def train(args):
# set seed
torch.manual_seed(2)
torch.cuda.manual_seed_all(2)
np.random.seed(2)
# create model
model = API_Net(args.device)
model = model.to(args.device)
model.conv = nn.DataParallel(model.conv)
# define optimizer
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# resume the previous state of training if possible
if args.resume:
if os.path.isfile(args.resume):
print('loading checkpoint {}'.format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
args.best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print('loaded checkpoint {}(epoch {})'.format(
args.resume, checkpoint['epoch']))
print('best acc:', args.best_prec1)
else:
print('no checkpoint found at {}'.format(args.resume))
cudnn.benchmark = True
# Load Data
train_dataset = BatchDataset(args.KFold, args.nFold, transform=train_tfms)
train_sampler = BalancedBatchSampler(train_dataset, args.n_classes,
args.n_samples)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_sampler=train_sampler,
num_workers=15)
val_dataset = RandomDataset(args.KFold, args.nFold, transform=val_tfms)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=15)
# Initialize Schedular
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, args.epochs)
for _ in range(args.start_epoch):
scheduler.step()
print("Let's use", torch.cuda.device_count(), "GPUs!")
for epoch in range(args.start_epoch, args.epochs):
train_one_epoch(train_loader, model, optimizer, epoch, val_loader,
args)
scheduler.step()
def train(train_path, labels, boxes, output_dir, num_epochs, hard, verbose):
df_train, df_val = _get_toy_dataset(labels, boxes)
if verbose:
logging.info("Train size: {}, validation size: {}".format(
len(df_train), len(df_val)))
sampler = None
if hard:
sampler = BalancedBatchSampler(df_train, n_classes=4, n_samples=4)
train_dl, single_train_dl, val_dl = get_dataloaders(
df_train, df_val, train_path, sampler)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if verbose:
logging.info("Using device {}".format(device))
net = TripletNetwork(embedding_size=128).to(device)
criterion = TripletLoss()
selector = None
if hard:
selector = HardBatchTripletSelector()
optimizer = optim.Adam(net.parameters(), lr=1e-4)
net, history = _train(
model=net,
optimizer=optimizer,
criterion=criterion,
train_dataloader=train_dl,
single_train_dataloader=single_train_dl,
val_dataloader=val_dl,
num_epochs=num_epochs,
save_path=output_dir,
device=device,
selector=selector,
)
_plot_history(history)
n_epochs = 20
log_interval = 100
fit(triplet_train_loader, triplet_test_loader, model, loss_fn, optimizer,
scheduler, n_epochs, cuda, log_interval)
train_embeddings_tl, train_labels_tl = extract_embeddings(train_loader, model)
plot_embeddings(train_embeddings_tl, train_labels_tl)
val_embeddings_tl, val_labels_tl = extract_embeddings(test_loader, model)
plot_embeddings(val_embeddings_tl, val_labels_tl)
from datasets import BalancedBatchSampler
# We'll create mini batches by sampling labels that will be present in the mini batch and number of examples from each class
train_batch_sampler = BalancedBatchSampler(train_dataset,
n_classes=10,
n_samples=25)
test_batch_sampler = BalancedBatchSampler(test_dataset,
n_classes=10,
n_samples=25)
kwargs = {'num_workers': 1, 'pin_memory': True} if cuda else {}
online_train_loader = torch.utils.data.DataLoader(
train_dataset, batch_sampler=train_batch_sampler, **kwargs)
online_test_loader = torch.utils.data.DataLoader(
test_dataset, batch_sampler=test_batch_sampler, **kwargs)
# Set up the network and training parameters
from networks import EmbeddingNet
from losses import OnlineContrastiveLoss
from utils import AllPositivePairSelector, HardNegativePairSelector # Strategies for selecting pairs within a minibatch
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
if config.mode == 'train':
#torch.autograd.set_detect_anomaly(True)
""" Load data """
print('dataset path', dataset_path)
train_dataset_path = dataset_path #+ '/train/train_data'
img_dataset = train_data_loader(data_path=train_dataset_path, img_size=input_size,
use_augment=use_augmentation)
# Balanced batch sampler and online train loader
train_batch_sampler = BalancedBatchSampler(img_dataset, n_classes=num_classes, n_samples=num_samples)
#train_batch_sampler = NegativeClassMiningBatchSampler(img_dataset, n_classes=num_classes, n_samples=num_samples)
online_train_loader = torch.utils.data.DataLoader(img_dataset,
batch_sampler=train_batch_sampler,
num_workers=4,
pin_memory=True)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Gather the parameters to be optimized/updated.
