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train.py
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train.py
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import paths
import torch
from torch import optim
import json
import os
import os.path
import sys
import time
import datetime
import numpy as np
import pandas as pd
from collections import defaultdict
import argparse
import shutil
from tqdm import tqdm
import utils
from utils import read_vocab, Tokenizer, vocab_pad_idx, timeSince, try_cuda
from utils import colorize, filter_param
from utils import get_confusion_matrix_image, get_bar_image
from model import BertEncoder, EncoderLSTM, AttnDecoderLSTM
from model import CogroundDecoderLSTM, ProgressMonitor, DeviationMonitor
from model import HallucinationDecoderLSTM0
from model import HallucinationDecoderLSTM1
from model import HallucinationDecoderLSTM2
from model import HallucinationDecoderLSTM3
from model import HallucinationDecoderLSTM4
from model import HallucinationDecoderLSTM5
from model import SpeakerEncoderLSTM, DotScorer, BacktrackButton
from follower import Seq2SeqAgent, RandomAgent
from scorer import Scorer
from env import R2RBatch, ImageFeatures
from refer360_env import Refer360Batch, Refer360ImageFeatures, make_sim
import eval
import refer360_eval
from vocab import SUBTRAIN_VOCAB, TRAIN_VOCAB
from tensorboardX import SummaryWriter
DECODER2PREFIX = {
'coground': '_cg',
'attention': '_att',
'hal0': '_hal0',
'hal1': '_hal1',
'hal2': '_hal2',
'hal3': '_hal3',
'hal4': '_hal4',
'hal5': '_hal5',
}
DECODER2MODEL = {
'coground': CogroundDecoderLSTM,
'attention': AttnDecoderLSTM,
'hal0': HallucinationDecoderLSTM0,
'hal1': HallucinationDecoderLSTM1,
'hal2': HallucinationDecoderLSTM2,
'hal3': HallucinationDecoderLSTM3,
'hal4': HallucinationDecoderLSTM4,
'hal5': HallucinationDecoderLSTM5
}
def get_max_num_a(args):
if args.decoder in ['coground', 'attention']:
return -1
else:
if args.prefix in ['touchdown', 'td', 'refer360', 'r360tiny']:
return 9
elif args.prefix in ['r2r', 'R2R', 'REVERIE', 'RxR_en-ALL']:
return 36
else:
raise NotImplementedError()
def get_word_embedding_size(args):
if args.decoder == 'coground' or args.bidirectional:
return int(
args.hidden_size / 2)
else:
return args.hidden_size
def get_action_embedding_size(args, action_embedding_size):
if not args.decoder == 'coground' and args.use_visited_embeddings:
action_embedding_size -= 64
return action_embedding_size
def get_model_prefix(args, image_feature_list,
dump_args=False):
image_feature_name = '+'.join(
[featurizer.get_name() for featurizer in image_feature_list])
nn = ('{}{}{}{}{}{}{}{}{}{}{}{}{}{}'.format(
('_bt' if args.bert else ''),
('_sc' if args.scorer else ''),
('_mh' if args.num_head > 1 else ''),
(DECODER2PREFIX[args.decoder]),
('_pm' if args.prog_monitor else ''),
('_sa' if args.soft_align else ''),
('_bi' if args.bidirectional else ''),
('_wv' if args.use_wordvec else ''),
('FT' if args.wordvec_finetune else ''),
('_GT' if args.use_gt_actions else ''),
('_ve'+args.use_visited_embeddings if args.use_visited_embeddings else ''),
('_ale' if args.use_absolute_location_embeddings else ''),
('_stop' if args.use_stop_embeddings else ''),
('_tse' if args.use_timestep_embeddings else ''),
))
model_prefix = 'follower{}_F{}_IMGF{}_NHe{}_Hid{}_ENL{}_DR{}'.format(
nn,
args.feedback_method,
image_feature_name,
args.num_head,
args.hidden_size,
args.encoder_num_layers,
args.dropout_ratio)
if args.use_train_subset:
model_prefix = 'trainsub_' + model_prefix
if args.use_pretraining:
model_prefix = model_prefix.replace(
'follower', 'follower_with_pretraining', 1)
if dump_args:
now = datetime.datetime.now()
