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from tqdm import tqdm
import torch.nn.functional as F
import clip.clip as clip
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from .utils import get_class_ids_per_task, get_class_names
from . import utils
from .dynamic_dataset import DynamicDataset
DEFAULT_THRESHOLD = 0.985
TOP_SELECT = 1
EPOCH_NUM = 4
TOP_K_RATIO = 0.1
LAMBDA_SCALE = 30
LAYER_NUM = 12
class ClassIncremental(nn.Module):
def __init__(self, cfg, device, origin_flag, jit=False):
super().__init__()
self.prompt_template = cfg.prompt_template
self.device = device
self.classes_names = None
self.origin_flag = origin_flag
self.model, self.transforms, _ = clip.load(cfg.model_name, device=device, jit=jit)
self.ref_model = None
self.class_ids_per_task = list(get_class_ids_per_task(cfg))
self.current_class_names = []
self.text_tokens = None
self.dynamic_dataset = DynamicDataset(cfg)
self.prev_gradients = None
self.visual_cur_matrix = {}
self.visual_U = {}
self.loss_list = []
def forward(self, image, taskid):
with torch.no_grad():
logits_per_image, _ = self.model(image, self.text_tokens, 0, is_train=False)
probs = logits_per_image.softmax(dim=-1)
return probs
def adaptation(self, task_id, cfg, train_dataset, train_classes_names, world):
self.current_class_names += get_class_names(self.classes_names, self.class_ids_per_task[task_id])
self.text_tokens = clip.tokenize(
[self.prompt_template.format(c) for c in self.current_class_names]
).cuda(device=2)
if cfg.method != "zeroshot":
self.train(task_id, cfg, train_dataset, train_classes_names, world)
def train(self, task_id, cfg, train_dataset, train_classes_names, world):
train_loader = DataLoader(train_dataset[task_id:task_id + 1],
batch_size=cfg.batch_size,
shuffle=True, num_workers=8)
train_iter = iter(train_loader)
EPOCH = EPOCH_NUM
num_batches = len(train_loader)
total_iterations = EPOCH * num_batches
for k, v in self.model.named_parameters():
if "adapt" not in k:
v.requires_grad = False
params = [
v for k, v in self.model.named_parameters() if "adapt" in k
]
params_name = [
k for k, v in self.model.named_parameters() if "adapt" in k
]
print('========trainable params============', params_name)
# optimizer
optimizer = torch.optim.AdamW(params, lr=cfg.lr, weight_decay=cfg.weight_decay)
scheduler = utils.cosine_lr(
optimizer, cfg.lr, 30, total_iterations
)
self.model = self.model.cuda(device=2)
classnames = get_class_names(self.classes_names, self.class_ids_per_task[task_id])
print(classnames)
texts = [self.prompt_template.format(c) for c in classnames]
texts = clip.tokenize(texts).cuda(device=2)
self.model.train()
batch_count = 0
lamda = [[0 for _ in range(LAYER_NUM)] for _ in range(LAYER_NUM)]
for iteration in tqdm(range(total_iterations + 1)):
scheduler(iteration)
try:
inputs, targets, task_ids = next(train_iter)
except:
train_iter = iter(train_loader)
inputs, targets, task_ids = next(train_iter)
if cfg.dataset == "tinyimagenet" and task_id != 0:
shift = 100 + (task_id - 1) * cfg.increment
targets -= shift
elif cfg.dataset == "imagenet100" and task_id != 0:
shift = cfg.initial_increment + (task_id - 1) * cfg.increment
targets -= shift
else:
shift = task_id * cfg.increment
targets -= shift
inputs, targets = inputs.cuda(device=2), targets.cuda(device=2)
logits_per_image, _ = self.model.cuda(device=2)(inputs, texts.cuda(device=2), 0, is_train=True) # 分开
loss = F.cross_entropy(logits_per_image, targets, label_smoothing=cfg.ls)
