Add model path argument to scGPT

src/methods/scgpt/config.vsh.yaml

@@ -1,4 +1,4 @@
-__merge__: ../../api/comp_method.yaml
+__merge__: ../../api/base_method.yaml
 
 name: scgpt
 label: scGPT
@@ -24,11 +24,18 @@ info:
       model: "scGPT_CP"
 
 arguments:
-  - name: --model
+  - name: --model_name
     type: string
-    description: String giving the scGPT model to use
+    description: String giving the name of the scGPT model to use
     choices: ["scGPT_human", "scGPT_CP"]
     default: "scGPT_human"
+  - name: --model
+    type: file
+    description: |
+      Path to the directory containing the scGPT model specified by model_name
+      or a .zip/.tar.gz archive to extract. If not given the model will be
+      downloaded.
+    required: false
   - name: --n_hvg
     type: integer
     default: 3000

src/methods/scgpt/script.py

@@ -1,5 +1,8 @@
+import os
 import sys
+import tarfile
 import tempfile
+import zipfile
 
 import anndata as ad
 import gdown
@@ -12,6 +15,7 @@
 par = {
     "input": "resources_test/.../input.h5ad",
     "output": "output.h5ad",
+    "model_name": "scGPT_human",
     "model": "scGPT_human",
     "n_hvg": 3000,
 }
@@ -43,23 +47,54 @@
 
 print(adata, flush=True)
 
-print(f"\n>>> Downloading '{par['model']}' model...", flush=True)
-model_drive_ids = {
-    "scGPT_human": "1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y",
-    "scGPT_CP": "1_GROJTzXiAV8HB4imruOTk6PEGuNOcgB",
-}
-drive_path = f"https://drive.google.com/drive/folders/{model_drive_ids[par['model']]}"
-model_dir = tempfile.TemporaryDirectory()
-print(f"Downloading from '{drive_path}'", flush=True)
-gdown.download_folder(drive_path, output=model_dir.name, quiet=True)
-print(f"Model directory: '{model_dir.name}'", flush=True)
+if par["model"] is None:
+    print(f"\n>>> Downloading '{par['model_name']}' model...", flush=True)
+    model_drive_ids = {
+        "scGPT_human": "1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y",
+        "scGPT_CP": "1_GROJTzXiAV8HB4imruOTk6PEGuNOcgB",
+    }
+    drive_path = (
+        f"https://drive.google.com/drive/folders/{model_drive_ids[par['model_name']]}"
+    )
+    model_temp = tempfile.TemporaryDirectory()
+    model_dir = model_temp.name
+    print(f"Downloading from '{drive_path}'", flush=True)
+    gdown.download_folder(drive_path, output=model_dir, quiet=True)
+else:
+    if os.path.isdir(par["model"]):
+        print(f"\n>>> Using model directory...", flush=True)
+        model_temp = None
+        model_dir = par["model"]
+    else:
+        model_temp = tempfile.TemporaryDirectory()
+        model_dir = model_temp.name
+
+        if zipfile.is_zipfile(par["model"]):
+            print(f"\n>>> Extracting model from .zip...", flush=True)
+            print(f".zip path: '{par['model']}'", flush=True)
+            with zipfile.ZipFile(par["model"], "r") as zip_file:
+                zip_file.extractall(model_dir)
+        elif tarfile.is_tarfile(par["model"]) and par["model"].endswith(
+            ".tar.gz"
+        ):
+            print(f"\n>>> Extracting model from .tar.gz...", flush=True)
+            print(f".tar.gz path: '{par['model']}'", flush=True)
+            with tarfile.open(par["model"], "r:gz") as tar_file:
+                tar_file.extractall(model_dir)
+                model_dir = os.path.join(model_dir, os.listdir(model_dir)[0])
+        else:
+            raise ValueError(
+                f"The 'model' argument should be a directory a .zip file or a .tar.gz file"
+            )
+
+print(f"Model directory: '{model_dir}'", flush=True)
 
 print("\n>>> Embedding data...", flush=True)
 device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"Device: '{device}'", flush=True)
 embedded = scgpt.tasks.embed_data(
     adata,
-    model_dir.name,
+    model_dir,
     gene_col="feature_name",
     batch_size=64,
     use_fast_transformer=False,  # Disable fast-attn as not installed
@@ -86,7 +121,8 @@
 print(f"Output H5AD file: '{par['output']}'", flush=True)
 output.write_h5ad(par["output"], compression="gzip")
 
-print("\n>>> Cleaning up temporary directories...", flush=True)
-model_dir.cleanup()
+if model_temp is not None:
+    print("\n>>> Cleaning up temporary directories...", flush=True)
+    model_temp.cleanup()
 
 print("\n>>> Done!", flush=True)

src/workflows/run_benchmark/main.nf

@@ -29,7 +29,9 @@ methods = [
   scalex,
   scanorama,
   scanvi,
-  scgpt,
+  scgpt.run(
+    args: [model_path: file("s3://openproblems-work/cache/scGPT_human.zip")]
+  ),
   scimilarity.run(
     args: [model: file("s3://openproblems-work/cache/scimilarity-model_v1.1.tar.gz")]
   ),