import spaghettini
from spaghettini import load, quick_register
@quick_register
class Composite:
def __init__(self, a, b):
self.a = a
self.b = b(-3, 2)
def __call__(self, x):
return self.b(self.a(x))
@quick_register
class Linear:
def __init__(self, w, b):
self.w = w
self.b = b
def __call__(self, x):
return self.w * x + self.b
if __name__ == "__main__":
m = load("assets/test.yaml")
print(m(2))
from spaghettini import load
import os
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="PyTorch CIFAR-10 Training")
parser.add_argument("--cfg",
type=str,
help="the path to the configuration file")
parser.add_argument("--resume",
"-r",
action="store_true",
help="resume from checkpoint")
parser.add_argument("--test",
"-t",
action="store_true",
help="test the model only")
args = parser.parse_args()
assert os.path.basename(args.cfg) == "cfg.yaml"
exp = load(args.cfg)
if exp.exp_dir is None:
exp_dir = os.path.dirname(args.cfg)
print("generating default exp_dir: {}".format(exp_dir))
exp.register_exp_dir(exp_dir)
exp.launch(args.resume, cfg_path_source=args.cfg, test_only=args.test)
print("finished")
os._exit(0)
import torch.nn as nn
import spaghettini
from spaghettini import register, quick_register, load, check, check_registered
quick_register(nn.Linear)
quick_register(nn.Linear)
register("relu")(nn.ReLU)
quick_register(nn.Sequential)
print(check())
print(check_registered())
net = load("./examples/assets/pytorch.yaml", verbose=False)
print(net)
import torch.nn as nn
import spaghettini
from spaghettini import register, quick_register, load, check
quick_register(nn.Linear)
register("relu")(nn.ReLU)
quick_register(nn.Sequential)
print(check())
net = load("assets/pytorch.yaml")
print(net)
import torch.nn as nn
import spaghettini
from spaghettini import register, quick_register, load, check
quick_register(nn.Linear)
quick_register(nn.ReLU)
@quick_register
class MLP(nn.Module):
def __init__(self, units, activation, linear_module):
super().__init__()
model = []
for index, (in_units, out_units) in enumerate(zip(units[:-1], units[1:])):
if index != 0:
model.append(activation)
model.append(linear_module(in_units, out_units))
self.model = nn.Sequential(*model)
def forward(self, x):
return self.model(x)
print(check())
net = load("assets/pytorch_mlp.yaml")
print(net)
import torch.nn as nn
import spaghettini
from spaghettini import register, quick_register, load, check, check_registered
quick_register(nn.Linear)
quick_register(nn.ReLU)
@quick_register
class MLP(nn.Module):
def __init__(self, units, activation, linear_module):
super().__init__()
model = []
for index, (in_units,
out_units) in enumerate(zip(units[:-1], units[1:])):
if index != 0:
model.append(activation)
model.append(linear_module(in_units, out_units))
self.model = nn.Sequential(*model)
def forward(self, x):
return self.model(x)
print(check())
check_registered()
net = load("examples/assets/pytorch_mlp.yaml", verbose=True)
print("Printing loaded network. ")
print(net)