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 infer_speaker(env, tok):
import tqdm
from utils import load_datasets
listner = Seq2SeqAgent(env, "", tok, args.maxAction)
speaker = Speaker(env, listner, tok)
speaker.load(args.load)
dataset = load_datasets(env.splits)
key_map = {}
for i, item in enumerate(dataset):
key_map[item["path_id"]] = i
path2inst = speaker.get_insts(wrapper=tqdm.tqdm)
for path_id in path2inst.keys():
speaker_pred = tok.decode_sentence(path2inst[path_id])
dataset[key_map[path_id]]['instructions'] = [speaker_pred]
with open("tasks/R2R/data/aug_paths_unseen_infer.json", "w") as f:
json.dump(dataset, f, indent=4, sort_keys=True)
def valid_speaker(train_env, tok, val_envs):
import tqdm
listner = Seq2SeqAgent(train_env, "", tok, args.maxAction)
speaker = Speaker(train_env, 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[str(path_id)]['instructions'])
bleu_score, precisions = evaluator.bleu_score(path2inst)
print(len(env.data), len(path2inst.keys()))
import pdb; pdb.set_trace()
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 beam_valid(train_env, tok, val_envs={}):
listener = Seq2SeqAgent(train_env, "", tok, args.maxAction)
speaker = Speaker(train_env, listener, tok)
if args.speaker is not None:
print("Load the speaker from %s." % args.speaker)
speaker.load(args.speaker)
print("Loaded the listener model at iter % d" % listener.load(args.load))
final_log = ""
for env_name, (env, evaluator) in val_envs.items():
listener.logs = defaultdict(list)
listener.env = env
listener.beam_search_test(speaker)
results = listener.results
def cal_score(x, alpha, avg_speaker, avg_listener):
speaker_score = sum(x["speaker_scores"]) * alpha
if avg_speaker:
speaker_score /= len(x["speaker_scores"])
# normalizer = sum(math.log(k) for k in x['listener_actions'])
normalizer = 0.
listener_score = (sum(x["listener_scores"]) + normalizer) * (1 -
alpha)
if avg_listener:
listener_score /= len(x["listener_scores"])
return speaker_score + listener_score
if args.param_search:
# Search for the best speaker / listener ratio
interval = 0.01
logs = []
for avg_speaker in [False, True]:
for avg_listener in [False, True]:
for alpha in np.arange(0, 1 + interval, interval):
result_for_eval = []
for key in results:
result_for_eval.append({
"instr_id":
key,
"trajectory":
max(results[key]['paths'],
key=lambda x: cal_score(
x, alpha, avg_speaker, avg_listener))
['trajectory']
})
score_summary, _ = evaluator.score(result_for_eval)
for metric, val in score_summary.items():
if metric in ['success_rate']:
print(
"Avg speaker %s, Avg listener %s, For the speaker weight %0.4f, the result is %0.4f"
% (avg_speaker, avg_listener, alpha, val))
logs.append(
(avg_speaker, avg_listener, alpha, val))
tmp_result = "Env Name %s\n" % (env_name) + \
"Avg speaker %s, Avg listener %s, For the speaker weight %0.4f, the result is %0.4f\n" % max(logs, key=lambda x: x[3])
print(tmp_result)
# print("Env Name %s" % (env_name))
# print("Avg speaker %s, Avg listener %s, For the speaker weight %0.4f, the result is %0.4f" %
# max(logs, key=lambda x: x[3]))
final_log += tmp_result
print()
else:
avg_speaker = True
avg_listener = True
alpha = args.alpha
result_for_eval = []
for key in results:
result_for_eval.append({
"instr_id": key,
"trajectory": [(vp, 0, 0) for vp in results[key]['dijk_path']] + \
max(results[key]['paths'],
key=lambda x: cal_score(x, alpha, avg_speaker, avg_listener)
)['trajectory']
})
# result_for_eval = utils.add_exploration(result_for_eval)
score_summary, _ = evaluator.score(result_for_eval)
if env_name != 'test':
loss_str = "Env Name: %s" % env_name
for metric, val in score_summary.items():
if metric in ['success_rate']:
print(
"Avg speaker %s, Avg listener %s, For the speaker weight %0.4f, the result is %0.4f"
% (avg_speaker, avg_listener, alpha, val))
loss_str += ",%s: %0.4f " % (metric, val)
print(loss_str)
print()
if args.submit:
json.dump(result_for_eval,
open(
os.path.join(log_dir,
