no more using of torch while exact solving with Google OR

Author: AnasNeumann

common.py

@@ -1,10 +1,7 @@
 import pickle
 import os
 import resource
-import torch
 from typing import Union, Any, Dict
-from model.instance import Instance
-from torch import Tensor
 
 # =====================================================================
 # =*= COMMON TOOLS (objects and functions) USED ACCROSS THE PROJECT =*=
@@ -27,13 +24,6 @@ def __init__(self, data: str, instances: str, models: str, out: str, scripts: st
 all_types_feature = Union[int, float, bool, list]
 generic_object = Union[object, Dict[Any, Any]]
 
-def add_into_tensor(tensor_list: Tensor | None, tensor_val: Tensor):
-    if tensor_list is None:
-        tensor_list = tensor_val
-    else:
-        tensor_list = torch.cat((tensor_list, tensor_val), dim=0)
-    return tensor_list
-
 def load_instance(path: str):
     with open(path, 'rb') as file:
         return pickle.load(file)
@@ -49,9 +39,6 @@ def load_instances(path: str):
     print("end of loading!")
     return instances
 
-def to_binary(booleanVal: bool):
-    return 1 if booleanVal else 0
-
 def set_memory_limit(max_ram_bytes: num_feature):
     _, hard = resource.getrlimit(resource.RLIMIT_AS)
     resource.setrlimit(resource.RLIMIT_AS, (max_ram_bytes * 1024 * 1024 * 1024, hard))
@@ -77,15 +64,6 @@ def init_several_3D(a: int, b: int, c: int, default_value: all_types_feature, nb
 def to_bool(s: str):
     return s.lower() in ['true', '1', 't', 'y', 'yes']
 
-def features2tensor(features: list, device: str):
-    for f in features:
-        if isinstance(f, bool):
-            f = to_binary(f)
-    return torch.tensor([[f for f in features]], dtype=torch.float, device=device)
-
-def id2tensor(id1: int, id2: int, device: str):
-    return torch.tensor([[id1], [id2]], dtype=torch.long, device=device)
-
 def search_object_by_id(objects: list[generic_object], id: int):
     for obj in objects:
         if obj['id'] == id:

exact_solver.py

@@ -1,6 +1,4 @@
 import argparse
-import pickle
-import os
 from common import init_several_1D, init_2D, init_several_2D, init_3D, load_instance, directory
 from model.instance import Instance
 from model.solution import Solution

jobs/exact_builder.py

@@ -1,4 +1,3 @@
-import sys
 import argparse
 import os
 
@@ -44,7 +43,7 @@
             f.write("virtualenv --no-download $SLURM_TMPDIR/env\n")
             f.write("source $SLURM_TMPDIR/env/bin/activate\n")
             f.write("pip install --upgrade pip --no-index\n")
-            f.write("pip install --no-index -r "+BASIC_PATH+"requirements.txt\n")
+            f.write("pip install --no-index -r "+BASIC_PATH+"requirements_or.txt\n")
             f.write(f"python {BASIC_PATH}exact_solver.py --mode=prod --size={size} --number={instance} --path="+BASIC_PATH+" \n")
             f.write("desactivate\n")
             f.close()

jobs/gns_builder.py

@@ -1,4 +1,3 @@
-import sys
 import argparse
 
 # =====================================================

model/agent.py

@@ -4,7 +4,7 @@
 from typing import Tuple
 import numpy as np
 from torch.nn import Module
-from common import add_into_tensor
+from tensors import add_into_tensor
 
 # ===========================================================
 # =*= DATA MODEL FOR PPO AGENT CONFIGURATION AND RESULTS =*=

model/gnn.py

@@ -2,7 +2,6 @@
 from torch_geometric.data.storage import EdgeStorage
 from torch.nn import Sequential, Linear, ELU, Tanh, Parameter, LeakyReLU, Module, ModuleList
 import torch.nn.functional as F
-from torch_geometric.nn import global_mean_pool
 from torch import Tensor
 from .graph import FeatureConfiguration, State
 from torch_geometric.utils import scatter

model/graph.py

@@ -1,10 +1,10 @@
-import copy
 import torch
 from torch_geometric.data import HeteroData
 from torch_geometric.data.storage import EdgeStorage
 from torch_geometric.utils import to_dense_adj
 from torch import Tensor
-from common import features2tensor, id2tensor, num_feature
+from common import num_feature
+from tensors import features2tensor, id2tensor
 from .instance import Instance
 
 # =============================================================

requirements.txt

@@ -1,4 +1,3 @@
-ortools
 numpy
 pandas
 seaborn

requirements_or.txt

@@ -0,0 +1,2 @@
+ortools
+pandas

results_analysis.py

@@ -7,7 +7,6 @@
 from model.agent import MAPPO_Losses
 from torch import Tensor
 import matplotlib.pyplot as plt
-import numpy as np
 
 # =====================================================
 # =*= EXPERIMENTS: RESULTS ANALYSIS =*=

tensors.py

@@ -0,0 +1,28 @@
+import torch
+from torch import Tensor
+
+# =====================================================
+# =*= TENSOR-RELATED TOOLS USED ACCROSS THE PROJECT =*=
+# ======================================================
+__author__ = "Anas Neumann - [email protected]"
+__version__ = "1.0.0"
+__license__ = "Apache 2.0 License"
+
+def add_into_tensor(tensor_list: Tensor | None, tensor_val: Tensor):
+    if tensor_list is None:
+        tensor_list = tensor_val
+    else:
+        tensor_list = torch.cat((tensor_list, tensor_val), dim=0)
+    return tensor_list
+
+def to_binary(booleanVal: bool):
+    return 1 if booleanVal else 0
+
+def features2tensor(features: list, device: str):
+    for f in features:
+        if isinstance(f, bool):
+            f = to_binary(f)
+    return torch.tensor([[f for f in features]], dtype=torch.float, device=device)
+
+def id2tensor(id1: int, id2: int, device: str):
+    return torch.tensor([[id1], [id2]], dtype=torch.long, device=device)