params_to_update = model.parameters()
print("Params to learn:")
if feature_extracting:
params_to_update = []
for name, param in model.named_parameters():
if param.requires_grad:
cfg_fname = 'configs/retriever_in_shop/triplet_hnm.py' # Triplet network with hard negative mining
# cfg_fname = 'configs/retriever_in_shop/triplet_vanilla.py' # Triplet network
cfg = Config.fromfile(cfg_fname)
cuda = True
from datasets import BalancedBatchSampler
# Datasets
train_set = build_dataset(cfg.data.train)
query_set = build_dataset(cfg.data.query)
gallery_set = build_dataset(cfg.data.gallery)
print('datasets loaded')
# Mini batch selector
train_batch_sampler = BalancedBatchSampler(torch.tensor(
train_set.train_labels),
n_classes=8,
n_samples=12)
test_batch_sampler = BalancedBatchSampler(torch.tensor(query_set.train_labels),
n_classes=8,
n_samples=12)
# Dataloaders
kwargs = {'num_workers': 1, 'pin_memory': True} if cuda else {}
online_train_loader = torch.utils.data.DataLoader(
train_set, batch_sampler=train_batch_sampler, **kwargs)
online_test_loader = torch.utils.data.DataLoader(
query_set, batch_sampler=test_batch_sampler, **kwargs)
print('dataloaders built')
# Build model and load checkpoint
# model = build_retriever(cfg.model)
model = embedding_net
if cuda:
model.cuda()
loss_fn = OnlineTripletLoss(margin, SemihardNegativeTripletSelector(margin))
lr = 1e-3
optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=1e-4)
#optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=0.0005)
scheduler = lr_scheduler.StepLR(optimizer, 8, gamma=0.1, last_epoch=-1)
n_epochs = 25
log_interval = 50
from datasets import BalancedBatchSampler
train_batch_sampler = BalancedBatchSampler(train_dataset.targets,
n_classes=100,
n_samples=5)
test_batch_sampler = BalancedBatchSampler(test_dataset.targets,
n_classes=100,
n_samples=5)
kwargs = {'num_workers': 0, 'pin_memory': True} if cuda else {}
online_train_loader = torch.utils.data.DataLoader(
train_dataset, batch_sampler=train_batch_sampler, **kwargs)
online_test_loader = torch.utils.data.DataLoader(
test_dataset, batch_sampler=test_batch_sampler, **kwargs)
fit(online_train_loader,
online_test_loader,
model,
loss_fn,
test_dataset.test_labels = torch.Tensor(l_te)
fashion_mnist_classes = [
'11_Starbucks', '12_Vans', '13_Burberry', '14_ALDO', '15_Polo',
'16_ClubMonaco', '17_HatsOn', '18_Guess', '19_Victoria', '20_TheBodyShop',
'21_Brooks', '22_Zara', '23_VanHart', '24_Starfield', '25_Lacoste',
'26_Hollys', '27_Converse', '28_Fendi', '29_Chicor', '30_Custom',
'31_Yankee', '32_Tommy', '33_GS', '34_KizDom', '35_Cartier', '36_Hermes',
'37_HM', '38_Gucci', '39_AT', '40_Chanel'
]
mnist_classes = fashion_mnist_classes
train_batch_sampler = BalancedBatchSampler(train_dataset.train_labels,
n_classes=30,
n_samples=16)
test_batch_sampler = BalancedBatchSampler(test_dataset.test_labels,
n_classes=30,
n_samples=16)
kwargs = {'num_workers': 1, 'pin_memory': True} if cuda else {}
online_train_loader = data.DataLoader(train_dataset,
batch_sampler=train_batch_sampler,
**kwargs)
online_test_loader = data.DataLoader(test_dataset,
batch_sampler=test_batch_sampler,
**kwargs)
margin = 1.
