def valid_speaker(tok, val_envs):
import tqdm
listner = Seq2SeqAgent(None, "", tok, args.maxAction)
speaker = Speaker(None, listner, tok)
speaker.load(os.path.join(log_dir, 'state_dict', 'best_val_seen_bleu'))
# speaker.load(os.path.join(log_dir, 'state_dict', 'best_val_unseen_loss'))
for args.beam in [False, True]:
print("Using Beam Search %s" % args.beam)
for env_name, (env, evaluator) in val_envs.items():
if env_name == 'train':
continue
print("............ Evaluating %s ............." % env_name)
speaker.env = env
path2inst, loss, word_accu, sent_accu = speaker.valid(
beam=args.beam, wrapper=tqdm.tqdm)
path_id = next(iter(path2inst.keys()))
print("Inference: ", tok.decode_sentence(path2inst[path_id]))
print("GT: ", evaluator.gt[path_id]['instructions'])
bleu_score, precisions, _ = evaluator.bleu_score(path2inst)
print(
"Bleu, Loss, Word_Accu, Sent_Accu for %s is: %0.4f, %0.4f, %0.4f, %0.4f"
% (env_name, bleu_score, loss, word_accu, sent_accu))
print(
"Bleu 1: %0.4f Bleu 2: %0.4f, Bleu 3 :%0.4f, Bleu 4: %0.4f" %
tuple(precisions))
print("Average Length %0.4f" % utils.average_length(path2inst))
def food(game):
"""Return good moves towards food goodness is determined by the weighted value function."""
moves = []
snake_length_ratio = utils.average_length(
snakes=game.snakes) / game.me.length()
closest_foods = []
# Only consider food we are the closest snake too
for f in game.foods:
min_dist = game.width * game.height
for s in game.other_snakes:
if s.head().distance(f) < min_dist:
min_dist = s.head().distance(f)
if game.me.head().distance(f) <= min_dist:
closest_foods.append(f)
if len(closest_foods) <= 0:
closest_foods = game.foods
def weighted_value(distance, health):
distance_weight = 1 / distance
health_weight = 1 / (health + 0.01)
length_compare_weight = 1 if snake_length_ratio > 1 else 0
if health < 50:
health_weight += 0.20 * (50 - health)
# Return a value between 0 and 1
return (distance_weight + health_weight + length_compare_weight) / 3
if len(closest_foods) <= 0:
return moves
# Get a list of distances to all foods
food_distances = map(lambda c: game.me.head().distance(c), closest_foods)
health = game.me.health_points
for idx, d in enumerate(food_distances):
# Find possible moves towards the foods
# i.e. if food is to the top right, possible moves are up and right
mt = game.me.moves_to(closest_foods[idx])
for m in mt:
# Add possible move to possible moves with goodness from weighted values
moves.append(Move(m, weighted_value(d, health), 'food'))
return moves
def value(game, distance):
"""Return a value representing how much we want to move towards the food.
