def basic_callback_dict(identifier, save="val_loss"):
callback_dict = defaultdict(list)
path = Path(__file__).resolve().parent / (identifier + ".pt" if identifier
else "best_model.pt")
if save == "val_loss":
ModelSaver(path=path).register(callback_dict)
else:
raise NotImplemented(f"Saving by {save} not implemented.")
return callback_dict
def train(args):
Arguments.save_args(args, args.args_path)
train_loader, val_loader, _ = get_dataloaders(args)
model = UNetVgg16(n_classes=args.n_classes).to(args.device)
optimizer = get_optimizer(args.optimizer, model)
lr_scheduler = LRScheduler(args.lr_scheduler, optimizer)
criterion = get_loss_fn(args.loss_type, args.ignore_index).to(args.device)
model_saver = ModelSaver(args.model_path)
recorder = Recorder(['train_miou', 'train_acc', 'train_loss',
'val_miou', 'val_acc', 'val_loss'])
for epoch in range(args.n_epochs):
print(f"{args.experim_name} Epoch {epoch+1}:")
train_loss, train_acc, train_miou, train_ious = train_epoch(
model=model,
dataloader=train_loader,
n_classes=args.n_classes,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
criterion=criterion,
device=args.device,
)
print(f"train | mIoU: {train_miou:.3f} | accuracy: {train_acc:.3f} | loss: {train_loss:.3f}")
val_loss, val_scores = eval_epoch(
model=model,
dataloader=val_loader,
n_classes=args.n_classes,
criterion=criterion,
device=args.device,
)
val_miou, val_ious, val_acc = val_scores['mIoU'], val_scores['IoUs'], val_scores['accuracy']
print(f"valid | mIoU: {val_miou:.3f} | accuracy: {val_acc:.3f} | loss: {val_loss:.3f}")
recorder.update([train_miou, train_acc, train_loss, val_miou, val_acc, val_loss])
recorder.save(args.record_path)
if args.metric.startswith("IoU"):
metric = val_ious[int(args.metric.split('_')[1])]
else: metric = val_miou
model_saver.save_models(metric, epoch+1, model,
ious={'train': train_ious, 'val': val_ious})
print(f"best model at epoch {model_saver.best_epoch} with miou {model_saver.best_score:.5f}")
def train(train_sets: tuple,
test_sets: tuple,
input_shape: tuple = (1, 128, 128, 1),
model_version="1.0.0",
epochs: int = 100,
classes: int = 2,
batch_size: int = 1,
verbose=1,
out_dir: str = "saved_models"):
"""
The function to train the model.
Parameters:
train_sets (tuple): A tuple of np.array for train images and train labels.
test_sets (tuple): A tuple of np.array for test images and test labels.
input shape (tuple): Input shape of the model. It should be in the form of (1, ..., ...).
model_version (str): The version of the model in d.d.d format.
epochs (int): The number of epochs.
classes (int): The number of classes.
batch_size (int): The number of batch size.
verbose (bool): Wether to show the progress of each epoch.
out_dir (str): The output dir for saving the model in.