self.loss_list.append(loss)
print('CELoss: {}'.format(loss))
optimizer.zero_grad()
loss.backward()
if task_id != 0:
if batch_count == 0:
for j in range(LAYER_NUM):
activation_visual = self.model.visual.transformer.lora_feature[j]
activation_visual = torch.bmm(activation_visual.detach().permute(1, 2, 0),
activation_visual.detach().permute(1, 0, 2)).sum(dim=0)
U_visual, S, Vh = torch.linalg.svd(activation_visual, full_matrices=False)
U_visual = U_visual[:, :TOP_SELECT]
for k in range(LAYER_NUM):
v_visual = self.visual_U[k]
normalized_vector_visual = U_visual / torch.norm(U_visual)
similarities_visual = []
for column_visual in v_visual.t():
normalized_column_visual = column_visual / torch.norm(column_visual)
cos_sim_visual = torch.dot(normalized_vector_visual.squeeze(),
normalized_column_visual.squeeze())
similarities_visual.append(cos_sim_visual)
dot_products_visual = torch.mean(
torch.topk(torch.stack(similarities_visual), int(len(similarities_visual) * TOP_K_RATIO))[0])
lamda[j][k] = torch.exp(-dot_products_visual) * LAMBDA_SCALE
batch_count = batch_count + 1
for name, params in self.model.named_parameters():
for i in range(LAYER_NUM):
if 'visual' in name and 'adapt' in name and 'down' in name and 'weight' in name:
v = self.visual_U[i]
v_ = torch.mm(params.grad.data, v)
params.grad.data = torch.mm(v_, v.T)* lamda[int(name.split(".")[3])][i]
elif 'visual' in name and 'adapt' in name and 'up' in name and 'weight' in name:
v = self.visual_U[i]
v_ = torch.mm(v.T, params.grad.data)
params.grad.data = torch.mm(v, v_)* lamda[int(name.split(".")[3])][i]
optimizer.step()
torch.cuda.empty_cache()
train_loader_ = DataLoader(train_dataset[task_id:task_id + 1],
batch_size=128,
shuffle=True, num_workers=8)
counts = 0
models = self.model.cuda(2)
for inputs, targets, task_ids in tqdm(train_loader_):
inputs = inputs.cuda(device=2)
with torch.no_grad():
outputs = models(inputs, texts.cuda(2), 0, is_train=False)
for i in range(LAYER_NUM):
if len(self.visual_cur_matrix) == i:
activation = models.visual.transformer.lora_feature[i]
activation = torch.bmm(activation.detach().permute(1, 2, 0),
activation.detach().permute(1, 0, 2)).sum(dim=0)
self.visual_cur_matrix[i] = activation
U, S, Vh = torch.linalg.svd(activation, full_matrices=False)
self.visual_U[i] = U[:,TOP_SELECT:]
else:
activation = models.visual.transformer.lora_feature[i]
activation = torch.bmm(activation.detach().permute(1, 2, 0),
activation.detach().permute(1, 0, 2)).sum(dim=0)
U1, S1, Vh1 = torch.linalg.svd(activation, full_matrices=False)
Ui = torch.cat((self.visual_U[i], U1[:, TOP_SELECT:]), dim=1)
self.visual_U[i] = Ui
counts = counts + 1
if counts == 1:
break
torch.cuda.empty_cache()
self.model.eval()
class DomainIncremental(nn.Module):
pass
class TaskAgnostic(nn.Module):
pass
def load_model(cfg: DictConfig, device: torch.device, origin_flag) -> nn.Module:
r"""Load a CLIP model in different continual scenarios.
Arguments:
cfg (DictConfig): Experiment configurations.
device (torch.device): Device to train (or) evaluate the model on.
Returns:
nn.Module: Return scenario specific CLIP model.
"""
if cfg.scenario == "class":
return ClassIncremental(cfg, device, origin_flag)
elif cfg.scenario == "domain":
return DomainIncremental(cfg, device)
elif cfg.scenario == "task-aganostic":
return TaskAgnostic(cfg, device)
else:
raise ValueError(f"""
`{cfg.scenarios}` is not a valid scenario,
Please choose from ['class', "domain', 'task-agnostic']
""") |