forked from yujiali/gmmn
-
Notifications
You must be signed in to change notification settings - Fork 1
/
visualize.py
154 lines (123 loc) · 4.9 KB
/
visualize.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
import matplotlib.pyplot as plt
import numpy as np
import gnumpy as gnp
import vistools as vt
import core.generative as gen
import os
import time
import core.util as util
import scipy.misc as misc
from mpl_toolkits.axes_grid1 import AxesGrid
plt.ion()
def nn_search(samples, database, top_k=1, imsz=[28,28], orientation='horizontal', output_file=None, pad=0.1):
if orientation not in ['horizontal', 'vertical']:
print '[Error] orientation must be either horizontal or vertical'
return
g_samples = util.to_garray(samples)
g_database = util.to_garray(database)
if isinstance(database, gnp.garray):
database = database.asarray()
if isinstance(samples, gnp.garray):
samples = samples.asarray()
n_samples, n_dims = samples.shape
nn = np.empty((n_samples * top_k, n_dims), dtype=np.float)
for i in range(n_samples):
v = g_samples[i]
d = ((g_database - v)**2).sum(axis=1)
idx = d.asarray().argsort()
top_candidates = database[idx[:top_k]]
if orientation == 'horizontal':
nn[np.arange(i, i+n_samples*top_k, n_samples)] = top_candidates
elif orientation == 'vertical':
nn[i*top_k:(i+1)*top_k] = top_candidates
f = plt.figure()
grid = AxesGrid(f, 111, nrows_ncols=(2,1), axes_pad=pad)
vt.bwpatchview(samples, imsz, 1, gridintensity=1, ax=grid[0])
if orientation == 'horizontal':
vt.bwpatchview(nn, imsz, top_k, gridintensity=1, ax=grid[1])
elif orientation == 'vertical':
vt.bwpatchview(nn, imsz, n_samples, gridintensity=1, ax=grid[1])
if output_file is not None:
plt.savefig(output_file, bbox_inches='tight')
def view_checkpoints(model_dir, sigma, imsz=[28,28], figid=101):
"""
checkpoint files should have a name matching the following:
<model_dir>/checkpoint_<sigma>_<iter>.pdata
"""
prefix = '%s/checkpoint_%s' % (model_dir, str(sigma))
checkpoint_numbers = sorted([int(fpath.split('.')[0].split('_')[-1]) for fpath in os.listdir(model_dir) if fpath.startswith('checkpoint_%s' % str(sigma))])
net = gen.StochasticGenerativeNet()
plt.figure(figid, figsize=(10,8))
ax = plt.subplot(111)
for i in checkpoint_numbers:
net.load_model_from_file(prefix + '_%d.pdata' % i)
w = net.layers[-1].params.W.asarray()
ax.cla()
vt.bwpatchview(w[:400], imsz, int(np.sqrt(w[:400].shape[0])), rowmajor=True, gridintensity=1, ax=ax)
plt.draw()
plt.show()
print 'Checkpoint %d' % i
time.sleep(0.04)
def generation_on_a_line(net, n_points=100, imsz=[28,28], nrows=10, h_seeds=None):
if h_seeds is None:
h = net.sample_hiddens(2)
z = gnp.zeros((n_points, h.shape[1]))
diff = h[1] - h[0]
step = diff / (n_points - 1)
for i in range(n_points):
z[i] = h[0] + step * i
else:
n_seeds = h_seeds.shape[0]
z = gnp.zeros((n_points * n_seeds, h_seeds.shape[1]))
for i in range(n_seeds):
h0 = h_seeds[i]
h1 = h_seeds[(i+1) % n_seeds]
diff = h1 - h0
step = diff / (n_points - 1)
for j in range(n_points):
z[i*n_points+j] = h0 + step * j
x = net.generate_samples(z=z)
vt.bwpatchview(x.asarray(), imsz, nrows, rowmajor=True, gridintensity=1)
def generate_morphing_video(net, h_seeds, n_points=100, imsz=[28,28], output_dir='video'):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
n_seeds = h_seeds.shape[0]
z = gnp.zeros((n_points * n_seeds, h_seeds.shape[1]))
for i in range(n_seeds):
h0 = h_seeds[i]
h1 = h_seeds[(i+1) % n_seeds]
diff = h1 - h0
step = diff / (n_points - 1)
for j in range(n_points):
z[i*n_points+j] = h0 + step * j
x = net.generate_samples(z=z).asarray()
for i in range(x.shape[0]):
misc.imsave(output_dir + '/%d.png' % i, x[i].reshape(imsz))
###################################
# For old experiments
###################################
def plot_dataset(x, t, ax=None):
if ax is None:
plt.figure()
ax = plt.subplot(111)
ax.plot(x[t==0,0], x[t==0,1], 'o')
ax.plot(x[t==1,0], x[t==1,1], 'o')
x_min = x[:,0].min()
x_max = x[:,0].max()
y_min = x[:,1].min()
y_max = x[:,1].max()
ax.set_xlim([x_min - (x_max - x_min) / 10.0, x_max + (x_max - x_min) / 10.0])
ax.set_ylim([y_min - (y_max - y_min) / 10.0, y_max + (y_max - y_min) / 10.0])
plt.show()
return ax
def plot_decision_boundary(f, x_range, y_range, density, ax=None, **kwargs):
if ax is None:
plt.figure()
ax = plt.subplot(111)
x, y = np.meshgrid(np.arange(x_range[0], x_range[1], density),
np.arange(y_range[0], y_range[1], density))
data = np.c_[x.reshape(x.size,1), y.reshape(y.size,1)]
z = f(data).reshape(x.shape)
ax.contour(x, y, z, levels=[0], **kwargs)
plt.show()
return ax