add app.py

app.py

@@ -0,0 +1,77 @@
+import os
+import numpy as np
+import cv2
+import codecs
+import torch
+import torchvision.transforms as transforms
+import gradio as gr
+
+from PIL import Image
+
+from unetplusplus import NestedUNet
+
+torch.manual_seed(0)
+
+if torch.cuda.is_available():
+    torch.backends.cudnn.deterministic = True
+
+# Device
+DEVICE = "cpu"
+print(DEVICE)
+
+# Load color map
+cmap = np.load('cmap.npy')
+
+# Make directories
+os.system("mkdir ./models")
+
+# Get model weights
+if not os.path.exists("./models/masksupnyu39.31d.pth"):
+    os.system("wget -O ./models/masksupnyu39.31d.pth https://github.com/hasibzunair/masksup-segmentation/releases/download/v0.1/masksupnyu39.31iou.pth")
+
+# Load model
+model = NestedUNet(num_classes=40)
+checkpoint = torch.load("./models/masksupnyu39.31d.pth")
+model.load_state_dict(checkpoint)
+model = model.to(DEVICE)
+model.eval()
+
+
+# Main inference function
+def inference(img_path):
+    image = Image.open(img_path).convert("RGB")
+    transforms_image = transforms.Compose(
+        [
+            transforms.Resize((224, 224)),
+            transforms.CenterCrop((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+        ]
+    )
+
+    image = transforms_image(image)
+    image = image[None, :]
+    # Predict
+    with torch.no_grad():
+        output = torch.sigmoid(model(image.to(DEVICE).float()))
+        output = torch.softmax(output, dim=1).argmax(dim=1)[0].float().cpu().numpy().astype(np.uint8)
+        pred = cmap[output]
+    return pred
+
+# App
+title = "Masked Supervised Learning for Semantic Segmentation"
+description = codecs.open("description.html", "r", "utf-8").read()
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2210.00923' target='_blank'>Masked Supervised Learning for Semantic Segmentation</a> | <a href='https://github.com/hasibzunair/masksup-segmentation' target='_blank'>Github</a></p>"
+
+gr.Interface(
+    inference,
+    gr.inputs.Image(type='file', label="Input Image"),
+    gr.outputs.Image(type="file", label="Predicted Output"),
+    examples=["./sample_images/a.png", "./sample_images/b.png", 
+    "./sample_images/c.png", "./sample_images/d.png"],
+    title=title,
+    description=description,
+    article=article,
+    allow_flagging=False,
+    analytics_enabled=False,
+    ).launch(debug=True, enable_queue=True)

nyu.ipynb

@@ -1,165 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import cv2\n",
-    "import codecs\n",
-    "import torch\n",
-    "import torchvision.transforms as transforms\n",
-    "import gradio as gr\n",
-    "\n",
-    "from PIL import Image\n",
-    "\n",
-    "from unetplusplus import NestedUNet\n",
-    "\n",
-    "torch.manual_seed(0)\n",
-    "\n",
-    "if torch.cuda.is_available():\n",
-    "    torch.backends.cudnn.deterministic = True\n",
-    "\n",
-    "# Device\n",
-    "DEVICE = \"cpu\"\n",
-    "print(DEVICE)\n",
-    "\n",
-    "# Load color map\n",
-    "cmap = np.load('cmap.npy')\n",
-    "\n",
-    "# Make directories\n",
-    "os.system(\"mkdir ./models\")\n",
-    "\n",
-    "# Get model weights\n",
-    "if not os.path.exists(\"./models/masksupnyu39.31d.pth\"):\n",
-    "    os.system(\"wget -O ./models/masksupnyu39.31d.pth https://github.com/hasibzunair/masksup-segmentation/releases/download/v0.1/masksupnyu39.31iou.pth\")\n",
-    "\n",
-    "# Load model\n",
-    "model = NestedUNet(num_classes=40)\n",
-    "checkpoint = torch.load(\"./models/masksupnyu39.31d.pth\")\n",
-    "model.load_state_dict(checkpoint)\n",
-    "model = model.to(DEVICE)\n",
-    "model.eval()\n",
-    "\n",
-    "\n",
-    "# Main inference function\n",
-    "def inference(img_path):\n",
-    "    image = Image.open(img_path).convert(\"RGB\")\n",
-    "    transforms_image = transforms.Compose(\n",
-    "        [\n",
-    "            transforms.Resize((224, 224)),\n",
-    "            transforms.CenterCrop((224, 224)),\n",
-    "            transforms.ToTensor(),\n",
-    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
-    "        ]\n",
-    "    )\n",
-    "\n",
-    "    image = transforms_image(image)\n",
-    "    image = image[None, :]\n",
-    "    # Predict\n",
-    "    with torch.no_grad():\n",
-    "        output = torch.sigmoid(model(image.to(DEVICE).float()))\n",
-    "        output = torch.softmax(output, dim=1).argmax(dim=1)[0].float().cpu().numpy().astype(np.uint8)\n",
-    "        pred = cmap[output]\n",
-    "    return pred\n",
-    "\n",
-    "# App\n",
-    "title = \"Masked Supervised Learning for Semantic Segmentation\"\n",
-    "description = codecs.open(\"description.html\", \"r\", \"utf-8\").read()\n",
-    "article = \"<p style='text-align: center'><a href='https://arxiv.org/abs/2210.00923' target='_blank'>Masked Supervised Learning for Semantic Segmentation</a> | <a href='https://github.com/hasibzunair/masksup-segmentation' target='_blank'>Github</a></p>\"\n",
-    "\n",
-    "gr.Interface(\n",
-    "    inference,\n",
-    "    gr.inputs.Image(type='file', label=\"Input Image\"),\n",
-    "    gr.outputs.Image(type=\"file\", label=\"Predicted Output\"),\n",
-    "    examples=[\"./sample_images/a.png\", \"./sample_images/b.png\", \n",
-    "    \"./sample_images/c.png\", \"./sample_images/d.png\"],\n",
-    "    title=title,\n",
-    "    description=description,\n",
-    "    article=article,\n",
-    "    allow_flagging=False,\n",
-    "    analytics_enabled=False,\n",
-    "    ).launch(debug=True, enable_queue=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.8.12 ('fifa')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.12"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "5a4cff4f724f20f3784f32e905011239b516be3fadafd59414871df18d0dad63"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}