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