args_fn = '%s.args-%d-%d-%d,%d:%d:%d' % (model_prefix, now.year, now.month,
now.day, now.hour, now.minute, now.second)
with open(os.path.join(args.PLOT_DIR, args_fn), 'w') as out_file:
out_file.write(' '.join(sys.argv))
out_file.write('\n')
json.dump(dict(args), out_file)
out_file.write('\n')
return model_prefix
def eval_model(agent, results_path, use_dropout, feedback, allow_cheat=False):
agent.results_path = results_path
agent.test(
use_dropout=use_dropout, feedback=feedback, allow_cheat=allow_cheat)
def train(args, train_env, agent, optimizers, n_iters, val_envs=None):
''' Train on training set, validating on both seen and unseen. '''
if val_envs is None:
val_envs = {}
split_string = '-'.join(train_env.splits)
print('Training with %s feedback' % args.feedback_method)
writer_path = os.path.join(args.PLOT_DIR, get_model_prefix(
args, train_env.image_features_list, dump_args=True))
writer = SummaryWriter(writer_path)
print('tensorboard path is', writer_path)
data_log = defaultdict(list)
start = time.time()
def make_path(n_iter):
return os.path.join(
args.SNAPSHOT_DIR, '%s_%s_iter_%d' % (
get_model_prefix(args, train_env.image_features_list),
split_string, n_iter))
best_metrics = {}
last_model_saved = {}
for idx in range(0, n_iters, args.log_every):
agent.env = train_env
interval = min(args.log_every, n_iters-idx)
iter = idx + interval
data_log['iteration'].append(iter)
loss_str = ''
# Train for log_every interval
env_name = 'train'
agent.train(optimizers, interval, feedback=args.feedback_method,
training_counter=idx,
max_iters=n_iters)
_loss_str, losses, images = agent.get_loss_info()
loss_str += env_name + ' ' + _loss_str
for k, v in losses.items():
data_log['%s %s' % (env_name, k)].append(v)
writer.add_scalar('{} {}'.format(env_name, k), v, iter)
for k, v in images.items():
img_conf = get_confusion_matrix_image(
[[str(v) for v in range(v.size(1))], [str(v) for v in range(v.size(0))]], v.cpu().numpy(), '')
writer.add_image(k, img_conf, iter)
save_log = []
# Run validation
for env_name, (val_env, evaluator) in sorted(val_envs.items()):
agent.env = val_env
# Get validation loss under the same conditions as training
agent.test(use_dropout=True, feedback=args.feedback_method,
allow_cheat=True)
_loss_str, losses, _ = agent.get_loss_info()
loss_str += ', ' + env_name + ' ' + _loss_str
for k, v in losses.items():
data_log['%s %s' % (env_name, k)].append(v)
writer.add_scalar('{} {}'.format(env_name, k), v, iter)
agent.results_path = '%s/%s_%s_iter_%d.json' % (
args.RESULT_DIR, get_model_prefix(
args, train_env.image_features_list),
env_name, iter)
best_path = '%s/%s_%s.best.' % (
args.RESULT_DIR, get_model_prefix(
args, train_env.image_features_list),
env_name)
# Get validation distance from goal under evaluation conditions
agent.test(use_dropout=False, feedback='argmax')
print('evaluating on {}'.format(env_name))
score_summary, all_scores, score_analysis = evaluator.score_results(
agent.results)
scores_path = make_path(iter) + '_%s_scores.npy' % (
env_name)
print('scores stats are dumped to %s' % scores_path)
with open(scores_path, 'wb') as f:
np.save(f, all_scores)
for metric, val in sorted(score_summary.items()):
writer.add_scalar('{} {}'.format(env_name, metric), val, iter)
data_log['%s %s' % (env_name, metric)].append(val)
if metric in args.metrics.split(','):
for analysis in score_analysis:
keys = sorted(score_analysis[analysis][metric].keys())
means = [np.mean(score_analysis[analysis][metric][key])
for key in keys]
stds = [np.std(score_analysis[analysis][metric][key])
for key in keys]
x_pos = np.arange(len(keys))
img_bar = get_bar_image(x_pos, keys, means, stds)
