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from dataset import get_finetune_training_set, get_finetune_validation_set
import argparse, time, os, random
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.utils.data
import numpy as np
from sklearnex import patch_sklearn, unpatch_sklearn
patch_sklearn()
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from tools import sum_para_cnt, remove_prefix
# change for action recogniton
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=80, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N',
help='mini-batch size (default: 256), this is the total '
'batch size of all GPUs on the current node when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=30., type=float,
metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--schedule', default=[50, 70, ], nargs='*', type=int,
help='learning rate schedule (when to drop lr by a ratio)')
parser.add_argument('--wd', '--weight-decay', default=0., type=float,
metavar='W', help='weight decay (default: 0.)',
dest='weight_decay')
parser.add_argument('--pretrained', default='', type=str,
help='path to moco pretrained checkpoint')
parser.add_argument('--finetune-dataset', default='ntu60', type=str,
help='which dataset to use for finetuning')
parser.add_argument('--protocol', default='cross_view', type=str,
help='traiining protocol of ntu')
parser.add_argument('--moda', default='joint', type=str,
help='joint, motion , bone')
parser.add_argument('--backbone', default='DSTE', type=str,
help='DSTE or STTR')
parser.add_argument('--knn-neighbours', default=1, type=int,
help='number of neighbours used for KNN.')
best_acc1 = 0
def load_pretrained(model, pretrained):
if os.path.isfile(pretrained):
print("=> loading checkpoint '{}'".format(pretrained))
checkpoint = torch.load(pretrained, map_location="cpu")
# rename pre-trained keys
state_dict = checkpoint['state_dict']
state_dict = remove_prefix(state_dict)
msg = model.load_state_dict(state_dict, strict=False)
print("message", msg)
assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}
print("=> loaded pre-trained model '{}'".format(pretrained))
else:
print("=> no checkpoint found at '{}'".format(pretrained))
def knn(data_train, data_test, label_train, label_test, nn=9):
label_train = np.asarray(label_train)
label_test = np.asarray(label_test)
print("Number of KNN Neighbours = ", nn)
print("training feature and labels", data_train.shape, len(label_train))
print("test feature and labels", data_test.shape, len(label_test))
# preprocessing.normalize(data_train)
# packge <prereocessing> use cpu, we can use gpu(CUDA) to accelaerate this operation.
# preprocessing.normalize(data_test)
Xtr_Norm = data_train
Xte_Norm = data_test
knn = KNeighborsClassifier(n_neighbors=nn,
metric='cosine',
n_jobs=24) # , metric='cosine'#'mahalanobis', metric_params={'V': np.cov(data_train)})
a = time.time()
knn.fit(Xtr_Norm, label_train)
b = time.time()
pred = knn.predict(Xte_Norm)
acc = accuracy_score(pred, label_test)
return acc, str(b-a)[:5]
def test_extract_hidden(model, data_train, data_eval, args):
model.eval()
print("Extracting training features")
label_train_list = []
hidden_array_train_list = []
for ith, (jt, js, bt, bs, mt, ms, label) in tqdm(enumerate(data_train)):
jt = jt.float().cuda(non_blocking=True)
js = js.float().cuda(non_blocking=True)
bt = bt.float().cuda(non_blocking=True)
bs = bs.float().cuda(non_blocking=True)
mt = mt.float().cuda(non_blocking=True)
ms = ms.float().cuda(non_blocking=True)
label = label.long().cuda()
en_hi = model(jt, js, bt, bs, mt, ms, knn_eval=True)
label_train_list.append(label)
hidden_array_train_list.append(en_hi)
