"""
Quick-and-dirty visualization scripts for a variety of tasks.
"""
import sequential_e2c as e2c
#import e2c_plane_z as e2c
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from data.plane_data2 import PlaneData, get_params
import ipdb as pdb
def show_recons_samples(sess, ckptfile):
# visualize sample reconstructions
e2c.saver.restore(sess, ckptfile) # restore variable values
dataset=PlaneData("data/plane1.npz","data/env1.png")
dataset.initialize()
(x_val,u_val,x_next_val)=dataset.sample(e2c.batch_size)
xr,xp=sess.run([e2c.x_recons, e2c.x_predict],feed_dict={e2c.x:x_val,e2c.u:u_val,e2c.x_next:x_next_val})
#xr,xp=sess.run([e2c.x_recons0, e2c.x_predict0],feed_dict={e2c.x0:x_val,e2c.u0:u_val,e2c.x1:x_next_val})
A,B=e2c.A,e2c.B
def getimgs(x,xnext):
padsize=1
padval=.5
ph=B+2*padsize
pw=A+2*padsize
img=np.ones((10*ph,2*pw))*padval
for i in range(10):
startr=i*ph+padsize
img[startr:startr+B,padsize:padsize+A]=x[i,:].reshape((A,B))
for i in range(10):
startr=i*ph+padsize
img[startr:startr+B,pw+padsize:pw+padsize+A]=xnext[i,:].reshape((A,B))
return img
fig,arr=plt.subplots(1,2)
arr[0].matshow(getimgs(x_val,x_next_val),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[0].set_title('Data')
arr[1].matshow(getimgs(xr,xp),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[1].set_title('Reconstruction')
plt.show()
def show_recons_seq(sess, ckptfile):
e2c.saver.restore(sess, ckptfile) # restore variable values
dataset=PlaneData("data/plane1.npz","data/env1.png")
dataset.initialize()
T=e2c.T
print(T)
(x_vals,u_vals)=dataset.sample_seq(e2c.batch_size,T)
feed_dict={}
for t in range(T):
feed_dict[e2c.xs[t]] = x_vals[t]
for t in range(T-1):
feed_dict[e2c.us[t]] = u_vals[t]
fetches=e2c.x_recons + e2c.x_predicts
results=sess.run(fetches,feed_dict)
xr=results[:T-1]
xp=results[T-1:]
A,B=e2c.A,e2c.B
def getimgs(x):
padsize=1
padval=.5
ph=B+2*padsize
pw=A+2*padsize
img=np.ones((ph,len(x)*pw))*padval
for t in range(len(x)):
startc=t*pw+padsize
img[padsize:padsize+B, startc:startc+A]=x[t][1,:].reshape((A,B))
return img
fig,arr=plt.subplots(3,1)
arr[0].matshow(getimgs(x_vals),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[0].set_title('X')
arr[1].matshow(getimgs(xr),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[1].set_title('Reconstruction')
arr[2].matshow(getimgs(xp),cmap=plt.cm.gray,vmin=0,vmax=1)
arr[2].set_title('Prediction')
plt.show()
def viz_z(sess, ckptfile):
e2c.saver.restore(sess,ckptfile) # restore variable values
dataset=PlaneData("data/plane1.npz","data/env1.png")
Ps,NPs=dataset.getPSpace()
batch_size=e2c.batch_size
n0=NPs.shape[0]
if False:
Ps=np.vstack((Ps,NPs))
xy=np.zeros([Ps.shape[0], 2])
xy[:,0]=Ps[:,1]
xy[:,1]=20-Ps[:,0] # for the purpose of computing theta, map centered @ origin
Zs=np.zeros([Ps.shape[0], e2c.z_dim])
theta=np.arctan(xy[:,1]/xy[:,0])
for i in range(Ps.shape[0] // batch_size):
print("batch %d" % i)
x_val=dataset.getXPs(Ps[i*batch_size:(i+1)*batch_size,:])
Zs[i*batch_size:(i+1)*batch_size,:]=sess.run(e2c.z, {e2c.x:x_val})
# last remaining points may not fit precisely into 1 minibatch.
x_val=dataset.getXPs(Ps[-batch_size:,:])
Zs[-batch_size:,:]=sess.run(e2c.z, {e2c.x:x_val})
if False:
theta[-n0:]=1;
fig,arr=plt.subplots(1,2)
arr[0].scatter(Ps[:,1], 40-Ps[:,0], c=(np.pi+theta)/(2*np.pi))
arr[0].set_title('True State Space')
arr[1].scatter(Zs[:,0],Zs[:,1], c=(np.pi+theta)/(2*np.pi))
arr[1].set_title('Latent Space Z')
#plt.show()
return fig
def viz_z_unfold(sess, cpktprefix):
d=100 # interval
for i in range(int(1e5) // d):
f="%s-%05d" % (cpktprefix,i*d)
ckptfile=f+".ckpt"
print(ckptfile)
fig=viz_z(sess,ckptfile)
fig.suptitle('%d'%(i*d))
fig.savefig(f+".png")
# combine with convert -delay 10 -loop 0 e2c-plane-*.png out.gif
# then reduce size using gifsicle -O3-colors 256 < out.gif > new.gif
print('done!')
if __name__=="__main__":
sess=tf.InteractiveSession()
#viz_z_unfold(sess, "/ltmp/e2c-plane")
fig=viz_z(sess,"/ltmp/e2c-plane-199000.ckpt")
#show_recons_samples(sess,"/ltmp/e2c-plane-186000.ckpt")
show_recons_seq(sess, "/ltmp/e2c-plane-199000.ckpt")
plt.show()
sess.close()
