Spaces:
Runtime error
Runtime error
File size: 8,900 Bytes
cb80c28 |
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 |
# Please note that the current implementation of DER only contains the dynamic expansion process, since masking and pruning are not implemented by the source repo.
import logging
import numpy as np
from tqdm import tqdm
import torch
from torch import nn
from torch import optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from models.base import BaseLearner
from utils.inc_net import DERNet, IncrementalNet
from utils.toolkit import count_parameters, target2onehot, tensor2numpy
EPSILON = 1e-8
init_epoch = 100
init_lr = 0.1
init_milestones = [40, 60, 80]
init_lr_decay = 0.1
init_weight_decay = 0.0005
epochs = 80
lrate = 0.1
milestones = [30, 50, 70]
lrate_decay = 0.1
batch_size = 32
weight_decay = 2e-4
num_workers = 8
T = 2
class DER(BaseLearner):
def __init__(self, args):
super().__init__(args)
self._network = DERNet(args, False)
def after_task(self):
self._known_classes = self._total_classes
logging.info("Exemplar size: {}".format(self.exemplar_size))
def incremental_train(self, data_manager):
self._cur_task += 1
self._total_classes = self._known_classes + data_manager.get_task_size(
self._cur_task
)
self._network.update_fc(self._total_classes)
logging.info(
"Learning on {}-{}".format(self._known_classes, self._total_classes)
)
if self._cur_task > 0:
for i in range(self._cur_task):
for p in self._network.convnets[i].parameters():
p.requires_grad = False
logging.info("All params: {}".format(count_parameters(self._network)))
logging.info(
"Trainable params: {}".format(count_parameters(self._network, True))
)
train_dataset = data_manager.get_dataset(
np.arange(self._known_classes, self._total_classes),
source="train",
mode="train",
appendent=self._get_memory(),
)
self.train_loader = DataLoader(
train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers
)
test_dataset = data_manager.get_dataset(
np.arange(0, self._total_classes), source="test", mode="test"
)
self.test_loader = DataLoader(
test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers
)
if len(self._multiple_gpus) > 1:
self._network = nn.DataParallel(self._network, self._multiple_gpus)
self._train(self.train_loader, self.test_loader)
self.build_rehearsal_memory(data_manager, self.samples_per_class)
if len(self._multiple_gpus) > 1:
self._network = self._network.module
def train(self):
self._network.train()
if len(self._multiple_gpus) > 1 :
self._network_module_ptr = self._network.module
else:
self._network_module_ptr = self._network
self._network_module_ptr.convnets[-1].train()
if self._cur_task >= 1:
for i in range(self._cur_task):
self._network_module_ptr.convnets[i].eval()
def _train(self, train_loader, test_loader):
self._network.to(self._device)
if self._cur_task == 0:
optimizer = optim.SGD(
filter(lambda p: p.requires_grad, self._network.parameters()),
momentum=0.9,
lr=init_lr,
weight_decay=init_weight_decay,
)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer=optimizer, milestones=init_milestones, gamma=init_lr_decay
)
self._init_train(train_loader, test_loader, optimizer, scheduler)
else:
optimizer = optim.SGD(
filter(lambda p: p.requires_grad, self._network.parameters()),
lr=lrate,
momentum=0.9,
weight_decay=weight_decay,
)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer=optimizer, milestones=milestones, gamma=lrate_decay
)
self._update_representation(train_loader, test_loader, optimizer, scheduler)
if len(self._multiple_gpus) > 1:
self._network.module.weight_align(
self._total_classes - self._known_classes
)
else:
self._network.weight_align(self._total_classes - self._known_classes)
def _init_train(self, train_loader, test_loader, optimizer, scheduler):
prog_bar = tqdm(range(init_epoch))
for _, epoch in enumerate(prog_bar):
self.train()
losses = 0.0
correct, total = 0, 0
for i, (_, inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(self._device), targets.to(self._device)
logits = self._network(inputs)["logits"]
loss = F.cross_entropy(logits, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses += loss.item()
_, preds = torch.max(logits, dim=1)
correct += preds.eq(targets.expand_as(preds)).cpu().sum()
total += len(targets)
scheduler.step()
train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2)
if epoch % 5 == 0:
test_acc = self._compute_accuracy(self._network, test_loader)
info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format(
self._cur_task,
epoch + 1,
init_epoch,
losses / len(train_loader),
train_acc,
test_acc,
)
else:
info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format(
self._cur_task,
epoch + 1,
init_epoch,
losses / len(train_loader),
train_acc,
)
prog_bar.set_description(info)
logging.info(info)
def _update_representation(self, train_loader, test_loader, optimizer, scheduler):
prog_bar = tqdm(range(epochs))
for _, epoch in enumerate(prog_bar):
self.train()
losses = 0.0
losses_clf = 0.0
losses_aux = 0.0
correct, total = 0, 0
for i, (_, inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(self._device), targets.to(self._device)
outputs = self._network(inputs)
logits, aux_logits = outputs["logits"], outputs["aux_logits"]
loss_clf = F.cross_entropy(logits, targets)
aux_targets = targets.clone()
aux_targets = torch.where(
aux_targets - self._known_classes + 1 > 0,
aux_targets - self._known_classes + 1,
torch.tensor([0]).to(self._device),
)
loss_aux = F.cross_entropy(aux_logits, aux_targets)
loss = loss_clf + loss_aux
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses += loss.item()
losses_aux += loss_aux.item()
losses_clf += loss_clf.item()
_, preds = torch.max(logits, dim=1)
correct += preds.eq(targets.expand_as(preds)).cpu().sum()
total += len(targets)
scheduler.step()
train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2)
if epoch % 5 == 0:
test_acc = self._compute_accuracy(self._network, test_loader)
info = "Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_aux {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format(
self._cur_task,
epoch + 1,
epochs,
losses / len(train_loader),
losses_clf / len(train_loader),
losses_aux / len(train_loader),
train_acc,
test_acc,
)
else:
info = "Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_aux {:.3f}, Train_accy {:.2f}".format(
self._cur_task,
epoch + 1,
epochs,
losses / len(train_loader),
losses_clf / len(train_loader),
losses_aux / len(train_loader),
train_acc,
)
prog_bar.set_description(info)
logging.info(info)
|