"submit_%s.json" % env_name), 'w'),
sort_keys=True,
indent=4,
separators=(',', ': '))
print(final_log)
def train(train_env, tok, n_iters, log_every=100, val_envs={}, aug_env=None):
writer = SummaryWriter(logdir=log_dir)
listner = Seq2SeqAgent(train_env, "", tok, args.maxAction)
speaker = None
if args.self_train:
speaker = Speaker(train_env, listner, tok)
if args.speaker is not None:
print("Load the speaker from %s." % args.speaker)
speaker.load(args.speaker)
start_iter = 0
if args.load is not None:
print("LOAD THE listener from %s" % args.load)
start_iter = listner.load(os.path.join(args.load))
start = time.time()
best_val = {
'val_seen': {
"accu": 0.,
"state": "",
'update': False
},
'val_unseen': {
"accu": 0.,
"state": "",
'update': False
}
}
if args.fast_train:
log_every = 40
for idx in range(start_iter, start_iter + n_iters, log_every):
listner.logs = defaultdict(list)
interval = min(log_every, n_iters - idx)
iter = idx + interval
# Train for log_every interval
if aug_env is None: # The default training process
listner.env = train_env
listner.train(interval,
feedback=feedback_method) # Train interval iters
else:
if args.accumulate_grad:
for _ in range(interval // 2):
listner.zero_grad()
listner.env = train_env
# Train with GT data
args.ml_weight = 0.2
listner.accumulate_gradient(feedback_method)
listner.env = aug_env
# Train with Back Translation
args.ml_weight = 0.6 # Sem-Configuration
listner.accumulate_gradient(feedback_method,
speaker=speaker)
listner.optim_step()
else:
for _ in range(interval // 2):
# Train with GT data
listner.env = train_env
args.ml_weight = 0.2
listner.train(1, feedback=feedback_method)
# Train with Back Translation
listner.env = aug_env
args.ml_weight = 0.6
listner.train(1, feedback=feedback_method, speaker=speaker)
# Log the training stats to tensorboard
total = max(sum(listner.logs['total']), 1)
length = max(len(listner.logs['critic_loss']), 1)
critic_loss = sum(
listner.logs['critic_loss']) / total #/ length / args.batchSize
entropy = sum(
listner.logs['entropy']) / total #/ length / args.batchSize
predict_loss = sum(listner.logs['us_loss']) / max(
len(listner.logs['us_loss']), 1)
writer.add_scalar("loss/critic", critic_loss, idx)
writer.add_scalar("policy_entropy", entropy, idx)
writer.add_scalar("loss/unsupervised", predict_loss, idx)
writer.add_scalar("total_actions", total, idx)
writer.add_scalar("max_length", length, idx)
print("total_actions", total)
print("max_length", length)
# Run validation
loss_str = ""
for env_name, (env, evaluator) in val_envs.items():
listner.env = env
# Get validation loss under the same conditions as training
iters = None if args.fast_train or env_name != 'train' else 20 # 20 * 64 = 1280
# Get validation distance from goal under test evaluation conditions
listner.test(use_dropout=False, feedback='argmax', iters=iters)
result = listner.get_results()
score_summary, _ = evaluator.score(result)
loss_str += ", %s " % env_name
for metric, val in score_summary.items():
if metric in ['success_rate']:
writer.add_scalar("accuracy/%s" % env_name, val, idx)
if env_name in best_val:
if val > best_val[env_name]['accu']:
best_val[env_name]['accu'] = val
best_val[env_name]['update'] = True
loss_str += ', %s: %.3f' % (metric, val)
for env_name in best_val:
if best_val[env_name]['update']:
best_val[env_name]['state'] = 'Iter %d %s' % (iter, loss_str)
best_val[env_name]['update'] = False
listner.save(
idx,
os.path.join("snap", args.name, "state_dict",
"best_%s" % (env_name)))
print(('%s (%d %d%%) %s' % (timeSince(start,
float(iter) / n_iters), iter,
float(iter) / n_iters * 100, loss_str)))
if iter % 1000 == 0:
print("BEST RESULT TILL NOW")
for env_name in best_val:
print(env_name, best_val[env_name]['state'])
if iter % 50000 == 0:
listner.save(
idx,
os.path.join("snap", args.name, "state_dict",
"Iter_%06d" % (iter)))
listner.save(
idx,
os.path.join("snap", args.name, "state_dict", "LAST_iter%d" % (idx)))
