-
Notifications
You must be signed in to change notification settings - Fork 56
/
generate_svg_fp.py
231 lines (203 loc) · 7.55 KB
/
generate_svg_fp.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
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
import torch
import torch.optim as optim
import torch.nn as nn
import argparse
import os
import random
from torch.autograd import Variable
from torch.utils.data import DataLoader
import utils
import itertools
import progressbar
import numpy as np
from scipy.ndimage.filters import gaussian_filter
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--data_root', default='data', help='root directory for data')
parser.add_argument('--model_path', default='', help='path to model')
parser.add_argument('--log_dir', default='', help='directory to save generations to')
parser.add_argument('--seed', default=1, type=int, help='manual seed')
parser.add_argument('--n_past', type=int, default=2, help='number of frames to condition on')
parser.add_argument('--n_future', type=int, default=28, help='number of frames to predict')
parser.add_argument('--num_threads', type=int, default=0, help='number of data loading threads')
parser.add_argument('--nsample', type=int, default=100, help='number of samples')
parser.add_argument('--N', type=int, default=256, help='number of samples')
opt = parser.parse_args()
os.makedirs('%s' % opt.log_dir, exist_ok=True)
opt.n_eval = opt.n_past+opt.n_future
opt.max_step = opt.n_eval
print("Random Seed: ", opt.seed)
random.seed(opt.seed)
torch.manual_seed(opt.seed)
torch.cuda.manual_seed_all(opt.seed)
dtype = torch.cuda.FloatTensor
# ---------------- load the models ----------------
tmp = torch.load(opt.model_path)
frame_predictor = tmp['frame_predictor']
posterior = tmp['posterior']
frame_predictor.eval()
posterior.eval()
encoder = tmp['encoder']
decoder = tmp['decoder']
encoder.eval()
decoder.eval()
frame_predictor.batch_size = opt.batch_size
posterior.batch_size = opt.batch_size
opt.g_dim = tmp['opt'].g_dim
opt.z_dim = tmp['opt'].z_dim
opt.num_digits = tmp['opt'].num_digits
# --------- transfer to gpu ------------------------------------
frame_predictor.cuda()
posterior.cuda()
encoder.cuda()
decoder.cuda()
# ---------------- set the options ----------------
opt.dataset = tmp['opt'].dataset
opt.last_frame_skip = tmp['opt'].last_frame_skip
opt.channels = tmp['opt'].channels
opt.image_width = tmp['opt'].image_width
print(opt)
# --------- load a dataset ------------------------------------
train_data, test_data = utils.load_dataset(opt)
train_loader = DataLoader(train_data,
num_workers=opt.num_threads,
batch_size=opt.batch_size,
shuffle=True,
drop_last=True,
pin_memory=True)
test_loader = DataLoader(test_data,
num_workers=opt.num_threads,
batch_size=opt.batch_size,
shuffle=True,
drop_last=True,
pin_memory=True)
def get_training_batch():
while True:
for sequence in train_loader:
batch = utils.normalize_data(opt, dtype, sequence)
yield batch
training_batch_generator = get_training_batch()
def get_testing_batch():
while True:
for sequence in test_loader:
batch = utils.normalize_data(opt, dtype, sequence)
yield batch
testing_batch_generator = get_testing_batch()
# --------- eval funtions ------------------------------------
def make_gifs(x, idx, name):
# get approx posterior sample
frame_predictor.hidden = frame_predictor.init_hidden()
posterior.hidden = posterior.init_hidden()
posterior_gen = []
posterior_gen.append(x[0])
x_in = x[0]
for i in range(1, opt.n_eval):
h = encoder(x_in)
h_target = encoder(x[i])[0].detach()
if opt.last_frame_skip or i < opt.n_past:
h, skip = h
else:
h, _ = h
h = h.detach()
_, z_t, _= posterior(h_target) # take the mean
if i < opt.n_past:
frame_predictor(torch.cat([h, z_t], 1))
posterior_gen.append(x[i])
x_in = x[i]
else:
h_pred = frame_predictor(torch.cat([h, z_t], 1)).detach()
x_in = decoder([h_pred, skip]).detach()
posterior_gen.append(x_in)
nsample = opt.nsample
ssim = np.zeros((opt.batch_size, nsample, opt.n_future))
psnr = np.zeros((opt.batch_size, nsample, opt.n_future))
progress = progressbar.ProgressBar(max_value=nsample).start()
all_gen = []
for s in range(nsample):
progress.update(s+1)
gen_seq = []
gt_seq = []
frame_predictor.hidden = frame_predictor.init_hidden()
posterior.hidden = posterior.init_hidden()
x_in = x[0]
all_gen.append([])
all_gen[s].append(x_in)
for i in range(1, opt.n_eval):
h = encoder(x_in)
if opt.last_frame_skip or i < opt.n_past:
h, skip = h
else:
h, _ = h
h = h.detach()
if i < opt.n_past:
h_target = encoder(x[i])[0].detach()
_, z_t, _ = posterior(h_target)
else:
z_t = torch.cuda.FloatTensor(opt.batch_size, opt.z_dim).normal_()
if i < opt.n_past:
frame_predictor(torch.cat([h, z_t], 1))
x_in = x[i]
all_gen[s].append(x_in)
else:
h = frame_predictor(torch.cat([h, z_t], 1)).detach()
x_in = decoder([h, skip]).detach()
gen_seq.append(x_in.data.cpu().numpy())
gt_seq.append(x[i].data.cpu().numpy())
all_gen[s].append(x_in)
_, ssim[:, s, :], psnr[:, s, :] = utils.eval_seq(gt_seq, gen_seq)
progress.finish()
utils.clear_progressbar()
###### ssim ######
for i in range(opt.batch_size):
gifs = [ [] for t in range(opt.n_eval) ]
text = [ [] for t in range(opt.n_eval) ]
mean_ssim = np.mean(ssim[i], 1)
ordered = np.argsort(mean_ssim)
rand_sidx = [np.random.randint(nsample) for s in range(3)]
for t in range(opt.n_eval):
# gt
gifs[t].append(add_border(x[t][i], 'green'))
text[t].append('Ground\ntruth')
#posterior
if t < opt.n_past:
color = 'green'
else:
color = 'red'
gifs[t].append(add_border(posterior_gen[t][i], color))
text[t].append('Approx.\nposterior')
# best
if t < opt.n_past:
color = 'green'
else:
color = 'red'
sidx = ordered[-1]
gifs[t].append(add_border(all_gen[sidx][t][i], color))
text[t].append('Best SSIM')
# random 3
for s in range(len(rand_sidx)):
gifs[t].append(add_border(all_gen[rand_sidx[s]][t][i], color))
text[t].append('Random\nsample %d' % (s+1))
fname = '%s/%s_%d.gif' % (opt.log_dir, name, idx+i)
utils.save_gif_with_text(fname, gifs, text)
def add_border(x, color, pad=1):
w = x.size()[1]
nc = x.size()[0]
px = Variable(torch.zeros(3, w+2*pad+30, w+2*pad))
if color == 'red':
px[0] =0.7
elif color == 'green':
px[1] = 0.7
if nc == 1:
for c in range(3):
px[c, pad:w+pad, pad:w+pad] = x
else:
px[:, pad:w+pad, pad:w+pad] = x
return px
for i in range(0, opt.N, opt.batch_size):
# plot train
train_x = next(training_batch_generator)
make_gifs(train_x, i, 'train')
# plot test
test_x = next(testing_batch_generator)
make_gifs(test_x, i, 'test')
print(i)