Spaces:
Sleeping
Sleeping
File size: 7,675 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 |
import logging
import numpy as np
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 IncrementalNetWithBias
epochs = 170
lrate = 0.1
milestones = [60, 100, 140]
lrate_decay = 0.1
batch_size = 128
split_ratio = 0.1
T = 2
weight_decay = 2e-4
num_workers = 8
class BiC(BaseLearner):
def __init__(self, args):
super().__init__(args)
self._network = IncrementalNetWithBias(
args, False, bias_correction=True
)
self._class_means = None
def after_task(self):
self._old_network = self._network.copy().freeze()
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 >= 1:
train_dset, val_dset = data_manager.get_dataset_with_split(
np.arange(self._known_classes, self._total_classes),
source="train",
mode="train",
appendent=self._get_memory(),
val_samples_per_class=int(
split_ratio * self._memory_size / self._known_classes
),
)
self.val_loader = DataLoader(
val_dset, batch_size=batch_size, shuffle=True, num_workers=num_workers
)
logging.info(
"Stage1 dset: {}, Stage2 dset: {}".format(
len(train_dset), len(val_dset)
)
)
self.lamda = self._known_classes / self._total_classes
logging.info("Lambda: {:.3f}".format(self.lamda))
else:
train_dset = data_manager.get_dataset(
np.arange(self._known_classes, self._total_classes),
source="train",
mode="train",
appendent=self._get_memory(),
)
test_dset = data_manager.get_dataset(
np.arange(0, self._total_classes), source="test", mode="test"
)
self.train_loader = DataLoader(
train_dset, batch_size=batch_size, shuffle=True, num_workers=num_workers
)
self.test_loader = DataLoader(
test_dset, batch_size=batch_size, shuffle=False, num_workers=num_workers
)
self._log_bias_params()
self._stage1_training(self.train_loader, self.test_loader)
if self._cur_task >= 1:
self._stage2_bias_correction(self.val_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
self._log_bias_params()
def _run(self, train_loader, test_loader, optimizer, scheduler, stage):
for epoch in range(1, epochs + 1):
self._network.train()
losses = 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"]
if stage == "training":
clf_loss = F.cross_entropy(logits, targets)
if self._old_network is not None:
old_logits = self._old_network(inputs)["logits"].detach()
hat_pai_k = F.softmax(old_logits / T, dim=1)
log_pai_k = F.log_softmax(
logits[:, : self._known_classes] / T, dim=1
)
distill_loss = -torch.mean(
torch.sum(hat_pai_k * log_pai_k, dim=1)
)
loss = distill_loss * self.lamda + clf_loss * (1 - self.lamda)
else:
loss = clf_loss
elif stage == "bias_correction":
loss = F.cross_entropy(torch.softmax(logits, dim=1), targets)
else:
raise NotImplementedError()
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses += loss.item()
scheduler.step()
train_acc = self._compute_accuracy(self._network, train_loader)
test_acc = self._compute_accuracy(self._network, test_loader)
info = "{} => Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.3f}, Test_accy {:.3f}".format(
stage,
self._cur_task,
epoch,
epochs,
losses / len(train_loader),
train_acc,
test_acc,
)
logging.info(info)
def _stage1_training(self, train_loader, test_loader):
"""
if self._cur_task == 0:
loaded_dict = torch.load('./dict_0.pkl')
self._network.load_state_dict(loaded_dict['model_state_dict'])
self._network.to(self._device)
return
"""
ignored_params = list(map(id, self._network.bias_layers.parameters()))
base_params = filter(
lambda p: id(p) not in ignored_params, self._network.parameters()
)
network_params = [
{"params": base_params, "lr": lrate, "weight_decay": weight_decay},
{
"params": self._network.bias_layers.parameters(),
"lr": 0,
"weight_decay": 0,
},
]
optimizer = optim.SGD(
network_params, lr=lrate, momentum=0.9, weight_decay=weight_decay
)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer=optimizer, milestones=milestones, gamma=lrate_decay
)
if len(self._multiple_gpus) > 1:
self._network = nn.DataParallel(self._network, self._multiple_gpus)
self._network.to(self._device)
if self._old_network is not None:
self._old_network.to(self._device)
self._run(train_loader, test_loader, optimizer, scheduler, stage="training")
def _stage2_bias_correction(self, val_loader, test_loader):
if isinstance(self._network, nn.DataParallel):
self._network = self._network.module
network_params = [
{
"params": self._network.bias_layers[-1].parameters(),
"lr": lrate,
"weight_decay": weight_decay,
}
]
optimizer = optim.SGD(
network_params, lr=lrate, momentum=0.9, weight_decay=weight_decay
)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer=optimizer, milestones=milestones, gamma=lrate_decay
)
if len(self._multiple_gpus) > 1:
self._network = nn.DataParallel(self._network, self._multiple_gpus)
self._network.to(self._device)
self._run(
val_loader, test_loader, optimizer, scheduler, stage="bias_correction"
)
def _log_bias_params(self):
logging.info("Parameters of bias layer:")
params = self._network.get_bias_params()
for i, param in enumerate(params):
logging.info("{} => {:.3f}, {:.3f}".format(i, param[0], param[1]))
|