lr = 2e-4
cuda = torch.cuda.is_available()
kwargs = {'num_workers': 1, 'pin_memory': True} if cuda else {}
if args.classify:
train_dataset.classify_train_labels()
test_dataset.classify_test_labels()
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.samples,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.samples,
shuffle=False,
**kwargs)
else:
train_batch_sampler = BalancedBatchSampler(train_dataset,
n_classes=args.classes,
n_samples=args.samples)
test_batch_sampler = BalancedBatchSampler(test_dataset,
n_classes=args.classes,
n_samples=args.samples)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_sampler=train_batch_sampler, **kwargs)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_sampler=test_batch_sampler, **kwargs)
if args.network == 'resnet':
model = EmbeddingResNet(args.tuning)
if args.network == 'vgg16':
model = EmbeddingVgg16(args.tuning)
if args.network == 'inception':
model = EmbeddingInception(args.tuning)
img_file = '/home/yixiong/exercise/In-shop_Clothes_Retrieval_Benchmark/Anno/query_img.txt'
id_file = '/home/yixiong/exercise/In-shop_Clothes_Retrieval_Benchmark/Anno/query_id.txt'
test_dataset = InShopDataset(img_path, img_file, id_file, train=False)
# Set up data loaders
kwargs = {'num_workers': 1, 'pin_memory': True} if cuda else {}
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, **kwargs)
# gallery_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, **kwargs)
# We'll create mini batches by sampling labels that will be present in the mini batch and number of examples
# from each class
if target_type == 'item_id':
train_batch_sampler = BalancedBatchSampler(train_dataset.ids, n_classes=n_classes, n_samples=n_samples)
test_batch_sampler = BalancedBatchSampler(test_dataset.ids, n_classes=n_classes, n_samples=n_samples)
else:
train_batch_sampler = BalancedBatchSampler(train_dataset.catogories_id, n_classes=n_classes, n_samples=n_samples)
test_batch_sampler = BalancedBatchSampler(test_dataset.catogories_id, n_classes=n_classes, n_samples=n_samples)
online_train_loader = torch.utils.data.DataLoader(train_dataset, batch_sampler=train_batch_sampler, **kwargs)
online_test_loader = torch.utils.data.DataLoader(test_dataset, batch_sampler=test_batch_sampler, **kwargs)
# Set up the network and training parameters
from networks import EmbeddingNet
from losses import OnlineTripletLoss
from utils import AllTripletSelector,HardestNegativeTripletSelector, RandomNegativeTripletSelector, SemihardNegativeTripletSelector # Strategies for selecting triplets within a minibatch
from metrics import AverageNonzeroTripletsMetric
margin = 1.
def main(args):
if os.path.exists('models') is False:
os.makedirs('models')
# img_list, base_path, item_dict = read_data(args.dataset, args.bbox)
img_list, base_path, item_dict = read_data("DeepFashion2", bbox_gt=False)
model_save_path = args.model_path # 'models/siames_triplet_df2.pth'
# writer = SummaryWriter('runs/fashion_mnist_experiment_1')
model = ResNetbasedNet()
if os.path.exists(model_save_path):
model.load_state_dict(torch.load(model_save_path))
cuda = torch.cuda.is_available()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if cuda:
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.to(device)
kwargs = {'num_workers': 8, 'pin_memory': True} if cuda else {}
if not args.phase:
train_dataset = DeepFashionDataset(img_list['train'], root=base_path)
train_batch_sampler = BalancedBatchSampler(train_dataset.labels,
train_dataset.source,
n_classes=32,
n_samples=4)
online_train_loader = torch.utils.data.DataLoader(
train_dataset, batch_sampler=train_batch_sampler, **kwargs)
test_dataset = DeepFashionDataset(img_list['validation'],
root=base_path)
test_batch_sampler = BalancedBatchSampler(test_dataset.labels,
test_dataset.source,
n_classes=32,
n_samples=4)
online_test_loader = torch.utils.data.DataLoader(
test_dataset, batch_sampler=test_batch_sampler, **kwargs)
margin = 1.
