-
Notifications
You must be signed in to change notification settings - Fork 5
/
utils.py
397 lines (322 loc) · 14 KB
/
utils.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
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
import os,sys
import numpy as np
import random
from copy import deepcopy
import math
import torch
import torch.nn as nn
from torch.optim import Optimizer
from tqdm import tqdm
from sklearn.feature_extraction import image
import torchvision.transforms.functional as tvF
import torchvision.transforms as transforms
from torchvision import models
from torchvision.models.resnet import *
from arguments import get_args
import torch.nn.functional as F
args = get_args()
class Adam(Optimizer):
r"""Implements Adam algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False)
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params, lr=1e-3, lr_rho=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, amsgrad=False, param_name=None, lr_scale=None):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay, amsgrad=amsgrad)
self.param_name = param_name
self.lr_rho = lr_rho
self.lr_scale = lr_scale
super(Adam, self).__init__(params, defaults)
def __setstate__(self, state):
super(Adam, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for i,p in enumerate(group['params']):
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
if group['weight_decay'] != 0:
grad.add_(group['weight_decay'], p.data)
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
n = self.param_name[i]
if 'rho' in self.param_name[i]:
step_size = self.lr_rho * math.sqrt(bias_correction2) / bias_correction1
else:
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
# p.data.addcdiv_(-step_size, self.lr_scale[n] * exp_avg, denom)
p.data.addcdiv_(-step_size, exp_avg, denom)
return loss
def gs_cal(t, x, y, criterion, model, sbatch=20):
# Init
param_R = {}
for name, param in model.named_parameters():
if len(param.size()) <= 1:
continue
name = name.split('.')[:-1]
name = '.'.join(name)
param = param.view(param.size(0), -1)
param_R['{}'.format(name)]=torch.zeros((param.size(0)))
# Compute
model.train()
for i in range(0,x.size(0),sbatch):
b=torch.LongTensor(np.arange(i,np.min([i+sbatch,x.size(0)]))).cuda()
images=x[b]
target=y[b]
# Forward and backward
outputs = model.forward(images, True)[t]
cnt = 0
for idx, j in enumerate(model.act):
j = torch.mean(j, dim=0)
if len(j.size())>1:
j = torch.mean(j.view(j.size(0), -1), dim = 1).abs()
model.act[idx] = j
for name, param in model.named_parameters():
if len(param.size()) <= 1 or 'last' in name or 'downsample' in name:
continue
name = name.split('.')[:-1]
name = '.'.join(name)
param_R[name] += model.act[cnt].abs().detach()*sbatch
cnt+=1
with torch.no_grad():
for key in param_R.keys():
param_R[key]=(param_R[key]/x.size(0))
return param_R
def gs_cal_resnet18(t, x, y, criterion, model, sbatch=20):
# Init
param_R = {}
for name, param in model.named_parameters():
if 'last' in name:
continue
if (('conv' in name) or ('fc' in name)) and 'weight' in name:
key = '.'.join(name.split('.')[:-1])
param_R[key] = torch.zeros(param.shape[0])
print (param_R[key].shape)
# Compute
model.train()
for i in range(0,x.size(0),sbatch):
b=torch.LongTensor(np.arange(i,np.min([i+sbatch,x.size(0)]))).cuda()
images=x[b]
target=y[b]
# Forward and backward
outputs = model.forward(images, True)[t]
cnt = 0
for idx, j in enumerate(model.act):
j = torch.mean(j, dim=0)
if len(j.size())>1:
j = torch.mean(j.view(j.size(0), -1), dim = 1).abs()
model.act[idx] = j
for name, param in model.named_parameters():
if len(param.size()) <= 1 or 'last' in name or 'downsample' in name:
continue
name = name.split('.')[:-1]
name = '.'.join(name)
param_R[name] += model.act[cnt].abs().detach()*sbatch
cnt+=1
with torch.no_grad():
for key in param_R.keys():
param_R[key]=(param_R[key]/x.size(0))
return param_R
########################################################################################################################
def crop(x, patch_size, mode = 'train'):
cropped_image = []
arr_len = len(x)
if mode == 'train':
for idx in range(arr_len):
patch = image.extract_patches_2d(image = x[idx].data.cpu().numpy(),
patch_size = (patch_size, patch_size), max_patches = 1)[0]
# Random horizontal flipping
if random.random() > 0.5:
patch = np.fliplr(patch)
# Random vertical flipping
if random.random() > 0.5:
patch = np.flipud(patch)
