def ball_batch_imitate(initial_config, start_index, pic_path, file_path,
batch_size, state_followed, image_followed, force_label,
force_followed):
'''
output: two images: past and now
@image_followed: contain original image
@state_following: state_input
@force_followed: the regressed force (new force by network)
'''
As = []
for it in range(start_index, start_index + batch_size):
new_force_0 = force_followed[it - start_index] * force_scale
new_force_0 = [round(i / 2) * 2 for i in new_force_0]
old_pos = state_followed[it - start_index, 1, 0] * pos_scale
new_pos_real = state_followed[it - start_index, 1, 1] * pos_scale
old_vel = state_followed[it - start_index, 0, 0] * vel_scale
# print(new_force_0)
A, B, _ = simulation_func(initial_config,
count=it * 2,
iteration=1,
pic_path=pic_path,
file_path=file_path,
new_force=new_force_0,
is_newstate=True,
new_pos=old_pos,
new_vel=old_vel,
force_func=generate_,
verbose=False,
iswrite=True,
isdraw=False)
new_pos_fake = B[1, 1] * pos_scale
img_concat(image_followed[it - start_index, 1],
A[1],
path=pic_path,
count=it - start_index,
pos1=new_pos_real,
pos2=new_pos_fake,
ball_config=initial_config,
force_real=force_label[it - start_index],
force_fake=force_followed[it - start_index],
is_force_line=False)
As.append(A[1])
As = np.array(As) #32,256,256,3
return As
def ball_continuous_input(initial_config,
total_step,
start_index,
pic_path,
file_path,
isdraw=False):
'''
similar to ball_batch_input, except that a continuous process of actor movements, of steps of @total_step
'''
As = []
Bs = []
Cs = []
new_pos_0 = generate_([i for i in range(ball_radius, 65 - ball_radius)])
new_vel_0 = generate_(
[i for i in range(int(-1 * vel_scale),
int(vel_scale) + 1)])
for it in range(total_step):
new_force_0 = generate_(
[i for i in range(int(-1 * force_scale),
int(force_scale) + 1, 2)])
A, B, C = simulation_func(initial_config,
count=it,
iteration=1,
pic_path=pic_path,
file_path=file_path,
new_force=new_force_0,
is_newstate=True,
new_pos=new_pos_0,
new_vel=new_vel_0,
verbose=False,
iswrite=False,
isdraw=isdraw)
new_vel_0 = B[0, 1, :] * vel_scale
new_pos_0 = B[1, 1, :] * pos_scale
# print("new_vel: ",new_vel_0)
As.append(A)
Bs.append(B)
Cs.append(C)
# print("force_label:",C)
As = np.array(As) #32,2,256,256,3 [pic0,pic1]
Bs = np.array(Bs) #32,2,2,2 [[vel(0,1)],[pos(0,1)]] each is [x,y]
Cs = np.array(Cs) #32,2 [Fx,Fy]
return As, Bs, Cs
def ball_batch_input(initial_config, start_index, pic_path, file_path,
batch_size):
'''
single movement of actor in batch
@start_index: the number of picture in direcotory while saving
output:
As batch_size,2,256,256,3 [pic0,pic1]
Bs batch_size,2,2,2 [[vel(past,now)],[pos(past,now)]] each is [x,y]
Cs batch_size,2 [Fx,Fy]
'''
As = []
Bs = []
Cs = []
force_record = []
for it in range(start_index, start_index + batch_size):
new_force_0 = generate_([i for i in range(-10, 11, 2)])
force_record.append(new_force_0)
new_pos_0 = generate_(
[i for i in range(ball_radius, 65 - ball_radius)])
new_vel_0 = generate_([i for i in range(-5, 6)])
A, B, C = simulation_func(initial_config,
count=it * 2,
iteration=1,
pic_path=pic_path,
file_path=file_path,
new_force=new_force_0,
is_newstate=True,
new_pos=new_pos_0,
new_vel=new_vel_0,
verbose=False,
iswrite=False,
isdraw=False)
As.append(A)
Bs.append(B)
Cs.append(C)
As = np.array(As) #32,2,256,256,3 [pic0,pic1]
Bs = np.array(Bs) #32,2,2,2 [[vel(0,1)],[pos(0,1)]] each is [x,y]
Cs = np.array(Cs) #32,2 [Fx,Fy]
return As, Bs, Cs
ball_config=initial_config,
force_real=force_label_[it],
force_fake=[0, 0],
is_force_line=False,
issave=False)
image_record.append(img_result)
# update the current state of imitator
if (it < opt.total_step - 1):
_, state_single_out, _ = simulation_func(
initial_config,
count=it,
iteration=1,
pic_path=opt.log_dir + '/images/imitator/' + str(epoch) + '_',
file_path=opt.log_dir + '/inputs/imitator/' + str(epoch) + '_',
new_force=[0, 0],
is_newstate=True,
new_pos=[32, 32],
new_vel=starting_vel * vel_scale,
force_func=generate_,
verbose=False,
iswrite=False,
isdraw=False)
starting_vel = state_single_out[0, 1]
image_concat = np.array([step_image_o_e, image_input_[it + 1, 1]
])[np.newaxis, :] #1,2,256,256,3
print(epoch, loss_e, loss_dyn_e, loss_i_e)
# draw the gif
# create_gif_byreading(path=opt.log_dir+'/images/imitator/',epoch=epoch,total_step=opt.total_step, gif_name=opt.log_dir+'/images/imitator/gifs/'+str(epoch)+'.gif')