def __init__(self, checkpoint_path):
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.model = EmbeddingNet()
self.model.load_state_dict(torch.load(checkpoint_path))
self.model.to(self.device)
self.model.eval()
def main(args):
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda is True:
print("##########Running in CUDA GPU##########")
kwargs = {'num_workers': 4, 'pin_memory': True}
else:
print("##########Running in CPU##########")
kwargs = {}
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
train_data = myDataset("./data", [1, 2, 3, 4, 5, 6], args.nframes, "train",
None)
train_loader = Data.DataLoader(dataset=train_data,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
# dev_data = myDataset("./data", None, args.nframes, "dev", "enrol")
# dev_loader = Data.DataLoader(dataset=dev_data, batch_size=args.test_batch_size, shuffle=False, **kwargs)
test_data = myDataset("./data", None, args.nframes, "test", "enrol")
test_loader = Data.DataLoader(dataset=test_data,
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
embeddingNet = EmbeddingNet(embedding_size=64,
num_classes=train_data.nspeakers)
# embeddingNet = embeddingNet.double()
if args.cuda:
print("##########model is in cuda mode##########")
gpu_ids = [0, 1, 2, 3, 4, 5, 6, 7]
embeddingNet.cuda()
# embeddingNet = nn.DataParallel(embeddingNet, device_ids=[0])
criterion = nn.CrossEntropyLoss()
# optimizer = optim.SGD(embeddingNet.parameters(), lr=args.lr, momentum=args.momentum)
optimizer = optim.Adam(embeddingNet.parameters(),
lr=args.lr,
weight_decay=0.001)
scheduler = lr_scheduler.StepLR(optimizer, 5)
start_epoch = 0
if args.resume is True:
embeddingNet, optimizer, start_epoch, loss = load_model(
args.load_epoch, args.load_step, args.load_loss, embeddingNet,
optimizer, "./weights/")
# for epoch in range(start_epoch, args.epochs):
# scheduler.step()
# train(train_loader, embeddingNet, optimizer, criterion, epoch, args)
# dev(dev_loader, embeddingNet, epoch)
scores = test(test_loader, embeddingNet)
write_scores(scores, "scores.npy")
def __init__(self, checkpoint_path):
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.model = EmbeddingNet()
self.model.load_state_dict(torch.load(checkpoint_path))
self.model.to(self.device)
self.model.eval()
self.transform = transforms.Compose([transforms.Lambda(lambda image: image.convert('RGB')),
transforms.Resize((224, 224)),
transforms.ToTensor()])
def train(args):
adapt = args.adapt
train_transforms = torchvision.transforms.Compose([
transforms.Resize((255, 255)),
transforms.ToTensor(),
])
cell_dataset = Cell_Dataset(transform=train_transforms)
train_loader = DataLoader(cell_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
net = EmbeddingNet()
if adapt:
print("Adapt the adapt module for new dataset")
for name, param in net.named_parameters():
if 'adapt' not in name:
param.require_grad = False
else:
print("Train with ILSV2015")
# Optimizer
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=args.weight_decay)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer=optimizer,
milestones=args.lr_steps)
criterion = nn.MSELoss()
net.cuda()
for epoch in range(args.epochs):
# Each epoch has a training and validation phase
net.train()
log_loss = []
for i_batch, (input_im, patch_im,
input_label) in enumerate(train_loader):
inputs_im, patch_im, input_label = input_im.cuda(), patch_im.cuda(
), input_label.cuda()
output_heatmap = net(inputs_im, patch_im)
# print(output_heatmap.shape, input_label.shape)
loss = criterion(output_heatmap, input_label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
log_loss += [loss.item()]
if i_batch % 10 == 0:
log = 'Epoch: %3d, Batch: %5d, ' % (epoch + 1, i_batch)
log += 'Total Loss: %6.3f, ' % (np.mean(log_loss))
print(log, datetime.datetime.now())
scheduler.step()
def create_classifier(embedding_model='', model=''):
embedder = EmbeddingNet()
if embedding_model != '':
embedder.load_state_dict(torch.load(embedding_model))
classifier = FullNet(embedder)
if model != '':
classifier.load_state_dict(torch.load(model))
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
classifier = torch.nn.DataParallel(classifier)
classifier.cuda()
return classifier
def create_lcc(embedding_model='', model=''):
embedder = EmbeddingNet()
if embedding_model != '':
embedder.load_state_dict(torch.load(embedding_model))
lcc = LCCNet(embedder)
if model != '':
lcc.load_state_dict(torch.load(model))
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
lcc = torch.nn.DataParallel(lcc)
lcc.cuda()
return lcc
def create_distance_model(embedding_model='', model=''):
embedder = EmbeddingNet()
if embedding_model != '':
embedder.load_state_dict(torch.load(embedding_model))
distanceModel = DistanceNet(embedder)
if model != '':
distanceModel.load_state_dict(torch.load(model))
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
distanceModel = torch.nn.DataParallel(distanceModel)
distanceModel.cuda()
return distanceModel
def main(args):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
p = args.labels_per_batch
k = args.samples_per_label
batch_size = p * k
model = EmbeddingNet()
if args.resume:
model.load_state_dict(torch.load(args.resume))
model.to(device)
criterion = TripletMarginLoss(margin=args.margin)
optimizer = Adam(model.parameters(), lr=args.lr)
transform = transforms.Compose([
transforms.Lambda(lambda image: image.convert("RGB")),
transforms.Resize((224, 224)),
transforms.PILToTensor(),
transforms.ConvertImageDtype(torch.float),
])
# Using FMNIST to demonstrate embedding learning using triplet loss. This dataset can
# be replaced with any classification dataset.
train_dataset = FashionMNIST(args.dataset_dir,
train=True,
transform=transform,
download=True)
test_dataset = FashionMNIST(args.dataset_dir,
train=False,
transform=transform,
download=True)