# Corrupt source image
patch = np.transpose(patch, (2,0,1))
patch = tvF.to_tensor(patch.copy())
cropped_image.append(patch)
elif mode == 'valid' or mode == 'test':
for idx in range(arr_len):
patch = x[idx].data.cpu().numpy()
H,W,C = patch.shape
patch = patch[H//2-patch_size//2:H//2+patch_size//2, W//2-patch_size//2:W//2+patch_size//2,:]
# Corrupt source image
patch = np.transpose(patch, (2,0,1))
patch = tvF.to_tensor(patch.copy())
cropped_image.append(patch)
image_tensor=torch.stack(cropped_image).view(-1,3,patch_size,patch_size).cuda()
return image_tensor
def print_model_report(model):
print('-'*100)
print(model)
print('Dimensions =',end=' ')
count=0
for p in model.parameters():
print(p.size(),end=' ')
count+=np.prod(p.size())
print()
print('Num parameters = %s'%(human_format(count)))
print('-'*100)
return count
def human_format(num):
magnitude=0
while abs(num)>=1000:
magnitude+=1
num/=1000.0
return '%.1f%s'%(num,['','K','M','G','T','P'][magnitude])
def print_optimizer_config(optim):
if optim is None:
print(optim)
else:
print(optim,'=',end=' ')
opt=optim.param_groups[0]
for n in opt.keys():
if not n.startswith('param'):
print(n+':',opt[n],end=', ')
print()
return
########################################################################################################################
def get_model(model):
return deepcopy(model.state_dict())
def set_model_(model,state_dict):
model.load_state_dict(deepcopy(state_dict))
return
def freeze_model(model):
for param in model.parameters():
param.requires_grad = False
return
########################################################################################################################
def compute_conv_output_size(Lin,kernel_size,stride=1,padding=0,dilation=1):
return int(np.floor((Lin+2*padding-dilation*(kernel_size-1)-1)/float(stride)+1))
########################################################################################################################
def fisher_matrix_diag(t,x,y,model,criterion,sbatch=20, split = False):
# Init
fisher={}
for n,p in model.named_parameters():
fisher[n]=0*p.data
# Compute
model.train()
criterion = torch.nn.CrossEntropyLoss()
for i in tqdm(range(0,x.size(0),sbatch),desc='Fisher diagonal',ncols=100,ascii=True):
b=torch.LongTensor(np.arange(i,np.min([i+sbatch,x.size(0)]))).cuda()
images=x[b]
target=y[b]
# Forward and backward
model.zero_grad()
if split:
outputs = model.forward(images)[t]
else:
outputs=model.forward(images)
loss= criterion(outputs, target)
# loss=criterion(t,outputs,target)
loss.backward()
# Get gradients
for n,p in model.named_parameters():
if p.grad is not None:
fisher[n]+=sbatch*p.grad.data.pow(2)
# Mean
with torch.no_grad():
for n,_ in model.named_parameters():
fisher[n]=fisher[n]/x.size(0)
return fisher
########################################################################################################################
class CPR(nn.Module):
def __init__(self):
super(CPR, self).__init__()
def forward(self, x):
b = F.softmax(x, dim=1) * F.log_softmax(x, dim=1)
b = -1.0 * b.sum(dim=1)
return b.mean()
def crop_CUB200(data, _type = 'train'):
mean = torch.FloatTensor([[[123.77, 127.55, 110.25]]])
std = torch.FloatTensor([[[59.16, 58.06, 67.99]]])
size=[3,224,224]
cropped_data = []
for i in range(len(data)):
if _type == 'train':
img = data[i] * std + mean
img = transforms.ToPILImage()(img.permute(2,0,1)).convert("RGB")
# i, j, h, w = transforms.RandomCrop.get_params(img, output_size=(224, 224))
# cropped_patch = tvF.crop(img, i, j, h, w)
i, j, h, w = transforms.RandomResizedCrop.get_params(img, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.))
cropped_patch = tvF.resized_crop(img, i, j, h, w, (224, 224))
# Random horizontal flipping
if random.random() > 0.5:
cropped_patch = tvF.hflip(cropped_patch)
# Random vertical flipping
if random.random() > 0.5:
cropped_patch = tvF.vflip(cropped_patch)
cropped_data.append(transforms.Normalize((123.77, 127.55, 110.25), (59.16, 58.06, 67.99))(tvF.to_tensor(cropped_patch)*255.))
else:
c, h, w = data[i].shape
if h == 224 and w == 224:
return data
else:
img = data[i] * std + mean
img = transforms.ToPILImage()(img.permute(2,0,1)).convert("RGB")
img = tvF.center_crop(img, 224)
cropped_data.append(transforms.Normalize((123.77, 127.55, 110.25), (59.16, 58.06, 67.99))(tvF.to_tensor(img)*255.))
return torch.stack(cropped_data).view(-1,size[0],size[1],size[2]).cuda()