['mines'][mine_counter]] + mine_counter += 1 + mine = onehot(mine, len(self.materials_mines_dict)) + self.player2_plan = self._add_node(self.player2_plan, self.plan['mines'][mine_counter], features=mine) + self.player2_plan = self._add_edge(self.player2_plan, self.plan['mines'][mine_counter], n, tool=t) + #self._plot_plan_graph(self.player2_plan, filename=f"plots/player2_{game_path.split('/')[-2]}_{game_path.split('/')[-1]}.png") + self.player2_plan = from_networkx(self.player2_plan) + + # with open('graphs.pkl', 'wb') as f: + # pickle.dump([self.global_plan, self.player1_plan, self.player2_plan], f) + + # construct a dict mapping materials to node indexes for each graph + p1_dict = {self.inverse_materials_mines_dict[torch.argmax(features).item()+1]: node_index for node_index, features in enumerate(self.player1_plan.features)} + p2_dict = {self.inverse_materials_mines_dict[torch.argmax(features).item()+1]: node_index for node_index, features in enumerate(self.player2_plan.features)} + # candidate edge = (u,v) + # u is from nodes with no out degree, v is from nodes with no in degree + p1_u_candidates = [p1_dict[i] for i in self.find_nodes_with_less_than_four_out_degree(self.player1_plan)] + p1_v_candidates = [p1_dict[i] for i in self.find_nodes_with_no_in_degree(self.player1_plan)] + p2_u_candidates = [p2_dict[i] for i in self.find_nodes_with_less_than_four_out_degree(self.player2_plan)] + p2_v_candidates = [p2_dict[i] for i in self.find_nodes_with_no_in_degree(self.player2_plan)] + # convert candidates to indexes + p1_edge_candidates = torch.tensor([(start, end) for start in p1_u_candidates for end in p1_v_candidates]) + p2_edge_candidates = torch.tensor([(start, end) for start in p2_u_candidates for end in p2_v_candidates]) + # find missing edges + gl_edges = [[self.inverse_materials_mines_dict[torch.argmax(self.global_plan.features[edge[0]]).item()+1], self.inverse_materials_mines_dict[torch.argmax(self.global_plan.features[edge[1]]).item()+1]] for edge in self.global_plan.edge_index.t().tolist()] + p1_edges = [[self.inverse_materials_mines_dict[torch.argmax(self.player1_plan.features[edge[0]]).item()+1], self.inverse_materials_mines_dict[torch.argmax(self.player1_plan.features[edge[1]]).item()+1]] for edge in self.player1_plan.edge_index.t().tolist()] + p2_edges = [[self.inverse_materials_mines_dict[torch.argmax(self.player2_plan.features[edge[0]]).item()+1], self.inverse_materials_mines_dict[torch.argmax(self.player2_plan.features[edge[1]]).item()+1]] for edge in self.player2_plan.edge_index.t().tolist()] + p1_missing_edges = [list(sublist) for sublist in set(map(tuple, gl_edges)) - set(map(tuple, p1_edges))] + p2_missing_edges = [list(sublist) for sublist in set(map(tuple, gl_edges)) - set(map(tuple, p2_edges))] + # convert missing edges as indexes + p1_missing_edges_idx = torch.tensor([(p1_dict[e[0]], p1_dict[e[1]]) for e in p1_missing_edges]) + p2_missing_edges_idx = torch.tensor([(p2_dict[e[0]], p2_dict[e[1]]) for e in p2_missing_edges]) + # check if all missing edges are present in the candidates + assert all(any(torch.equal(element, row) for row in p1_edge_candidates) for element in p1_missing_edges_idx) + assert all(any(torch.equal(element, row) for row in p2_edge_candidates) for element in p2_missing_edges_idx) + # concat candidates to plan graph + if p1_edge_candidates.numel() != 0: + self.player1_edge_label_index = torch.cat([self.player1_plan.edge_index, p1_edge_candidates.permute(1, 0)], dim=-1) + # create labels + self.player1_edge_label_own_missing_knowledge = torch.cat((torch.ones(self.player1_plan.edge_index.shape[1]), torch.zeros(p1_edge_candidates.shape[0]))) + else: + # no missing knowledge + self.player1_edge_label_index = self.player1_plan.edge_index + # create labels + self.player1_edge_label_own_missing_knowledge = torch.ones(self.player1_plan.edge_index.shape[1]) + if p2_edge_candidates.numel() != 0: + self.player2_edge_label_index = torch.cat([self.player2_plan.edge_index, p2_edge_candidates.permute(1, 0)], dim=-1) + # create labels + self.player2_edge_label_own_missing_knowledge = torch.cat((torch.ones(self.player2_plan.edge_index.shape[1]), torch.zeros(p2_edge_candidates.shape[0]))) + else: + # no missing knowledge + self.player2_edge_label_index = self.player2_plan.edge_index + # create labels + self.player2_edge_label_own_missing_knowledge = torch.ones(self.player2_plan.edge_index.shape[1]) + p1_edge_list = [tuple(x) for x in self.player1_edge_label_index.T.tolist()] + p1_missing_edges_idx_list = [tuple(x) for x in p1_missing_edges_idx.tolist()] + self.player1_edge_label_own_missing_knowledge[[p1_edge_list.index(x) for x in p1_missing_edges_idx_list]] = 1. + p2_edge_list = [tuple(x) for x in self.player2_edge_label_index.T.tolist()] + p2_missing_edges_idx_list = [tuple(x) for x in p2_missing_edges_idx.tolist()] + self.player2_edge_label_own_missing_knowledge[[p2_edge_list.index(x) for x in p2_missing_edges_idx_list]] = 1. + # compute other's missing knowledge == identify which one of my edges is unknown to the other player + p1_original_edges_list = [tuple(x) for x in self.player1_plan.edge_index.T.tolist()] + p2_original_edges_list = [tuple(x) for x in self.player2_plan.edge_index.T.tolist()] + p1_other_missing_edges_idx = [(p1_dict[e[0]], p1_dict[e[1]]) for e in p2_missing_edges] # note here is p2_missing_edges + p2_other_missing_edges_idx = [(p2_dict[e[0]], p2_dict[e[1]]) for e in p1_missing_edges] # note here is p1_missing_edges + self.player1_edge_label_other_missing_knowledge = torch.zeros(self.player1_plan.edge_index.shape[1]) + self.player1_edge_label_other_missing_knowledge[[p1_original_edges_list.index(x) for x in p1_other_missing_edges_idx]] = 1. + self.player2_edge_label_other_missing_knowledge = torch.zeros(self.player2_plan.edge_index.shape[1]) + self.player2_edge_label_other_missing_knowledge[[p2_original_edges_list.index(x) for x in p2_other_missing_edges_idx]] = 1. + + self.__iter_ts = self.start_ts + + self.action_labels = None + self.materials = sorted(self.plan['materials']) + + def _add_node(self, g, material, features): + if material not in g.nodes: + #print(f'Add node {material}') + g.add_node(material, features=features) + return g + + def _add_edge(self, g, u, v, tool): + if not g.has_edge(u, v): + #print(f'Add edge ({u}, {v})') + g.add_edge(u, v, tool=tool) + return g + + def _plot_plan_graph(self, g, filename): + plt.figure(figsize=(20,20)) + pos = nx.spring_layout(g, seed=42) + nx.draw(g, pos, with_labels=True, node_color='lightblue', edge_color='gray') + plt.savefig(filename) + plt.close() + + def find_nodes_with_less_than_four_out_degree(self, data): + edge_index = data.edge_index + num_nodes = data.num_nodes + degrees = degree(edge_index[0], num_nodes) # out degrees + # find all nodes that have out degree less than 2 + nodes = torch.nonzero(degrees < 4).view(-1) + nodes = [self.inverse_materials_mines_dict[torch.argmax(data.features[i]).item()+1] for i in nodes] + # remove planks (bc all planks have out degree less than 2) + nodes = [n for n in nodes if n.split('_')[-1] != 'PLANKS'] + # now check for planks with out degree 0 + check_zero_out_degree_planks = torch.nonzero(degrees < 1).view(-1) + check_zero_out_degree_planks = [self.inverse_materials_mines_dict[torch.argmax(data.features[i]).item()+1] for i in check_zero_out_degree_planks] + check_zero_out_degree_planks = [n for n in nodes if n.split('_')[-1] == 'PLANKS'] + nodes = nodes + check_zero_out_degree_planks + return nodes + + def find_nodes_with_no_in_degree(self, data): + edge_index = data.edge_index + num_nodes = data.num_nodes + degrees = degree(edge_index[1], num_nodes) # in degrees + nodes = torch.nonzero(degrees < 1).view(-1) + nodes = [self.inverse_materials_mines_dict[torch.argmax(data.features[i]).item()+1] for i in nodes] + nodes = [n for n in nodes if n.split('_')[-1] != 'PLANKS'] + return nodes + + def __len__(self): + return self.end_ts - self.start_ts + + def __next__(self): + if self.__iter_ts < self.end_ts: + if self.load_dialogue: + d = [x for x in self.dialogue_events if x[0] == self.__iter_ts] + l = [x for x in self.dialogue_act_labels if x[0] == self.__iter_ts] + d = d if d else None + l = l if l else None + else: + d = None + l = None + if self.use_dialogue_moves: + m = [x for x in self.dialogue_move_labels if x[0] == self.__iter_ts] + m = m if m else None + else: + m = None + if self.action_labels: + a = [x for x in self.action_labels if (x.TickIndex//30 + self.start_ts) >= self.__iter_ts] + if a: + try: + while not a[0].items: + a = a[1:] + al = self.materials.index(a[0].items[0]) if a else 0 + except Exception: + print(a) + print(a[0]) + print(a[0].items) + print(a[0].items[0]) + exit() + at = a[0].TickIndex//30 + self.start_ts + an = int(a[0].Name[-1]) + a = [(at,al,an)] + else: + a = [(self.__iter_ts, self.materials.index(self.plan['materials'][0]), 1)] + a = None + else: + if self.end_ts - self.__iter_ts < 10: + a = None + else: + a = None + if not a is None: + if not a[0][0] == self.__iter_ts: + a = None + q = [x for x in self.question_pairs if (x[0][1] == self.__iter_ts)] + q = q[0] if q else None + frame_idx = self.__iter_ts - self.start_ts + if self.load_third_person: + frames = self.third_pers_frames + elif self.load_player1: + frames = self.player1_pov_frames + elif self.load_player2: + frames = self.player2_pov_frames + else: + frames = np.array([0]) + if len(frames) == 1: + f = np.zeros((self.img_h,self.img_w,3)) + else: + if frame_idx < frames.shape[0]: + f = frames[frame_idx] + else: + f = np.zeros((self.img_h,self.img_w,3)) + if self.do_upperbound: + if not q is None: + qnum = 0 + base_rep = np.concatenate([ + onehot(q[0][2],2), + onehot(q[0][3],2), + onehot(q[0][4][qnum][0]+1,2), + onehot(self.materials_dict[q[0][5][qnum][1]],len(self.materials_dict)), + onehot(q[0][4][qnum][0]+1,2), + onehot(self.materials_dict[q[0][5][qnum][1]],len(self.materials_dict)), + onehot(['YES','MAYBE','NO'].index(q[1][0][qnum])+1,3), + onehot(['YES','MAYBE','NO'].index(q[1][1][qnum])+1,3) + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + ToM6 = base_rep if self.ToM6 is not None else np.zeros(1024) + qnum = 1 + base_rep = np.concatenate([ + onehot(q[0][2],2), + onehot(q[0][3],2), + onehot(q[0][4][qnum][0]+1,2), + onehot(self.materials_dict[q[0][5][qnum][1]],len(self.materials_dict)), + onehot(q[0][4][qnum][0]+1,2), + onehot(self.materials_dict[q[0][5][qnum][1]],len(self.materials_dict)), + onehot(['YES','MAYBE','NO'].index(q[1][0][qnum])+1,3), + onehot(['YES','MAYBE','NO'].index(q[1][1][qnum])+1,3) + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + ToM7 = base_rep if self.ToM7 is not None else np.zeros(1024) + qnum = 2 + base_rep = np.concatenate([ + onehot(q[0][2],2), + onehot(q[0][3],2), + onehot(q[0][4][qnum]+1,2), + onehot(q[0][4][qnum]+1,2), + onehot(self.materials_dict[q[1][0][qnum]] if