# targets is a list where the i_th element corresponds to the label of i_th dataset element.
# This is required for PKSampler to randomly sample from exactly p classes. You will need to
# construct targets while building your dataset. Some datasets (such as ImageFolder) have a
# targets attribute with the same format.
targets = train_dataset.targets.tolist()
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
sampler=PKSampler(targets, p, k),
num_workers=args.workers)
test_loader = DataLoader(test_dataset,
batch_size=args.eval_batch_size,
shuffle=False,
num_workers=args.workers)
for epoch in range(1, args.epochs + 1):
print("Training...")
train_epoch(model, optimizer, criterion, train_loader, device, epoch,
args.print_freq)
print("Evaluating...")
evaluate(model, test_loader, device)
print("Saving...")
save(model, epoch, args.save_dir, "ckpt.pth")
class Predictor:
def __init__(self, checkpoint_path):
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.model = EmbeddingNet()
self.model.load_state_dict(torch.load(checkpoint_path))
self.model.to(self.device)
self.model.eval()
self.transform = transforms.Compose([transforms.Lambda(lambda image: image.convert('RGB')),
transforms.Resize((224, 224)),
transforms.ToTensor()])
def _preprocess(self, image):
image = transforms.ToPILImage()(image)
image = self.transform(image)
return image
def predict(self, image_list):
image_tensor = torch.cat([self._preprocess(im).unsqueeze(0) for im in image_list], dim=0)
with torch.no_grad():
image_tensor = image_tensor.cuda()
embedings = self.model(image_tensor)
return embedings.cpu().numpy()
class TensorPredictor:
def __init__(self, checkpoint_path):
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.model = EmbeddingNet()
self.model.load_state_dict(torch.load(checkpoint_path))
self.model.to(self.device)
self.model.eval()
def predict(self, image_list):
tensor_list = []
for image_tensor in image_list:
#image_tensor = torch.index_select(image_tensor, 2, torch.tensor([2, 1, 0], device=self.device)) # BGR -> RBG ?
image_tensor = F.interpolate(torch.unsqueeze(image_tensor,0), size=(224, 224))[0]
tensor_list.append(image_tensor)
input_tensor = torch.stack(tensor_list)
with torch.no_grad():
input_tensor = input_tensor.to(self.device)
embeddings = self.model(input_tensor)
return embeddings
def main(args):
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
p = args.labels_per_batch
k = args.samples_per_label
batch_size = p * k
model = EmbeddingNet(backbone=None, pretrained=args.pretrained)
print('pretrained: ', args.pretrained)
if args.resume:
model.load_state_dict(torch.load(args.resume))
model.to(device)
criterion = TripletMarginLoss(margin=args.margin)
optimizer = Adam(model.parameters(), lr=args.lr)
train_transform = transforms.Compose([transforms.Lambda(lambda image: image.convert('RGB')),
transforms.RandomHorizontalFlip(),
transforms.Resize((224, 224)),
transforms.ToTensor()])
test_transform = transforms.Compose([transforms.Lambda(lambda image: image.convert('RGB')),
transforms.Resize((224, 224)),
transforms.ToTensor()])
# Using FMNIST to demonstrate embedding learning using triplet loss. This dataset can
# be replaced with any classification dataset.
train_dataset = ImageFolder(os.path.join(args.dataset_dir, 'train'), transform=train_transform)
test_dataset = ImageFolder(os.path.join(args.dataset_dir, 'test'), transform=test_transform)
# targets is a list where the i_th element corresponds to the label of i_th dataset element.
# This is required for PKSampler to randomly sample from exactly p classes. You will need to
# construct targets while building your dataset. Some datasets (such as ImageFolder) have a
# targets attribute with the same format.
targets = train_dataset.targets
train_loader = DataLoader(train_dataset, batch_size=batch_size,
sampler=PKSampler(targets, p, k),
num_workers=args.workers)
test_loader = DataLoader(test_dataset, batch_size=args.eval_batch_size,
shuffle=False,
num_workers=args.workers)
for epoch in range(1, args.epochs + 1):
print('Training...')
train_epoch(model, optimizer, criterion, train_loader, device, epoch, args.print_freq)
print('Evaluating...')
evaluate(model, test_loader, device)
print('Saving...')
save(model, epoch, args.save_dir, 'ckpt.pth')
def create_embedder(embedding_model=''):
embedder = EmbeddingNet()
if embedding_model != '':
embedder.load_state_dict(torch.load(embedding_model))
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
embedder = torch.nn.DataParallel(embedder)
embedder.cuda()
return embedder
def get_model(args):
preserved = None
model = EmbeddingNet(network=args.model_name, pretrained=args.pretrained, embedding_len=args.embedding_size)
if args.increment_phase == 0:
pass
elif args.increment_phase == -1:
model = ClassificationNet(model)
elif args.increment_phase == 1:
try:
pkl = torch.load(args.train_set_old+'/pkl/state_best.pth')
model = pkl['model']
preserved = {'fts_means': pkl['fts_means'],
'preserved_embedding': pkl['embeddings']}
except OSError as reason:
print(args.train_set_old+'.........')
