Merge pull request #12 from haddocking/ntxxt-patch-1

silence pyg warnings
haddocking · May 29, 2024 · 6ca9d04 · 6ca9d04
2 parents 2e4c090 + e697d7f
commit 6ca9d04
Show file tree

Hide file tree

Showing 2 changed files with 20 additions and 14 deletions.
diff --git a/README.md b/README.md
@@ -18,11 +18,11 @@ $ cd DeepRank-GNN-esm
 
 2. Install either the CPU or GPU version of DeepRank-GNN-esm
 ```bash
-$ conda env create -f environment-cpu.yml && conda activate deeprank-gnn-esm-cpu-env
+$ conda env create -f environment-cpu.yml && conda activate deeprank-gnn-esm-cpu
 ```
 OR
 ```bash
-$ conda env create -f environment-gpu.yml && conda activate deeprank-gnn-esm-gpu-env
+$ conda env create -f environment-gpu.yml && conda activate deeprank-gnn-esm-gpu
 ```
 
 3. Install the command line tool
@@ -63,8 +63,8 @@ $ wget https://files.rcsb.org/view/1B6C.pdb -q
 
 # make sure the environment is activated
 $ conda activate deeprank-gnn-esm-gpu-env
-(deeprank-gnn-esm-gpu-env) $ export MODEL=../paper_pretrained_models/scoring_of_docking_models/gnn_esm/treg_yfnat_b64_e20_lr0.001_foldall_esm.pth.tar
-(deeprank-gnn-esm-gpu-env) $ deeprank-gnn-esm-predict 1B6C.pdb A B $MODEL
+(deeprank-gnn-esm-gpu) $ export MODEL=../paper_pretrained_models/scoring_of_docking_models/gnn_esm/treg_yfnat_b64_e20_lr0.001_foldall_esm.pth.tar
+(deeprank-gnn-esm-gpu) $ deeprank-gnn-esm-predict 1B6C.pdb A B $MODEL
  2023-06-28 06:08:21,889 predict:64 INFO - Setting up workspace - /home/DeepRank-GNN-esm/1B6C-gnn_esm_pred_A_B
  2023-06-28 06:08:21,945 predict:72 INFO - Renumbering PDB file.
  2023-06-28 06:08:22,294 predict:104 INFO - Reading sequence of PDB 1B6C.pdb

diff --git a/tutorial.ipynb b/tutorial.ipynb
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {
     "vscode": {
      "languageId": "powershell"
@@ -117,12 +117,12 @@
     "embedding_path = \"embedding\"\n",
     "nproc = 20\n",
     "outfile = \"1ATN_residue.hdf5\"\n",
-    "GraphHDF5(pdb_path = pdb_path,\n",
-    "               embedding_path = embedding_path,\n",
-    "               graph_type = \"residue\",\n",
-    "               outfile = outfile,\n",
-    "               nproc = nproc,\n",
-    "               tmpdir=\"./tmpdir\")"
+    "hdf5 = GraphHDF5(pdb_path = pdb_path,\n",
+    "                 embedding_path = embedding_path,\n",
+    "                 graph_type = \"residue\",\n",
+    "                 outfile = outfile,\n",
+    "                 nproc = nproc,\n",
+    "                 tmpdir=\"./tmpdir\")"
    ]
   },
   {
@@ -138,16 +138,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')\n",
+    "\n",
     "from deeprank_gnn.ginet import GINet\n",
     "from deeprank_gnn.NeuralNet import NeuralNet as NN\n",
+    "import os\n",
+    "\n",
     "database_test = \"1ATN_residue.hdf5\"\n",
     "gnn = GINet\n",
     "target = \"fnat\"\n",
     "edge_feature = [\"dist\"]\n",
     "node_features=[\"type\", \"polarity\", \"bsa\", \"charge\", \"embedding\"]\n",
     "threshold = 0.3\n",
     "pretrained_model=\"../paper_pretrained_models/scoring_of_docking_models/gnn_esm/treg_yfnat_b64_e20_lr0.001_foldall_esm.pth.tar\"\n",
-    "device_name = \"cuda:0\"\n",
+    "device_name = \"cuda:0\" if 'CUDA_VISIBLE_DEVICES' in os.environ and os.environ['CUDA_VISIBLE_DEVICES'] else 'cpu'\n",
     "num_workers = 10\n",
     "model = NN(\n",
     "            database_test,\n",
@@ -159,7 +164,8 @@
     "            num_workers = num_workers,\n",
     "            pretrained_model=pretrained_model,\n",
     "            threshold = threshold)\n",
-    "model.test(hdf5 = \"GNN_esm_prediction.hdf5\")"
+    "model.test(hdf5 = \"GNN_esm_prediction.hdf5\")\n",
+    "print(f'Fnat predictions for input PDBs saved in GNN_esm_prediction.hdf5')"
    ]
   },
   {
@@ -180,7 +186,7 @@
     "mol_names = f[\"epoch_0000\"][\"test\"][\"mol\"][()]\n",
     "fnats = f[\"epoch_0000\"][\"test\"][\"outputs\"][()]\n",
     "for mol, fnat in zip(mol_names, fnats):\n",
-    "    print(mol.decode(), fnat)"
+    "    print(f'Fnat for {mol.decode()}: {fnat}')"
    ]
   }
  ],