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=(',', ': '))
def train(train_env, tok, n_iters, log_every=100, val_envs={}, aug_env=None):
writer = SummaryWriter(logdir=log_dir)
listner = Seq2SeqAgent(train_env, "", tok, args.maxAction)
speaker = None
if args.self_train:
speaker = Speaker(train_env, listner, tok)
if args.speaker is not None:
if args.upload:
print("Load the speaker from %s." % args.speaker)
speaker.load(
get_sync_dir(os.path.join(args.upload_path, args.speaker)))
else:
print("Load the speaker from %s." % args.speaker)
speaker.load(os.path.join(args.R2R_Aux_path, args.speaker))
start_iter = 0
if args.load is not None:
if args.upload:
refs_paths = get_outputs_refs_paths()['experiments'][0]
print(refs_paths)
load_model = os.path.join(refs_paths, args.load)
print(load_model)
print("LOAD THE listener from %s" % load_model)
start_iter = listner.load(load_model)
else:
print("LOAD THE listener from %s" % args.load)
start_iter = listner.load(
os.path.join(args.R2R_Aux_path, args.load))
start = time.time()
best_val = {
'val_seen': {
"accu": 0.,
"state": "",
'update': False
},
'val_unseen': {
"accu": 0.,
"state": "",
'update': False
}
}
if args.fast_train:
log_every = 40
for idx in range(start_iter, start_iter + n_iters, log_every):
listner.logs = defaultdict(list)
interval = min(log_every, start_iter + n_iters - idx)
iter = idx + interval
# Train for log_every interval
if aug_env is None: # The default training process
listner.env = train_env
listner.train(interval,
feedback=feedback_method) # Train interval iters
else:
if args.accumulate_grad:
for _ in range(interval // 2):
listner.zero_grad()
listner.env = train_env
# Train with GT data
args.ml_weight = 0.2
listner.accumulate_gradient(feedback_method)
listner.env = aug_env
# Train with Back Translation
args.ml_weight = 0.6 # Sem-Configuration
listner.accumulate_gradient(feedback_method,
speaker=speaker)
listner.optim_step()
else:
for _ in range(interval // 2):
# Train with GT data
listner.env = train_env
args.ml_weight = 0.2
listner.train(1, feedback=feedback_method)
# Train with Back Translation
listner.env = aug_env
args.ml_weight = 0.6
listner.train(1, feedback=feedback_method, speaker=speaker)
# Log the training stats to tensorboard
total = max(sum(listner.logs['total']), 1)
# import pdb; pdb.set_trace() # length_rl == length_ml ? entropy length
assert (max(len(listner.logs['rl_loss']),
1) == max(len(listner.logs['ml_loss']), 1))
max_rl_length = max(len(listner.logs['critic_loss']), 1)
log_length = max(len(listner.logs['rl_loss']), 1)
rl_loss = sum(listner.logs['rl_loss']) / log_length
ml_loss = sum(listner.logs['ml_loss']) / log_length
critic_loss = sum(listner.logs['critic_loss']
) / log_length #/ length / args.batchSize
spe_loss = sum(listner.logs['spe_loss']) / log_length
pro_loss = sum(listner.logs['pro_loss']) / log_length
mat_loss = sum(listner.logs['mat_loss']) / log_length
fea_loss = sum(listner.logs['fea_loss']) / log_length
ang_loss = sum(listner.logs['ang_loss']) / log_length
entropy = sum(
listner.logs['entropy']) / log_length #/ length / args.batchSize
predict_loss = sum(listner.logs['us_loss']) / log_length
writer.add_scalar("loss/rl_loss", rl_loss, idx)
writer.add_scalar("loss/ml_loss", ml_loss, idx)
writer.add_scalar("policy_entropy", entropy, idx)
writer.add_scalar("loss/spe_loss", spe_loss, idx)
writer.add_scalar("loss/pro_loss", pro_loss, idx)
writer.add_scalar("loss/mat_loss", mat_loss, idx)
writer.add_scalar("loss/fea_loss", fea_loss, idx)
writer.add_scalar("loss/ang_loss", ang_loss, idx)
writer.add_scalar("total_actions", total, idx)
writer.add_scalar("max_rl_length", max_rl_length, idx)
writer.add_scalar("loss/critic", critic_loss, idx)
writer.add_scalar("loss/unsupervised", predict_loss, idx)
print("total_actions", total)
print("max_rl_length", max_rl_length)
# Run validation
loss_str = ""
for env_name, (env, evaluator) in val_envs.items():
listner.env = env