print(reason)
else:
print(args.increment_phase)
raise NotImplementedError
return model, preserved
def main():
# 4. dataset
mean, std = 0.1307, 0.3081
transform = tfs.Compose([tfs.Normalize((mean, ), (std, ))])
test_transform = tfs.Compose([tfs.ToTensor(),
tfs.Normalize((mean,), (std,))])
train_set = MNIST('./data/MNIST',
train=True,
download=True,
transform=None)
train_set = SEMI_MNIST(train_set,
transform=transform,
num_samples=100)
test_set = MNIST('./data/MNIST',
train=False,
download=True,
transform=test_transform)
test_set = SEMI_MNIST(test_set,
transform=transform,
num_samples=100)
# 5. data loader
train_loader = DataLoader(dataset=train_set,
shuffle=True,
batch_size=1,
num_workers=8,
pin_memory=True
)
test_loader = DataLoader(dataset=test_set,
shuffle=False,
batch_size=1,
)
# 6. model
model = EmbeddingNet().cuda()
model.load_state_dict(torch.load('./saves/state_dict.{}'.format(15)))
# 7. criterion
criterion = MetricCrossEntropy()
data = []
y = []
is_known_ = []
# for idx, (imgs, targets, samples, is_known) in enumerate(train_loader):
# model.train()
# batch_size = 1
# imgs = imgs.cuda() # [N, 1, 28, 28]
# targets = targets.cuda() # [N]
# samples = samples.cuda() # [N, 1, 32, 32]
# is_known = is_known.cuda()
#
# output = model(imgs)
# y.append(targets.cpu().detach().numpy())
# is_known_.append(is_known.cpu().detach().numpy())
#
# if idx % 100 == 0:
# print(idx)
# print(output.size())
#
# data.append(output.cpu().detach().numpy())
#
# data_numpy = np.array(data)
# y_numpy = np.array(y)
# is_known_numpy = np.array(is_known_)
#
# np.save('data', data_numpy)
# np.save('known', is_known_numpy)
# np.save('y', y)
data_numpy = np.load('data.npy')
y_numpy = np.load('y.npy')
is_known_numpy = np.load('known.npy')
print(data_numpy.shape)
print(y_numpy.shape)
data_numpy = np.squeeze(data_numpy)
y_numpy = np.squeeze(y_numpy)
is_known_numpy = np.squeeze(is_known_numpy)
print(data_numpy.shape)
print(y_numpy.shape)
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
colors = ['#476A2A', '#7851B8', '#BD3430', '#4A2D4E', '#875525',
'#A83683', '#4E655E', '#853541', '#3A3120', '#535D8E']
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
'#9467bd', '#8c564b', '#e377c2', '#7f7f7f',
'#bcbd22', '#17becf', '#ada699']
# t-SNE 모델 생성 및 학습
# tsne = TSNE(random_state=0)
# digits_tsne = tsne.fit_transform(data_numpy)
# np.save('tsne', digits_tsne)
digits_tsne = np.load('tsne.npy')
print('complete t-sne')
# ------------------------------ 1 ------------------------------
plt.figure(figsize=(10, 10))
for i in range(11):
inds = np.where(y_numpy == i)[0]
known = is_known_numpy[inds]
known_idx = np.where(known == 1)
unknown_idx = np.where(known == 0)
plt.scatter(digits_tsne[inds[unknown_idx], 0], digits_tsne[inds[unknown_idx], 1], alpha=0.5, color=colors[10])
plt.scatter(digits_tsne[inds[known_idx], 0], digits_tsne[inds[known_idx], 1], alpha=0.5, color=colors[i])
# plt.scatter(digits_tsne[inds, 0], digits_tsne[inds, 1], alpha=0.5, color=colors[i])
plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'unknown'])
plt.show() # 그래프 출력
def main():
# 1. argparse
parser = argparse.ArgumentParser()
parser.add_argument('--epoch', type=int, default=100)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--resume', type=int, default=0)
opts = parser.parse_args()
print(opts)
# 2. device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 3. visdom
vis = visdom.Visdom()
# 4. dataset
mean, std = 0.1307, 0.3081
transform = tfs.Compose([tfs.Normalize((mean, ), (std, ))])
test_transform = tfs.Compose(
[tfs.ToTensor(), tfs.Normalize((mean, ), (std, ))])
train_set = MNIST('./data/MNIST',
train=True,
download=True,
transform=None)
train_set = SEMI_MNIST(train_set, transform=transform, num_samples=100)
test_set = MNIST('./data/MNIST',
train=False,
download=True,
transform=test_transform)
# 5. data loader
train_loader = DataLoader(dataset=train_set,
shuffle=True,
batch_size=opts.batch_size,
num_workers=8,
pin_memory=True)
test_loader = DataLoader(
dataset=test_set,
shuffle=False,
batch_size=opts.batch_size,
)
# 6. model
model = EmbeddingNet().to(device)
# 7. criterion
criterion = MetricCrossEntropy().to(device)
# 8. optimizer
optimizer = torch.optim.SGD(params=model.parameters(),
lr=opts.lr,
momentum=0.9,
weight_decay=5e-4)
# 9. scheduler
scheduler = StepLR(optimizer=optimizer, step_size=50, gamma=1)
# 10. resume
if opts.resume:
model.load_state_dict(
torch.load('./saves/state_dict.{}'.format(opts.resume)))
print("resume from {} epoch..".format(opts.resume - 1))
else:
print("no checkpoint to resume.. train from scratch.")
