Ejemplos de CASIAB en Python

Ejemplo n.º 1

Archivo: cb_tb4_bg_pami_hgs_hgd_train.py Proyecto: OsamaAyman/GaitNet-CVPR2019

# optimizerLstm = optim.Adam(lstm.parameters(), lr=opt.lr, betas=(0.9, 0.999))

mse_loss = nn.MSELoss()
cse_loss = nn.CrossEntropyLoss()
l1_loss = nn.L1Loss()

netE.cuda()
netD.cuda()
lstm.cuda()
mse_loss.cuda()
cse_loss.cuda()

# #################################################################################################################
# DATASET PREPARATION
train_data = CASIAB(is_train_data=True,
                    train_structure=train_structure,
                    test_structure=test_structure,
                    opt=opt)
train_loader = DataLoader(train_data,
                          num_workers=opt.data_threads,
                          batch_size=opt.batch_size,
                          shuffle=True,
                          drop_last=False,
                          pin_memory=True)

training_batch_generator = get_training_batch(train_loader)

test_data = CASIAB(is_train_data=False,
                   train_structure=train_structure,
                   test_structure=test_structure,
                   opt=opt)
test_loader = DataLoader(test_data,

Ejemplo n.º 2

schedulerLSTM = torch.optim.lr_scheduler.StepLR(optimizerLstm, 500, 0.9)

mse_loss = nn.MSELoss()
cse_loss = nn.CrossEntropyLoss()
l1_loss = nn.L1Loss()

netE.cuda()
netD.cuda()
lstm.cuda()
mse_loss.cuda()
cse_loss.cuda()

# #################################################################################################################
# DATASET PREPARATION
train_data = CASIAB(is_train_data=True,
                    train_structure=train_structure,
                    test_structure=test_structure,
                    opt=opt)
train_loader = DataLoader(train_data,
                          num_workers=opt.data_threads,
                          batch_size=opt.batch_size,
                          shuffle=True,
                          drop_last=False,
                          pin_memory=True)

training_batch_generator = get_training_batch(train_loader)

test_data = CASIAB(is_train_data=False,
                   train_structure=train_structure,
                   test_structure=test_structure,
                   opt=opt)
test_loader = DataLoader(test_data,

Ejemplo n.º 3

Archivo: old_code-new_data-padtopdown-newdataloader.py Proyecto: OsamaAyman/GaitNet-CVPR2019

trp_loss.cuda()

# l1_crit = nn.L1Loss(size_average=False)
# reg_loss = 0
# for param in netE.parameters():
#     reg_loss += l1_crit(param)
#
# factor = 0.0005
# loss = factor * reg_loss
# #################################################################################################################
# DATASET PREPARATION
from utils.dataloader import get_training_batch

train_data1 = CASIAB(is_train_data=True,
                     data_root=opt.data_root,
                     clip_len=opt.max_step,
                     im_height=opt.im_height,
                     im_width=opt.im_width,
                     seed=opt.seed)
train_loader = DataLoader(train_data1,
                          num_workers=opt.data_threads,
                          batch_size=opt.batch_size,
                          shuffle=True,
                          drop_last=True,
                          pin_memory=True)

training_batch_generator1 = get_training_batch(train_loader)

test_data = CASIAB(is_train_data=False,
                   data_root=opt.data_root,
                   clip_len=opt.max_step,
                   im_height=opt.im_height,

Ejemplo n.º 4

mse_loss = nn.MSELoss()
cse_loss = nn.CrossEntropyLoss()
l1_loss = nn.L1Loss()

netE.cuda()
netD.cuda()
lstm.cuda()
mse_loss.cuda()
cse_loss.cuda()

# #################################################################################################################
# DATASET PREPARATION
train_data = CASIAB(
    is_train_data=True,
    train_structure=train_structure,
    test_structure=test_structure,
    opt=opt
)
train_loader = DataLoader(train_data,
                          num_workers=opt.data_threads,
                          batch_size=opt.batch_size,
                          shuffle=True,
                          drop_last=False,
                          pin_memory=True)

training_batch_generator = get_training_batch(train_loader)

test_data = CASIAB(
    is_train_data=False,
    train_structure=train_structure,
    test_structure=test_structure,

Ejemplo n.º 5

# optimizerLstm = optim.Adam(lstm.parameters(), lr=opt.lr, betas=(0.9, 0.999))

mse_loss = nn.MSELoss()
cse_loss = nn.CrossEntropyLoss()
l1_loss = nn.L1Loss()

netE.cuda()
netD.cuda()
lstm.cuda()
mse_loss.cuda()
cse_loss.cuda()

# #################################################################################################################
# DATASET PREPARATION
train_data = CASIAB(is_train_data=True,
                    train_structure=train_structure,
                    test_structure=test_structure,
                    opt=opt)
train_loader = DataLoader(train_data,
                          num_workers=opt.data_threads,
                          batch_size=opt.batch_size,
                          shuffle=True,
                          drop_last=False,
                          pin_memory=True)

training_batch_generator = get_training_batch(train_loader)

test_data = CASIAB(is_train_data=False,
                   train_structure=train_structure,
                   test_structure=test_structure,
                   opt=opt)
test_loader = DataLoader(test_data,