325 lines
16 KiB
Python
325 lines
16 KiB
Python
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import os
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import torch, 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, set_seed
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from src.models.plan_model_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):
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print(f'{acc_loss:9.4f}',end='; ',flush=True)
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acc = []
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f1 = []
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for x in data:
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a, b, _, _, _, _, _, _ = x
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acc.append(accuracy_score(b, a))
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f1.append(f1_score(b, a, zero_division=1))
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print(f'{np.mean(f1):5.3f},', end=' ', flush=True)
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print('', end='; ', flush=True)
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return np.mean(acc), np.mean(f1), f1
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def do_split(model, lst, exp, criterion, device, optimizer=None, global_plan=False, player_plan=False, incremental=False):
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data = []
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acc_loss = 0
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for batch, game in enumerate(lst):
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if (exp != 2) and (exp != 3):
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raise ValueError('This script is only for exp == 2 or exp == 3.')
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prediction, ground_truth, sel = model(game, experiment=exp, global_plan=global_plan, player_plan=player_plan, incremental=incremental)
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if exp == 2:
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if sel[0]:
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prediction = prediction[game.player1_plan.edge_index.shape[1]:]
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ground_truth = ground_truth[game.player1_plan.edge_index.shape[1]:]
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if sel[1]:
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prediction = prediction[game.player2_plan.edge_index.shape[1]:]
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ground_truth = ground_truth[game.player2_plan.edge_index.shape[1]:]
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if prediction.numel() == 0 and ground_truth.numel() == 0: continue
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if incremental:
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ground_truth = ground_truth.to(device).repeat(prediction.shape[0], 1)
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data += list(zip(torch.round(torch.sigmoid(prediction)).float().cpu().data.numpy(),
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ground_truth.cpu().data.numpy(),
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[game.player1_plan.edge_index.shape[1]]*len(prediction),
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[game.player2_plan.edge_index.shape[1]]*len(prediction),
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[game.global_plan.edge_index.shape[1]]*len(prediction),
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[sel]*len(prediction),
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[game.game_path]*len(prediction),
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[batch]*len(prediction)))
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else:
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ground_truth = ground_truth.to(device)
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data.append((
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torch.round(torch.sigmoid(prediction)).float().cpu().data.numpy(),
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ground_truth.cpu().data.numpy(),
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game.player1_plan.edge_index.shape[1],
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game.player2_plan.edge_index.shape[1],
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game.global_plan.edge_index.shape[1],
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sel,
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game.game_path,
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batch,
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))
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loss = criterion(prediction, ground_truth)
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# loss += 1e-5 * sum(p.pow(2.0).sum() for p in model.parameters())
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acc_loss += loss.item()
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if model.training and (not optimizer is None):
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loss.backward()
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if (batch+1) % 2 == 0: # gradient accumulation
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# nn.utils.clip_grad_norm_(model.parameters(), 1)
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optimizer.step()
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optimizer.zero_grad()
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acc_loss /= len(lst)
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acc, f1, f1_list = print_epoch(data, acc_loss)
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if not incremental:
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data = [data[i] + (f1_list[i],) for i in range(len(data))]
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return acc_loss, data, acc, f1
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def init_weights(m):
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if isinstance(m, nn.Linear):
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torch.nn.init.xavier_uniform_(m.weight)
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m.bias.data.fill_(0.00)
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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_new.json')
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# dataset_splits = make_splits('config/dataset_splits_dev.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 not args.intermediate in list(range(32)):
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print('Intermediate must be in',list(range(32)),', but got',args.intermediate)
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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.use_int0_instead_of_intermediate:
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if args.pov=='None':
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val = [GameParser(f,d_flag,0,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])]
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train = [GameParser(f,d_flag,0,args.intermediate,d_move_flag,load_int0_feats=True) 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,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])]
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train += [GameParser(f,d_flag,4,args.intermediate,d_move_flag,load_int0_feats=True) 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,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])]
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train = [GameParser(f,d_flag,3,args.intermediate,d_move_flag,load_int0_feats=True) 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,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])]
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train = [GameParser(f,d_flag,1,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['training'])]
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val += [GameParser(f,d_flag,2,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['validation'])]
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train += [GameParser(f,d_flag,2,args.intermediate,d_move_flag,load_int0_feats=True) 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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else:
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if args.pov=='None':
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val = [GameParser(f,d_flag,0,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])]
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train = [GameParser(f,d_flag,0,args.intermediate,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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])]
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train += [GameParser(f,d_flag,4,args.intermediate,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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])]
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train = [GameParser(f,d_flag,3,args.intermediate,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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])]
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train = [GameParser(f,d_flag,1,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['training'])]
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val += [GameParser(f,d_flag,2,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['validation'])]
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train += [GameParser(f,d_flag,2,args.intermediate,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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# model.apply(init_weights)
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print(model)
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model.train()
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learning_rate = args.lr
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weight_decay = args.weight_decay
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optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
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if args.experiment == 2:
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pos_weight = torch.tensor([2.5], device=DEVICE)
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if args.experiment == 3:
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pos_weight = torch.tensor([10.0], device=DEVICE)
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criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weight)
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# criterion = nn.BCEWithLogitsLoss()
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print(str(criterion), str(optimizer))
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num_epochs = 200
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min_acc_loss = 1e6
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best_f1 = 0.0
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epochs_since_improvement = 0
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wait_epoch = 15
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max_fails = 5
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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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model.eval()
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# acc_loss, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=True)
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acc_loss0, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False)
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# if np.mean([acc_loss, acc_loss0]) < min_acc_loss:
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if f1 > best_f1:
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# min_acc_loss = np.mean([acc_loss, acc_loss0])
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best_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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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, device=DEVICE, optimizer=optimizer, global_plan=global_plan, player_plan=player_plan, incremental=True)
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do_split(model, train, args.experiment, criterion, device=DEVICE, optimizer=optimizer, global_plan=global_plan, player_plan=player_plan, incremental=False)
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model.eval()
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# acc_loss, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=True)
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acc_loss0, data, acc, f1 = do_split(model, val, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False)
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# if np.mean([acc_loss, acc_loss0]) < min_acc_loss:
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# if acc_loss0 < min_acc_loss:
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if f1 > best_f1:
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# min_acc_loss = np.mean([acc_loss, acc_loss0])
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# min_acc_loss = acc_loss0
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best_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 epoch > wait_epoch and epochs_since_improvement > max_fails:
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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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if args.use_int0_instead_of_intermediate:
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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,args.intermediate,d_move_flag,load_int0_feats=True) 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,args.intermediate,d_move_flag,load_int0_feats=True) 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,args.intermediate,d_move_flag,load_int0_feats=True) 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,args.intermediate,d_move_flag,load_int0_feats=True) for f in tqdm(dataset_splits['test'])]
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test += [GameParser(f,d_flag,2,args.intermediate,d_move_flag,load_int0_feats=True) 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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else:
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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,args.intermediate,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,args.intermediate,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,args.intermediate,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,args.intermediate,d_move_flag) for f in tqdm(dataset_splits['test'])]
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test += [GameParser(f,d_flag,2,args.intermediate,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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# acc_loss, data, acc, f1 = do_split(model, test, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=True)
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acc_loss, data, acc, f1 = do_split(model, test, args.experiment, criterion, device=DEVICE, global_plan=global_plan, player_plan=player_plan, incremental=False)
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|
|
|
||
|
|
print()
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||
|
|
print(data)
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||
|
|
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())
|