label_train = torch.cat(label_train_list).cpu().numpy()
hidden_array_train = torch.nn.functional.normalize(torch.cat(hidden_array_train_list), p=2, dim=1).cpu().numpy()
print("Extracting validation features")
label_eval_list = []
hidden_array_eval_list = []
for ith, (jt, js, bt, bs, mt, ms, label) in tqdm(enumerate(data_eval)):
jt = jt.float().cuda(non_blocking=True)
js = js.float().cuda(non_blocking=True)
bt = bt.float().cuda(non_blocking=True)
bs = bs.float().cuda(non_blocking=True)
mt = mt.float().cuda(non_blocking=True)
ms = ms.float().cuda(non_blocking=True)
label = label.long().cuda()
en_hi = model(jt, js, bt, bs, mt, ms, knn_eval=True)
label_eval_list.append(label)
hidden_array_eval_list.append(en_hi)
label_eval = torch.cat(label_eval_list).cpu().numpy()
hidden_array_eval = torch.nn.functional.normalize(torch.cat(hidden_array_eval_list), p=2, dim=1).cpu().numpy()
return hidden_array_train, hidden_array_eval, label_train, label_eval
def clustering_knn_acc(model, train_loader, eval_loader, knn_neighbours=1, args=None):
with torch.no_grad():
hi_train, hi_eval, label_train, label_eval = test_extract_hidden(model, train_loader, eval_loader, args)
knn_acc_1, time_cost = knn(hi_train, hi_eval, label_train, label_eval, nn=knn_neighbours)
knn_acc_au = knn_acc_1
return knn_acc_1, knn_acc_au, time_cost
def main():
args = parser.parse_args()
if not os.path.exists(args.pretrained):
print(args.pretrained, ' not found!')
exit(0)
# Simply call main_worker function
main_worker(args)
def main_worker(args):
# training dataset
from options import options_downstream as options
if args.finetune_dataset== 'pku_v2' and args.protocol == 'cross_subject':
opts = options.opts_pku_v2_xsub()
elif args.finetune_dataset== 'ntu60' and args.protocol == 'cross_view':
opts = options.opts_ntu_60_cross_view()
elif args.finetune_dataset== 'ntu60' and args.protocol == 'cross_subject':
opts = options.opts_ntu_60_cross_subject()
elif args.finetune_dataset== 'ntu120' and args.protocol == 'cross_setup':
opts = options.opts_ntu_120_cross_setup()
elif args.finetune_dataset== 'ntu120' and args.protocol == 'cross_subject':
opts = options.opts_ntu_120_cross_subject()
# create model
print("=> creating model")
if args.backbone == 'DSTE':
from model.DSTE import Downstream
model = Downstream(**opts.encoder_args)
elif args.backbone == 'STTR':
from model.STTR import Downstream
model = Downstream(**opts.encoder_args)
print(sum_para_cnt(model)/1e6)
print(model)
print("options: \n",opts.encoder_args,'\n',opts.train_feeder_args,'\n',opts.test_feeder_args)
# freeze all layers
for _, param in model.named_parameters():
param.requires_grad = False
# load from pre-trained model
load_pretrained(model, args.pretrained)
model = model.cuda()
# Data loading code
train_dataset = get_finetune_training_set(opts)
val_dataset = get_finetune_validation_set(opts)
trainloader_params = {
'batch_size': args.batch_size,
'shuffle': True,
'num_workers': 1,
'pin_memory': True,
'prefetch_factor': 1,
'persistent_workers': False
}
valloader_params = {
'batch_size': args.batch_size,
'shuffle': False,
'num_workers': 1,
'pin_memory': True,
'prefetch_factor': 1,
'persistent_workers': False
}
train_loader = torch.utils.data.DataLoader(train_dataset, **trainloader_params)
val_loader = torch.utils.data.DataLoader(val_dataset, **valloader_params)
# Extract frozen features of the pre-trained query encoder
# train and evaluate a KNN classifier on extracted features
acc1, _, tc = clustering_knn_acc(model, train_loader, val_loader,
knn_neighbours=args.knn_neighbours, args=args)
print(args.pretrained, 'Knn time cost:' + tc + "s\tKnn Without AE= ", acc1)#, " Knn With AE=", acc_au)
if __name__ == '__main__':
main() |