# --
for epoch in range(opts.resume, opts.epoch):
# 11. trian
for idx, (imgs, targets, samples, is_known) in enumerate(train_loader):
model.train()
batch_size = opts.batch_size
imgs = imgs.to(device) # [N, 1, 28, 28]
targets = targets.to(device) # [N]
samples = samples.to(device) # [N, 1, 32, 32]
is_known = is_known.to(device)
samples = samples.view(batch_size * 10, 1, 28, 28)
out_x = model(imgs) # [N, 10]
out_z = model(samples).view(batch_size, 10,
out_x.size(-1)) # [N * 10 , 2]
loss = criterion(out_x, targets, out_z, is_known, 10, 1)
optimizer.zero_grad()
loss.backward()
optimizer.step()
for param_group in optimizer.param_groups:
lr = param_group['lr']
if idx % 100 == 0:
print('Epoch : {}\t'
'step : [{}/{}]\t'
'loss : {}\t'
'lr : {}\t'.format(epoch, idx, len(train_loader), loss,
lr))
vis.line(X=torch.ones(
(1, 1)) * idx + epoch * len(train_loader),
Y=torch.Tensor([loss]).unsqueeze(0),
update='append',
win='loss',
opts=dict(x_label='step',
y_label='loss',
title='loss',
legend=['total_loss']))
torch.save(model.state_dict(), './saves/state_dict.{}'.format(epoch))
# 12. test
correct = 0
avg_loss = 0
for idx, (img, target) in enumerate(test_loader):
model.load_state_dict(
torch.load('./saves/state_dict.{}'.format(epoch)))
model.eval()
img = img.to(device) # [N, 1, 28, 28]
target = target.to(device) # [N]
output = model(img) # [N, 10]
output = torch.softmax(output, -1)
pred, idx_ = output.max(-1)
print(idx_)
correct += torch.eq(target, idx_).sum()
#loss = criterion(output, target)
#avg_loss += loss.item()
print('Epoch {} test : '.format(epoch))
accuracy = correct.item() / len(test_set)
print("accuracy : {:.4f}%".format(accuracy * 100.))
#avg_loss = avg_loss / len(test_loader)
#print("avg_loss : {:.4f}".format(avg_loss))
vis.line(X=torch.ones((1, 1)) * epoch,
Y=torch.Tensor([accuracy]).unsqueeze(0),
update='append',
win='test',
opts=dict(x_label='epoch',
y_label='test_',
title='test_loss',
legend=['accuracy']))
scheduler.step()
def main():
parser = argparse.ArgumentParser(
description='Classifiar using triplet loss.')
parser.add_argument('--CVDs',
type=str,
default='0',
metavar='CUDA_VISIBLE_DEVICES',
help='CUDA_VISIBLE_DEVICES')
parser.add_argument('--server',
type=int,
default=82,
metavar='T',
help='which server is being used')
parser.add_argument(
'--train-set',
type=str,
default='/home/zili/memory/FaceRecognition-master/data/cifar100/train2',
metavar='dir',
help='path of train set.')
parser.add_argument(
'--test-set',
type=str,
default='/home/zili/memory/FaceRecognition-master/data/cifar100/test2',
metavar='dir',
help='path of test set.')
parser.add_argument(
'--train-set-csv',
type=str,
default=
'/home/zili/memory/FaceRecognition-master/data/cifar100/train.csv',
metavar='file',
help='path of train set.csv.')
parser.add_argument('--num-triplet',
type=int,
default=1000,
metavar='number',
help='number of triplet in dataset (default: 32)')
parser.add_argument('--train-batch-size',
type=int,
default=96,
metavar='number',
help='input batch size for training (default: 32)')
parser.add_argument('--test-batch-size',
type=int,
default=192,
metavar='number',
help='input batch size for testing (default: 64)')
parser.add_argument('--epochs',
type=int,
default=200,
metavar='number',
help='number of epochs to train (default: 100)')
parser.add_argument('--embedding-size',
type=int,
default=128,
metavar='number',
help='embedding size of model (default: 256)')
parser.add_argument('--lr',
type=float,
default=0.1,
metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--margin',
type=float,
default=1.,
metavar='margin',
help='loss margin (default: 1.0)')
parser.add_argument('--num-classes',
type=int,
default=10,
metavar='number',
help='classes number of dataset')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument('--momentum',
type=float,
default=0.9,
metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='number',
help='how many batches to wait before logging training status')
parser.add_argument('--model-name',
type=str,
default='resnet34',
metavar='M',
help='model name (default: resnet34)')
parser.add_argument('--dropout-p',
type=float,
default=0.2,
metavar='D',
help='Dropout probability (default: 0.2)')
parser.add_argument(
'--check-path',
type=str,
default='/home/zili/memory/FaceRecognition-master/checkpoints',
metavar='folder',
help='Checkpoint path')
parser.add_argument(
'--is-semihard',
type=bool,
default=True,
metavar='R',
help='whether the dataset is selected in semi-hard way.')
parser.add_argument('--is-pretrained',
type=bool,
default=False,
metavar='R',
help='whether model is pretrained.')
