def test_restore(self): r = Restoreable('one','two') r.metadata['fisk'] = 'frisky' r.save('8834739829') r = Storeable.load('8834739829') print(r.one, r.two) assert r is not None and r.one == 'one' m = Storeable.load_metadata('8834739829') assert m['fisk'] == 'frisky' print(m)
def test_save_to_data_dir_random(self): model = AtariConv_v6([64,64,64,64,64]) print(model) name = model.save(data_dir='c:\data') model = Storeable.load(name, data_dir='c:\data') print(model) assert model is not None
def test_save(self): model = AtariConv_v6() import inspect print(inspect.getmro(AtariConv_v6)) model.save('8834739821') model = Storeable.load('8834739821') assert model is not None
from mentalitystorm import Storeable, config, Demo, MseKldLoss, OpenCV import torchvision import torchvision.transforms as TVT if __name__ == '__main__': dataset = torchvision.datasets.ImageFolder( root=config.datapath('spaceinvaders/images/raw'), transform=TVT.Compose([TVT.ToTensor()]) ) convolutions = Storeable.load(r'C:\data\runs\399\B-VAE loss 2.0\epoch0004') # todo demo of effect of each z parameter demo = Demo() convolutions.registerView('z_corr', OpenCV('z_corr', (512, 512))) #lossfunc = MseKldLoss() #demo.test(convolutions, dataset, 128, lossfunc) demo.rotate(convolutions, 2) #demo.sample(convolutions, 2, samples=20) #demo.demo(convolutions, dataset)
def __init__(self): encoder = self.Encoder() decoder = self.Decoder() BaseVAE.__init__(self, encoder, decoder) Storeable.__init__(self)
def test_reloading(self): model = Storeable.load('C4CP0C45CJ7Z0JHZ', 'c:\data') optim = torch.optim.Adam(model.parameters(), lr=1e-3) fac = OneShotTrainer(model, optim) run(fac, 'spaceinvaders/images/dev/', 2)
datadir = Path(config.DATA_PATH) / 'spaceinvaders/images/raw' dataset = torchvision.datasets.ImageFolder( root=datadir.absolute(), transform=torchvision.transforms.Compose( [torchvision.transforms.ToTensor()])) test_image = dataset.__getitem__(0) test_image = test_image[0].unsqueeze(0) imageio.imwrite('test_image.jpg', NumpyRGBWrapper(test_image[0], 'tensorPIL').getImage()) mdb = ModelDb(config.DATA_PATH) best_maxpooling = mdb.best_loss_for_model_class('MaxPooling') max_pool = Storeable.load(best_maxpooling, config.DATA_PATH) maxpool_image = max_pool(test_image, noise=False) imageio.imwrite( 'maxpool.jpg', NumpyRGBWrapper(maxpool_image[0].data, 'tensorPIL').getImage()) imageio.imwrite( 'maxpoolz.jpg', NumpyRGBWrapper(maxpool_image[1].data, 'tensorPIL').getImage()) best_convpooling = mdb.best_loss_for_model_class('ConvolutionalPooling') conv_pool = Storeable.load(best_convpooling, config.DATA_PATH) convpool_image = conv_pool(test_image, noise=False) imageio.imwrite( 'convpool.jpg',
def __init__(self, one, two): self.one = one self.two = two Storeable.__init__(self, one, two)
def test_save_to_data_dir(self): model = AtariConv_v6() model.save('8834739821','c:\data') model = Storeable.load('8834739821','c:\data') assert model is not None