351 lines
No EOL
15 KiB
Python
351 lines
No EOL
15 KiB
Python
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()) |