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.CVDs
if args.server == 31:
args.train_set = '/share/zili/code/triplet/data/cifar100/train2'
args.test_set = '/share/zili/code/triplet/data/cifar100/test2'
args.train_set_csv = '/share/zili/code/triplet/data/cifar100/train.csv'
args.check_path = '/share/zili/code/triplet/checkpoints'
if args.server == 16:
args.train_set = '/data0/zili/code/triplet/data/cifar100/train2'
args.test_set = '/data0/zili/code/triplet/data/cifar100/test2'
args.train_set_csv = '/data0/zili/code/triplet/data/cifar100/train.csv'
args.check_path = '/data0/zili/code/triplet/checkpoints'
if args.server == 17:
args.train_set = '/data/jiaxin/zili/data/cifar100/train2'
args.test_set = '/data/jiaxin/zili/data/cifar100/test'
args.train_set_csv = '/data/jiaxin/zili/data/cifar100/train.csv'
args.check_path = '/data/jiaxin/zili/checkpoints'
if args.server == 15:
args.train_set = '/home/zili/code/triplet/data/cifar100/train2'
args.test_set = '/home/zili/code/triplet/data/cifar100/test2'
args.train_set_csv = '/home/zili/code/triplet/data/cifar100/train.csv'
args.check_path = '/home/zili/code/triplet/checkpoints'
now_time = str(datetime.datetime.now())
if not os.path.exists(args.check_path):
os.mkdir(args.check_path)
args.check_path = os.path.join(args.check_path, now_time)
if not os.path.exists(args.check_path):
os.mkdir(args.check_path)
shutil.copy('tripletloss.py', args.check_path)
output1 = 'main_' + now_time
f = open(args.check_path + os.path.sep + output1 + '.txt', 'w+')
writer = SummaryWriter()
print('Loading model...')
# model = FaceModel(model_name = args.model_name,
# embedding_size = args.embedding_size,
# pretrained = args.is_pretrained)
# model = model_.ResNet34(True, args.embedding_size)
model = EmbeddingNet(embedding_len=args.embedding_size)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
f.write(" model: {}".format(model.module) + '\r\n')
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=1e-5)
print('start training...')
features, labels, clf, destination = select_three_sample(
model, args, -1, writer)
for epoch in range(args.epochs):
file_operation(f, args, optimizer)
if (epoch + 1) % 10 == 0:
args.lr = args.lr / 3
update_lr(optimizer, args.lr)
train(epoch, model, optimizer, args, f, features, destination, writer)
features, labels, clf, destination = select_three_sample(
model, args, epoch, writer)
validate(epoch, model, clf, args, f, writer, False)
validate(epoch, model, clf, args, f, writer, True)
f.write('\r\n')
if epoch < 80 and epoch > 10:
torch.save(
model,
args.check_path + os.path.sep + 'epoch' + str(epoch) + '.pth')
writer.export_scalars_to_json("./all_scalars.json")
writer.close()
def main():
args = get_args()
logdir = 'log/{}-emb{}-{}layers-{}resblk-lr{}-wd{}-maxlen{}-alpha10-margin{}'\
'{}class-{}sample-{}selector'\
.format(args.name,
args.embedding_size,
args.layers,
args.resblk,
args.lr,
args.wd,
args.maxlen,
args.margin,
args.n_classes,
args.n_samples,
args.selection)
if not os.path.exists(logdir):
os.makedirs(logdir)
resblock = []
for i in range(args.layers):
resblock.append(args.resblk)
if args.train:
logger = Logger(logdir)
if not os.path.exists(args.trainfeature):
os.mkdir(args.trainfeature)
extractFeature(args.training-dataset, args.trainfeature)
trainset = DeepSpkDataset(args.trainfeature, args.maxlen)
pre_loader = DataLoader(trainset, batch_size = 128, shuffle = True, num_workers = 8)
train_batch_sampler = BalancedBatchSampler(trainset.train_labels,
n_classes = args.n_classes,
n_samples = args.n_samples)
kwargs = {'num_workers' : 1, 'pin_memory' : True}
online_train_loader = torch.utils.data.DataLoader(trainset,
batch_sampler=train_batch_sampler,
**kwargs)
margin = args.margin
embedding_net = EmbeddingNet(resblock,
embedding_size = args.embedding_size,
layers = args.layers)
model = DeepSpeaker(embedding_net, trainset.get_num_class())
device = torch.device('cuda:0')
model.to(device) # 要在初始化optimizer之前把model转换到GPU上，这样初始化optimizer的时候也是在GPU上
optimizer = optim.SGD(model.embedding_net.parameters(),
lr = args.lr,
momentum = 0.99,
weight_decay = args.wd)
start_epoch = 0
if args.resume:
if os.path.isfile(args.resume):
print('=> loading checkpoint {}'.format(args.resume))
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch']
model.embedding_net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
else:
print('=> no checkpoint found at {}'.format(args.resume))
pretrain_epoch = args.pretrain_epoch
if args.selection == 'randomhard':
selector = RandomNegativeTripletSelector(margin)
if args.selection == 'hardest':
selector = HardestNegativeTripletSelector(margin)
if args.selection == 'semihard':
selector = SemihardNegativeTripletSelector(margin)
if args.selection == 'all':
print('warning : select all triplet may take very long time')
selector = AllTripletSelector()
loss_fn = OnlineTripletLoss(margin, selector)
scheduler = lr_scheduler.StepLR(optimizer,
step_size = args.lr_adjust_step,
gamma = args.lr_decay,
last_epoch = -1)
n_epochs = args.n_epochs
log_interval = 50
fit(online_train_loader,
pre_loader,
model,
loss_fn,
optimizer,
scheduler,
pretrain_epoch,
n_epochs,
True,
device,
log_interval,
log_dir = logdir,
eval_path = args.evalfeature,
logger = logger,
metrics = [AverageNonzeroTripletsMetric()],
evaluatee = args.eval,
start_epoch = start_epoch)
else:
if not os.path.exists(args.testfeature):
os.mkdir(args.testfeature)
extractFeature(args.test-dataset, args.testfeature)
model = EmbeddingNet(resblock,
embedding_size = args.embedding_size,
layers = args.layers)
model.cpu()
if args.model:
if os.path.isfile(args.model):
print('=> loading checkpoint {}'.format(args.model))
checkpoint = torch.load(args.model)
model.load_state_dict(checkpoint['state_dict'])
else:
print('=> no checkpoint found at {}'.format(args.model))
thres = np.loadtxt(logdir + '/thres.txt')
acc = np.loadtxt(logdir + '/acc.txt')
idx = np.argmax(acc)
best_thres = thres[idx]
predict(model, args.testfeature, best_thres)
import torch
from model import RelationNet, EmbeddingNet
from runner import Runner
import yaml
feature_encoder = EmbeddingNet()
relation_network = RelationNet()
feature_encoder_optim = torch.optim.SGD(feature_encoder.parameters(),
lr=0.001,
momentum=0.9)
relation_network_optim = torch.optim.SGD(relation_network.parameters(),
lr=0.001,
momentum=0.9)
feature_encoder_scheduler = torch.optim.lr_scheduler.StepLR(
feature_encoder_optim, step_size=30, gamma=0.1)
relation_network_scheduler = torch.optim.lr_scheduler.StepLR(
relation_network_optim, step_size=30, gamma=0.1)
loss = torch.nn.MSELoss()
with open("./default.yaml", 'r') as f:
cfg = yaml.load(f, Loader=yaml.SafeLoader)
runner = Runner(feature_encoder, relation_network, feature_encoder_optim,
relation_network_optim, feature_encoder_scheduler,
relation_network_scheduler, loss, cfg)
runner.run()
import tensorflow as tf
from dataset import TripletLossDataset
from train import train_step
from model import EmbeddingNet
num_steps = 1000
learning_rate = 0.001
display_step = 100
data = TripletLossDataset(num_clusters=4, num_examples=256, batch_size=16)
data.build_dataset()
model = EmbeddingNet()
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
loss = None
for step in range(num_steps):
for batch in data.dataset:
x_batch = tf.cast(batch[0], dtype=tf.float32)
y_batch = tf.cast(batch[1], dtype=tf.float32)
loss = train_step(model, x_batch, y_batch, optimizer)
if step % display_step == 0:
print(f"Step: {step:04d}\tLoss: {loss.numpy():.4f}")
def test_triplet():
parser = argparse.ArgumentParser(
description='Face recognition using triplet loss.')
parser.add_argument('--CVDs', type=str, default='1,2', metavar='CUDA_VISIBLE_DEVICES',
help='CUDA_VISIBLE_DEVICES')
parser.add_argument('--server', type=int, default=82, metavar='T',
help='which server is being used')
parser.add_argument('--train-set', type=str, default='/home/zili/memory/FaceRecognition-master/data/cifar100/train2', metavar='dir',
help='path of train set.')
parser.add_argument('--test-set', type=str, default='/home/zili/memory/FaceRecognition-master/data/cifar100/test2', metavar='dir',
help='path of train set.')
parser.add_argument('--batch-size', type=int, default=128, metavar='N',
help='input batch size for training (default: 32)')
parser.add_argument('--test-batch-size', type=int, default=128, metavar='N',
help='input batch size for testing (default: 64)')
parser.add_argument('--epochs', type=int, default=300, metavar='N',
help='number of epochs to train (default: 100)')
parser.add_argument('--embedding-size', type=int, default=128, metavar='N',
help='embedding size of model (default: 256)')
parser.add_argument('--num-classes', type=int, default=10, metavar='N',
help='classes number of dataset')
parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.8, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=2, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--model-name', type=str, default='resnet34', metavar='M',
help='model name (default: resnet50)')
parser.add_argument('--dropout-p', type=float, default=0.2, metavar='D',
help='Dropout probability (default: 0.2)')
parser.add_argument('--check-path', type=str, default='/home/zili/memory/FaceRecognition-master/checkpoints2', metavar='C',
help='Checkpoint path')
parser.add_argument('--pretrained', type=bool, default=False,metavar='R',
help='whether model is pretrained.')
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.CVDs
if args.server == 31:
args.train_set = '/share/zili/code/triplet/data/mnist/train'
args.test_set = '/share/zili/code/triplet/data/test_class'
args.check_path = '/share/zili/code/triplet/checkpoints2'
if args.server == 16:
args.train_set = '/data0/zili/code/triplet/data/cifar100/train2'
args.test_set = '/data0/zili/code/triplet/data/cifar100/test2'
args.check_path = '/data0/zili/code/triplet/checkpoints2'
if args.server == 17:
args.train_set = '/data/jiaxin/zili/data/cifar100/train2'
args.test_set = '/data/jiaxin/zili/data/cifar100/test'
args.check_path = '/data/jiaxin/zili/checkpoints2'
if args.server == 15:
args.train_set = '/home/zili/code/triplet/data/cifar100/train2'
args.test_set = '/home/zili/code/triplet/data/cifar100/test2'
args.train_set_csv = '/home/zili/code/triplet/data/cifar100/train.csv'
args.check_path = '/home/zili/code/triplet/checkpoints'
now_time = str(datetime.datetime.now())
args.check_path = os.path.join(args.check_path, now_time)
if not os.path.exists(args.check_path):
os.mkdir(args.check_path)
shutil.copy('crossentropyloss.py', args.check_path)
# os.path.join(args.check_path)
f = open(args.check_path + os.path.sep + now_time + 'CrossEntropy.txt', 'w+')
print('Loading model...')
