[ADD] vgg16 tils model

dockerfiles/tils-vgg16.dockerfile

@@ -0,0 +1,14 @@
+# Tumor-infiltrating lymphocyte detection model.
+#
+# Note about versioning: We should not use the 'latest' tag because it is a moving
+# target. We should prefer using a versioned release of the wsinfer pipeline.
+FROM kaczmarj/wsinfer:0.3.6
+
+# The CLI will use these env vars for model and weights.
+ENV WSINFER_MODEL="vgg16"
+ENV WSINFER_WEIGHTS="TCGA-TILs-v1"
+
+# Download the weights.
+RUN python -c "from wsinfer import get_model_weights; get_model_weights(architecture=\"$WSINFER_MODEL\", name=\"$WSINFER_WEIGHTS\").load_model()" \
+    # Downloaded models are mode 0600. Make them readable by all users.
+    && chmod -R +r $TORCH_HOME/hub/checkpoints/

scripts/build_docker_images.sh

@@ -44,6 +44,9 @@ docker build -t kaczmarj/wsinfer:$version .
 # TILs
 build $version-tils dockerfiles/tils.dockerfile
 
+# TILs VGG16
+build $version-tils-vgg16 dockerfiles/tils-vgg16.dockerfile
+
 # Tumor BRCA
 build $version-tumor-brca dockerfiles/tumor-brca.dockerfile
 
@@ -64,6 +67,7 @@ else
     echo "Pushing images."
     docker push kaczmarj/wsinfer:$version
     docker push kaczmarj/wsinfer:$version-tils
+    docker push kaczmarj/wsinfer:$version-tils-vgg16
     docker push kaczmarj/wsinfer:$version-tumor-brca
     docker push kaczmarj/wsinfer:$version-tumor-luad
     docker push kaczmarj/wsinfer:$version-tumor-paad

scripts/convert_tf_to_pytorch_til_vgg16.py

@@ -66,6 +66,7 @@ def convert_tf_to_pytorch(input_path, output_path, num_classes: int):
         elif "fc" in tf_weights:
             if tf_weights == "vgg_16/fc6/weights":
                 # [7, 7, 512, 4096] -> [25088, 4096]
+                tf_weight_array = tf_weight_array.transpose([2, 0, 1, 3])
                 tf_weight_array = tf_weight_array.reshape((25088, 4096))
             # E.g., go from shape [1, 1, 4096, 1000] to [1000, 4096]
             tf_weight_array = tf_weight_array.squeeze().T

tests/test_all.py

@@ -239,6 +239,15 @@ def test_cli_run_args(tmp_path: Path):
             200,
             441,
         ),
+        # VGG16 TCGA-TILs-v1
+        (
+            "vgg16",
+            "TCGA-TILs-v1",
+            ["notils", "tils"],
+            [0.9987693428993224, 0.0012305785203352],
+            200,
+            441,
+        ),
         # Vgg16mod TCGA-BRCA-v1
         (
             "vgg16mod",
@@ -950,7 +959,7 @@ def test_jit_compile(model_name: str, weights_name: str):
             out_nojit = model(x).detach().cpu()
         time_nojit = time.perf_counter() - t0
     model_nojit = model
-    model = jit_compile(model)
+    model = jit_compile(model)  # type: ignore
     if model is model_nojit:
         pytest.skip("Failed to compile model (would use original model)")
     with torch.no_grad():

wsinfer/modeldefs/vgg16_tcga-tils-v1.yaml

@@ -0,0 +1,20 @@
+# Configuration of a tumor infiltrating lymphocyte detection model (VGG16).
+version: "1.0"
+architecture: vgg16
+name: TCGA-TILs-v1
+url: https://stonybrookmedicine.box.com/shared/static/0orxxw2aai3l3lztetvukwqetvr3z4lr.pt
+url_file_name: vgg16-tils-20220112-3088cb70.pt
+num_classes: 2
+transform:
+  resize_size: 224
+  # Normalize to [-1, 1]
+  mean: [0.5, 0.5, 0.5]
+  std: [0.5, 0.5, 0.5]
+patch_size_pixels: 100
+spacing_um_px: 0.5
+class_names:
+  - notils
+  - tils
+metadata:
+  notes: |
+    Original code available at https://github.com/SBU-BMI/u24_lymphocyte.