q[1][0][qnum] in self.materials_dict else len(self.materials_dict)+1,len(self.materials_dict)+1), + onehot(self.materials_dict[q[1][1][qnum]] if q[1][1][qnum] in self.materials_dict else len(self.materials_dict)+1,len(self.materials_dict)+1) + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + ToM8 = base_rep if self.ToM8 is not None else np.zeros(1024) + else: + ToM6 = np.zeros(1024) + ToM7 = np.zeros(1024) + ToM8 = np.zeros(1024) + if not l is None: + base_rep = np.concatenate([ + onehot(l[0][1],2), + onehot(l[0][2],len(self.dialogue_act_labels_dict)) + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + DAct = base_rep if self.DAct is not None else np.zeros(1024) + else: + DAct = np.zeros(1024) + if not m is None: + base_rep = np.concatenate([ + onehot(m[0][1],2), + onehot(m[0][2][0],len(self.dialogue_move_labels_dict)), + onehot(m[0][2][1],len(self.tools_dict) + len(self.materials_dict) + len(self.mines_dict)+1), + onehot(m[0][2][2],len(self.tools_dict) + len(self.materials_dict) + len(self.mines_dict)+1), + onehot(m[0][2][3],len(self.tools_dict) + len(self.materials_dict) + len(self.mines_dict)+1), + ]) + base_rep = np.concatenate([base_rep, np.zeros(1024-base_rep.shape[0])]) + DMove = base_rep if self.DMove is not None else np.zeros(1024) + else: + DMove = np.zeros(1024) + else: + ToM6 = self.ToM6[frame_idx] if self.ToM6 is not None else np.zeros(1024) + ToM7 = self.ToM7[frame_idx] if self.ToM7 is not None else np.zeros(1024) + ToM8 = self.ToM8[frame_idx] if self.ToM8 is not None else np.zeros(1024) + DAct = self.DAct[frame_idx] if self.DAct is not None else np.zeros(1024) + DMove = self.DAct[frame_idx] if self.DMove is not None else np.zeros(1024) + intermediate = np.concatenate([ToM6,ToM7,ToM8,DAct,DMove]) + if self.load_int0: + intermediate = np.zeros(1024*5) + intermediate[:1024] = self.int0_exp2_feats[frame_idx] + retval = ((self.__iter_ts,self.pov),d,l,q,f,a,intermediate,m) + self.__iter_ts += 1 + return retval + self.__iter_ts = self.start_ts + raise StopIteration() + + def __iter__(self): + return self + + def __load_videos(self): + d = self.end_ts - self.start_ts + if self.load_third_person: + try: + self.third_pers_file = glob(os.path.join(self.game_path,'third*gif'))[0] + np_file = self.third_pers_file[:-3]+'npz' + if os.path.isfile(np_file): + self.third_pers_frames = np.load(np_file)['data'] + else: + frames = imageio.get_reader(self.third_pers_file, '.gif') + reshaper = lambda x: cv2.resize(x,(self.img_h,self.img_w)) + if 'main' in self.game_path: + self.third_pers_frames = np.array([reshaper(f[95:4*95,250:-249,2::-1]) for f in frames]) + else: + self.third_pers_frames = np.array([reshaper(f[-3*95:,250:-249,2::-1]) for f in frames]) + print(np_file,end=' ') + np.savez_compressed(open(np_file,'wb'), data=self.third_pers_frames) + print('saved') + except Exception as e: + self.third_pers_frames = np.array([0]) + if self.third_pers_frames.shape[0]//d < 10: + self.third_pov_frame_rate = 6 + else: + if self.third_pers_frames.shape[0]//d < 20: + self.third_pov_frame_rate = 12 + else: + if self.third_pers_frames.shape[0]//d < 45: + self.third_pov_frame_rate = 30 + else: + self.third_pov_frame_rate = 60 + self.third_pers_frames = self.third_pers_frames[::self.third_pov_frame_rate] + else: + self.third_pers_frames = np.array([0]) + if self.load_player1: + try: + search_str = 'play2*gif' if self.flip_video else 'play1*gif' + self.player1_pov_file = glob(os.path.join(self.game_path,search_str))[0] + np_file = self.player1_pov_file[:-3]+'npz' + if os.path.isfile(np_file): + self.player1_pov_frames = np.load(np_file)['data'] + else: + frames = imageio.get_reader(self.player1_pov_file, '.gif') + reshaper = lambda x: cv2.resize(x,(self.img_h,self.img_w)) + self.player1_pov_frames = np.array([reshaper(f[:,:,2::-1]) for f in frames]) + print(np_file,end=' ') + np.savez_compressed(open(np_file,'wb'), data=self.player1_pov_frames) + print('saved') + except Exception as e: + self.player1_pov_frames = np.array([0]) + if self.player1_pov_frames.shape[0]//d < 10: + self.player1_pov_frame_rate = 6 + else: + if self.player1_pov_frames.shape[0]//d < 20: + self.player1_pov_frame_rate = 12 + else: + if self.player1_pov_frames.shape[0]//d < 45: + self.player1_pov_frame_rate = 30 + else: + self.player1_pov_frame_rate = 60 + self.player1_pov_frames = self.player1_pov_frames[::self.player1_pov_frame_rate] + else: + self.player1_pov_frames = np.array([0]) + if self.load_player2: + try: + search_str = 'play1*gif' if self.flip_video else 'play2*gif' + self.player2_pov_file = glob(os.path.join(self.game_path,search_str))[0] + np_file = self.player2_pov_file[:-3]+'npz' + if os.path.isfile(np_file): + self.player2_pov_frames = np.load(np_file)['data'] + else: + frames = imageio.get_reader(self.player2_pov_file, '.gif') + reshaper = lambda x: cv2.resize(x,(self.img_h,self.img_w)) + self.player2_pov_frames = np.array([reshaper(f[:,:,2::-1]) for f in frames]) + print(np_file,end=' ') + np.savez_compressed(open(np_file,'wb'), data=self.player2_pov_frames) + print('saved') + except Exception as e: + self.player2_pov_frames = np.array([0]) + if self.player2_pov_frames.shape[0]//d < 10: + self.player2_pov_frame_rate = 6 + else: + if self.player2_pov_frames.shape[0]//d < 20: + self.player2_pov_frame_rate = 12 + else: + if self.player2_pov_frames.shape[0]//d < 45: + self.player2_pov_frame_rate = 30 + else: + self.player2_pov_frame_rate = 60 + self.player2_pov_frames = self.player2_pov_frames[::self.player2_pov_frame_rate] + else: + self.player2_pov_frames = np.array([0]) + + def __parse_question_pairs(self): + question_dict = {} + for q in self.questions: + k = q[2][0][1] + q[2][1][1] + if not k in question_dict: + question_dict[k] = [] + question_dict[k].append(q) + self.question_pairs = [] + for k,v in question_dict.items(): + if len(v) == 2: + if v[0][1]+v[1][1] == 3: + self.question_pairs.append(v) + else: + while len(v) > 1: + pair = [] + pair.append(v.pop(0)) + pair.append(v.pop(0)) + while not pair[0][1]+pair[1][1] == 3: + if not v: + break + # print(game_path,pair) + pair.append(v.pop(0)) + pair.pop(0) + if not v: + break + self.question_pairs.append(pair) + self.question_pairs = sorted(self.question_pairs, key=lambda x: x[0][0]) + if self.load_player2 or self.pov==4: + self.question_pairs = [sorted(q, key=lambda x: x[1],reverse=True) for q in self.question_pairs] + else: + self.question_pairs = [sorted(q, key=lambda x: x[1]) for q in self.question_pairs] + self.question_pairs = [((a[0], b[0], a[1], b[1], a[2], b[2]), (a[3], b[3])) for a,b in self.question_pairs] + + def __parse_dialogue(self): + self.dialogue_events = [] + save_path = os.path.join(self.game_path,f'dialogue_{self.game_path.split("/")[-1]}.pkl') + if os.path.isfile(save_path): + self.dialogue_events = pickle.load(open( save_path, "rb" )) + return + for x in open(self.dialogue_file): + if '[Async Chat Thread' in x: + ts = list(map(int,x.split(' [')[0].strip('[]').split(':'))) + ts = 3600*ts[0] + 60*ts[1] + ts[2] + player, event = x.strip().split('/INFO]: [] ',1) + event = event.lower() + event = ''.join([x if x in string.ascii_lowercase else f' {x} ' for x in event]).strip() + event = event.replace(' ',' ').replace(' ',' ') + player = int(player) + if GameParser.tokenizer is None: + GameParser.tokenizer = BertTokenizer.from_pretrained('bert-large-uncased', do_lower_case=True) + if self.model is None: + GameParser.model = BertModel.from_pretrained('bert-large-uncased', output_hidden_states=True).to(DEVICE) + encoded_dict = GameParser.tokenizer.encode_plus( + event, # Sentence to encode. + add_special_tokens=True, # Add '[CLS]' and '[SEP]' + return_tensors='pt', # Return pytorch tensors. + ) + token_ids = encoded_dict['input_ids'].to(DEVICE) + segment_ids = torch.ones(token_ids.size()).long().to(DEVICE) + GameParser.model.eval() + with torch.no_grad(): + outputs = GameParser.model(input_ids=token_ids, token_type_ids=segment_ids) + outputs = outputs[1][0].cpu().data.numpy() + self.dialogue_events.append((ts,player,event,outputs)) + pickle.dump(self.dialogue_events, open( save_path, "wb" )) + print(f'Saved to {save_path}',flush=True) + + def __parse_questions(self): + self.questions = [] + for x in open(self.questions_file): + if x[0] == '#': + ts, qs = x.strip().split(' Number of records inserted: 1 # player') + ts = list(map(int,ts.split(' ')[5].split(':'))) + ts = 3600*ts[0] + 60*ts[1] + ts[2] + player = int(qs[0]) + questions = qs[2:].split(';') + answers =[x[7:] for x in questions[3:]] + questions = [x[9:].split(' ') for x in questions[:3]] + questions[0] = (int(questions[0][0] == 'Have'), questions[0][-3]) + questions[1] = (int(questions[1][2] == 'know'), questions[1][-1]) + questions[2] = int(questions[2][1] == 'are') + + self.questions.append((ts,player,questions,answers)) + def __parse_start_end(self): + self.start_ts = [x.strip() for x in open(self.dialogue_file) if 'THEY ARE PLAYER' in x][1] + self.start_ts = list(map(int,self.start_ts.split('] [')[0][1:].split(':'))) + self.start_ts = 3600*self.start_ts[0] + 60*self.start_ts[1] + self.start_ts[2] + try: + self.start_ts = max(self.start_ts, self.questions[0][0]-75) + except Exception as e: + pass + self.end_ts = [x.strip() for x in open(self.dialogue_file) if 'Stopping' in x] + if self.end_ts: + self.end_ts = self.end_ts[0] + self.end_ts = list(map(int,self.end_ts.split('] [')[0][1:].split(':'))) + self.end_ts = 3600*self.end_ts[0] + 60*self.end_ts[1] + self.end_ts[2] + else: + self.end_ts = self.dialogue_events[-1][0] + try: + self.end_ts = max(self.end_ts, self.questions[-1][0]) + 1 + except Exception as e: + pass + + def __load_dialogue_act_labels(self): + file_name = 'config/dialogue_act_labels.json' + if not os.path.isfile(file_name): + files = sorted(glob('/home/*/MCC/*done.txt')) + dialogue_act_dict = {} + for file in files: + game_str = '' + for line in open(file): + line = line.strip() + if '_logs/' in line: + game_str = line + else: + if line: + line = line.split() + key = f'{game_str}#{line[0]}' + dialogue_act_dict[key] = line[-1] + json.dump(dialogue_act_dict,open(file_name,'w'), indent=4) + self.dialogue_act_dict = json.load(open(file_name)) + self.dialogue_act_labels_dict = {l : i for i, l in enumerate(sorted(list(set(self.dialogue_act_dict.values()))))} + self.dialogue_act_bias = {l : sum([int(x==l) for x in self.dialogue_act_dict.values()]) for l in self.dialogue_act_labels_dict.keys()} + json.dump(self.dialogue_act_labels_dict,open('config/dialogue_act_label_names.json','w'), indent=4) + + def __assign_dialogue_act_labels(self): + log_file = glob('/'.join([self.game_path,'mcc*log']))[0].split('mindcraft/')[1] + self.dialogue_act_labels = [] + for emb in self.dialogue_events: + ts = emb[0] + h = ts//3600 + m = (ts%3600)//60 + s = ts%60 + key = f'{log_file}#[{h:02d}:{m:02d}:{s:02d}]:{emb[1]}>' + self.dialogue_act_labels.append((emb[0],emb[1],self.dialogue_act_labels_dict[self.dialogue_act_dict[key]])) + + def __load_dialogue_move_labels(self): + file_name = "config/dialogue_move_labels.json" + dialogue_move_dict = {} + if not os.path.isfile(file_name): + file_text = '' + dialogue_moves = set() + for line in open("XXX"): + line = line.strip() + if not line: + continue + if line[0] == '#': + continue + if line[0] == '[': + tag_text = glob(f'data/*/*/mcc_{file_text}.log')[0].split('/',1)[-1] + key = f'{tag_text}#{line.split()[0]}' + value = line.split()[-1].split('#') + if len(value) < 4: + value += ['IGNORE']*(4-len(value)) + dialogue_moves.add(value[0]) + value = '#'.join(value) + dialogue_move_dict[key] = value + else: + file_text = line + dialogue_moves = sorted(list(dialogue_moves)) + + json.dump(dialogue_move_dict,open(file_name,'w'), indent=4) + self.dialogue_move_dict = json.load(open(file_name)) + self.dialogue_move_labels_dict = {l : i for i, l in enumerate(sorted(list(set([lbl.split('#')[0] for lbl in self.dialogue_move_dict.values()]))))} + self.dialogue_move_bias = {l : sum([int(x==l) for x in self.dialogue_move_dict.values()]) for l in self.dialogue_move_labels_dict.keys()} + json.dump(self.dialogue_move_labels_dict,open('config/dialogue_move_label_names.json','w'), indent=4) + + def __assign_dialogue_move_labels(self): + log_file = glob('/'.join([self.game_path,'mcc*log']))[0].split('mindcraft/')[1] + self.dialogue_move_labels = [] + for emb in self.dialogue_events: + ts = emb[0] + h = ts//3600 + m = (ts%3600)//60 + s = ts%60 + key = f'{log_file}#[{h:02d}:{m:02d}:{s:02d}]:{emb[1]}>' + move = self.dialogue_move_dict[key].split('#') + move[0] = self.dialogue_move_labels_dict[move[0]] + for i,m in enumerate(move[1:]): + if m == 'IGNORE': + move[i+1] = 0 + elif m in self.materials_dict: + move[i+1] = self.materials_dict[m] + elif m in self.mines_dict: + move[i+1] = self.mines_dict[m] + len(self.materials_dict) + elif m in self.tools_dict: + move[i+1] = self.tools_dict[m] + len(self.materials_dict) + len(self.mines_dict) + else: + print(move) + exit() + self.dialogue_move_labels.append((emb[0],emb[1],move)) + + def __load_replay_data(self): + self.actions = None + + def __load_intermediate(self): + if self.intermediate > 15: + self.do_upperbound = True + else: + self.do_upperbound = False + if self.pov in [1,2]: + self.ToM6 = np.load(glob(f'{self.game_path}/intermediate_ToM6*player{self.pov}.npz')[0])['data'] if self.intermediate % 2 else None + self.intermediate = self.intermediate // 2 + self.ToM7 = np.load(glob(f'{self.game_path}/intermediate_ToM7*player{self.pov}.npz')[0])['data'] if self.intermediate % 2 else None + self.intermediate = self.intermediate // 2 + self.ToM8 = np.load(glob(f'{self.game_path}/intermediate_ToM8*player{self.pov}.npz')[0])['data'] if self.intermediate % 2 else None + self.intermediate = self.intermediate // 2 + self.DAct = np.load(glob(f'{self.game_path}/intermediate_DAct*player{self.pov}.npz')[0])['data'] if self.intermediate % 2 else None + self.intermediate = self.intermediate // 2 + self.DMove = None + else: + self.ToM6 = None + self.ToM7 = None + self.ToM8 = None + self.DAct = None + self.DMove = None + + def __load_int0_feats(self): + self.int0_exp2_feats = np.load(glob(f'{self.game_path}/int0_exp2*player{self.pov}.npz')[0])['data'] + # self.int0_exp3_feats = np.load(np.load(glob(f'{self.game_path}/int0_exp3*player{self.pov}.npz')[0])['data']) + diff --git a/src/models/.DS_Store b/src/models/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 0: + retval[x-1] = 1 + return retval + +class PlanLoss(nn.Module): + def __init__(self): + super(PlanLoss, self).__init__() + def getWeights(self, output, target): + # return 1 + f1 = (1+5*torch.stack([2-torch.sum(target.reshape(-1,21,21),dim=-1)]*21,dim=-1)).reshape(-1,21*21) + f2 = 100*target + 1 + return (f1+f2)/60 + exit(0) + # print(max(torch.sum(target.reshape(21,21),dim=-1))) + return (target*torch.sum(target,dim=-1) + 1) + def MSELoss(self, output, target): + retval = (output - target)**2 + retval *= self.getWeights(output,target) + return torch.mean(retval) + def BCELoss(self, output, target, loss_mask=None): + mask_factor = torch.ones(target.shape).to(output.device) + if loss_mask is not None: + loss_mask = loss_mask.reshape(-1,21,21) + mask_factor = mask_factor.reshape(-1,21,21) + # print(mask_factor.shape,loss_mask.shape,output.shape,target.shape) + for idx, tgt in enumerate(loss_mask): + for jdx, tgt_node in enumerate(tgt): + if sum(tgt_node) == 0: + mask_factor[idx,jdx] *= 0 + + # print(loss_mask[0].data.cpu().numpy()) + # print(mask_factor[0].data.cpu().numpy()) + # print() + # print(loss_mask[45].data.cpu().numpy()) + # print(mask_factor[45].data.cpu().numpy()) + # print() + # print(loss_mask[-1].data.cpu().numpy()) + # print(mask_factor[-1].data.cpu().numpy()) + # print() + + + loss_mask = loss_mask.reshape(-1,21*21) + mask_factor = mask_factor.reshape(-1,21*21) + # print(loss_mask.shape, target.shape, mask_factor.shape) + # exit() + + factor = (10 if target.shape[-1]==441 else 1)# * torch.sum(target,dim=-1)+1 + retval = -1 * mask_factor * (factor * target * torch.log(1e-6+output) + (1-target) * torch.log(1e-6+1-output)) + factor = torch.stack([torch.sum(target,dim=-1)+1]*target.shape[-1],dim=-1) + return torch.mean(factor*retval) + return torch.mean(retval) + def forward(self, output, target, loss_mask=None): + return self.BCELoss(output,target,loss_mask) + 0.01*torch.sum(output - 1/21) + # return self.MSELoss(output,target) + +class DialogueActLoss(nn.Module): + def __init__(self): + super(DialogueActLoss, self).__init__() + self.bias = torch.tensor([289,51,45,57,14,12,1,113,6,264,27,63,22,66,2,761,129,163,5]).float() + self.bias = max(self.bias) - self.bias + 1 + self.bias /= torch.sum(self.bias) + self.bias = 1-self.bias + # self.bias *= self.bias + def BCELoss(self, output, target): + target = torch.stack([torch.tensor(onehot(x + 1,19)).long() for x in target]).to(output.device) + retval = -1 * (target * torch.log(1e-6+output) + (1-target) * torch.log(1e-6+1-output)) + retval *= torch.stack([self.bias] * output.shape[0]).to(output.device) + # print(output) + # print(target) + # print(retval) + # print(torch.mean(retval)) + # exit() + return torch.mean(retval) + def forward(self, output, target): + return self.BCELoss(output,target) + # return self.MSELoss(output,target) + +class DialogueMoveLoss(nn.Module): + def __init__(self, device): + super(DialogueMoveLoss, self).__init__() + # self.bias = torch.tensor([289,51,45,57,14,12,1,113,6,264,27,63,22,66,2,761,129,163,5]).float() + # self.bias = max(self.bias) - self.bias + 1 + # self.bias /= torch.sum(self.bias) + # self.bias = 1-self.bias + move_weights = torch.tensor(np.array([202, 34, 34, 48, 4, 2, 420, 10, 54, 1, 10, 11, 30, 28, 14, 2, 16, 6, 2, 86, 4, 12, 28, 2, 2, 16, 12, 14, 4, 1, 12, 258, 12, 26, 2])).float().to(device) + move_weights = 1+ max(move_weights) - move_weights + self.loss1 = CrossEntropyLoss(weight=move_weights) + zero_bias = 0.773 + num_classes = 40 + + weight = torch.tensor(np.array([50 if not x else 1 for x in range(num_classes)])).float().to(device) + weight = 1+ max(weight) - weight + self.loss2 = CrossEntropyLoss(weight=weight) + # self.bias *= self.bias + def BCELoss(self, output, target,zero_bias): + # # print(output.shape,target.shape) + # bias = torch.tensor(np.array([1 if t else zero_bias for t in target])).to(output.device) + # target = torch.stack([torch.tensor(onehot(x,output.shape[-1])).long() for x in target]).to(output.device) + # # print(target.shape, bias.shape, bias) + + # retval = -1 * (target * torch.log(1e-6+output) + (1-target) * torch.log(1e-6+1-output)) + # retval = torch.mean(retval,-1) + + # # print(retval.shape) + # retval *= bias + # # retval *= torch.stack([self.bias] * output.shape[0]).to(output.device) + # # print(output) + # # print(target) + # # print(retval) + # # print(torch.mean(retval)) + # # exit() + # # retval = self.loss(output,target) + # return torch.mean(retval) # retval # + # weight = [zero_bias if x else (1-zero_bias)/(output.shape[-1]-1) for x in range(output.shape[-1])] + retval = self.loss2(output,target) if zero_bias else self.loss1(output,target) + return retval # + def forward(self, output, target): + o1, o2, o3, o4 = output + t1, t2, t3, t4 = target + + # print(t2,t2.shape, o2.shape) + + # if sum(t2): + # o2, t2 = zip(*[(a,b) for a,b in zip(o2,t2) if b]) + # o2 = torch.stack(o2) + # t2 = torch.stack(t2) + # if sum(t3): + # o3, t3 = zip(*[(a,b) for a,b in zip(o3,t3) if b]) + # o3 = torch.stack(o3) + # t3 = torch.stack(t3) + # if sum(t4): + # o4, t4 = zip(*[(a,b) for a,b in zip(o4,t4) if b]) + # o4 = torch.stack(o4) + # t4 = torch.stack(t4) + + # print(t2,t2.shape, o2.shape) + # exit() + + retval = sum([ + 1*self.BCELoss(output[0],target[0],0), + 0*self.BCELoss(output[1],target[1],1), + 0*self.BCELoss(output[2],target[2],1), + 0*self.BCELoss(output[3],target[3],1) + ]) + return retval #sum([fact*self.BCELoss(o,t,zbias) for fact,zbias,o,t in zip([1,0,0,0],[0,1,1,1],output,target)]) + # return self.MSELoss(output,target) + +class DialoguePredLoss(nn.Module): + def __init__(self): + super(DialoguePredLoss, self).__init__() + self.bias = torch.tensor([289,51,45,57,14,12,1,113,6,264,27,63,22,66,2,761,129,163,5,0]).float() + self.bias[-1] = 1460#2 * torch.sum(self.bias) // 3 + self.bias = max(self.bias) - self.bias + 1 + self.bias /= torch.sum(self.bias) + self.bias = 1-self.bias + # self.bias *= self.bias + def BCELoss(self, output, target): + target = torch.stack([torch.tensor(onehot(x + 1,20)).long() for x in target]).to(output.device) + retval = -1 * (target * torch.log(1e-6+output) + (1-target) * torch.log(1e-6+1-output)) + retval *= torch.stack([self.bias] * output.shape[0]).to(output.device) + # print(output) + # print(target) + # print(retval) + # print(torch.mean(retval)) + # exit() + return torch.mean(retval) + def forward(self, output, target): + return self.BCELoss(output,target) + # return self.MSELoss(output,target) + +class ActionLoss(nn.Module): + def __init__(self): + super(ActionLoss, self).