# model = FaceModelForCls(model_name=args.model_name,
# num_classes=args.num_classes,
# pretrained=args.pretrained)
# model = model_.ResNet34(False, num_classes=args.num_classes)
embedding_net = EmbeddingNet()
model = ClassificationNet(embedding_net, 10)
f.write(" model: {}".format(model))
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
transform = transforms.Compose([
# transforms.Resize(32),
# transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
mean=np.array([0.4914, 0.4822, 0.4465]),
std=np.array([0.2023, 0.1994, 0.2010])),
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
print('Loading data...')
train_set = torchvision.datasets.ImageFolder(root=args.train_set, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=20)
test_set = torchvision.datasets.ImageFolder(root=args.test_set,transform=test_transform)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batch_size, shuffle=True, num_workers=1)
weight = torch.FloatTensor([1.,1.,1.,1.,1.,1.,1,10.,10.,10.]).cuda()
criterion = nn.CrossEntropyLoss(weight=weight)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=1e-4)
# optimizer = optim.Adam(model.parameters(),lr=args.lr)
writer = SummaryWriter()
def train(epoch):
model.train()
total_loss, correct = 0.0, 0
fea, l = torch.zeros(0), torch.zeros(0)
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model.forward(data)
loss = criterion(output, target)
total_loss += loss.item()
loss.backward()
optimizer.step()
_, predicted = output.max(1)
fea = torch.cat((fea, output.data.cpu()))
l = torch.cat((l, target.data.cpu().float()))
correct += predicted.eq(target).sum().item()
writer.add_scalar('/Loss', loss.item(), epoch * len(train_loader) + batch_idx)
if (batch_idx+1) % args.log_interval == 0:
context = 'Train Epoch: {} [{}/{} ({:.0f}%)], Average loss: {:.6f}'.format(
epoch, fea.size()[0], len(train_loader.dataset),
100.0 * batch_idx / len(train_loader), total_loss / (batch_idx+1))
print(context)
f.write(context + '\r\n')
writer.add_embedding(mat=fea, metadata=l, global_step=epoch)
context = 'Train Epoch: {} [{}/{} ({:.0f}%)], Average loss: {:.4f}'.format(
epoch, len(train_loader.dataset), len(train_loader.dataset),
100.0 * len(train_loader) / len(train_loader), total_loss / len(train_loader))
print(context)
f.write(context + '\r\n')
context = 'Train set: Accuracy: {}/{} ({:.3f}%)\n'.format(
correct, len(train_loader.dataset),
100. * float(correct) / len(train_loader.dataset))
print(context)
f.write(context+'\r\n')
def test(epoch):
model.eval()
test_loss, correct = 0, 0
target_arr, predict_arr = [], []
data1 = [0] * args.num_classes
data2 = [0] * args.num_classes
data3 = [0] * args.num_classes
with torch.no_grad():
for i , (data, target) in enumerate(test_loader):
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
output = model(data)
test_loss += criterion(output, target)
_, pred = output.data.max(1)
for i in range(0, target.size(0)):
data1[target[i]] += 1
data3[pred[i]] += 1
if target[i] == pred[i]:
data2[target[i]] += 1
batch_correct = pred.eq(target).sum().item()
correct += batch_correct
predict_arr.append(pred.cpu().numpy())
target_arr.append(target.data.cpu().numpy())
writer.add_scalar('/Acc', 100 * float(batch_correct) / data.size(0), epoch * len(test_loader) + i)
cm_path = args.check_path + '/' + str(epoch) + '_confusematrix'
cm = metrics.get_confuse_matrix(predict_arr, target_arr)
np.save(cm_path, cm)
for j in range(10):
recall = 0 if data1[j] == 0 else data2[j] / data1[j]
precision = 0 if data3[j] == 0 else data2[j] / data3[j]
context = 'Class%1s: recall is %.2f%% (%d in %d), precision is %.2f%% (%d in %d)' % (
str(j), 100 * recall, data2[j], data1[j],
100 * precision, data2[j], data3[j])
print(context)
f.write(context + "\r\n")
test_loss /= len(test_loader)
context = 'Test set: Average loss: {:.6f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / float(len(test_loader.dataset)))
print(context)
f.write(context + '\r\n')
def update_lr(optimizer, lr):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print('start training')
for epoch in range(args.epochs):
if (epoch ) % 2 == 0:
print(" server: {}".format(args.server))
print(" train-size: {}".format(args.train_set))
print(" embedding-size: {}".format(args.embedding_size))
print(" model: {}".format(args.model_name))
print(" dropout: {}".format(args.dropout_p))
print(" num_train_set: {}".format(train_set.__len__()))
print(" check_path: {}".format(args.check_path))
print(" learing_rate: {}".format(args.lr))
print(" batch_size: {}".format(args.batch_size))
print(" pretrained: {}".format(args.pretrained))
print(" optimizer: {}".format(optimizer))
f.write(" server: {}".format(args.server) + '\r\n')
f.write(" train-size: {}".format(args.train_set) + '\r\n')
f.write(" embedding-size: {}".format(args.embedding_size) + '\r\n')
f.write(" model: {}".format(args.model_name) + '\r\n')
f.write(" dropout: {}".format(args.dropout_p) + '\r\n')
f.write(" num_train_set: {}".format(train_set.__len__()) + '\r\n')
f.write(" check_path: {}".format(args.check_path) + '\r\n')
f.write(" learing_rate: {}".format(args.lr) + '\r\n')
f.write(" batch_size: {}".format(args.batch_size) + '\r\n')
f.write(" pretrained: {}".format(args.pretrained) + '\r\n')
f.write(" optimizer: {}".format(optimizer) + '\r\n')
train(epoch)
test(epoch)
if (epoch + 1) % 20 == 0 :
args.lr = args.lr / 3
update_lr(optimizer, args.lr)
if epoch > 30 and epoch < 80:
torch.save(model, args.check_path+ os.path.sep + 'epoch' + str(epoch)+'.pth')
f.write('\r\n')
writer.export_scalars_to_json("./all_scalars.json")
writer.close()
f.close()
def main():
sys_time = str(datetime.datetime.now())
if not os.path.exists(args.check_path):
os.mkdir(args.check_path)
log_path = args.check_path + os.path.sep + sys_time + 'CE.txt'
log_file = open(log_path, 'w+')
print('Loading model......')
