def log_visuals(self, paths, epoch, log_dir):
# turn the xy pos into arrays you can work with
# N_paths x path_len x 2
# xy_pos = np.array([[d['xy_pos'] for d in path["env_info"]] for path in paths])
# plot using seaborn heatmap
xy_pos = [np.array([d['xy_pos'] for d in path["env_info"]]) for path in paths]
xy_pos = np.array([d['xy_pos'] for path in paths for d in path['env_info']])
PLOT_BOUND = int(self.env_bound * 1.25)
plot_seaborn_heatmap(
xy_pos[:,0],
xy_pos[:,1],
30,
'Point-Mass Heatmap Epoch %d'%epoch,
os.path.join(log_dir, 'heatmap_epoch_%d.png'%epoch),
[[-PLOT_BOUND,PLOT_BOUND], [-PLOT_BOUND,PLOT_BOUND]]
)
plot_scatter(
xy_pos[:,0],
xy_pos[:,1],
30,
'Point-Mass Scatter Epoch %d'%epoch,
os.path.join(log_dir, 'scatter_epoch_%d.png'%epoch),
[[-PLOT_BOUND,PLOT_BOUND], [-PLOT_BOUND,PLOT_BOUND]]
)
return {}
def log_new_ant_multi_statistics(self, paths, epoch, log_dir):
# turn the xy pos into arrays you can work with
# N_paths x path_len x 2
# xy_pos = np.array([[d['xy_pos'] for d in path["env_infos"]] for path in paths])
# plot using seaborn heatmap
xy_pos = [
np.array([d['xy_pos'] for d in path["env_infos"]])
for path in paths
]
xy_pos = np.array(
[d['xy_pos'] for path in paths for d in path['env_infos']])
PLOT_BOUND = 6
plot_seaborn_heatmap(
xy_pos[:, 0], xy_pos[:, 1], 30, 'Ant Heatmap Epoch %d' % epoch,
os.path.join(log_dir, 'heatmap_epoch_%d.png' % epoch),
[[-PLOT_BOUND, PLOT_BOUND], [-PLOT_BOUND, PLOT_BOUND]])
plot_scatter(xy_pos[:, 0], xy_pos[:, 1], 30,
'Ant Heatmap Epoch %d' % epoch,
os.path.join(log_dir, 'scatter_epoch_%d.png' % epoch),
[[-PLOT_BOUND, PLOT_BOUND], [-PLOT_BOUND, PLOT_BOUND]])
return {}
def gen_plus_sign(distance, num_points_per_direction, save_path):
SCALE = 1.0
# CENTER_BUFFER = 3*SCALE
# CENTER_BUFFER = 6*SCALE
CENTER_BUFFER = 0.0
print(SCALE, CENTER_BUFFER)
X = []
Y = []
# East
for _ in range(num_points_per_direction):
x = np.random.uniform(CENTER_BUFFER, float(distance))
y = np.random.normal(loc=0.0, scale=SCALE)
X.append(x)
Y.append(y)
# West
for _ in range(num_points_per_direction):
x = np.random.uniform(-float(distance), -CENTER_BUFFER)
y = np.random.normal(loc=0.0, scale=SCALE)
X.append(x)
Y.append(y)
# North
for _ in range(num_points_per_direction):
y = np.random.uniform(CENTER_BUFFER, float(distance))
x = np.random.normal(loc=0.0, scale=SCALE)
X.append(x)
Y.append(y)
# South
for _ in range(num_points_per_direction):
y = np.random.uniform(-float(distance), -CENTER_BUFFER)
x = np.random.normal(loc=0.0, scale=SCALE)
X.append(x)
Y.append(y)
plot_2dhistogram(X, Y, 30, 'test', 'plots/junk_vis/test_plus.png',
[[-distance, distance], [-distance, distance]])
plot_scatter(X, Y, 30, 'test', 'plots/junk_vis/test_plus_scatter.png',
[[-distance, distance], [-distance, distance]])
plot_seaborn_heatmap(X, Y, 30, 'test', 'plots/junk_vis/test_plus_sns.png',
[[-distance, distance], [-distance, distance]])
XY_DATA = np.array([X, Y]).T
print(XY_DATA.shape)
print(np.mean(XY_DATA, axis=0).shape)
print(np.std(XY_DATA, axis=0).shape)
joblib.dump(
{
'xy_data': XY_DATA,
'xy_mean': np.mean(XY_DATA, axis=0),
'xy_std': np.std(XY_DATA, axis=0)
},
save_path,
compress=3)
def log_new_ant_multi_statistics(self, paths, epoch, log_dir):
xy_pos = np.array(
