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import copy
import logging
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from models.foster import FOSTER
from utils.toolkit import count_parameters, tensor2numpy, accuracy
from utils.inc_net import IncrementalNet
from scipy.spatial.distance import cdist
from models.base import BaseLearner
from models.icarl import iCaRL
from tqdm import tqdm
import torch.optim as optim
EPSILON = 1e-8
batch_size = 32
weight_decay = 2e-4
num_workers = 8
class RMMBase(BaseLearner):
def __init__(self, args):
self._args = args
self._m_rate_list = args.get("m_rate_list", [])
self._c_rate_list = args.get("c_rate_list", [])
@property
def samples_per_class(self):
return int(self.memory_size // self._total_classes)
@property
def memory_size(self):
if self._args["dataset"] == "cifar100":
img_per_cls = 500
else:
img_per_cls = 1300
if self._m_rate_list[self._cur_task] != 0:
print(self._total_classes)
self._memory_size = min(int(self._total_classes*img_per_cls-1),self._args["memory_size"] + int(
self._m_rate_list[self._cur_task]
* self._args["increment"]
* img_per_cls
))
return self._memory_size
@property
def new_memory_size(self):
if self._args["dataset"] == "cifar100":
img_per_cls = 500
else:
img_per_cls = 1300
return int(
(1 - self._m_rate_list[self._cur_task])
* self._args["increment"]
* img_per_cls
)
def build_rehearsal_memory(self, data_manager, per_class):
self._reduce_exemplar(data_manager, per_class)
self._construct_exemplar(data_manager, per_class)
def _construct_exemplar(self, data_manager, m):
if self._args["dataset"] == "cifar100":
img_per_cls = 500
else:
img_per_cls = 1300
ns = [
min(img_per_cls,int(m * (1 - self._c_rate_list[self._cur_task]))),
min(img_per_cls,int(m * (1 + self._c_rate_list[self._cur_task]))),
]
logging.info(
"Constructing exemplars...({} or {} per classes)".format(ns[0], ns[1])
)
all_cls_entropies = []
ms = []
for class_idx in range(self._known_classes, self._total_classes):
data, targets, idx_dataset = data_manager.get_dataset(
np.arange(class_idx, class_idx + 1),
source="train",
mode="test",
ret_data=True,
)
idx_loader = DataLoader(
idx_dataset, batch_size=batch_size, shuffle=False, num_workers=4
)
with torch.no_grad():
cidx_cls_entropies = []
for idx, (_, inputs, targets) in enumerate(idx_loader):
inputs, targets = inputs.to(self._device), targets.to(self._device)
logits = self._network(inputs)["logits"]
cross_entropy = (
F.cross_entropy(logits, targets, reduction="none")
.detach()
.cpu()
.numpy()
)
cidx_cls_entropies.append(cross_entropy)
# print(cidx_cls_entropies)
cidx_cls_entropies = np.mean(np.concatenate(cidx_cls_entropies))
all_cls_entropies.append(cidx_cls_entropies)
entropy_median = np.median(all_cls_entropies)
for the_entropy in all_cls_entropies:
if the_entropy > entropy_median:
ms.append(ns[0])
else:
ms.append(ns[1])
logging.info(f"ms: {ms}")
for class_idx in range(self._known_classes, self._total_classes):
data, targets, idx_dataset = data_manager.get_dataset(
np.arange(class_idx, class_idx + 1),
source="train",
mode="test",
ret_data=True,
)
idx_loader = DataLoader(
idx_dataset, batch_size=batch_size, shuffle=False, num_workers=4
)
vectors, _ = self._extract_vectors(idx_loader)
vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T
class_mean = np.mean(vectors, axis=0)
# Select
selected_exemplars = []
exemplar_vectors = [] # [n, feature_dim]
for k in range(1, ms[class_idx - self._known_classes] + 1):
S = np.sum(
exemplar_vectors, axis=0
) # [feature_dim] sum of selected exemplars vectors
mu_p = (vectors + S) / k # [n, feature_dim] sum to all vectors
i = np.argmin(np.sqrt(np.sum((class_mean - mu_p) ** 2, axis=1)))
selected_exemplars.append(
np.array(data[i])
) # New object to avoid passing by inference
exemplar_vectors.append(
np.array(vectors[i])
) # New object to avoid passing by inference
vectors = np.delete(
vectors, i, axis=0
) # Remove it to avoid duplicative selection
data = np.delete(
data, i, axis=0
) # Remove it to avoid duplicative selection
selected_exemplars = np.array(selected_exemplars)
exemplar_targets = np.full(ms[class_idx - self._known_classes], class_idx)
self._data_memory = (
np.concatenate((self._data_memory, selected_exemplars))
if len(self._data_memory) != 0
else selected_exemplars
)
self._targets_memory = (
np.concatenate((self._targets_memory, exemplar_targets))
if len(self._targets_memory) != 0
else exemplar_targets
)
# Exemplar mean
idx_dataset = data_manager.get_dataset(
[],
source="train",
mode="test",
appendent=(selected_exemplars, exemplar_targets),
)
idx_loader = DataLoader(
idx_dataset, batch_size=batch_size, shuffle=False, num_workers=4
)
vectors, _ = self._extract_vectors(idx_loader)
vectors = (vectors.T / (np.linalg.norm(vectors.T, axis=0) + EPSILON)).T
mean = np.mean(vectors, axis=0)
mean = mean / np.linalg.norm(mean)
self._class_means[class_idx, :] = mean
class RMM_iCaRL(
RMMBase, iCaRL
): # RMM Base is supposed to be prior to the orginal method.
def __init__(self, args):
RMMBase.__init__(self, args)
iCaRL.__init__(self, args)
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)
)
train_dataset = data_manager.get_dataset(
np.arange(self._known_classes, self._total_classes),
source="train",
mode="train",
appendent=self._get_memory(),
m_rate=self._m_rate_list[self._cur_task] if self._cur_task > 0 else None,
)
self.train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
)
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
class RMM_FOSTER(RMMBase, FOSTER):
def __init__(self, args):
RMMBase.__init__(self, args)
FOSTER.__init__(self, args)
def incremental_train(self, data_manager):
self.data_manager = data_manager
self._cur_task += 1
if self._cur_task > 1:
self._network = self._snet
self._total_classes = self._known_classes + data_manager.get_task_size(
self._cur_task
)
self._network.update_fc(self._total_classes)
self._network_module_ptr = self._network
logging.info(
"Learning on {}-{}".format(self._known_classes, self._total_classes)
)
if self._cur_task > 0:
for p in self._network.convnets[0].parameters():
p.requires_grad = False
for p in self._network.oldfc.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(),
m_rate=self._m_rate_list[self._cur_task] if self._cur_task > 0 else None,
)
self.train_loader = DataLoader(
train_dataset,
batch_size=self.args["batch_size"],
shuffle=True,
num_workers=self.args["num_workers"],
pin_memory=True,
)
test_dataset = data_manager.get_dataset(
np.arange(0, self._total_classes), source="test", mode="test"
)
self.test_loader = DataLoader(
test_dataset,
batch_size=self.args["batch_size"],
shuffle=False,
num_workers=self.args["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
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