"""
(x_train, y_train), (x_test, y_test) = train_sets, test_sets
y_train = keras.utils.to_categorical(y_train, classes)
y_test = keras.utils.to_categorical(y_test, classes)
m = get_model(model_version)
if not m:
return
model = m.build_model(input_shape)
model.compile(loss=BinaryCrossentropy(),
optimizer=RMSprop(learning_rate=0.0001),
metrics=['accuracy'])
saver = ModelSaver(out_dir)
csv_logger = CSVLogger(
"%s/%s/log.csv" %
(out_dir, datetime.datetime.now().date().strftime("%Y_%m_%d")),
append=True,
separator=',')
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
validation_data=(x_test, y_test),
callbacks=[saver, csv_logger])
model.save("%s/%s/final.hd5" %
(out_dir, datetime.datetime.now().date().strftime("%Y_%m_%d")))
print("Model saved in %s as final.hd5" % out_dir)
plot_results(history, epochs, out_dir)
def __init__(self, weights=None, biases=None):
self.weights = weights if weights else self.weights
self.biases = biases if biases else self.biases
self.datasets = get_datasets(heart_diseases, nr_inputs)
self.label_data = get_labels(self.datasets)
self.saver = ModelSaver(save_dir="saved_models/cnn/")
logs_path = "tensorboard_data/cnn/"
self.tensorboard_handler = TensorBoardHandler(logs_path)
self.tensorboard_handler.add_histograms(self.weights)
self.tensorboard_handler.add_histograms(self.biases)
self.build()
def build_trainer(config, seed, args):
monitoring_metrics = [
'epoch', 'iteration', 'total_loss', 'latent_loss', 'seg_loss', 'NET',
'ED', 'ET'
]
logger = Logger(save_dir=config.save.save_dir,
config=config,
seed=seed,
name=config.save.study_name,
monitoring_metrics=monitoring_metrics)
save_dir_path = logger.log_dir
checkpoint_callback = ModelSaver(limit_num=10,
monitor=None,
filepath=os.path.join(
save_dir_path,
'ckpt-{epoch:04d}-{total_loss:.2f}'),
save_top_k=-1)
if config.run.resume_checkpoint:
print('Training will resume from: {}'.format(
config.run.resume_checkpoint))
model = TumorSegmentation.load_from_checkpoint(
config.run.resume_checkpoint,
config=config,
save_dir_path=save_dir_path,
)
else:
model = TumorSegmentation(config, save_dir_path)
trainer = pl.Trainer(gpus=config.run.visible_devices,
num_nodes=1,
max_epochs=config.run.n_epochs,
progress_bar_refresh_rate=1,
automatic_optimization=True,
distributed_backend=config.run.distributed_backend,
deterministic=True,
logger=logger,
sync_batchnorm=True,
checkpoint_callback=checkpoint_callback,
resume_from_checkpoint=config.run.resume_checkpoint,
limit_val_batches=10)
return model, trainer
parser.add_argument('--unfreeze', type=str, metavar='UF', default='',
help='Provide an option for unfreezeing given layers')
parser.add_argument('--freeze', type=str, metavar='F', default='',
help='Provide an option for freezeing given layers')
parser.add_argument('--pretrain', action='store_true')
parser.add_argument('--fc-only', action='store_true')
parser.add_argument('--except-fc', action='store_true')
parser.add_argument('--load-best', action='store_true')
parser.add_argument('--load-last', action='store_true')
parser.add_argument('--continue-step', action='store_true')
parser.add_argument('--train-all', action='store_true', help='Train all layers')
args = parser.parse_args()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
l2_dist = PairwiseDistance(2)
modelsaver = ModelSaver()
def save_if_best(state, acc):
modelsaver.save_if_best(acc, state)
def main():
init_log_just_created("log/valid.csv")
init_log_just_created("log/train.csv")
import pandas as pd
valid = pd.read_csv('log/valid.csv')
max_acc = valid['acc'].max()
pretrain = args.pretrain
fc_only = args.fc_only
betas=(
CONFIG.hyperparam.optimization.beta1,
CONFIG.hyperparam.optimization.beta2,
),
weight_decay=CONFIG.hyperparam.optimization.weight_decay,
)
else:
raise NotImplementedError("only Adam implemented")
#########################################################
################# evaluator, saver ######################
print("loading evaluator and model saver...")