loss_fn = OnlineTripletLoss(margin,
RandomNegativeTripletSelector(margin))
lr = 1e-3
optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=1e-4)
scheduler = lr_scheduler.StepLR(optimizer, 5, gamma=0.1, last_epoch=-1)
n_epochs = 20
log_interval = 200
fit(online_train_loader,
online_test_loader,
model,
loss_fn,
optimizer,
scheduler,
n_epochs,
cuda,
log_interval,
model_save_path,
metrics=[AverageNonzeroTripletsMetric()])
else:
model.eval()
gallery_dataset = DeepFashionDataset(img_list['validation'],
root=base_path)
acc, query_cnt = get_topK_acc(gallery_dataset, item_dict['validation'],
model, cuda,
open('retrieval_result.txt', 'a'), 100)
np.savetxt('TopK_accuracy.txt',
np.concatenate((acc, np.asarray([query_cnt]))),
fmt='%1.5f',
delimiter=',')
elif mining_tech == 'Triplet':
train_dataset = TripletVGG10(dataset_tr)
val_dataset = TripletVGG10(datset_val)
loss_fn = OnlineTripletLoss(margin, RandomNegativeTripletSelector(margin))
if cuda:
loss_fn = loss_fn.cuda()
elif mining_tech == 'Doppleganger':
pass
else:
raise Exception('Wrong choice for mining technique')"""
train_batch_sampler = BalancedBatchSampler(dataset_tr.train_labels,
n_classes=27,
n_samples=3)
val_batch_sampler = BalancedBatchSampler(dataset_val.val_labels,
n_classes=27,
n_samples=3)
train_loader = DataLoader(dataset_tr,
batch_sampler=train_batch_sampler,
**kwargs)
val_loader = DataLoader(dataset_val, batch_sampler=val_batch_sampler, **kwargs)
embedding_net = EmbeddingNet()
model = embedding_net
if cuda:
model = nn.DataParallel(model).cuda()
print(train_classes, test_classes)
# In[17]:
from losses import TripletLoss
from torch.optim import lr_scheduler
import torch.optim as optim
from trainer import fit
cuda = torch.cuda.is_available()
# In[18]:
from datasets import BalancedBatchSampler
train_batch_sampler = BalancedBatchSampler(train_dataset.train_labels,
n_classes=train_classes,
n_samples=train_classes_samples)
test_batch_sampler = BalancedBatchSampler(test_dataset.test_labels,
n_classes=test_classes,
n_samples=train_classes_samples)
train_batch_sampler_rgb = BalancedBatchSampler(train_dataset_rgb.train_labels,
n_classes=train_classes,
n_samples=train_classes_samples)
test_batch_sampler_rgb = BalancedBatchSampler(test_dataset_rgb.test_labels,
n_classes=test_classes,
n_samples=train_classes_samples)
kwargs = {'num_workers': 4, 'pin_memory': True} if cuda else {}
online_train_loader = torch.utils.data.DataLoader(
def main(args):
model_path = args.model_path
save_dir = args.save_dir
vec_dim = 128
data_type = ['validation'
] if args.phase == 'test' else ['train', 'validation']
img_list, base_path, item_dict = read_data("DeepFashion2",
bbox_gt=True,
type_list=data_type)
# model = ResNetbasedNet(vec_dim=vec_dim, max_pool=True, load_path=model_path, clf2_num=2, adv_eta=1e-4)
model = ResNetbasedNet(vec_dim=vec_dim,
max_pool=True,
load_path=model_path,
clf2_num=2)
domain_adap = args.domain_adap
adv_train = args.adv_train
is_cud = torch.cuda.is_available()
device = torch.device("cuda" if is_cud else "cpu")
if is_cud:
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.to(device)
kwargs = {'num_workers': 8, 'pin_memory': True} if is_cud else {}
if args.phase == 'train':
train_dataset = DeepFashionDataset(img_list['train'],
root=base_path,
augment=True)
train_batch_sampler = BalancedBatchSampler(train_dataset.labels,
train_dataset.source,
n_classes=64,
n_samples=4)
online_train_loader = torch.utils.data.DataLoader(
train_dataset, batch_sampler=train_batch_sampler, **kwargs)