__init__() + self.bias1 = torch.tensor([134,1370,154,128,220,166,46,76,106,78,88,124,102,120,276,122,112,106,44,174,20]).float() + # self.bias[-1] = 1460#2 * torch.sum(self.bias) // 3 + # self.bias1 = torch.ones(21).float() + self.bias1 = max(self.bias1) - self.bias1 + 1 + self.bias1 /= torch.sum(self.bias1) + self.bias1 = 1-self.bias1 + self.bias2 = torch.tensor([1168,1310]).float() + # self.bias2[-1] = 1460#2 * torch.sum(self.bias) // 3 + # self.bias2 = torch.ones(21).float() + self.bias2 = max(self.bias2) - self.bias2 + 1 + self.bias2 /= torch.sum(self.bias2) + self.bias2 = 1-self.bias2 + # self.bias *= self.bias + def BCELoss(self, output, target): + # target = torch.stack([torch.tensor(onehot(x + 1,20)).long() for x in target]).to(output.device) + retval = -1 * (target * torch.log(1e-6+output) + (1-target) * torch.log(1e-6+1-output)) + # print(self.bias1.shape,self.bias2.shape,output.shape[-1]) + retval *= torch.stack([self.bias2 if output.shape[-1]==2 else self.bias1] * output.shape[0]).to(output.device) + # print(output) + # print(target) + # print(retval) + # print(torch.mean(retval)) + # exit() + return torch.mean(retval) + def forward(self, output, target): + return self.BCELoss(output,target) + # return self.MSELoss(output,target) \ No newline at end of file diff --git a/src/models/model_with_dialogue_moves.py b/src/models/model_with_dialogue_moves.py new file mode 100755 index 0000000..04ba889 --- /dev/null +++ b/src/models/model_with_dialogue_moves.py @@ -0,0 +1,205 @@ +import sys, torch, random +from numpy.core.fromnumeric import reshape +import torch.nn as nn, numpy as np +from src.data.game_parser import DEVICE + +def onehot(x,n): + retval = np.zeros(n) + if x > 0: + retval[x-1] = 1 + return retval + +class Model(nn.Module): + def __init__(self, seq_model_type=0,device=DEVICE): + super(Model, self).__init__() + self.device = device + print("model set to device", self.device) + + my_rnn = lambda i,o: nn.GRU(i,o) + #my_rnn = lambda i,o: nn.LSTM(i,o) + + plan_emb_in = 81 + plan_emb_out = 32 + q_emb = 100 + + self.plan_embedder0 = my_rnn(plan_emb_in,plan_emb_out) + self.plan_embedder1 = my_rnn(plan_emb_in,plan_emb_out) + self.plan_embedder2 = my_rnn(plan_emb_in,plan_emb_out) + + # self.dialogue_listener = my_rnn(1126,768) + dlist_hidden = 1024 + frame_emb = 512 + self.move_emb = 157 + drnn_in = 1024 + 2 + q_emb + frame_emb + self.move_emb + # drnn_in = 1024 + 2 + + # my_rnn = lambda i,o: nn.GRU(i,o) + my_rnn = lambda i,o: nn.LSTM(i,o) + + if seq_model_type==0: + self.dialogue_listener_rnn = nn.GRU(drnn_in,dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1,1,drnn_in))[0] + elif seq_model_type==1: + self.dialogue_listener_rnn = nn.LSTM(drnn_in,dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1,1,drnn_in))[0] + elif seq_model_type==2: + mask_fun = lambda x: torch.triu(torch.ones(x.shape[0],x.shape[0]),diagonal=1).bool().to(self.device) + sincos_fun = lambda x:torch.transpose(torch.stack([ + torch.sin(2*np.pi*torch.tensor(list(range(x)))/x), + torch.cos(2*np.pi*torch.tensor(list(range(x)))/x) + ]),0,1).reshape(-1,1,2) + self.dialogue_listener_lin1 = nn.Linear(drnn_in,dlist_hidden-2) + self.dialogue_listener_attn = nn.MultiheadAttention(dlist_hidden, 8) + self.dialogue_listener_wrap = lambda x: self.dialogue_listener_attn(x,x,x,attn_mask=mask_fun(x)) + self.dialogue_listener = lambda x: self.dialogue_listener_wrap(torch.cat([ + sincos_fun(x.shape[0]).float().to(self.device), + self.dialogue_listener_lin1(x).reshape(-1,1,dlist_hidden-2) + ], axis=-1))[0] + else: + print('Sequence model type must be in (0: GRU, 1: LSTM, 2: Transformer), but got ', seq_model_type) + exit() + + conv_block = lambda i,o,k,p,s: nn.Sequential( + nn.Conv2d( i, o, k, padding=p, stride=s), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.MaxPool2d(2), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.ReLU() + ) + + self.conv = nn.Sequential( + conv_block( 3, 8, 3, 1, 1), + # conv_block( 3, 8, 5, 2, 2), + conv_block( 8, 32, 5, 2, 2), + conv_block( 32, frame_emb//4, 5, 2, 2), + nn.Conv2d( frame_emb//4, frame_emb, 3),nn.ReLU(), + ) + + qlayer = lambda i,o : nn.Sequential( + nn.Linear(i,512), + nn.Dropout(0.5), + nn.GELU(), + nn.Dropout(0.5), + nn.Linear(512,o), + nn.Dropout(0.5), + # nn.Softmax(-1) + ) + + q_in_size = 3*plan_emb_out+dlist_hidden+q_emb + + self.q01 = qlayer(q_in_size,2) + self.q02 = qlayer(q_in_size,2) + self.q03 = qlayer(q_in_size,2) + + self.q11 = qlayer(q_in_size,3) + self.q12 = qlayer(q_in_size,3) + self.q13 = qlayer(q_in_size,22) + + self.q21 = qlayer(q_in_size,3) + self.q22 = qlayer(q_in_size,3) + self.q23 = qlayer(q_in_size,22) + + def forward(self,game,global_plan=False, player_plan=False, intermediate=False): + retval = [] + + l = list(game) + _,d,_,q,f,_,_,m = zip(*list(game)) + + + parse_move = lambda m: np.concatenate([ + onehot(m[0][1], 2), + onehot(m[0][2][0]+1, len(game.dialogue_move_labels_dict)), + onehot(m[0][2][1]+1, len(game.materials_dict)+len(game.mines_dict)+len(game.tools_dict)+1), + onehot(m[0][2][2]+1, len(game.materials_dict)+len(game.mines_dict)+len(game.tools_dict)+1), + onehot(m[0][2][3]+1, len(game.materials_dict)+len(game.mines_dict)+len(game.tools_dict)+1) + ]) + # print(2+len(game.dialogue_move_labels_dict)+3*(len(game.materials_dict)+len(game.mines_dict)+len(game.tools_dict)+1)) + # print(len(game.dialogue_move_labels_dict)) + m = np.stack([np.zeros(self.move_emb) if move is None else parse_move(move) for move in m]) + + + h = None + f = np.array(f, dtype=np.uint8) + # f = torch.tensor(f).permute(0,3,1,2).float().to(self.device) + # flt_lst = [(a,b) for a,b in zip(d,q) if (not a is None) or (not b is None)] + # if not flt_lst: + # return [] + # d,q = zip(*flt_lst) + d = np.stack([np.concatenate(([int(x[0][1]==2),int(x[0][1]==1)],x[0][-1])) if not x is None else np.zeros(1026) for x in d]) + def parse_q(q): + if not q is None: + q ,l = q + q = np.concatenate([ + onehot(q[2],2), + onehot(q[3],2), + onehot(q[4][0][0]+1,2), + onehot(game.materials_dict[q[4][0][1]],len(game.materials_dict)), + onehot(q[4][1][0]+1,2), + onehot(game.materials_dict[q[4][1][1]],len(game.materials_dict)), + onehot(q[4][2]+1,2), + onehot(q[5][0][0]+1,2), + onehot(game.materials_dict[q[5][0][1]],len(game.materials_dict)), + onehot(q[5][1][0]+1,2), + onehot(game.materials_dict[q[5][1][1]],len(game.materials_dict)), + onehot(q[5][2]+1,2) + ]) + else: + q = np.zeros(100) + l = None + return q, l + try: + sel1 = int([x[0][2] for x in q if not x is None][0] == 1) + sel2 = 1 - sel1 + except Exception as e: + sel1 = 0 + sel2 = 0 + q = [parse_q(x) for x in q] + q, l = zip(*q) + q = np.stack(q) + + if not global_plan and not player_plan: + plan_emb = torch.cat([ + self.plan_embedder0(torch.stack(list(map(torch.tensor,game.global_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder1(torch.stack(list(map(torch.tensor,game.player1_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder2(torch.stack(list(map(torch.tensor,game.player2_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0] + ]) + plan_emb = 0*plan_emb + elif global_plan: + plan_emb = torch.cat([ + self.plan_embedder0(torch.stack(list(map(torch.tensor,game.global_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder1(torch.stack(list(map(torch.tensor,game.player1_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder2(torch.stack(list(map(torch.tensor,game.player2_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0] + ]) + else: + plan_emb = torch.cat([ + 0*self.plan_embedder0(torch.stack(list(map(torch.tensor,game.global_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + sel1*self.plan_embedder1(torch.stack(list(map(torch.tensor,game.player1_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + sel2*self.plan_embedder2(torch.stack(list(map(torch.tensor,game.player2_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0] + ]) + + u = torch.cat(( + torch.tensor(d).float().to(self.device), + torch.tensor(q).float().to(self.device), + self.conv(torch.tensor(f).permute(0,3,1,2).float().to(self.device)).reshape(-1,512), + torch.tensor(m).float().to(self.device) + ),axis=-1) + u = u.float().to(self.device) + + y = self.dialogue_listener(u) + y = y.reshape(-1,y.shape[-1]) + + if intermediate: + return y + + if all([x is None for x in l]): + return [] + + fun_lst = [self.q01,self.q02,self.q03,self.q11,self.q12,self.q13,self.q21,self.q22,self.q23] + fun = lambda x: [f(x) for f in fun_lst] + + retval = [(_l,fun(torch.cat((plan_emb,torch.tensor(_q).float().to(self.device),_y)))) for _y, _q, _l in zip(y,q,l)if not _l is None] + return retval \ No newline at end of file diff --git a/src/models/model_with_dialogue_moves_graphs.py b/src/models/model_with_dialogue_moves_graphs.py new file mode 100644 index 0000000..21edc19 --- /dev/null +++ b/src/models/model_with_dialogue_moves_graphs.py @@ -0,0 +1,226 @@ +import torch +import torch.nn as nn, numpy as np +from src.data.game_parser import DEVICE +from torch_geometric.nn import GATv2Conv, MeanAggregation + + +def onehot(x,n): + retval = np.zeros(n) + if x > 0: + retval[x-1] = 1 + return retval + + +class PlanGraphEmbedder(nn.Module): + def __init__(self, h_dim, dropout=0.0, heads=4): + super().__init__() + self.proj_x = nn.Sequential( + nn.Linear(27, h_dim), + nn.GELU(), + nn.Dropout(dropout) + ) + self.proj_edge_attr = nn.Sequential( + nn.Linear(12, h_dim), + nn.GELU(), + nn.Dropout(dropout) + ) + self.conv1 = GATv2Conv(h_dim, h_dim, heads=heads, edge_dim=h_dim) + self.conv2 = GATv2Conv(h_dim*heads, h_dim, heads=1, edge_dim=h_dim) + self.act = nn.GELU() + self.dropout = nn.Dropout(dropout) + self.pool = MeanAggregation() + + def forward(self, data): + x, edge_index, edge_attr = data.features.to(DEVICE), data.edge_index.to(DEVICE), data.tool.to(DEVICE) + x = self.proj_x(x) + edge_attr = self.proj_edge_attr(edge_attr) + x = self.conv1(x, edge_index, edge_attr) + x = self.act(x) + x = self.dropout(x) + x = self.conv2(x, edge_index, edge_attr) + x = self.pool(x) + return x.squeeze(0) + + +class Model(nn.Module): + def __init__(self, seq_model_type=0,device=DEVICE): + super(Model, self).