evaluator = NLGEval(no_skipthoughts=True, no_glove=True)
# evaluator = NLGEval(metrics_to_omit=["METEOR"])
model_path = os.path.join(outdir, "best_score.ckpt")
saver = ModelSaver(model_path, init_val=0)
offset_ep = 1
offset_ep = saver.load_ckpt(model, optimizer, device)
if offset_ep > CONFIG.hyperparam.misc.max_epoch:
raise RuntimeError(
"trying to restart at epoch {} while max training is set to {} \
epochs".format(offset_ep, CONFIG.hyperparam.misc.max_epoch))
########################################################
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
if CONFIG.use_wandb:
wandb.watch(model)
################### training loop #####################
return x
model = NIMA()
model = model.to(device)
#########
# Train #
#########
parameters = [
{"params": model.base_model.parameters()},
{"params": model.head.parameters(), "lr": 3e-5},
]
optimizer = torch.optim.Adam(parameters, lr=3e-6)
scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.95)
saver = ModelSaver()
def train(model, train_loader, scheduler, optimizer):
criterion = EMDLoss() # r=2 for train
logging.info("Train Phase, Epoch: {}".format(epoch))
scheduler.step()
emd_losses = AverageMeter()
model.train()
for batch_num, batch in enumerate(train_loader, 1):
imgs, labels = batch
imgs, labels = imgs.to(device).float(), labels.to(device).float()
scores = model(imgs)
emd_loss = criterion(scores, labels)
def run(opt):
train_loader, test_loader = create_loaders(opt)
# Initialize generator and discriminator
generator = load_or_init_models(RetouchGenerator(opt.device, opt.pw_guide), opt)
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, weight_decay=1e-8)
# Losses
# criterion_GAN = torch.nn.MSELoss()
# criterion_pixelwise = torch.nn.L1Loss()
# if opt.cuda:
# generator = generator.cuda()
# discriminator = discriminator.cuda()
generator, criterion_pixelwise = to_variables((generator,torch.nn.MSELoss()), cuda=opt.cuda, device=opt.device)
saverG = ModelSaver(f'{opt.checkpoint_dir}/saved_models/{opt.name}')
train_writer = SummaryWriter(log_dir=os.path.join(opt.checkpoint_dir, 'train'))
test_writer = SummaryWriter(log_dir=os.path.join(opt.checkpoint_dir, 'test'))
for epoch in tqdm(range(opt.epoch, opt.n_epochs), desc='Training'):
####
# Train
###
avg_stats = defaultdict(float)
for i, data in enumerate(train_loader):
data = to_variables(data, cuda=opt.cuda, device=opt.device)
y_hat, loss_G = trainG(generator, criterion_pixelwise, optimizer_G, data)
update_stats(avg_stats, loss_G)
# Print image to tensorboard
if (epoch % opt.sample_interval == 0) and (i % 50 == 0):
train_writer.add_image('RetouchNet', y_hat[0], epoch)
train_writer.add_image('Edited', data[2][0], epoch)
train_writer.add_image('Original', data[0][0], epoch)
# Log Progress
str_out = '[train] {}/{} '.format(epoch, opt.n_epochs)
for k, v in avg_stats.items():
avg = v / len(train_loader)
train_writer.add_scalar(k, avg, epoch)
str_out += '{}: {:.6f} '.format(k, avg)
print(str_out)
####
# Test
###
avg_stats = defaultdict(float)
images = None
with torch.no_grad():
for i, data in enumerate(test_loader):
data = to_variables(data, cuda=opt.cuda, device=opt.device, test=True)
images, losses = test(generator, criterion_pixelwise, data)
update_stats(avg_stats, losses)
# Print image to tensorboard
if (epoch % opt.sample_interval == 0) and (i % 5 == 0):
test_writer.add_image('RetouchNet', images[0], epoch)
test_writer.add_image('Edited', data[2][0], epoch)
test_writer.add_image('Original', data[0][0], epoch)
# Log Progress
str_out = '[test] {}/{} '.format(epoch, opt.n_epochs)
for k, v in avg_stats.items():
avg = v / len(test_loader)
test_writer.add_scalar(k, avg, epoch)
str_out += '{}: {:.6f} '.format(k, avg)
print(str_out)
# If at sample interval save image
# if epoch % opt.sample_interval == 0:
# x_hr, x_lr, y_hr, y_lr = data
# test_writer.add_image('RetouchNet', images[0], epoch)
# test_writer.add_image('GroundTruth', y_hr[0], epoch)
# test_writer.add_image('raw', x_hr[0], epoch)
if epoch % opt.checkpoint_interval == 0:
# Save model checkpoints
saverG.save_if_best(generator, loss_G['loss_G'])
optimizer = optim.Adam(
model.parameters(),
lr=CONFIG.hyperparam.optimization.lr,
betas=(
CONFIG.hyperparam.optimization.beta1,
CONFIG.hyperparam.optimization.beta2,
),
weight_decay=CONFIG.hyperparam.optimization.weight_decay,
)
logging.info("done!")
