Torch working

notebooks/model-training-v1.ipynb

@@ -51,6 +51,14 @@
     "        print(f\"{class_name}: {count} images\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "64122ad4",
+   "metadata": {},
+   "source": [
+    "Check out sample image from dataset"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -91,6 +99,80 @@
     "plt.title(first_class)\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c19ec00a",
+   "metadata": {},
+   "source": [
+    "Ensure that all images are RGB, all of same resolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3cedd586",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique sizes: {(64, 64)}\n",
+      "Unique modes: {'RGB'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "sizes = set()\n",
+    "modes = set()\n",
+    "\n",
+    "for class_name in os.listdir(data_dir):\n",
+    "    class_path = os.path.join(data_dir, class_name)\n",
+    "    if not os.path.isdir(class_path):\n",
+    "        continue\n",
+    "    for img_name in os.listdir(class_path):\n",
+    "        img = Image.open(os.path.join(class_path, img_name))\n",
+    "        sizes.add(img.size)\n",
+    "        modes.add(img.mode)\n",
+    "\n",
+    "print(f\"Unique sizes: {sizes}\")\n",
+    "print(f\"Unique modes: {modes}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88ac961b",
+   "metadata": {},
+   "source": [
+    "Ensure that torch works with GPU (5080) [Credit: Claude]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8f556b22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "NVIDIA GeForce RTX 5080\n",
+      "GPU works! torch.Size([1000, 1000])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "print(torch.cuda.is_available())  \n",
+    "print(torch.cuda.get_device_name(0)) \n",
+    "\n",
+    "x = torch.randn(1000, 1000).cuda()\n",
+    "y = x @ x\n",
+    "print(\"GPU works!\", y.shape)"
+   ]
   }
  ],
  "metadata": {