def main():
if os.path.exists('models') is False:
os.makedirs('models')
# get train, test dataloader
train_df, test_df = get_train_test()
train_dataset = DeepFashionInShopDataset(train_df, 'RANDOM')
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
test_dataset = DeepFashionInShopDataset(test_df, 'CENTER')
test_dataloader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
#get network
print("Creating model: {}".format(const.net_name))
print(
"Batch_size:{}; Model learning rate:{}; Center learning rate:{}; Total Epoch:{}; "
.format(args.batch_size, args.lr_model, args.lr_cent, args.epoch))
net = const.USE_NET(const.NUM_CLASSES)
net = net.to(const.device) # 转移到cpu/gpu上
#center loss and parameters
criterion_xent = nn.CrossEntropyLoss()
criterion_cent = CenterLoss(num_classes=const.NUM_CLASSES,
feat_dim=const.FEATURE_EMBEDDING,
use_gpu=True)
criterion_pcent = VGG_perceptual_loss_16()
optimizer_model = torch.optim.SGD(net.parameters(),
lr=args.lr_model,
weight_decay=5e-04,
momentum=0.9)
optimizer_centloss = torch.optim.SGD(criterion_cent.parameters(),
lr=args.lr_cent)
optimizer_ploss = torch.optim.SGD(criterion_pcent.parameters(),
lr=args.lr_ploss)
#write to tensorboardX
writer = SummaryWriter(const.TRAIN_DIR)
scheduler = lr_scheduler.StepLR(optimizer_model,
step_size=const.STEP_SIZE,
gamma=const.LEARNING_RATE_DECAY)
step = 0
for epoch in range(args.epoch):
print("==> Epoch {}/{}".format(epoch + 1, args.epoch))
train(net, criterion_xent, criterion_cent, criterion_pcent,
optimizer_model, optimizer_centloss, optimizer_ploss,
train_dataloader, const.NUM_CLASSES, epoch, writer, step)
test(net, test_dataloader, const.NUM_CLASSES, epoch, writer, step)
step += 1
print('Saving Model....')
torch.save(net.state_dict(), 'models/' + const.MODEL_NAME)
print('Finished')
mode=const.DATASET_PROC_METHOD_TRAIN)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=const.BATCH_SIZE,
shuffle=True,
num_workers=4)
val_df = df[df['evaluation_status'] == 'test']
val_dataset = DeepFashionCAPDataset(val_df,
mode=const.DATASET_PROC_METHOD_VAL)
val_dataloader = torch.utils.data.DataLoader(
val_dataset,
batch_size=const.VAL_BATCH_SIZE,
shuffle=False,
num_workers=4)
val_step = len(val_dataloader)
net = const.USE_NET()
net = net.to(const.device)
learning_rate = const.LEARNING_RATE
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
writer = SummaryWriter(const.TRAIN_DIR)
total_step = len(train_dataloader)
step = 0
for epoch in range(const.NUM_EPOCH):
net.train()
for i, sample in enumerate(train_dataloader):
step += 1
for key in sample:
sample[key] = sample[key].to(const.device)
df = pd.read_csv(const.base_path + const.USE_CSV)
inf_df = df
inf_dataset = DeepFashionCAPDataset(inf_df,
random_state=random_state,
mode=const.DATASET_PROC_METHOD_INF,
base_path=const.base_path)
inf_dataloader = torch.utils.data.DataLoader(
inf_dataset,
batch_size=1, #const.INF_BATCH_SIZE,
shuffle=False,
num_workers=6)
inf_step = len(inf_dataloader)
net = const.USE_NET(const.USE_IORN)
net = net.to(const.device)
net.load_state_dict(torch.load(const.INIT_MODEL), strict=False)
writer = SummaryWriter(const.INF_DIR)
inf_step = len(inf_dataloader)
with open(
'/home/thomasz/SA-FL/data/AttributePrediction/Anno/list_attr_cloth.txt'
) as f:
ret = []
f.readline()
f.readline()
for line in f:
line = line.split(' ')
mode=const.DATASET_PROC_METHOD_TRAIN)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=const.BATCH_SIZE,
shuffle=True,
num_workers=4)
val_df = df[df['evaluation_status'] == 'test']
val_dataset = DeepFashionCAPDataset(val_df,
mode=const.DATASET_PROC_METHOD_VAL)
val_dataloader = torch.utils.data.DataLoader(
val_dataset,
batch_size=const.VAL_BATCH_SIZE,
shuffle=False,
num_workers=4)
val_step = len(val_dataloader)
net = const.USE_NET()
net = net.to(const.device)
learning_rate = const.LEARNING_RATE
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
writer = SummaryWriter(const.TRAIN_DIR)
total_step = len(train_dataloader)
step = 0
for epoch in range(const.NUM_EPOCH):
print("startin epochs")
net.train()
for i, sample in enumerate(train_dataloader):
step += 1
for key in sample:
