File size: 14,083 Bytes
b84549f |
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 |
from turtle import forward
from typing import Optional
import torch
import copy
from torch import nn
#from methods.utils.data import get_source_dataloader
from utils.dl.common.model import get_model_device, get_model_latency, get_model_size, get_module, get_super_module, set_module
from utils.common.log import logger
"""
No real speedup.
But it's ok because our big model just forward for one time to find the best sub-model.
The sub-model doesn't contain filter selection modules. It's just a normal model.
"""
class KTakesAll(nn.Module):
def __init__(self, k):
super(KTakesAll, self).__init__()
self.k = k
def forward(self, g: torch.Tensor):
# if self.k == 0.:
# t = g
# t = t / torch.sum(t, dim=1).unsqueeze(1) * t.size(1)
# return t.unsqueeze(2).unsqueeze(3)
# t = g
# t = t / torch.sum(t, dim=1).unsqueeze(1) * t.size(1)
# # print('000', t.size())
# t = t.unsqueeze(2).unsqueeze(3).mean((0, 2, 3)).unsqueeze(0).unsqueeze(2).unsqueeze(3)
# # print('111', t.size())
# # print(t)
# return t
# # assert x.dim() == 2
# print(g)
k = int(g.size(1) * self.k)
i = (-g).topk(k, 1)[1]
t = g.scatter(1, i, 0)
# t = t / torch.sum(t, dim=1).unsqueeze(1) * t.size(1)
# print(t)
return t.unsqueeze(2).unsqueeze(3)
# g = g.mean(0).unsqueeze(0)
# k = int(g.size(1) * self.k)
# i = (-g).topk(k, 1)[1]
# t = g.scatter(1, i, 0)
# t = t / torch.sum(t, dim=1).unsqueeze(1) * t.size(1)
# return t.unsqueeze(2).unsqueeze(3)
# class NoiseAdd(nn.Module):
# def __init__(self):
# super(NoiseAdd, self).__init__()
# self.training = True
# def forward(self, x):
# if self.training:
# return x + torch.randn_like(x, device=x.device)
# else:
# return x
class Abs(nn.Module):
def __init__(self):
super(Abs, self).__init__()
def forward(self, x):
return x.abs()
class DomainDynamicConv2d(nn.Module):
def __init__(self, raw_conv2d: nn.Conv2d, raw_bn: nn.BatchNorm2d, k: float, bn_after_fc=False):
super(DomainDynamicConv2d, self).__init__()
assert not bn_after_fc
self.filter_selection_module = nn.Sequential(
Abs(),
nn.AdaptiveAvgPool2d(1),
nn.Flatten(),
nn.Linear(raw_conv2d.in_channels, raw_conv2d.out_channels),
# nn.Conv2d(raw_conv2d.in_channels, raw_conv2d.out_channels // 16, kernel_size=1, bias=False),
# nn.Linear(raw_conv2d.in_channels, raw_conv2d.out_channels // 16),
# nn.BatchNorm1d(raw_conv2d.out_channels // 16) if bn_after_fc else nn.Identity(),
# nn.ReLU(),
# nn.Linear(raw_conv2d.out_channels // 16, raw_conv2d.out_channels),
# nn.BatchNorm1d(raw_conv2d.out_channels),
nn.ReLU(),
# NoiseAdd(),
# nn.Sigmoid()
# L1RegTrack(),
# KTakesAll(k)
)
self.k_takes_all = KTakesAll(k)
self.raw_conv2d = raw_conv2d
self.bn = raw_bn # remember clear the original BNs in the network
nn.init.constant_(self.filter_selection_module[3].bias, 1.)
nn.init.kaiming_normal_(self.filter_selection_module[3].weight)
self.cached_raw_w = None
self.l1_reg_of_raw_w = None
self.cached_w = None
self.static_w = None
self.pruning_ratios = None
def forward(self, x):
raw_x = self.bn(self.raw_conv2d(x))
# if self.k_takes_all.k < 1e-7:
# return raw_x
if self.static_w is None:
raw_w = self.filter_selection_module(x)
self.cached_raw_w = raw_w
# self.l1_reg_of_raw_w = raw_w.norm(1, dim=1).mean()
self.l1_reg_of_raw_w = raw_w.norm(1)
w = self.k_takes_all(raw_w)
# w = w.unsqueeze(2).unsqueeze(3)
# if self.training:
# soft_w = torch.max(torch.zeros_like(raw_w), torch.min(torch.ones_like(raw_w),
# 1.2 * (torch.sigmoid(raw_w + torch.randn_like(raw_w))) - 0.1))
# else:
# soft_w = torch.max(torch.zeros_like(raw_w), torch.min(torch.ones_like(raw_w),
# 1.2 * (torch.sigmoid(raw_w)) - 0.1))
# w = soft_w.detach().clone()
# w[w < 0.5] = 0.
# w[w >= 0.5] = 1.
# w = w + soft_w - soft_w.detach()
# w = w.unsqueeze(2).unsqueeze(3)
# soft_w = soft_w.unsqueeze(2).unsqueeze(3)
# self.l1_reg_of_raw_w = soft_w.norm(1)
self.cached_w = w
# print(w.size(), x.size(), raw_x.size())
else:
w = self.static_w.unsqueeze(0).unsqueeze(2).unsqueeze(3)
if self.pruning_ratios is not None:
# self.pruning_ratios += [1. - float((w_of_a_asample > 0.).sum() / w_of_a_asample.numel()) for w_of_a_asample in w]
self.pruning_ratios += [torch.sum(w > 0.) / w.numel()]
return raw_x * w
# def to_static(self):
# global_w = self.cached_raw_w.detach().topk(0.25, 1)[0].mean(0).unsqueeze(0)
# global_w = self.k_takes_all(global_w).squeeze(0)
# self.static_w = global_w
# def to_dynamic(self):
# self.static_w = None
def boost_raw_model_with_filter_selection(model: nn.Module, init_k: float, bn_after_fc=False, ignore_layers=None, perf_test=True, model_input_size: Optional[tuple]=None):
model = copy.deepcopy(model)
device = get_model_device(model)
if perf_test:
before_model_size = get_model_size(model, True)
before_model_latency = get_model_latency(
model, model_input_size, 50, device, 50)
# clear original BNs
num_original_bns = 0
last_conv_name = None
conv_bn_map = {}
for name, module in model.named_modules():
if isinstance(module, nn.Conv2d):
last_conv_name = name
if isinstance(module, nn.BatchNorm2d) and (ignore_layers is not None and last_conv_name not in ignore_layers):
# set_module(model, name, nn.Identity())
num_original_bns += 1
conv_bn_map[last_conv_name] = name
num_conv = 0
for name, module in model.named_modules():
if isinstance(module, nn.Conv2d) and (ignore_layers is not None and name not in ignore_layers):
set_module(model, name, DomainDynamicConv2d(module, get_module(model, conv_bn_map[name]), init_k, bn_after_fc))
num_conv += 1
assert num_conv == num_original_bns
for bn_layer in conv_bn_map.values():
set_module(model, bn_layer, nn.Identity())
if perf_test:
after_model_size = get_model_size(model, True)
after_model_latency = get_model_latency(
model, model_input_size, 50, device, 50)
logger.info(f'raw model -> raw model w/ filter selection:\n'
f'model size: {before_model_size:.3f}MB -> {after_model_size:.3f}MB '
f'latency: {before_model_latency:.6f}s -> {after_model_latency:.6f}s')
return model, conv_bn_map
def get_l1_reg_in_model(boosted_model):
res = 0.
for name, module in boosted_model.named_modules():
if isinstance(module, DomainDynamicConv2d):
res += module.l1_reg_of_raw_w
return res
def get_cached_w(model):
res = []
for name, module in model.named_modules():
if isinstance(module, DomainDynamicConv2d):
res += [module.cached_w]
return torch.cat(res, dim=1)
def set_pruning_rate(model, k):
for name, module in model.named_modules():
if isinstance(module, KTakesAll):
module.k = k
def get_cached_raw_w(model):
res = []
for name, module in model.named_modules():
if isinstance(module, DomainDynamicConv2d):
res += [module.cached_raw_w]
return torch.cat(res, dim=1)
def start_accmu_flops(model):
for name, module in model.named_modules():
if isinstance(module, DomainDynamicConv2d):
module.pruning_ratios = []
def get_accmu_flops(model):
layer_res = {}
total_res = []
for name, module in model.named_modules():
if isinstance(module, DomainDynamicConv2d):
layer_res[name] = module.pruning_ratios
total_res += module.pruning_ratios
module.pruning_ratios = None
avg_pruning_ratio = sum(total_res) / len(total_res)
return layer_res, total_res, avg_pruning_ratio
def convert_boosted_model_to_static(boosted_model, a_few_data):
boosted_model(a_few_data)
for name, module in boosted_model.named_modules():