# Get validation loss under the same conditions as training
iters = None if args.fast_train or env_name != 'train' else 20 # 20 * 64 = 1280
# Get validation distance from goal under test evaluation conditions
listner.test(use_dropout=False, feedback='argmax', iters=iters)
result = listner.get_results()
score_summary, _ = evaluator.score(result)
loss_str += "%s " % env_name
for metric, val in score_summary.items():
if metric in ['success_rate']:
loss_str += ', %s: %.4f' % (metric, val)
writer.add_scalar("%s/accuracy" % env_name, val, idx)
if env_name in best_val:
if val > best_val[env_name]['accu']:
best_val[env_name]['accu'] = val
best_val[env_name]['update'] = True
if metric in ['spl']:
writer.add_scalar("%s/spl" % env_name, val, idx)
loss_str += ', %s: %.4f' % (metric, val)
loss_str += '\n'
loss_str += '\n'
for env_name in best_val:
if best_val[env_name]['update']:
best_val[env_name]['state'] = 'Iter %d \n%s' % (iter, loss_str)
best_val[env_name]['update'] = False
file_dir = os.path.join(output_dir, "snap", args.name,
"state_dict", "best_%s" % (env_name))
listner.save(idx, file_dir)
print(('%s (%d %d%%) \n%s' % (timeSince(start,
float(iter) / n_iters), iter,
float(iter) / n_iters * 100, loss_str)))
if iter % 1000 == 0:
print("BEST RESULT TILL NOW")
for env_name in best_val:
print(env_name, best_val[env_name]['state'])
if iter % args.save_iter == 0:
file_dir = os.path.join(output_dir, "snap", args.name,
"state_dict", "Iter_%06d" % (iter))
listner.save(idx, file_dir)
def train_speaker(train_env, tok, n_iters, log_every=500, val_envs={}):
writer = SummaryWriter(logdir=log_dir)
listner = Seq2SeqAgent(train_env, "", tok, args.maxAction)
speaker = Speaker(train_env, listner, tok)
if args.load is not None:
print("LOAD THE Speaker from %s" % args.load)
speaker.load(os.path.join(args.load))
if args.fast_train:
log_every = 40
best_bleu = defaultdict(lambda: 0)
best_loss = defaultdict(lambda: 1232)
for idx in range(0, n_iters, log_every):
interval = min(log_every, n_iters - idx)
# Train for log_every interval
speaker.env = train_env
speaker.train(interval) # Train interval iters
print()
print("Iter: %d" % idx)
# Evaluation
for env_name, (env, evaluator) in val_envs.items():
if 'train' in env_name: # Ignore the large training set for the efficiency
continue
print("............ Evaluating %s ............." % env_name)
speaker.env = env
path2inst, loss, word_accu, sent_accu = speaker.valid()
path_id = next(iter(path2inst.keys()))
print('path_id:', path_id)
print("Inference: ", tok.decode_sentence(path2inst[path_id]))
print("GT: ", evaluator.gt[str(path_id)]['instructions'])
bleu_score, precisions = evaluator.bleu_score(path2inst)
# Tensorboard log
writer.add_scalar("bleu/%s" % (env_name), bleu_score, idx)
writer.add_scalar("loss/%s" % (env_name), loss, idx)
writer.add_scalar("word_accu/%s" % (env_name), word_accu, idx)
writer.add_scalar("sent_accu/%s" % (env_name), sent_accu, idx)
writer.add_scalar("bleu4/%s" % (env_name), precisions[3], idx)
# Save the model according to the bleu score
if bleu_score > best_bleu[env_name]:
best_bleu[env_name] = bleu_score
print('Save the model with %s BEST env bleu %0.4f' %
(env_name, bleu_score))
speaker.save(
idx,
os.path.join(log_dir, 'state_dict',
'best_%s_bleu' % env_name))
if loss < best_loss[env_name]:
best_loss[env_name] = loss
print('Save the model with %s BEST env loss %0.4f' %
(env_name, loss))
speaker.save(
idx,
os.path.join(log_dir, 'state_dict',
'best_%s_loss' % env_name))
# Screen print out
print(
"Bleu 1: %0.4f Bleu 2: %0.4f, Bleu 3 :%0.4f, Bleu 4: %0.4f" %
tuple(precisions))
def train(train_env, tok, n_iters, log_every=100, val_envs={}, aug_env=None,stok=None,press_env=None):
writer = SummaryWriter(logdir=log_dir)
listner = Seq2SeqAgent(train_env, "", tok, args.maxAction,encoder_type=args.encoderType)
speaker = None
if args.self_train:
if args.encoderType in ['DicEncoder','CEncoder','Dic']:
print("Note that u r using bert-based encoder,use speaker tokenizer to decode instructions!")
