-
Notifications
You must be signed in to change notification settings - Fork 199
/
IAN_simple.py
243 lines (223 loc) · 7.98 KB
/
IAN_simple.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
231
232
233
234
235
236
237
238
239
240
241
242
243
### Simple IAN model for use with Neural Photo Editor
# This model is a simplified version of the Introspective Adversarial Network that does not
# make use of Multiscale Dilated Convolutional blocks, Ternary Adversarial Loss, or an
# autoregressive RGB-Beta layer. It's designed to be sleeker and to run on laptop GPUs with <1GB of memory.
import numpy as np
import lasagne
import lasagne.layers
from lasagne.layers import SliceLayer as SL
from lasagne.layers import batch_norm as BN
from lasagne.layers import ElemwiseSumLayer as ESL
from lasagne.layers import NonlinearityLayer as NL
from lasagne.layers import DenseLayer as DL
from lasagne.init import Normal as initmethod
from lasagne.nonlinearities import elu
from lasagne.nonlinearities import rectify as relu
from lasagne.nonlinearities import LeakyRectify as lrelu
from lasagne.layers import TransposedConv2DLayer as TC2D
from lasagne.layers import ConcatLayer as CL
import theano.tensor as T
from math import sqrt
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from layers import GaussianSampleLayer,MinibatchLayer
lr_schedule = { 0: 0.0002}
cfg = {'batch_size' : 128,
'learning_rate' : lr_schedule,
'optimizer' : 'Adam',
'beta1' : 0.5,
'update_ratio' : 1,
'decay_rate' : 0,
'reg' : 1e-5,
'momentum' : 0.9,
'shuffle' : True,
'dims' : (64,64),
'n_channels' : 3,
'n_classes' : 10,
'batches_per_chunk': 64,
'max_epochs' :250,
'checkpoint_every_nth' : 1,
'num_latents': 100,
'recon_weight': 3.0,
'feature_weight': 1.0,
}
def get_model(dnn=True):
if dnn:
import lasagne.layers.dnn
from lasagne.layers.dnn import Conv2DDNNLayer as C2D
from theano.sandbox.cuda.basic_ops import (as_cuda_ndarray_variable,
host_from_gpu,
gpu_contiguous, HostFromGpu,
gpu_alloc_empty)
from theano.sandbox.cuda.dnn import GpuDnnConvDesc, GpuDnnConv, GpuDnnConvGradI, dnn_conv, dnn_pool
from layers import DeconvLayer
else:
import lasagne.layers
from lasagne.layers import Conv2DLayer as C2D
dims, n_channels, n_classes = tuple(cfg['dims']), cfg['n_channels'], cfg['n_classes']
shape = (None, n_channels)+dims
l_in = lasagne.layers.InputLayer(shape=shape)
l_enc_conv1 = C2D(
incoming = l_in,
num_filters = 128,
filter_size = [5,5],
stride = [2,2],
pad = (2,2),
W = initmethod(0.02),
nonlinearity = lrelu(0.2),
flip_filters=False,
name = 'enc_conv1'
)
l_enc_conv2 = BN(C2D(
incoming = l_enc_conv1,
num_filters = 256,
filter_size = [5,5],
stride = [2,2],
pad = (2,2),
W = initmethod(0.02),
nonlinearity = lrelu(0.2),
flip_filters=False,
name = 'enc_conv2'
),name = 'bnorm2')
l_enc_conv3 = BN(C2D(
incoming = l_enc_conv2,
num_filters = 512,
filter_size = [5,5],
stride = [2,2],
pad = (2,2),
W = initmethod(0.02),
nonlinearity = lrelu(0.2),
flip_filters=False,
name = 'enc_conv3'
),name = 'bnorm3')
l_enc_conv4 = BN(C2D(
incoming = l_enc_conv3,
num_filters = 1024,
filter_size = [5,5],
stride = [2,2],
pad = (2,2),
W = initmethod(0.02),
nonlinearity = lrelu(0.2),
flip_filters=False,
name = 'enc_conv4'
