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visualize_lstm.py
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visualize_lstm.py
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import argparse
import json
import os
import random
import imageio
import torch
import torch.utils.data as data
import torch.nn as nn
import torch.optim as optim
import cv2
import h5py
import numpy as np
import time
import matplotlib.pyplot as plt
import robosuite
from robosuite.utils.mjcf_utils import postprocess_model_xml
from predict_lstm import LSTMPredictor
def sim_environment(data_folder, env, run_id, predictor, save=False, sequence_length=5, frame_size=(64,64)):
jointdata_folder = os.path.join(data_folder, 'demo_{}_jointdata'.format(run_id))
video_writer = imageio.get_writer(os.path.join(data_folder, "visualize_demo_{}.mp4".format(run_id)), fps=120)
past_frames = []
for i in range(0, sequence_length):
action = np.load(os.path.join(jointdata_folder, 'frame_{:04d}.npy'.format(i)))
obs, reward, done, info = env.step(action)
# env.render()
render_img = obs['image']
if (save):
# frame = cv2.cvtColor(render_img, cv2.COLOR_BGR2RGB)
frame = cv2.flip(render_img, 0)
# cv2.imshow('whee', frame)
# cv2.waitKey(100)
video_writer.append_data(frame)
render_img = frame_to_tensor(render_img, frame_size)
past_frames.append(render_img)
count = 5
while (os.path.exists(os.path.join(jointdata_folder, 'frame_{:04d}.npy'.format(count)))):
frames = torch.stack(past_frames, dim=0)
frames = frames.detach()
frames.requires_grad = False
frames = frames.permute(1, 0, 2, 3)
frames = frames[None,:]
action = predictor.predict(frames).numpy()
obs, reward, done, info = env.step(action[0,:])
# env.render()
render_img = obs['image']
if (save):
# frame = cv2.cvtColor(render_img, cv2.COLOR_BGR2RGB)
frame = cv2.flip(render_img, 0)
# cv2.imshow('whee', frame)
# cv2.waitKey(100)
video_writer.append_data(frame)
render_img = frame_to_tensor(render_img, frame_size)
past_frames.append(render_img)
past_frames = past_frames[1:]
count += 1
print(count)
print(action)
if (save):
video_writer.close()
def init_environment(demo_path, run_id):
hdf5_path = os.path.join(demo_path, 'demo.hdf5')
f = h5py.File(hdf5_path, "r")
env_info = f['data'].attrs['env']
env = robosuite.make(
env_info,
has_renderer=True,
has_offscreen_renderer=True,
ignore_done=True,
use_camera_obs=True,
camera_depth=True,
use_object_obs=False,
reward_shaping=True,
camera_name="frontview",
control_freq=100,
)
model_xml = f["data/demo_{}".format(run_id)].attrs["model_file"]
with open(os.path.join(demo_path, 'models', model_xml)) as xml_file:
model_xml_str = xml_file.read()
env.reset()
xml = postprocess_model_xml(model_xml_str)
env.reset_from_xml_string(xml)
env.sim.reset()
# env.viewer.set_camera(0)
states = f["data/demo_{}/states".format(run_id)][()]
env.sim.set_state_from_flattened(states[0])
env.sim.forward()
return env
def frame_to_tensor(frame, frame_size):
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = cv2.flip(frame, 0)
frame = cv2.resize(frame, frame_size)
frame = torch.from_numpy(frame)
frame = frame.permute(2, 0, 1)
frame = frame.float()/255.0
return frame
if __name__ == "__main__":
frame_size = (64,64)
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_folder",
type=str,
),
parser.add_argument(
"--demo_path",
type=str,
)
parser.add_argument(
"--run_id",
type=int
)
parser.add_argument(
"--index",
type=str
)
parser.add_argument(
"--save",
action="store_true"
)
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
predictor = LSTMPredictor(num_channels=3, num_kernels=64, kernel_size=(3,3), padding=(1,1), activation="relu", frame_size=frame_size, num_layers=3, index=args.index, device=device)
env = init_environment(args.demo_path, args.run_id)
sim_environment(args.data_folder, env, args.run_id, predictor, save=args.save)