__init__() + self.device = device + print("model set to device", self.device) + + plan_emb_out = 32*3 + q_emb = 100 + + self.plan_embedder0 = PlanGraphEmbedder(plan_emb_out) + self.plan_embedder1 = PlanGraphEmbedder(plan_emb_out) + self.plan_embedder2 = PlanGraphEmbedder(plan_emb_out) + + dlist_hidden = 1024 + frame_emb = 512 + self.move_emb = 157 + drnn_in = 1024 + 2 + q_emb + frame_emb + self.move_emb + + if seq_model_type==0: + self.dialogue_listener_rnn = nn.GRU(drnn_in,dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1,1,drnn_in))[0] + elif seq_model_type==1: + self.dialogue_listener_rnn = nn.LSTM(drnn_in,dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1,1,drnn_in))[0] + elif seq_model_type==2: + mask_fun = lambda x: torch.triu(torch.ones(x.shape[0],x.shape[0]),diagonal=1).bool().to(self.device) + sincos_fun = lambda x:torch.transpose(torch.stack([ + torch.sin(2*np.pi*torch.tensor(list(range(x)))/x), + torch.cos(2*np.pi*torch.tensor(list(range(x)))/x) + ]),0,1).reshape(-1,1,2) + self.dialogue_listener_lin1 = nn.Linear(drnn_in,dlist_hidden-2) + self.dialogue_listener_attn = nn.MultiheadAttention(dlist_hidden, 8) + self.dialogue_listener_wrap = lambda x: self.dialogue_listener_attn(x,x,x,attn_mask=mask_fun(x)) + self.dialogue_listener = lambda x: self.dialogue_listener_wrap(torch.cat([ + sincos_fun(x.shape[0]).float().to(self.device), + self.dialogue_listener_lin1(x).reshape(-1,1,dlist_hidden-2) + ], axis=-1))[0] + else: + print('Sequence model type must be in (0: GRU, 1: LSTM, 2: Transformer), but got ', seq_model_type) + exit() + + conv_block = lambda i,o,k,p,s: nn.Sequential( + nn.Conv2d( i, o, k, padding=p, stride=s), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.MaxPool2d(2), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.ReLU() + ) + + self.conv = nn.Sequential( + conv_block( 3, 8, 3, 1, 1), + conv_block( 8, 32, 5, 2, 2), + conv_block( 32, frame_emb//4, 5, 2, 2), + nn.Conv2d( frame_emb//4, frame_emb, 3),nn.ReLU(), + ) + + qlayer = lambda i,o : nn.Sequential( + nn.Linear(i,512), + nn.Dropout(0.5), + nn.GELU(), + nn.Dropout(0.5), + nn.Linear(512,o), + nn.Dropout(0.5), + ) + + q_in_size = plan_emb_out+dlist_hidden+q_emb + + self.q01 = qlayer(q_in_size,2) + self.q02 = qlayer(q_in_size,2) + self.q03 = qlayer(q_in_size,2) + + self.q11 = qlayer(q_in_size,3) + self.q12 = qlayer(q_in_size,3) + self.q13 = qlayer(q_in_size,22) + + self.q21 = qlayer(q_in_size,3) + self.q22 = qlayer(q_in_size,3) + self.q23 = qlayer(q_in_size,22) + + def forward(self,game,global_plan=False, player_plan=False, intermediate=False): + retval = [] + + l = list(game) + _,d,_,q,f,_,_,m = zip(*list(game)) + + parse_move = lambda m: np.concatenate([ + onehot(m[0][1], 2), + onehot(m[0][2][0]+1, len(game.dialogue_move_labels_dict)), + onehot(m[0][2][1]+1, len(game.materials_dict)+len(game.mines_dict)+len(game.tools_dict)+1), + onehot(m[0][2][2]+1, len(game.materials_dict)+len(game.mines_dict)+len(game.tools_dict)+1), + onehot(m[0][2][3]+1, len(game.materials_dict)+len(game.mines_dict)+len(game.tools_dict)+1) + ]) + m = np.stack([np.zeros(self.move_emb) if move is None else parse_move(move) for move in m]) + + f = np.array(f, dtype=np.uint8) + d = np.stack([np.concatenate(([int(x[0][1]==2),int(x[0][1]==1)],x[0][-1])) if not x is None else np.zeros(1026) for x in d]) + def parse_q(q): + if not q is None: + q ,l = q + q = np.concatenate([ + onehot(q[2],2), + onehot(q[3],2), + onehot(q[4][0][0]+1,2), + onehot(game.materials_dict[q[4][0][1]],len(game.materials_dict)), + onehot(q[4][1][0]+1,2), + onehot(game.materials_dict[q[4][1][1]],len(game.materials_dict)), + onehot(q[4][2]+1,2), + onehot(q[5][0][0]+1,2), + onehot(game.materials_dict[q[5][0][1]],len(game.materials_dict)), + onehot(q[5][1][0]+1,2), + onehot(game.materials_dict[q[5][1][1]],len(game.materials_dict)), + onehot(q[5][2]+1,2) + ]) + else: + q = np.zeros(100) + l = None + return q, l + try: + sel1 = int([x[0][2] for x in q if not x is None][0] == 1) + sel2 = 1 - sel1 + except Exception as e: + sel1 = 0 + sel2 = 0 + q = [parse_q(x) for x in q] + q, l = zip(*q) + q = np.stack(q) + + if not global_plan and not player_plan: + # plan_emb = torch.cat([ + # self.plan_embedder0(game.global_plan), + # self.plan_embedder1(game.player1_plan), + # self.plan_embedder2(game.player2_plan) + # ]) + plan_emb = self.plan_embedder0(game.global_plan) + plan_emb = 0*plan_emb + elif global_plan: + # plan_emb = torch.cat([ + # self.plan_embedder0(game.global_plan), + # self.plan_embedder1(game.player1_plan), + # self.plan_embedder2(game.player2_plan) + # ]) + plan_emb = self.plan_embedder0(game.global_plan) + else: + # plan_emb = torch.cat([ + # 0*self.plan_embedder0(game.global_plan), + # sel1*self.plan_embedder1(game.player1_plan), + # sel2*self.plan_embedder2(game.player2_plan) + # ]) + if sel1: + plan_emb = self.plan_embedder1(game.player1_plan) + elif sel2: + plan_emb = self.plan_embedder2(game.player2_plan) + else: + plan_emb = self.plan_embedder0(game.global_plan) + plan_emb = 0*plan_emb + + u = torch.cat(( + torch.tensor(d).float().to(self.device), + torch.tensor(q).float().to(self.device), + self.conv(torch.tensor(f).permute(0,3,1,2).float().to(self.device)).reshape(-1,512), + torch.tensor(m).float().to(self.device) + ),axis=-1) + u = u.float().to(self.device) + + y = self.dialogue_listener(u) + y = y.reshape(-1,y.shape[-1]) + + if intermediate: + return y + + if all([x is None for x in l]): + return [] + + fun_lst = [self.q01,self.q02,self.q03,self.q11,self.q12,self.q13,self.q21,self.q22,self.q23] + fun = lambda x: [f(x) for f in fun_lst] + + retval = [(_l,fun(torch.cat((plan_emb,torch.tensor(_q).float().to(self.device),_y)))) for _y, _q, _l in zip(y,q,l)if not _l is None] + return retval \ No newline at end of file diff --git a/src/models/plan_model.py b/src/models/plan_model.py new file mode 100755 index 0000000..9b6d486 --- /dev/null +++ b/src/models/plan_model.py @@ -0,0 +1,225 @@ +import sys, torch, random +from numpy.core.fromnumeric import reshape +import torch.nn as nn, numpy as np +from torch.nn import functional as F +from src.data.game_parser import DEVICE + +def onehot(x,n): + retval = np.zeros(n) + if x > 0: + retval[x-1] = 1 + return retval + +class Model(nn.Module): + def __init__(self, seq_model_type=0,device=DEVICE): + super(Model, self).__init__() + + self.device = device + + my_rnn = lambda i,o: nn.GRU(i,o) + #my_rnn = lambda i,o: nn.LSTM(i,o) + + plan_emb_in = 81 + plan_emb_out = 32 + q_emb = 100 + + self.plan_embedder0 = my_rnn(plan_emb_in,plan_emb_out) + self.plan_embedder1 = my_rnn(plan_emb_in,plan_emb_out) + self.plan_embedder2 = my_rnn(plan_emb_in,plan_emb_out) + + # self.dialogue_listener = my_rnn(1126,768) + dlist_hidden = 1024 + frame_emb = 512 + drnn_in = 5*1024 + 2 + frame_emb + 1024 + # drnn_in = 1024 + 2 + + # my_rnn = lambda i,o: nn.GRU(i,o) + my_rnn = lambda i,o: nn.LSTM(i,o) + + if seq_model_type==0: + self.dialogue_listener_rnn = nn.GRU(drnn_in,dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1,1,drnn_in))[0] + elif seq_model_type==1: + self.dialogue_listener_rnn = nn.LSTM(drnn_in,dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1,1,drnn_in))[0] + elif seq_model_type==2: + mask_fun = lambda x: torch.triu(torch.ones(x.shape[0],x.shape[0]),diagonal=1).bool().to(device) + sincos_fun = lambda x:torch.transpose(torch.stack([ + torch.sin(2*np.pi*torch.tensor(list(range(x)))/x), + torch.cos(2*np.pi*torch.tensor(list(range(x)))/x) + ]),0,1).reshape(-1,1,2) + self.dialogue_listener_lin1 = nn.Linear(drnn_in,dlist_hidden-2) + self.dialogue_listener_attn = nn.MultiheadAttention(dlist_hidden, 8) + self.dialogue_listener_wrap = lambda x: self.dialogue_listener_attn(x,x,x,attn_mask=mask_fun(x)) + self.dialogue_listener = lambda x: self.dialogue_listener_wrap(torch.cat([ + sincos_fun(x.shape[0]).float().to(self.device), + self.dialogue_listener_lin1(x).reshape(-1,1,dlist_hidden-2) + ], axis=-1))[0] + else: + print('Sequence model type must be in (0: GRU, 1: LSTM, 2: Transformer), but got ', seq_model_type) + exit() + + conv_block = lambda i,o,k,p,s: nn.Sequential( + nn.Conv2d( i, o, k, padding=p, stride=s), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.MaxPool2d(2), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.ReLU() + ) + + self.conv = nn.Sequential( + conv_block( 3, 8, 3, 1, 1), + # conv_block( 3, 8, 5, 2, 2), + conv_block( 8, 32, 5, 2, 2), + conv_block( 32, frame_emb//4, 5, 2, 2), + nn.Conv2d( frame_emb//4, frame_emb, 3),nn.ReLU(), + ) + + plan_layer = lambda i,o : nn.Sequential( + # nn.Linear(i,(i+o)//2), + nn.Linear(i,(i+2*o)//3), + nn.Dropout(0.5), + nn.GELU(), + nn.Dropout(0.5), + # nn.Linear((i+o)//2,o), + nn.Linear((i+2*o)//3,o), + nn.GELU(), + nn.Dropout(0.5), + # nn.Sigmoid() + ) + + plan_mat_size = 21*21 + q_in_size = 3*plan_emb_out+dlist_hidden + q_in_size = 3*plan_emb_out+dlist_hidden+plan_mat_size + q_in_size = dlist_hidden+plan_mat_size + + # self.plan_out = plan_layer(q_in_size,plan_mat_size) + self.plan_out = plan_layer(q_in_size,plan_mat_size) + # self.q01 = qlayer(q_in_size,2) + # self.q02 = qlayer(q_in_size,2) + # self.q03 = qlayer(q_in_size,2) + + # self.q11 = qlayer(q_in_size,3) + # self.q12 = qlayer(q_in_size,3) + # self.q13 = qlayer(q_in_size,22) + + # self.q21 = qlayer(q_in_size,3) + # self.q22 = qlayer(q_in_size,3) + # self.q23 = qlayer(q_in_size,22) + + def forward(self,game,global_plan=False, player_plan=False,evaluation=False, incremental=False): + retval = [] + + l = list(game) + _,d,l,q,f,_,intermediate,_ = zip(*list(game)) + # print(np.array(intermediate).shape) + # exit() + + h = None + intermediate = np.array(intermediate) + f = np.array(f, dtype=np.uint8) + # f = torch.tensor(f).permute(0,3,1,2).float().to(self.device) + # flt_lst = [(a,b) for a,b in zip(d,q) if (not a is None) or (not b is None)] + # if not flt_lst: + # return [] + # d,q = zip(*flt_lst) + d = np.stack([np.concatenate(([int(x[0][1]==2),int(x[0][1]==1)],x[0][-1])) if not x is None else np.zeros(1026) for x in d]) + # def parse_q(q): + # if not q is None: + # q ,l = q + # q = np.concatenate([ + # onehot(q[2],2), + # onehot(q[3],2), + # onehot(q[4][0][0]+1,2), + # onehot(game.materials_dict[q[4][0][1]],len(game.materials_dict)), + # onehot(q[4][1][0]+1,2), + # onehot(game.materials_dict[q[4][1][1]],len(game.materials_dict)), + # onehot(q[4][2]+1,2), + # onehot(q[5][0][0]+1,2), + # onehot(game.materials_dict[q[5][0][1]],len(game.materials_dict)), + # onehot(q[5][1][0]+1,2), + # onehot(game.materials_dict[q[5][1][1]],len(game.materials_dict)), + # onehot(q[5][2]+1,2) + # ]) + # else: + # q = np.zeros(100) + # l = None + # return q, l + try: + sel1 = int([x[0][2] for x in q if not x is None][0] == 1) + sel2 = 1 - sel1 + except Exception as e: + sel1 = 0 + sel2 = 0 + # q = [parse_q(x) for x in q] + # q, l = zip(*q) + + + if not global_plan and not player_plan: + plan_emb = torch.cat([ + self.plan_embedder0(torch.stack(list(map(torch.tensor,game.global_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder1(torch.stack(list(map(torch.tensor,game.player1_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder2(torch.stack(list(map(torch.tensor,game.player2_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0] + ]) + plan_emb = 0*plan_emb + elif global_plan: + plan_emb = torch.cat([ + self.plan_embedder0(torch.stack(list(map(torch.tensor,game.global_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder1(torch.stack(list(map(torch.tensor,game.player1_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder2(torch.stack(list(map(torch.tensor,game.player2_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0] + ]) + else: + plan_emb = torch.cat([ + 0*self.plan_embedder0(torch.stack(list(map(torch.tensor,game.global_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + sel1*self.plan_embedder1(torch.stack(list(map(torch.tensor,game.player1_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + sel2*self.plan_embedder2(torch.stack(list(map(torch.tensor,game.player2_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0] + ]) + + # if sel1 == 0 and sel2 == 0: + # print(torch.unique(plan_emb)) + + u = torch.cat(( + torch.tensor(d).float().to(self.device), + # torch.tensor(q).float().to(self.device), + self.conv(torch.tensor(f).permute(0,3,1,2).float().to(self.device)).reshape(-1,512), + torch.tensor(intermediate).float().to(self.device) + ),axis=-1) + u = u.float().to(self.device) + # print(d.shape) + # print(self.conv(torch.tensor(f).permute(0,3,1,2).float().to(self.device)).reshape(-1,512).shape) + # print(intermediate.shape) + # print(u.shape) + + y = self.dialogue_listener(u) + y = y.reshape(-1,y.shape[-1]) + # print(y[-1].shape,plan_emb.shape,torch.tensor(game.plan_repr).float().to(self.device).shape) + # return self.plan_out(torch.cat((y[-1],plan_emb))), y + # return self.plan_out(torch.cat((y[-1],plan_emb,torch.tensor(game.plan_repr.reshape(-1)).float().to(self.device)))), y + if incremental: + prediction = torch.stack([ + self.plan_out(torch.cat((y[0],torch.tensor(game.plan_repr.reshape(-1)).float().to(self.device))))] + [ + self.plan_out(torch.cat((f,torch.tensor(game.plan_repr.reshape(-1)).float().to(self.device)))) for f in y[len(y)%10-1::10] + ]) + prediction = F.softmax(prediction.reshape(-1,21,21),-1).reshape(-1,21*21) + else: + prediction = self.plan_out(torch.cat((y[-1],torch.tensor(game.plan_repr.reshape(-1)).float().to(self.device)))) + prediction = F.softmax(prediction.reshape(21,21),-1).reshape(21*21) + # prediction = F.softmax(prediction,-1) + # prediction = F.softmax(prediction,-1) + # exit() + return prediction, y + + # exit() + # if all([x is None for x in l]): + # return [] + + # fun_lst = [self.q01,self.q02,self.q03,self.q11,self.q12,self.q13,self.q21,self.q22,self.q23] + # fun = lambda x: [f(x) for f in fun_lst] + + + # retval = [(_l,fun(torch.cat((plan_emb,torch.tensor(_q).float().to(self.device),_y)))) for _y, _q, _l in zip(y,q,l) if not _l is None] + # return retval \ No newline at end of file diff --git a/src/models/plan_model_graphs.py b/src/models/plan_model_graphs.py new file mode 100644 index 0000000..b7cc1bf --- /dev/null +++ b/src/models/plan_model_graphs.py @@ -0,0 +1,230 @@ +import torch +import torch.nn as nn, numpy as np +from torch.nn import functional as F +from torch_geometric.nn import MeanAggregation, GATv2Conv + + +class PlanGraphEmbedder(nn.Module): + def __init__(self, device, h_dim, dropout=0.0, heads=4): + super().__init__() + self.device = device + self.proj_x = nn.Sequential( + nn.Linear(27, h_dim), + nn.GELU(), + nn.Dropout(dropout) + ) + self.proj_edge_attr = nn.Sequential( + nn.Linear(12, h_dim), + nn.GELU(), + nn.Dropout(dropout) + ) + self.conv1 = GATv2Conv(h_dim, h_dim, heads=heads, edge_dim=h_dim) + self.conv2 = GATv2Conv(h_dim*heads, h_dim, heads=1, edge_dim=h_dim) + self.act = nn.GELU() + self.dropout = nn.Dropout(dropout) + self.dec = nn.Linear(h_dim*3, 1) + + def encode(self, data): + x, edge_index, edge_attr = data.features.to(self.device), data.edge_index.to(self.device), data.tool.to(self.device) + x = self.proj_x(x) + edge_attr = self.proj_edge_attr(edge_attr) + x = self.conv1(x, edge_index, edge_attr) + x = self.act(x) + x = self.dropout(x) + x = self.conv2(x, edge_index, edge_attr) + return x, edge_attr + + def decode(self, z, context, edge_label_index): + u = z[edge_label_index[0]] + v = z[edge_label_index[1]] + return self.dec(torch.cat((u, v, context), -1)) + + # def decode(self, z, edge_index, edge_attr, edge_label_index): + # z = self.conv3(z, edge_index.to(self.device), edge_attr) + # return (z[edge_label_index[0]] * z[edge_label_index[1]]).sum(dim=-1) + +class Model(nn.Module): + def __init__(self, seq_model_type, device): + super(Model, self).__init__() + + self.device = device + plan_emb_out = 128 + self.plan_embedder0 = PlanGraphEmbedder(device, plan_emb_out) + self.plan_embedder1 = PlanGraphEmbedder(device, plan_emb_out) + self.plan_embedder2 = PlanGraphEmbedder(device, plan_emb_out) + self.plan_pool = MeanAggregation() + dlist_hidden = 1024 + frame_emb = 512 + drnn_in = 5*1024 + 2 + frame_emb + 1024 + self.dialogue_listener_pre_ln = nn.LayerNorm(drnn_in) + if seq_model_type==0: + self.dialogue_listener_rnn = nn.GRU(drnn_in, dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1, 1, drnn_in))[0] + elif seq_model_type==1: + self.dialogue_listener_rnn = nn.LSTM(drnn_in, dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1, 1, drnn_in))[0] + elif seq_model_type==2: + mask_fun = lambda x: torch.triu(torch.ones(x.shape[0], x.shape[0]), diagonal=1).bool().to(device) + sincos_fun = lambda x:torch.transpose(torch.stack([ + torch.sin(2*np.pi*torch.tensor(list(range(x)))/x), + torch.cos(2*np.pi*torch.tensor(list(range(x)))/x) + ]),0,1).reshape(-1,1,2) + self.dialogue_listener_lin1 = nn.Linear(drnn_in, dlist_hidden-2) + self.dialogue_listener_attn = nn.MultiheadAttention(dlist_hidden, 8) + self.dialogue_listener_wrap = lambda x: self.dialogue_listener_attn(x, x, x, attn_mask=mask_fun(x)) + self.dialogue_listener = lambda x: self.dialogue_listener_wrap(torch.cat([ + sincos_fun(x.shape[0]).float().to(self.device), + self.dialogue_listener_lin1(x).reshape(-1, 1, dlist_hidden-2) + ], axis=-1))[0] + else: + print('Sequence model type must be in (0: GRU, 1: LSTM, 2: Transformer), but got ', seq_model_type) + exit() + conv_block = lambda i, o, k, p, s: nn.Sequential( + nn.Conv2d(i, o, k, padding=p, stride=s), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.MaxPool2d(2), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.ReLU() + ) + self.conv = nn.Sequential( + conv_block(3, 8, 3, 1, 1), + conv_block(8, 32, 5, 2, 2), + conv_block(32, frame_emb//4, 5, 2, 2), + nn.Conv2d(frame_emb//4, frame_emb, 3), + nn.ReLU(), + ) + self.proj_y = nn.Sequential( + nn.Linear(1024, 512), + nn.GELU(), + nn.Dropout(0.5), + nn.Linear(512, plan_emb_out) + ) + + def forward(self, game, experiment, global_plan=False, player_plan=False, incremental=False, return_feats=False): + + _, d, l, q, f, _, intermediate, _ = zip(*list(game)) + intermediate = np.array(intermediate) + f = np.array(f, dtype=np.uint8) + d = np.stack([np.concatenate(([int(x[0][1]==2), int(x[0][1]==1)], x[0][-1])) if not x is None else np.zeros(1026) for x in d]) + try: + sel1 = int([x[0][2] for x in q if not x is None][0] == 1) + sel2 = 1 - sel1 + except Exception as e: + sel1 = 0 + sel2 = 0 + + if player_plan: + if sel1: + z, _ = self.plan_embedder1.encode(game.player1_plan) + elif sel2: + z, _ = self.plan_embedder2.encode(game.player2_plan) + else: + z, _ = self.plan_embedder0.encode(game.global_plan) + else: + raise ValueError('There should never be a global plan!') + + u = torch.cat(( + torch.tensor(d).float().to(self.device), + self.conv(torch.tensor(f).permute(0, 3, 1, 2).float().to(self.device) / 255.0).reshape(-1, 512), + torch.tensor(intermediate).float().to(self.device) + ), axis=-1) + u = u.float().to(self.device) + u = self.dialogue_listener_pre_ln(u) + y = self.dialogue_listener(u) + y = y.reshape(-1, y.shape[-1]) + if return_feats: + _y = y.clone().detach().cpu().numpy() + y = self.proj_y(y) + + if experiment == 2: + pred, label = self.decode_own_missing_knowledge(z, y, game, sel1, sel2, incremental) + elif experiment == 3: + pred, label = self.decode_partner_missing_knowledge(z, y, game, sel1, sel2, incremental) + else: + raise ValueError('Wrong experiment id! Valid values are 2 and 3.') + + if return_feats: + return pred, label, [sel1, sel2], _y + + return pred, label, [sel1, sel2] + + def decode_own_missing_knowledge(self, z, y, game, sel1, sel2, incremental): + if incremental: + if sel1: + pred = torch.stack( + [self.plan_embedder1.decode(z, y[0].repeat(game.player1_edge_label_index.shape[1], 1), game.player1_edge_label_index).view(-1)] + + + [self.plan_embedder1.decode(z, f.repeat(game.player1_edge_label_index.shape[1], 1), game.player1_edge_label_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = game.player1_edge_label_own_missing_knowledge.to(self.device) + elif sel2: + pred = torch.stack( + [self.plan_embedder2.decode(z, y[0].repeat(game.player2_edge_label_index.shape[1], 1), game.player2_edge_label_index).view(-1)] + + + [self.plan_embedder2.decode(z, f.repeat(game.player2_edge_label_index.shape[1], 1), game.player2_edge_label_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = game.player2_edge_label_own_missing_knowledge.to(self.device) + else: + pred = torch.stack( + # NOTE: here I use game.global_plan.edge_index instead of edge_label_index => no negative sampling + [self.plan_embedder0.decode(z, y[0].repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1)] + + + [self.plan_embedder0.decode(z, f.repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = torch.zeros(game.global_plan.edge_index.shape[1]) + else: + if sel1: + pred = self.plan_embedder1.decode(z, y.mean(0, keepdim=True).repeat(game.player1_edge_label_index.shape[1], 1), game.player1_edge_label_index).view(-1) + label = game.player1_edge_label_own_missing_knowledge.to(self.device) + elif sel2: + pred = self.plan_embedder2.decode(z, y.mean(0, keepdim=True).repeat(game.player2_edge_label_index.shape[1], 1), game.player2_edge_label_index).view(-1) + label = game.player2_edge_label_own_missing_knowledge.to(self.device) + else: + # NOTE: here I use game.global_plan.edge_index instead of edge_label_index => no negative sampling + pred = self.plan_embedder0.decode(z, y.mean(0, keepdim=True).repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1) + label = torch.zeros(game.global_plan.edge_index.shape[1]) + + return (pred, label) + + def decode_partner_missing_knowledge(self, z, y, game, sel1, sel2, incremental): + if incremental: + if sel1: + pred = torch.stack( + [self.plan_embedder1.decode(z, y[0].repeat(game.player1_plan.edge_index.shape[1], 1), game.player1_plan.edge_index).view(-1)] + + + [self.plan_embedder1.decode(z, f.repeat(game.player1_plan.edge_index.shape[1], 1), game.player1_plan.edge_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = game.player1_edge_label_other_missing_knowledge.to(self.device) + elif sel2: + pred = torch.stack( + [self.plan_embedder2.decode(z, y[0].repeat(game.player2_plan.edge_index.shape[1], 1), game.player2_plan.edge_index).view(-1)] + + + [self.plan_embedder2.decode(z, f.repeat(game.player2_plan.edge_index.shape[1], 1), game.player2_plan.edge_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = game.player2_edge_label_other_missing_knowledge.to(self.device) + else: + pred = torch.stack( + # NOTE: here I use game.global_plan.edge_index instead of edge_label_index => no negative sampling + [self.plan_embedder0.decode(z, y[0].repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1)] + + + [self.plan_embedder0.decode(z, f.repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = torch.zeros(game.global_plan.edge_index.shape[1]) + else: + if sel1: + pred = self.plan_embedder1.decode(z, y.mean(0, keepdim=True).repeat(game.player1_plan.edge_index.shape[1], 1), game.player1_plan.edge_index).view(-1) + label = game.player1_edge_label_other_missing_knowledge.to(self.device) + elif sel2: + pred = self.plan_embedder2.decode(z, y.mean(0, keepdim=True).repeat(game.player2_plan.edge_index.shape[1], 1), game.player2_plan.edge_index).view(-1) + label = game.player2_edge_label_other_missing_knowledge.to(self.device) + else: + # NOTE: here I use game.global_plan.edge_index instead of edge_label_index => no negative sampling + pred = self.plan_embedder0.decode(z, y.mean(0, keepdim=True).repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1) + label = torch.zeros(game.global_plan.edge_index.shape[1]) + + return (pred, label) + \ No newline at end of file diff --git a/src/models/plan_model_graphs_oracle.py b/src/models/plan_model_graphs_oracle.py new file mode 100644 index 0000000..f8a048d --- /dev/null +++ b/src/models/plan_model_graphs_oracle.py @@ -0,0 +1,291 @@ +import torch +import torch.nn as nn, numpy as np +from torch.nn import functional as F +from torch_geometric.nn import MeanAggregation, GATv2Conv + + +def onehot(x,n): + retval = np.zeros(n) + if x > 0: + retval[x-1] = 1 + return retval + +class PlanGraphEmbedder(nn.Module): + def __init__(self, device, h_dim, dropout=0.0, heads=4): + super().