[d['xy_pos'] for path in paths for d in path['env_infos']])
PLOT_BOUND = 60
plot_scatter(xy_pos[:, 0], xy_pos[:, 1], 30, 'XY Pos Epoch %d' % epoch,
os.path.join(log_dir, 'xy_pos_epoch_%d.png' % epoch),
[[-PLOT_BOUND, PLOT_BOUND], [-PLOT_BOUND, PLOT_BOUND]])
return {}
def gen_plus_sign(distance, num_points_per_direction, save_path):
RADIUS = 2.0
SCALE = 1.0
# CENTER_BUFFER = 3*SCALE
# CENTER_BUFFER = 6*SCALE
CENTER_BUFFER = 0.0
print(SCALE, CENTER_BUFFER)
X = []
Y = []
for _ in range(num_points_per_direction * 4):
# y = np.random.uniform(-float(distance), -CENTER_BUFFER)
# x = np.random.normal(loc=0.0, scale=SCALE)
xy = np.random.normal(size=2)
xy = xy / np.linalg.norm(xy)
xy *= RADIUS
xy += np.random.normal(size=2)*0.15
X.append(xy[0])
Y.append(xy[1])
plot_2dhistogram(
X, Y, 30, 'test', 'plots/junk_vis/test_plus.png', [[-distance,distance], [-distance,distance]]
)
plot_scatter(
X, Y, 30, 'test', 'plots/junk_vis/test_plus_scatter.png', [[-distance,distance], [-distance,distance]]
)
plot_seaborn_heatmap(
X, Y, 30, 'test', 'plots/junk_vis/test_plus_sns.png', [[-distance,distance], [-distance,distance]]
)
XY_DATA = np.array([X,Y]).T
print(XY_DATA.shape)
print(np.mean(XY_DATA, axis=0).shape)
print(np.std(XY_DATA, axis=0).shape)
joblib.dump(
{
'xy_data': XY_DATA,
'xy_mean': np.mean(XY_DATA, axis=0),
'xy_std': np.std(XY_DATA, axis=0)
},
save_path,
compress=3
)
def log_visuals(self, paths, epoch, log_dir):
xyz = np.array([d['xyz'] for path in paths for d in path['env_info']])
a = np.array([d['a'] for path in paths for d in path['env_info']])
plot_scatter(
xyz[:,0],
xyz[:,1],
30,
'Top-Down Epoch %d'%epoch,
os.path.join(log_dir, 'top_down_epoch_%d.png'%epoch),
[[-1,1], [-1.6,0.4]]
)
plot_scatter(
a.flatten(),
xyz[:,2],
30,
'AZ Epoch %d'%epoch,
os.path.join(log_dir, 'a_z_epoch%d.png'%epoch),
[[-np.pi,np.pi], [-1,1]]
)
return {}
def log_visuals(self, paths, epoch, log_dir):
grip_pos = np.array(
[d['grip_pos'] for path in paths for d in path['env_info']])
obj_pos = np.array(
[d['obj_pos'] for path in paths for d in path['env_info']])
# PLOT_BOUND = 2
plot_scatter(
grip_pos[:, 0],
grip_pos[:, 1],
30,
'Grip Pos Epoch %d' % epoch,
os.path.join(log_dir, 'grip_pos_epoch_%d.png' % epoch),
# [[-PLOT_BOUND,PLOT_BOUND], [-PLOT_BOUND,PLOT_BOUND]]
[
# [0.98697072 - 0.3, 0.98697072 + 0.175],
# [0.74914774 - 0.35, 0.74914774 + 0.45]
[1.34196849 - 0.3, 1.34196849 + 0.2],
[0.74910081 - 0.35, 0.74910081 + 0.35]
])
plot_scatter(
obj_pos[:, 0],
obj_pos[:, 1],
30,
'Obj Pos Epoch %d' % epoch,
os.path.join(log_dir, 'obj_pos_epoch_%d.png' % epoch),
# [[-PLOT_BOUND,PLOT_BOUND], [-PLOT_BOUND,PLOT_BOUND]]
[
# [0.98697072 - 0.3, 0.98697072 + 0.175],
# [0.74914774 - 0.35, 0.74914774 + 0.45]
[1.34196849 - 0.3, 1.34196849 + 0.2],
[0.74910081 - 0.35, 0.74910081 + 0.35]
])
return {}
def log_visuals(self, paths, epoch, log_dir):
arm_xyz = np.array([d['arm_xyz'] for path in paths for d in path['env_info']])
obj_xyz = np.array([d['obj_xyz'] for path in paths for d in path['env_info']])
min_arm_to_obj = np.array([min(-d['reward_near'] for d in path['env_info']) for path in paths])
min_obj_to_goal = np.array([min(-d['reward_dist'] for d in path['env_info']) for path in paths])
