Bugfixes and solutions

part3/2023-CoDaS-HEP-Exercises-3.ipynb

@@ -100,12 +100,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "3c5b8b78",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "def convolution(img, kernel, stride=None, padding=None):\n",
+   "outputs": [
+    {
+     "ename": "IndentationError",
+     "evalue": "unindent does not match any outer indentation level (<tokenize>, line 7)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m<tokenize>:7\u001b[0;36m\u001b[0m\n\u001b[0;31m    H = img.shape[0] # height of the image\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unindent does not match any outer indentation level\n"
+     ]
+    }
+   ],
+   "source": [
+    "def convolution(img, kernel, stride=None, padding=True):\n",
+    "\n",
+    "    # Implement padding\n",
+    "#     if padding:\n",
+    "#         img = np.pad(img, 1, 'constant', constant_values=0)\n",
     "    \n",
     "    H = img.shape[0] # height of the image\n",
     "    W = img.shape[1] # width of the image\n",
@@ -115,12 +128,12 @@
     "    filt_h = kernel.shape[1]\n",
     "    \n",
     "    # In our case we only have one filer, so n=1. We will drop the 3rd dim for simplicity\n",
-    "    img_out = np.zeros((H+1,W+1))\n",
+    "    img_out = np.zeros((H-(S-1), W-(S-1)))\n",
     "\n",
     "    # Nested loops over V and U\n",
-    "    for v in range(S//2, H-S//2):\n",
-    "        for u in range(S//2, W-S//2):\n",
-    "            img_out[v, u] = np.sum(img[v-S//2:v+S//2+1,u-S//2:u+S//2+1]*kernel)\n",
+    "    for v in range(0, H-(S-1)):\n",
+    "        for u in range(0, W-(S-1)):\n",
+    "            img_out[v, u] = np.sum(img[v:v+S, u:u+S] * kernel)\n",
     "    return img_out"
    ]
   },