__init__() + self.device = device + self.proj_x = nn.Sequential( + nn.Linear(27, h_dim), + nn.GELU(), + nn.Dropout(dropout) + ) + self.proj_edge_attr = nn.Sequential( + nn.Linear(12, h_dim), + nn.GELU(), + nn.Dropout(dropout) + ) + self.conv1 = GATv2Conv(h_dim, h_dim, heads=heads, edge_dim=h_dim) + self.conv2 = GATv2Conv(h_dim*heads, h_dim, heads=1, edge_dim=h_dim) + self.act = nn.GELU() + self.dropout = nn.Dropout(dropout) + self.dec = nn.Linear(h_dim*3, 1) + + def encode(self, data): + x, edge_index, edge_attr = data.features.to(self.device), data.edge_index.to(self.device), data.tool.to(self.device) + x = self.proj_x(x) + edge_attr = self.proj_edge_attr(edge_attr) + x = self.conv1(x, edge_index, edge_attr) + x = self.act(x) + x = self.dropout(x) + x = self.conv2(x, edge_index, edge_attr) + return x, edge_attr + + def decode(self, z, context, edge_label_index): + u = z[edge_label_index[0]] + v = z[edge_label_index[1]] + return self.dec(torch.cat((u, v, context), -1)) + +class Model(nn.Module): + def __init__(self, seq_model_type, device): + super(Model, self).__init__() + + self.device = device + plan_emb_out = 128 + self.plan_embedder0 = PlanGraphEmbedder(device, plan_emb_out) + self.plan_embedder1 = PlanGraphEmbedder(device, plan_emb_out) + self.plan_embedder2 = PlanGraphEmbedder(device, plan_emb_out) + self.plan_pool = MeanAggregation() + dlist_hidden = 1024 + frame_emb = 512 + drnn_in = 5*1024 + 2 + frame_emb + 1024 + self.dialogue_listener_pre_ln = nn.LayerNorm(drnn_in) + if seq_model_type==0: + self.dialogue_listener_rnn = nn.GRU(drnn_in, dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1, 1, drnn_in))[0] + elif seq_model_type==1: + self.dialogue_listener_rnn = nn.LSTM(drnn_in, dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1, 1, drnn_in))[0] + elif seq_model_type==2: + mask_fun = lambda x: torch.triu(torch.ones(x.shape[0], x.shape[0]), diagonal=1).bool().to(device) + sincos_fun = lambda x:torch.transpose(torch.stack([ + torch.sin(2*np.pi*torch.tensor(list(range(x)))/x), + torch.cos(2*np.pi*torch.tensor(list(range(x)))/x) + ]),0,1).reshape(-1,1,2) + self.dialogue_listener_lin1 = nn.Linear(drnn_in, dlist_hidden-2) + self.dialogue_listener_attn = nn.MultiheadAttention(dlist_hidden, 8) + self.dialogue_listener_wrap = lambda x: self.dialogue_listener_attn(x, x, x, attn_mask=mask_fun(x)) + self.dialogue_listener = lambda x: self.dialogue_listener_wrap(torch.cat([ + sincos_fun(x.shape[0]).float().to(self.device), + self.dialogue_listener_lin1(x).reshape(-1, 1, dlist_hidden-2) + ], axis=-1))[0] + else: + print('Sequence model type must be in (0: GRU, 1: LSTM, 2: Transformer), but got ', seq_model_type) + exit() + conv_block = lambda i, o, k, p, s: nn.Sequential( + nn.Conv2d(i, o, k, padding=p, stride=s), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.MaxPool2d(2), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.ReLU() + ) + self.conv = nn.Sequential( + conv_block(3, 8, 3, 1, 1), + conv_block(8, 32, 5, 2, 2), + conv_block(32, frame_emb//4, 5, 2, 2), + nn.Conv2d(frame_emb//4, frame_emb, 3), + nn.ReLU(), + ) + self.proj_y = nn.Sequential( + nn.Linear(1024, 512), + nn.GELU(), + nn.Dropout(0.5), + nn.Linear(512, plan_emb_out) + ) + self.proj_tom = nn.Linear(154, 5*1024) + + def parse_ql(self, q, game, intermediate): + tom12_answ = ['YES', 'NO', 'MAYBE'] + materials_dict = game.materials_dict.copy() + materials_dict['NOT_SURE'] = 0 + if not q is None: + q, l = q + tom_gt = np.concatenate([onehot(q[2],2), onehot(q[3],2)]) + #### q1 + q1_1 = np.concatenate([onehot(q[4][0][0]+1,2), onehot(materials_dict[q[4][0][1]], len(game.materials_dict))]) + ## r1 + r1_1 = np.eye(len(tom12_answ))[tom12_answ.index(l[0][0])] + #### q2 + q2_1 = np.concatenate([onehot(q[4][1][0]+1,2), onehot(materials_dict[q[4][1][1]], len(game.materials_dict))]) + ## r2 + r2_1 = np.eye(len(tom12_answ))[tom12_answ.index(l[0][1])] + #### q3 + q3_1 = onehot(q[4][2]+1, 2) + ## r3 + r3_1 = onehot(materials_dict[l[0][2]], len(game.materials_dict)) + #### q1 + q1_2 = np.concatenate([onehot(q[5][0][0]+1,2), onehot(materials_dict[q[5][0][1]], len(game.materials_dict))]) + ## r1 + r1_2 = np.eye(len(tom12_answ))[tom12_answ.index(l[1][0])] + #### q2 + q2_2 = np.concatenate([onehot(q[5][1][0]+1,2), onehot(materials_dict[q[5][1][1]], len(game.materials_dict))]) + ## r2 + r2_2 = np.eye(len(tom12_answ))[tom12_answ.index(l[1][1])] + #### q3 + q3_2 = onehot(q[5][2]+1,2) + ## r3 + r3_2 = onehot(materials_dict[l[1][2]], len(game.materials_dict)) + if intermediate == 0: + tom_gt = np.zeros(154) + elif intermediate == 1: + # tom6 + tom_gt = np.concatenate([tom_gt, q1_1, r1_1, np.zeros(q2_1.shape[0] + r2_1.shape[0] + q3_1.shape[0] + r3_1.shape[0]), q1_2, r1_2, np.zeros(q2_2.shape[0] + r2_2.shape[0] + q3_2.shape[0] + r3_2.shape[0])]) + elif intermediate == 2: + # tom7 + tom_gt = np.concatenate([tom_gt, np.zeros(q1_1.shape[0] + r1_1.shape[0]), q2_1, r2_1, np.zeros(q3_1.shape[0] + r3_1.shape[0] + q1_2.shape[0] + r1_2.shape[0]), q2_2, r2_2, np.zeros(q3_2.shape[0] + r3_2.shape[0])]) + elif intermediate == 3: + # tom6 + tom7 + tom_gt = np.concatenate([tom_gt, q1_1, r1_1, q2_1, r2_1, np.zeros(q3_1.shape[0] + r3_1.shape[0]), q1_2, r1_2, q2_2, r2_2, np.zeros(q3_2.shape[0] + r3_2.shape[0])]) + elif intermediate == 4: + # tom8 + tom_gt = np.concatenate([tom_gt, np.zeros(q1_1.shape[0] + r1_1.shape[0] + q2_1.shape[0] + r2_1.shape[0]), q3_1, r3_1, np.zeros(q1_2.shape[0] + r1_2.shape[0] + q2_2.shape[0] + r2_2.shape[0]), q3_2, r3_2]) + elif intermediate == 5: + # tom6 + tom8 + tom_gt = np.concatenate([tom_gt, q1_1, r1_1, np.zeros(q2_1.shape[0] + r2_1.shape[0]), q3_1, r3_1, q1_2, r1_2, np.zeros(q2_2.shape[0] + r2_2.shape[0]), q3_2, r3_2]) + elif intermediate == 6: + # tom7 + tom8 + tom_gt = np.concatenate([tom_gt, np.zeros(q1_1.shape[0] + r1_1.shape[0]), q2_1, r2_1, q3_1, r3_1, np.zeros(q1_2.shape[0] + r1_2.shape[0]), q2_2, r2_2, q3_2, r3_2]) + elif intermediate == 7: + # tom6 + tom7 + tom8 + tom_gt = np.concatenate([tom_gt, q1_1, r1_1, q2_1, r2_1, q3_1, r3_1, q1_2, r1_2, q2_2, r2_2, q3_2, r3_2]) + else: + tom_gt = np.zeros(154) + if tom_gt.shape[0] != 154: breakpoint() + return tom_gt + + def forward(self, game, experiment, global_plan=False, player_plan=False, incremental=False, intermediate=0): + + l = list(game) + _, d, l, q, f, _, _, _ = zip(*list(game)) + + tom_gt = [self.parse_ql(x, game, intermediate) for x in q] + tom_gt = torch.tensor(np.stack(tom_gt), device=self.device, dtype=torch.float32) + tom_gt = self.proj_tom(tom_gt) + + f = np.array(f, dtype=np.uint8) + d = np.stack([np.concatenate(([int(x[0][1]==2), int(x[0][1]==1)], x[0][-1])) if not x is None else np.zeros(1026) for x in d]) + try: + sel1 = int([x[0][2] for x in q if not x is None][0] == 1) + sel2 = 1 - sel1 + except Exception as e: + sel1 = 0 + sel2 = 0 + + if player_plan: + if sel1: + z, _ = self.plan_embedder1.encode(game.player1_plan) + elif sel2: + z, _ = self.plan_embedder2.encode(game.player2_plan) + else: + z, _ = self.plan_embedder0.encode(game.global_plan) + else: + raise ValueError('There should never be a global plan!') + + u = torch.cat(( + torch.tensor(d).float().to(self.device), + self.conv(torch.tensor(f).permute(0, 3, 1, 2).float().to(self.device) / 255.0).reshape(-1, 512), + tom_gt + ), axis=-1) + u = u.float().to(self.device) + u = self.dialogue_listener_pre_ln(u) + y = self.dialogue_listener(u) + y = y.reshape(-1, y.shape[-1]) + y = self.proj_y(y) + + if experiment == 2: + pred, label = self.decode_own_missing_knowledge(z, y, game, sel1, sel2, incremental) + elif experiment == 3: + pred, label = self.decode_partner_missing_knowledge(z, y, game, sel1, sel2, incremental) + else: + raise ValueError('Wrong experiment id! Valid values are 2 and 3.') + + return pred, label, [sel1, sel2] + + def decode_own_missing_knowledge(self, z, y, game, sel1, sel2, incremental): + if incremental: + if sel1: + pred = torch.stack( + [self.plan_embedder1.decode(z, y[0].repeat(game.player1_edge_label_index.shape[1], 1), game.player1_edge_label_index).view(-1)] + + + [self.plan_embedder1.decode(z, f.repeat(game.player1_edge_label_index.shape[1], 1), game.player1_edge_label_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = game.player1_edge_label_own_missing_knowledge.to(self.device) + elif sel2: + pred = torch.stack( + [self.plan_embedder2.decode(z, y[0].repeat(game.player2_edge_label_index.shape[1], 1), game.player2_edge_label_index).view(-1)] + + + [self.plan_embedder2.decode(z, f.repeat(game.player2_edge_label_index.shape[1], 1), game.player2_edge_label_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = game.player2_edge_label_own_missing_knowledge.to(self.device) + else: + pred = torch.stack( + # NOTE: here I use game.global_plan.edge_index instead of edge_label_index => no negative sampling + [self.plan_embedder0.decode(z, y[0].repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1)] + + + [self.plan_embedder0.decode(z, f.repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = torch.zeros(game.global_plan.edge_index.shape[1]) + else: + if sel1: + pred = self.plan_embedder1.decode(z, y.mean(0, keepdim=True).repeat(game.player1_edge_label_index.shape[1], 1), game.player1_edge_label_index).view(-1) + label = game.player1_edge_label_own_missing_knowledge.to(self.device) + elif sel2: + pred = self.plan_embedder2.decode(z, y.mean(0, keepdim=True).repeat(game.player2_edge_label_index.shape[1], 1), game.player2_edge_label_index).view(-1) + label = game.player2_edge_label_own_missing_knowledge.to(self.device) + else: + # NOTE: here I use game.global_plan.edge_index instead of edge_label_index => no negative sampling + pred = self.plan_embedder0.decode(z, y.mean(0, keepdim=True).repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1) + label = torch.zeros(game.global_plan.edge_index.shape[1]) + + return (pred, label) + + def