writer.add_image(analysis+'_'+metric, img_bar, iter)
loss_str += ', %s: %.3f' % (metric, val)
key = (env_name, metric)
if key not in best_metrics or best_metrics[key] < val:
best_metrics[key] = val
if not args.no_save:
model_path = make_path(iter) + '_%s-%s=%.3f' % (
env_name, metric, val)
save_log.append(
'new best, saved model to %s' % model_path)
agent.save(model_path)
agent.write_results()
if key in last_model_saved:
for old_model_path in last_model_saved[key]:
if os.path.isfile(old_model_path):
os.remove(old_model_path)
# last_model_saved[key] = [agent.results_path] +\
last_model_saved[key] = [] +\
list(agent.modules_paths(model_path))
best_file = best_path + '%s' % (metric) + '.json'
shutil.copyfile(agent.results_path, best_file)
print(('%s (%d %d%%) %s' % (
timeSince(start, float(iter)/n_iters),
iter, float(iter)/n_iters*100, loss_str)))
for s in save_log:
print(colorize(s))
if not args.no_save:
if args.save_every and iter % args.save_every == 0:
agent.save(make_path(iter))
df = pd.DataFrame(data_log)
df.set_index('iteration')
df_path = '%s/%s_%s_log.csv' % (
args.PLOT_DIR, get_model_prefix(
args, train_env.image_features_list), split_string)
print('data_log written to', df_path)
df.to_csv(df_path)
def setup(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
def make_more_train_env(args, train_vocab_path, train_splits):
setup(args.seed)
if args.env == 'r2r':
EnvBatch = R2RBatch
ImgFeatures = ImageFeatures
elif args.env in ['refer360']:
EnvBatch = Refer360Batch
ImgFeatures = Refer360ImageFeatures
else:
raise NotImplementedError(
'this {} environment is not implemented.'.format(args.env))
image_features_list = ImgFeatures.from_args(args)
vocab = read_vocab(train_vocab_path, args.language)
tok = Tokenizer(vocab=vocab)
sim_cache = None
if args.use_raw:
sim_cache = cache_sim(args)
train_env = EnvBatch(image_features_list,
splits=train_splits,
tokenizer=tok,
sim_cache=sim_cache,
args=args)
return train_env
def make_scorer(args,
action_embedding_size=-1,
feature_size=-1):
bidirectional = args.bidirectional
enc_hidden_size = int(
args.hidden_size/2) if bidirectional else args.hidden_size
traj_encoder = try_cuda(SpeakerEncoderLSTM(action_embedding_size, feature_size,
enc_hidden_size, args.dropout_ratio,
bidirectional=args.bidirectional))
scorer_module = try_cuda(DotScorer(enc_hidden_size, enc_hidden_size))
scorer = Scorer(scorer_module, traj_encoder)
if args.load_scorer != '':
scorer.load(args.load_scorer)
print(colorize('load scorer traj ' + args.load_scorer))
elif args.load_traj_encoder != '':
scorer.load_traj_encoder(args.load_traj_encoder)
print(colorize('load traj encoder ' + args.load_traj_encoder))
return scorer
def make_follower(args, vocab,
action_embedding_size=-1,
feature_size=-1):
if args.random_baseline:
print('using random agent')
agent = RandomAgent
return agent
enc_hidden_size = int(
args.hidden_size//2) if args.bidirectional else args.hidden_size
wordvec = np.load(args.wordvec_path) if args.use_wordvec else None
if args.bert:
Encoder = BertEncoder
args.hidden_size = 768
else:
Encoder = EncoderLSTM
args.visual_hidden_size = args.hidden_size * 2
Decoder = DECODER2MODEL[args.decoder]
word_embedding_size = get_word_embedding_size(args)
encoder = try_cuda(Encoder(len(vocab), word_embedding_size, enc_hidden_size, vocab_pad_idx, args.dropout_ratio,
bidirectional=args.bidirectional,
num_layers=args.encoder_num_layers,
wordvec=wordvec,
wordvec_finetune=args.wordvec_finetune,
))
max_num_a = get_max_num_a(args)
decoder = try_cuda(Decoder(
action_embedding_size, args.hidden_size, args.dropout_ratio,
feature_size=feature_size,
num_head=args.num_head,
max_len=args.max_input_length,
visual_hidden_size=args.visual_hidden_size,
visual_context_size=feature_size,
num_actions=max_num_a))
action_embedding_size = get_action_embedding_size(
args, action_embedding_size)
prog_monitor = try_cuda(ProgressMonitor(action_embedding_size,
args.hidden_size, text_len=args.max_input_length)) if args.prog_monitor else None
bt_button = try_cuda(BacktrackButton()) if args.bt_button else None
dev_monitor = try_cuda(DeviationMonitor(action_embedding_size,
args.hidden_size)) if args.dev_monitor else None
agent = Seq2SeqAgent(
None, '', encoder, decoder, args.max_episode_len,
max_instruction_length=args.max_input_length,
attn_only_verb=args.attn_only_verb,
clip_rate=args.clip_rate,
max_num_a=max_num_a,
reading=args.use_reading)
agent.prog_monitor = prog_monitor
agent.dev_monitor = dev_monitor
agent.bt_button = bt_button
agent.soft_align = args.soft_align
if args.scorer:
agent.scorer = make_scorer(args,
action_embedding_size=action_embedding_size,
feature_size=feature_size)
if args.load_follower != '':
scorer_exists = os.path.isfile(args.load_follower + '_scorer_enc')
agent.load(args.load_follower, load_scorer=(
args.load_scorer == '' and scorer_exists))
print(colorize('load follower ' + args.load_follower))
return agent
def cache_sim(args):
sim_cache = {}
if args.env == 'r2r':
scans_root = './data/v1/scans/'
pbar = tqdm(os.listdir(scans_root))
sim_cache = {}
for scan in pbar:
sim_cache_file = os.path.join(scans_root, scan, 'sim_cache.npy')
if not os.path.exists(sim_cache_file):
print('{} does not exist! generate cache file!', sim_cache_file)
quit(0)
sc = np.load(sim_cache_file, allow_pickle=True)[()]
sim_cache[scan] = sc
else:
image_list = [line.strip()
for line in open(args.image_list_file)]
if args.prefix in ['refer360', 'r360tiny']:
width, height = 4552, 2276
elif args.prefix in ['touchdown', 'td']:
width, height = 3000, 1500
pbar = tqdm(image_list)
for fname in pbar:
pano = fname.split('/')[-1].split('.')[0]
sim = make_sim(args.cache_root,
image_w=Refer360ImageFeatures.IMAGE_W,
image_h=Refer360ImageFeatures.IMAGE_H,
fov=Refer360ImageFeatures.VFOV,
height=height,
width=width,
reading=args.use_reading,
raw=args.use_raw)
sim.set_pano(pano)
sim.load_fovs()
sim.look_fov(0)
sim_cache[pano] = sim
return sim_cache
def make_env_and_models(args, train_vocab_path, train_splits, test_splits):
setup(args.seed)
if args.prefix in ['refer360', 'r360tiny', 'RxR_en-ALL']:
width, height = 4552, 2276
elif args.prefix in ['touchdown', 'td']:
width, height = 3000, 1500
if args.env == 'r2r':
EnvBatch = R2RBatch
ImgFeatures = ImageFeatures
Eval = eval.Evaluation
env_sim = None
elif args.env in ['refer360']:
EnvBatch = Refer360Batch
ImgFeatures = Refer360ImageFeatures
Eval = refer360_eval.Refer360Evaluation
sim = make_sim(args.cache_root,
image_w=Refer360ImageFeatures.IMAGE_W,
image_h=Refer360ImageFeatures.IMAGE_H,
fov=Refer360ImageFeatures.VFOV,
height=height,
width=width,
reading=args.use_reading)
sim.load_maps()
env_sim = sim
else:
raise NotImplementedError(
'this {} environment is not implemented.'.format(args.env))
image_features_list = ImgFeatures.from_args(args)
vocab = read_vocab(train_vocab_path, args.language)
print('vocab size:',len(vocab))
tok = Tokenizer(vocab=vocab)
sim_cache = None
if args.use_raw:
sim_cache = cache_sim(args)
train_env = EnvBatch(image_features_list,
splits=train_splits,
tokenizer=tok,
sim_cache=sim_cache,
args=args) if len(train_splits) > 0 else None
test_envs = {
split: (EnvBatch(image_features_list,
splits=[split],
tokenizer=tok,
sim_cache=sim_cache,
args=args),
Eval([split],
sim=env_sim,
args=args))
for split in test_splits}
feature_size = sum(
