Increase more code coverage in _chgnet.py #261
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…in TensorNet class
…oat_th and including linear layer in TensorNet to match the original implementations
WalkthroughThe recent modifications involve enhancing the Changes
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| for readout_field in ["atom_feat", "bond_feat", "angle_feat"]: | ||
| for final_mlp_type in ["gated", "mlp"]: | ||
| model = CHGNet( | ||
| element_types=["Mo", "S"], | ||
| activation_type=activation, | ||
| bond_update_hidden_dims=bond_dim, | ||
| learn_basis=learn_basis, | ||
| angle_update_hidden_dims=angle_dim, | ||
| conv_dropout=dropout, | ||
| readout_field=readout_field, | ||
| final_mlp_type=final_mlp_type, | ||
| ) | ||
| global_out = model(g=graph) | ||
| assert torch.numel(global_out) == 1 | ||
| assert torch.numel(graph.ndata["magmom"]) == graph.num_nodes() | ||
| model.save(".") | ||
| CHGNet.load(".") | ||
| os.remove("model.pt") | ||
| os.remove("model.json") | ||
| os.remove("state.pt") | ||
|
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Refactor repeated code in test_model method.
The instantiation of CHGNet and subsequent operations are repeated for each combination of readout_field and final_mlp_type. Consider refactoring this to reduce code duplication and improve maintainability. You could define a helper function that takes readout_field and final_mlp_type as parameters and performs the instantiation and checks.
+ def instantiate_and_test_model(readout_field, final_mlp_type):
+ model = CHGNet(
+ element_types=["Mo", "S"],
+ activation_type=activation,
+ bond_update_hidden_dims=bond_dim,
+ learn_basis=learn_basis,
+ angle_update_hidden_dims=angle_dim,
+ conv_dropout=dropout,
+ readout_field=readout_field,
+ final_mlp_type=final_mlp_type,
+ )
+ global_out = model(g=graph)
+ assert torch.numel(global_out) == 1
+ assert torch.numel(graph.ndata["magmom"]) == graph.num_nodes()
+ model.save(".")
+ CHGNet.load(".")
+ os.remove("model.pt")
+ os.remove("model.json")
+ os.remove("state.pt")
- for readout_field in ["atom_feat", "bond_feat", "angle_feat"]:
- for final_mlp_type in ["gated", "mlp"]:
- model = CHGNet(
- element_types=["Mo", "S"],
- activation_type=activation,
- bond_update_hidden_dims=bond_dim,
- learn_basis=learn_basis,
- angle_update_hidden_dims=angle_dim,
- conv_dropout=dropout,
- readout_field=readout_field,
- final_mlp_type=final_mlp_type,
- )
- global_out = model(g=graph)
- assert torch.numel(global_out) == 1
- assert torch.numel(graph.ndata["magmom"]) == graph.num_nodes()
- model.save(".")
- CHGNet.load(".")
- os.remove("model.pt")
- os.remove("model.json")
- os.remove("state.pt")Committable suggestion
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| for readout_field in ["atom_feat", "bond_feat", "angle_feat"]: | |
| for final_mlp_type in ["gated", "mlp"]: | |
| model = CHGNet( | |
| element_types=["Mo", "S"], | |
| activation_type=activation, | |
| bond_update_hidden_dims=bond_dim, | |
| learn_basis=learn_basis, | |
| angle_update_hidden_dims=angle_dim, | |
| conv_dropout=dropout, | |
| readout_field=readout_field, | |
| final_mlp_type=final_mlp_type, | |
| ) | |
| global_out = model(g=graph) | |
| assert torch.numel(global_out) == 1 | |
| assert torch.numel(graph.ndata["magmom"]) == graph.num_nodes() | |
| model.save(".") | |
| CHGNet.load(".") | |
| os.remove("model.pt") | |
| os.remove("model.json") | |
| os.remove("state.pt") | |
| def instantiate_and_test_model(readout_field, final_mlp_type): | |
| model = CHGNet( | |
| element_types=["Mo", "S"], | |
| activation_type=activation, | |
| bond_update_hidden_dims=bond_dim, | |
| learn_basis=learn_basis, | |
| angle_update_hidden_dims=angle_dim, | |
| conv_dropout=dropout, | |
| readout_field=readout_field, | |
| final_mlp_type=final_mlp_type, | |
| ) | |
| global_out = model(g=graph) | |
| assert torch.numel(global_out) == 1 | |
| assert torch.numel(graph.ndata["magmom"]) == graph.num_nodes() | |
| model.save(".") | |
| CHGNet.load(".") | |
| os.remove("model.pt") | |
| os.remove("model.json") | |
| os.remove("state.pt") |
Summary
Increase more code coverage in _chgnet.py
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