Coverage for biobb_pytorch / mdae / encode_model.py: 97%
65 statements
« prev ^ index » next coverage.py v7.13.2, created at 2026-02-02 16:33 +0000
1import torch
2from torch.utils.data import DataLoader
3import os
4from biobb_common.tools.file_utils import launchlogger
5from biobb_common.tools import file_utils as fu
6from biobb_pytorch.mdae.utils.log_utils import get_size
7from biobb_common.generic.biobb_object import BiobbObject
8from mlcolvar.data import DictDataset
9import numpy as np
12class EvaluateEncoder(BiobbObject):
13 """
14 | biobb_pytorch EvaluateEncoder
15 | Encode data with a Molecular Dynamics AutoEncoder (MDAE) model.
16 | Evaluates a PyTorch autoencoder from the given properties.
18 Args:
19 input_model_pth_path (str): Path to the trained model file whose encoder will be used. File type: input. `Sample file <https://github.com/bioexcel/biobb_pytorch/raw/master/biobb_pytorch/test/reference/mdae/output_model.pth>`_. Accepted formats: pth (edam:format_2333).
20 input_dataset_pt_path (str): Path to the input dataset file (.pt) to encode. File type: input. `Sample file <https://github.com/bioexcel/biobb_pytorch/raw/master/biobb_pytorch/test/reference/mdae/output_model.pt>`_. Accepted formats: pt (edam:format_2333).
21 output_results_npz_path (str): Path to the output latent-space results file (compressed NumPy archive, typically containing 'z'). File type: output. `Sample file <https://github.com/bioexcel/biobb_pytorch/raw/master/biobb_pytorch/test/reference/mdae/output_results.npz>`_. Accepted formats: npz (edam:format_2333).
22 properties (dict - Python dictionary object containing the tool parameters, not input/output files):
23 * **Dataset** (*dict*) - ({}) mlcolvar DictDataset / DataLoader options (e.g. batch_size, shuffle).
25 Examples:
26 This example shows how to use the EvaluateEncoder class to evaluate a PyTorch autoencoder model::
28 from biobb_pytorch.mdae.evaluate_model import evaluateEncoder
30 input_model_pth_path='input_model.pth'
31 input_dataset_pt_path='input_dataset.npy'
32 output_results_npz_path='output_results.npz'
34 prop={
35 'Dataset': {
36 'batch_size': 32
37 }
38 }
40 evaluateEncoder(input_model_pth_path=input_model.pth,
41 input_dataset_pt_path=input_dataset.npy,
42 output_results_npz_path=output_results.npz,
43 properties=prop)
45 Info:
46 * wrapped_software:
47 * name: PyTorch
48 * version: >=1.6.0
49 * license: BSD 3-Clause
50 * ontology:
51 * name: EDAM
52 * schema: http://edamontology.org/EDAM.owl
53 """
55 def __init__(
56 self,
57 input_model_pth_path: str,
58 input_dataset_pt_path: str,
59 output_results_npz_path: str,
60 properties: dict,
61 **kwargs,
62 ) -> None:
64 properties = properties or {}
66 super().__init__(properties)
68 self.input_model_pth_path = input_model_pth_path
69 self.input_dataset_pt_path = input_dataset_pt_path
70 self.output_results_npz_path = output_results_npz_path
71 self.properties = properties.copy()
72 self.locals_var_dict = locals().copy()
74 # Input/Output files
75 self.io_dict = {
76 "in": {
77 "input_model_pth_path": input_model_pth_path,
78 "input_dataset_pt_path": input_dataset_pt_path,
79 },
80 "out": {
81 "output_results_npz_path": output_results_npz_path,
82 },
83 }
85 self.Dataset = self.properties.get('Dataset', {})
86 self.results = None
88 # Check the properties
89 self.check_properties(properties)
90 self.check_arguments()
92 def load_model(self):
93 return torch.load(self.io_dict["in"]["input_model_pth_path"],
94 weights_only=False)
96 def load_dataset(self):
97 dataset = torch.load(self.io_dict["in"]["input_dataset_pt_path"],
98 weights_only=False)
99 return DictDataset(dataset)
101 def create_dataloader(self, dataset):
102 ds_cfg = self.properties['Dataset']
103 return DataLoader(
104 dataset,
105 batch_size=ds_cfg.get('batch_size', 16),
106 shuffle=ds_cfg.get('shuffle', False),
107 )
109 def evaluate_encoder(self, model, dataloader):
110 """Evaluate the encoder part of the model."""
111 model.eval()
112 with torch.no_grad():
113 z_all = []
114 for batch in dataloader:
115 z = model.forward_cv(batch['data'])
116 z_all.append(z)
117 return {"z": torch.cat(z_all, dim=0)}
119 @launchlogger
120 def launch(self) -> int:
121 """
122 Execute the :class:`EvaluateEncoder` class and its `.launch()` method.
123 """
125 fu.log('## BioBB Model Evaluator ##', self.out_log)
127 # Setup Biobb
128 if self.check_restart():
129 return 0
131 self.stage_files()
133 # Start Pipeline
135 # load the model
136 fu.log(f'Load model from {os.path.abspath(self.io_dict["in"]["input_model_pth_path"])}', self.out_log)
137 model = self.load_model()
139 # load the dataset
140 fu.log(f'Load dataset from {os.path.abspath(self.io_dict["in"]["input_dataset_pt_path"])}', self.out_log)
141 dataset = self.load_dataset()
143 # create the dataloader
144 fu.log('Start evaluating...', self.out_log)
145 dataloader = self.create_dataloader(dataset)
147 # evaluate the model
148 results = self.evaluate_encoder(model, dataloader)
150 # Save the results
151 np.savez_compressed(self.io_dict["out"]["output_results_npz_path"], **results)
152 fu.log(f'Evaluation Results saved to {os.path.abspath(self.io_dict["out"]["output_results_npz_path"])}', self.out_log)
153 fu.log(f'File size: {get_size(self.io_dict["out"]["output_results_npz_path"])}', self.out_log)
155 # Copy files to host
156 self.copy_to_host()
158 # Remove temporal files
159 self.remove_tmp_files()
161 self.check_arguments(output_files_created=True, raise_exception=False)
163 return 0
166def evaluateEncoder(
167 properties: dict,
168 input_model_pth_path: str,
169 input_dataset_pt_path: str,
170 output_results_npz_path: str,
171 **kwargs,
172) -> int:
173 """Create the :class:`EvaluateEncoder <EvaluateEncoder.EvaluateEncoder>` class and
174 execute the :meth:`launch() <EvaluateEncoder.evaluateEncoder.launch>` method."""
175 return EvaluateEncoder(**dict(locals())).launch()
178evaluateEncoder.__doc__ = EvaluateEncoder.__doc__
179main = EvaluateEncoder.get_main(evaluateEncoder, "Encode data with a Molecular Dynamics AutoEncoder (MDAE) model.")
181if __name__ == "__main__":
182 main()