if isinstance(module, DomainDynamicConv2d):
module.to_static()
# TODO: use fn3 techniques
def ensure_boosted_model_to_dynamic(boosted_model):
for name, module in boosted_model.named_modules():
if isinstance(module, DomainDynamicConv2d):
module.to_dynamic()
def train_only_gate(model):
gate_params = []
for n, p in model.named_parameters():
if 'filter_selection_module' in n:
gate_params += [p]
else:
p.requires_grad = False
return gate_params
if __name__ == '__main__':
# rand_input = torch.rand((256, 3, 32, 32))
# conv = nn.Conv2d(3, 64, 3, 1, 1, bias=False)
# new_conv = DomainDynamicConv2d(conv, 0.1)
# train_dataloader = get_source_dataloader('CIFAR100', 256, 4, 'train', True, None, True)
# rand_input, _ = next(train_dataloader)
# start_accmu_flops(new_conv)
# new_conv(rand_input)
# _, total_pruning_ratio, avg_pruning_ratio = get_accmu_flops(new_conv)
# import matplotlib.pyplot as plt
# plt.hist(total_pruning_ratio)
# plt.savefig('./tmp.png')
# plt.clf()
# print(avg_pruning_ratio)
# with torch.no_grad():
# conv(rand_input)
# new_conv(rand_input)
# from torchvision.models import resnet18
# model = resnet18()
# boost_raw_model_with_filter_selection(model, 0.5, True, (1, 3, 224, 224))
# rand_input = torch.rand((2, 3, 32, 32))
# conv = nn.Conv2d(3, 4, 3, 1, 1, bias=False)
# w = torch.rand((1, 4)).repeat(2, 1)
# with torch.no_grad():
# o1 = conv(rand_input) * w.unsqueeze(2).unsqueeze(3)
# print(w)
# w = w.mean(0).unsqueeze(1).unsqueeze(2).unsqueeze(3)
# print(w)
# conv.weight.data.mul_(w)
# o2 = conv(rand_input)
# diff = ((o1 - o2) ** 2).sum()
# print(diff)
# rand_input = torch.rand((2, 3, 32, 32))
# conv1 = nn.Conv2d(3, 6, 3, 1, 1, bias=False)
# conv2 = nn.Conv2d(3, 3, 3, 1, 1, bias=False, groups=3)
# print(conv1.weight.data.size(), conv2.weight.data.size())
# import time
# import torch
# from utils.dl.common.model import get_model_latency
# # s, e = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
# # s.record()
# # # TODO
# # e.record()
# # torch.cuda.synchronize()
# # time_usage = s.elapsed_time(e) / 1000.
# # print(time_usage)
# data = [torch.rand((512, 3, 3)).cuda() for _ in range(512)]
# # t1 = time.time()
# # for i in range(300): d = torch.stack(data)
# # t2 = time.time()
# # for i in range(300): d = torch.cat(data).view(512, 512, 3, 3)
# # t3 = time.time()
# # print("torch.stack time: {}, torch.cat time: {}".format(t2 - t1, t3 - t2))
# s, e = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
# s.record()
# for i in range(300): d = torch.stack(data)
# e.record()
# torch.cuda.synchronize()
# time_usage = s.elapsed_time(e) / 1000.
# print(time_usage)
# s, e = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
# s.record()
# for i in range(300): d = torch.cat(data).view(512, 512, 3, 3)
# e.record()
# torch.cuda.synchronize()
# time_usage = s.elapsed_time(e) / 1000.
# print(time_usage)
# from models.resnet_cifar.resnet_cifar_3 import resnet18
# model = resnet18()
# full_l1_reg = 0.
# for name, module in model.named_modules():
# if isinstance(module, nn.Conv2d):
# w = torch.ones((256, module.out_channels))
# w[:, (module.out_channels // 2):] = 0.
# full_l1_reg += w.norm(1)
# full_l1_reg /= 2
# print(f'{full_l1_reg:.3e}')
# def f(x):
# # x = x - 0.5
# return torch.max(torch.zeros_like(x), torch.min(torch.ones_like(x), 1.2 * torch.sigmoid(x) - 0.1))
# x = torch.arange(-2, 2, 0.01).float()
# y = f(x)
# print(f(torch.FloatTensor([0.])))
# print(f(torch.FloatTensor([0.5])))
# import matplotlib.pyplot as plt
# plt.plot(x, y)
# plt.savefig('./tmp.png')
# rand_input = torch.rand((256, 3, 32, 32))
# conv = nn.Conv2d(3, 64, 3, 1, 1, bias=False)
# new_conv = DomainDynamicConv2d(conv, 0.1)
# new_conv(rand_input)
# conv = nn.Conv2d(3, 64, 3, 1, 1, bias=False)
# new_conv = DomainDynamicConv2d(conv, nn.BatchNorm2d(64), 0.1)
# print(new_conv.filter_selection_module[5].training)
# new_conv.eval()
# print(new_conv.filter_selection_module[5].training)
n = KTakesAll(0.6)
rand_input = torch.rand((1, 5))
print(n(rand_input)) |