speaker = Speaker(train_env, listner, stok)
else:
speaker = Speaker(train_env, listner, tok)
if args.speaker is not None:
print("Load the speaker from %s." % args.speaker)
speaker.load(args.speaker)
start_iter = 0
if args.load is not None:
print("LOAD THE listener from %s" % args.load)
start_iter = listner.load(os.path.join(args.load))
listner.encoder.bert.update_lang_bert,listner.encoder.bert.config.update_lang_bert = args.d_transformer_update, args.d_transformer_update
listner.encoder.bert.update_add_layer, listner.encoder.bert.config.update_add_layer = args.d_update_add_layer, args.d_update_add_layer
myidx = 0
best_spl = 0
best_sr_sum = 0
start = time.time()
best_val = {'val_seen': {"accu": 0., "state":"", 'update':False},
'val_unseen': {"accu": 0., "state":"", 'update':False}}
best_val_sr_sum = {'sr_sum': {"accu": 0., "state":"", 'update':False},
'spl_unseen': {"accu": 0., "state":"", 'update':False}}
if args.fast_train:
log_every = 40
for idx in range(start_iter, start_iter+n_iters, log_every):
listner.logs = defaultdict(list)
interval = min(log_every, n_iters-idx)
iter = idx + interval
myidx += interval
print()
print("PROGRESS: {}%".format(round((myidx) * 100 / n_iters, 4)))
print()
# Train for log_every interval
if aug_env is None: # The default training process
listner.env = train_env
listner.train(interval, feedback=feedback_method) # Train interval iters
else:
if args.accumulate_grad:
for _ in range(interval // 2):
listner.zero_grad()
listner.env = train_env
# Train with GT data
args.ml_weight = 0.2
listner.accumulate_gradient(feedback_method)
listner.env = aug_env
# Train with Back Translation
args.ml_weight = 0.6 # Sem-Configuration
listner.accumulate_gradient(feedback_method, speaker=speaker)
listner.optim_step()
else:
for _ in range(interval // 2):
# Train with GT data
listner.env = train_env
args.ml_weight = 0.2
listner.train(1, feedback=feedback_method)
# Train with Back Translation
listner.env = aug_env
args.ml_weight = 0.6
listner.train(1, feedback=feedback_method, speaker=speaker)
# Log the training stats to tensorboard
total = max(sum(listner.logs['total']), 1)
length = max(len(listner.logs['critic_loss']), 1)
critic_loss = sum(listner.logs['critic_loss']) / total #/ length / args.batchSize
entropy = sum(listner.logs['entropy']) / total #/ length / args.batchSize
predict_loss = sum(listner.logs['us_loss']) / max(len(listner.logs['us_loss']), 1)
writer.add_scalar("loss/critic", critic_loss, idx)
writer.add_scalar("policy_entropy", entropy, idx)
writer.add_scalar("loss/unsupervised", predict_loss, idx)
writer.add_scalar("total_actions", total, idx)
writer.add_scalar("max_length", length, idx)
print("total_actions", total)
print("max_length", length)
data_log['iteration'].append(iter)
# Run validation
loss_str = ""
current_sr_sum = 0
for env_name, (env, evaluator) in val_envs.items():
listner.env = env
# Get validation loss under the same conditions as training
iters = None if args.fast_train or env_name != 'train' else 20 # 20 * 64 = 1280
# Get validation distance from goal under test evaluation conditions
listner.test(use_dropout=False, feedback='argmax', iters=iters)
result = listner.get_results()
score_summary, _ = evaluator.score(result)
loss_str += ", %s " % env_name
for metric,val in score_summary.items():
if metric in ['success_rate']:
writer.add_scalar("accuracy/%s" % env_name, val, idx)
if env_name in best_val:
current_sr_sum += val