#########################################################
################# load model params ######################
logging.info("loading model params...")
model_path = os.path.join(outdir, "best_score.ckpt")
saver = ModelSaver(model_path)
offset_ep = saver.load_ckpt(model, optimizer, device)
if offset_ep == 1:
raise RuntimeError("aborting, no pretrained model")
logging.info("done!")
##########################################################
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
################# make submission file ######################
logging.info("making submission file...")
submission = {
"version": "VERSION 1.3",
"external_data": {
"used":
def main():
logging = get_root_logger(args.log_path, mode='a')
logging.info('Command Line Arguments:')
for key, i in vars(args).items():
logging.info(key + ' = ' + str(i))
logging.info('End Command Line Arguments')
batch_size = args.batch_size
num_epochs = args.num_epochs
resume_from = args.resume_from
steps_per_checkpoint = args.steps_per_checkpoint
gpu_id = args.gpu_id
configure_process(args, gpu_id)
if gpu_id > -1:
logging.info('Using CUDA on GPU ' + str(gpu_id))
args.cuda = True
else:
logging.info('Using CPU')
args.cuda = False
'''Load data'''
logging.info('Data base dir ' + args.data_base_dir)
logging.info('Loading vocab from ' + args.vocab_file)
with open(args.vocab_file, "r", encoding='utf-8') as f:
args.target_vocab_size = len(f.readlines()) + 4
logging.info('Load training data from ' + args.data_path)
train_data = UIDataset(args.data_base_dir, args.data_path, args.label_path, args.vocab_file)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True,
num_workers=2, drop_last=True, collate_fn=collate_fn)
logging.info('Load validation data from ' + args.val_data_path)
val_data = UIDataset(args.data_base_dir, args.val_data_path, args.label_path, args.vocab_file)
val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=True,
num_workers=2, drop_last=True, collate_fn=collate_fn)
# Build model
logging.info('Building model')
if args.resume_from:
logging.info('Loading checkpoint from %s' % resume_from)
checkpoint = torch.load(resume_from)
else:
checkpoint = None
logging.info('Creating model with fresh parameters')
model = build_model(args, gpu_id, checkpoint)
logging.info(model)
n_params, enc, dec = cal_parameters(model)
logging.info('encoder: %d' % enc)
logging.info('decoder: %d' % dec)
logging.info('number of parameters: %d' % n_params)
# Build optimizer
optimier = torch.optim.SGD(model.parameters(), lr=args.learning_rate)
optim = Optimizer(optimier)
if checkpoint:
optim.load_state_dict(checkpoint['optim'])
optim.training_step += 1
# Build model saver
model_saver = ModelSaver(args.model_dir, model, optim)
train(model, optim, model_saver, num_epochs, train_loader, val_loader, steps_per_checkpoint,
args.valid_steps, args.lr_decay, args.start_decay_at, args.cuda)
obs_size = 2 * player_size + hand_size + hand_size + 32
num_actions = 4
hidden_1 = 256
hidden_2 = 64
# Actor maps state to actions' probabilities
actor = nn.Sequential(nn.Linear(obs_size, hidden_1), nn.ReLU(),
nn.Linear(hidden_1, hidden_2), nn.ReLU(),
nn.Linear(hidden_2, num_actions), nn.Softmax(dim=1))
optimizer = optim.Adam(actor.parameters(), lr=0.01)
discounting = 0.99999
saver = ModelSaver({
'actor': actor,
'optim_actor': optimizer
}, './models/Sedma/VPG-3')
# saver.load()
# saver.load(ignore_errors=True)
def card2tensor(c):
suit, rank = c
suit = suit2ix[suit]
rank = rank2ix[rank]
return torch.cat((one_hot(suit, len(suits)), one_hot(rank, len(ranks))))
def state2tensor(state):
