upload v1667110895 model

.gitattributes

@@ -31,3 +31,4 @@ saved_model/**/* 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
+test-image.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,7 @@
 ---
-title: Segmentation Training Pipeline
-emoji: 🐨
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.8
+title: "semantic segmentation with TFX ML pipeline"
+sdk: "gradio"
 app_file: app.py
+tags: ["tfx", "auto-generated"]
 pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---

app.py

@@ -0,0 +1,94 @@
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+from PIL import Image
+
+MODEL_CKPT = "chansung/segmentation-training-pipeline@v1667110895"
+MODEL = from_pretrained_keras(MODEL_CKPT)
+
+RESOLTUION = 128
+
+RESOLTUION = 128
+
+ADE_PALETTE = []
+with open(r"./palette.txt", "r") as fp:
+    for line in fp:
+        tmp_list = list(map(int, line[:-1].strip("][").split(", ")))
+        ADE_PALETTE.append(tmp_list)
+
+
+def preprocess_input(image: Image) -> tf.Tensor:
+    image = np.array(image)
+    image = tf.convert_to_tensor(image)
+
+    image = tf.image.resize(image, (RESOLTUION, RESOLTUION))
+    image = image / 255
+
+    return tf.expand_dims(image, 0)
+
+
+# The below utility get_seg_overlay() are from:
+# https://github.com/deep-diver/semantic-segmentation-ml-pipeline/blob/main/notebooks/inference_from_SavedModel.ipynb
+
+
+def get_seg_overlay(image, seg):
+    color_seg = np.zeros(
+        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
+    )  # height, width, 3
+    palette = np.array(ADE_PALETTE)
+
+    for label, color in enumerate(palette):
+        color_seg[seg == label, :] = color
+
+    # Show image + mask
+    img = np.array(image) * 0.5 + color_seg * 0.5
+
+    img *= 255
+    img = np.clip(img, 0, 255)
+    img = img.astype(np.uint8)
+    return img
+
+
+def run_model(image: Image) -> tf.Tensor:
+    preprocessed_image = preprocess_input(image)
+    prediction = MODEL.predict(preprocessed_image)
+
+    seg_mask = tf.math.argmax(prediction, -1)
+    seg_mask = tf.squeeze(seg_mask)
+    return seg_mask
+
+
+def get_predictions(image: Image):
+    predicted_segmentation_mask = run_model(image)
+    preprocessed_image = preprocess_input(image)
+    preprocessed_image = tf.squeeze(preprocessed_image, 0)
+
+    pred_img = get_seg_overlay(
+        preprocessed_image.numpy(), predicted_segmentation_mask.numpy()
+    )
+    return Image.fromarray(pred_img)
+
+
+title = (
+    "Simple demo for a semantic segmentation model trained on the Sidewalks dataset."
+)
+
+description = """
+
+Note that the outputs obtained in this demo won't be state-of-the-art. The underlying project has a different objective focusing more on the ops side of
+deploying a semantic segmentation model. For more details, check out the repository: https://github.com/deep-diver/semantic-segmentation-ml-pipeline/.
+
+"""
+
+demo = gr.Interface(
+    get_predictions,
+    gr.inputs.Image(type="pil"),
+    "pil",
+    allow_flagging="never",
+    title=title,
+    description=description,
+    examples=[["test-image.jpg"]],
+)
+
+demo.launch()

palette.txt

@@ -0,0 +1,35 @@
+[0, 0, 0]
+[216, 82, 24]
+[255, 255, 0]
+[125, 46, 141]
+[118, 171, 47]
+[161, 19, 46]
+[255, 0, 0]
+[0, 128, 128]
+[190, 190, 0]
+[0, 255, 0]
+[0, 0, 255]
+[170, 0, 255]
+[84, 84, 0]
+[84, 170, 0]
+[84, 255, 0]
+[170, 84, 0]
+[170, 170, 0]
+[170, 255, 0]
+[255, 84, 0]
+[255, 170, 0]
+[255, 255, 0]
+[33, 138, 200]
+[0, 170, 127]
+[0, 255, 127]
+[84, 0, 127]
+[84, 84, 127]
+[84, 170, 127]
+[84, 255, 127]
+[170, 0, 127]
+[170, 84, 127]
+[170, 170, 127]
+[170, 255, 127]
+[255, 0, 127]
+[255, 84, 127]
+[255, 170, 127]

requirements.txt

@@ -0,0 +1,4 @@
+numpy==1.23.0
+pillow==9.1.1
+tensorflow==2.9.0
+huggingface_hub