update

README.md

@@ -32,6 +32,7 @@ pip install torch-scatter torch-sparse torch-cluster torch-geometric -f https://
 pip install scanpy e3nn
 pip install lightning==2.1.0
 pip install numpy==1.26.4
+pip install transformers
 ```
 where the compatibility between ```torch``` and ```torch-geometric``` is not always guaranteed (the first two lines), since these two guys are picky on the platform and the version of other packages. You may trial and error to find the right version of ```torch``` and ```torch-geometric``` that works for you, e.g., in some machine I once installed via:
 ```

{models → models_cifm}/cifm.py

@@ -3,8 +3,8 @@ import torch.nn as nn
 from torch_geometric.nn import radius_graph
 import scanpy as sc
 from huggingface_hub import PyTorchModelHubMixin
-from models.mlp_and_gnn import MLPBiasFree
-from models.egnn_void_invariant import VIEGNNModel
+from models_cifm.mlp_and_gnn import MLPBiasFree
+from models_cifm.egnn_void_invariant import VIEGNNModel
 
 
 class CIFM(

{models → models_cifm}/egnn_void_invariant.py

@@ -2,8 +2,8 @@ import torch
 from torch.nn import functional as F
 from torch_geometric.nn import global_add_pool, global_mean_pool
 
-from models.layers.egnn_layer_void_invariant import EGNNLayer
-from models.mlp_and_gnn import MLPBiasFree
+from models_cifm.layers.egnn_layer_void_invariant import EGNNLayer
+from models_cifm.mlp_and_gnn import MLPBiasFree
 
 
 class VIEGNNModel(torch.nn.Module):

{models → models_cifm}/layers/egnn_layer_void_invariant.py

@@ -3,7 +3,7 @@ from torch.nn import Linear, ReLU, SiLU, Sequential
 from torch_geometric.nn import MessagePassing
 from torch_scatter import scatter
 
-from models.mlp_and_gnn import MLPBiasFree
+from models_cifm.mlp_and_gnn import MLPBiasFree
 
 
 class EGNNLayer(MessagePassing):

test.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "import torch\n",
     "import numpy as np\n",
-    "from models.cifm import CIFM\n",
+    "from models_cifm.cifm import CIFM\n",
     "import scanpy as sc"
    ]
   },
@@ -100,7 +100,7 @@
     }
    ],
    "source": [
-    "args_model = torch.load('./model_files/args.pt')\n",
+    "args_model = torch.load('./models_cifm/args.pt')\n",
     "device = 'cpu' # or 'cuda\n",
     "model = CIFM.from_pretrained('ynyou/CIFM', args=args_model).to(device)\n",
     "model.eval()"
@@ -172,7 +172,7 @@
    ],
    "source": [
     "channel2ensembl_ids_target = [[i] for i in adata.var.index.tolist()]\n",
-    "channel2ensembl_ids_source = torch.load('./model_files/channel2ensembl.pt')\n",
+    "channel2ensembl_ids_source = torch.load('./models_cifm/channel2ensembl.pt')\n",
     "model.channel_matching(channel2ensembl_ids_target, channel2ensembl_ids_source)"
    ]
   },