successes = [np.sum([d['success'] for d in path['env_info']]) > 0 for path in paths]
success_rate = float(np.sum(successes)) / float(len(successes))
plot_scatter(
arm_xyz[:,0],
arm_xyz[:,1],
30,
'Pusher Arm X-Y Epoch %d'%epoch,
os.path.join(log_dir, 'arm_x_y_epoch_%d.png'%epoch),
[[-0.5,1], [-1,0.5]]
)
plot_scatter(
arm_xyz[:,1],
arm_xyz[:,2],
30,
'Pusher Arm Y-Z Epoch %d'%epoch,
os.path.join(log_dir, 'arm_y_z_epoch_%d.png'%epoch),
[[-1,0.5], [-0.5,0.5]]
)
plot_scatter(
obj_xyz[:,0],
obj_xyz[:,1],
30,
'Obj X-Y Epoch %d'%epoch,
os.path.join(log_dir, 'obj_x_y_epoch_%d.png'%epoch),
[[-0.5,1], [-1,0.5]]
)
return {}
# XY_DATA = np.array([X,Y]).T
# print(XY_DATA.shape)
# print(np.mean(XY_DATA, axis=0).shape)
# print(np.std(XY_DATA, axis=0).shape)
# joblib.dump(
# {
# 'data': XY_DATA,
# 'xy_mean': np.mean(XY_DATA, axis=0),
# 'xy_std': np.std(XY_DATA, axis=0)
# },
# save_path,
# compress=3
# )
bound = 2
plot_scatter(pusher_points[:, 0], pusher_points[:, 1], 30, 'test',
'plots/data_gen/pusher_top_down.png', [[-1, 1], [-1.6, 0.4]])
plot_scatter(
a,
# np.arctan2(pusher_points[:,1], pusher_points[:,0]),
pusher_points[:, 2],
30,
'test',
'plots/data_gen/pusher_a_z.png',
[[-np.pi, np.pi], [-1, 1]])
print(pusher_points.shape)
joblib.dump({
'data': pusher_points,
}, save_path, compress=3)
# save_path = '/scratch/hdd001/home/kamyar/expert_demos/data_gen/slide_column.pkl'
# data = column(8000)
save_path = '/scratch/hdd001/home/kamyar/expert_demos/data_gen/corl_box_pushing_from_center.pkl'
data = uniform_box(10000)
X = data[:,0]
Y = data[:,1]
plot_scatter(
X, Y, 30, 'test', 'plots/data_gen/obj_scatter.png',
[
[1.34196849 - 0.3, 1.34196849 + 0.2],
[0.74910081 - 0.35, 0.74910081 + 0.35]
]
)
X = data[:,2]
Y = data[:,3]
plot_scatter(
X, Y, 30, 'test', 'plots/data_gen/grip_scatter.png',
[
[1.34196849 - 0.3, 1.34196849 + 0.2],
[0.74910081 - 0.35, 0.74910081 + 0.35]
]
)
print(data.shape)
joblib.dump(
all_samples.append(np.concatenate([arm_traj, obj_traj[:, :2]], axis=1))
all_samples = np.concatenate(all_samples, axis=0)
return all_samples
if __name__ == '__main__':
# save_path = '/scratch/hdd001/home/kamyar/expert_demos/data_gen/pusher_task_states_200_eps_correct.pkl'
# save_path = '/scratch/hdd001/home/kamyar/expert_demos/data_gen/test.pkl'
# pusher_points = pusher_to_obj_to_goal(200)
# save_path = '/scratch/hdd001/home/kamyar/expert_demos/data_gen/pusher_task_states_no_inbetween.pkl'
save_path = '/scratch/hdd001/home/kamyar/expert_demos/data_gen/corl_pusher_push_states.pkl'
pusher_points = pusher_to_obj_to_goal_gaussian_target(200)
# save_path = '/scratch/hdd001/home/kamyar/expert_demos/data_gen/with_gaussian_line_pusher_task_states_no_inbetween.pkl'
# pusher_points = pusher_to_obj_to_goal_gaussian_target_with_gaussian_line(200)
plot_scatter(pusher_points[:, 0], pusher_points[:, 1], 30, 'test',
'plots/data_gen/pusher_arm_x_y.png', [[-0.5, 1], [-1, 0.5]])
plot_scatter(pusher_points[:, 1], pusher_points[:, 2], 30, 'test',
'plots/data_gen/pusher_arm_y_z.png', [[-1, 0.5], [-0.5, 0.5]])
plot_scatter(pusher_points[:, 3], pusher_points[:, 4], 30, 'test',
'plots/data_gen/pusher_obj.png', [[-0.5, 1], [-1, 0.5]])
print(pusher_points.shape)
joblib.dump({
'data': pusher_points,
}, save_path, compress=3)