# model = torch.load(log_path)
embedding_net = EmbeddingNet()
model = ClassificationNet(embedding_net, 10)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
train_transform = transforms.Compose([
transforms.Resize(224),
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=np.array([0.485, 0.456, 0.406]),
std=np.array([0.229, 0.224, 0.225])),
])
test_transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=np.array([0.485, 0.456, 0.406]),
std=np.array([0.229, 0.224, 0.225])),
])
print('Loading data...')
train_set = torchvision.datasets.ImageFolder(root=args.train_set,
transform=train_transform)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True)
test_set = torchvision.datasets.ImageFolder(root=args.test_set,
transform=test_transform)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=args.test_batch_size,
shuffle=True)
classweight = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 10.0, 10.0]
classweight = np.array(classweight)
classweight = torch.from_numpy(classweight).float().cuda()
criterion = nn.CrossEntropyLoss(weight=classweight)
# ignored_params = list(map(id, model.fc.parameters()))
# base_params = filter(lambda p: id(p) not in ignored_params,
# model.parameters())
#
# optimizer = torch.optim.SGD([
# {'params': base_params},
# {'params': model.fc.parameters(), 'lr': 1e-2}
# ], lr=1e-3, momentum=0.9)
optimizer = optim.SGD([{
'params': model.module.model.parameters()
}, {
'params': model.module.fc.parameters(),
'lr': 1e-2
}],
lr=1e-1,
momentum=0.9)
writer = SummaryWriter()
def train(epoch):
model.train()
total_loss = 0.0
correct = 0
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
total_loss += loss.item()
loss.backward()
optimizer.step()
_, predicted = output.max(1)
correct += predicted.eq(target).sum().item()
writer.add_scalar('/Loss', loss.item(),
epoch * len(train_loader) + batch_idx)
if batch_idx % args.log_interval == 0:
context = 'Train Epoch: {} [{}/{} ({:.0f}%)], Average loss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100.0 * batch_idx / len(train_loader),
total_loss / (batch_idx + 1))
print(context)
log_file.write(context + '\r\n')
context = 'Train set: Accuracy: {}/{} ({:.3f}%)\n'.format(
correct, len(train_loader.dataset),
100. * float(correct) / len(train_loader.dataset))
print(context)
log_file.write(context + '\r\n')
def test(epoch):
model.eval()
test_loss = 0
correct = 0
target_arr = []
predict_arr = []
with torch.no_grad():
for batch_idx, (data, target) in enumerate(test_loader):
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
output = model(data)
test_loss += criterion(output, target)
_, pred = output.data.max(1)
batch_correct = pred.eq(target).sum().item()
correct += batch_correct
predict_arr.append(pred.cpu().numpy())
target_arr.append(target.data.cpu().numpy())
writer.add_scalar('/Acc',
100 * float(batch_correct) / data.size(0),
epoch * len(test_loader) + batch_idx)
cm_path = './' + str(epoch) + '_confusematrix'
cm = metrics.get_confuse_matrix(predict_arr, target_arr)
np.save(cm_path, cm)
test_loss /= len(test_loader)
context = 'Test set: Average loss: {:.6f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / float(len(test_loader.dataset)))
print(context)
log_file.write(context + '\r\n')
def update_lr(optimizer, lr):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print('start training')
for epoch in range(args.epochs):
if epoch % 5 == 0:
print(" model: {}".format(args.model_name))
print(" num_triplet: {}".format(train_set.__len__()))
print(" check_path: {}".format(args.check_path))
print(" learing_rate: {}".format(args.lr))
print(" batch_size: {}".format(args.batch_size))
print(" is_pretrained: {}".format(args.is_pretrained))
print(" optimizer: {}".format(optimizer))
log_file.write(" model: {}".format(args.model_name) +
'\r\n')
log_file.write(
" num_triplet: {}".format(train_set.__len__()) +
'\r\n')
log_file.write(
" check_path: {}".format(args.check_path) + '\r\n')
log_file.write(" learing_rate: {}".format(args.lr) +
'\r\n')
log_file.write(
" batch_size: {}".format(args.batch_size) + '\r\n')
log_file.write(" optimizer: {}".format(optimizer) +
'\r\n')
train(epoch)
test(epoch)
if epoch == 50:
args.lr = args.lr / 5
update_lr(optimizer, args.lr)
elif epoch == 100:
args.lr = args.lr / 5
update_lr(optimizer, args.lr)
elif epoch == 150:
args.lr = args.lr / 5
update_lr(optimizer, args.lr)
log_file.write('\r\n')
model_path = args.check_path + os.path.sep + sys_time + 'model.pkt'
writer.export_scalars_to_json("./all_scalars.json")
writer.close()
log_file.close()
torch.save(model, model_path)
