def __init__(self, experiment, device):
config_file = os.path.join(CONFIG_DIR, experiment + '.json')
assert os.path.exists(
config_file), 'config file {} does not exist'.format(config_file)
self.experiment = experiment
with open(config_file, 'r') as f:
configs = json.load(f)
self.device = int(device)
self.lr = configs['lr']
self.max_epochs = configs['max-epochs']
self.train_batch_size = configs['train-batch-size']
self.test_batch_size = configs['test-batch-size']
self.n_epochs = 0
self.log_dir = os.path.join(LOG_DIR, experiment)
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
self.tboard_writer = tensorboardX.SummaryWriter(log_dir=self.log_dir)
self.checkpoint_dir = os.path.join(CHECKPOINT_DIR, experiment)
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
self.model = mstcn.MultiStageModel(
num_stages=N_STAGES,
num_layers=N_LAYERS,
num_f_maps=N_FEATURE_MAPS,
dim=IN_CHANNELS,
num_classes=breakfast.N_MSTCN_CLASSES)
self.model = self.model.cuda(self.device)
self.ce_loss = nn.CrossEntropyLoss(ignore_index=-100).cuda(self.device)
self.mse_loss = nn.MSELoss(reduction='none').cuda(self.device)
if configs['optim'] == 'adam':
self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
elif configs['optim'] == 'sgd':
self.optimizer = optim.SGD(self.model.parameters(),
lr=self.lr,
momentum=configs['momentum'],
nesterov=configs['nesterov'])
else:
raise ValueError('no such optimizer')
if configs['scheduler'] == 'step':
self.scheduler = optim.lr_scheduler.StepLR(
self.optimizer,
step_size=configs['lr-step'],
gamma=configs['lr-decay'])
elif configs['scheduler'] == 'plateau':
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer, mode='min', patience=configs['lr-step'])
else:
raise ValueError('no such scheduler')
self._load_checkpoint()
self.submission_dir = os.path.join(SUBMISSION_DIR, self.experiment)
def __init__(self, experiment, device):
config_file = os.path.join(CONFIG_DIR, experiment + '.json')
assert os.path.exists(
config_file), 'config file {} does not exist'.format(config_file)
self.experiment = experiment
with open(config_file, 'r') as f:
configs = json.load(f)
self.device = int(device)
self.gen_lr = configs['gen-lr']
self.dis_lr = configs['dis-lr']
self.max_epochs = configs['max-epochs']
self.train_batch_size = configs['train-batch-size']
self.test_batch_size = configs['test-batch-size']
self.n_epochs = 0
self.log_dir = os.path.join(LOG_DIR, experiment)
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
self.tboard_writer = tensorboardX.SummaryWriter(log_dir=self.log_dir)
self.checkpoint_dir = os.path.join(CHECKPOINT_DIR, experiment)
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
self.source_to_target_gen = cyclegan.VideoFeatGenerator(
n_layers=10, in_channels=IN_CHANNELS,
reduction_factor=4).cuda(self.device)
self.target_to_source_gen = cyclegan.VideoFeatGenerator(
n_layers=10, in_channels=IN_CHANNELS,
reduction_factor=4).cuda(self.device)
self.source_dis = cyclegan.VideoFeatDiscriminator(
num_layers=N_LAYERS, num_f_maps=N_FEATURE_MAPS,
dim=IN_CHANNELS).cuda(self.device)
self.target_dis = cyclegan.VideoFeatDiscriminator(
num_layers=N_LAYERS, num_f_maps=N_FEATURE_MAPS,
dim=IN_CHANNELS).cuda(self.device)
self.mstcn_model = mstcn.MultiStageModel(num_stages=N_STAGES, num_layers=N_LAYERS,
num_f_maps=N_FEATURE_MAPS,
dim=IN_CHANNELS, num_classes=breakfast.N_MSTCN_CLASSES)\
.cuda(self.device)
self.mstcn_model.eval()
self.mstcn_model_config = configs['mstcn-config']
self._load_mstcn_model()
self.ce_loss = nn.CrossEntropyLoss(ignore_index=-100).cuda(self.device)
self.mse_loss = nn.MSELoss(reduction='none').cuda(self.device)
self.reconstruction_loss_fn = nn.L1Loss()
gen_parameters = list(self.target_to_source_gen.parameters()) + list(
self.source_to_target_gen.parameters())
dis_parameters = list(self.target_dis.parameters()) + list(
self.source_dis.parameters())
if configs['optim'] == 'adam':
self.gen_optimizer = optim.Adam(gen_parameters, lr=self.gen_lr)
self.dis_optimizer = optim.Adam(dis_parameters, lr=self.dis_lr)
elif configs['optim'] == 'sgd':
self.gen_optimizer = optim.SGD(gen_parameters,
lr=self.gen_lr,
momentum=configs['momentum'],
nesterov=configs['nesterov'])
self.dis_optimizer = optim.SGD(dis_parameters,
lr=self.gen_lr,
momentum=configs['momentum'],
nesterov=configs['nesterov'])
else:
raise ValueError('no such optimizer')
if configs['scheduler'] == 'step':
self.gen_scheduler = optim.lr_scheduler.StepLR(
self.gen_optimizer,
step_size=configs['gen-lr-step'],
gamma=configs['gen-lr-decay'])
self.dis_scheduler = optim.lr_scheduler.StepLR(
self.dis_optimizer,
step_size=configs['dis-lr-step'],
gamma=configs['dis-lr-decay'])
elif configs['scheduler'] == 'plateau':
self.gen_scheduler = optim.lr_scheduler.ReduceLROnPlateau(
self.gen_optimizer,
mode='min',
patience=configs['gen-lr-step'])
self.dis_scheduler = optim.lr_scheduler.ReduceLROnPlateau(
self.dis_optimizer,
mode='min',
patience=configs['dis-lr-step'])
else:
raise ValueError('no such scheduler')
self._load_checkpoint()
