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train_bc.py
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import sys
if '/opt/ros/kinetic/lib/python2.7/dist-packages' in sys.path:
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
from configs.default import get_config
from model.policy import *
from trainer.il.bc_trainer import BC_trainer
from gym.spaces.dict import Dict as SpaceDict
from gym.spaces.box import Box
from gym.spaces.discrete import Discrete
import os
import argparse
from dataset.multidemodataset import HabitatDemoMultiGoalDataset
from torch.utils.data import DataLoader
import torch
torch.backends.cudnn.enable = True
import time
import numpy as np
from torch.utils.tensorboard import SummaryWriter
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, required=True, help="path to config yaml containing info about experiment")
parser.add_argument("--gpu", type=str, default="0", help="gpus",)
parser.add_argument("--version", type=str, default="test", help="name to save")
parser.add_argument("--stop", action='store_true', default=False, help="include stop action or not",)
parser.add_argument('--data-dir', default='daaaaaa', type=str)
parser.add_argument('--resume', default='none', type=str)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
device = 'cpu' if args.gpu == '-1' else 'cuda'
def train():
observation_space = SpaceDict({
'panoramic_rgb': Box(low=0, high=256, shape=(144, 192, 3), dtype=np.float32),
'panoramic_depth': Box(low=0, high=256, shape=(144,192, 1), dtype=np.float32),
'target_goal': Box(low=0, high=256, shape=(144, 192, 3), dtype=np.float32),
'step': Box(low=0, high=500, shape=(1,), dtype=np.float32),
'prev_act': Box(low=0, high=3, shape=(1,), dtype=np.int32),
'gt_action': Box(low=0, high=3, shape=(1,), dtype=np.int32)
})
config = get_config(args.config)
s = time.time()
action_space = Discrete(4) if args.stop else Discrete(3)
stop_info = 'INCLUDING' if args.stop else 'EXCLUDING'
print('POLICY : {}'.format(config.POLICY))
print('TRAINING INFO : {} STOP ACTION'.format(stop_info))
config.defrost()
config.NUM_PROCESSES = config.BC.batch_size
config.TORCH_GPU_ID = "0"#args.gpu
config.freeze()
policy = eval(config.POLICY)(
observation_space=observation_space,
action_space=action_space,
hidden_size=config.features.hidden_size,
rnn_type=config.features.rnn_type,
num_recurrent_layers=config.features.num_recurrent_layers,
backbone=config.features.backbone,
goal_sensor_uuid=config.TASK_CONFIG.TASK.GOAL_SENSOR_UUID,
normalize_visual_inputs=True,
cfg=config
)
trainer = eval(config.IL_TRAINER_NAME)(config, policy)
DATA_DIR = args.data_dir
train_data_list = [os.path.join(DATA_DIR, 'train', x) for x in sorted(os.listdir(os.path.join(DATA_DIR, 'train')))]
valid_data_list = [os.path.join(DATA_DIR, 'val', x) for x in sorted(os.listdir(os.path.join(DATA_DIR, 'val')))]
params = {'batch_size': config.BC.batch_size,
'shuffle': True,
'num_workers': config.BC.num_workers,
'pin_memory': True}
train_dataset = HabitatDemoMultiGoalDataset(config, train_data_list, args.stop)
train_dataloader = DataLoader(train_dataset, **params)
train_iter = iter(train_dataloader)
valid_dataset = HabitatDemoMultiGoalDataset(config, valid_data_list, args.stop)
valid_params = params
valid_dataloader = DataLoader(valid_dataset, **valid_params)
valid_iter = iter(valid_dataloader)
version_name = config.saving.name if args.version == 'none' else args.version
version_name = version_name
version_name += '_start_time:{}'.format(time.ctime())
IMAGE_DIR = os.path.join('data', 'images', version_name)
SAVE_DIR = os.path.join('data', 'new_checkpoints', version_name)
LOG_DIR = os.path.join('data', 'logs', version_name)
os.makedirs(IMAGE_DIR, exist_ok=True)
os.makedirs(SAVE_DIR, exist_ok=True)
os.makedirs(LOG_DIR, exist_ok=True)
start_step = 0
start_epoch = 0
if args.resume != 'none':
sd = torch.load(args.resume)
start_epoch, start_step = sd['trained']
trainer.agent.load_state_dict(sd['state_dict'])
print('load {}, start_ep {}, strat_step {}'.format(args.resume, start_epoch, start_step))
print_every = config.saving.log_interval
save_every = config.saving.save_interval
eval_every = config.saving.eval_interval
writer = SummaryWriter(log_dir=LOG_DIR)
start = time.time()
temp = start
step = start_step
step_values = [10000, 50000, 100000]
step_index = 0
lr = config.BC.lr
def adjust_learning_rate(optimizer, step_index, lr_decay):
lr = config.BC.lr * (lr_decay ** step_index)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
trainer.to(device)
trainer.train()
for epoch in range(start_epoch, config.BC.max_epoch):
train_dataloader = DataLoader(train_dataset, **params)
train_iter = iter(train_dataloader)
loss_summary_dict = {}
s = time.time()
for batch in train_iter:
results, loss_dict = trainer(batch)
for k,v in loss_dict.items():
if k not in loss_summary_dict.keys():
loss_summary_dict[k] = []
loss_summary_dict[k].append(v)
if step in step_values:
step_index += 1
lr = adjust_learning_rate(trainer.optim, step_index, 0.1)
if step % print_every == 0:
loss_str = ''
writer_dict = {}
for k,v in loss_summary_dict.items():
value = np.array(v).mean()
loss_str += '%s: %.3f '%(k,value)
writer_dict[k] = value
print("time = %.2fm, epo %d, step %d, lr: %.5f, %ds per %d iters || loss : " % ((time.time() - start) // 60, epoch + 1,
step + 1, lr, time.time() - temp, print_every), loss_str)
loss_summary_dict = {}
temp = time.time()
writer.add_scalars('loss', writer_dict, step)
#trainer.visualize(results, os.path.join(IMAGE_DIR, 'train_{}_{}'.format(results['scene'],step)))
if step % save_every == 0 :
trainer.save(file_name=os.path.join(SAVE_DIR, 'epoch%04diter%05d.pt' % (epoch, step)),epoch=epoch, step=step)
if step % eval_every == 0 and step > 0:
trainer.eval()
eval_start = time.time()
with torch.no_grad():
val_loss_summary_dict = {}
for j in range(100):
try:
batch = next(valid_iter)
except:
valid_dataloader = DataLoader(valid_dataset, **valid_params)
valid_iter = iter(valid_dataloader)
batch = next(valid_iter)
results, loss_dict = trainer(batch, train=False)
#if j % 100 == 0:
#trainer.visualize(results,os.path.join(IMAGE_DIR, 'validate_{}_{}_{}'.format(results['scene'], step, j)))
for k, v in loss_dict.items():
if k not in val_loss_summary_dict.keys():
val_loss_summary_dict[k] = []
val_loss_summary_dict[k].append(v)
loss_str = ''
writer_dict = {}
for k, v in val_loss_summary_dict.items():
value = np.array(v).mean()
loss_str += '%s: %.3f ' %(k, value)
writer_dict[k] = value
print("validation = time = %.2fm, epo %d, step %d, lr: %.5f, %ds per %d iters || loss : " % (
(time.time() - start) // 60,
epoch + 1, step + 1,
lr, time.time() - eval_start, print_every), loss_str)
loss_summary_dict = {}
temp = time.time()
writer.add_scalars('val_loss', writer_dict, step)
trainer.train()
step += 1
print('===> end training')
if __name__ == '__main__':
train()