upload files manually

.gitattributes

@@ -1,17 +1,36 @@
-*.bin.* filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tar.gz filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
 *.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
 *.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pk1 filter=lfs diff=lfs merge=lfs -text

config.json

@@ -0,0 +1,13 @@
+{
+    "id2label": {
+      "0": "1",
+      "1": "1-2",
+      "2": "2",
+      "3": "2-3",
+      "4": "3",
+      "5": "3-4",
+      "6": "4",
+      "7": "4-5",
+      "8": "5"
+     }
+}

pipeline.py

@@ -1,16 +1,62 @@
 from typing import Dict, List, Any
 from PIL import Image
 
+import os
+import json
+import numpy as np
+from fastai.learner import load_learner
+
+class OrdinalRegressionMetric(Metric):
+    def __init__(self):
+        super().__init__()
+        self.total = 0
+        self.count = 0
+
+    def accumulate(self, learn):
+        # Get predictions and targets
+        preds, targs = learn.pred, learn.y
+
+        # Your custom logic to convert predictions and targets to numeric values
+        preds_numeric = torch.argmax(preds, dim=1)
+        targs_numeric = targs
+
+        #print("preds_numeric: ",preds_numeric)
+        #print("targs_numeric: ",targs_numeric)
+
+        # Calculate the metric (modify this based on your specific needs)
+        squared_diff = torch.sum(torch.sqrt((preds_numeric - targs_numeric)**2))
+
+        # Normalize by the maximum possible difference
+        max_diff = torch.sqrt((torch.max(targs_numeric) - torch.min(targs_numeric))**2)
+
+        #print("squared_diff: ",squared_diff)
+        #print("max_diff: ",max_diff)
+
+        # Update the metric value
+        self.total += squared_diff
+        #print("self.total: ",self.total)
+        self.count += max_diff
+        #print("self.count: ",self.count)
+    @property
+    def value(self):
+        if self.count == 0:
+            return 0.0  # or handle this case appropriately
+        #print("self.total / self.count: ", (self.total / self.count))
+        # Calculate the normalized metric value
+        metric_value = 1/(self.total / self.count)
+        return metric_value
+
 class PreTrainedPipeline():
     def __init__(self, path=""):
         # IMPLEMENT_THIS
         # Preload all the elements you are going to need at inference.
         # For instance your model, processors, tokenizer that might be needed.
         # This function is only called once, so do all the heavy processing I/O here"""
-        raise NotImplementedError(
-            "Please implement PreTrainedPipeline __init__ function"
-        )
-    
+        self.model = load_learner(os.path.join(path, "textfile3-2.pk1"))
+        with open(os.path.join(path, "config.json")) as config:
+            config = json.load(config)
+        self.id2label = config["id2label"]
+
     def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
         """
         Args:
@@ -22,6 +68,18 @@ class PreTrainedPipeline():
                 It is preferred if the returned list is in decreasing `score` order
         """
         # IMPLEMENT_THIS
-        raise NotImplementedError(
-            "Please implement PreTrainedPipeline __call__ function"
-        )
+        # FastAI expects a np array, not a PIL Image.
+        _, _, preds = self.model.predict(np.array(inputs))
+        preds = preds.tolist()
+        labels = [
+            {"label": str(self.id2label["0"]), "score": preds[0]},
+            {"label": str(self.id2label["1"]), "score": preds[1]},
+            {"label": str(self.id2label["2"]), "score": preds[2]},
+            {"label": str(self.id2label["3"]), "score": preds[3]},
+            {"label": str(self.id2label["4"]), "score": preds[4]},
+            {"label": str(self.id2label["5"]), "score": preds[5]},
+            {"label": str(self.id2label["6"]), "score": preds[6]},
+            {"label": str(self.id2label["7"]), "score": preds[7]},
+            {"label": str(self.id2label["8"]), "score": preds[8]},
+        ]
+        return labels

requirements.txt

@@ -0,0 +1,4 @@
+fastai
+torch
+gradio
+numpy

textfile3-2.pk1

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c86bf454f4bcf7dd0fba8c4628ac89872ce3431fb4d2997ccbdbab5ba70c3dbb
+size 46998504