diff --git a/hooknet/inference/apply_torch.py b/hooknet/inference/apply_torch.py
new file mode 100644
index 0000000..1f31e6f
--- /dev/null
+++ b/hooknet/inference/apply_torch.py
@@ -0,0 +1,153 @@
+import time
+import traceback
+from pathlib import Path
+import numpy as np
+from hooknet.configuration.config import create_hooknet
+from hooknet.inference.utils import (
+    create_lock_file,
+    create_output_folders,
+    files_exists,
+    get_files,
+    release_lock_file,
+)
+from hooknet.model_torch import HookNet
+from hooknet.inference.writing import create_writers
+from tqdm import tqdm
+from wholeslidedata.iterators import create_batch_iterator
+from wholeslidedata.source.configuration.config import (
+    get_paths,
+    insert_paths_into_config,
+)
+import torch
+
+
+def _execute_inference_single(
+    iterator,
+    model,
+    image_path,
+    files,
+    output_folder,
+    tmp_folder,
+):
+
+    print("Init writers...")
+    writers = create_writers(
+        image_path=image_path,
+        files=files,
+        output_folder=output_folder,
+        tmp_folder=tmp_folder,
+    )
+
+    if not writers:
+        print(f"Nothing to process for image {image_path}")
+        return
+
+    prediction_times = []
+    batch_times = []
+    print("Applying...")
+    index = 0
+    batch_time = -1
+    for x_batch, y_batch, info in tqdm(iterator):
+        if index > 0:
+            batch_times.append(time.time()-batch_time)
+        prediction_time = time.time()
+        predictions = model(*x_batch)
+        print(predictions.shape)
+        print(y_batch.shape)
+        if index > 0:
+            prediction_times.append(time.time()-prediction_time)
+
+        for idx, prediction in enumerate(predictions):
+            point = info["sample_references"][idx]["point"]
+            c, r = (
+                point.x - 70 // 4, # 70 is model_outputshape
+                point.y - 70 // 4,
+            )
+
+            for writer in writers:
+                writer.write_tile(
+                    tile=prediction,
+                    coordinates=(int(c), int(r)),
+                    mask=y_batch[idx][0],
+                )
+        index += 1
+        batch_time = time.time()
+
+    print(f"average batch time: {np.mean(batch_times)}")
+    print(f"average prediction time: {np.mean(prediction_times)}")
+    
+    # save predictions
+    print("Saving...")
+    for writer in writers:
+        writer.save()
+
+
+def execute_inference(
+    user_config,
+    mode,
+    model_name,
+    output_folder,
+    tmp_folder,
+    cpus,
+    source_preset,
+    heatmaps,
+):
+    print("Create output folder")
+    create_output_folders(tmp_folder=tmp_folder, output_folder=output_folder)
+    model = torch.load('/data/pathology/projects/retraining_hooknet_breast/torch_model/last_model.h5')
+    for image_path, annotation_path in get_paths(user_config, preset=source_preset):
+        print(f"PROCESSING: {image_path}, with {annotation_path}....")
+
+        lock_file_path = output_folder / (image_path.stem + f"{model_name}.lock")
+        if lock_file_path.exists():
+            print("Lock file exists, skipping inference.")
+            continue
+
+        files = get_files(
+            image_path=image_path, model_name=model_name, heatmaps=heatmaps
+        )
+
+        if files_exists(files=files, output_folder=output_folder):
+            print(f"All output files already exist, skipping inference.")
+            continue
+
+        try:
+            create_lock_file(lock_file_path=lock_file_path)
+
+            # Create iterator
+            print("Creating iterator...")
+            user_config_dict = insert_paths_into_config(
+                user_config, image_path, annotation_path
+            )
+            iterator = create_batch_iterator(
+                mode=mode,
+                user_config=user_config_dict["wholeslidedata"],
+                presets=(
+                    "files",
+                    "slidingwindow",
+                ),
+                cpus=cpus,
+                number_of_batches=-1,
+                search_paths=(str(Path(user_config).parent),),
+            )
+            with torch.no_grad():
+                model.eval()  # Optional when not using Model Specific layer
+                print("Run inference")
+                _execute_inference_single(
+                    iterator=iterator,
+                    model=model,
+                    image_path=image_path,
+                    files=files,
+                    output_folder=output_folder,
+                    tmp_folder=tmp_folder,
+                )
+                print('Stopping iterator')
+                iterator.stop()
+
+        except Exception as e:
+            print("Exception")
+            print(e)
+            print(traceback.format_exc())
+        finally:
+            release_lock_file(lock_file_path=lock_file_path)
+        print("--------------")