from Trainer import Trainer
from utils.plots import plot_image, plot_label, plot_transformations, plot_stats, plot_final
from utils.download import unzip_results, download_models
if "__main__" == __name__:
# Set seed
seed = 0
np.random.seed(seed)
torch.manual_seed(seed)
random.seed(seed)
# NOTE: Illustration
model = Trainer(device='cpu', batch_size=4, experiment='Real')
trainset, valset, testset = model.trainset, model.valset, model.testset
model.build()
model.visualize_results(epoch='pre', pre_training=True)
# model.fit()
shouldTrain = False
downloadModels = False
if shouldTrain:
## Experiments:
# Real
for exp in ['Real']:
model = Trainer(device='cuda:0', batch_size=4, experiment=exp)
model.trainset, model.valset, model.testset = trainset, valset, testset
for optim in ['Adam', 'SGD']:
for scheduler in ['StepLR', 'ReduceLROnPlateau']:
print(f'Running: {exp}, {optim}, {scheduler}')
model.build(optim=optim, scheduler=scheduler)
