forked from GATECH-EIC/Early-Bird-Tickets
-
Notifications
You must be signed in to change notification settings - Fork 0
/
resprune_50.py
511 lines (454 loc) · 21.6 KB
/
resprune_50.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
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
import argparse
import numpy as np
import os, sys
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
from compute_flops import print_model_param_nums, print_model_param_flops
import models
from models import *
from models.preresnet_imagenet import BasicBlock
# Prune settings
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR prune')
parser.add_argument('--dataset', type=str, default='cifar100',
help='training dataset (default: cifar10)')
parser.add_argument('--data', type=str, default=None,
help='path to dataset')
parser.add_argument('--test-batch-size', type=int, default=256, metavar='N',
help='input batch size for testing (default: 256)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--depth', type=int, default=164,
help='depth of the resnet')
parser.add_argument('--percent', type=float, default=0.5,
help='scale sparse rate (default: 0.5)')
parser.add_argument('--model', default='', type=str, metavar='PATH',
help='path to the model (default: none)')
parser.add_argument('--init', default='', type=str, metavar='PATH',
help='path to the rewind init (default: none)')
parser.add_argument('--save', default='', type=str, metavar='PATH',
help='path to save pruned model (default: none)')
parser.add_argument('--arch', default="resnet", type=str, help='model name')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(1)
if args.cuda:
torch.cuda.manual_seed(1)
if not os.path.exists(args.save):
os.makedirs(args.save)
args.gpu_ids = [4,5,6]
if len(args.gpu_ids) > 0:
torch.cuda.set_device(args.gpu_ids[0])
if args.dataset == 'imagenet':
model = models.__dict__['resnet50_official'](pretrained=False)
model = torch.nn.DataParallel(model, device_ids=args.gpu_ids)
else:
model = resnet(depth=args.depth, dataset=args.dataset)
if args.cuda:
model.cuda()
if args.model:
if os.path.isfile(args.model):
print("=> loading checkpoint '{}'".format(args.model))
checkpoint = torch.load(args.model)
# args.start_epoch = checkpoint['epoch']
# best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
# print("=> loaded checkpoint '{}' (epoch {}) Prec1: {:f}"
# .format(args.model, checkpoint['epoch'], best_prec1))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
exit()
if args.dataset == 'imagenet':
print('original model param: ', print_model_param_nums(model))
print('original model flops: ', print_model_param_flops(model, 224, True))
else:
print('original model param: ', print_model_param_nums(model))
print('original model flops: ', print_model_param_flops(model, 32, True))
total = 0
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
total += m.weight.data.shape[0]
bn = torch.zeros(total)
index = 0
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
size = m.weight.data.shape[0]
bn[index:(index+size)] = m.weight.data.abs().clone()
index += size
p_flops = 0
y, i = torch.sort(bn)
p_flops += total * np.log2(total) * 3
thre_index = int(total * args.percent)
thre = y[thre_index]
pruned = 0
cfg = []
cfg_mask = []
for k, m in enumerate(model.modules()):
if isinstance(m, nn.BatchNorm2d):
weight_copy = m.weight.data.abs().clone()
mask = weight_copy.gt(thre.cuda()).float().cuda()
pruned = pruned + mask.shape[0] - torch.sum(mask)
m.weight.data.mul_(mask)
m.bias.data.mul_(mask)
num = int(torch.sum(mask))
if num != 0:
cfg.append(num)
cfg_mask.append(mask.clone())
elif num == 0:
cfg.append(1)
_mask = mask.clone()
_mask[0] = 1
cfg_mask.append(_mask)
print('layer index: {:d} \t total channel: {:d} \t remaining channel: {:d}'.
format(k, mask.shape[0], int(torch.sum(mask))))
elif isinstance(m, nn.MaxPool2d):
# cfg.append('M')
pass
pruned_ratio = pruned/total
# compare two mask distance
p_flops += 2 * total #(minus and sum)
print(' + Memory Request: %.2fKB' % float(total * 32 / 1024 / 8))
print(' + Flops for pruning: %.2fM' % (p_flops / 1e6))
print('Pre-processing Successful!')
# simple test model after Pre-processing prune (simple set BN scales to zeros)
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def test(model):
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
if args.dataset == 'cifar10':
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
batch_size=args.test_batch_size, shuffle=False, **kwargs)
elif args.dataset == 'cifar100':
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])),
batch_size=args.test_batch_size, shuffle=False, **kwargs)
elif args.dataset == 'imagenet':
# Data loading code
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.test_batch_size, shuffle=False,
num_workers=16, pin_memory=True)
else:
raise ValueError("No valid dataset is given.")
model.eval()
test_acc = 0
for data, target in test_loader:
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
prec1, prec5 = accuracy(output.data, target.data, topk=(1, 5))
test_acc += prec1.item()
print('\nTest set: Accuracy: {}/{} ({:.1f}%)\n'.format(
test_acc, len(test_loader), test_acc / len(test_loader)))
return np.round(test_acc / len(test_loader), 2)
# acc = test(model)
print("Cfg:")
print(cfg)
print(len(cfg))
original_cfg_mask = cfg_mask
if args.dataset == 'imagenet':
downsample = [5, 15, 28, 47] #[11, 37, 71, 121]
last_block = [4, 8, 11, 14, 18, 21, 24, 27, 31, 34, 37, 40, 43, 46, 50, 53]
new_cfg = []
for i, item in enumerate(cfg):
if (i+1) in downsample:
new_cfg[-1] = max(new_cfg[-1], item)
# elif i in last_block:
# new_cfg[-1] = min(new_cfg[-1], item)
else:
new_cfg.append(item)
new_cfg = [cfg[0]] + new_cfg
print(new_cfg)
newmodel = models.__dict__[args.arch](pretrained=False, cfg=new_cfg)
newmodel = torch.nn.DataParallel(newmodel, device_ids=args.gpu_ids)
newmodel.cuda()
else:
newmodel = resnet(depth=args.depth, dataset=args.dataset, cfg=cfg)
if args.init != '':
if os.path.isfile(args.init):
print("=> loading checkpoint '{}'".format(args.init))
checkpoint = torch.load(args.init)
model.load_state_dict(checkpoint['state_dict'])
else:
print("=> no checkpoint found at '{}'".format(args.init))
# if args.cuda:
# newmodel.cuda()
# model.cuda()
num_parameters = sum([param.nelement() for param in newmodel.parameters()])
savepath = os.path.join(args.save, "prune.txt")
with open(savepath, "w") as fp:
fp.write("Configuration: \n"+str(cfg)+"\n")
fp.write("Number of parameters: \n"+str(num_parameters)+"\n")
# fp.write("Test accuracy: \n"+str(acc))
# old_modules = list(model.modules())
# new_modules = list(newmodel.modules())
# useful_i = []
# for i, module in enumerate(old_modules):
# if isinstance(module, nn.Conv2d) or isinstance(module, nn.BatchNorm2d) or isinstance(module, nn.Linear) or isinstance(module, nn.ReLU) or isinstance(module, channel_selection):
# useful_i.append(i)
# temp = []
# for i, item in enumerate(useful_i):
# temp.append(old_modules[item])
# # for i, item in enumerate(temp):
# # print(i, item)
# # sys.exit()
# layer_id_in_cfg = 0
# start_mask = torch.ones(3)
# end_mask = cfg_mask[layer_id_in_cfg]
# conv_count = 0
# bn_count = 0
# indexes = []
# if args.dataset == 'imagenet':
# downsample = [5, 15, 28, 47] #[11, 37, 71, 121]
# last_block = [4, 8, 11, 14, 18, 21, 24, 27, 31, 34, 37, 40, 43, 46, 50, 53] #[8, 18, 26, 34, 44, 52, 60, 68, 78, 86, 94, 102, 110, 118, 128, 136]
# channel_select = [5, 15, 23, 31, 41, 49, 57, 65, 75, 83, 91, 99, 107, 115, 125, 133, 140]
# channel_select_bn = [1, 4, 8, 11, 14, 18, 21, 24, 27, 31, 34, 37, 40, 43, 46, 50, 53]#[1, 9, 19, 27, 35, 45, 53, 61, 69, 79, 87, 95, 103, 111, 119, 129, 137]
# for layer_id in range(len(temp)):
# m0 = old_modules[useful_i[layer_id]]
# m1 = new_modules[useful_i[layer_id]]
# # print(m0, m1)
# if isinstance(m0, nn.BatchNorm2d):
# # print(len(start_mask), len(end_mask))
# bn_count += 1
# idx1 = np.squeeze(np.argwhere(np.asarray(end_mask.cpu().numpy())))
# # print(layer_id_in_cfg, len(cfg_mask))
# if idx1.size == 1:
# idx1 = np.resize(idx1,(1,))
# if bn_count == 1:
# print('bn=1')
# # m1.weight.data = m0.weight.data.clone()
# # m1.bias.data = m0.bias.data.clone()
# # m1.running_mean = m0.running_mean.clone()
# # m1.running_var = m0.running_var.clone()
# m1.weight.data = m0.weight.data[idx1.tolist()].clone()
# m1.bias.data = m0.bias.data[idx1.tolist()].clone()
# m1.running_mean = m0.running_mean[idx1.tolist()].clone()
# m1.running_var = m0.running_var[idx1.tolist()].clone()
# m2 = new_modules[useful_i[layer_id+4]] # channel selection
# assert isinstance(m2, channel_selection)
# # m2.indexes = np.zeros_like(m2.indexes)
# # m2.indexes[idx1.tolist()] = 1.0
# m2.indexes.data.zero_()
# m2.indexes.data[idx1.tolist()] = 1.0
# indexes.append(m2.indexes)
# layer_id_in_cfg += 1
# start_mask = end_mask.clone()
# if layer_id_in_cfg < len(cfg_mask):
# end_mask = cfg_mask[layer_id_in_cfg]
# elif bn_count in downsample:
# # If the current layer is the downsample layer, then the current batchnorm 2d layer won't be pruned.
# m1.weight.data = m0.weight.data.clone()
# m1.bias.data = m0.bias.data.clone()
# m1.running_mean = m0.running_mean.clone()
# m1.running_var = m0.running_var.clone()
# elif bn_count in last_block:
# # If the current layer is the last conv-bn layer in block, then the current batchnorm 2d layer won't be pruned.
# m1.weight.data = m0.weight.data.clone()
# m1.bias.data = m0.bias.data.clone()
# m1.running_mean = m0.running_mean.clone()
# m1.running_var = m0.running_var.clone()
# # We need to set the channel selection layer.
# if bn_count + 1 in downsample:
# m2 = new_modules[useful_i[layer_id+6]]
# assert isinstance(m2, channel_selection)
# else:
# if layer_id + 4 < len(useful_i):
# m2 = new_modules[useful_i[layer_id+4]]
# assert isinstance(m2, channel_selection) #or isinstance(m2, nn.Linear)
# else:
# m2 = None
# if isinstance(m2, channel_selection):
# # m2.indexes = np.zeros_like(m2.indexes)
# # m2.indexes[idx1.tolist()] = 1.0
# m2.indexes.data.zero_()
# m2.indexes.data[idx1.tolist()] = 1.0
# indexes.append(m2.indexes)
# layer_id_in_cfg += 1
# start_mask = end_mask.clone()
# if layer_id_in_cfg < len(cfg_mask):
# end_mask = cfg_mask[layer_id_in_cfg]
# else:
# m1.weight.data = m0.weight.data[idx1.tolist()].clone()
# m1.bias.data = m0.bias.data[idx1.tolist()].clone()
# m1.running_mean = m0.running_mean[idx1.tolist()].clone()
# m1.running_var = m0.running_var[idx1.tolist()].clone()
# layer_id_in_cfg += 1
# start_mask = end_mask.clone()
# if layer_id_in_cfg < len(cfg_mask): # do not change in Final FC
# end_mask = cfg_mask[layer_id_in_cfg]
# # elif layer_id in channel_select[:-1]:
# # # We need to set the channel selection layer.