test_dataset = DeepFashionDataset(img_list['validation'],
root=base_path)
test_batch_sampler = BalancedBatchSampler(test_dataset.labels,
test_dataset.source,
n_classes=64,
n_samples=4)
online_test_loader = torch.utils.data.DataLoader(
test_dataset, batch_sampler=test_batch_sampler, **kwargs)
margin = 0.2
loss_fn = OnlineTripletLoss(margin,
HardestNegativeTripletSelector(margin),
domain_adap)
# loss_fn = AllTripletLoss(margin)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-5, weight_decay=5e-4)
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="max", patience=4, threshold=0.001, cooldown=2, min_lr=1e-4 / (10 * 2),)
scheduler = lr_scheduler.ReduceLROnPlateau(
optimizer,
mode="max",
patience=4,
threshold=1,
cooldown=2,
min_lr=1e-5 / (10 * 2),
)
n_epochs = 300
log_interval = 200
fit(online_train_loader,
online_test_loader,
model,
loss_fn,
optimizer,
scheduler,
n_epochs,
is_cud,
log_interval,
save_dir,
metrics=[AverageNonzeroTripletsMetric()],
start_epoch=200,
criterion=criterion,
domain_adap=domain_adap,
adv_train=adv_train)
# fit(online_train_loader, online_test_loader, model, loss_fn, optimizer, scheduler, n_epochs, is_cud, log_interval,
# save_dir, metrics=[AverageNonzeroTripletsMetric()], start_epoch=0, criterion=criterion,
# adv_train=True, adv_epsilon=0.01, adv_alph=0.007, adv_iter=1)
else:
with torch.no_grad():
model.eval()
test_dataset = DeepFashionDataset(img_list['validation'],
root=base_path)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=256,
shuffle=False,
num_workers=4)
embedding_mtx = torch.zeros((len(test_dataset), vec_dim))
labels = np.zeros(len(test_dataset))
top_k = 500
idx_ = 0
start_time = time.time()
cf_mtx = np.zeros(
4, dtype=float
) # predict_user_real_user / predict_user_real_shop / predict_shop_real_user / predict_shop_real_shop
for idx, (data, target, _, source) in enumerate(test_loader):
emb_vecs = model(data.cuda())
embedding_mtx[idx_:idx_ + len(data)] = emb_vecs[0]
predict = torch.argmax(emb_vecs[1], dim=1).cpu().numpy()
real = source.cpu().numpy()
cf_mtx[0] += np.sum((predict == 0) & (real == 0))
cf_mtx[1] += np.sum((predict == 0) & (real == 1))
cf_mtx[2] += np.sum((predict == 1) & (real == 0))
cf_mtx[3] += np.sum((predict == 1) & (real == 1))
labels[idx_:idx_ + len(data)] = np.asarray(target)
idx_ += len(data)
if idx % 20 == 0:
print('processing {}/{}... elapsed time {}s'.format(
idx + 1, len(test_loader),
time.time() - start_time))
print('Total: {}, Domain Classification Acc: {:.5f}'.format(
np.sum(cf_mtx), (cf_mtx[0] + cf_mtx[3]) / np.sum(cf_mtx)))
print('Recall User Photo: {:.5f}'.format(cf_mtx[0] /
(cf_mtx[0] + cf_mtx[2])))
print('Recall Shop Photo: {:.5f}'.format(cf_mtx[3] /
(cf_mtx[1] + cf_mtx[3])))
np.save(os.path.join(save_dir, 'emb_mtx.npy'), embedding_mtx)
with open(os.path.join(save_dir, 'file_info.txt'), 'w') as f:
for i in range(len(test_dataset)):
f.write('{},{},{},{}\n'.format(img_list['validation'][i][0],
test_dataset[i][1],
test_dataset[i][2],
test_dataset[i][3]))
print('save files!')
distance_mtx = pdist(embedding_mtx)
sorted_idx = torch.argsort(distance_mtx, dim=1).cpu().numpy()
result_arr = np.zeros((sorted_idx.shape[0], top_k))
for idx in range(sorted_idx.shape[0]):
result_arr[idx] = sorted_idx[idx][sorted_idx[idx] != idx][:top_k]
result_arr[idx] = labels[result_arr[idx].astype(
np.int)] == labels[idx]
if idx % 1000 == 0:
print(idx)
for k in [1, 5, 10, 20, 100, 200, 500]:
topk_accuracy = np.sum(
np.sum(result_arr[:, :k], axis=1) > 0) / result_arr.shape[0]
print('Top-{} Accuracy: {:.5f}'.format(k, topk_accuracy))