[featurizer.feature_dim for featurizer in image_features_list]) + 128
if args.use_visited_embeddings:
feature_size += 64
if args.use_oracle_embeddings:
feature_size += 64
if args.use_absolute_location_embeddings:
feature_size += 64
if args.use_stop_embeddings:
feature_size += 64
if args.use_timestep_embeddings:
feature_size += 64
if args.use_reading:
feature_size += 64
agent = make_follower(args, vocab,
action_embedding_size=feature_size,
feature_size=feature_size)
agent.env = train_env
return train_env, test_envs, agent
def train_setup(args, train_splits=['train']):
if args.prefix in ['refer360', 'r2r', 'R2R', 'REVERIE', 'r360tiny', 'RxR_en-ALL']:
val_splits = ['val_seen', 'val_unseen']
elif args.prefix in ['touchdown', 'td']:
val_splits = ['dev']
else:
raise NotImplementedError()
if args.use_test_set:
raise NotImplementedError()
# val_splits = ['val_seen', 'val_unseen']
# val_splits = ['test_seen','test_unseen']
if args.debug:
args.log_every = 3
args.n_iters = 2
args.image_feature_type = ['none']
args.refer360_image_feature_type = ['none']
vocab = TRAIN_VOCAB
if args.use_train_subset:
train_splits = ['sub_' + split for split in train_splits]
val_splits = ['sub_' + split for split in val_splits]
vocab = SUBTRAIN_VOCAB
train_env, val_envs, agent = make_env_and_models(
args, vocab, train_splits, val_splits)
if args.use_pretraining:
pretrain_splits = args.pretrain_splits
assert len(pretrain_splits) > 0, \
'must specify at least one pretrain split'
pretrain_env = make_more_train_env(
args, vocab, pretrain_splits)
if args.use_pretraining:
return agent, train_env, val_envs, pretrain_env
else:
return agent, train_env, val_envs
def train_val(args):
''' Train on the training set, and validate on seen and unseen splits. '''
if args.use_pretraining:
agent, train_env, val_envs, pretrain_env = train_setup(args)
else:
agent, train_env, val_envs = train_setup(args)
m_dict = {
'follower': [agent.encoder, agent.decoder],
'pm': [agent.prog_monitor],
'follower+pm': [agent.encoder, agent.decoder, agent.prog_monitor],
'all': agent.modules()
}
if agent.scorer:
m_dict['scorer_all'] = agent.scorer.modules()
m_dict['scorer_scorer'] = [agent.scorer.scorer]
optimizers = [optim.Adam(filter_param(m),
lr=args.learning_rate,
weight_decay=args.weight_decay) for m in m_dict[args.grad] if len(filter_param(m))]
if args.use_pretraining:
train(args, pretrain_env, agent, optimizers,
args.n_pretrain_iters, val_envs=val_envs)
print('will use device:', torch.cuda.get_device_name(0))
train(args, train_env, agent, optimizers,
args.n_iters, val_envs=val_envs)
# TODO
# def test_submission(args):
# ''' Train on combined training and validation sets, and generate test
# submission. '''
def make_arg_parser():
parser = argparse.ArgumentParser()
ImageFeatures.add_args(parser)
Refer360ImageFeatures.add_args(parser)
parser.add_argument('--load_scorer', type=str, default='')
parser.add_argument('--load_follower', type=str, default='')
parser.add_argument('--load_traj_encoder', type=str, default='')
parser.add_argument('--feedback_method',
choices=['sample', 'teacher', 'sample1step', 'sample2step', 'sample3step', 'teacher+sample', 'recover', 'argmax',
'scheduledsampling', 'sss', 'sshalf', 'ssquarter', 'ss01', 'ss05', 'ss10', 'ss15','ss20','ss25','ss50'], default='sample')
parser.add_argument('--debug', action='store_true')
parser.add_argument('--bidirectional', action='store_true')
parser.add_argument('--hidden_size', type=int, default=256)
parser.add_argument('--encoder_num_layers', type=int, default=2)
parser.add_argument('--learning_rate', type=float, default=0.0001)
parser.add_argument('--clip_rate', type=float, default=0.)