# # _mask = original_cfg_mask[channel_select_bn[channel_select.index(layer_id)] - 1]
# # idx1 = np.squeeze(np.argwhere(np.asarray(_mask.cpu().numpy())))
# # m2 = new_modules[useful_i[layer_id]]
# # assert isinstance(m2, channel_selection)
# # if isinstance(m2, channel_selection):
# # m2.indexes = np.zeros_like(m2.indexes)
# # m2.indexes[idx1.tolist()] = 1.0
# # # print(m2.indexes)
# # layer_id_in_cfg += 1
# # start_mask = end_mask.clone()
# # if layer_id_in_cfg < len(cfg_mask):
# # end_mask = cfg_mask[layer_id_in_cfg]
# elif isinstance(m0, nn.Conv2d):
# conv_count += 1
# if conv_count == 1:
# print('yes!!!')
# # m1.weight.data = m0.weight.data.clone()
# idx0 = np.squeeze(np.argwhere(np.asarray(start_mask.cpu().numpy())))
# idx1 = np.squeeze(np.argwhere(np.asarray(end_mask.cpu().numpy())))
# print('In shape: {:d}, Out shape {:d}.'.format(idx0.size, idx1.size))
# if idx0.size == 1:
# idx0 = np.resize(idx0, (1,))
# if idx1.size == 1:
# idx1 = np.resize(idx1, (1,))
# w1 = m0.weight.data[:, idx0.tolist(), :, :].clone()
# w1 = w1[idx1.tolist(), :, :, :].clone()
# m1.weight.data = w1.clone()
# continue
# elif conv_count in downsample: # downsample
# # We need to consider the case where there are downsampling convolutions.
# # For these convolutions, we just copy the weights.
# m1.weight.data = m0.weight.data.clone()
# continue
# elif conv_count in last_block:
# # the last convolution in the residual block.
# idx0 = np.squeeze(np.argwhere(np.asarray(start_mask.cpu().numpy())))
# idx1 = np.squeeze(np.argwhere(np.asarray(end_mask.cpu().numpy())))
# print('In shape: {:d}, Out shape {:d}.'.format(idx0.size, idx1.size))
# if idx0.size == 1:
# idx0 = np.resize(idx0, (1,))
# if idx1.size == 1:
# idx1 = np.resize(idx1, (1,))
# w1 = m0.weight.data[:, idx0.tolist(), :, :].clone()
# m1.weight.data = w1.clone()
# continue
# else:
# idx0 = np.squeeze(np.argwhere(np.asarray(start_mask.cpu().numpy())))
# idx1 = np.squeeze(np.argwhere(np.asarray(end_mask.cpu().numpy())))
# print('In shape: {:d}, Out shape {:d}.'.format(idx0.size, idx1.size))
# if idx0.size == 1:
# idx0 = np.resize(idx0, (1,))
# if idx1.size == 1:
# idx1 = np.resize(idx1, (1,))
# w1 = m0.weight.data[:, idx0.tolist(), :, :].clone()
# w1 = w1[idx1.tolist(), :, :, :].clone()
# m1.weight.data = w1.clone()
# elif isinstance(m0, nn.Linear):
# # end_mask = cfg_mask[-1]
# # idx0 = np.squeeze(np.argwhere(np.asarray(end_mask.cpu().numpy())))
# # print(idx0)
# # if idx0.size == 1:
# # idx0 = np.resize(idx0, (1,))
# # m1.weight.data = m0.weight.data[:, idx0].clone()
# m1.weight.data = m0.weight.data.clone()
# m1.bias.data = m0.bias.data.clone()
# else:
# for layer_id in range(len(old_modules)):
# m0 = old_modules[layer_id]
# m1 = new_modules[layer_id]
# if isinstance(m0, nn.BatchNorm2d):
# idx1 = np.squeeze(np.argwhere(np.asarray(end_mask.cpu().numpy())))
# if idx1.size == 1:
# idx1 = np.resize(idx1,(1,))
# if isinstance(old_modules[layer_id + 1], channel_selection):
# # If the next layer is the channel selection layer, then the current batchnorm 2d layer won't be pruned.