game: Game object
distance: manhattan distance to food
"""
health = game.me.health()
avg_length = utils.average_length(snakes=game.snakes)
if health > params.FOOD_THRESH and game.me.length() >= avg_length:
return 0
if health < distance:
return 100
distance_weight = 1 / distance
health_weight = (100 / health) * params.FOOD_HEALTH_MULTI
return distance_weight + health_weight
def valid_speaker(tok, val_envs):
import tqdm
listner = Seq2SeqAgent(None, "", tok, args.maxAction)
speaker = Speaker(None, listner, tok)
speaker.load(args.load)
for env_name, (env, evaluator) in val_envs.items():
if env_name == 'train':
continue
print("............ Evaluating %s ............." % env_name)
speaker.env = env
path2inst, loss, word_accu, sent_accu = speaker.valid(wrapper=tqdm.tqdm)
path_id = next(iter(path2inst.keys()))
print("Inference: ", tok.decode_sentence(path2inst[path_id]))
print("GT: ", evaluator.gt[path_id]['instructions'])
pathXinst = list(path2inst.items())
name2score = evaluator.lang_eval(pathXinst, no_metrics={'METEOR'})
score_string = " "
for score_name, score in name2score.items():
score_string += "%s_%s: %0.4f " % (env_name, score_name, score)
print("For env %s" % env_name)
print(score_string)
print("Average Length %0.4f" % utils.average_length(path2inst))
def create_augment_data():
setup()
# Create a batch training environment that will also preprocess text
vocab = read_vocab(TRAIN_VOCAB)
tok = Tokenizer(vocab=vocab, encoding_length=args.maxInput)
# Load features
feat_dict = read_img_features(features)
candidate_dict = utils.read_candidates(CANDIDATE_FEATURES)
# The datasets to be augmented
print("Start to augment the data")
aug_envs = []
# aug_envs.append(
# R2RBatch(
# feat_dict, candidate_dict, batch_size=args.batchSize, splits=['train'], tokenizer=tok
# )
# )
# aug_envs.append(
# SemiBatch(False, 'tasks/R2R/data/all_paths_46_removetrain.json',
# feat_dict, candidate_dict, batch_size=args.batchSize, splits=['train', 'val_seen'], tokenizer=tok)
# )
aug_envs.append(
SemiBatch(False,
'tasks/R2R/data/all_paths_46_removevalunseen.json',
"unseen",
feat_dict,
candidate_dict,
batch_size=args.batchSize,
splits=['val_unseen'],
tokenizer=tok))
aug_envs.append(
SemiBatch(False,
'tasks/R2R/data/all_paths_46_removetest.json',
"test",
feat_dict,
candidate_dict,
batch_size=args.batchSize,
splits=['test'],
tokenizer=tok))
# aug_envs.append(
# R2RBatch(
# feat_dict, candidate_dict, batch_size=args.batchSize, splits=['val_seen'], tokenizer=tok
# )
# )
# aug_envs.append(
# R2RBatch(
# feat_dict, candidate_dict, batch_size=args.batchSize, splits=['val_unseen'], tokenizer=tok
# )
# )
for snapshot in os.listdir(os.path.join(log_dir, 'state_dict')):
# if snapshot != "best_val_unseen_bleu": # Select a particular snapshot to process. (O/w, it will make for every snapshot)
if snapshot != "best_val_unseen_bleu":
continue
# Create Speaker
listner = Seq2SeqAgent(aug_envs[0], "", tok, args.maxAction)
speaker = Speaker(aug_envs[0], listner, tok)
# Load Weight
load_iter = speaker.load(os.path.join(log_dir, 'state_dict', snapshot))
print("Load from iter %d" % (load_iter))
# Augment the env from aug_envs
for aug_env in aug_envs:
speaker.env = aug_env
# Create the aug data
import tqdm
path2inst = speaker.get_insts(beam=args.beam, wrapper=tqdm.tqdm)
data = []
for datum in aug_env.fake_data:
datum = datum.copy()
path_id = datum['path_id']
if path_id in path2inst:
datum['instructions'] = [
tok.decode_sentence(path2inst[path_id])
]
datum.pop('instr_encoding') # Remove Redundant keys
datum.pop('instr_id')
data.append(datum)
print("Totally, %d data has been generated for snapshot %s." %
(len(data), snapshot))
print("Average Length %0.4f" % utils.average_length(path2inst))
print(datum) # Print a Sample
# Save the data
import json
os.makedirs(os.path.join(log_dir, 'aug_data'), exist_ok=True)
beam_tag = "_beam" if args.beam else ""
json.dump(data,
open(
os.path.join(
log_dir, 'aug_data', '%s_%s%s.json' %
(snapshot, aug_env.name, beam_tag)), 'w'),
sort_keys=True,
indent=4,
separators=(',', ': '))