forked from tomgoldstein/loss-landscape
-
Notifications
You must be signed in to change notification settings - Fork 1
/
net_plotter.py
executable file
·337 lines (279 loc) · 12.3 KB
/
net_plotter.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
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
"""
Manipulate network parameters and setup random directions with normalization.
"""
import torch
import copy
from os.path import exists, commonprefix
import numpy as np
import h5py
import h5_util
import model_loader
################################################################################
# Supporting functions for weights manipulation
################################################################################
def get_weights(net):
""" Extract parameters from net, and return a list of tensors"""
return [p.data for p in net.parameters()]
def set_weights(net, weights, directions=None, step=None):
"""
Overwrite the network's weights with a specified list of tensors
or change weights along directions with a step size.
"""
if directions is None:
# You cannot specify a step length without a direction.
for (p, w) in zip(net.parameters(), weights):
p.data.copy_(w.type(type(p.data)))
else:
assert step is not None, 'If a direction is specified then step must be specified as well'
if len(directions) == 2:
dx = directions[0]
dy = directions[1]
changes = [d0*step[0] + d1*step[1] for (d0, d1) in zip(dx, dy)]
else:
changes = [d*step for d in directions[0]]
for (p, w, d) in zip(net.parameters(), weights, changes):
p.data = w + torch.Tensor(d).type(type(w))
def set_states(net, states, directions=None, step=None):
"""
Overwrite the network's state_dict or change it along directions with a step size.
"""
if directions is None:
net.load_state_dict(states)
else:
assert step is not None, 'If direction is provided then the step must be specified as well'
if len(directions) == 2:
dx = directions[0]
dy = directions[1]
changes = [d0*step[0] + d1*step[1] for (d0, d1) in zip(dx, dy)]
else:
changes = [d*step for d in directions[0]]
new_states = copy.deepcopy(states)
assert (len(new_states) == len(changes))
for (k, v), d in zip(new_states.items(), changes):
d = torch.tensor(d)
v.add_(d.type(v.type()))
net.load_state_dict(new_states)
def get_random_weights(weights):
"""
Produce a random direction that is a list of random Gaussian tensors
with the same shape as the network's weights, so one direction entry per weight.
"""
return [torch.randn(w.size()) for w in weights]
def get_random_states(states):
"""
Produce a random direction that is a list of random Gaussian tensors
with the same shape as the network's state_dict(), so one direction entry
per weight, including BN's running_mean/var.
"""
return [torch.randn(w.size()) for k, w in states.items()]
def get_diff_weights(weights, weights2):
""" Produce a direction from 'weights' to 'weights2'."""
return [w2 - w for (w, w2) in zip(weights, weights2)]
def get_diff_states(states, states2):
""" Produce a direction from 'states' to 'states2'."""
return [v2 - v for (k, v), (k2, v2) in zip(states.items(), states2.items())]
################################################################################
# Normalization Functions
################################################################################
def normalize_direction(direction, weights, norm='filter'):
"""
Rescale the direction so that it has similar norm as their corresponding
model in different levels.
Args:
direction: a variables of the random direction for one layer
weights: a variable of the original model for one layer
norm: normalization method, 'filter' | 'layer' | 'weight'
"""
if norm == 'filter':
# Rescale the filters (weights in group) in 'direction' so that each
# filter has the same norm as its corresponding filter in 'weights'.
for d, w in zip(direction, weights):
d.mul_(w.norm()/(d.norm() + 1e-10))
elif norm == 'layer':
# Rescale the layer variables in the direction so that each layer has
# the same norm as the layer variables in weights.
direction.mul_(weights.norm()/direction.norm())
elif norm == 'weight':
# Rescale the entries in the direction so that each entry has the same
# scale as the corresponding weight.
direction.mul_(weights)
elif norm == 'dfilter':
# Rescale the entries in the direction so that each filter direction
# has the unit norm.
for d in direction:
d.div_(d.norm() + 1e-10)
elif norm == 'dlayer':
# Rescale the entries in the direction so that each layer direction has
# the unit norm.
direction.div_(direction.norm())
def normalize_directions_for_weights(direction, weights, norm='filter', ignore='biasbn'):
"""
The normalization scales the direction entries according to the entries of weights.
"""
assert(len(direction) == len(weights))
for d, w in zip(direction, weights):
if d.dim() <= 1:
if ignore == 'biasbn':
d.fill_(0) # ignore directions for weights with 1 dimension
else:
d.copy_(w) # keep directions for weights/bias that are only 1 per node
else:
normalize_direction(d, w, norm)
def normalize_directions_for_states(direction, states, norm='filter', ignore='ignore'):
assert(len(direction) == len(states))
for d, (k, w) in zip(direction, states.items()):
if d.dim() <= 1:
if ignore == 'biasbn':
d.fill_(0) # ignore directions for weights with 1 dimension
else:
d.copy_(w) # keep directions for weights/bias that are only 1 per node
else:
normalize_direction(d, w, norm)
def ignore_biasbn(directions):
""" Set bias and bn parameters in directions to zero """
for d in directions:
if d.dim() <= 1:
d.fill_(0)
################################################################################
# Create directions
################################################################################
def create_target_direction(net, net2, dir_type='states'):
"""
Setup a target direction from one model to the other
Args:
net: the source model
net2: the target model with the same architecture as net.
dir_type: 'weights' or 'states', type of directions.