parser.add_argument('--weight_decay', type=float, default=0.0005)
parser.add_argument('--dropout_ratio', type=float, default=0.5)
parser.add_argument('--bert', action='store_true')
parser.add_argument('--num_head', type=int, default=1)
parser.add_argument('--scorer', action='store_true')
parser.add_argument('--decoder', type=str, default='coground',
help='decoder type, default=coground')
parser.add_argument('--prog_monitor', action='store_false')
parser.add_argument('--dev_monitor', action='store_true')
parser.add_argument('--soft_align', action='store_true')
parser.add_argument('--bt_button', action='store_true')
parser.add_argument('--use_wordvec', action='store_true')
parser.add_argument('--attn_only_verb', action='store_true')
parser.add_argument('--use_gt_actions', action='store_true')
parser.add_argument('--use_absolute_location_embeddings',
action='store_true')
parser.add_argument('--use_stop_embeddings', action='store_true')
parser.add_argument('--use_timestep_embeddings', action='store_true')
parser.add_argument('--use_visited_embeddings',
type=str,
choices=['', 'ones', 'zeros', 'count', 'pe'],
default='')
parser.add_argument('--use_oracle_embeddings', action='store_true')
parser.add_argument(
'--use_object_embeddings', action='store_true')
parser.add_argument('--n_iters', type=int, default=100000)
parser.add_argument('--log_every', type=int, default=5000)
parser.add_argument('--save_every', type=int, default=5000)
parser.add_argument('--max_input_length', type=int, default=80)
parser.add_argument('--max_episode_len', type=int, default=20)
parser.add_argument('--grad', type=str, default='all')
parser.add_argument('--metrics', type=str,
default='success',
help='Success metric, default=success')
parser.add_argument('--use_pretraining', action='store_true')
parser.add_argument('--pretrain_splits', nargs='+', default=[])
parser.add_argument('--n_pretrain_iters', type=int, default=50000)
parser.add_argument('--use_train_subset', action='store_true',
help='use a subset of the original train data for validation')
parser.add_argument('--use_test_set', action='store_true')
parser.add_argument('--no_save', action='store_true')
parser.add_argument('--random_baseline', action='store_true')
parser.add_argument('--seed', type=int, default=10)
parser.add_argument('--beam_size', type=int, default=1)
parser.add_argument('--prefix', type=str, default='R2R')
parser.add_argument('--language', type=str, default='')
parser.add_argument('--wordvec_path', type=str,
default='tasks/R2R/data/train_glove')
parser.add_argument('--wordvec_finetune', action='store_true')
parser.add_argument('--error_margin', type=float, default=3.0)
parser.add_argument('--use_intermediate', action='store_true')
parser.add_argument('--use_reading', action='store_true')
parser.add_argument('--use_raw', action='store_true')
parser.add_argument('--add_asterix', action='store_true')
parser.add_argument('--img_features_root', type=str,
default='./img_features')
parser.add_argument('--refer360_root', type=str,
default='refer360_data')
parser.add_argument('--angle_inc', type=int, default=30)
parser.add_argument('--deaf', action='store_true')
parser.add_argument('--blind', action='store_true')
parser.add_argument('--no_lookahead', action='store_true')
parser.add_argument('--nextstep', action='store_true')
parser.add_argument('--verbose', action='store_true')
return parser
if __name__ == '__main__':
torch.backends.cudnn.enabled = False
utils.run(make_arg_parser(), train_val)