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
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)
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 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 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