class DatasetRealSmolBulletV2NoSim(Dataset):
def __init__(self,
path="~/data/sim2real/data-realigned-v3-{}-bullet.npz",
train=True):
super().__init__()
ds_name = "train"
if not train:
ds_name = "test"
path_ext = path.format(ds_name)
print("using dataset:", path_ext)
self.ds = DatasetProduction()
self.ds.load(path_ext)
def __len__(self):
return len(self.ds.current_real)
def format_data(self, idx):
return (np.hstack((self.ds.current_real[idx], self.ds.action[idx])),
self.ds.next_real[idx] - self.ds.current_real[idx])
def __getitem__(self, idx):
x, y = self.format_data(idx)
return {"x": x, "y": y}
def __init__(self,
path="~/data/sim2real/data-realigned-v3-{}-bullet.npz",
train=True):
super().__init__()
ds_name = "train"
if not train:
ds_name = "test"
path_ext = path.format(ds_name)
print("using dataset:", path_ext)
self.ds = DatasetProduction()
self.ds.load(path_ext)
import time
import numpy as np
import os
import matplotlib.pyplot as plt
import gym
import gym_ergojr
import torch
from gym_ergojr.sim.single_robot import SingleRobot
from hyperdash import Experiment
from s2rr.movements.dataset import DatasetProduction
from torch.autograd import Variable
from simple_joints_lstm.lstm_net_real_v3 import LstmNetRealv3
import torch.nn.functional as F
ds = DatasetProduction()
ds.load("~/data/sim2real/data-realigned-v3-{}-bullet.npz".format("train"))
net = LstmNetRealv3(nodes=128, layers=3)
if torch.cuda.is_available():
net = net.cuda()
HIDDEN_NODES = 128
LSTM_LAYERS = 3
EXPERIMENT = 1
EPOCHS = 5
MODEL_PATH = "./trained_models/lstm_real_vX5_exp{}_l{}_n{}.pt".format(
EXPERIMENT, LSTM_LAYERS, HIDDEN_NODES)
def double_unsqueeze(data):
return torch.unsqueeze(torch.unsqueeze(data, dim=0), dim=0)