),name = 'bnorm4')
l_enc_fc1 = BN(DL(
incoming = l_enc_conv4,
num_units = 1000,
W = initmethod(0.02),
nonlinearity = elu,
name = 'enc_fc1'
),
name = 'bnorm_enc_fc1')
l_enc_mu,l_enc_logsigma = [BN(DL(incoming = l_enc_fc1,num_units=cfg['num_latents'],nonlinearity = None,name='enc_mu'),name='mu_bnorm'),
BN(DL(incoming = l_enc_fc1,num_units=cfg['num_latents'],nonlinearity = None,name='enc_logsigma'),name='ls_bnorm')]
l_Z = GaussianSampleLayer(l_enc_mu, l_enc_logsigma, name='l_Z')
l_dec_fc2 = BN(DL(
incoming = l_Z,
num_units = 1024*16,
nonlinearity = relu,
W=initmethod(0.02),
name='l_dec_fc2'),
name = 'bnorm_dec_fc2')
l_unflatten = lasagne.layers.ReshapeLayer(
incoming = l_dec_fc2,
shape = ([0],1024,4,4),
)
if dnn:
l_dec_conv1 = BN(DeconvLayer(
incoming = l_unflatten,
num_filters = 512,
filter_size = [5,5],
stride = [2,2],
crop = (2,2),
W = initmethod(0.02),
nonlinearity = relu,
name = 'dec_conv1'
),name = 'bnorm_dc1')
l_dec_conv2 = BN(DeconvLayer(
incoming = l_dec_conv1,
num_filters = 256,
filter_size = [5,5],
stride = [2,2],
crop = (2,2),
W = initmethod(0.02),
nonlinearity = relu,
name = 'dec_conv2'
),name = 'bnorm_dc2')
l_dec_conv3 = BN(DeconvLayer(
incoming = l_dec_conv2,
num_filters = 128,
filter_size = [5,5],
stride = [2,2],
crop = (2,2),
W = initmethod(0.02),
nonlinearity = relu,
name = 'dec_conv3'
),name = 'bnorm_dc3')
l_out = DeconvLayer(
incoming = l_dec_conv3,
num_filters = 3,
filter_size = [5,5],
stride = [2,2],
crop = (2,2),
W = initmethod(0.02),
b = None,
nonlinearity = lasagne.nonlinearities.tanh,
name = 'dec_out'
)
else:
l_dec_conv1 = SL(SL(BN(TC2D(
incoming = l_unflatten,
num_filters = 512,
filter_size = [5,5],
stride = [2,2],
crop = (1,1),
W = initmethod(0.02),
nonlinearity = relu,
name = 'dec_conv1'
),name = 'bnorm_dc1'),indices=slice(1,None),axis=2),indices=slice(1,None),axis=3)
l_dec_conv2 = SL(SL(BN(TC2D(
incoming = l_dec_conv1,
num_filters = 256,
filter_size = [5,5],
stride = [2,2],
crop = (1,1),
W = initmethod(0.02),
nonlinearity = relu,
name = 'dec_conv2'
),name = 'bnorm_dc2'),indices=slice(1,None),axis=2),indices=slice(1,None),axis=3)
l_dec_conv3 = SL(SL(BN(TC2D(
incoming = l_dec_conv2,
num_filters = 128,
filter_size = [5,5],
stride = [2,2],
crop = (1,1),
W = initmethod(0.02),
nonlinearity = relu,
name = 'dec_conv3'
),name = 'bnorm_dc3'),indices=slice(1,None),axis=2),indices=slice(1,None),axis=3)
l_out = SL(SL(TC2D(
incoming = l_dec_conv3,
num_filters = 3,
filter_size = [5,5],
stride = [2,2],
crop = (1,1),
W = initmethod(0.02),
b = None,
nonlinearity = lasagne.nonlinearities.tanh,
name = 'dec_out'
),indices=slice(1,None),axis=2),indices=slice(1,None),axis=3)
# l_in,num_filters=1,filter_size=[5,5],stride=[2,2],crop=[1,1],W=dc.W,b=None,nonlinearity=None)
minibatch_discrim = MinibatchLayer(lasagne.layers.GlobalPoolLayer(l_enc_conv4), num_kernels=500,name='minibatch_discrim')
l_discrim = DL(incoming = minibatch_discrim,
num_units = 1,
nonlinearity = lasagne.nonlinearities.sigmoid,
b = None,
W=initmethod(),
name = 'discrimi')
return {'l_in':l_in,
'l_out':l_out,
'l_mu':l_enc_mu,
'l_ls':l_enc_logsigma,
'l_Z':l_Z,
'l_introspect':[l_enc_conv1, l_enc_conv2,l_enc_conv3,l_enc_conv4],
'l_discrim' : l_discrim}