Ginevra's_task1

2.0-tl-pytorch.ipynb

@@ -28,9 +28,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Ginevra\\anaconda3\\envs\\3dcv-test\\lib\\site-packages\\scipy\\__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.25.2\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -63,68 +72,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]\n",
+      "tensor([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,\n",
+      "        14., 15., 16., 17., 18., 19.])\n",
+      "tensor([ 0.0000,  0.3226,  0.7881,  0.9376,  0.5649,  2.0675,  5.9284,  5.9023,\n",
+      "         4.8933,  4.6293,  4.3573,  2.0781, 10.1377,  2.3673, 10.2661,  1.1669,\n",
+      "         3.2232, 10.6823,  3.8795,  5.6676])\n",
+      "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+      "        1., 1.])\n",
+      "tensor(1.)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Create a numpy array that looks like this: [0, 1, 2, ..., 19]\n",
-    "arr = \n",
+    "arr = np.arange(0,20)\n",
+    "print(arr)\n",
     "\n",
     "# Convert the numpy array to a torch tensor\n",
-    "tensor = \n",
+    "tensor = torch.from_numpy(arr.astype(np.float32))\n",
     "print(tensor)\n",
     "\n",
     "# Create a tensor that contains random numbers.\n",
     "# It should have the same size like the numpy array.\n",
     "# Multiply it with the previous tensor.\n",
-    "rand_tensor = \n",
-    "tensor = \n",
+    "rand_tensor = torch.rand_like(tensor, dtype=torch.float)\n",
+    "tensor = tensor * rand_tensor\n",
     "print(tensor)\n",
     "\n",
     "# Create a tensor that contains only 1s.\n",
     "# It should have the same size like the numpy array.\n",
     "# Substract it from the previous tensor.\n",
-    "tensor = \n",
+    "tensor = torch.ones_like(tensor)\n",
     "print(tensor)\n",
     "\n",
     "# Get the 5th element using a index.\n",
-    "element = \n",
+    "element = tensor[4]\n",
     "print(element)\n",
     "\n",
     "# Create a tensor that contains only 0s.\n",
     "# It should have the same size like the numpy array.\n",
-    "# Multiply it with the previous tensor without any assignment (in place).\n"
+    "# Multiply it with the previous tensor without any assignment (in place).\n",
+    "zero_tensor=torch.zeros_like(tensor)\n",
+    "tensor.mul_(zero_tensor)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([512, 512, 3])\n",
+      "786432\n",
+      "torch.Size([1, 786432])\n",
+      "torch.Size([786432])\n",
+      "torch.Size([512, 512, 3])\n",
+      "tensor(91404744.)\n",
+      "tensor(116.2271)\n",
+      "tensor(255.)\n"
+     ]
+    }
+   ],
    "source": [
     "# Load the image from the last exercise as RGB image.\n",
-    "image = \n",
+    "image = skimage.io.imread('./data/pepo.jpg')\n",
     "\n",
     "# Convert the image to a tensor\n",
-    "image = \n",
-    "\n",
+    "image = torch.from_numpy(image.astype(np.float32))\n",
+    "height, width, channels = image.shape\n",
     "# Print its shape\n",
     "print(image.shape)\n",
     "\n",
     "# Flatten the image\n",
-    "image = \n",
+    "image = torch.flatten(image)\n",
     "print(len(image))\n",
     "\n",
     "# Add another dimension resulting in a 1x78642 tensor\n",
-    "\n",
+    "image = image.unsqueeze(0)\n",
     "print(image.shape)\n",
     "\n",
     "# Revert the last action\n",
-    "\n",
+    "image = image.squeeze(0)\n",
     "print(image.shape)\n",
     "\n",
     "# Reshape the tensor, so that it has the original 2D dimensions\n",
-    "image = \n",
+    "image = image.view(height, width, channels)\n",
     "print(image.shape)\n",
     "\n",
     "# Calculate the sum, mean and max of the tensor\n",
@@ -337,7 +390,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "3dcv-test",
    "language": "python",
    "name": "python3"
   },
@@ -351,7 +404,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.19"
   }
  },
  "nbformat": 4,