decode_partner_missing_knowledge(self, z, y, game, sel1, sel2, incremental): + if incremental: + if sel1: + pred = torch.stack( + [self.plan_embedder1.decode(z, y[0].repeat(game.player1_plan.edge_index.shape[1], 1), game.player1_plan.edge_index).view(-1)] + + + [self.plan_embedder1.decode(z, f.repeat(game.player1_plan.edge_index.shape[1], 1), game.player1_plan.edge_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = game.player1_edge_label_other_missing_knowledge.to(self.device) + elif sel2: + pred = torch.stack( + [self.plan_embedder2.decode(z, y[0].repeat(game.player2_plan.edge_index.shape[1], 1), game.player2_plan.edge_index).view(-1)] + + + [self.plan_embedder2.decode(z, f.repeat(game.player2_plan.edge_index.shape[1], 1), game.player2_plan.edge_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = game.player2_edge_label_other_missing_knowledge.to(self.device) + else: + pred = torch.stack( + # NOTE: here I use game.global_plan.edge_index instead of edge_label_index => no negative sampling + [self.plan_embedder0.decode(z, y[0].repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1)] + + + [self.plan_embedder0.decode(z, f.repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1) for f in y[len(y)%10-1::10]] + ) + label = torch.zeros(game.global_plan.edge_index.shape[1]) + else: + if sel1: + pred = self.plan_embedder1.decode(z, y.mean(0, keepdim=True).repeat(game.player1_plan.edge_index.shape[1], 1), game.player1_plan.edge_index).view(-1) + label = game.player1_edge_label_other_missing_knowledge.to(self.device) + elif sel2: + pred = self.plan_embedder2.decode(z, y.mean(0, keepdim=True).repeat(game.player2_plan.edge_index.shape[1], 1), game.player2_plan.edge_index).view(-1) + label = game.player2_edge_label_other_missing_knowledge.to(self.device) + else: + # NOTE: here I use game.global_plan.edge_index instead of edge_label_index => no negative sampling + pred = self.plan_embedder0.decode(z, y.mean(0, keepdim=True).repeat(game.global_plan.edge_index.shape[1], 1), game.global_plan.edge_index).view(-1) + label = torch.zeros(game.global_plan.edge_index.shape[1]) + + return (pred, label) \ No newline at end of file diff --git a/src/models/plan_model_oracle.py b/src/models/plan_model_oracle.py new file mode 100644 index 0000000..e13b0cb --- /dev/null +++ b/src/models/plan_model_oracle.py @@ -0,0 +1,214 @@ +import sys, torch, random +from numpy.core.fromnumeric import reshape +import torch.nn as nn, numpy as np +from torch.nn import functional as F +from src.data.game_parser import DEVICE + +def onehot(x,n): + retval = np.zeros(n) + if x > 0: + retval[x-1] = 1 + return retval + +class Model(nn.Module): + def __init__(self, seq_model_type=0,device=DEVICE): + super(Model, self).__init__() + + self.device = device + + my_rnn = lambda i,o: nn.GRU(i,o) + + plan_emb_in = 81 + plan_emb_out = 32 + q_emb = 100 + + self.plan_embedder0 = my_rnn(plan_emb_in,plan_emb_out) + self.plan_embedder1 = my_rnn(plan_emb_in,plan_emb_out) + self.plan_embedder2 = my_rnn(plan_emb_in,plan_emb_out) + + dlist_hidden = 1024 + frame_emb = 512 + drnn_in = 5*1024 + 2 + frame_emb + 1024 + + my_rnn = lambda i,o: nn.LSTM(i,o) + + if seq_model_type==0: + self.dialogue_listener_rnn = nn.GRU(drnn_in,dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1,1,drnn_in))[0] + elif seq_model_type==1: + self.dialogue_listener_rnn = nn.LSTM(drnn_in,dlist_hidden) + self.dialogue_listener = lambda x: \ + self.dialogue_listener_rnn(x.reshape(-1,1,drnn_in))[0] + elif seq_model_type==2: + mask_fun = lambda x: torch.triu(torch.ones(x.shape[0],x.shape[0]),diagonal=1).bool().to(device) + sincos_fun = lambda x:torch.transpose(torch.stack([ + torch.sin(2*np.pi*torch.tensor(list(range(x)))/x), + torch.cos(2*np.pi*torch.tensor(list(range(x)))/x) + ]),0,1).reshape(-1,1,2) + self.dialogue_listener_lin1 = nn.Linear(drnn_in,dlist_hidden-2) + self.dialogue_listener_attn = nn.MultiheadAttention(dlist_hidden, 8) + self.dialogue_listener_wrap = lambda x: self.dialogue_listener_attn(x,x,x,attn_mask=mask_fun(x)) + self.dialogue_listener = lambda x: self.dialogue_listener_wrap(torch.cat([ + sincos_fun(x.shape[0]).float().to(self.device), + self.dialogue_listener_lin1(x).reshape(-1,1,dlist_hidden-2) + ], axis=-1))[0] + else: + print('Sequence model type must be in (0: GRU, 1: LSTM, 2: Transformer), but got ', seq_model_type) + exit() + + conv_block = lambda i,o,k,p,s: nn.Sequential( + nn.Conv2d( i, o, k, padding=p, stride=s), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.MaxPool2d(2), + nn.BatchNorm2d(o), + nn.Dropout(0.5), + nn.ReLU() + ) + + self.conv = nn.Sequential( + conv_block( 3, 8, 3, 1, 1), + conv_block( 8, 32, 5, 2, 2), + conv_block( 32, frame_emb//4, 5, 2, 2), + nn.Conv2d( frame_emb//4, frame_emb, 3),nn.ReLU(), + ) + + plan_layer = lambda i,o : nn.Sequential( + nn.Linear(i,(i+2*o)//3), + nn.Dropout(0.5), + nn.GELU(), + nn.Dropout(0.5), + nn.Linear((i+2*o)//3,o), + nn.GELU(), + nn.Dropout(0.5), + ) + + plan_mat_size = 21*21 + q_in_size = 3*plan_emb_out+dlist_hidden + q_in_size = 3*plan_emb_out+dlist_hidden+plan_mat_size + q_in_size = dlist_hidden+plan_mat_size + + self.plan_out = plan_layer(q_in_size,plan_mat_size) + + self.proj_tom = nn.Linear(154, 5*1024) + + def forward(self,game,global_plan=False, player_plan=False,evaluation=False, incremental=False, intermediate=0): + + _,d,l,q,f,_,_,_ = zip(*list(game)) + + tom_gt = [self.parse_ql(x, game, intermediate) for x in q] + tom_gt = torch.tensor(np.stack(tom_gt), device=self.device, dtype=torch.float32) + tom_gt = self.proj_tom(tom_gt) + + f = np.array(f, dtype=np.uint8) + + d = np.stack([np.concatenate(([int(x[0][1]==2),int(x[0][1]==1)],x[0][-1])) if not x is None else np.zeros(1026) for x in d]) + + try: + sel1 = int([x[0][2] for x in q if not x is None][0] == 1) + sel2 = 1 - sel1 + except Exception as e: + sel1 = 0 + sel2 = 0 + + if not global_plan and not player_plan: + plan_emb = torch.cat([ + self.plan_embedder0(torch.stack(list(map(torch.tensor,game.global_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder1(torch.stack(list(map(torch.tensor,game.player1_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder2(torch.stack(list(map(torch.tensor,game.player2_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0] + ]) + plan_emb = 0*plan_emb + elif global_plan: + plan_emb = torch.cat([ + self.plan_embedder0(torch.stack(list(map(torch.tensor,game.global_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder1(torch.stack(list(map(torch.tensor,game.player1_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + self.plan_embedder2(torch.stack(list(map(torch.tensor,game.player2_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0] + ]) + else: + plan_emb = torch.cat([ + 0*self.plan_embedder0(torch.stack(list(map(torch.tensor,game.global_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + sel1*self.plan_embedder1(torch.stack(list(map(torch.tensor,game.player1_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0], + sel2*self.plan_embedder2(torch.stack(list(map(torch.tensor,game.player2_plan))).reshape(-1,1,81).float().to(self.device))[0][-1][0] + ]) + + u = torch.cat(( + torch.tensor(d).float().to(self.device), + self.conv(torch.tensor(f).permute(0,3,1,2).float().to(self.device)).reshape(-1,512), + tom_gt + ),axis=-1) + u = u.float().to(self.device) + + y = self.dialogue_listener(u) + y = y.reshape(-1,y.shape[-1]) + + if incremental: + prediction = torch.stack([ + self.plan_out(torch.cat((y[0],torch.tensor(game.plan_repr.reshape(-1)).float().to(self.device))))] + [ + self.plan_out(torch.cat((f,torch.tensor(game.plan_repr.reshape(-1)).float().to(self.device)))) for f in y[len(y)%10-1::10] + ]) + prediction = F.softmax(prediction.reshape(-1,21,21),-1).reshape(-1,21*21) + else: + prediction = self.plan_out(torch.cat((y[-1],torch.tensor(game.plan_repr.reshape(-1)).float().to(self.device)))) + prediction = F.softmax(prediction.reshape(21,21),-1).reshape(21*21) + + return prediction, y + + def parse_ql(self, q, game, intermediate): + tom12_answ = ['YES', 'NO', 'MAYBE'] + materials_dict = game.materials_dict.copy() + materials_dict['NOT_SURE'] = 0 + if not q is None: + q, l = q + tom_gt = np.concatenate([onehot(q[2],2), onehot(q[3],2)]) + #### q1 + q1_1 = np.concatenate([onehot(q[4][0][0]+1,2), onehot(materials_dict[q[4][0][1]], len(game.materials_dict))]) + ## r1 + r1_1 = np.eye(len(tom12_answ))[tom12_answ.index(l[0][0])] + #### q2 + q2_1 = np.concatenate([onehot(q[4][1][0]+1,2), onehot(materials_dict[q[4][1][1]], len(game.materials_dict))]) + ## r2 + r2_1 = np.eye(len(tom12_answ))[tom12_answ.index(l[0][1])] + #### q3 + q3_1 = onehot(q[4][2]+1, 2) + ## r3 + r3_1 = onehot(materials_dict[l[0][2]], len(game.materials_dict)) + #### q1 + q1_2 = np.concatenate([onehot(q[5][0][0]+1,2), onehot(materials_dict[q[5][0][1]], len(game.materials_dict))]) + ## r1 + r1_2 = np.eye(len(tom12_answ))[tom12_answ.index(l[1][0])] + #### q2 + q2_2 = np.concatenate([onehot(q[5][1][0]+1,2), onehot(materials_dict[q[5][1][1]], len(game.materials_dict))]) + ## r2 + r2_2 = np.eye(len(tom12_answ))[tom12_answ.index(l[1][1])] + #### q3 + q3_2 = onehot(q[5][2]+1,2) + ## r3 + r3_2 = onehot(materials_dict[l[1][2]], len(game.materials_dict)) + if intermediate == 0: + tom_gt = np.zeros(154) + elif intermediate == 1: + # tom6 + tom_gt = np.concatenate([tom_gt, q1_1, r1_1, np.zeros(q2_1.shape[0] + r2_1.shape[0] + q3_1.shape[0] + r3_1.shape[0]), q1_2, r1_2, np.zeros(q2_2.shape[0] + r2_2.shape[0] + q3_2.shape[0] + r3_2.shape[0])]) + elif intermediate == 2: + # tom7 + tom_gt = np.concatenate([tom_gt, np.zeros(q1_1.shape[0] + r1_1.shape[0]), q2_1, r2_1, np.zeros(q3_1.shape[0] + r3_1.shape[0] + q1_2.shape[0] + r1_2.shape[0]), q2_2, r2_2, np.zeros(q3_2.shape[0] + r3_2.shape[0])]) + elif intermediate == 3: + # tom6 + tom7 + tom_gt = np.concatenate([tom_gt, q1_1, r1_1, q2_1, r2_1, np.zeros(q3_1.shape[0] + r3_1.shape[0]), q1_2, r1_2, q2_2, r2_2, np.zeros(q3_2.shape[0] + r3_2.shape[0])]) + elif intermediate == 4: + # tom8 + tom_gt = np.concatenate([tom_gt, np.zeros(q1_1.shape[0] + r1_1.shape[0] + q2_1.shape[0] + r2_1.shape[0]), q3_1, r3_1, np.zeros(q1_2.shape[0] + r1_2.shape[0] + q2_2.shape[0] + r2_2.shape[0]), q3_2, r3_2]) + elif intermediate == 5: + # tom6 + tom8 + tom_gt = np.concatenate([tom_gt, q1_1, r1_1, np.zeros(q2_1.shape[0] + r2_1.shape[0]), q3_1, r3_1, q1_2, r1_2, np.zeros(q2_2.shape[0] + r2_2.shape[0]), q3_2, r3_2]) + elif intermediate == 6: + # tom7 + tom8 + tom_gt = np.concatenate([tom_gt, np.zeros(q1_1.shape[0] + r1_1.shape[0]), q2_1, r2_1, q3_1, r3_1, np.zeros(q1_2.shape[0] + r1_2.shape[0]), q2_2, r2_2, q3_2, r3_2]) + elif intermediate == 7: + # tom6 + tom7 + tom8 + tom_gt = np.concatenate([tom_gt, q1_1, r1_1, q2_1, r2_1, q3_1, r3_1, q1_2, r1_2, q2_2, r2_2, q3_2, r3_2]) + else: + tom_gt = np.zeros(154) + if tom_gt.shape[0] != 154: breakpoint() + return tom_gt \ No newline at end of file