def get_train_test_loaders(is_vid, config, dataset):
# TODO: REFACTOR LOGIC @!
batch_size = config['batch_size']
test_split = .2
shuffle_dataset = True
random_seed = 42
dataset_size = 32560
indices = list(range(dataset_size))
split = int(np.floor(test_split * dataset_size))
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, test_indices = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_indices)
test_sampler = SubsetRandomSampler(test_indices)
train_dataset = dataset(
config=config,
excluded_indices=test_indices,
transform=NpToTensor(),
is_vid=is_vid,
)
test_dataset = dataset(
config=config,
excluded_indices=train_indices,
transform=NpToTensor(),
is_vid=is_vid,
)
train_loader = DataLoader(train_dataset, batch_size=batch_size,
sampler=train_sampler)
test_loader = DataLoader(test_dataset, batch_size=batch_size,
sampler=test_sampler)
return train_loader, test_loader
def get_validation_loader(is_vid, config, dataset, augment):
validation_dataset = dataset(
config,
transform=NpToTensor(),
is_vid=is_vid,
augment=augment
)
validation_dataloader = DataLoader(
validation_dataset,
batch_size=config['batch_size'],
shuffle=True
)
return validation_dataloader
import torch
from configs.config_s1 import CONFIG_S1
from datasets.frei_dataset_s1 import FreiDatasetS1
from models.model_s1 import ModelS1
from trainers.trainer_s1 import TrainerS1
from transforms.np_to_tensor import NpToTensor
from utils.data_utils import get_loaders
from utils.utils import init_build_id
if __name__ == '__main__':
config = init_build_id(CONFIG_S1)
print(f"INITIATED BUILD WITH ID: {config['build_id']}")
dataset = FreiDatasetS1(
config=config,
transform=NpToTensor()
)
train_loader, test_loader = get_loaders(config['batch_size'], dataset)
model = ModelS1()
test_in = next(iter(train_loader))
# print(test_in['img'])
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# model.to(device)
#
# hz = model(test_in['img'])
trainer = TrainerS1(config, train_loader, test_loader, model)
trainer.train(config['epochs'])
print(f"INITIATED BUILD WITH ID: {config['build_id']}")
test_split = .2
shuffle_dataset = True
random_seed = 42
# Creating data indices for training and validation splits:
dataset_size = 32560
indices = list(range(dataset_size))
split = int(np.floor(test_split * dataset_size))
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, test_indices = indices[split:], indices[:split]
train_dataset = ManoDatasetC(config['train_dataset_path'], NpToTensor(), train_indices)
test_dataset = ManoXYZDataset(config['test_dataset_path'], NpToTensor())
# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
test_sampler = SubsetRandomSampler(test_indices)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,
sampler=train_sampler)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,
sampler=test_sampler)
models = {
"C0": ModelC(7),
"C1": ModelC(9)
}