# MIT License
#
# Copyright (c) 2022-2023, Alex M. Maldonado
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# SOFTWARE.
import hashlib
import numpy as np
from .base import Model
from ..logger import GDMLLogger
try:
import torch
_HAS_TORCH = True
except ImportError:
_HAS_TORCH = False
log = GDMLLogger(__name__)
# pylint: disable-next=invalid-name
[docs]class schnetModel(Model):
def __init__(self, model_path, comp_ids, device, criteria=None):
"""
Parameters
----------
model_path : :obj:`str`
Path to SchNet PyTorch model.
comp_ids : ``iterable``
Model component IDs that relate entity IDs of a structure to a
fragment label.
device : :obj:`str`
The device where the model and tensors will be stored. For example,
``'cpu'`` and ``'cuda'``.
criteria : :obj:`mbgdml.descriptors.Criteria`, default: :obj:`None`
Initialized descriptor criteria for accepting a structure based on
a descriptor and cutoff.
"""
super().__init__(criteria)
self.type = "schnet"
# pylint: disable-next=no-member
self.spk_model = torch.load(model_path, map_location=torch.device(device))
self.device = device
if isinstance(comp_ids, (list, tuple)):
comp_ids = np.array(comp_ids)
self.comp_ids = comp_ids
self.nbody_order = len(comp_ids)
# SchNet MD5
md5_hash = hashlib.md5()
for param in self.spk_model.parameters():
md5_hash.update(
hashlib.md5(param.cpu().detach().numpy().flatten()).digest()
)
self.md5 = md5_hash.hexdigest()