from config import setSeed, getConfig
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from main.encoder import PixelEncoder
from main.model import CURL
from IPython import embed
setSeed(2)
assert len(sys.argv) == 2, "Indicate a configuration file like 'config_0.0'"
conf = getConfig(sys.argv[1])
MINERL_GYM_ENV = os.getenv('MINERL_GYM_ENV', 'MineRLNavigate-v0')
# MINERL_GYM_ENV = os.getenv('MINERL_GYM_ENV', 'MineRLTreechop-v0')
MINERL_DATA_ROOT = os.getenv(
'MINERL_DATA_ROOT', '/home/usuaris/imatge/juan.jose.nieto/mineRL/data/')
data = minerl.data.make(MINERL_GYM_ENV,
data_dir=MINERL_DATA_ROOT,
num_workers=1)
feature_dim = conf['curl']['embedding_dim']
img_size = conf['curl']['img_size']
obs_shape = (3, img_size, img_size)
batch_size = conf['batch_size']
from customLoader import MinecraftData
from pprint import pprint
from os.path import join
from pathlib import Path
from scipy.signal import savgol_filter
from torchvision.utils import make_grid
from torchvision.transforms import transforms
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from IPython import embed
setSeed(0)
assert len(sys.argv) == 2, "Indicate a configuration file like 'config_0.0'"
conf = getConfig(sys.argv[1])
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5), (1.0,1.0,1.0))
])
mrl_val = MinecraftData(conf['environment'], 'val', conf['split'], False, transform=transform)
validation_loader = DataLoader(mrl_val, batch_size=16, shuffle=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")