Returns:
direction: the target direction from net to net2 with the same dimension
as weights or states.
"""
assert (net2 is not None)
# direction between net2 and net
if dir_type == 'weights':
w = get_weights(net)
w2 = get_weights(net2)
direction = get_diff_weights(w, w2)
elif dir_type == 'states':
s = net.state_dict()
s2 = net2.state_dict()
direction = get_diff_states(s, s2)
return direction
def create_random_direction(net, dir_type='weights', ignore='biasbn', norm='filter'):
"""
Setup a random (normalized) direction with the same dimension as
the weights or states.
Args:
net: the given trained model
dir_type: 'weights' or 'states', type of directions.
ignore: 'biasbn', ignore biases and BN parameters.
norm: direction normalization method, including
'filter" | 'layer' | 'weight' | 'dlayer' | 'dfilter'
Returns:
direction: a random direction with the same dimension as weights or states.
"""
# random direction
if dir_type == 'weights':
weights = get_weights(net) # a list of parameters.
direction = get_random_weights(weights)
normalize_directions_for_weights(direction, weights, norm, ignore)
elif dir_type == 'states':
states = net.state_dict() # a dict of parameters, including BN's running mean/var.
direction = get_random_states(states)
normalize_directions_for_states(direction, states, norm, ignore)
return direction
def setup_direction(args, dir_file, net):
"""
Setup the h5 file to store the directions.
- xdirection, ydirection: The pertubation direction added to the mdoel.
The direction is a list of tensors.
"""
print('-------------------------------------------------------------------')
print('setup_direction')
print('-------------------------------------------------------------------')
# Skip if the direction file already exists
if exists(dir_file):
f = h5py.File(dir_file, 'r')
if (args.y and 'ydirection' in f.keys()) or 'xdirection' in f.keys():
f.close()
print ("%s is already setted up" % dir_file)
return
f.close()
# Create the plotting directions
f = h5py.File(dir_file,'w') # create file, fail if exists
if not args.dir_file:
print("Setting up the plotting directions...")
if args.model_file2:
net2 = model_loader.load(args.dataset, args.model, args.model_file2)
xdirection = create_target_direction(net, net2, args.dir_type)
else:
xdirection = create_random_direction(net, args.dir_type, args.xignore, args.xnorm)
h5_util.write_list(f, 'xdirection', xdirection)
if args.y:
if args.same_dir:
ydirection = xdirection
elif args.model_file3:
net3 = model_loader.load(args.dataset, args.model, args.model_file3)
ydirection = create_target_direction(net, net3, args.dir_type)
else:
ydirection = create_random_direction(net, args.dir_type, args.yignore, args.ynorm)
h5_util.write_list(f, 'ydirection', ydirection)
f.close()
print ("direction file created: %s" % dir_file)
def name_direction_file(args):
""" Name the direction file that stores the random directions. """
if args.dir_file:
assert exists(args.dir_file), "%s does not exist!" % args.dir_file
return args.dir_file
dir_file = ""
file1, file2, file3 = args.model_file, args.model_file2, args.model_file3
# name for xdirection
if file2:
# 1D linear interpolation between two models
assert exists(file2), file2 + " does not exist!"
if file1[:file1.rfind('/')] == file2[:file2.rfind('/')]:
# model_file and model_file2 are under the same folder
dir_file += file1 + '_' + file2[file2.rfind('/')+1:]
else:
# model_file and model_file2 are under different folders
prefix = commonprefix([file1, file2])
prefix = prefix[0:prefix.rfind('/')]
dir_file += file1[:file1.rfind('/')] + '_' + file1[file1.rfind('/')+1:] + '_' + \
file2[len(prefix)+1: file2.rfind('/')] + '_' + file2[file2.rfind('/')+1:]
else:
dir_file += file1
dir_file += '_' + args.dir_type
if args.xignore:
dir_file += '_xignore=' + args.xignore
if args.xnorm:
dir_file += '_xnorm=' + args.xnorm
# name for ydirection
if args.y:
if file3:
assert exists(file3), "%s does not exist!" % file3
if file1[:file1.rfind('/')] == file3[:file3.rfind('/')]:
dir_file += file3
else:
# model_file and model_file3 are under different folders
dir_file += file3[:file3.rfind('/')] + '_' + file3[file3.rfind('/')+1:]
else:
if args.yignore:
dir_file += '_yignore=' + args.yignore
if args.ynorm:
dir_file += '_ynorm=' + args.ynorm
if args.same_dir: # ydirection is the same as xdirection
dir_file += '_same_dir'
# index number
if args.idx > 0: dir_file += '_idx=' + str(args.idx)
dir_file += ".h5"
return dir_file
def load_directions(dir_file):
""" Load direction(s) from the direction file."""
f = h5py.File(dir_file, 'r')
if 'ydirection' in f.keys(): # If this is a 2D plot
xdirection = h5_util.read_list(f, 'xdirection')
ydirection = h5_util.read_list(f, 'ydirection')
directions = [xdirection, ydirection]
else:
directions = [h5_util.read_list(f, 'xdirection')]
return directions