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commented
3 weeks ago Walkthrough
## Walkthrough The recent updates primarily introduce new classes and methods to handle property fitting, property evaluation, and atomic model updates in the DeepMD framework. Significant additions include the `PropertyFittingNet`, `DeepProperty`, and `EnergyModel` classes, modifications to support intensive property handling, and new methods across various files for property evaluation and intensivity checks. ## Changes | Files | Change Summary | |--------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `deepmd/dpmodel/fitting/property_fitting.py` | Added the `PropertyFittingNet` class for property fitting neural networks, introducing initialization, serialization, and deserialization methods. | | `deepmd/dpmodel/output_def.py` | Updated `OutputVariableDef` class to include an `intensive` boolean attribute and its getter method. | | `deepmd/infer/deep_eval.py` | Added mappings for properties and reducing properties, including a new abstract method `get_intensive` in `DeepEvalBackend`. | | `deepmd/infer/deep_property.py` | Introduced the `DeepProperty` class to evaluate properties of structures based on deep learning models. | | `deepmd/pt/model/atomic_model/dp_atomic_model.py` | Added the `get_intensive` method to the `AtomicModel` class. | | `deepmd/pt/model/atomic_model/property_atomic_model.py` | Introduced the `DPPropertyAtomicModel` class with the `get_intensive` method. | | `deepmd/pt/model/model/property_model.py` | Added `PropertyModel` class with methods for forward pass, task dimension retrieval, and determining if the output is intensive. | | `deepmd/pt/train/training.py` | Added import for `PropertyLoss` and handled `loss_type == "property"` condition in `training.py` to create a `PropertyLoss` instance. | | `deepmd/pt/utils/stat.py` | Added `intensive` parameter to `compute_output_stats` and `compute_output_stats_global` functions to indicate intensive or extensive fitting property. | | `examples/property/README.md`, `examples/property/data/data_0/type.raw`, ... | Provided examples and dataset for QM9 property fitting, with files describing labels for properties like `lumo`, `homo`, and `band_gap`. | | `deepmd/pt/model/atomic_model/base_atomic_model.py` | Added check for `intensive` attribute in `fitting_net` for output statistics computation and introduced `get_intensive` method. | | `deepmd/dpmodel/atomic_model/property_atomic_model.py`, `deepmd/dpmodel/model/property_model.py` | Introduced `DPPropertyAtomicModel` and `EnergyModel` classes, including methods for property intensiveness retrieval and model initialization. | | `deepmd/pt/model/model/__init__.py` | Updated import for `PropertyModel`, modified `get_standard_model` logic, and introduced `embed()` call from `IPython`. |Recent review details
**Configuration used: CodeRabbit UI** **Review profile: CHILL**Commits
Files that changed from the base of the PR and between 79153731279842a48dfc53cc22acae1b72bc788b and 692ac978a46319e2ad48328ea79685b5fb4bd156.Files selected for processing (4)
* deepmd/dpmodel/atomic_model/property_atomic_model.py (1 hunks) * deepmd/dpmodel/model/property_model.py (1 hunks) * deepmd/pt/model/model/__init__.py (2 hunks) * examples/property/README.md (1 hunks)Files skipped from review as they are similar to previous changes (2)
* deepmd/pt/model/model/__init__.py * examples/property/README.mdAdditional comments not posted (2)
deepmd/dpmodel/atomic_model/property_atomic_model.py (1)
`16-18`: **Clarify the source of `atomic_output_def()`.** The method `get_intensive` uses `atomic_output_def()` which is not defined in this file or in the parent class as per the provided context. This could lead to a runtime error if not properly implemented.Verification successful
--- **Clarify the source of `atomic_output_def()`.** The method `atomic_output_def` is defined in `deepmd/dpmodel/atomic_model/base_atomic_model.py` and `deepmd/pt/model/atomic_model/base_atomic_model.py`. This indicates that `property_atomic_model.py` likely inherits this method, ensuring it is available and preventing potential runtime errors. - **Locations of `atomic_output_def` definitions:** - `deepmd/dpmodel/atomic_model/base_atomic_model.py` - `deepmd/pt/model/atomic_model/base_atomic_model.py`Scripts executed
The following scripts were executed for the analysis: --- Script: ```shell #!/bin/bash # Description: Verify the definition of `atomic_output_def` in the parent class or elsewhere in the codebase. # Test: Search for the method definition. Expect: At least one definition in the codebase. ast-grep --lang python --pattern $'class $$_ { atomic_output_def($$) { $$$ } }' ``` Length of output: 562 --- --- Script: ```shell #!/bin/bash # Description: Verify the definition of `atomic_output_def` in the codebase. # Test: Search for the method definition. Expect: At least one definition in the codebase. ast-grep --lang python --pattern 'def atomic_output_def($$): $$$' ``` Length of output: 67 --- --- Script: ```shell #!/bin/bash # Description: Search for the definition or assignment of `atomic_output_def` in the codebase. # Test: Search for `atomic_output_def` in the codebase, including inheritance and dynamic assignments. rg 'atomic_output_def' -t py ``` Length of output: 4081deepmd/dpmodel/model/property_model.py (1)
`19-27`: **Check the order and completeness of superclass initializations.** The constructor initializes `DPModelCommon` and `DPPropertyModel_`. However, the order of initialization might not correctly set up the state if the superclasses have overlapping state modifications. Furthermore, the use of positional and keyword arguments in `DPPropertyModel_`'s initialization needs to be verified for correctness.
codecov[bot]
Chengqian-Zhang
Solve issue #3866
Summary by CodeRabbit
New Features
PropertyFittingNetclass.DeepPropertyclass for evaluating properties of structures using a deep learning model.EnergyModelclass for energy-related property predictions.Enhancements
intensiveproperty to several classes to indicate whether a fitting property is intensive or extensive.Documentation
examples/property/providing descriptions of the included dataset and properties.