# m1.weight.data = m0.weight.data.clone()
# m1.bias.data = m0.bias.data.clone()
# m1.running_mean = m0.running_mean.clone()
# m1.running_var = m0.running_var.clone()
# # We need to set the channel selection layer.
# m2 = new_modules[layer_id + 1]
# m2.indexes.data.zero_()
# m2.indexes.data[idx1.tolist()] = 1.0
# layer_id_in_cfg += 1
# start_mask = end_mask.clone()
# if layer_id_in_cfg < len(cfg_mask):
# end_mask = cfg_mask[layer_id_in_cfg]
# else:
# m1.weight.data = m0.weight.data[idx1.tolist()].clone()
# m1.bias.data = m0.bias.data[idx1.tolist()].clone()
# m1.running_mean = m0.running_mean[idx1.tolist()].clone()
# m1.running_var = m0.running_var[idx1.tolist()].clone()
# layer_id_in_cfg += 1
# start_mask = end_mask.clone()
# if layer_id_in_cfg < len(cfg_mask): # do not change in Final FC
# end_mask = cfg_mask[layer_id_in_cfg]
# elif isinstance(m0, nn.Conv2d):
# if conv_count == 0:
# m1.weight.data = m0.weight.data.clone()
# conv_count += 1
# continue
# if isinstance(old_modules[layer_id-1], channel_selection) or isinstance(old_modules[layer_id-1], nn.BatchNorm2d):
# # This convers the convolutions in the residual block.
# # The convolutions are either after the channel selection layer or after the batch normalization layer.
# conv_count += 1
# idx0 = np.squeeze(np.argwhere(np.asarray(start_mask.cpu().numpy())))
# idx1 = np.squeeze(np.argwhere(np.asarray(end_mask.cpu().numpy())))
# print('In shape: {:d}, Out shape {:d}.'.format(idx0.size, idx1.size))
# if idx0.size == 1:
# idx0 = np.resize(idx0, (1,))
# if idx1.size == 1:
# idx1 = np.resize(idx1, (1,))
# w1 = m0.weight.data[:, idx0.tolist(), :, :].clone()
# # If the current convolution is not the last convolution in the residual block, then we can change the
# # number of output channels. Currently we use `conv_count` to detect whether it is such convolution.
# if conv_count % 3 != 1:
# w1 = w1[idx1.tolist(), :, :, :].clone()
# m1.weight.data = w1.clone()
# continue
# # We need to consider the case where there are downsampling convolutions.
# # For these convolutions, we just copy the weights.
# m1.weight.data = m0.weight.data.clone()
# elif isinstance(m0, nn.Linear):
# idx0 = np.squeeze(np.argwhere(np.asarray(start_mask.cpu().numpy())))
# if idx0.size == 1:
# idx0 = np.resize(idx0, (1,))
# m1.weight.data = m0.weight.data[:, idx0].clone()
# m1.bias.data = m0.bias.data.clone()
torch.save({'cfg': new_cfg, 'state_dict': newmodel.state_dict()}, os.path.join(args.save, 'pruned.pth.tar'))
# print(model)
# print('*'*34)
print(newmodel)
model = newmodel
# old_modules = list(model.modules())
# for i, module in enumerate(old_modules):
# print(i, module)
# sys.exit()
# test(model)
param = print_model_param_nums(model)
if args.dataset == 'imagenet':
flops = print_model_param_flops(model, 224, True)
else:
flops = print_model_param_flops(model.cpu(), 32, True)
with open(savepath, "w") as fp:
fp.write("new model param: \n"+str(param)+"\n")
fp.write("new model flops: \n"+str(flops)+"\n")
print('new model param: ', param)
print('new model flops: ', flops)