if val > best_val[env_name]['accu']:
best_val[env_name]['accu'] = val
best_val[env_name]['update'] = True
loss_str += ', %s: %.3f' % (metric, val)
if metric == 'spl' and env_name == 'val_unseen':
if val > best_spl:
best_spl = val
best_val_sr_sum['spl_unseen']['accu'] = best_spl
best_val_sr_sum['spl_unseen']['update'] = True
data_log['%s %s' % (env_name, metric)].append(val)
if current_sr_sum > best_sr_sum:
best_sr_sum = current_sr_sum
best_val_sr_sum['sr_sum']['accu'] = best_sr_sum
best_val_sr_sum['sr_sum']['update'] = True
print()
print("EVALERR: {}%".format(best_spl))
print()
for env_name in best_val:
if best_val[env_name]['update']:
best_val[env_name]['state'] = 'Iter %d %s' % (iter, loss_str)
best_val[env_name]['update'] = False
if args.philly:
listner.save(idx, os.path.join(log_dir, "state_dict", "best_%s" % (env_name)))
else:
listner.save(idx, os.path.join("snap", args.name, "state_dict", "best_%s" % (env_name)))
for metric in best_val_sr_sum:
if best_val_sr_sum[metric]['update']:
best_val_sr_sum[metric]['state'] = 'Iter %d %s' % (iter, loss_str)
best_val_sr_sum[metric]['update'] = False
if args.philly:
listner.save(idx, os.path.join(log_dir, "state_dict", "best_%s" % (metric)))
else:
listner.save(idx, os.path.join("snap", args.name, "state_dict", "best_%s" % (metric)))
print(('%s (%d %d%%) %s' % (timeSince(start, float(iter)/n_iters),
iter, float(iter)/n_iters*100, loss_str)))
if iter % 1000 == 0:
print("BEST RESULT TILL NOW")
for env_name in best_val:
print(env_name, best_val[env_name]['state'])
df = pd.DataFrame(data_log)
df.set_index('iteration')
df_path = '%s/plot_log.csv' % (plot_dir)
write_num = 0
while (write_num < 20):
try:
df.to_csv(df_path)
break
except:
write_num += 1
#if iter % 50000 == 0:
# if args.philly:
# listner.save(idx, os.path.join(log_dir, "state_dict", "Iter_%06d" % (iter)))
# else:
# listner.save(idx, os.path.join(log_dir, "state_dict", "Iter_%06d" % (iter)))
# #listner.save(idx, os.path.join(log_dir, "state_dict", "Iter_%06d" % (iter)))
if args.philly:
listner.save(idx, os.path.join(log_dir, "state_dict", "LAST_iter%d" % (idx)))
else:
listner.save(idx, os.path.join('snap', args.name, "state_dict", "LAST_iter%d" % (idx)))
# torch_ds = torch.utils.data.Subset(torch_ds, range(1000))
print("The size of data split %s is %d" % (split, len(torch_ds)))
loader = torch.utils.data.DataLoader(
torch_ds,
batch_size=args.batch_size, shuffle=shuffle,
num_workers=args.workers, pin_memory=True,
drop_last=drop_last)
return dataset, torch_ds, loader
if 'speaker' in args.train:
train_tuple = get_tuple(args.dataset, 'train', shuffle=False, drop_last=True)
valid_tuple = get_tuple(args.dataset, 'valid', shuffle=False, drop_last=False)
speaker = Speaker(train_tuple[0]) # [0] is the dataset
if args.load is not None:
print("Load speaker from %s." % args.load)
speaker.load(args.load)
scores, result = speaker.evaluate(valid_tuple)
print("Have result for %d data" % len(result))
print("The validation result is:")
print(scores)
if args.train == 'speaker':
speaker.train(train_tuple, valid_tuple, args.epochs)
if args.train == 'rlspeaker':
speaker.train(train_tuple, valid_tuple, args.epochs, rl=True)
elif args.train == 'validspeaker':
scores, result = speaker.evaluate(valid_tuple)
print(scores)
elif args.train == 'testspeaker':
test_tuple = get_tuple(args.dataset, 'test', shuffle=False, drop_last=False)
scores, result = speaker.evaluate(test_tuple)
print("Test:")
