From f6379fc6a91b8764e38b3fba0b4a45e9f143ef62 Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Mon, 9 Oct 2023 10:25:41 +0200
Subject: [PATCH] Made PyTorch optional, so it runs even without it.
---
.gitlab-ci.yml | 31 +++++++++++++++++++++++++++++++
pes_to_spec/bnn.py | 18 +++++++++++++-----
pes_to_spec/exception.py | 13 +++++++++++++
pes_to_spec/model.py | 17 +++++++++++++++--
4 files changed, 72 insertions(+), 7 deletions(-)
create mode 100644 .gitlab-ci.yml
create mode 100644 pes_to_spec/exception.py
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
new file mode 100644
index 0000000..dd1851a
--- /dev/null
+++ b/.gitlab-ci.yml
@@ -0,0 +1,31 @@
+stages:
+ - environment
+ - test
+ - deploy
+
+cache:
+ paths:
+ - .cache/pip
+ - .venv
+
+setup-environment:
+ stage: environment
+ image: python3.9-slim-buster
+ script:
+ - python3 -m venv .venv
+ - source .venv/bin/activate
+ - python3 -m pip install --upgrade pip
+ - python3 -m pip install --force-reinstall --index-url https://pypi.anaconda.org/intel/simple --no-dependencies numpy scipy==1.7.3
+ - python3 -m pip install numpy scipy==1.7.3
+ #- python3 -m pip install torch --index-url https://download.pytorch.org/whl/cpu
+ - python3 -m pip install joblib scikit-learn
+ - python3 -m pip install matplotlib lmfit seaborn extra_data
+
+test_ard:
+ stage: test
+ image: python3.9-slim-buster
+ script:
+ - python3 -m venv .venv
+ - source .venv/bin/activate
+ - ./pes_to_spec/test/offline_analysis.py -p 900331 -r 69 -t 70 -d results_ard --model-type ard
+
diff --git a/pes_to_spec/bnn.py b/pes_to_spec/bnn.py
index 5f8832e..242a507 100644
--- a/pes_to_spec/bnn.py
+++ b/pes_to_spec/bnn.py
@@ -1,14 +1,22 @@
+"""
+BNN implementation.
+"""
+
from sklearn.base import BaseEstimator, RegressorMixin
from typing import Any, Dict, Optional, Union, Tuple
import numpy as np
-import math
from scipy.special import gamma
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import TensorDataset, DataLoader
+from pes_to_spec.exception import MethodNotAvailableException
+
+try:
+ import torch
+ import torch.nn as nn
+ import torch.nn.functional as F
+ from torch.utils.data import TensorDataset, DataLoader
+except ImportError:
+ raise MethodNotAvailableException("PyTorch not available. BNN deactivated.")
class BayesLinearEmpiricalPrior(nn.Module):
"""
diff --git a/pes_to_spec/exception.py b/pes_to_spec/exception.py
new file mode 100644
index 0000000..75a169d
--- /dev/null
+++ b/pes_to_spec/exception.py
@@ -0,0 +1,13 @@
+"""
+Module containing package-specific exceptions.
+"""
+
+class MethodNotAvailableException(Exception):
+ """
+ Flags that one of the methods is not available.
+ """
+ def __init__(self, msg: str="Method not available."):
+ self.msg = msg
+ def __str__(self) -> str:
+ return self.msg
+
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index 7f2fe33..99734a3 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -1,5 +1,7 @@
from __future__ import annotations
+from typing import Any, Dict, List, Optional, Union, Tuple, Literal
+
import joblib
import numpy as np
@@ -19,9 +21,15 @@ from sklearn.base import clone, MetaEstimatorMixin
from joblib import Parallel, delayed
from copy import deepcopy
-from pes_to_spec.bnn import BNNModel
+from pes_to_spec.exception import MethodNotAvailableException
+
+is_bnn_available = False
+try:
+ from pes_to_spec.bnn import BNNModel
+ is_bnn_available = True
+except MethodNotAvailableException:
+ print("Warning: BNN model disabled. It requires PyTorch. Check if it is installed.")
-from typing import Any, Dict, List, Optional, Union, Tuple, Literal
def matching_ids(a: np.ndarray, b: np.ndarray, c: np.ndarray) -> np.ndarray:
"""Returns list of train IDs common to sets a, b and c."""
@@ -629,6 +637,8 @@ class Model(TransformerMixin, BaseEstimator):
n_peaks: int=0,
n_bnn_epochs: int=500,
):
+ if model_type in ["bnn", "bnn_rvm"] and not is_bnn_available:
+ raise MethodNotAvailableException("The BNN model requires a PyTorch installation. Please do `pip install torch` or `conda install pytorch` to be able to use the BNN model.")
self.high_res_sigma = high_res_sigma
# models
self.x_select = SelectRelevantLowResolution(channels, tof_start, delta_tof, poly=False) #(model_type not in ["bnn", "bnn_rvm"]))
@@ -1090,6 +1100,9 @@ class Model(TransformerMixin, BaseEstimator):
obj.x_select = x_select
obj.x_model = x_model
obj.y_model = y_model
+ if obj.model_type in ["bnn", "bnn_rvm"] and not is_bnn_available:
+ raise MethodNotAvailableException("Attempted to load a BNN model, but it requires a PyTorch installation. "
+ "Please do `pip install torch` or `conda install pytorch` to be able to load this model.")
if obj.model_type == "bnn":
obj.fit_model = BNNModel(state_dict=fit_model, rvm=False)
elif obj.model_type == "bnn_rvm":
--
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