Fix test bugs
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@ -82,7 +82,14 @@ def main(args):
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historical_x, historical_y = subsample(historical_x, historical_y)
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# build model
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mean, std = historical_x.mean().item(), historical_x.std().item()
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model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
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model_kwargs = dict(
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input_dim=1,
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output_dim=1,
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act_cls="leaky_relu",
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norm_cls="simple_norm",
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mean=mean,
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std=std,
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)
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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# build optimizer
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optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
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@ -78,7 +78,14 @@ def main(args):
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historical_y = env_info["{:}-y".format(idx)]
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# build model
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mean, std = historical_x.mean().item(), historical_x.std().item()
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model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
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model_kwargs = dict(
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input_dim=1,
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output_dim=1,
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act_cls="leaky_relu",
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norm_cls="simple_norm",
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mean=mean,
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std=std,
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)
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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# build optimizer
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optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
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@ -24,6 +24,8 @@ from models.xcore import get_model
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class Population:
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"""A population used to maintain models at different timestamps."""
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def __init__(self):
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self._time2model = dict()
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@ -64,7 +64,7 @@ class TestSuperSimpleNorm(unittest.TestCase):
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model.apply_verbose(True)
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print(model.super_run_type)
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self.assertTrue(model[1].bias)
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self.assertTrue(model[2].bias)
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inputs = torch.rand(20, 10)
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print("Input shape: {:}".format(inputs.shape))
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@ -80,6 +80,6 @@ class TestSuperSimpleNorm(unittest.TestCase):
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model.set_super_run_type(super_core.SuperRunMode.Candidate)
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model.apply_candidate(abstract_child)
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output_shape = (20, abstract_child["1"]["_out_features"].value)
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output_shape = (20, abstract_child["2"]["_out_features"].value)
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outputs = model(inputs)
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self.assertEqual(tuple(outputs.shape), output_shape)
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