416 lines
17 KiB
Python
416 lines
17 KiB
Python
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from glob import glob
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import os, json, sys
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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, precision_score, recall_score
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from src.data.game_parser import GameParser, make_splits, onehot, DEVICE, set_seed
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from src.models.plan_model import Model
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from src.models.losses import PlanLoss
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import argparse
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from tqdm import tqdm
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def print_epoch(data,acc_loss,lst,exp, incremental=False):
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print(f'{acc_loss:9.4f}',end='; ',flush=True)
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acc = []
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prec = []
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rec = []
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f1 = []
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iou = []
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total = []
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predicts = []
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targets = []
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# for x,game in zip(data,lst):
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for x in data:
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game = lst[x[2]]
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game_mats = game.plan['materials']
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pov_plan = game.plan[f'player{game.pov}']
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pov_plan_mat = game.__dict__[f'player{game.pov}_plan_mat']
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possible_mats = [game.materials_dict[x]-1 for x,_ in zip(game_mats[1:],pov_plan[1:])]
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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]
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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]
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a, b = x[:2]
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if exp == 3:
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a = a.reshape(21,21)
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for idx,aa in enumerate(a):
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if idx in possible_extra:
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cand_idxs = set([i for i,x in enumerate(pov_plan_mat[idx]) if x])
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th, _ = zip(*sorted([(i, x) for i, x in enumerate(aa) if i in possible_mats], key=lambda x:x[1])[-2:])
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if len(cand_idxs.intersection(set(th))):
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for jdx, _ in enumerate(aa):
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a[idx,jdx] = pov_plan_mat[idx,jdx]
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else:
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for jdx, _ in enumerate(aa):
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a[idx,jdx] = 0
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else:
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for jdx, aaa in enumerate(aa):
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a[idx,jdx] = 0
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elif exp == 2:
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a = a.reshape(21,21)
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for idx,aa in enumerate(a):
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if idx in possible_cand:
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th = [x for i, x in enumerate(aa) if i in possible_mats]
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th = sorted(th)
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th = th[-2]
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th = 1.1 if th < (1/21) else th
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for jdx, aaa in enumerate(aa):
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if idx in possible_mats:
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a[idx,jdx] = 0 if aaa < th else 1
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else:
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a[idx,jdx] = 0
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else:
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for jdx, aaa in enumerate(aa):
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a[idx,jdx] = 0
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else:
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a = a.reshape(21,21)
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for idx,aa in enumerate(a):
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th = sorted(aa)[-2]
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th = 1.1 if th < (2.1/21) else th
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for jdx, aaa in enumerate(aa):
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a[idx,jdx] = 0 if aaa < th else 1
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a = a.reshape(-1)
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predicts.append(np.argmax(a))
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targets.append(np.argmax(a) if np.argmax(a) in [x for x in b if x] else np.argmax(b))
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acc.append(accuracy_score(a,b))
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sa = set([i for i,x in enumerate(a) if x])
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sb = set([i for i,x in enumerate(b) if x])
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i = len(sa.intersection(sb))
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u = len(sa.union(sb))
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if u > 0:
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a,b = zip(*[(x,y) for x,y in zip(a,b) if x+y > 0])
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f1.append(f1_score(b,a,zero_division=1))
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prec.append(precision_score(b,a,zero_division=1))
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rec.append(recall_score(b,a,zero_division=1))
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iou.append(i/u if u > 0 else 1)
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total.append(sum(a))
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print(
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# f'({accuracy_score(targets,predicts):5.3f},'
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# f'{np.mean(acc):5.3f},'
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# f'{np.mean(prec):5.3f},'
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# f'{np.mean(rec):5.3f},'
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f'{np.mean(f1):5.3f},'
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f'{np.mean(iou):5.3f},'
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f'{np.std(iou):5.3f},',
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# f'{np.mean(total):5.3f})',
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end=' ',flush=True)
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print('', end='; ',flush=True)
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return accuracy_score(targets,predicts), np.mean(acc), np.mean(f1), np.mean(iou)
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def do_split(model,lst,exp,criterion,optimizer=None,global_plan=False, player_plan=False, incremental=False, device=DEVICE):
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data = []
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acc_loss = 0
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p = []
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g = []
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masks = []
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for batch, game in enumerate(lst):
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if model.training and (not optimizer is None): optimizer.zero_grad()
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if exp==0:
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ground_truth = torch.tensor(game.global_plan_mat.reshape(-1)).float()
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elif exp==1:
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ground_truth = torch.tensor(game.partner_plan.reshape(-1)).float()
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elif exp==2:
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ground_truth = torch.tensor(game.global_diff_plan_mat.reshape(-1)).float()
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loss_mask = torch.tensor(game.global_plan_mat.reshape(-1)).float()
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else:
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ground_truth = torch.tensor(game.partner_diff_plan_mat.reshape(-1)).float()
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loss_mask = torch.tensor(game.plan_repr.reshape(-1)).float()
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prediction, _ = model(game, global_plan=global_plan, player_plan=player_plan, incremental=incremental)
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if incremental:
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ground_truth = ground_truth.to(device)
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g += [ground_truth for _ in prediction]
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masks += [loss_mask for _ in prediction]
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p += [x for x in prediction]
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data += list(zip(prediction.cpu().data.numpy(), [ground_truth.cpu().data.numpy()]*len(prediction),[batch]*len(prediction)))
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else:
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ground_truth = ground_truth.to(device)
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g.append(ground_truth)
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masks.append(loss_mask)
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p.append(prediction)
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data.append((prediction.cpu().data.numpy(), ground_truth.cpu().data.numpy(),batch))
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if (batch+1) % 2 == 0:
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loss = criterion(torch.stack(p),torch.stack(g), torch.stack(masks))
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loss += 1e-5 * sum(p.pow(2.0).sum() for p in model.parameters())
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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(), 1)
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# nn.utils.clip_grad_norm_(model.parameters(), 10)
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optimizer.step()
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acc_loss += loss.item()
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p = []
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g = []
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masks = []
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acc_loss /= len(lst)
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acc0, acc, f1, iou = print_epoch(data,acc_loss,lst,exp)
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return acc0, acc_loss, data, acc, f1, iou
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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_(0.01)
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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 args.seed=='Random':
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# pass
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# elif args.seed=='Fixed':
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# random.seed(0)
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# torch.manual_seed(1)
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# np.random.seed(0)
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# else:
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# print('Seed must be in [Random, Fixed], but got',args.seed)
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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 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.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 = 1e-5
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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.BCELoss()
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criterion = PlanLoss()
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# criterion = torch.hub.load(
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# 'adeelh/pytorch-multi-class-focal-loss',
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# model='focal_loss',
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# alpha=[.25, .75],
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# gamma=10,
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# reduction='mean',
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# device=device,
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# dtype=torch.float32,
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# force_reload=False
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# )
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# criterion = nn.BCEWithLogitsLoss(pos_weight=10*torch.ones(21*21).to(device))
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# criterion = nn.MSELoss()
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print(str(criterion), str(optimizer))
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num_epochs = 200#1#
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min_acc_loss = 1e6
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max_f1 = 0
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epochs_since_improvement = 0
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wait_epoch = 15#150#1000#
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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, acc_loss, data, _, f1, iou = do_split(model,val,args.experiment,criterion,global_plan=global_plan, player_plan=player_plan, incremental=True, device=DEVICE)
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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)
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if np.mean([acc_loss,acc_loss0]) < min_acc_loss:
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min_acc_loss = np.mean([acc_loss,acc_loss0])
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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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# 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')
|
||
|
|
# 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())
|